Methods and systems for monitoring, verifying and correcting carbon emissions from new energy freight vehicles

Abstract

This invention discloses a method and system for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles, belonging to the field of new energy vehicle carbon management technology. It aims to solve the problems of incomplete data dimensions, large calculation biases, distorted attribution of charging carbon emissions, and lack of dynamic control throughout the entire process in existing new energy freight vehicle carbon emission monitoring data. The method includes: acquiring basic information about the cargo and the new energy freight vehicle during the loading stage, generating an initial loading vector and parameter correlation matrix, splitting alternative paths into transport sub-paths and constructing a feature table, and generating an initial carbon emission benchmark value by matching historical data; during the transport stage, correcting parameters and actual carbon emissions based on real-time data, updating the carbon emission values ​​of alternative paths, and selecting recommended paths; accurately calculating charging carbon emissions upon connection to charging piles and splitting and correcting them according to the transportation progress; and finally identifying abnormal paths, generating standardized reports, and updating the historical database. This application achieves accurate monitoring and dynamic correction throughout the entire process, providing reliable data support for low-carbon transportation management.

Description

Technical Field

[0001] This invention relates to the field of carbon management technology for new energy vehicles, and more specifically to a method and system for monitoring, verifying and correcting carbon emissions from new energy freight vehicles. Background Technology

[0002] Driven by dual carbon targets, new energy trucks, with their low emissions during operation, have become one of the core carriers for low-carbon transformation in the transportation sector. However, new energy trucks are not zero-carbon emissions throughout their entire life cycle. Their carbon emissions cover the entire process from upstream raw material production, midstream new energy truck operation, to downstream scrapping and recycling. Among these, carbon emission accounting and control during the operation phase is a key link in achieving accurate quantification of the carbon footprint throughout the entire life cycle.

[0003] Currently, the field of carbon emission monitoring for new energy freight vehicles still faces numerous technical bottlenecks. Regarding the setting of carbon emission benchmarks, existing technologies often employ standardized parameter models, neglecting the individual differences in cargo load, route terrain, and other factors across different transportation tasks. This results in low compatibility between initial carbon emission benchmarks and actual transportation scenarios, leading to significant prediction deviations. In the management of the driving process, traditional monitoring methods struggle to capture the real-time impact of dynamic factors such as road congestion, sudden terrain changes, and load fluctuations on energy consumption, failing to promptly correct carbon emission calculation results and causing a disconnect between actual and predicted carbon emissions. For carbon emission handling during charging, existing solutions often centrally account for charging carbon emissions in a single transportation phase, neglecting the correlation between charging behavior and the transportation process. This leads to distorted carbon emission attribution and affects the accuracy of the entire process calculation. Furthermore, existing systems lack effective mechanisms for identifying abnormal carbon emission paths, making it difficult to pinpoint key nodes leading to excessive carbon emissions. They also lack a standardized and compliant carbon emission reporting system, failing to meet the practical needs of enterprise carbon management and regulatory verification. These problems severely restrict the accuracy and effectiveness of carbon emission monitoring for new energy freight vehicles, hindering the implementation of carbon reduction targets in the transportation sector. Therefore, to overcome these limitations, this invention proposes a method and system for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention aims to provide a method and system for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles. This solution addresses the problem of accurately calculating carbon emissions throughout the entire lifecycle of new energy freight vehicles, specifically overcoming technical pain points such as large deviations in the initial carbon emission baseline value prediction, untimely dynamic correction of carbon emissions during transportation, distorted attribution of carbon emissions during charging, difficulty in identifying abnormal routes, and lack of ability to generate compliant carbon emission reports.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] Methods for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles include:

[0007] The system acquires cargo information and basic information of new energy freight vehicles, generates an initial loading vector and constructs a parameter correlation matrix, and splits each cargo transportation candidate route into a transportation sub-route, constructs a transportation sub-route feature table, and combines historical transportation database to match benchmark reference records to generate an initial carbon emission benchmark value for cargo transportation.

[0008] During the transportation phase of new energy freight vehicles, based on the real-time transportation data of the current transportation sub-path that the new energy freight vehicles have entered, the loading initial vector and the associated transportation sub-path feature table are monitored and corrected in real time to obtain the actual carbon emissions of the current transportation sub-path and update the initial carbon emission sub-values ​​of each candidate path of the next transportation sub-path for screening the recommended path of the next transportation sub-path.

[0009] If new energy trucks connect to charging piles during the transportation phase, the actual carbon emissions of each transportation sub-path and the initial carbon emission baseline value are corrected through charging pile data interaction, charging carbon emission calculation and dynamic splitting, and the total carbon emissions of cargo transportation are generated by combining the actual carbon emissions of the transportation sub-paths.

[0010] Based on the initial carbon emission values ​​and actual carbon emissions of the transport sub-routes, abnormal transport sub-routes are identified, carbon emission reports are generated, and the historical transport database is updated.

[0011] Specifically, the steps for generating the initial loading vector and constructing the parameter correlation matrix include:

[0012] The collected information on onboard cargo and basic information on new energy trucks are standardized and processed, and an initial loading vector is constructed according to a preset parameter sequence.

[0013] Based on the cargo transport origin and destination, alternative transport routes are generated. Each alternative transport route is divided into transport sub-routes according to the differences in road segment characteristics. The path features of each transport sub-routes are extracted to form a transport sub-routes feature table and associated with the loading initial vector.

[0014] A parameter association matrix is ​​constructed using the merged parameters of vehicle cargo information and transport sub-path features as the matrix row dimension and the parameters in the basic information of new energy freight vehicles as the matrix column dimension.

[0015] Each element of the parameter correlation matrix corresponds to a set of carbon emission value accounting logic identifiers, which are used to constrain the carbon emission value accounting logic between corresponding parameters.

[0016] The carbon emission value accounting logic refers to the set of rules that describe the synergistic effect between vehicle cargo information parameters, transportation sub-route characteristic parameters, and basic information parameters of new energy freight vehicles to derive the carbon emission value influencing factors.

[0017] Specifically, the steps for matching the baseline reference record include:

[0018] By using the new energy vehicle identification of the new energy vehicle, combined with the starting point and ending point of each sub-route of this cargo transportation and the route characteristics of the sub-route, route matching is performed in the historical transportation database. Historical transportation records in which the coordinate error of the starting point and ending point of the sub-route is within a preset distance range and the similarity of the sub-route features is greater than a preset transportation similarity threshold are used as benchmark reference records.

[0019] If the historical transport records are not unique, the difference between the current loading initial vector and the loading initial vector of each historical record is calculated, and the historical transport records are selected as the benchmark reference records based on the difference between the loading initial vector and the historical loading initial vector.

[0020] Specifically, the steps for generating initial carbon emission baselines for cargo transportation include:

[0021] If no benchmark reference record is matched, the impact factors of carbon emission values ​​of each carrier sub-path are calculated based on the current loading initial vector and the associated carrier sub-path feature table, including comprehensive energy consumption per unit mileage, basic mileage of the route, and basic carbon emission factors of the power grid.

[0022] Based on the mileage of the transport sub-routes, and according to the preset carbon emission value calculation logic, the initial carbon emission sub-value of each transport sub-routes is calculated; the initial carbon emission sub-values ​​of all transport sub-routes are summed to obtain the carbon emission benchmark value of each alternative transport route, and the initial carbon emission benchmark value of this cargo transport is obtained by weighted averaging.

[0023] If a benchmark reference record is matched, a carrier sub-path difference vector is constructed according to the carrier sub-path, and the carbon emission value accounting logic corresponding to the difference parameter in the parameter association matrix is ​​called to calculate the difference component of the carbon emission value impact factor.

[0024] Based on the mileage of the transport sub-paths and according to the preset carbon emission value calculation logic, the carbon emission difference sub-value of each transport sub-path is obtained; the actual carbon emission sub-value of the corresponding transport sub-path in the benchmark reference record is corrected to obtain the current carbon emission sub-value of each transport sub-path; the carbon emission sub-values ​​of all current transport sub-paths are summed to obtain the initial carbon emission benchmark value of this cargo transport.

[0025] Specifically, the steps to obtain the actual carbon emissions of the current carrier sub-path include:

[0026] After the new energy truck enters the current transport sub-path, transport data is collected in real time to build a real-time transport dataset. The loading initial vector and the associated transport sub-path feature table are retrieved to extract the initial parameters corresponding to the real-time transport data.

[0027] The parameter values ​​of the real-time vehicle data dataset are compared with the corresponding initial parameter values ​​in the loading initial vector, and the parameter deviation rate is calculated. The corresponding parameters in the loading initial vector whose parameter deviation rate exceeds the deviation threshold are marked as deviation abnormal parameters.

[0028] When an abnormal parameter is identified, the carbon emission value calculation logic corresponding to the abnormal parameter in the parameter correlation matrix is ​​invoked to correct the impact factor of the carbon emission value of the current carrier sub-path.

[0029] The system retrieves the initial carbon emission baseline value for this cargo transport and the initial carbon emission sub-values ​​for each transport sub-route. Based on the influence factors of the corrected carbon emission values ​​of the transport sub-routes and the preset carbon emission value calculation logic, it updates the initial carbon emission sub-values ​​of the transport sub-routes that have already been traveled, obtains the actual carbon emission amount of the current transport sub-route, and then updates the initial carbon emission baseline value for this cargo transport.

[0030] Specifically, the steps for selecting the recommended path for the next carrier sub-path include:

[0031] When a new energy truck reaches a preset distance node of the current transport sub-path, the on-board route recommendation operation is triggered, the list of alternative routes for the next transport sub-path is retrieved, and the initial data corresponding to each alternative route is extracted.

[0032] Based on the list of alternative paths, obtain the real-time path data of each alternative path to form a real-time dynamic path database for alternative paths;

[0033] Based on the real-time dynamic path database of alternative routes, the impact factors of carbon emission values ​​of carrier sub-paths are updated, and the carbon emission value accounting logic corresponding to the parameter correlation matrix is ​​called to correct the initial carbon emission sub-values ​​of each alternative route.

[0034] Obtain the current real-time remaining power data of new energy freight vehicles, and combine the unit mileage comprehensive energy consumption of the carbon emission value impact factor of each alternative route with the basic mileage of the route to calculate the estimated energy consumption demand of each alternative route.

[0035] The power supply adaptability is assessed based on the comparison between estimated energy demand and real-time remaining power.

[0036] A quantitative scoring standard was established for each alternative route's evaluation dimensions. The evaluation dimensions include the revised initial carbon emission sub-values ​​for each alternative route, energy adaptability, and route accessibility. The comprehensive score for each alternative route was calculated by weighted summation.

[0037] Based on the comprehensive scores of each candidate path, the candidate path is selected as the recommended path for the next carrier sub-path.

[0038] Specifically, the steps for generating the total carbon emissions from cargo transportation include:

[0039] After a new energy truck connects to a charging pile, the charging pile identifier is read and matched with the current transport sub-path to which the charging pile belongs.

[0040] Obtain the current power source composition of the charging pile and retrieve the carbon emission factors of various power sources;

[0041] After charging is started, data for the entire charging cycle is collected, including the start time of charging, the end time of charging, the real-time charging amount, the initial remaining power before charging, and the real-time remaining power after charging. After charging is completed, the collected real-time charging amount data is accumulated to obtain the total charging amount.

[0042] By combining the initial remaining power before charging and the real-time remaining power after charging, the actual charging power consumed by the current cargo transport is obtained.

[0043] Based on the collected electricity source composition and electricity carbon emission factor, the carbon emission of a single electricity source is calculated by multiplying the proportion of each type of electricity source by the corresponding electricity carbon emission factor, and the total carbon emission of the total charging amount is obtained by summing the carbon emissions of each electricity source.

[0044] Specifically, the steps for generating the total carbon emissions from cargo transportation also include:

[0045] Retrieve the total mileage of the sub-route completed during this charging and the total mileage of the cargo transportation route, calculate the percentage of completed mileage, and use it as the allocation weight;

[0046] The total carbon emissions from the actual charging power consumed during the current cargo transport are divided into the corrected carbon emission value for charging on the already traveled road segments and the estimated corrected carbon emission value for charging on the remaining road segments, according to the allocation weight.

[0047] The carbon emission correction value for charging on the already driven route is used to be superimposed on the actual cumulative carbon emission value of each driven vehicle sub-route, correcting and updating the actual cumulative carbon emission value of the corresponding vehicle sub-route.

[0048] The revised carbon emission estimates for charging on the remaining routes are used to add to the initial carbon emission sub-values ​​for each remaining vehicle sub-route, thereby correcting and updating the actual carbon emission estimates for the corresponding vehicle sub-route.

[0049] Specifically, the steps for identifying anomalous carrier sub-routes and generating carbon emission reports include:

[0050] Retrieve the initial carbon emission values ​​and corrected actual carbon emissions for all transport sub-routes in this cargo transportation, and calculate the carbon emission deviation rate for each transport sub-routes.

[0051] Set a dynamic deviation threshold, mark the carrier sub-paths whose carbon emission deviation rate exceeds the dynamic deviation threshold as abnormal carrier sub-paths, and form a list of abnormal carrier sub-paths.

[0052] Retrieve real-time transport data records corresponding to abnormal transport sub-paths, locate the root cause of the abnormality, summarize the core data of the identified abnormal transport sub-paths, and form a structured abnormal transport sub-path information table.

[0053] The basic information of this cargo transportation is retrieved, and the initial carbon emission values, actual carbon emissions and carbon emission deviation rates of each transport sub-route are integrated. This information is then combined with the abnormal transport sub-route information table to form a full-route carbon emission detail. The total carbon emissions of this cargo transportation are calculated, and a standardized carbon emission report is generated.

[0054] The carbon emission monitoring, verification and correction system for new energy freight vehicles includes a vehicle initialization module, a vehicle monitoring module, a charging monitoring module, an anomaly identification module and a dynamic update module;

[0055] The initial loading module acquires information on the onboard cargo and basic information on the new energy trucks, generates an initial loading vector, constructs a parameter correlation matrix, and builds a feature table for the loading sub-path. Combined with the historical transportation database, it generates an initial carbon emission baseline value for cargo loading. The loading monitoring module obtains the actual carbon emissions of the current loading sub-path based on real-time loading data from the new energy trucks entering the current sub-path, updates the initial carbon emission sub-values ​​of each candidate path for the next loading sub-path, and filters the recommended path for the next loading sub-path. The charging monitoring module corrects the actual carbon emissions of each loading sub-path and the initial carbon emission baseline value through charging pile data interaction, charging carbon emission calculation, and dynamic splitting, generating the total carbon emissions for cargo loading. The anomaly identification module identifies abnormal loading sub-paths based on the initial carbon emission sub-values ​​and actual carbon emissions of the loading sub-paths. The dynamic update module updates the historical transportation database.

[0056] The beneficial effects of this invention are:

[0057] This application effectively improves the accuracy of carbon emission accounting for the entire life cycle of new energy freight vehicles. It not only significantly reduces the estimation deviation of the initial carbon emission benchmark value, ensuring its high compatibility with actual transportation scenarios, but also enables real-time dynamic correction of carbon emissions during transportation, allowing the accounting results to respond promptly to sudden changes in road conditions, load, and other factors. Simultaneously, it solves the problem of distorted attribution of carbon emissions during charging, making the overall carbon emission statistics more closely aligned with the actual transportation process. Furthermore, by identifying abnormal carbon emission paths and generating standardized compliance reports, it provides a reliable basis for corporate carbon management and regulatory department verification, comprehensively enhancing the scientific rigor and practicality of carbon emission monitoring and control for new energy freight vehicles, and providing strong support for the implementation of carbon reduction targets in the transportation sector. Attached Figure Description

[0058] Figure 1This is a flowchart of the carbon emission monitoring, verification and correction method for new energy freight vehicles of the present invention;

[0059] Figure 2 This is a flowchart illustrating the matching benchmark reference record of the present invention;

[0060] Figure 3 A flowchart for generating initial carbon emission baseline values ​​for cargo transportation according to this invention;

[0061] Figure 4 This is a flowchart illustrating how the actual carbon emissions of the current carrier sub-path are obtained according to the present invention.

[0062] Figure 5 This is a flowchart for generating the total carbon emissions of cargo transportation according to the present invention. Detailed Implementation

[0063] Please see Figure 1 This embodiment introduces a method for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles, including:

[0064] Step S1: During the loading stage of the new energy freight truck, acquire the cargo information and basic information of the new energy freight truck, generate an initial loading vector, generate alternative transport routes by combining the cargo transport start and end points, and break them down into transport sub-routes. Then, construct a parameter association matrix through multi-dimensional data association, match benchmark reference records with the historical transport database of the new energy freight truck, calculate the initial carbon emission sub-values ​​according to the transport sub-routes, and summarize them to generate the initial carbon emission benchmark value for cargo transport. Specifically, the cargo information refers to the dynamic data related to the cargo and transport task collected by the vehicle-mounted weighing device and positioning device, including the total weight of the cargo, loading distribution status, cargo transport start and end points; the basic information of the new energy freight truck refers to the basic data related to the inherent attributes and energy consumption characteristics of the new energy freight truck extracted by the new energy freight truck control system, including the new energy freight truck's tare weight, battery rated capacity, initial battery charge, unit mileage energy consumption benchmark value, and load energy consumption coefficient; the transport sub-routes refer to the alternative complete routes from the cargo transport start to the transport end, segmented according to road terrain and mileage characteristics. Divided into several local path units, the core features of each transport sub-path, such as mileage, terrain type, and distribution of charging stations along the route, need to be extracted. The parameter correlation matrix is ​​a structured correlation tool used to clarify the carbon emission calculation logic among parameters in the vehicle cargo information, transport sub-path features, and new energy truck basic information. It is established by mapping the influence relationship of cargo parameters, transport sub-path parameters, and new energy truck parameters on carbon emissions. For example, the total weight of cargo and the self-weight of new energy trucks are associated with the total load calculation logic, and the terrain of transport sub-paths and the energy consumption benchmark value per unit mileage are associated with the terrain energy consumption adaptation logic. The historical transportation database is a structured data set that stores the vehicle cargo information, new energy truck basic information, initial carbon emission sub-values ​​of transport sub-paths, initial carbon emission benchmark values, and actual carbon emission results corresponding to the past transportation tasks of the new energy truck. It is used to provide historical parameters and initial carbon emission sub-values ​​as references when the start point, end point, and transport sub-path features of the current cargo transportation are matched with historical transportation records. The current initial carbon emission benchmark value is generated by correcting the difference between the current parameters and historical parameters.

[0065] In this embodiment, step S1 integrates the scattered dynamic data of onboard cargo, basic data of new energy trucks, and characteristic data of transportation sub-routes in a structured manner. It clarifies the carbon emission calculation logic between the data based on the parameter correlation matrix and achieves difference correction by combining the historical transportation database. The final generated initial carbon emission benchmark value can accurately match the actual conditions of this cargo transportation task. This not only avoids the calculation deviation caused by parameter confusion and neglect of route segment differences in traditional solutions, but also improves the adaptability of the benchmark value through historical data reuse and dynamic difference adjustment. At the same time, it can assess the carbon emission compliance risk of this cargo transportation in advance, provide a low-carbon target reference for route recommendation in the subsequent transportation stage, and lay an accurate and reliable foundation for real-time monitoring, verification and correction of carbon emissions throughout the process.

[0066] Please see Figure 2 Preferably, the specific steps for matching the baseline reference record include:

[0067] When the loading phase of the new energy truck is completed, the collected onboard cargo information and the basic information of the new energy truck are standardized. By unifying the data units and formats, the parameters from different sources are made consistent and can be directly used for subsequent correlation calculations. The standardized onboard cargo information and the basic information of the new energy truck are sequentially entered into a vector structure according to the preset parameter sequence to construct the initial loading vector.

[0068] The map service system is invoked to generate alternative transport routes based on the origin and destination of the cargo transport. Each alternative transport route is then divided into several sub-routes according to the differences in road segment characteristics. The route characteristics of each sub-routes are extracted, including route mileage, terrain type, and default power source of charging stations along the route, forming a sub-routes feature table and linking it to the loading initial vector. The differences in road segment characteristics refer to the significant differences between different road segments in the alternative transport routes in terms of terrain attributes, mileage, traffic control attributes, distribution density of charging facilities along the route, and default power supply type. These differences directly affect the energy consumption level and carbon emission calculation logic of new energy trucks on that road segment, and therefore need to be used as the core basis for route splitting.

[0069] A parameter association matrix is ​​constructed using the combined parameters of vehicle cargo information and transport sub-path features as the matrix row dimension and the parameters in the basic information of new energy freight vehicles as the matrix column dimension. Each element in the parameter correlation matrix corresponds to a set of carbon emission value calculation logic identifiers, used to constrain the carbon emission value calculation logic between corresponding parameters, providing association rules for subsequent initial carbon emission benchmark value calculation. The carbon emission value calculation logic refers to a set of rules describing the synergistic effect between vehicle cargo information parameters, transport sub-path characteristic parameters, and new energy truck basic information parameters to derive carbon emission value influencing factors. By clarifying the operational relationships, influence directions, and correction methods between parameters, it is used to achieve structured derivation from raw data to carbon emission benchmark values. It includes total load calculation logic, load energy consumption correction logic, center of gravity energy consumption correction logic, terrain energy consumption adaptation logic, and power status energy consumption adjustment logic, etc. For example, the element identifier corresponding to the total weight of cargo and the self-weight of the new energy truck is the total load calculation logic, used to obtain the total load of this cargo transportation by adding the two; the element identifier corresponding to the terrain type of the transport sub-path and the unit mileage energy consumption benchmark value is the terrain energy consumption adaptation logic, used to adjust the unit mileage energy consumption benchmark value according to the terrain differences of the transport sub-path. By clarifying the correlation between parameters through the matrix, it replaces the rigid calculation mode of traditional fixed formulas and improves the calculation flexibility.

[0070] Specifically, the steps for constructing the parameter correlation matrix include:

[0071] The standardized vehicle cargo information, combined parameters of transport sub-path features, and basic information of new energy freight vehicles are divided into two parameter sets. The combined parameters of vehicle cargo information and transport sub-path features are used as the row dimension of the matrix, and the parameters of basic information of new energy freight vehicles are used as the column dimension of the matrix. The sub-parameters under each dimension are sorted according to the data collection priority and labeled with parameter identifiers to ensure the uniqueness of dimension division and parameter identification.

[0072] It iterates through all sub-parameter combinations in the row and column dimensions, and by calling preset association judgment rules, such as whether the parameters jointly participate in the calculation of energy consumption and carbon emissions of the carrier sub-path, it filters out parameter combinations with accounting associations, and marks parameter combinations without associations, forming a list of association relationships.

[0073] Based on the list of relationships, a carbon emission value accounting logic identifier is assigned to the matrix cell corresponding to each parameter combination with accounting relationship. The carbon emission value accounting logic identifier and the carbon emission value accounting logic are bound by an index.

[0074] For cells corresponding to unrelated parameter combinations, assign a uniform unrelated identifier to explicitly exclude invalid calculation paths. Integrate the row dimensions, column dimensions, and corresponding cell identifiers into a two-dimensional table structure to generate a parameter association matrix;

[0075] Simultaneously, a parameter association matrix update interface is configured. When new parameters are added or the carbon emission value calculation logic is adjusted, the corresponding dimension or cell identifier is updated through the update interface without reconstructing the overall matrix structure, thus achieving dynamic matrix adaptation.

[0076] By using the new energy vehicle identification of the new energy vehicles, and combining the starting point, ending point, and route characteristics of each sub-route of the cargo transport, path matching is performed in the historical transport database. Historical transport records with the same new energy vehicle identification, where the coordinate errors of the starting point and ending point of the sub-route are within a preset distance range, and the similarity of the sub-route features is greater than a preset transport similarity threshold, are selected as benchmark reference records. Furthermore, the loading initial vector fragment, initial carbon emission sub-value, actual carbon emission sub-value, and initial carbon emission benchmark value of the complete route are extracted from the historical reference records for the corresponding sub-route. This ensures that the historical data is highly matched with the scenario of each sub-route in this transport, and avoids the invalidation of historical reference data due to differences in the characteristics of the sub-route. The preset distance range is used to determine the upper limit of geographic coordinate deviation between the current and historical sub-paths for matching in terms of spatial start and end points. This is combined with the transportation scenario, positioning device accuracy, and path segmentation granularity to ensure consistency between the matched historical and current paths in terms of spatial start and end points. For example, the preset distance range is less than 50 meters in urban road scenarios and less than 100 meters in intercity highway scenarios. The preset transport similarity threshold is used to quantify the critical value for determining the similarity between the features of the current and historical sub-paths. Feature similarity is calculated by integrating core feature dimensions such as path terrain type, road segment mileage ratio, speed limit level distribution, charging station density along the route, and road slope range, and then weighted to obtain a normalized value in the 0-1 range.

[0077] If the selected historical transport records are not unique, calculate the difference in sub-path level parameters between the current loading initial vector and each historical loading initial vector. Then, according to the proportion of each sub-path mileage to the total mileage of the complete path, calculate the weighted sum to obtain the total parameter difference of the complete path. Select the historical transport record with the smallest total parameter difference as the final benchmark reference record.

[0078] Please see Figure 3 Preferably, the specific steps for generating the initial carbon emission baseline value for cargo transportation include:

[0079] If no baseline reference record is found, the impact factors of carbon emission values ​​for each carrier sub-path are calculated based on the current loading initial vector and the associated carrier sub-path feature table, including:

[0080] The total load capacity of each transport sub-path is calculated by calling the total weight of goods and the tare weight of the new energy truck in the parameter association matrix. The total load capacity of each transport sub-path is then calculated by calling the load capacity energy consumption correction logic corresponding to the total load capacity and the energy consumption benchmark value per unit mileage. Combined with the terrain energy consumption adaptation logic corresponding to the terrain type of the transport sub-path and the energy consumption benchmark value per unit mileage, the comprehensive energy consumption per unit mileage of each transport sub-path is corrected. The grid carbon emission baseline factor corresponding to the default power source of the charging stations along each transport sub-path is extracted.

[0081] Based on the mileage of each sub-route, and according to the preset carbon emission value calculation logic, the initial carbon emission sub-value of each sub-route is calculated. The initial carbon emission sub-values ​​of all sub-routes are summed to obtain the carbon emission benchmark value of each candidate route. The initial carbon emission benchmark value of this cargo transport is obtained by weighted averaging. The preset carbon emission value calculation logic refers to the structured operation rules used to convert carbon emission value influencing factors into carbon emission benchmark values. For example, the product of the comprehensive energy consumption per unit mileage, the basic mileage of the route, and the basic carbon emission factor of the power grid is used as the carbon emission benchmark value.

[0082] If a benchmark reference record is matched, a sub-path difference vector is constructed based on the sub-paths: For each current sub-path, the difference parameters with the corresponding sub-paths in the historical benchmark reference record are extracted and entered into the vector structure in sub-path order; the carbon emission value calculation logic corresponding to each difference parameter in the parameter association matrix is ​​called to calculate the difference components of the unit mileage comprehensive energy consumption difference component, the grid carbon emission base factor, and other difference parameters for each sub-path; combined with the sub-path mileage, the carbon emission difference sub-value for each sub-path is obtained according to the preset carbon emission value calculation logic; the actual carbon emission sub-values ​​of the corresponding sub-paths in the benchmark reference record are corrected to obtain the current carbon emission sub-values ​​for each sub-path; the carbon emission sub-values ​​of all current sub-paths are summed to obtain the initial carbon emission benchmark value for this cargo transport.

[0083] After the initial carbon emission baseline value is calculated, the initial loading vector, the characteristic table of the transport sub-path, the carbon emission sub-values ​​of each transport sub-path, and the initial carbon emission baseline value are synchronously stored in the local vehicle database to provide initial data support for route recommendation, real-time carbon emission monitoring and verification correction in the subsequent transport phase.

[0084] Step S2: During the transportation phase of the new energy truck, based on the real-time transportation data of the current transportation sub-path entered by the new energy truck, the initial loading vector and the associated transportation sub-path feature table are monitored and corrected in real time to obtain the actual carbon emissions of the current transportation sub-path. The initial carbon emission sub-values ​​of each candidate path for the next transportation sub-path are then updated. These updated sub-values ​​are used to select recommended paths for the next transportation sub-path based on the corrected initial carbon emission sub-values, the real-time remaining battery power adaptability of the new energy truck, and the real-time traffic and charging station operation status of the next transportation sub-path. Specifically, after the new energy truck enters the current transportation sub-path, transportation data is collected in real time, including... Battery output energy consumption, driving range, real-time road terrain, and real-time load are used to specifically correct the unit mileage energy consumption parameter in the initial loading vector and the terrain influence coefficient in the transport sub-path feature table. By correcting these two core parameters, the initial carbon emission baseline value of this cargo transport is directly linked and updated. At the same time, real-time path data of each alternative path for the next transport sub-path is retrieved, including real-time traffic congestion and real-time charging station operation status, to correct the initial carbon emission sub-value of each alternative path. Then, combined with the real-time remaining power of the new energy truck, it is determined whether it can support the entire journey of the alternative path. Finally, the recommended path is selected to ensure that the recommendation result takes into account both low-carbon goals and transportation feasibility.

[0085] In this embodiment, step S2 achieves a crucial connection between pre-prediction and in-process dynamic adjustment of carbon emission control through real-time data feedback, dynamic parameter correction, and precise route recommendation during the transportation phase. On the one hand, by collecting data such as battery output energy consumption and road terrain in real time, it accurately corrects the unit mileage energy consumption in the initial loading vector and the terrain coefficient in the transportation sub-path feature table. This effectively solves the problem of carbon emission benchmark values ​​deviating from reality due to fixed initial parameters and neglect of unexpected situations during driving in traditional solutions, ensuring that the updated initial carbon emission benchmark value always matches the real-time transportation status of the new energy truck. On the other hand, when recommending the next transportation sub-path, it does not only use low carbon as the sole criterion, but also combines the real-time traffic status of the alternative path, the operation status of charging stations, and the adaptability of the remaining power of the new energy truck. This ensures both the low carbon attributes of the recommended path and the timeliness and safety of the transportation task. At the same time, this process also provides continuous and reliable dynamic data support for the accurate accumulation, verification, and final settlement of carbon emissions throughout the entire journey, helping drivers balance low carbon goals and transportation efficiency during actual driving, and avoiding the impact on the completion of transportation tasks due to blindly pursuing low carbon.

[0086] Please see Figure 4 Preferably, the specific steps for obtaining the actual carbon emissions of the current carrier sub-path include:

[0087] After a new energy truck enters its current transport sub-path, it collects transport data in real time. This includes obtaining real-time battery output energy consumption data through the battery management system, recording the route information already traveled within the current transport sub-path through the onboard high-precision positioning device, collecting real-time route features of the road segment through the onboard terrain perception module, and obtaining real-time load data through the onboard dynamic weighing device. For the collected transport data, a dynamic threshold filtering algorithm based on sensor characteristics is used to remove transient fluctuation abnormal data, and data temporal consistency verification is used to ensure the matching of data from different sources in the time dimension. Finally, a standardized real-time transport dataset is formed, providing a reliable data foundation for subsequent parameter correction.

[0088] The system retrieves the constructed initial loading vector and associated vehicle sub-path feature table from the local vehicle database, extracting the initial parameters corresponding to the real-time vehicle data. It then compares the vehicle data parameter values ​​in the real-time vehicle dataset with the corresponding initial parameter values ​​in the initial loading vector, calculating the parameter deviation rate and determining if deviation exists based on a preset deviation threshold. Parameters in the initial loading vector whose deviation rate exceeds the deviation threshold are marked as abnormal deviation parameters. The deviation threshold is used to quantify the critical standard for determining the degree of deviation between the real-time vehicle data parameters and the corresponding initial parameters in the initial loading vector. Its core function is to distinguish whether the parameter deviation is an acceptable deviation caused by normal equipment measurement errors or minor environmental fluctuations, or a substantial deviation that significantly affects the accuracy of carbon emission calculation, thereby determining whether to trigger the parameter correction process. The threshold is set differently based on the weight of the parameter's impact on carbon emissions, the measurement accuracy of the vehicle sensors, and the fluctuating characteristics of the transportation scenario.

[0089] When an abnormal parameter deviation is detected, the parameter correlation matrix is ​​retrieved from the local vehicle database. The carbon emission calculation logic corresponding to the abnormal parameter in the matrix is ​​then called for targeted correction: If the abnormal parameter is a terrain feature parameter, the terrain energy consumption adaptation logic is called, and the baseline value of energy consumption per unit mileage in the initial loading vector is adjusted by gradient based on the mapping model between terrain type and energy consumption per unit mileage; if the abnormal parameter is a load parameter, the load energy consumption correction logic is called, and the energy consumption per unit mileage parameter is further optimized based on the correlation algorithm between load change and energy consumption increment; if the abnormal parameter is energy consumption data, the energy consumption per unit mileage parameter is calibrated a second time by combining the terrain and load correction results and the energy consumption self-calibration logic, and finally, the influence factor of the corrected carbon emission value of the transport sub-path adapted to the current driving state is generated.

[0090] The system retrieves the initial carbon emission baseline value for this cargo transport and the initial carbon emission sub-values ​​for each transport sub-route from the local vehicle database. Based on the influence factors of the corrected carbon emission values ​​of the transport sub-routes and the preset carbon emission value calculation logic, it updates the initial carbon emission sub-values ​​of the already driven transport sub-routes, obtains the actual carbon emission of the current transport sub-route, and then updates the initial carbon emission baseline value for this cargo transport, realizing the transformation of the baseline value from static preset to dynamic adaptation to the already driven scenario.

[0091] The updated initial carbon emission baseline, the corrected carbon emission impact factor of the transport sub-route, the initial carbon emission sub-value of the transport sub-route already driven, and the correction records of deviation from abnormal parameters are synchronously written to the dynamic update partition of the local vehicle database.

[0092] Preferably, the specific steps for selecting the recommended path for the next carrier sub-path include:

[0093] When the new energy truck reaches the preset distance node of the current transport sub-path, the on-board route recommendation operation is triggered. The list of alternative routes for the next transport sub-path is retrieved from the local on-board database. At the same time, the initial data corresponding to each alternative route is extracted, including the initial carbon emission sub-value, the total length of the route, the distribution of charging stations along the route, and the initial power source type. The preset distance node is a distance node set before the end of the current transport sub-path to allow time for route decision-making and preparation. It is necessary to ensure that the driver has sufficient time to confirm the recommended route and adjust the driving status. It is set in a coordinated manner based on the total mileage of the current transport sub-path, the real-time driving speed of the new energy truck, and the time required for processing the route recommendation data.

[0094] Based on the list of alternative routes, real-time route data for each alternative route is obtained. Real-time traffic status query requests are sent to the map service platform to obtain real-time traffic flow distribution, road segment efficiency, and estimated travel time for each alternative route. Road segment speed is calculated based on the real-time traffic flow distribution, and the speed matching rate is obtained by combining it with the road segment's design speed. Simultaneously, the ratio of the estimated travel time to the shortest travel time for that route is correlated, and a weighted fusion algorithm is used to obtain a quantified value for route smoothness. Real-time status query requests for charging stations are sent to the charging station operation platform to obtain the operating status of charging stations along each alternative route, the number of idle devices, and charging power parameters. Power source query requests are sent to the regional power grid data platform to obtain the current power source composition ratio for each charging station. The real-time route data returned from multiple platforms is formatted and mapped, and integrated to form a real-time dynamic route database for alternative routes.

[0095] Based on the real-time dynamic path database of candidate routes, the impact factors of carbon emission values ​​of carrier sub-paths are updated. The carbon emission value calculation logic corresponding to the parameter correlation matrix is ​​called to correct the initial carbon emission sub-values ​​of each candidate route. If the real-time traffic data of a candidate route shows congestion, the congestion energy consumption correction logic in the parameter correlation matrix is ​​called to adjust the energy consumption parameter per unit mileage of the route according to the correlation algorithm between the degree of congestion and the energy consumption increment. If the real-time power source composition of the charging station differs from the initial preset, the real-time grid carbon emission base factor corresponding to the route is recalculated. Combined with the corrected energy consumption per unit mileage and the total length of the route, the initial carbon emission sub-values ​​of each candidate route after correction are calculated.

[0096] The system acquires real-time remaining battery power data for new energy freight vehicles. It then combines this data with the unit mileage comprehensive energy consumption of the carbon emission impact factors of each alternative route after correction, along with the route's base mileage, to calculate the estimated energy consumption demand for each alternative route. Based on the comparison between the estimated energy consumption demand and the real-time remaining battery power, the system assesses battery power suitability: if the real-time remaining battery power can cover the estimated energy consumption demand and retains redundant power, the system is deemed to be suited for battery power; if the real-time remaining battery power is insufficient, the system further queries the availability of charging stations along the alternative route. If a charging station exists that can meet the energy replenishment demand, the system is deemed to be suited for battery power; otherwise, the system is deemed to be unsuitable for battery power.

[0097] For each alternative route, a quantitative scoring standard is established, including the revised initial carbon emission sub-values, energy adaptability, and route accessibility. The comprehensive score of each alternative route is calculated by weighted summation, thereby achieving a systematic evaluation of the alternative routes.

[0098] Based on the comprehensive scores of each candidate route, the candidate route with the highest comprehensive score is selected as the recommended route for the next transportation sub-route. The revised initial carbon emission sub-value, estimated energy consumption demand, charging station locations along the route, and refueling suggestions of the recommended route are presented to the driver through a visual interface on the vehicle display terminal. At the same time, the recommended route data is written into the route recommendation partition of the local vehicle database and linked to the current driving status information of the new energy truck, providing data support for real-time carbon emission monitoring and parameter adjustment after the new energy truck enters the recommended route.

[0099] Step S3: If the new energy truck connects to the charging pile during the transportation phase, the actual carbon emissions of each transportation sub-path and the initial carbon emission baseline value are corrected through charging pile data interaction, charging carbon emission calculation and dynamic breakdown. Combined with the actual carbon emissions of the transportation sub-path, the total carbon emissions of cargo transportation are generated. The core is to solve the data distortion problem caused by the concentrated inclusion of charging carbon emissions in a single stage. Specifically, after the new energy truck connects to the charging pile, the on-board terminal first locates the transportation sub-path to which the charging pile belongs and identifies its unique identifier. It obtains the current power source composition and the corresponding grid carbon emission basic factor from the charging pile operation platform. The battery management system records key charging data and calculates the total charging carbon emissions. Then, based on the ratio of the total mileage of the completed transportation sub-path to the total mileage of the complete path during charging, the allocation weight is determined. The total charging carbon emissions are broken down into the corrected value of charging carbon emissions for the already traveled road segment and the estimated corrected value of charging carbon emissions for the remaining road segment. Finally, the relevant data is synchronously stored in the local and cloud databases to provide accurate data support for subsequent real-time monitoring, route recommendation and destination summary.

[0100] In this embodiment, by accurately calculating and dynamically breaking down charging carbon emissions according to the transportation process, the distortion problem of charging carbon emissions being centrally included in a single stage in the traditional solution is effectively solved, making the carbon emission accounting throughout the process highly compatible with the actual transportation process; it provides continuous and accurate data support for real-time carbon emission monitoring in the subsequent transportation stage, recommendation of the next transportation sub-path, and carbon emission summary at the final transportation destination, further improving the closed-loop logic of carbon emission management.

[0101] Please see Figure 5 Preferably, the specific steps for generating the total carbon emissions from cargo transportation include:

[0102] After a new energy truck connects to a charging pile, the on-board terminal reads the charging pile's identifier using radio frequency identification or near-field communication technology. Simultaneously, it combines the on-board high-precision positioning device with the vehicle sub-path feature table to match and determine the current vehicle sub-path to which the charging pile belongs. This enables precise correlation between each charging action and the transportation process, providing a positioning basis for correcting the actual carbon emissions of the corresponding vehicle sub-path and avoiding data confusion in different charging scenarios.

[0103] The vehicle-mounted vehicle-to-everything (V2X) communication device initiates a targeted data request to the charging pile operation platform to obtain the current power source composition of the charging pile. At the same time, it retrieves various power carbon emission factors from the regional power grid carbon emission database that are consistent with the basic carbon emission factors of the power grid along the transportation sub-path, ensuring that the carbon emission accounting standard is uniform for each charging, which is different from the problem of scattered power carbon emission factor sources and inconsistent standards in traditional solutions.

[0104] After charging begins, the vehicle battery management system collects full-cycle data for the current charging session at preset intervals. This includes the start and end times of charging, real-time charge level, initial remaining charge before charging, and real-time remaining charge after charging. All data is linked to a unified timestamp from the vehicle system to ensure the consistency of data sequence for each charging session. The preset interval refers to a fixed time interval at which the vehicle battery management system continuously collects charging-related data throughout the entire charging cycle. Its core purpose is to accurately capture the dynamic characteristics of key information such as changes in charge level and time points during the charging process. This ensures that the data granularity meets the accuracy requirements for charging carbon emission accounting and energy consumption breakdown, while avoiding excessive system energy consumption or data redundancy due to overly frequent data collection. It also adapts to the rate of change of different charging power scenarios.

[0105] After charging is completed, the collected real-time charging data is summed to obtain the total charging amount for this charge, and the charging unit is standardized and unified. At the same time, the total energy consumption data of this cargo transportation up to the current charging time is retrieved from the local vehicle database. Combined with the initial remaining power before charging and the real-time remaining power after charging, the actual charging power consumed in this transportation is calculated to eliminate the interference of charging amount in subsequent transportation and avoid the carbon emission attribution deviation caused by ignoring the unused charging amount in the traditional solution.

[0106] Based on the electricity source composition and electricity carbon emission factor obtained this time, the carbon emission of a single electricity type is first calculated by multiplying the proportion of each type of electricity source by the corresponding electricity carbon emission factor. Then, the carbon emission of all types of electricity is summed to obtain the total carbon emission corresponding to the total charging amount. Furthermore, combined with the actual charging amount consumed by the current transportation, the carbon emission of the current transportation should bear for this charging is calculated to ensure that only the carbon emission of the actual consumption of the current transportation is included in the calculation.

[0107] The system retrieves the total mileage of the completed sub-route and the total mileage of the cargo transport route during this charging session from the local vehicle database. It calculates the percentage of completed mileage and uses it as the allocation weight. This allocation weight reflects the position of this charging behavior in the entire transportation process, achieving dynamic matching between the allocation weight and the actual transportation progress. This is different from the static mode of allocating charging carbon emissions with a fixed ratio in traditional solutions.

[0108] The total carbon emissions from the actual charging power consumed during the current cargo transport are divided into two weighted values: the corrected carbon emission value for charging on the already traveled routes and the estimated corrected carbon emission value for charging on the remaining routes. The corrected carbon emission value for charging on the already traveled routes is added to the actual cumulative carbon emission value of each already traveled sub-route, correcting and updating the actual cumulative carbon emission value of the corresponding sub-route. The estimated corrected carbon emission value for charging on the remaining routes is added to the initial carbon emission value of each remaining sub-route, correcting and updating the actual estimated carbon emission value of the corresponding sub-route. By accurately correcting the actual carbon emissions of each sub-route, and combining the corrected actual values ​​of all sub-routes, the total carbon emissions of cargo transport are generated.

[0109] After each charge completes the above correction, the total carbon emissions from cargo transportation and the initial carbon emission baseline value in the local vehicle database are updated in real time. This provides the latest data basis for possible subsequent recharge corrections and ensures the consistency and accuracy of carbon emission data throughout the entire charging process.

[0110] Step S4: By comparing the initial carbon emission values ​​of each transport sub-path with the actual carbon emissions, identify abnormal transport sub-paths that exceed the preset dynamic deviation threshold and complete the source tracing of the abnormalities. Integrate the basic information, carbon emission details, abnormal descriptions and calculation basis of the entire transportation process to generate a standardized carbon emission report. At the same time, at key nodes in the entire transportation process, update the basic data, carbon emission data and auxiliary records of this cargo transportation to the historical transportation database and add multi-dimensional tags to provide reliable data support and compliance basis for accurate carbon emission calculation, route optimization and monitoring model iteration of subsequent transportation tasks.

[0111] Preferably, the specific steps for generating a standardized carbon emission report include:

[0112] The system retrieves the initial carbon emission values ​​and corrected actual carbon emission values ​​for all transport sub-routes in this freight transport operation from the local vehicle database, ensuring a one-to-one correspondence between the two types of data for each route. It also calculates the carbon emission deviation rate for each transport sub-route to accurately quantify the degree of deviation between the actual value and the estimated baseline.

[0113] Set a dynamic deviation threshold, which is a critical value for deviation judgment that is dynamically adjusted based on transportation scenarios, route characteristics, and historical data, rather than a fixed value; set the threshold based on the terrain complexity of the transport sub-route, the average carbon emission deviation rate of similar historical routes, and the carbon emission control level of the transportation task; mark transport sub-routes with carbon emission deviation rates exceeding the dynamic deviation threshold as abnormal transport sub-routes, and form an abnormal transport sub-route list.

[0114] Retrieve real-time transport data records corresponding to abnormal transport sub-paths, including road congestion duration, real-time terrain changes, load fluctuation data, charging power source adjustment records, parameter correction logs, and path feature tables. Through correlation analysis, locate the root cause of the anomaly, such as increased energy consumption due to congestion, inaccurate energy consumption calculation caused by terrain prediction deviation, and increased carbon emissions due to the proportion of green electricity in charging being lower than the preset value. Generate a detailed source tracing document.

[0115] The core data of the identified abnormal vehicle sub-paths are summarized, including the sub-path number, start and end point coordinates, mileage, initial carbon emission sub-value, actual carbon emission, and carbon emission deviation rate. Combined with the root cause of the anomaly, a structured abnormal vehicle sub-path information table is formed.

[0116] Retrieve basic information about this cargo transportation from the local vehicle database, including the new energy truck identification, cargo name and total weight, transportation start and end times, coordinates of the starting and ending points, the final transportation route, and the basis for dividing each sub-route, to ensure that the information is associated with the route numbers in the abnormal sub-route information table.

[0117] The initial carbon emission values, actual carbon emissions, and carbon emission deviation rates of each transport sub-route are integrated and merged with the abnormal transport sub-route information table to form a detailed carbon emission report for the entire route. At the same time, the total carbon emissions of each charging during this cargo transportation process are summarized, and the correction values ​​of the driven and remaining road segments are broken down by mileage percentage. Finally, the total carbon emissions of this cargo transportation are calculated. A standardized carbon emission report is generated through structured layout, visualization, and compliance verification. The report is arranged in the following order: report identification information, basic transportation information, detailed carbon emissions for the entire route, special explanation of abnormal transport sub-routes, summary of charging carbon emissions, total carbon emissions, and appendix on calculation basis. The report identification information includes: report number, generation time, associated new energy freight vehicle and task number; basic transportation information includes: new energy freight vehicle identification, cargo name and total weight, transportation start and end time, coordinates of the starting and ending points, the final transportation route and the basis for dividing each transportation sub-route; the full-route carbon emission details are presented in tabular form, showing the initial carbon emission sub-values, actual carbon emissions and carbon emission deviation rates for all transportation sub-routes, with abnormal transportation sub-routes marked with special identifiers; a special explanation for abnormal transportation sub-routes includes an information table of abnormal transportation sub-routes and source analysis, highlighting key nodes where the carbon emission deviation rate exceeds the dynamic deviation threshold; the charging carbon emission breakdown summary lists the total carbon emissions for each charging session, the carbon emission correction values ​​for the already driven sections and the estimated correction values ​​for the remaining sections, and the correction results, broken down by mileage; the total carbon emissions clearly state the final calculated value and calculation logic; the calculation basis appendix includes the core logic of the parameter correlation matrix, the source of the grid carbon emission factor, and data verification records. For key data such as the composition of total carbon emissions and the distribution of carbon emission deviation rates for each carrier sub-path, visual charts such as pie charts and bar charts are used to enhance readability. A unique data traceability QR code is added to the report, linking it to the original data storage address. A compliance verification rule base is invoked to check the consistency of data logic, the compliance of accounting basis, and the completeness of required fields. After verification, a standardized carbon emission report is generated, synchronously archived to local and cloud databases, and pushed to relevant management terminals.

[0118] Preferably, the specific steps for updating the historical transportation database include:

[0119] The system pre-defines key trigger points for database updates, including the completion of each transport sub-route, the end of each charging process, and the arrival of the new energy truck at the transport destination. For each key trigger point, the corresponding data update scope is clearly defined: upon completion of a transport sub-route, the system updates the actual carbon emissions, parameter correction records, and recommended route execution status for that sub-route; after charging, it updates the charging carbon emission calculation data, the carbon emission correction values ​​for each transport sub-route, and the total amount; upon arrival at the destination, it updates the complete transport dataset and standardized carbon emission report.

[0120] Extract the data to be updated from the corresponding key trigger nodes from the local vehicle database, and remove duplicate data and invalid redundant information. Perform a unified conversion on data of different formats to ensure that the new data is consistent with the existing data format in the database, and avoid storage or query anomalies caused by format differences.

[0121] Add category tags to the data to be updated. Tag dimensions include transportation scenario tags, terrain feature tags, seasonal climate tags, power source tags, and abnormal situation tags. The tags are in the form of keyword combinations, which facilitates quick matching of similar historical transportation records through subsequent multi-condition filtering.

[0122] Standardized, tagged data is written to the corresponding partition of the historical transportation database via a database interface, according to a preset data storage structure. Simultaneously, a cloud synchronization mechanism is activated to push locally updated data to the cloud database in real time, ensuring consistency between local and cloud data. After writing is complete, a data update log is generated, recording information such as update time, data type, and number of updated records, facilitating subsequent data maintenance and troubleshooting.

[0123] To address the tag characteristics of newly added data, the database retrieval index was updated, query algorithms were optimized, and the efficiency of matching historical records for similar transportation tasks was improved. Simultaneously, invalid data in the database was regularly cleaned up to ensure database performance.

[0124] This embodiment also introduces a carbon emission monitoring, verification and correction system for new energy freight vehicles, including a vehicle initialization module, a vehicle monitoring module, a charging monitoring module, an anomaly identification module and a dynamic update module;

[0125] The initial loading module acquires information about the onboard cargo and the basic information of the new energy trucks, generates an initial loading vector, constructs a parameter correlation matrix, and breaks down each cargo transport candidate route into sub-routes, constructs a sub-routes feature table, and generates an initial carbon emission baseline value for cargo transport by matching benchmark reference records with the historical transport database. The loading monitoring module, during the transport phase of the new energy trucks, monitors and corrects deviations in the initial loading vector and associated sub-routes based on real-time transport data from the current sub-routes the new energy trucks enter, thereby obtaining the current sub-routes' carbon emission baseline value. The system calculates the actual carbon emissions of each transport sub-path and updates the initial carbon emission sub-values ​​of each candidate sub-path for the next transport sub-path, which is used to select the recommended path for the next transport sub-path; the charging monitoring module is used to correct the actual carbon emissions of each transport sub-path and the initial carbon emission benchmark value through charging pile data interaction, charging carbon emission calculation and dynamic splitting, and generates the total carbon emissions of cargo transportation based on the actual carbon emissions of the transport sub-path; the anomaly identification module is used to identify abnormal transport sub-paths based on the initial carbon emission sub-values ​​and actual carbon emissions of the transport sub-paths; the dynamic update module is used to update the historical transportation database;

[0126] In the loading phase of new energy freight vehicles, the initial loading module collects and standardizes information on the onboard cargo and basic information of the new energy freight vehicles, and constructs an initial loading vector according to a preset parameter sequence. It then calls upon the map service system to generate alternative transport routes based on the cargo's starting and ending points. These alternative routes are further divided into sub-routes based on differences in road segment characteristics. Features such as route mileage, terrain type, and default power source for charging stations along the route are extracted from each sub-route to form a sub-route feature table, which is then linked to the initial loading vector. Finally, using the combined parameters of the onboard cargo information and sub-route features as the row dimension and the basic information parameters of the new energy freight vehicles as the column dimension, a logical identifier for carbon emission value calculation is constructed. The parameter correlation matrix is ​​configured, and a parameter correlation matrix update interface is configured to achieve dynamic adaptation. By using the new energy truck identifier of the new energy truck, and combining the start point, end point and route characteristics of each transport sub-path, the baseline reference record is matched in the historical transportation database. If a match is found, the initial carbon emission sub-value of each transport sub-path is generated through difference analysis. If no match is found, the corresponding carbon emission value calculation logic is called based on the parameter correlation matrix to calculate the initial carbon emission sub-value of each transport sub-path. Finally, the initial carbon emission baseline value of cargo transportation is obtained by summarizing the data. The initial loading vector, transport sub-path feature table, initial carbon emission sub-value of each transport sub-path and initial carbon emission baseline value are synchronously stored in the local vehicle database.

[0127] The transport monitoring module is responsible for the dynamic management and control of the entire transport phase of new energy trucks. After a new energy truck enters the current transport sub-path, it collects real-time data on battery output energy consumption, mileage, road terrain, and load. This data is then processed using a dynamic threshold filtering algorithm and data time-series consistency verification to form a standardized real-time transport dataset. The module retrieves the initial loading vector and transport sub-path feature table, compares the real-time transport data with the initial parameters, calculates the parameter deviation rate, identifies abnormal parameters, and calls the corresponding carbon emission value calculation logic in the parameter correlation matrix for targeted correction. It updates the actual carbon emissions of the current transport sub-path and the initial carbon emission benchmark value for cargo transport, and synchronously writes this data to the local vehicle database for dynamic updates. When a new energy truck enters the current transport sub-path, it dynamically updates the partition. When a truck reaches a preset distance node on the current transport sub-route, it retrieves the list of alternative routes and initial data for the next transport sub-route. It then requests real-time traffic status, charging station operation status, and power source composition data from the map service platform, charging pile operation platform, and regional power grid data platform, respectively, and integrates them to form a real-time dynamic route database for alternative routes. Based on this database, it corrects the initial carbon emission sub-values ​​of each alternative route, assesses the power suitability by combining the real-time remaining power of the new energy truck, calculates the comprehensive score of each alternative route by weighted summation, selects the route with the highest comprehensive score as the recommended route, presents the relevant information of the recommended route to the driver through the vehicle display terminal, and writes the recommended route data into the local vehicle database route recommendation partition.

[0128] The charging monitoring module is used to perform carbon emission calculation and correction when new energy trucks connect to charging piles during the transportation phase. The onboard terminal reads the charging pile identifier using RFID or near-field communication technology, and determines the current transportation sub-path to which the charging pile belongs by combining the onboard high-precision positioning device and the transportation sub-path feature table. It obtains the power source composition from the charging pile operation platform through the onboard vehicle-to-everything (V2X) communication device and retrieves the corresponding power carbon emission factor from the regional power grid carbon emission database. During charging, the onboard battery management system collects full-cycle charging data at preset intervals, and the total charging amount is accumulated after charging is completed to calculate the actual charging power consumed during the current transportation. Based on the power... The system calculates the total carbon emissions from this charging operation based on the source composition and electricity carbon emission factor, as well as the carbon emissions from charging that should be borne by the current transportation. Then, it determines the allocation weight based on the ratio of the total mileage of the completed sub-route to the total mileage of the complete route during charging. The carbon emissions to be borne are then broken down into the corrected value of the carbon emissions from charging on the already traveled route and the estimated corrected value of the carbon emissions from charging on the remaining route. The actual cumulative carbon emissions of each traveled sub-route and the initial carbon emissions of each remaining sub-route are updated using the two types of correction values, respectively. The total carbon emissions from cargo transportation are then aggregated to generate the total carbon emissions from cargo transportation. The total carbon emissions and the initial carbon emission baseline value in the local vehicle database are updated in real time and stored synchronously in the cloud database.

[0129] The anomaly detection module retrieves the initial carbon emission values ​​and corrected actual carbon emissions for all transport sub-routes from the local vehicle database, and calculates the carbon emission deviation rate for each sub-route. It sets a dynamic deviation threshold based on the terrain complexity of the sub-routes, the average carbon emission deviation rate of similar historical routes, and the carbon emission control level of the transport task. Sub-routes with carbon emission deviation rates exceeding this threshold are marked as abnormal and listed. The module retrieves real-time transport data records, parameter correction logs, and route feature tables corresponding to the abnormal sub-routes, uses correlation analysis to locate the root cause of the anomaly, and generates a source tracing document. Finally, it summarizes the core data of the abnormal sub-routes. Based on the structured abnormal transport sub-path information table formed with the root cause of the anomaly, and at the same time retrieving the basic information of this cargo transportation, the carbon emission details of the whole path and the charging carbon emission breakdown data are integrated to calculate the total carbon emission of transportation. The content is arranged in the order of report identification information, basic transportation information, carbon emission details of the whole path, special explanation of abnormal transport sub-path, summary of charging carbon emission breakdown, total carbon emission, and calculation basis appendix. The key data is presented with visual charts, a unique data traceability QR code is added, and after the compliance verification rule library is called to complete the verification, a standardized carbon emission report is generated, which is simultaneously archived to the local and cloud databases and pushed to the relevant management terminals.

[0130] The dynamic update module is used to achieve full lifecycle management of historical transportation data. It presets three key trigger nodes: completion of each transportation sub-route, end of each charging process, and arrival of the new energy truck at the transportation destination, and clarifies the update data range corresponding to each node. It extracts the data to be updated for the corresponding node from the local vehicle database, removes duplicate and invalid redundant information, and uniformly converts data of different formats to ensure format consistency. It adds multi-dimensional classification tags such as transportation scenario tags, terrain feature tags, seasonal climate tags, power source tags, and abnormal situation tags to the data to be updated, and uses keyword combinations to facilitate subsequent filtering and matching. It writes the standardized tagged data to the corresponding partition of the historical transportation database through the database interface, and starts the cloud synchronization mechanism to ensure data consistency between local and cloud. After writing, it generates an update log containing update time, data type, and number of updated entries. It updates the database retrieval index for the tag features of newly added data, optimizes the query algorithm to improve the matching efficiency of historical records, and regularly cleans up invalid data to ensure database operation performance.

[0131] Working principle and its effects:

[0132] The core working logic of this invention is to achieve precise management of the entire chain from initial benchmark setting to final report generation by refining the control of carbon emissions from new energy trucks and combining multi-dimensional data integration and dynamic correction mechanisms. This effectively solves the problems of large accounting deviations, slow dynamic response and chaotic data attribution in traditional monitoring, and provides scientific and reliable technical support for the carbon emission reduction and control of new energy trucks.

[0133] During the loading phase, basic information on the cargo and new energy freight vehicles is collected and standardized to construct an initial loading vector and parameter correlation matrix. Simultaneously, alternative routes are split into transport sub-paths, and features are extracted. This data is then combined with high-similarity benchmark reference records from a historical transportation database to calculate the initial carbon emission baseline value. This design fully considers the individual differences in transportation tasks, avoids the limitations of a uniform parameter model, and ensures that the initial carbon emission baseline value is highly adapted to the actual scenario, significantly reducing prediction bias. During the transport phase, dynamic data such as road conditions and load are collected in real time. Abnormal deviations are identified by comparing with the initial parameters, and the carbon emission value calculation logic is invoked to correct the influencing factors of the carbon emission value, synchronously updating the actual carbon emissions. At the same time, the optimal recommended route is selected based on real-time road conditions and battery availability, ensuring both the dynamic accuracy of carbon emission calculation and achieving a balance between low-carbon goals and transportation feasibility. When new energy freight vehicles connect to charging stations, the system matches the sub-path of the vehicle to which the charging belongs, calculates the carbon emissions corresponding to the actual electricity consumption, and splits the correction value according to the transportation progress, updating the carbon emission data for the already traveled and remaining routes separately. This solves the problem of distorted attribution when charging carbon emissions are centrally included, ensuring the integrity of the entire process accounting. Finally, abnormal routes are identified by calculating the carbon emission deviation rate, and standardized reports are generated by integrating the entire process data and updating the historical database, providing a more accurate reference for subsequent monitoring and meeting the dual needs of enterprise management and regulatory verification.

[0134] In summary, this invention achieves precise control over the entire chain of initial carbon emission benchmark values, dynamic accounting values, charging carbon emissions, and anomaly identification. Furthermore, it forms a closed-loop management system through standardized reports and historical database updates, significantly improving the scientific rigor, timeliness, and compliance of carbon emission monitoring, and providing strong technical support for the implementation of dual-carbon targets in the transportation sector.

[0135] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles, characterized in that, include: The system acquires cargo information and basic information of new energy freight vehicles, generates an initial loading vector and constructs a parameter correlation matrix, and splits each cargo transportation candidate route into a transportation sub-route, constructs a transportation sub-route feature table, and combines historical transportation database to match benchmark reference records to generate an initial carbon emission benchmark value for cargo transportation. During the transportation phase of new energy freight vehicles, based on the real-time transportation data of the current transportation sub-path that the new energy freight vehicles have entered, the loading initial vector and the associated transportation sub-path feature table are monitored and corrected in real time to obtain the actual carbon emissions of the current transportation sub-path and update the initial carbon emission sub-values ​​of each candidate path of the next transportation sub-path for screening the recommended path of the next transportation sub-path. If new energy trucks connect to charging piles during the transportation phase, the actual carbon emissions of each transportation sub-path and the initial carbon emission baseline value are corrected through charging pile data interaction, charging carbon emission calculation and dynamic splitting, and the total carbon emissions of cargo transportation are generated by combining the actual carbon emissions of the transportation sub-paths. Based on the initial carbon emission sub-values ​​and actual carbon emissions of the transport sub-routes, abnormal transport sub-routes are identified, carbon emission reports are generated, and the historical transport database is updated. The steps of generating the initial loading vector and constructing the parameter correlation matrix include: The collected information on onboard cargo and basic information on new energy trucks are standardized and processed, and an initial loading vector is constructed according to a preset parameter sequence. Based on the cargo transport origin and destination, alternative transport routes are generated. According to the differences in the road segment characteristics of the alternative transport routes, each alternative transport route is divided into transport sub-routes. The path features of each transport sub-routes are extracted to form a transport sub-routes feature table and associated with the loading initial vector. A parameter association matrix is ​​constructed using the merged parameters of vehicle cargo information and transport sub-path features as the matrix row dimension and the parameters in the basic information of new energy freight vehicles as the matrix column dimension. Each element of the parameter correlation matrix corresponds to a set of carbon emission value accounting logic identifiers, which are used to constrain the carbon emission value accounting logic between corresponding parameters. The carbon emission value calculation logic refers to a set of rules that describe the synergistic effect between vehicle cargo information parameters, transportation sub-route characteristic parameters, and basic information parameters of new energy freight vehicles to derive the carbon emission value influencing factors. The steps for obtaining the actual carbon emissions of the current carrier sub-path include: After the new energy truck enters the current transport sub-path, transport data is collected in real time to build a real-time transport dataset. The loading initial vector and the associated transport sub-path feature table are retrieved to extract the initial parameters corresponding to the real-time transport data. The parameter values ​​of the real-time vehicle data dataset are compared with the corresponding initial parameter values ​​in the loading initial vector, and the parameter deviation rate is calculated. The corresponding parameters in the loading initial vector whose parameter deviation rate exceeds the deviation threshold are marked as deviation abnormal parameters. When an abnormal parameter is identified, the carbon emission value calculation logic corresponding to the abnormal parameter in the parameter correlation matrix is ​​invoked to correct the impact factor of the carbon emission value of the current carrier sub-path. The system retrieves the initial carbon emission baseline value for this cargo transport and the initial carbon emission sub-values ​​for each transport sub-route. Based on the influence factors of the corrected carbon emission values ​​of the transport sub-routes and the preset carbon emission value calculation logic, it updates the initial carbon emission sub-values ​​of the transport sub-routes that have already been traveled, obtains the actual carbon emission amount of the current transport sub-route, and then updates the initial carbon emission baseline value for this cargo transport.

2. The method for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles as described in claim 1, characterized in that, The steps for matching the baseline reference record include: By using the new energy vehicle identification of the new energy vehicle, combined with the starting point and ending point of each sub-route of this cargo transportation and the route characteristics of the sub-route, route matching is performed in the historical transportation database. Historical transportation records in which the coordinate error of the starting point and ending point of the sub-route is within a preset distance range and the similarity of the sub-route features is greater than a preset transportation similarity threshold are used as benchmark reference records. If the historical transport records are not unique, the difference between the current loading initial vector and the loading initial vector of each historical record is calculated, and the historical transport records are selected as the benchmark reference records based on the difference between the loading initial vector and the historical loading initial vector.

3. The method for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles as described in claim 2, characterized in that, The steps for generating the initial carbon emission baseline for cargo transportation include: If no benchmark reference record is matched, the impact factors of carbon emission values ​​of each carrier sub-path are calculated based on the current loading initial vector and the associated carrier sub-path feature table, including comprehensive energy consumption per unit mileage, basic mileage of the route, and basic carbon emission factors of the power grid. Based on the mileage of the transport sub-routes, and according to the preset carbon emission value calculation logic, the initial carbon emission sub-value of each transport sub-routes is calculated; the initial carbon emission sub-values ​​of all transport sub-routes are summed to obtain the carbon emission benchmark value of each alternative transport route, and the initial carbon emission benchmark value of this cargo transport is obtained by weighted averaging. If a benchmark reference record is matched, a carrier sub-path difference vector is constructed according to the carrier sub-path, and the carbon emission value accounting logic corresponding to the difference parameter in the parameter association matrix is ​​called to calculate the difference component of the carbon emission value impact factor. Based on the mileage of the transport sub-paths and according to the preset carbon emission value calculation logic, the carbon emission difference sub-value of each transport sub-path is obtained; the actual carbon emission sub-value of the corresponding transport sub-path in the benchmark reference record is corrected to obtain the current carbon emission sub-value of each transport sub-path; the carbon emission sub-values ​​of all current transport sub-paths are summed to obtain the initial carbon emission benchmark value of this cargo transport.

4. The method for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles as described in claim 1, characterized in that, The step of selecting the recommended path for the next carrier sub-path includes: When a new energy truck reaches a preset distance node of the current transport sub-path, the on-board route recommendation operation is triggered, the list of alternative routes for the next transport sub-path is retrieved, and the initial data corresponding to each alternative route is extracted. Based on the list of alternative paths, obtain the real-time path data of each alternative path to form a real-time dynamic path database for alternative paths; Based on the real-time dynamic path database of alternative routes, the impact factors of carbon emission values ​​of carrier sub-paths are updated, and the carbon emission value accounting logic corresponding to the parameter correlation matrix is ​​called to correct the initial carbon emission sub-values ​​of each alternative route. Obtain the current real-time remaining power data of new energy freight vehicles, and combine the unit mileage comprehensive energy consumption of the carbon emission value impact factor of each alternative route with the basic mileage of the route to calculate the estimated energy consumption demand of each alternative route. The power supply adaptability is assessed based on the comparison between estimated energy demand and real-time remaining power. A quantitative scoring standard is established for the evaluation dimensions of each alternative route. The evaluation dimensions include the corrected initial carbon emission sub-values ​​of each alternative route, energy adaptability, and route accessibility. The comprehensive score of each alternative route is calculated by weighted summation. Based on the comprehensive scores of each candidate path, the candidate path is selected as the recommended path for the next carrier sub-path.

5. The method for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles as described in claim 1, characterized in that, The steps for generating the total carbon emissions from cargo transportation include: After a new energy truck connects to a charging pile, the charging pile identifier is read and matched with the current transport sub-path to which the charging pile belongs. Obtain the current power source composition of the charging pile and retrieve the carbon emission factors of various power sources; After charging is started, data for the entire charging cycle is collected, including the start time of charging, the end time of charging, the real-time charging amount, the initial remaining power before charging, and the real-time remaining power after charging. After charging is completed, the collected real-time charging amount data is accumulated to obtain the total charging amount. By combining the initial remaining power before charging and the real-time remaining power after charging, the actual charging power consumed by the current cargo transport is obtained. Based on the collected electricity source composition and electricity carbon emission factor, the carbon emission of a single electricity source is calculated by multiplying the proportion of each type of electricity source by the corresponding electricity carbon emission factor, and the total carbon emission of the total charging amount is obtained by summing the carbon emissions of each electricity source.

6. The method for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles as described in claim 5, characterized in that, The step of generating the total carbon emissions from cargo transportation also includes: Retrieve the total mileage of the sub-route completed during this charging and the total mileage of the cargo transportation route, calculate the percentage of completed mileage, and use it as the allocation weight; The total carbon emissions from the actual charging electricity consumed during the current cargo transport are divided into adjusted carbon emission values ​​for charging on the already traveled routes and estimated carbon emission values ​​for charging on the remaining routes, according to the allocation weight: The carbon emission correction value for charging on the already driven road segment is used to be superimposed on the actual cumulative carbon emission value of each already driven vehicle sub-path segment, correcting and updating the actual cumulative carbon emission value of the corresponding vehicle sub-path. The revised carbon emission estimates for charging on the remaining routes are used to add to the initial carbon emission sub-values ​​for each remaining transport sub-path, thereby correcting and updating the actual carbon emission estimates for the corresponding transport sub-paths.

7. The method for monitoring, verifying, and correcting carbon emissions from new energy freight vehicles as described in claim 1, characterized in that, The steps of identifying abnormal carrier sub-paths and generating carbon emission reports include: Retrieve the initial carbon emission values ​​and corrected actual carbon emissions for all transport sub-routes in this cargo transportation, and calculate the carbon emission deviation rate for each transport sub-routes. Set a dynamic deviation threshold, mark the carrier sub-paths whose carbon emission deviation rate exceeds the dynamic deviation threshold as abnormal carrier sub-paths, and form a list of abnormal carrier sub-paths. Retrieve real-time transport data records corresponding to abnormal transport sub-paths, locate the root cause of the abnormality, summarize the core data of the identified abnormal transport sub-paths, and form a structured abnormal transport sub-path information table. The basic information of this cargo transportation is retrieved, and the initial carbon emission values, actual carbon emissions and carbon emission deviation rates of each transport sub-route are integrated. This information is then combined with the abnormal transport sub-route information table to form a full-route carbon emission detail. The total carbon emissions of this cargo transportation are calculated, and a standardized carbon emission report is generated.

8. A carbon emission monitoring, verification, and correction system for new energy freight vehicles, implemented based on the carbon emission monitoring, verification, and correction method for new energy freight vehicles as described in any one of claims 1-7, characterized in that, It includes a vehicle initialization module, a vehicle monitoring module, a charging monitoring module, an anomaly identification module, and a dynamic update module; The initial loading module is used to acquire vehicle cargo information and basic information of new energy trucks, generate an initial loading vector and construct a parameter correlation matrix, construct a loading sub-path feature table, and combine it with the historical transportation database to generate an initial carbon emission benchmark value for cargo transportation. The transport monitoring module is used to obtain the actual carbon emissions of the current transport sub-path based on the real-time transport data of the new energy truck entering the current transport sub-path, update the initial carbon emission sub-values ​​of each candidate path of the next transport sub-path, and screen the recommended path of the next transport sub-path. The charging monitoring module is used to correct the actual carbon emissions of each transport sub-path and the initial carbon emission benchmark value through charging pile data interaction, charging carbon emission calculation and dynamic splitting, and generate the total carbon emissions of cargo transport. The anomaly identification module is used to identify abnormal transport sub-routes based on the initial carbon emission sub-value and the actual carbon emission amount; the dynamic update module is used to update the historical transport database.

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