Transportation process carbon footprint accounting and dynamic allocation method based on multi-dimensional data
By constructing a multi-dimensional database and a dynamic allocation method, the issues of accuracy and fairness in carbon footprint accounting during transportation have been resolved, enabling refined and dynamic accounting and allocation of carbon footprint, and supporting enterprises' low-carbon decision-making.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUANENG ENERGY & COMM HLDG CO LTD
- Filing Date
- 2025-12-26
- Publication Date
- 2026-04-28
AI Technical Summary
Existing technologies for calculating carbon footprints in the transportation process suffer from problems such as a single calculation dimension, unfair allocation mechanism, and fragmented data, resulting in insufficient accuracy, unfair allocation, and low calculation efficiency. They cannot meet the market's demand for more accurate, fair, and transparent carbon footprints at the product level.
A multi-dimensional basic database is constructed, including databases of transportation vehicles, transportation tasks, and commodity attributes. The total carbon footprint is dynamically calculated by combining actual transportation distance, energy consumption factor per unit distance, and load correction coefficient. The carbon footprint is then corrected and normalized by the emission weight coefficient of commodity type, thereby achieving a refined and dynamic allocation of the carbon footprint.
It enables accurate accounting and fair allocation of carbon footprint in the transportation process, improves accounting accuracy, optimizes data integration and calculation efficiency, and provides enterprises with scientific low-carbon consumption choices and supply chain optimization strategy support.
Smart Images
Figure CN121936792A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, and in particular to a method and apparatus for calculating and dynamically allocating the carbon footprint of transportation processes based on multi-dimensional data. Background Technology
[0002] With the deepening global consensus on addressing climate change and the advancement of China's "dual carbon" goals, the accurate measurement and management of carbon emissions has become a core concern for governments, businesses, and consumers. Product carbon footprint, as a key indicator for measuring greenhouse gas emissions throughout a product's lifecycle, is an important tool for promoting green consumption and guiding low-carbon production. The transportation sector, as the connecting artery of the industrial chain, accounts for a significant portion of the total carbon footprint of a product, especially in industries such as commerce, logistics, and manufacturing. Therefore, accurate calculation and reasonable allocation of the carbon footprint during transportation are not only fundamental to accurately assessing the environmental impact of products, but also crucial for enterprises to optimize their supply chains, formulate emission reduction strategies, respond to carbon tariff policies such as the EU's CBAM, and fulfill their social responsibilities.
[0003] Currently, the accounting and allocation of carbon footprints in transportation processes both domestically and internationally mainly rely on international frameworks such as ISO 14083:2023 and the GHG Protocol (Scope 3). However, these standards have revealed prominent problems in practice, including insufficient accounting granularity, unfair allocation mechanisms, and difficulties in technology implementation. These problems are specifically reflected in three aspects: First, the single accounting dimension leads to insufficient accuracy and reliability. The mainstream "average emission factor method" (carbon emissions = transport volume × emission factor), although simple to operate, has inherent defects: First, it ignores the differences in tools, mixing up transport vehicles of different models, fuel types, energy efficiency levels, and load conditions, and fails to reflect the differences in emission intensity between old heavy trucks and new electric light trucks; second, it ignores the specificity of the route, using straight-line distance or default distance to replace the actual driving trajectory of the vehicle, which leads to a serious underestimation of the calculation results due to road conditions, congestion and other factors in urban delivery; third, it lacks dynamic adjustment, and the static average factor cannot reflect the impact of real-time road conditions, vehicle speed, temperature and other factors on energy consumption, and the calculation results are only a rough estimate.
[0004] Secondly, the allocation mechanism is unfair and fails to drive emission reduction behavior. When a single shipment contains multiple goods, the current allocation method based on mass or volume assumes that all goods share the same "responsibility" for transportation resource consumption and carbon emissions, leading to unfair allocation results. For example, ordinary plastic pellets transported at the same time and an X-ray diffractometer requiring a constant temperature and pressure environment may share the same carbon footprint by mass, but the differences in emissions caused by the energy consumption of maintaining a constant temperature and pressure environment are ignored. This allocation method distorts product costs and fails to provide effective economic incentives for cargo owners to choose low-carbon products and services.
[0005] Third, data fragmentation and technological lag hinder refined accounting. Transportation activities involve multi-source heterogeneous data on tools, energy consumption, routes, and goods. Currently, there is a lack of an effective technological framework to achieve organic data integration. Enterprises must manually extract data from TMS, ERP systems, and fuel consumption statistics and perform calculations using spreadsheets, which is inefficient and prone to errors. At the same time, the existing accounting system treats goods as passive "transported goods," systematically ignoring the additional impact of their physical and chemical properties, such as temperature control requirements, fragility, and value density, on carbon emissions. It has not established a database linking "goods type" and "emission weights," resulting in a lack of data foundation and calculation basis for fair allocation.
[0006] In summary, current technologies remain at a macroscopic, average, and static level, failing to meet the market's demand for precise, fair, and transparent carbon footprint analysis at the product level. The industry urgently needs a calculation and allocation method that deeply integrates multi-dimensional dynamic data and scientifically reflects the differences in emissions from the transportation of different commodities, in order to bridge the gap between standards and practice. Summary of the Invention
[0007] The main objective of this invention is to provide a method for calculating and dynamically allocating the carbon footprint of transportation processes based on multi-dimensional data.
[0008] Another objective of this invention is to propose a device for calculating and dynamically allocating the carbon footprint of a transportation process based on multi-dimensional data.
[0009] The third objective of this invention is to provide an electronic device.
[0010] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.
[0011] To achieve the above objectives, a first aspect of the present invention proposes a method for calculating and dynamically allocating the carbon footprint of a transportation process based on multi-dimensional data, comprising: Construct a multi-dimensional basic database, which includes a transportation vehicle database, a transportation task database, and a commodity attribute database; Based on the aforementioned basic database, the total carbon footprint of a single transportation task is dynamically calculated by using the actual transportation distance of the transportation task, the energy consumption factor per unit distance of the transportation vehicle, and the load correction coefficient. The total carbon footprint is allocated to each smallest unit of goods in this transportation task. The standard allocation amount of each goods is calculated, and the standard allocation amount is corrected using the emission weighting coefficient of each goods type. Through normalization, it is ensured that the sum of the allocation values of all goods is consistent with the total carbon footprint, thus completing the dynamic allocation.
[0012] Optionally, the transportation database can cover different transportation types and their corresponding energy consumption factors per unit distance; The transportation task database is used to record the actual transportation distance, transportation vehicle type, and load factor for each transportation task. The commodity attribute database is used to define commodity type emission weight coefficients for different categories of commodities.
[0013] Optionally, the total carbon footprint of a single transport mission can be calculated using the following formula: CF_total=EF_transport×D×L, Wherein, EF_transport represents the energy consumption factor per unit distance of the selected vehicle for this transport, D represents the actual distance of this transport mission, and L represents the load correction factor.
[0014] Optionally, the load correction factor can be calculated using the following formula: L = Actual load / Rated load.
[0015] Optionally, the calculation of the standard allocation quantity of the goods further includes: The share that each commodity i should be allocated according to traditional physical quantities is calculated using the following formula: CF_standard_i=CF_total×(M_i / M_total), Wherein, CF_standard_i represents the standard allocation quantity of the product, CF_total represents the total carbon footprint, M_i represents the quality of the product, and M_total represents the total quality of all products in this batch.
[0016] Optionally, the standard allocation can be corrected by setting and utilizing weighting coefficients using the following formula: CF_final_i=CF_standard_i×K_good_i, Wherein, CF_final_i represents the final carbon footprint of the commodity during transportation, and K_good_i represents the emission weighting coefficient for commodity type.
[0017] Optionally, the weighting coefficients can be normalized using the following formula: CF_alloc_i=CF_total×(M_i×K_good_i) / Σ(M_i×K_good_i), Wherein, CF_alloc_i represents the carbon footprint of the i-th type of commodity transportation after normalization allocation.
[0018] To achieve the above objectives, a second aspect of the present invention provides a device for calculating and dynamically allocating the carbon footprint of a transportation process based on multi-dimensional data, comprising: A multi-dimensional basic database construction module is used to build a basic database, which includes a transportation tool database, a transportation task database, and a commodity attribute database. The dynamic carbon footprint calculation module for a single transportation trip is used to dynamically calculate the total carbon footprint of a single transportation trip based on the basic database, by taking into account the actual transportation distance of the transportation task, the energy consumption factor per unit distance of the transportation vehicle, and the load correction coefficient. The carbon footprint dynamic allocation and balancing module is used to allocate the total carbon footprint to each smallest unit of goods in this transportation task, calculate the standard allocation amount of goods, correct the standard allocation amount using the emission weight coefficient of goods type, and ensure that the sum of the allocation values of all goods is consistent with the total carbon footprint through normalization, thus completing the dynamic allocation.
[0019] To achieve the above objectives, a third aspect of this application provides an electronic device, including a processor and a memory; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, for implementing a method for calculating and dynamically allocating carbon footprints in transportation processes based on multi-dimensional data as described in the first aspect embodiment.
[0020] To achieve the above objectives, the fourth aspect of this application proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for calculating and dynamically allocating the carbon footprint of a transportation process based on multi-dimensional data, as described in the first aspect embodiment.
[0021] The embodiments of the present invention have the following beneficial effects: The beneficial effects of the present invention are as follows: 1. Significantly improved accounting accuracy: By constructing a multi-dimensional basic database of transportation vehicles, transportation tasks, and commodity attributes, and combining actual transportation distance, dynamic energy consumption factors, and load correction coefficients, the limitations of the traditional average emission factor method are broken through. The impact of differences in vehicles, route specificity, and dynamic operating conditions on carbon emissions is accurately captured, and the total carbon footprint of transportation is calculated in a refined and dynamic manner.
[0022] 2. The allocation mechanism is more equitable and reasonable: The emission weight coefficient associated with commodity attributes is introduced, and the allocation amount is adjusted according to the characteristics of commodity temperature control requirements, fragility and other characteristics. This avoids the distortion of responsibility caused by the traditional "one-size-fits-all" allocation method, so that the carbon footprint allocation results match the actual carbon emission contribution of the commodity and provide a scientific basis for low-carbon consumption choices.
[0023] 3. Data integration and computational efficiency optimization: Establish a technical framework for multi-source heterogeneous data fusion, integrate data resources across the entire transportation chain, replace manual, decentralized extraction and table calculation methods, reduce operational complexity and error rate, and provide enterprises with efficient and reliable carbon footprint accounting tools.
[0024] 4. Supporting the optimization of low-carbon supply chains: Accurate accounting results and fair allocation mechanisms can provide data support for enterprises to formulate emission reduction strategies for supply chains and select low-carbon transportation solutions, helping enterprises to cope with international policy requirements such as carbon tariffs and improve their environmental responsibility fulfillment capabilities and market competitiveness. Attached Figure Description
[0025] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a method for calculating and dynamically allocating the carbon footprint of a transportation process based on multi-dimensional data, provided in an embodiment of the present invention; Figure 2 This is a structural diagram of a transportation process carbon footprint accounting and dynamic allocation device based on multi-dimensional data, provided in an embodiment of the present invention. Detailed Implementation
[0026] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0028] The following description, with reference to the accompanying drawings, describes a method and apparatus for calculating and dynamically allocating the carbon footprint of a transportation process based on multi-dimensional data, according to an embodiment of the present invention.
[0029] Example 1 This invention provides a method for calculating and dynamically allocating the carbon footprint of transportation processes based on multi-dimensional data. Figure 1 This is a flowchart illustrating a method for calculating and dynamically allocating the carbon footprint of a transportation process based on multi-dimensional data, provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps: Step 101: Construct a multi-dimensional basic database, which includes a transportation tool database, a transportation task database, and a commodity attribute database.
[0030] In this embodiment of the application, the basic database includes three sub-databases: a transportation vehicle database, a transportation task database, and a commodity attribute database. The construction method and data content of each sub-database are as follows.
[0031] The transportation vehicle database stores the core energy consumption parameter for different types of transportation vehicles—the energy consumption factor per unit distance (EF_transport), which is one of the key parameters determining the accuracy of the calculation. Unlike traditional methods that use a single, averaged energy consumption factor, this application's embodiments perform refined classification of transportation vehicles to ensure that the energy consumption factor is highly matched with the actual characteristics of the vehicles.
[0032] In practice, the transportation tools are first categorized into major types based on their mode of transport, such as heavy-duty trucks, light-duty trucks, airplanes, ships, and trains. Each major category is then further subdivided according to fuel type (diesel, gasoline, electricity, natural gas, etc.), energy efficiency level (e.g., China V and China VI emission standards for vehicles), and load capacity. For each subdivided transportation tool type, its unit-distance energy consumption factor (EF_transport) is determined by combining measured data, authoritative industry datasets, and historical enterprise operating data, and a dynamic update mechanism is established. This basic data is structured and stored in a core database, and can be manually updated through the system's backend management interface or automatically synchronized with authoritative industry databases to ensure the timeliness of the energy consumption factor.
[0033] The transportation task database records dynamic core data for each transportation task, including actual transportation distance (D), vehicle type, and load factor. This data is automatically acquired through the system's data acquisition module. The actual transportation distance (D) is calculated by the data acquisition module through connection to the vehicle's GPS positioning device, collecting and analyzing the vehicle's real-time trajectory. This effectively avoids the deviation of traditional theoretical distances. For example, if a transportation task's planned distance on the map is 100 kilometers, but due to detours and stops, the actual distance traveled is 120 kilometers, the system can accurately collect the actual data for that 120 kilometers. Vehicle type data is extracted from the enterprise's TMS system by the acquisition module and used to match energy consumption factors in the vehicle database. Load factor data is calculated by the acquisition module through connection to the vehicle's weighing sensor to obtain the actual load, combined with the vehicle's rated load capacity, providing a basis for determining subsequent load correction coefficients. All collected transportation task data is stored in the core database according to task number for easy retrieval and traceability. The commodity attribute database is the core innovation of this application in achieving "fair allocation". Its core function is to define exclusive commodity type emission weight coefficients (K_good) for different categories of commodities. Through this coefficient, the "added value" impact of the commodity's own characteristics on transportation carbon emissions is quantified, thus solving the drawbacks of the traditional allocation method of "one-size-fits-all".
[0034] The product attribute database defines product type weighting coefficients (K_good) for different product categories, serving as the core data support for fair allocation. This data is constructed through a combination of system data acquisition and manual supplementation. The weighting coefficients (K_good) are primarily determined based on the product's temperature control requirements (room temperature, refrigeration, freezing, cryogenics, etc.), special handling requirements (shockproof, anti-tilt, hazardous materials protection, etc.), and value density and physical density. The more stringent the requirements and the higher the resource utilization rate, the larger the coefficient. For example, ordinary dried goods have K_good = 1.0, fresh fruits and vegetables (requiring refrigeration) have K_good = 1.8, and medical vaccines (requiring cryogenics) have K_good = 3.5. The basic attribute data of the products is extracted from the product list in the enterprise's ERP system by the acquisition module. The corresponding weighting coefficients are set by technical personnel based on industry standards and experimental data, then entered into the core database and associated with the product type. Dynamic adjustments based on actual application scenarios are also supported.
[0035] Step 102: Based on the aforementioned basic database, dynamically calculate the total carbon footprint of a single transportation task by considering the actual transportation distance, the unit distance energy consumption factor of the transportation vehicle, and the load correction coefficient.
[0036] In this embodiment, the total carbon footprint calculation is supported by a multi-dimensional basic database. By constructing a calculation formula that integrates tool characteristics, actual routes, and load status, the accurate calculation of the total carbon footprint of a single transportation task is achieved. The calculation formula is: CF_total = EF_transport × D × L, and the determination and calculation of each parameter are as follows.
[0037] In this embodiment of the invention, the data processing and calculation engine automatically retrieves the corresponding parameters from the core database, matches the "transport vehicle type" in the transportation task database with the EF_transport in the transportation vehicle database, extracts the actual transportation distance (D) collected by GPS, and calculates the load correction coefficient (L) based on the load factor data. Here, L reflects the efficiency loss due to empty runs or insufficient load, with a default value of 1 (load factor 100%). The calculation method can be a simple empirical formula (L = actual load / rated load) or a complex fitting formula (L = a × (actual load / rated load) + b, such as a = 0.9 and b = 0.1 for diesel trucks). Users can select the appropriate calculation method through the system settings interface.
[0038] Furthermore, the computing engine incorporates the aforementioned calculation model, performs real-time calculations after calling parameters, and verifies the rationality of the parameters. If it finds that EF_transport exceeds the normal range of similar tools, or that the D value deviates too much from the normal route, it will automatically trigger an anomaly warning and push it to the system interface to remind staff to check.
[0039] Step 103: Allocate the total carbon footprint to each smallest unit of goods in this transportation task, calculate the standard allocation amount of the goods, correct the standard allocation amount using the emission weight coefficient of the goods type, and through normalization processing, ensure that the sum of the allocation values of all goods is consistent with the total carbon footprint, thus completing the dynamic allocation.
[0040] In one embodiment of the invention, the computing engine extracts the quality data of all goods transported in this shipment from the core database (M_i is the quality of a single item, and M_total is the total quality), and calculates the standard allocation amount according to the proportion of traditional physical quantities, using the formula CF_standard_i = CF_total × (M_i / M_total). For example, if the total carbon footprint is 160 kg CO2e, and 5 tons of item A and 5 tons of item B are transported, the standard allocation amount for both is 80 kg CO2e.
[0041] Furthermore, the calculation engine calls the emission weight coefficient (K_good_i) corresponding to each product in the product attribute database to correct the standard allocation, initially obtaining CF_final_i = CF_standard_i × K_good_i. If product A is ordinary plastic granules (K_good_A = 1.0) and product B is an X-ray diffractometer requiring a constant temperature and pressure environment (K_good_B = 1.8), then after the initial correction, product A is 80 kg CO2e and product B is 144 kg CO2e.
[0042] In this embodiment of the invention, to ensure that the total allocation amount is consistent with the total carbon footprint, the calculation engine uses a normalized formula for accurate calculation. The final formula is CF_alloc_i=CF_total×(M_i×K_good_i) / Σ(M_i×K_good_i), which can achieve fair allocation and consistency with the total amount in one step. After the calculation is completed, the system's visualization output module displays the results in the form of tables, dashboards, etc., clearly showing the final carbon footprint and percentage of each product. It also supports the generation of detailed reports, which can trace the source of each parameter and the calculation process, meeting the needs of internal enterprise management and external requirements such as carbon tariffs.
[0043] In the application of one embodiment of the present invention, the implementation process is as follows: A specific transport task: Using a diesel refrigerated heavy-duty truck (EF_transport=1.2kgCO2e / km), travel 100 kilometers to transport a batch of goods, including: Product A: Ordinary cardboard box (K_good=1.0), total weight 500kg.
[0044] Product B: Laser interferometer (must be kept at constant temperature and pressure, K_good=1.8), total weight 500kg.
[0045] 1. Calculate the total carbon footprint (CF_total) CF_total = 1.2kgCO2e / km × 100km × 1 = 120kgCO2e.
[0046] 2. Allocate carbon footprint.
[0047] According to the traditional method (allocated only by quality): each of commodities A and B receives: 120kgCO2e × (500kg / 1000kg) = 60kgCO2e.
[0048] According to the method of this invention, the normalization formula is as follows: CF_alloc_A = 120 × (500 × 1.0) / [(500 × 1.0) + (500 × 1.8)] = 120 × (500 / 1400) ≈ 42.86 kg CO2e; CF_alloc_B = 120 × (500 × 1.8) / [(500 × 1.0) + (500 × 1.8)] = 120 × (900 / 1400) ≈ 77.14 kg CO2e.
[0049] Results Analysis: The method of this invention reasonably allocates more of the carbon footprint (77.14 kg) to the laser interferometer, which requires additional energy to maintain a constant temperature and pressure environment, while the carbon footprint of ordinary cardboard boxes (42.86 kg) is correspondingly reduced. This truly reflects the respective carbon emission responsibilities and provides accurate data for enterprises to optimize their supply chains (e.g., whether it is worthwhile to invest in high-carbon-emission constant temperature and pressure services for the transportation of this batch of laser interferometers).
[0050] Example 2 This invention provides a device for calculating and dynamically allocating the carbon footprint of a transportation process based on multi-dimensional data. Figure 2 This is a schematic diagram of a transportation process carbon footprint accounting and dynamic allocation device based on multi-dimensional data, provided in an embodiment of the present invention. Figure 2 As shown, the device includes: A multi-dimensional basic database construction module 100 is used to construct a basic database, wherein the basic database includes a transportation tool database, a transportation task database, and a commodity attribute database. The dynamic carbon footprint calculation module 200 for a single transportation trip is used to dynamically calculate the total carbon footprint of a single transportation trip based on the basic database, by taking into account the actual transportation distance of the transportation task, the energy consumption factor per unit distance of the transportation vehicle, and the load correction coefficient. The carbon footprint dynamic allocation and balancing module 300 is used to allocate the total carbon footprint to each smallest unit of goods in this transportation task, calculate the standard allocation amount of the goods, correct the standard allocation amount using the emission weight coefficient of the goods type, and ensure that the sum of the allocation values of all goods is consistent with the total carbon footprint through normalization, thus completing the dynamic allocation.
[0051] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0052] Example 3 To implement the methods of the above embodiments, the present invention also provides an electronic device, which includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the various steps of the methods described above.
[0053] Example 4 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.
[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
[0055] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0056] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.
Claims
1. A method for calculating and dynamically allocating the carbon footprint of a transportation process based on multi-dimensional data, characterized in that, include: Construct a multi-dimensional basic database, which includes a transportation vehicle database, a transportation task database, and a commodity attribute database; Based on the aforementioned basic database, the total carbon footprint of a single transportation task is dynamically calculated by using the actual transportation distance of the transportation task, the energy consumption factor per unit distance of the transportation vehicle, and the load correction coefficient. The total carbon footprint is allocated to each smallest unit of goods in this transportation task. The standard allocation amount of each goods is calculated, and the standard allocation amount is corrected using the emission weighting coefficient of each goods type. Through normalization, it is ensured that the sum of the allocation values of all goods is consistent with the total carbon footprint, thus completing the dynamic allocation.
2. The method according to claim 1, characterized in that, The transportation database can cover different transportation types and their corresponding energy consumption factors per unit distance. The transportation task database is used to record the actual transportation distance, transportation vehicle type, and load factor for each transportation task. The commodity attribute database is used to define commodity type emission weight coefficients for different categories of commodities.
3. The method according to claim 2, characterized in that, The total carbon footprint of a single transportation mission can be calculated using the following formula: CF_total=EF_transport×D×L, Wherein, EF_transport represents the energy consumption factor per unit distance of the selected vehicle for this transport, D represents the actual distance of this transport mission, and L represents the load correction factor.
4. The method according to claim 3, characterized in that, The load correction factor is calculated using the following formula: L = Actual load / Rated load.
5. The method according to claim 4, characterized in that, The calculation of the standard allocation quantity of the goods also includes: The share that each commodity i should be allocated according to traditional physical quantities is calculated using the following formula: CF_standard_i=CF_total×(M_i / M_total), Wherein, CF_standard_i represents the standard allocation quantity of the product, CF_total represents the total carbon footprint, M_i represents the quality of the product, and M_total represents the total quality of all products in this batch.
6. The method according to claim 5, characterized in that, Set and utilize weighting coefficients to correct the standard allocation using the following formula: CF_final_i=CF_standard_i×K_good_i, Wherein, CF_final_i represents the final carbon footprint of the commodity during transportation, and K_good_i represents the emission weighting coefficient for commodity type.
7. The method according to claim 6, characterized in that, The weighting coefficients are normalized using the following formula: CF_alloc_i=CF_total×(M_i×K_good_i) / Σ(M_i×K_good_i), Wherein, CF_alloc_i represents the carbon footprint of the i-th type of commodity transportation after normalization allocation.
8. A device for calculating and dynamically allocating the carbon footprint of a transportation process based on multi-dimensional data, characterized in that, include: A multi-dimensional basic database construction module is used to build a basic database, which includes a transportation tool database, a transportation task database, and a commodity attribute database. The dynamic carbon footprint calculation module for a single transportation trip is used to dynamically calculate the total carbon footprint of a single transportation trip based on the basic database, by taking into account the actual transportation distance of the transportation task, the energy consumption factor per unit distance of the transportation vehicle, and the load correction coefficient. The carbon footprint dynamic allocation and balancing module is used to allocate the total carbon footprint to each smallest unit of goods in this transportation task, calculate the standard allocation amount of goods, correct the standard allocation amount using the emission weight coefficient of goods type, and ensure that the sum of the allocation values of all goods is consistent with the total carbon footprint through normalization, thus completing the dynamic allocation.
9. An electronic device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method as described in any one of claims 1-7.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.