Vehicle material carbon footprint evaluation method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of automobile manufacturing, and discloses a vehicle material carbon footprint evaluation method and device, computer equipment and a storage medium. The vehicle material carbon footprint evaluation method comprises the following steps: acquiring material production parameters of a target vehicle material; determining a carbon footprint evaluation index according to the material production parameters; and determining carbon footprint evaluation data of the target vehicle material according to the carbon footprint evaluation index. According to the method, the material production parameters of the target vehicle material serve as the basic data for determining the carbon footprint evaluation indexes, the carbon footprint influence factors of the vehicle material in the production stage can be comprehensively considered, and the accuracy of the carbon footprint evaluation result is improved. Meanwhile, the carbon footprint checking and evaluation indexes are determined based on the material production parameters, checking and evaluation analysis are carried out, the checking requirement of the current situation of the carbon footprint of the vehicle material can be met, evaluation and prediction of the future level of the carbon footprint of the vehicle material can be achieved, and therefore the dual purposes of static checking and dynamic evaluation can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile manufacturing, and in particular to a method, device, computer device and storage medium for evaluating the carbon footprint of vehicle materials. Background Art

[0002] With the implementation of green, low-carbon and circular economic development in countries around the world, the automobile manufacturing industry, as a key industry for carbon emission management, carbon footprint analysis and evaluation have become increasingly important. In the prior art, based on the Life Cycle Assessment (LCA) analysis method, the carbon footprint of vehicle materials is evaluated by tracking and evaluating the greenhouse gas emissions of vehicle materials in each stage of the vehicle life cycle.

[0003] However, the carbon footprint evaluation method in the prior art does not fully consider the carbon footprint influencing factors in the manufacturing process of vehicle materials itself. Since the carbon footprint of vehicle materials involves multiple production links and multiple participants, different materials, different production processes, and environmental factors in different regions will all affect the carbon footprint of vehicle materials. If the considered factors are not comprehensive enough, the carbon footprint evaluation result will be inaccurate. In addition, the existing carbon footprint evaluation method can only calculate the current status of the carbon footprint of vehicle materials, lacking the analysis of the carbon emission change trend of vehicle materials and unable to realize the assessment and prediction of the future level of the carbon footprint. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer device and storage medium for evaluating the carbon footprint of vehicle materials to solve the problems of incomplete consideration of factors in the existing carbon footprint evaluation process of vehicle materials and the lack of assessment and prediction of the future level of the carbon footprint.

[0005] A method for evaluating the carbon footprint of vehicle materials includes: Obtaining the material production parameters of the target vehicle material; Determining the carbon footprint assessment index according to the material production parameters; Determining the carbon footprint evaluation data of the target vehicle material according to the carbon footprint assessment index.

[0006] A device for evaluating the carbon footprint of vehicle materials includes: An obtaining module, configured to obtain the material production parameters of the target vehicle material; A first determination module, configured to determine the carbon footprint assessment index according to the material production parameters; A second determination module, configured to determine the carbon footprint evaluation data of the target vehicle material according to the carbon footprint assessment index.

[0007] A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the above-mentioned carbon footprint evaluation method for vehicle materials is implemented.

[0008] A computer-readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors are caused to execute the carbon footprint evaluation method for vehicle materials as described above.

[0009] In the above-mentioned carbon footprint evaluation method, device, computer device, and storage medium for vehicle materials, the carbon footprint evaluation method for vehicle materials uses the material production parameters of the target vehicle material as the basic data for determining the carbon footprint evaluation index, and then uses this carbon footprint evaluation index for subsequent carbon footprint evaluation and analysis. In this way, the carbon footprint influencing factors in the production stage of vehicle materials can be comprehensively considered, and the accuracy of the carbon footprint evaluation result can be improved. At the same time, by calculating and evaluating the carbon footprint evaluation index determined based on the material production parameters, the requirements for analyzing the current situation of the carbon footprint of vehicle materials can be met, and the prediction of the future level of the carbon footprint of vehicle materials can be achieved. Therefore, the dual purposes of static calculation and dynamic evaluation can be achieved. Description of the Drawings

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0011] Figure 1 is a flowchart of a carbon footprint evaluation method for vehicle materials in an embodiment of the present invention; Figure 2 is a structural diagram of a carbon footprint evaluation device for vehicle materials in an embodiment of the present invention; Figure 3 is a schematic diagram of a computer device in an embodiment of the present invention. Detailed Embodiments

[0012] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0013] In one embodiment, asFigure 1 As shown, a method for evaluating the carbon footprint of vehicle materials is provided, including the following steps S10 - S30: S10. Obtain the material production parameters of the target vehicle material.

[0014] Understandably, the carbon footprint is an indicator used to measure the amount of carbon dioxide emissions directly or indirectly caused by a product or service within a certain period of time. The analysis of the carbon footprint covers the emissions throughout the entire life cycle of a product or service, from production, transportation, final use to waste disposal. In the process of evaluating the carbon footprint of vehicle materials, the target vehicle material refers to the material used for manufacturing vehicles that requires carbon footprint analysis. During the assembly and manufacturing process of vehicles, various materials (such as plastics, steel, rubber, etc.) are involved, and the carbon emissions of different materials are different. Therefore, a material can be designated as the target evaluation material according to needs. Material production parameters are data used to characterize the characteristics of a material that distinguish it from other materials during the production process, including but not limited to origin, production process, and energy consumption or utilization rate, etc. Even for the same vehicle material, different carbon emissions will be generated when different production processes are involved.

[0015] In one embodiment, during the actual application process, the specific object of the target vehicle material is , , representing the selected material object that requires carbon footprint analysis, such as steel. In the automotive manufacturing industry, the usage of vehicle materials varies in different regions. The region of the target vehicle material is , , representing the region where the material for carbon footprint analysis is used, such as China. For example, through statistical research, it is known that 95% of the steel used in vehicle production within the Chinese region comes from the country itself, and the remaining 5% comes from other regions; the sources of vehicle steel materials during vehicle production within the European Union region are widely distributed, among which 31% comes from the European Union, 14% comes from Turkey, 12% comes from South Korea, 11% comes from Russia, 9% comes from India, 5% comes from China, and the remaining 18% comes from other regions. In the automotive manufacturing industry, the usage of vehicle materials also varies in different time periods. In this embodiment, the carbon footprint analysis is carried out on an annual basis, and the base year of the target vehicle material is , , representing the annual range for carbon footprint analysis, such as 2024.

[0016] In one embodiment, in step S10, that is, the obtaining of the material production parameters of the target vehicle material includes: S101. Obtain the material origin parameters of the target vehicle material; S102. Obtain the material process type parameters according to the material origin parameters; S103. Obtain the material process flow parameters according to the material process type parameters; S104. Obtain the process energy efficiency parameters according to the material process flow parameters; S105. Determine the material production parameters by using the material origin parameters, material process type parameters, material process flow parameters, and process energy efficiency parameters.

[0017] Understandably, the material origin parameters of the target vehicle-use material refer to the proportion parameters used to characterize the different origin sources of the target vehicle-use material, denoted by which represents different origin sources. For example, when producing vehicles within the territory of China, 95% of the steel is from the country itself, and the remaining 5% is from other regions, then and . The production process types of vehicle-use materials are also different in different material origins. Based on the material origin parameters, the material process type parameters can be further obtained. The material process type parameters refer to the proportion parameters used to characterize the different production process types of the target vehicle-use material, denoted by which represents different process types. For example, when the origin of steel is China, approximately 95% of the vehicle-use steel is produced by the blast furnace-converter long process, and approximately 5% is produced by the electric furnace process, then and . The process flows of vehicle-use materials are also different under different production process types. Based on the material process type parameters, the material process flow parameters can be further obtained. The material process flow parameters refer to the parameters used to characterize the different production process flows of the target vehicle-use material, denoted by which represents different process flows. For example, the blast furnace-converter long process flow includes iron ore mining and processing, coking, sintering, ironmaking, and steelmaking, while the electric furnace process includes iron ore mining and processing, ironmaking, and steelmaking. The energy efficiency levels of vehicle-use materials are also different under different process flows. Based on the material process flow parameters, the process energy efficiency parameters can be further obtained. The process energy efficiency parameters refer to the parameters used to characterize the energy utilization level under different production process flows of the target vehicle-use material. For example, the energy utilization rate of the ironmaking process flow.

[0018]

[0019] This embodiment comprehensively considers influencing factors such as the origin, process type, process flow, and energy efficiency level of vehicle-use materials as parameters, which helps to improve the accuracy and reliability of subsequent carbon footprint accounting and prediction of vehicle-use materials.

[0020] S20. Determine the carbon footprint assessment index according to the material production parameters.

[0020] Understandably, after obtaining the material production parameters, it is necessary to convert the material production parameters into carbon footprint assessment indicators according to the needs of carbon footprint accounting and assessment. Carbon footprint assessment indicators are quantitative indicators used to characterize the influencing factors in the process of carbon footprint accounting and assessment, including carbon footprint accounting indicators and carbon footprint assessment indicators. Accounting refers to the analysis of the current carbon footprint, and carbon footprint accounting indicators refer to the combined factors in the target vehicle materials that affect the results of the current carbon footprint accounting. Assessment refers to the prediction of the carbon footprint at a specific future time, and carbon footprint assessment indicators refer to the combined factors in the target vehicle materials that affect the results of the carbon footprint assessment at a future time.

[0021] S30. Determine the carbon footprint evaluation data of the target vehicle material according to the carbon footprint assessment indicator.

[0022] Understandably, through mathematical analysis (such as model analysis, statistical analysis, etc.) of the carbon footprint assessment indicators, the carbon footprint evaluation data of the target vehicle material can be further determined. Carbon footprint evaluation data are quantitative data used to characterize the results of carbon footprint accounting and assessment, including carbon footprint accounting values and carbon footprint prediction values. Carbon footprint accounting values refer to the accounting results based on the factors affecting the current carbon footprint in the target vehicle materials. Carbon footprint prediction values refer to the prediction results based on the factors affecting the carbon footprint in the target vehicle materials at a specific future time.

[0023] In this embodiment, the material production parameters of the target vehicle material are obtained; the carbon footprint assessment indicators are determined according to the material production parameters; and the carbon footprint evaluation data of the target vehicle material are determined according to the carbon footprint assessment indicators. In this embodiment, the material production parameters of the target vehicle material are used as the basic data for determining the carbon footprint assessment indicators, and then the carbon footprint assessment indicators are used for subsequent carbon footprint evaluation analysis. In this way, the influencing factors of the carbon footprint of vehicle materials in the production stage can be comprehensively considered, and the accuracy of the carbon footprint evaluation results can be improved. At the same time, through the accounting and assessment analysis of the carbon footprint assessment indicators determined based on the material production parameters, the accounting requirements for the current situation of the carbon footprint of vehicle materials can be met, and the assessment and prediction of the future level of the carbon footprint of vehicle materials can be realized. Therefore, the dual purposes of static accounting and dynamic assessment can be achieved.

[0024] In one embodiment, the carbon footprint assessment indicators include carbon footprint accounting indicators; the carbon footprint evaluation data include carbon footprint accounting values; in step S30, that is, determining the carbon footprint evaluation data of the target vehicle material according to the carbon footprint assessment indicator includes: S301. Perform accounting analysis and processing on the carbon footprint accounting indicator through a preset carbon footprint accounting model to obtain the carbon footprint accounting value.

[0025] Understandably, a preset carbon footprint accounting model refers to a pre-established mathematical model used to characterize the relationship between carbon footprint accounting indicators and carbon footprint accounting values. When the carbon footprint accounting indicators are input into the preset carbon footprint accounting model, the preset carbon footprint accounting model will output the corresponding carbon footprint accounting values. There is a one-to-one correspondence between the carbon footprint accounting indicators and the carbon footprint accounting values. For the same preset carbon footprint accounting model, when the input carbon footprint accounting indicators are different, the obtained carbon footprint accounting values are also different.

[0026] In this embodiment, the carbon footprint accounting indicators are used as the input of the preset carbon footprint accounting model, and the carbon footprint accounting values are used as the output of the preset carbon footprint accounting model, which can accurately analyze the current situation of the carbon footprint of vehicle materials.

[0027] In one embodiment, the carbon footprint accounting indicators include a first accounting indicator determined according to the material origin parameters, a second accounting indicator determined according to the material process type parameters, and a third accounting indicator determined according to the material process flow parameters, the process energy efficiency parameters, and the preset process carbon emission factors; in step S301, that is, the carbon footprint accounting indicators are subjected to accounting analysis processing through the preset carbon footprint accounting model to obtain the carbon footprint accounting values, including: S3011. Perform accounting analysis processing on the first accounting indicator, the second accounting indicator, and the third accounting indicator through the preset carbon footprint accounting model to obtain the carbon footprint accounting values.

[0028] Understandably, the carbon footprint accounting indicators include a first accounting indicator, a second accounting indicator, and a third accounting indicator. Among them, the first accounting indicator is a quantitative indicator used to characterize the influence degree of the target vehicle materials with different material origins on the current carbon footprint accounting results, represented by . The second accounting indicator is a quantitative indicator used to characterize the influence degree of the target vehicle materials with different material process types under different material origins on the current carbon footprint accounting results, represented by . The third accounting indicator is a quantitative indicator used to characterize the influence degree of the target vehicle materials with different material process flows under different material process types under different material origins on the current carbon footprint accounting results, represented by . The third accounting indicator is determined according to the material process flow parameters, the process energy efficiency parameters, and the preset process carbon emission factors. The preset process carbon emission factor is a pre-set carbon emission factor corresponding to each process. The carbon emission factor refers to the amount of carbon emissions generated per unit of energy during use. When the current time period is annual, the preset process carbon emission factor can be set as a fixed value within the annual range.

[0029] In one embodiment, the preset carbon footprint accounting model can be represented in the form of a formula, and the calculation formula is as follows: Among them, represents the annual carbon footprint accounting value of materials in the region; of the carbon footprint accounting value; represents the total number of material production areas; represents the proportion of materials in the material production area in the annual, that is, the first accounting indicator; represents the total number of material process types; represents the proportion of materials in the material production area and process type in the materials of the material production area, that is, the second accounting indicator; of the materials of the represents the production area, the carbon footprint of the unit material of the process type, that is, the third accounting indicator.

[0030] Furthermore, the calculation formula of the third accounting indicator is . Among them, represents the carbon footprint of the unit material of the material production area and process type , represents the total amount (such as mass, volume, etc.) of the material production area and process type of the materials, represents the total number of material production area and process type of the process flow. The process energy efficiency parameters include and , represents the resource utilization rate of the process flow, represents the resource consumption of the process flow, represents the preset process carbon emission factor of the process flow.

[0031] Based on the first accounting index, the second accounting index, and the third accounting index, this embodiment comprehensively considers the influence of factors such as the origin of materials, the type of material process, the material process flow, and the energy efficiency level, ensuring the comprehensiveness of data and improving the accuracy of the carbon footprint accounting value.

[0032] In one embodiment, the carbon footprint assessment index includes the carbon footprint evaluation index; the carbon footprint evaluation data includes the carbon footprint prediction value; in step S30, that is, determining the carbon footprint evaluation data of the target vehicle material according to the carbon footprint assessment index includes: S302. Perform predictive analysis processing on the carbon footprint assessment index through a preset carbon footprint prediction model to obtain the carbon footprint prediction value of the target vehicle material.

[0033] Understandably, the preset carbon footprint prediction model refers to a pre-established mathematical model used to characterize the relationship between the carbon footprint assessment index and the carbon footprint prediction value. Input the carbon footprint assessment index into the preset carbon footprint prediction model, and the preset carbon footprint prediction model will output the corresponding carbon footprint prediction value. There is a one-to-one correspondence between the carbon footprint assessment index and the carbon footprint prediction value. For the same preset carbon footprint prediction model, when the input carbon footprint assessment index is different, the obtained carbon footprint prediction value is also different.

[0034] This embodiment takes the carbon footprint assessment index as the input of the preset carbon footprint prediction model and the carbon footprint prediction value as the output of the preset carbon footprint accounting model, which can effectively predict the future level of the carbon footprint of vehicle materials.

[0035] In one embodiment, before step S302, that is, before performing predictive analysis processing on the carbon footprint assessment index through the preset carbon footprint prediction model, it includes: S3021. Obtain the historical carbon footprint sample data of the target vehicle material; S3022. Perform parameter analysis processing on the initial carbon footprint prediction model according to the historical carbon footprint sample data to obtain the parameter estimation result; the initial carbon footprint prediction model includes a constructed structural model and a measurement model; S3023. Determine the preset carbon footprint prediction model according to the initial carbon footprint prediction model and the parameter estimation result.

[0036] Understandably, the preset carbon footprint prediction model is a multivariate statistical analysis mathematical model constructed based on the Structural Equation Modeling (SEM) to characterize the relationship between carbon footprint assessment indicators and carbon footprint prediction values. The structural equation model combines factor analysis and path analysis, and can simultaneously examine multiple causal relationships and analyze the complex relationships among multiple variables. The historical carbon footprint sample data of the target vehicle material refers to the data set of the carbon footprint values of the target vehicle material and the material production parameters that affect the change of the carbon footprint statistically within a specific historical time period, such as the historical data in the past ten years. The initial carbon footprint prediction model is a pre-constructed structural equation model that includes observed variables and latent variables. For example, the carbon footprint is used as a latent variable (unobservable variable), and some or all of the material production parameters are used as observed variables (observable variables). The initial carbon footprint prediction model includes a structural model and a measurement model. The structural model focuses on the relationships between latent variables, and the measurement model focuses on the relationships between observed variables and latent variables. Latent variables are those abstract concepts or influencing factors that cannot be directly observed. For example, technological progress or policies that affect the carbon footprint. Latent variables can be indirectly measured through observed variables. Observed variables are data that can be directly measured. For example, the type of process that affects the carbon footprint.

[0037] Before applying the structural equation model to predict the carbon footprint, it is necessary to perform parameter analysis and processing on the initial carbon footprint prediction model according to the historical carbon footprint sample data to obtain the parameter estimation results. The methods of parameter estimation include the maximum likelihood method, the generalized least squares method, etc. The parameter estimation result refers to the parameter result obtained by testing a set of hypotheses about the relationships between variables in the structural equation model. The parameter estimation result includes path coefficients, loading coefficients, and error terms. Specifically, for an actual structural equation model, the parameter estimation result includes the path coefficients and structural error terms corresponding to the structural model, as well as the loading coefficients and measurement error terms corresponding to the measurement model. The path coefficient represents the degree of direct influence of one variable on another variable and reveals the causal relationship between variables. The loading coefficient is used to characterize the relationship between the observed variable and the latent variable and measure how strongly the observed variable and the latent variable are correlated. The error term is the random term in the structural equation model, which reflects the influence of the factors not considered in the model on the variables and can improve the interpretability and predictive ability of the model.

[0038] After obtaining the parameter estimation results, the preset carbon footprint prediction model can be determined based on the initial carbon footprint prediction model and the parameter estimation results. Further, it is also necessary to evaluate the degree of fit between the preset carbon footprint prediction model and the actual data through the model goodness-of-fit, that is, to compare the deviation between the carbon footprint prediction value based on the preset carbon footprint prediction model and the carbon footprint value in the historical carbon footprint sample data. The model goodness-of-fit can be in the form of, for example, chi-square value (χ²), comparative fit index (CFI), root mean square error approximation (RMSEA), etc. If the model fit is not good, the model needs to be adjusted, and the parameters of the modified model need to be estimated and the goodness-of-fit evaluated again until the model meets the conditions and the final preset carbon footprint prediction model is determined. If the model fit meets the conditions, the preset carbon footprint prediction model is directly used for subsequent prediction analysis.

[0039] In one embodiment, when constructing the structural model and measurement model of the initial carbon footprint prediction model, the material origin variable, material process type variable, carbon emission factor variable, and energy efficiency level variable are used as observed variables, and the carbon footprint variable and comprehensive influence factor (such as technological progress, policy impact, etc.) variable are used as latent variables. The specific form of the structural model is as follows: Among them, represents the carbon footprint variable and the path coefficients between each observed variable and the comprehensive influence factor variable Z; represents the material origin variable, such as the proportion of each material origin in the total amount of materials, represents the serial number of the material origin variable; represents the material process type variable, such as the proportion of each material process type in the total amount of materials, represents the serial number of the material process type variable; represents the carbon emission factor variable, and its manifestation form is the electricity carbon emission factor, with the unit of kgCO 2 e / kWh, that is, the amount of carbon dioxide equivalent generated per unit kWh; represents the energy efficiency level variable; represents the structural error term.

[0040] The specific form of the measurement model is as follows: Among them, represents the load coefficient between the comprehensive influence factor variable Z and each observed variable and the observed variable Observation variables showing correlation with the comprehensive impact factor variable Z Sequence numbers showing correlation with the comprehensive impact factor variable Z Measurement error term of the comprehensive impact factor variable Z Represents the carbon footprint variable and each observation variable The loading coefficient between them. The observation variable Represents an observation variable showing correlation with the carbon footprint variable Represents an observation variable showing correlation with the carbon footprint variable Sequence number of the observation variable showing correlation with the carbon footprint variable Represents the carbon footprint variable Measurement error term

[0041] In this embodiment, a preset carbon footprint prediction model including both observation variables and latent variables is constructed based on the structural equation model, and the reliability, interpretability, and prediction ability of the model are improved through parameter estimation, which can comprehensively consider the influence of various factors on the carbon footprint, thereby achieving the goal of dynamic evaluation.

[0042] In one embodiment, the carbon footprint evaluation indicators include a first evaluation indicator determined according to the material origin parameters, a second evaluation indicator determined according to the material process type parameters, a third evaluation indicator determined according to the preset expected carbon emission factor, and a fourth evaluation indicator determined according to the preset expected energy efficiency parameters; in step S302, that is, predicting and analyzing the carbon footprint evaluation indicators through the preset carbon footprint prediction model to obtain the carbon footprint prediction value of the target vehicle material, including: S3024. Predicting and analyzing the first evaluation indicator, the second evaluation indicator, the third evaluation indicator, and the fourth evaluation indicator through the preset carbon footprint prediction model to obtain the carbon footprint prediction value of the target vehicle material.

[0043] Understandably, the carbon footprint evaluation indicators include the first evaluation indicator, the second evaluation indicator, the third evaluation indicator, and the fourth evaluation indicator. Among them, the first evaluation indicator is a quantitative indicator used to characterize the influence degree of the target vehicle materials from different material origins on the future carbon footprint prediction result, represented by . The second evaluation indicator is a quantitative indicator used to characterize the influence degree of the target vehicle materials with different material process types under different material origins on the future carbon footprint prediction result, such as the proportion of future emerging process types, represented by ​representation. Since the changes in the material process flow within the future time period may be significant and prone to enlarging the prediction error, the influence degree of the target vehicle materials with different material process flows in different material process types under different material production locations on the future carbon footprint prediction results is no longer considered. The third evaluation index is a quantitative index used to characterize the influence degree of the carbon emission factor within a specific future time period on the future carbon footprint prediction results, which is represented by representation. The third evaluation index is determined according to the preset expected carbon emission factor. The preset expected carbon emission factor is a corresponding value preset to describe the change trend of the carbon emission factor within a specific future time period. The carbon emission factor refers to the amount of carbon emissions generated per unit of electric energy during use. The fourth evaluation index is a quantitative index used to characterize the influence degree of the energy efficiency level within a specific future time period on the future carbon footprint prediction results, which is represented by representation. The fourth evaluation index is determined according to the preset expected energy efficiency parameter. The preset expected energy efficiency parameter is a corresponding value preset to describe the change trend of the energy efficiency level within a specific future time period.

[0044] In this embodiment, the first evaluation index, the second evaluation index, the third evaluation index, and the fourth evaluation index are predicted and analyzed based on the preset carbon footprint prediction model. By comprehensively considering the influence of the observed variable index and the latent variable carbon footprint, it is possible to dynamically evaluate the future level of the carbon footprint, accurately reflect the carbon footprint of vehicle materials in a specific future time period, and find possible paths for subsequent carbon emission reduction.

[0045] In one embodiment, vehicle steel is used as the target vehicle material, and the geographical scope is China and the European Union. The carbon footprint accounting of vehicle steel in China and the European Union is compared and analyzed, as well as the future level. The corresponding base year is 2022, and the prediction time range is set to 8 years later, that is, 2030. For the material origin, 95% of the steel in vehicles produced in China is from the country itself, and the remaining 5% is from other regions; 31% of the vehicle steel in the European Union comes from the European Union, 14% from Turkey, 12% from South Korea, 11% from Russia, 9% from India, 5% from China, and the remaining 18% from other regions. For the material process type, about 95% of the vehicle steel in China is produced by the blast furnace-converter long process (the energy efficiency level is about 90%), and about 5% is produced by the electric furnace process (the energy efficiency level is about 87%); while more than 90% of the vehicle steel in the European Union uses the blast furnace-converter long process (the energy efficiency level is above 90%), and less than 10% is produced by the electric furnace process (the energy efficiency level is above 75%). For the material process flow, the blast furnace-converter long process flow includes iron ore mining and processing, coking, sintering, ironmaking, and steelmaking; the electric furnace process includes iron ore mining and processing, ironmaking, and steelmaking. The process energy consumption of the blast furnace-converter long process in China is 432 kgce / t, and the electric furnace process is 230 kgce / t, with an energy efficiency level of 90%; taking Germany in the European Union as an example, the process energy consumption of the blast furnace-converter long process is 415 kgce / t, and the electric furnace process is 238 kgce / t, with an energy efficiency level of 90%. Based on the above data, the material production parameters of vehicle steel in China and the material production parameters of vehicle steel in the European Union can be obtained respectively, and further the carbon footprint accounting indicators of vehicle steel in China and the carbon footprint accounting indicators of vehicle steel in the European Union can be obtained. Through the preset carbon footprint accounting model, the carbon footprint accounting indicators are accounted and analyzed, and the carbon footprint accounting value of vehicle steel in China is 2.51 kgCO 2 e / kg, and the carbon footprint accounting value of vehicle steel in the European Union is 2.74 kgCO 2 e / kg.

[0046] Electricity carbon emissions are an important source of the carbon footprint of vehicle materials. With the further optimization of the future power structure, the electricity carbon emission factor shows a downward trend. The maximum likelihood method is used to estimate that the future average electricity carbon emission factor in China will approach the southwest power grid factor. When the preset expected carbon emission factor in China in 2030 is 0.2113 kgCO 2 e / kWh, then the predicted average carbon footprint of vehicle steel in China obtained through the preset carbon footprint prediction model is 2.46 kgCO 2 e / kg. When the preset expected carbon emission factor of each member state in the European Union in 2030 decreases by 50% compared with 2022, then the predicted average carbon footprint of vehicle steel in the European Union obtained through the preset carbon footprint prediction model is 2.73 kgCO 2e / kg.

[0047] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0048] In one embodiment, a carbon footprint evaluation device for vehicle materials is provided, and the carbon footprint evaluation device for vehicle materials corresponds one-to-one with the carbon footprint evaluation method for vehicle materials in the above embodiment. As Figure 2 shown, the carbon footprint evaluation device for vehicle materials includes an acquisition module 10, a first determination module 20, and a second determination module 30. The detailed description of each functional module is as follows: The acquisition module 10 is used to acquire the material production parameters of the target vehicle material; The first determination module 20 is used to determine the carbon footprint nuclear evaluation index according to the material production parameters; The second determination module 30 is used to determine the carbon footprint evaluation data of the target vehicle material according to the carbon footprint nuclear evaluation index.

[0049] In one embodiment, the acquisition module 10 includes: The origin parameter acquisition unit is used to acquire the material origin parameters of the target vehicle material; The type parameter acquisition unit is used to acquire the material process type parameters according to the material origin parameters; The process parameter acquisition unit is used to acquire the material process flow parameters according to the material process type parameters; The energy efficiency parameter acquisition unit is used to acquire the process energy efficiency parameters according to the material process flow parameters; The material production parameter determination unit is used to determine the material production parameters by using the material origin parameters, material process type parameters, material process flow parameters, and process energy efficiency parameters.

[0050] In one embodiment, the second determination module 30 includes: The accounting model processing unit is used to perform accounting analysis processing on the carbon footprint accounting index through a preset carbon footprint accounting model to obtain the carbon footprint accounting value.

[0051] In one embodiment, the second determination module 30 further includes: The accounting index analysis unit is used to perform accounting analysis processing on the first accounting index, the second accounting index, and the third accounting index through the preset carbon footprint accounting model to obtain the carbon footprint accounting value.

[0052] In one embodiment, the second determination module 30 further includes: A prediction model processing unit, configured to perform prediction analysis processing on the carbon footprint evaluation index through a preset carbon footprint prediction model to obtain a carbon footprint prediction value of the target vehicle material.

[0053] In one embodiment, the second determination module 30 further includes: A sample data acquisition unit, configured to acquire historical carbon footprint sample data of the target vehicle material; A parameter analysis unit, configured to perform parameter analysis processing on an initial carbon footprint prediction model according to the historical carbon footprint sample data to obtain a parameter estimation result; the initial carbon footprint prediction model includes a constructed structural model and a measurement model; A prediction model generation unit, configured to determine the preset carbon footprint prediction model according to the initial carbon footprint prediction model and the parameter estimation result.

[0054] In one embodiment, the second determination module 30 further includes: An evaluation index analysis unit, configured to perform prediction analysis processing on the first evaluation index, the second evaluation index, the third evaluation index, and the fourth evaluation index through the preset carbon footprint prediction model to obtain a carbon footprint prediction value of the target vehicle material.

[0055] For the specific limitations of the vehicle material carbon footprint evaluation device, reference may be made to the limitations of the vehicle material carbon footprint evaluation method described above, which will not be elaborated here. Each module in the above vehicle material carbon footprint evaluation device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0056] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as Figure 3 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium and an internal memory. The readable storage medium stores an operating system, computer-readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The database of the computer device is used to store data involved in the vehicle material carbon footprint evaluation method. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a vehicle material carbon footprint evaluation method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0057] In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor. When the processor executes the computer-readable instructions, the following steps are implemented: Obtain the material production parameters of the target vehicle-use material; Determine the carbon footprint assessment indicators according to the material production parameters; Determine the carbon footprint evaluation data of the target vehicle-use material according to the carbon footprint assessment indicators.

[0058] In one embodiment, one or more computer-readable storage media storing computer-readable instructions are provided. The readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage media. Computer-readable instructions are stored on the readable storage media. When the computer-readable instructions are executed by one or more processors, the following steps are implemented: Obtain the material production parameters of the target vehicle-use material; Determine the carbon footprint assessment indicators according to the material production parameters; Determine the carbon footprint evaluation data of the target vehicle-use material according to the carbon footprint assessment indicators.

[0059] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they can include the processes of the embodiments of the above methods. Among them, any reference to the memory, storage, database, or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0060] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0061] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for evaluating the carbon footprint of automotive materials, characterized in that: include: Obtain material production parameters of target automotive materials; Determine the carbon footprint evaluation index based on the material production parameters; The carbon footprint evaluation data of the target vehicle material is determined according to the carbon footprint evaluation index.

2. The method for evaluating the carbon footprint of automotive materials according to claim 1, characterized in that: The step of obtaining material production parameters of a target automotive material includes: Obtaining material origin parameters of the target automotive material; Acquire material process type parameters according to the material origin parameters; Acquire material process flow parameters according to the material process type parameters; Obtaining process energy efficiency parameters according to the material process parameters; The material origin parameters, material process type parameters, material process flow parameters and process energy efficiency parameters are determined as material production parameters.

3. The method for evaluating the carbon footprint of automotive materials according to claim 2, characterized in that: The carbon footprint evaluation index includes a carbon footprint calculation index; the carbon footprint evaluation data includes a carbon footprint calculation value; The step of determining the carbon footprint evaluation data of the target vehicle material according to the carbon footprint evaluation index includes: The carbon footprint calculation index is calculated and analyzed by a preset carbon footprint calculation model to obtain the carbon footprint calculation value.

4. The method for evaluating the carbon footprint of automotive materials according to claim 3, characterized in that: The carbon footprint accounting index includes a first accounting index determined according to the material origin parameter, a second accounting index determined according to the material process type parameter, and a third accounting index determined according to the material process flow parameter, the process energy efficiency parameter and a preset process carbon emission factor; The carbon footprint accounting index is calculated and analyzed by a preset carbon footprint accounting model to obtain the carbon footprint accounting value, including: The first accounting indicator, the second accounting indicator and the third accounting indicator are calculated and analyzed by the preset carbon footprint calculation model to obtain the carbon footprint calculation value.

5. The method for evaluating the carbon footprint of automotive materials according to claim 2, wherein: The carbon footprint evaluation index includes a carbon footprint assessment index; the carbon footprint evaluation data includes a carbon footprint prediction value; The step of determining the carbon footprint evaluation data of the target vehicle material according to the carbon footprint evaluation index includes: The carbon footprint evaluation index is predicted and analyzed by a preset carbon footprint prediction model to obtain a carbon footprint prediction value of the target automotive material.

6. The method for evaluating the carbon footprint of automotive materials according to claim 5, characterized in that: Before the carbon footprint assessment index is predicted and analyzed by the preset carbon footprint prediction model, the method includes: Obtaining historical carbon footprint sample data of the target automotive materials; Performing parameter analysis on the initial carbon footprint prediction model according to the historical carbon footprint sample data to obtain parameter estimation results; the initial carbon footprint prediction model includes a constructed structural model and a measurement model; The preset carbon footprint prediction model is determined according to the initial carbon footprint prediction model and the parameter estimation result.

7. The method for evaluating the carbon footprint of automotive materials according to claim 5, characterized in that: The carbon footprint assessment index includes a first assessment index determined according to the material origin parameter, a second assessment index determined according to the material process type parameter, a third assessment index determined according to a preset expected carbon emission factor, and a fourth assessment index determined according to a preset expected energy efficiency parameter; The method of performing forecasting and analyzing processing on the carbon footprint assessment index by using a preset carbon footprint forecasting model to obtain a carbon footprint forecasting value of the target vehicle material includes: The first evaluation index, the second evaluation index, the third evaluation index and the fourth evaluation index are predicted and analyzed by the preset carbon footprint prediction model to obtain a predicted carbon footprint value of the target vehicle material.

8. A carbon footprint assessment device for automotive materials, characterized in that: include: An acquisition module, used to acquire material production parameters of target automotive materials; A first determination module is used to determine the carbon footprint evaluation index according to the material production parameters; The second determination module is used to determine the carbon footprint evaluation data of the target vehicle material according to the carbon footprint evaluation index.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the method for evaluating the carbon footprint of automotive materials as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by one or more processors, the one or more processors are caused to perform the vehicle material carbon footprint assessment method according to any one of claims 1 to 7.

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