Carbon emission reduction scheme generation method and device, electronic equipment, vehicle and storage medium

Abstract

The invention relates to the technical field of vehicles, in particular to a carbon emission reduction scheme generation method and device, electronic equipment, a vehicle and a storage medium, and the method comprises the steps: obtaining the carbon emission reduction project information of a target vehicle; identifying material attributes and material parameters in each carbon emission reduction project in the carbon emission reduction project information, and establishing a carbon footprint model according to the material attributes and material parameters in each carbon emission reduction project; and inputting the carbon emission reduction optimization data of the target vehicle into the carbon footprint model, and analyzing the carbon footprint model based on a genetic algorithm to obtain a target carbon emission reduction scheme of the target vehicle. Therefore, the problems that the generation efficiency of a carbon emission reduction scheme is low, calculation is complex, and optimization cannot be carried out for multiple targets and multiple conditions in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, device, electronic device, vehicle and storage medium for generating a carbon emission reduction solution. Background Art

[0002] As global climate change becomes increasingly serious, reducing greenhouse gas emissions has become a consensus of the international community. The transportation industry is one of the main sources of greenhouse gas emissions. Therefore, reducing carbon emissions in the transportation industry is crucial to achieving global climate goals.

[0003] The carbon footprint of a product is determined by the carbon emission levels at each stage of its life cycle. Automobiles are complex industrial products with a long product chain and involve many production links and processing techniques. There are many factors that affect the carbon footprint of products. It is crucial to accurately and efficiently select the optimal combination of emission reduction solutions while taking into account costs, development cycles and other conditions. Therefore, there are challenges in the development of low-carbon automobile products.

[0004] Carbon emissions in related technologies are mostly optimized and analyzed in the form of constraints or costs. The results obtained are difficult to directly provide theoretical guidance for related design requirements with carbon footprint as an indicator, and are mainly based on item-by-item replacement analysis. The carbon footprint optimization efficiency is low and it is difficult to deal with optimization problems under multiple objectives and conditions. Summary of the invention

[0005] The present application provides a carbon emission reduction scheme generation method, device, electronic device, vehicle and storage medium to solve the problems of low efficiency and complex calculation of carbon emission reduction scheme generation in related technologies.

[0006] The first aspect of the present application provides a method for generating a carbon emission reduction plan, including the following steps: obtaining carbon emission reduction project information of a target vehicle; identifying material properties and material parameters in each carbon emission reduction project in the carbon emission reduction project information, and establishing a carbon footprint model according to the material properties and material parameters in each carbon emission reduction project; inputting the carbon emission reduction optimization data of the target vehicle into the carbon footprint model, and analyzing the carbon footprint model based on a genetic algorithm to obtain a target carbon emission reduction plan for the target vehicle.

[0007] Optionally, the carbon emission reduction optimization data includes optimization objectives and constraints of carbon emission reduction, wherein the optimization objectives and constraints include at least one of emission reduction amount, emission reduction cost and project cycle.

[0008] Optionally, the carbon footprint model is analyzed based on a genetic algorithm to obtain a target carbon emission reduction plan for the target vehicle, including: randomly generating multiple initial carbon emission reduction plans that meet the optimization objectives and constraints; performing non-dominated sorting on the multiple initial carbon emission reduction plans to obtain a sorting result, and calculating the congestion of each initial carbon emission reduction plan based on the sorting result; sorting according to the level and congestion of the non-dominated sorting to select multiple initial carbon emission reduction plans that meet preset standards as first-generation carbon emission reduction plans, and mutating and crossing the first-generation carbon emission reduction plans to obtain a second-generation carbon emission reduction plan; generating multiple carbon emission reduction plans after a preset number of loop iterations, and selecting a preset number of carbon emission reduction plans whose deviations are within a preset range as the target carbon emission reduction plans for the target vehicle.

[0009] Optionally, a carbon footprint model is established based on the material properties and material parameters in each carbon emission reduction project, including: determining the data range of the materials in each carbon emission reduction project; establishing the basic flows required for the model based on the material properties and material parameters in each carbon emission reduction project, as well as the data range, wherein the basic flows include the materials, energy input and energy output required in the model; establishing model units based on the basic flow groups, and generating the carbon footprint model based on the basic flows and model units.

[0010] Optionally, identifying material properties and material parameters in each carbon emission reduction project in the carbon emission reduction project information includes: establishing a parameter database based on various carbon emission reduction project data of each vehicle model; determining parameter data of the target vehicle according to the target vehicle model and the parameter database; determining material properties and material parameters in each carbon emission reduction project according to the parameter data and the carbon emission reduction project information.

[0011] Optionally, before identifying the material properties and material parameters of each carbon emission reduction project in the carbon emission reduction project information, the method further includes: performing at least one function of editing, screening, copying, deleting and approving the carbon emission reduction project information.

[0012] The second aspect of the present application provides a carbon emission reduction scheme generating device, including: an acquisition module, used to obtain carbon emission reduction project information of a target vehicle; an establishment module, used to identify the material properties and material parameters of each carbon emission reduction project in the carbon emission reduction project information, and establish a carbon footprint model according to the material properties and material parameters of each carbon emission reduction project; an analysis module, used to input the carbon emission reduction optimization data of the target vehicle into the carbon footprint model, and analyze the carbon footprint model based on a genetic algorithm to obtain a target carbon emission reduction scheme for the target vehicle.

[0013] Optionally, the carbon emission reduction optimization data includes optimization objectives and constraints of carbon emission reduction, wherein the optimization objectives and constraints include at least one of emission reduction amount, emission reduction cost and project cycle.

[0014] Optionally, the analysis module is further used to: randomly generate multiple initial carbon emission reduction schemes that meet the optimization objectives and constraints; perform non-dominated sorting on the multiple initial carbon emission reduction schemes to obtain a sorting result, and calculate the congestion of each initial carbon emission reduction scheme based on the sorting result; sort according to the level and congestion of the non-dominated sorting to select multiple initial carbon emission reduction schemes that meet preset standards as the first-generation carbon emission reduction schemes, and mutate and cross the first-generation carbon emission reduction schemes to obtain a second-generation carbon emission reduction scheme; generate multiple carbon emission reduction schemes after a preset number of loop iterations, and select a preset number of carbon emission reduction schemes whose deviations are within a preset range as the target carbon emission reduction schemes for the target vehicle.

[0015] Optionally, the establishment module is further used to: determine the data range of materials in each carbon emission reduction project; establish the basic flows required for the model based on the material properties and material parameters of each carbon emission reduction project, and the data range, wherein the basic flows include the materials, energy input and energy output required in the model; establish model units based on the basic flow group, and generate a carbon footprint model based on the basic flows and model units.

[0016] Optionally, the establishment module is further used to: establish a parameter database based on various carbon emission reduction project data of each vehicle model; determine the parameter data of the target vehicle according to the target vehicle model and the parameter database; determine the material properties and material parameters in each carbon emission reduction project according to the parameter data and carbon emission reduction project information.

[0017] Optionally, it also includes: an execution module, which is used to perform at least one function of editing, screening, copying, deleting and approving the carbon emission reduction project information before identifying the material properties and material parameters in each carbon emission reduction project in the carbon emission reduction project information.

[0018] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for generating a carbon emission reduction plan as described in the above embodiment.

[0019] The fourth aspect of the present application provides a vehicle, which uses the carbon emission reduction plan generation method of the above embodiment to generate a carbon emission reduction plan for its entire life cycle.

[0020] The fifth aspect of the present application provides a computer-readable storage medium on which a computer program or instruction is stored. The computer program or instruction is executed by a processor to implement the carbon emission reduction scheme generation method as described in the above embodiment.

[0021] Therefore, this application has at least the following beneficial effects:

[0022] The embodiment of the present application can establish a carbon footprint model based on the material properties and material parameters in each carbon emission reduction project in the carbon emission reduction project information of the target vehicle. In the process of establishing the model, the detailed information of the material properties and material parameters can be taken into account, ensuring the accuracy and reliability of the carbon footprint model, and then the carbon emission reduction optimization data of the target vehicle is input into the carbon footprint model, and the target carbon emission reduction plan of the target vehicle is obtained by analyzing the carbon footprint model based on the genetic algorithm, thereby improving the effect and efficiency of the optimization analysis, and the calculation complexity is low. Therefore, the technical problems such as low efficiency of carbon emission reduction plan generation and complex calculation in the related technology, and inability to optimize for multiple objectives and multiple conditions are solved.

[0023] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 A flowchart of a method for generating a carbon emission reduction solution according to an embodiment of the present application;

[0026] Figure 2 A flowchart of a method for generating a carbon emission reduction solution according to an embodiment of the present application;

[0027] Figure 3 A schematic diagram of a device for generating a carbon emission reduction solution according to an embodiment of the present application;

[0028] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0030] The following describes the carbon emission reduction scheme generation method, device, electronic device, vehicle and storage medium of the embodiment of the present application with reference to the accompanying drawings. In view of the fact that the carbon emissions in the related technologies mentioned in the above background technology are mostly optimized and analyzed in the form of constraints or costs, the obtained results are difficult to directly provide theoretical guidance for the relevant design requirements with carbon footprint as an indicator, and mainly focus on item-by-item replacement analysis. The carbon footprint optimization efficiency is low, and it is difficult to deal with the optimization problems under multiple objectives and multiple conditions. The present application provides a carbon emission reduction scheme generation method, in which the material properties and material parameters in each carbon emission reduction project in the carbon emission reduction project information of the target vehicle can be used to establish a carbon footprint model according to the material properties and material parameters in each carbon emission reduction project, and then the carbon emission reduction optimization data of the target vehicle is input into the carbon footprint model, and the carbon footprint model is analyzed based on the genetic algorithm to obtain the target carbon emission reduction scheme of the target vehicle. As a result, the problems of low efficiency and complex calculation of carbon emission reduction scheme generation in the related technology and inability to optimize for multiple objectives and multiple conditions are solved.

[0031] Specifically, Figure 1 A flowchart of a method for generating a carbon emission reduction solution provided in an embodiment of the present application.

[0032] like Figure 1 As shown, the carbon emission reduction scheme generation method includes the following steps:

[0033] In step S101, the carbon emission reduction project information of the target vehicle is obtained.

[0034] Among them, the target vehicle can be any vehicle that needs to reduce carbon emissions; the carbon emission reduction project information is the carbon emission reduction project information about the target vehicle submitted by the user, including technology name, project category, application object, etc.

[0035] In step S102, the material properties and material parameters of each carbon emission reduction project in the carbon emission reduction project information are identified, and a carbon footprint model is established according to the material properties and material parameters of each carbon emission reduction project.

[0036] It is understandable that the embodiment of the present application can identify the material properties and material parameters in each carbon emission reduction project in the carbon emission reduction project information, and establish a carbon footprint model according to the material properties and material parameters in each carbon emission reduction project. The specific process is as follows:

[0037] In an embodiment of the present application, material properties and material parameters in each carbon emission reduction project in the carbon emission reduction project information are identified, including: establishing a parameter database based on various carbon emission reduction project data of each vehicle model; determining parameter data of the target vehicle according to the target vehicle model and the parameter database; determining material properties and material parameters in each carbon emission reduction project according to the parameter data and the carbon emission reduction project information.

[0038] Among them, the reference database can also become a low-carbon technology library; material properties include material name, material type, chemical composition, physical properties, mechanical properties, production process, etc., and material parameters include material quantity, material unit, production energy consumption, transportation energy consumption, processing energy consumption, etc.

[0039] It can be understood that the embodiment of the present application establishes a reference database, and determines the parameter data of the target vehicle based on the model and parameter database of the target vehicle, and then determines the material properties and material parameters in each carbon emission reduction project based on the parameter data and carbon emission reduction project information.

[0040] Specifically, the embodiments of the present application can establish a parameter database to collect various carbon emission reduction project data for various vehicle models. The data fields include technology name, project category, application object, actual emission reduction, relative emission reduction, unit emission reduction, investment cost, variable cost, project cycle, and maximum availability. The preferred data fields include technology name, product identification, technology description, project category, application object, scope of application, technology type, location, applicable area, actual emission reduction, relative emission reduction, unit emission reduction, investment cost, variable cost, project cycle, and maximum availability.

[0041] In an embodiment of the present application, a carbon footprint model is established based on the material properties and material parameters in each carbon emission reduction project, including: determining the data range of the materials in each carbon emission reduction project; establishing the basic flows required for the model based on the material properties and material parameters in each carbon emission reduction project, and the data range, wherein the basic flows include the materials, energy input and energy output required in the model; establishing a model unit based on the basic flow group, and generating a carbon footprint model based on the basic flows and the model unit.

[0042] It can be understood that the embodiments of the present application can determine the data range of materials in each carbon emission reduction project, such as material type, material quantity, etc., so as to ensure the accuracy and reliability of the carbon footprint model, and establish the basic flow required for the model according to the material properties and material parameters of each carbon emission reduction project, and the data range, establish the model unit according to the basic flow group, and generate the carbon footprint model according to the basic flow and model unit, wherein the basic flow includes the materials required in the model, energy input and energy output.

[0043] It should be noted that the data range of materials in each carbon emission reduction project can be determined by determining the system boundary and functional unit of the carbon emission reduction project research, and then determining the data range of materials based on the system boundary and functional unit.

[0044] Specifically, the establishment of the carbon footprint model of the embodiment of the present application includes: first, determining the system boundary and functional unit of the study, thereby determining the data range of the materials to be used, and establishing a corresponding list for subsequent use. Secondly, collecting the content of the specific materials based on the list, and establishing the basic flow required for the product model of the project. The basic flow includes the input and output of all basic materials and energy used in a model. Finally, the unit process is established using the established basic flow group, and all basic flows and unit processes are connected.

[0045] In an embodiment of the present application, before identifying the material properties and material parameters of each carbon emission reduction project in the carbon emission reduction project information, it also includes: performing at least one function of editing, screening, copying, deleting and approving the carbon emission reduction project information.

[0046] It can be understood that the embodiment of the present application performs at least one operation of editing, screening, copying, deleting and approving on the carbon emission reduction project information before identifying the material properties and material parameters of each carbon emission reduction project in the carbon emission reduction project information. Specifically, the operation can be performed by an administrator to form a list of project data that can be used for optimization analysis. The main purpose is to judge the authenticity of the project and retain practical and feasible projects.

[0047] In step S103, the carbon emission reduction optimization data of the target vehicle is input into the carbon footprint model, and the carbon footprint model is analyzed based on the genetic algorithm to obtain the target carbon emission reduction plan of the target vehicle.

[0048] The carbon emission reduction optimization data include the optimization objectives and constraints of carbon emission reduction, and the optimization objectives and constraints include at least one of emission reduction amount, emission reduction cost and project cycle.

[0049] It can be understood that the embodiment of the present application can input the carbon emission reduction optimization data of the target vehicle into the carbon footprint model, and analyze the carbon footprint model based on the genetic algorithm to obtain the target carbon emission reduction plan of the target vehicle, thereby realizing multiple combinations of carbon emission reduction plans for the entire life cycle of the target vehicle materials. The use of genetic algorithms to analyze the carbon footprint model can quickly find the optimal carbon emission reduction plan, improve the optimization efficiency and effect, and then help enterprises better formulate research and development plans for low-carbon models.

[0050] In an embodiment of the present application, a carbon footprint model is analyzed based on a genetic algorithm to obtain a target carbon emission reduction plan for a target vehicle, including: randomly generating multiple initial carbon emission reduction plans that meet the optimization objectives and constraints; performing non-dominated sorting on the multiple initial carbon emission reduction plans to obtain a sorting result, and calculating the congestion of each initial carbon emission reduction plan based on the sorting result; sorting and selecting multiple initial carbon emission reduction plans that meet preset standards as first-generation carbon emission reduction plans according to the level and congestion of the non-dominated sorting, and mutating and crossing the first-generation carbon emission reduction plans to obtain a second-generation carbon emission reduction plan; generating multiple carbon emission reduction plans after a preset number of loop iterations, and selecting a preset number of carbon emission reduction plans whose deviations are within a preset range as the target carbon emission reduction plans for the target vehicle.

[0051] Among them, the preset standard, preset number of times, preset range, and preset quantity can all be set according to specific circumstances. For example, the preset standard is that the non-dominated level is low and the congestion is high, the preset number of times is 1000 times, the preset range is that the deviation is less than certain values, and the preset quantity is 100. There is no specific limitation on this.

[0052] It can be understood that the embodiment of the present application analyzes the carbon footprint model based on the genetic algorithm to obtain the target carbon emission reduction plan of the target vehicle, and randomly generates multiple initial carbon emission reduction plans as the parent population, which ensures the diversity of the initial population and improves the optimization exploration ability. The optimal solution can be effectively screened out through non-dominated sorting to ensure the diversity and high quality of the optimization plan. By calculating the congestion degree, the distribution uniformity of the population is ensured, and the premature convergence of the local optimal solution is avoided. A new generation of child population is formed through operations such as mutation and crossover and retaining some excellent plans in the parent population. Through iterative selection of the parent population and the child population, the carbon emission reduction parameters are gradually optimized, and finally the optimal carbon emission reduction plan is generated, as follows:

[0053] Step 1: Randomly generate an initial solution that meets the optimization objectives and conditions, and use the initial solution as the parent population;

[0054] Step 2: Perform non-dominated sorting on multiple initial solutions and calculate the congestion of each solution based on the sorting results:

[0055] Step 3: According to the non-dominated hierarchy and crowding ranking, the scheme with lower non-dominated hierarchy and higher crowding is selected as the first-generation carbon emission reduction scheme. The second-generation carbon emission reduction scheme is formed through operations such as mutation and crossover and retaining some excellent schemes in the parent population, that is, the new generation of offspring population;

[0056] Step 4: Repeat steps 2-3 to meet the set number of iteration cycles, such as 1000 times, to gradually optimize the carbon emission reduction scheme, and finally select the 100 schemes with the smallest target deviation value as the final scheme output.

[0057] In general, the optimization goal and constraints of carbon emission reduction are set first, including emission reduction amount, emission reduction cost and project cycle. An initial plan is randomly generated according to the optimization goal and constraints, and multiple initial plans are non-dominated sorted. The congestion is calculated according to the sorting results. The carbon emission reduction parameters of the target vehicle in each life cycle are iteratively selected according to the parent population and the child population. The target carbon emission reduction plan for the target vehicle is generated according to the carbon emission reduction parameters of each life cycle, that is, the carbon emission reduction plan for the next round of optimization is selected according to the level and congestion of the non-dominated sorting, and multiple carbon emission reduction plans, i.e., new carbon emission reduction plans, are generated through crossover and mutation operations. The above steps are iterated many times, for example, 1,000 times, to achieve gradual optimization of the carbon emission reduction plan, and the plans with the top 100 deviation values ​​can be selected as the final carbon emission reduction plan, i.e., the target carbon emission reduction plan.

[0058] In summary, the carbon emission reduction solution generation method of the embodiment of the present application mainly includes the following steps:

[0059] S1: Establish a low-carbon technology library: General users need to register an account, log in to enter the low-carbon technology library to download project form samples, organize project list data, and report.

[0060] Collect data on various carbon emission reduction projects. The data fields include technology name, project category, application object, actual emission reduction, relative emission reduction, unit emission reduction, investment cost, variable cost, project cycle, and maximum availability. The preferred data fields include technology name, product identification, technology description, project category, application object, scope of application, technology type, location, applicable area, actual emission reduction, relative emission reduction, unit emission reduction, investment cost, variable cost, project cycle, and maximum availability. Prepare supporting materials to explain the source of the data.

[0061] S11: The user enters the optimization system website, registers an account, fills in basic information and uploads the business license.

[0062] S12: The administrator reviews the registration information and activates the application account.

[0063] S13: The user logs in to the optimization system, downloads the carbon emission reduction project form, organizes the form data, and prepares supporting materials.

[0064] S14: The user enters the data declaration port, uploads the carbon emission reduction project form, writes the project list data into the storage device, and marks it as unapproved.

[0065] S2: Project management: The administrator logs into the system to view the carbon emission reduction project information reported by different users, and performs operations such as approval, editing, and screening on the projects to form a list of project data that can be used for optimization analysis.

[0066] S21: The administrator user enters the project management interface and can view and approve emission reduction projects marked as unapproved.

[0067] S22: The status of the emission reduction project after approval is marked as approved, and the status of the project that has not been approved is marked as unapproved and returned to the account of the declaring user.

[0068] S23: Administrator users can edit, filter, copy, and delete approved emission reduction project information.

[0069] S3: Model building: Select the target vehicle model, import the BOM list data (i.e. the material properties and material parameters of each carbon reduction project in the carbon reduction project information of this application), build a carbon footprint model, set the emission reduction project screening conditions, and set the optimization goals and constraints. The optimization goals include carbon footprint emission reduction, investment cost, variable cost, and project cycle. The preferred optimization goal is carbon footprint emission reduction, and the constraints include investment cost, variable cost, project cycle, and project availability.

[0070] S31: BOM form import: The system supports two formats of BOM import, the first is column-level BOM, and the second is parent-child level BOM. To import BOM, you first need to select the BOM file and specify the worksheet. Then set the import data rules, which include clarifying the product name, product model, product quantity, product unit, material name, material model, material quantity, material unit, etc.

[0071] S32: Establishment of carbon footprint model: After importing the vehicle BOM and matching the data information of the materials in it, use this information to model the project. First, determine the system boundary and functional unit of the study to determine the data range of the materials to be used, and establish a corresponding list for subsequent use. Secondly, collect the content of specific materials based on the list, and establish the basic flow required for the product model of the project. The basic flow includes the input and output of all basic materials and energy used in a model. Finally, use the established basic flow group to establish the unit process and connect all basic flows and unit processes.

[0072] S33: Select optimization indicators and constraints: You can choose optimization targets such as emission reduction, emission reduction cost, project cycle, etc. Different targets can be combined with each other, but at least one optimization target needs to be selected. For unselected indicators, their selection range can be further restricted.

[0073] S4: Optimization analysis: setting the calculation parameters of the genetic algorithm and performing calculations, the preferred calculation steps include non-dominated sorting, calculation of clustering distance (ie, crowding degree), and population iterative calculation.

[0074] S41: Non-dominated sorting.

[0075] S42: Calculate the clustering distance.

[0076] S43: Determine the parent population and the child population according to the aggregation distance, iteratively select the carbon emission reduction parameters of the target vehicle in each life cycle according to the parent population and the child population, and generate a target carbon emission reduction plan for the target vehicle according to the carbon emission reduction parameters of each life cycle.

[0077] S431: Randomly generate an initial solution that meets the optimization objectives and conditions, and use the initial solution as the parent population;

[0078] S432: Perform non-dominated sorting on multiple initial solutions, and calculate the congestion degree of each solution based on the sorting result:

[0079] S433: According to the non-dominated level and crowding ranking, the scheme with lower non-dominated level and higher crowding is selected first, and a new generation of offspring population is formed through operations such as mutation and crossover and retaining some excellent schemes in the parent population.

[0080] S434: Repeat steps S432-S433 to meet the set number of iteration cycles, gradually optimize the carbon emission reduction scheme, and finally select the 100 schemes with the smallest target deviation value as the final scheme output.

[0081] S5: Result output: Export the list of emission reduction plans, and visualize the specific parameters of the optimization objectives and constraints. The optimization plan results can be exported in .xlsx format.

[0082] S51: Export the calculated emission reduction project scheme combination in .xlsx format.

[0083] S52: Graphically display the emission reduction amount, emission reduction cost and emission reduction cycle effect of the emission reduction plan, and display the selected plan combination in tabular form.

[0084] The following is a specific example to describe the method for generating a carbon reduction solution of the present application. Figure 2 As shown, the following steps are included:

[0085] 1. Low-carbon technology library: General users need to register an account, log in to enter the low-carbon technology library to download project form samples, organize project list data, and report. The main purpose is to collect data and provide optional projects for the optimization of emission reduction plans.

[0086] 1.1 The user enters the optimization system website, registers an account, fills in basic information, including user name, company name, industry, main products, application purpose, real name, position, department, contact number, email address, and uploads the business license.

[0087] 1.2 The administrator reviews the registration information. If the information is correct and there is no problem with the business license, the application account will be activated, otherwise the registration application will be returned.

[0088] 1.3 The user logs in to the optimization system, downloads the carbon emission reduction project form, organizes the form data, fills in the technology name, product identification, technology description, project category, application object, scope of application, technology type, location, applicable area, actual emission reduction, relative emission reduction, unit emission reduction, investment cost, variable cost, project cycle, maximum availability, and prepares supporting materials to explain the source of the data.

[0089] 1.4 The user enters the data declaration port, uploads the carbon emission reduction project form, writes the project list data into the storage device, and marks it as unapproved.

[0090] 2. Project management: The administrator logs into the system, views the carbon reduction project information reported by different users, and performs operations such as approval, editing, and screening on the projects to form a list of project data that can be used for optimization analysis. The main purpose is to judge the authenticity of the projects and retain practical and feasible projects.

[0091] 2.1 The administrator user enters the project management interface and can view and approve emission reduction projects marked as unapproved.

[0092] 2.2 The status of emission reduction projects that have passed the approval will be marked as approved, and the status of projects that have not passed the approval will be marked as unapproved and returned to the account of the declaring user.

[0093] 2.3 Administrator users can edit, filter, copy and delete approved emission reduction project information.

[0094] 3. Model building: Select the target model, import BOM data, build the carbon footprint model, set the emission reduction project screening conditions, and set the optimization goals and constraints. The main purpose is to use the life cycle method to establish a calculation framework for the carbon footprint of automotive products, clarify the target value of the carbon footprint, and the acceptable range of parameters such as cost and cycle.

[0095] 3.1 BOM form import: The system supports two formats of BOM import, the first is column-level BOM, and the second is parent-child level BOM. To import BOM, you first need to select the BOM file and specify the worksheet. Then set the import data rules, which include clarifying the product name, product model, product quantity, product unit, material name, material model, material quantity, material unit, etc.

[0096] 3.2 Establishment of carbon footprint model: After importing the vehicle BOM table and matching the data information of the materials in it, use this information to model the project. First, determine the system boundary and functional unit of the study to determine the data range of the materials to be used, and establish a corresponding list for subsequent use. Secondly, collect the content of specific materials based on the list, and establish the basic flow required for the product model of the project. The basic flow includes the input and output of all basic materials and energy used in a model. Finally, use the established basic flow group to establish the unit process and connect all basic flows and unit processes.

[0097] 3.3 Select optimization indicators and constraints: You can choose optimization targets such as emission reduction, emission reduction cost, project cycle, etc. Different targets can be combined with each other, but at least one optimization target needs to be selected. For unselected indicators, their selection range can be further restricted.

[0098] According to the project library selected in the project parameter setting and the set screening conditions, some unimportant description information is removed to form a model input list, as shown in Table 1. Table 1 is a model input list table.

[0099] Table 1

[0100]

[0101] According to the input data matrix, determine the value range of emission reduction, project cost and project period:

[0102]

[0103] The emission reduction range that can be achieved by the emission reduction scheme is [0, Value max ], where 0 represents the minimum emission reduction without any emission reduction measures, and Value max In order to consider the maximum emission reduction of all emission reduction measures, the emission reduction of different projects is superimposed on each other. Prod is the vehicle production. If the object is a product, the parameter is set to 1.

[0104]

[0105] Regardless of the actual use of the project, the fixed cost of the project is the same, that is, the fixed cost is only related to whether the project is used or not, not to the degree of use of the project. The fixed cost range of the emission reduction plan is [0,FC max ], where 0 represents the minimum fixed cost of emission reduction without taking any emission reduction measures, FC max In order to consider the maximum fixed cost of all emission reduction measures, the fixed costs of different projects are superimposed on each other.

[0106]

[0107] The variable cost of the emission reduction scheme ranges from [0, VC max ], where 0 represents the minimum variable cost of emission reduction without any emission reduction measures, VC max In order to consider the maximum variable cost of all emission reduction measures, the variable costs of different projects are superimposed on each other.

[0108] Period max =max{Period1,Period2,…Period n}; (4)

[0109] The project period of the emission reduction plan ranges from [0, Period max ], where 0 represents the minimum emission reduction period without any emission reduction measures, and Period max In order to consider the maximum emission reduction cost of all emission reduction measures, different project cycles are parallel and the maximum solution cycle is the maximum value among them.

[0110] After obtaining the value range of each parameter, the selection range of non-optimized indicators is controlled according to the user's choice of emission reduction target, while providing the user with the maximum value expectation.

[0111] The output of this tool is a combination of different items, that is, the degree of use of each item. Assume that the final solution is:

[0112] x=[x1,x2…x n ];(5)

[0113] Where is the maximum availability of each project, then the following relationship exists:

[0114]

[0115] Period x =max{Period1×F(x1),Period2×F(x2),…Period n ×F(x n )}; (9)

[0116]

[0117] According to the optimization index selected by the user, the corresponding objective function can be determined in the following cases.

[0118] Object1:min{-Value x};

[0119] Object2:min{FC x};

[0120] Object3:min{VC x};

[0121] Object4:min{Period x};

[0122] At the same time, there are the following restrictions:

[0123] Constraint1:min{Value set -Value x};

[0124] Constraint2:min{FC set -FC x};

[0125] Constraint3:min{VC set -VC x};

[0126] Constraint4:min{Period set -Period x}.

[0127] Depending on the optimization metric selected by the user, the following situations may occur:

[0128] A. Single optimization metric.

[0129] Taking the maximum emission reduction as the optimization index, we have:

[0130] Objective function Object 1, constraint functions Constraint 2, 3, 4;

[0131] Taking the minimum fixed cost as the optimization indicator, we have:

[0132] Objective function Object 2, constraint functions Constraint 1, 3, 4;

[0133] Taking the minimum variable cost as the optimization indicator, we have:

[0134] Objective function Object 3, constraint functions Constraint 1, 2, 4;

[0135] Taking the shortest cycle as the goal, we have:

[0136] Objective function Object 4, constraint functions Constraint 1, 2, 3;

[0137] Under a single optimization metric, a unique result will be output, but the result may be different due to different constraint functions.

[0138] B. Two optimization indicators.

[0139] Taking the maximum emission reduction and the minimum fixed cost as the optimization index, we have:

[0140] Objective function Object 1, 2, constraint function Constraint 3, 4;

[0141] Taking the maximum emission reduction and the minimum variable cost as the optimization index, we have:

[0142] Objective function Object 1, 3, constraint function Constraint 2, 4;

[0143] Taking the maximum emission reduction and the shortest period as the goal, we have:

[0144] Objective function Object 1, 4, constraint function Constraint 2, 3;

[0145] Taking the lowest fixed cost and the lowest variable cost as the optimization index, we have:

[0146] Objective function Object 2, 3, constraint function Constraint 1, 4;

[0147] Taking the lowest fixed cost and the shortest project cycle as the optimization indicators, we have:

[0148] Objective function Object 2, 4, constraint function Constraint 1, 3;

[0149] Taking the lowest variable cost and the shortest project cycle as the optimization indicators, we have:

[0150] Objective function Object 3, 4, constraint function Constraint 1, 2.

[0151] If the user selects two optimization indicators, the output result is not unique, but a curve. For any point on the curve, there is an optimization indicator that is optimal for any point in the optional range (for example, if emission reduction and emission reduction cost are used as optimization indicators, then for any point on the curve, the cost of the point with a larger emission reduction within the value range is higher than it, and the emission reduction is not as large as it is). If you need to narrow the range of the output results, you need to narrow the value of the constraint function.

[0152] 4. Optimization analysis: Based on the genetic algorithm, set the calculation parameters and perform optimization analysis. The main purpose is to use the genetic algorithm to quickly calculate the solution curve.

[0153] The specific process of the genetic algorithm has been described in the above embodiments and will not be repeated here.

[0154] 5. Result output: Export the list of emission reduction schemes and visualize the specific parameters of optimization objectives and constraints. The main purpose is to output a combination of emission reduction schemes that meet the requirements, including information such as individual projects, project usage, and emission reduction effects.

[0155] 5.1 Export the calculated emission reduction project scheme combination in .xlsx format.

[0156] Through the above process, the emission reduction project combination scheme under different demand settings is calculated in the following form:

[0157] For single target optimization, as shown in Table 2, Table 2 is the carbon emission reduction project combination plan under single target optimization.

[0158] Table 2

[0159] Solution No. Emission reduction Fixed costs Variable costs Project Cycle Program composition PL-001 Value FC VC Period x1=0.1,x2=0.5,…xn=0.4

[0160] For multiple target optimization, as shown in Table 3, Table 3 is a carbon emission reduction project combination plan under single target optimization.

[0161] Table 3

[0162] Solution No. Emission reduction Fixed costs Variable costs Project Cycle Program composition PL-001 Value-1 FC-1 VC-1 Period-1 x1=0.1,x2=0.5,…xn=0.4 PL-002 Value-2 FC-2 VC-2 Period-2 x1=0.2,x2=0.4,…xn=0.5 … … … … … … PL-n Value-n FC-n VC-n Period-n x1=0.4,x2=0.2,…xn=0.7

[0163] Based on the scheme composition of each scheme, the system can output the specific scheme combination in text form, the emission reduction, fixed cost, variable cost and cycle information of the additional scheme.

[0164] 5.2 Graphically display the emission reduction amount, emission reduction cost and emission reduction cycle effect of the emission reduction plan, and display the selected plan combination in tabular form.

[0165] The carbon emission reduction scheme generation method of the embodiment of the present application is an optimization method that directly uses carbon footprint as the main objective function, and uses parameters such as project cost and implementation cycle as constraints for optimization analysis. The results obtained can directly provide theoretical guidance for related design requirements with carbon footprint as an indicator. In addition, a genetic optimization algorithm is used to improve the efficiency of carbon footprint optimization analysis under the framework of the material life cycle, and it can solve optimization problems under multiple objectives and conditions, improve the application of optimization algorithms in life cycle theory, and provide efficient tools for the calculation and optimization of complex carbon footprints of automotive products.

[0166] According to the carbon emission reduction scheme generating method proposed in the embodiment of the present application, a carbon footprint model can be established based on the material properties and material parameters in each carbon emission reduction project in the carbon emission reduction project information of the target vehicle, and the detailed information of the material properties and material parameters can be taken into account in the process of establishing the model, thereby ensuring the accuracy and reliability of the carbon footprint model. The carbon emission reduction optimization data of the target vehicle is then input into the carbon footprint model, and the carbon footprint model is analyzed based on the genetic algorithm to obtain the target carbon emission reduction scheme of the target vehicle. The calculation complexity is low, which improves the effect and efficiency of the optimization analysis.

[0167] Next, a device for generating a carbon emission reduction scheme according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0168] Figure 3 It is a block diagram of a device for generating a carbon emission reduction solution according to an embodiment of the present application.

[0169] like Figure 3 As shown, the carbon emission reduction scheme generating device 10 includes: an acquisition module 100 , a building module 200 and an analysis module 300 .

[0170] Among them, the acquisition module 100 is used to obtain the carbon emission reduction project information of the target vehicle; the establishment module 200 is used to identify the material properties and material parameters of each carbon emission reduction project in the carbon emission reduction project information, and establish a carbon footprint model according to the material properties and material parameters of each carbon emission reduction project; the analysis module 300 is used to input the carbon emission reduction optimization data of the target vehicle into the carbon footprint model, and analyze the carbon footprint model based on the genetic algorithm to obtain the target carbon emission reduction plan for the target vehicle.

[0171] In an embodiment of the present application, the carbon emission reduction optimization data includes optimization objectives and constraints of carbon emission reduction, wherein the optimization objectives and constraints include at least one of emission reduction amount, emission reduction cost and project cycle.

[0172] In an embodiment of the present application, the analysis module 300 is further used to: randomly generate multiple initial carbon emission reduction schemes that meet the optimization objectives and constraints; perform non-dominated sorting on the multiple initial carbon emission reduction schemes to obtain a sorting result, and calculate the congestion of each initial carbon emission reduction scheme based on the sorting result; sort according to the level and congestion of the non-dominated sorting to select multiple initial carbon emission reduction schemes that meet preset standards as the first-generation carbon emission reduction schemes, and mutate and cross the first-generation carbon emission reduction schemes to obtain the second-generation carbon emission reduction schemes; generate multiple carbon emission reduction schemes after a preset number of loop iterations, and select a preset number of carbon emission reduction schemes whose deviations are within a preset range as the target carbon emission reduction schemes for the target vehicle.

[0173] In an embodiment of the present application, the establishment module 200 is further used to: determine the data range of materials in each carbon emission reduction project; establish the basic flows required for the model based on the material properties and material parameters of each carbon emission reduction project, and the data range, wherein the basic flows include the materials, energy input and energy output required in the model; establish a model unit based on the basic flow group, and generate a carbon footprint model based on the basic flows and model units.

[0174] In an embodiment of the present application, the establishment module 200 is further used to: establish a parameter database based on various carbon emission reduction project data of various vehicle models; determine the parameter data of the target vehicle according to the target vehicle model and the parameter database; determine the material properties and material parameters in each carbon emission reduction project according to the parameter data and the carbon emission reduction project information.

[0175] In the embodiment of the present application, the device 10 of the embodiment of the present application further includes: an execution module.

[0176] The execution module is used to perform at least one function of editing, screening, copying, deleting and approving the carbon emission reduction project information before identifying the material properties and material parameters in each carbon emission reduction project in the carbon emission reduction project information.

[0177] It should be noted that the above explanation of the embodiment of the carbon emission reduction scheme generating method is also applicable to the carbon emission reduction scheme generating device of this embodiment, and will not be repeated here.

[0178] According to the carbon emission reduction scheme generating device proposed in the embodiment of the present application, a carbon footprint model can be established based on the material properties and material parameters in each carbon emission reduction project in the carbon emission reduction project information of the target vehicle. In the process of establishing the model, the detailed information of the material properties and material parameters can be taken into account, thereby ensuring the accuracy and reliability of the carbon footprint model. The carbon emission reduction optimization data of the target vehicle is then input into the carbon footprint model, and the carbon footprint model is analyzed based on the genetic algorithm to obtain the target carbon emission reduction scheme of the target vehicle. The calculation complexity is low, which improves the effect and efficiency of the optimization analysis.

[0179] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0180] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0181] When the processor 402 executes the program, the carbon emission reduction scheme generation method provided in the above embodiment is implemented.

[0182] Furthermore, the vehicle also includes:

[0183] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0184] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0185] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0186] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0187] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0188] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0189] An embodiment of the present application also provides a vehicle, which uses the above carbon emission reduction plan generation method to generate a carbon emission reduction plan for its entire life cycle.

[0190] An embodiment of the present application also provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the above-mentioned method for generating a carbon emission reduction plan is implemented.

[0191] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0192] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0193] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0194] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0195] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

Claims

1. A method for generating a carbon emission reduction scheme, characterized in that: The following steps are involved: Obtain the carbon emission reduction project information of the target vehicle; Identify material properties and material parameters of each carbon emission reduction project in the carbon emission reduction project information, and establish a carbon footprint model according to the material properties and material parameters of each carbon emission reduction project; The carbon emission reduction optimization data of the target vehicle is input into the carbon footprint model, and the carbon footprint model is analyzed based on a genetic algorithm to obtain a target carbon emission reduction plan for the target vehicle.

2. The method for generating a carbon emission reduction plan according to claim 1, characterized in that: The carbon emission reduction optimization data includes optimization objectives and constraints of carbon emission reduction, wherein the optimization objectives and constraints include at least one of emission reduction amount, emission reduction cost and project cycle.

3. The method for generating a carbon emission reduction plan according to claim 2, characterized in that: The step of analyzing the carbon footprint model based on a genetic algorithm to obtain a target carbon emission reduction plan for the target vehicle includes: Randomly generate multiple initial carbon emission reduction plans that meet the optimization objectives and constraints; Performing non-dominated sorting on the multiple initial carbon emission reduction schemes to obtain a sorting result, and calculating the congestion degree of each initial carbon emission reduction scheme based on the sorting result; Sorting and selecting a plurality of initial carbon emission reduction schemes that meet preset standards according to the level and congestion degree of the non-dominated sorting as the first-generation carbon emission reduction schemes, and mutating and crossing the first-generation carbon emission reduction schemes to obtain the second-generation carbon emission reduction schemes; After a preset number of loop iterations, a plurality of carbon emission reduction schemes are generated, and a preset number of carbon emission reduction schemes whose deviations are within a preset range are selected as target carbon emission reduction schemes for the target vehicle.

4. The method for generating a carbon emission reduction plan according to claim 1, characterized in that: The carbon footprint model is established according to the material properties and material parameters in each carbon emission reduction project, including: Determine the data scope of the materials in each carbon reduction project; Establishing the basic flows required for the model according to the material properties and material parameters in each carbon emission reduction project and the data range, wherein the basic flows include the materials, energy input and energy output required in the model; A model unit is established according to the basic flow group, and the carbon footprint model is generated according to the basic flow and the model unit.

5. The method for generating a carbon emission reduction plan according to claim 1, characterized in that: The identifying of the material properties and material parameters of each carbon emission reduction project in the carbon emission reduction project information includes: Establish a parameter database based on various carbon emission reduction project data for each vehicle model; Determining parameter data of the target vehicle according to the target vehicle model and the parameter database; The material properties and material parameters in each carbon emission reduction project are determined according to the parameter data and the carbon emission reduction project information.

6. The method for generating a carbon emission reduction plan according to claim 1, characterized in that: Before identifying the material properties and material parameters of each carbon emission reduction project in the carbon emission reduction project information, it also includes: At least one function of editing, filtering, copying, deleting and approving is performed on the carbon emission reduction project information.

7. A device for generating a carbon emission reduction plan, characterized in that: include: An acquisition module is used to obtain carbon emission reduction project information of a target vehicle; An establishment module is used to identify the material properties and material parameters of each carbon emission reduction project in the carbon emission reduction project information, and establish a carbon footprint model according to the material properties and material parameters of each carbon emission reduction project; The analysis module is used to input the carbon emission reduction optimization data of the target vehicle into the carbon footprint model, and analyze the carbon footprint model based on a genetic algorithm to obtain a target carbon emission reduction plan for the target vehicle.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for generating a carbon emission reduction scheme as described in any one of claims 1 to 6.

9. A vehicle, characterized in that: The vehicle uses a carbon emission reduction plan for its entire life cycle generated by the carbon emission reduction plan generation method described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program or instruction is executed by a processor to implement the carbon emission reduction solution generation method as described in any one of claims 1-6.

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