Product supply chain carbon footprint accounting and optimizing method

Abstract

The invention relates to a product supply chain carbon footprint accounting and optimizing method, which comprises the following steps of S1, acquiring product information, inputting a refined product information splitting model, and deconstructing a product into a plurality of modules; s2, acquiring full life cycle information of a supply chain of each module of a product, and performing clustering analysis on the carbon footprint accounting record to obtain a multi-dimensional calculation power distribution coefficient; s3, obtaining a node source factor data set based on the multi-stage carbon footprint accounting node, and carrying out carbon footprint data acquisition and standardization processing on each module of the product to obtain multi-stage carbon source data; s4, performing carbon footprint accounting and correction on the multi-stage carbon source data based on the multi-dimensional computational power distribution coefficient to obtain a multi-stage corrected carbon footprint accounting result; and S5, generating a product carbon optimization scheme according to the basic information of each module of the product and the multi-stage correction carbon footprint accounting result, and optimizing the product supply chain carbon footprint. Compared with the prior art, the accuracy and comprehensiveness of product supply chain carbon footprint accounting can be further improved.

Description

Technical Field

[0001] The present invention belongs to the field of carbon footprint environmental protection technology, and in particular relates to a product supply chain carbon footprint accounting and optimization method. Background Art

[0002] As a key indicator for assessing the total amount of carbon dioxide and other greenhouse gases released directly or indirectly by individuals, enterprises, products or services during their entire life cycle, carbon footprint runs through every link from production, transportation, use to disposal. The product supply chain carbon footprint refers to the total amount of greenhouse gas emissions generated along the entire supply chain of the product (including the mining, production, transportation, distribution, use and final disposal of raw materials). Accurately quantifying and analyzing the product supply chain carbon footprint plays a vital role in accurately grasping and controlling carbon emissions in the supply chain.

[0003] At present, carbon footprint accounting usually adopts the life cycle assessment method (LCA), which is supported by advanced data collection and calculation technology, systematically collects and analyzes data, and evaluates the environmental impact of products or services throughout their life cycle. It generally includes steps such as determining system boundaries, data collection, and quantity accounting. Due to the complexity of the supply chain system, which often covers multiple links, multiple participants, and complicated data flows, coupled with the diversity of product structures, slight errors or data distortions in any link of the product's component composition, production technology, and manufacturing management may have a significant impact on the final carbon footprint assessment results. Therefore, this method faces the problem of insufficient accuracy in carbon footprint accounting in actual operation. Chinese patent application 202310352984.9 discloses a method for carbon footprint accounting of transformer products, which performs multi-dimensional carbon footprint accounting of transformer products based on the full life cycle information of transformer products, improves the accuracy of carbon footprint accounting of transformer products, and performs adaptive and reliable carbon footprint optimization analysis on transformer products based on the carbon footprint accounting results, thereby improving the carbon footprint optimization quality of transformer products. However, the above method ignores the impact of product structure itself on carbon footprint accounting, which may cause the accounting results to deviate from reality, resulting in insufficient accuracy and comprehensiveness of the accounting results, making it difficult for companies to accurately identify key areas for emission reduction, and thus difficult to formulate efficient and targeted emission reduction strategies. Therefore, it is necessary to redesign a product supply chain carbon footprint accounting and optimization method to further improve the accuracy of product supply chain carbon footprint accounting and achieve adaptive and reliable carbon footprint optimization analysis. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a product supply chain carbon footprint accounting and optimization method, so as to further improve the accuracy and comprehensiveness of product supply chain carbon footprint accounting.

[0005] The present invention provides a product supply chain carbon footprint accounting and optimization method, comprising the following steps:

[0006] S1. Obtain product information, input a refined product information splitting model, and deconstruct the product into multiple modules, wherein the refined product information splitting model includes a top layer, a middle layer, and a bottom layer connected in sequence, wherein the top layer is used to identify the overall function of the product, the middle layer is used to divide the product into multiple units according to the principle of independence, each unit corresponds to a sub-function, and the bottom layer is used to merge units with associated sub-functions into one module, thereby finally completing product deconstruction;

[0007] S2. Obtain the full life cycle information of the supply chain of each module of the product, obtain the multi-level carbon footprint accounting nodes, and perform cluster analysis on the carbon footprint accounting records to obtain the multi-dimensional computing power allocation coefficient;

[0008] S3, obtaining a node source factor data group based on the multi-level carbon footprint accounting node, collecting and standardizing the carbon footprint data of each module of the product based on the node source factor data group, and obtaining multi-level carbon source data;

[0009] S4. Based on the multi-dimensional computing power allocation coefficient, the carbon footprint of the multi-level carbon source data is calculated and corrected to obtain a multi-level corrected carbon footprint calculation result;

[0010] S5. Generate a product carbon optimization plan based on the basic information of each module of the product and the multi-level corrected carbon footprint calculation results, and optimize the supply chain carbon footprint of each module of the product.

[0011] Furthermore, in the middle layer, the product is divided into multiple modules by using 3Dmax or SolidWorks software.

[0012] Furthermore, the specific process of merging modules with associated sub-functions at the bottom layer is as follows:

[0013] Obtain all modules and their corresponding sub-functions obtained by dividing the intermediate layer;

[0014] Group all modules according to correlation influencing factors, including data call relationship, interface call relationship and business logic coupling;

[0015] Merge modules in the same group and finally complete product deconstruction.

[0016] Furthermore, the specific process of step S2 is as follows:

[0017] S210, obtaining the full life cycle information of the supply chain of each module of the product and performing node identification to obtain a multi-level carbon footprint accounting node, wherein the multi-level carbon footprint accounting node includes a raw material acquisition node, a manufacturing and assembly node, a transportation node, a use node, and a recycling and processing node of each module of the product;

[0018] S220, obtaining a carbon footprint accounting record, performing linear dimensionality reduction by singular value decomposition, and obtaining a standard carbon footprint accounting record, wherein the standard carbon footprint accounting record includes a plurality of historical standard dioxygen equivalent values ​​in the carbon footprint accounting record after dimensionality reduction processing;

[0019] S230, performing cluster analysis on the standard carbon footprint accounting records based on the multi-level carbon footprint accounting nodes, classifying multiple historical standard carbon dioxide equivalent values ​​corresponding to the same carbon footprint accounting node in the standard carbon footprint accounting records into one category, and obtaining multi-dimensional standard carbon footprint accounting data, wherein the multi-dimensional standard carbon footprint accounting data includes multiple groups of standard carbon footprint accounting data, and each group of standard carbon footprint accounting data includes multiple historical standard carbon dioxide equivalent values ​​corresponding to each carbon footprint accounting node;

[0020] S240: Calculate computing power characteristics based on the multi-dimensional standard carbon footprint accounting data to obtain a multi-dimensional computing power allocation coefficient.

[0021] Furthermore, the specific process of step S240 is as follows:

[0022] Performing data volume statistics on multiple groups of standard carbon footprint accounting data in the multi-dimensional standard carbon footprint accounting data to obtain multiple historical carbon footprint accounting data volumes corresponding to the multiple groups of standard carbon footprint accounting data;

[0023] Adding up multiple historical carbon footprint accounting data volumes to obtain the total historical carbon footprint accounting data volume;

[0024] The ratios of multiple historical carbon footprint accounting data volumes to the total historical carbon footprint accounting data volume are calculated to obtain a multi-dimensional computing power allocation coefficient.

[0025] Furthermore, in step S3, the specific process of obtaining the node source factor data group based on the multi-level carbon footprint accounting node is as follows:

[0026] S311. Based on the multi-level carbon footprint accounting nodes, obtain multi-dimensional node data items, wherein the multi-dimensional node data items include raw material acquisition node data item information, manufacturing and assembly node data item information, transportation node data item information, usage node data item information, and recycling and processing node data item information corresponding to the multi-level carbon footprint accounting nodes;

[0027] S312, performing carbon footprint association analysis based on the multidimensional node data item to obtain a carbon footprint association analysis result, wherein the data item association analysis result includes a carbon footprint association parameter corresponding to each data type information in the multidimensional node data item;

[0028] S313, obtaining a carbon source data record, wherein the carbon source data record includes historical carbon footprint associated data corresponding to each module of multiple historical products, and performing a carbon footprint confidence analysis on the multidimensional node data item based on the carbon source data record to obtain a data item confidence analysis result;

[0029] S314, obtaining a data item analysis weight constraint condition, wherein the data item analysis weight constraint condition includes a preset association parameter weight value and a confidence parameter weight value, and based on the data item analysis weight constraint condition, performing a weighted calculation on the carbon footprint association analysis result and the data item confidence analysis result to obtain a multi-dimensional node data item feature evaluation result;

[0030] S315, obtaining a data item feature evaluation constraint condition, wherein the data item feature evaluation constraint condition includes a preset data type feature evaluation threshold, determining whether the multidimensional node data item feature evaluation result satisfies the data item feature evaluation constraint condition, and obtaining a multidimensional node data item determination result;

[0031] S316. Filter the multidimensional node data items based on the judgment results of the multidimensional node data items to obtain a node source factor data group, wherein the node source factor data group includes a raw material acquisition node source factor group, a manufacturing and assembly node source factor group, a transportation node source factor group, a usage node source factor group, and a recycling and processing node source factor group corresponding to the multi-level carbon footprint accounting node.

[0032] Furthermore, in step S313, the specific process of carbon footprint confidence analysis is as follows:

[0033] Based on the data type information in the multidimensional node data item, cluster analysis is performed on the multiple historical carbon footprint associated data in the carbon source data record, and the historical carbon footprint associated data corresponding to the same data type information are classified into one category to obtain multiple data type associated data groups, each data type associated data group including multiple historical carbon footprint associated data corresponding to each data type information in the multidimensional node data item;

[0034] Performing data volume statistics on multiple data type-associated data groups respectively to obtain multiple data type-associated data volumes, and summing up the multiple data type-associated data volumes to obtain the total data volume associated with the data types;

[0035] Ratios of the data volumes associated with multiple data types to the total data volumes associated with the data types are calculated respectively to obtain multiple data type confidence parameters corresponding to the multiple data type information in the multidimensional node data item, and add them to the data item confidence analysis results.

[0036] Furthermore, the specific process in step S4 is as follows:

[0037] S410, based on the node source factor data group, data collection is performed on each module of the product to obtain a carbon footprint data set, and after standardization processing, multi-level carbon source data is obtained, wherein the carbon footprint data set includes the raw material acquisition node source factor group, the manufacturing and assembly node source factor group, the transportation node source factor group, the use node source factor group, and the recycling node source factor group corresponding to the raw material acquisition node carbon footprint data, the manufacturing and assembly node carbon footprint data, the transportation node carbon footprint data, the use node carbon footprint data, and the recycling node carbon footprint data;

[0038] S420, inputting the multi-level carbon source data into the carbon footprint calculation platform, and performing carbon footprint calculation on the multi-level carbon source data based on the multi-dimensional computing power allocation coefficient to obtain a multi-level carbon footprint calculation result;

[0039] S430, inputting the multi-level carbon source data into an intelligent accounting analysis model to obtain a plurality of carbon footprint accounting data, wherein the plurality of carbon footprint accounting data include a plurality of total values ​​of dioxygen equivalent nodes corresponding to the multi-level carbon footprint accounting nodes output by the intelligent accounting analysis model;

[0040] S440, performing difference calculation on the multi-level carbon footprint calculation result and the multiple carbon footprint calculation data to obtain multiple carbon footprint calculation difference coefficients, wherein the multiple carbon footprint calculation difference coefficients include multiple difference information between the total values ​​of the dioxygen equivalent of multiple nodes in the multi-level carbon footprint calculation result and the corresponding multiple carbon footprint calculation data;

[0041] S450, determining whether the plurality of carbon footprint accounting difference coefficients satisfy the carbon footprint accounting difference interval, obtaining a plurality of abnormal accounting data groups, and correcting the multi-level carbon footprint accounting results based on the plurality of abnormal accounting data groups to obtain a multi-level corrected carbon footprint accounting result.

[0042] Further, in step S450, the carbon footprint accounting difference interval includes a pre-set carbon footprint accounting difference coefficient range information. If the carbon footprint accounting difference coefficient satisfies the carbon footprint accounting difference interval, the node dioxygen equivalent total value in the multi-level carbon footprint accounting result corresponding to the carbon footprint accounting difference coefficient is set as the node corrected dioxygen equivalent total value, and the node corrected dioxygen equivalent total value is added to the multi-level corrected carbon footprint accounting result; if the carbon footprint accounting difference coefficient does not satisfy the carbon footprint accounting difference interval, the node dioxygen equivalent total value and carbon footprint accounting data corresponding to the carbon footprint accounting difference coefficient are added to the abnormal accounting data group; the mean of all abnormal accounting data groups is calculated to obtain multiple node corrected dioxygen equivalent total values, and add them to the multi-level corrected carbon footprint accounting result.

[0043] Furthermore, the specific process of step S5 is as follows:

[0044] S510, summing up the total values ​​of the modified dioxygen equivalent of multiple nodes in the multi-level modified carbon footprint calculation result to obtain a total carbon footprint calculation result, and performing ratio calculation on the total values ​​of the modified dioxygen equivalent of multiple nodes and the total carbon footprint calculation result to obtain multiple carbon footprint ratio characteristic coefficients;

[0045] S520, marking the multi-level carbon footprint accounting nodes based on the multiple carbon footprint ratio characteristic coefficients to obtain the multi-level carbon footprint node optimization value;

[0046] S530, obtaining a carbon footprint variable source data set based on the node source factor data set;

[0047] S540. Obtain basic information of each module of the product, including structural composition, model specifications, component size parameters, performance parameters, material parameters and other data information of each module of the product, input the basic information of each module of the product, the carbon footprint variable source data set and the multi-level carbon footprint node optimization value into the carbon footprint optimization analysis model, generate a product carbon optimization plan, and optimize the carbon footprint of the product supply chain based on the product carbon optimization plan.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention provides a product supply chain carbon footprint accounting and optimization method. Before performing product supply chain carbon footprint accounting and optimization operations, the product is first deconstructed into multiple modules through a refined product information splitting model, wherein the refined product information splitting model includes a top layer, a middle layer and a bottom layer connected in sequence, the top layer is used to identify the overall function of the product, the middle layer is used to divide the product into multiple modules according to the principle of independence, each module corresponds to a sub-function, and the bottom layer is used to merge modules with related sub-functions to complete product deconstruction. The above method takes into account the impact of the product structure itself on the carbon footprint accounting, and can split the product according to the function, which is convenient for subsequent carbon footprint accounting and management, and further improves the accuracy and comprehensiveness of the product supply chain carbon footprint accounting, provides enterprises with more scientific and effective emission reduction guidance, and promotes green and low-carbon transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flow chart of the method of the present invention is provided. DETAILED DESCRIPTION

[0051] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0052] Example:

[0053] This embodiment provides a product supply chain carbon footprint accounting and optimization method, such as Figure 1 As shown, the following steps are included:

[0054] S1. Obtain product information, input a refined product information splitting model, and deconstruct the product into multiple modules.

[0055] The refined product information splitting model includes the top layer, middle layer and bottom layer connected in sequence. The top layer is used to identify the overall functions of the product. The specific process is as follows: first, clarify the core functions and auxiliary functions of the product, as well as the main and secondary needs of the user; then, list all possible functional points of the product, and classify and organize them.

[0056] The middle layer is used to divide the product into multiple units according to the principle of independence. Each unit corresponds to a sub-function. The specific process is as follows: First, analyze the independence of each functional point to determine whether it can exist as an independent sub-function; then, use the segmentation, cutting, separation and other tools in 3D modeling software (such as 3Dmax, SolidWorks, etc.), or use special product splitting software to split and combine interdependent or weakly related functional points into independent sub-functions; finally, assign a unique identifier to each sub-function for subsequent management and tracking.

[0057] The bottom layer is used to merge units with related sub-functions into one module, and finally complete product deconstruction. The specific process is as follows: first, all units and their corresponding sub-functions obtained by the middle layer division are obtained, and then all units are grouped according to the correlation influencing factors. The correlation influencing factors include data call relationship, interface call relationship and business logic coupling; then the units in the same group are merged into one module, and a unique identifier is assigned to each module to finally complete product deconstruction.

[0058] S2. Obtain the full life cycle information of the supply chain of each module of the product, obtain the multi-level carbon footprint accounting nodes, and perform cluster analysis on the carbon footprint accounting records to obtain the multi-dimensional computing power allocation coefficient. The specific process is as follows:

[0059] S210. Based on big data, query the life cycle parameters of each module of the product, obtain the full life cycle information of the supply chain of each module of the product, identify the nodes, and obtain multi-level carbon footprint accounting nodes. The full life cycle information refers to the entire life cycle process of each module of the product from production, manufacturing, use to disposal. The multi-level carbon footprint accounting nodes include the raw material acquisition node, manufacturing and assembly node, transportation node, use node, and recycling and processing node of each module of the product.

[0060] S220: query the carbon footprint accounting information of each module of the product based on big data, obtain the carbon footprint accounting record, perform singular value decomposition, and obtain the standard carbon footprint accounting record.

[0061] Singular value decomposition is one of the most commonly used linear dimensionality reduction methods. Its goal is to map high-dimensional carbon footprint accounting records to low-dimensional space through some kind of linear projection, and to maximize the amount of information in the projected dimension, so as to use fewer data dimensions while retaining more characteristics of the original data points. The standard carbon footprint accounting record includes multiple historical standard dioxygen equivalent values ​​in the carbon footprint accounting record after the dimensionality reduction processing of principal component analysis. Each set of historical carbon footprint accounting data includes multiple node carbon footprint accounting data corresponding to each module of each historical product, and each node carbon footprint accounting data includes multiple historical dioxygen equivalent values ​​at each carbon footprint accounting node for each module of each historical product. Dioxygen equivalent is the basic unit for measuring the greenhouse effect. The larger the historical dioxygen equivalent value, the greater the corresponding carbon footprint contribution and the higher the contribution to the greenhouse effect. For example, the dioxygen equivalent value of a gas is the product of the mass of the gas and the global warming potential value corresponding to the gas.

[0062] S230. Perform cluster analysis on standard carbon footprint accounting records based on multi-level carbon footprint accounting nodes, classify multiple historical standard carbon dioxide equivalent values ​​corresponding to the same carbon footprint accounting node in the standard carbon footprint accounting record into one category, and obtain multidimensional standard carbon footprint accounting data. The multidimensional standard carbon footprint accounting data includes multiple groups of standard carbon footprint accounting data, and each group of standard carbon footprint accounting data includes multiple historical standard carbon dioxide equivalent values ​​corresponding to each carbon footprint accounting node.

[0063] S240: Calculate the computing power characteristics based on the multi-dimensional standard carbon footprint accounting data to obtain a multi-dimensional computing power allocation coefficient. The specific process is as follows:

[0064] Firstly, data volume statistics are performed on multiple groups of standard carbon footprint accounting data in the multi-dimensional standard carbon footprint accounting data to obtain multiple historical carbon footprint accounting data volumes corresponding to the multiple groups of standard carbon footprint accounting data; secondly, multiple historical carbon footprint accounting data volumes are added up to obtain the total historical carbon footprint accounting data volume; then, the ratio of multiple historical carbon footprint accounting data volumes to the total historical carbon footprint accounting data volume is calculated to obtain the multi-dimensional computing power allocation coefficient.

[0065] The above steps analyze the full life cycle information of each module of the product to obtain multi-level carbon footprint accounting nodes, and mark the computing power allocation of the multi-level carbon footprint accounting nodes to obtain accurate multi-dimensional computing power allocation coefficients, laying the foundation for subsequent carbon footprint accounting of the product supply chain.

[0066] S3. Obtain node source factor data groups based on multi-level carbon footprint accounting nodes, collect and standardize carbon footprint data of each module of the product based on the node source factor data groups, and obtain multi-level carbon source data.

[0067] The specific process of obtaining the node source factor data group based on the multi-level carbon footprint accounting node is as follows:

[0068] S311. Based on the multi-level carbon footprint accounting nodes, multi-dimensional node data items are obtained, and the multi-dimensional node data items include raw material acquisition node data item information, manufacturing and assembly node data item information, transportation node data item information, usage node data item information, and recycling and processing node data item information corresponding to the multi-level carbon footprint accounting nodes.

[0069] S312, based on the multidimensional node data items, a carbon footprint association analysis is performed to obtain a carbon footprint association analysis result, the data item association analysis result includes a carbon footprint association parameter corresponding to each data type information in the multidimensional node data items, and the larger the carbon footprint association parameter, the stronger the association between the corresponding data type information and the carbon footprint of each module of the product. Exemplarily, a historical data query is performed based on the multidimensional node data items to obtain a plurality of historical data type information and a plurality of historical data type-carbon footprint association parameters, the correspondence between the plurality of historical data type information and the plurality of historical data type-carbon footprint association parameters is analyzed, the plurality of historical data type information and the plurality of historical data type-carbon footprint association parameters are arranged according to the correspondence, a carbon footprint association analysis database is obtained, the multidimensional node data items are used as input information, and the carbon footprint association analysis database is input, and the data item association analysis result can be obtained.

[0070] S313. Based on big data, query the carbon source data of each module of the product to obtain carbon source data records, which include historical carbon footprint associated data corresponding to each module of multiple historical products. Based on the carbon source data records, perform carbon footprint confidence analysis on multidimensional node data items to obtain data item confidence analysis results.

[0071] The specific process of carbon footprint confidence analysis is as follows:

[0072] Based on the data type information in the multidimensional node data item, a cluster analysis is performed on multiple historical carbon footprint associated data in the carbon source data record, and the historical carbon footprint associated data corresponding to the same data type information are classified into one category to obtain multiple data type associated data groups, each data type associated data group includes multiple historical carbon footprint associated data corresponding to each data type information in the multidimensional node data item;

[0073] Performing data volume statistics on multiple data type-associated data groups respectively to obtain multiple data type-associated data volumes, and summing up the multiple data type-associated data volumes to obtain the total data volume associated with the data types;

[0074] Ratios of the data volumes associated with multiple data types to the total data volumes associated with the data types are calculated respectively to obtain multiple data type confidence parameters corresponding to the multiple data type information in the multidimensional node data item, and add them to the data item confidence analysis results.

[0075] S314, obtaining a data item analysis weight constraint condition, the data item analysis weight constraint condition includes a preset association parameter weight value and a confidence parameter weight value, and based on the data item analysis weight constraint condition, weighted calculation is performed on the carbon footprint association analysis result and the data item confidence analysis result to obtain a multidimensional node data item feature evaluation result, the multidimensional node data item feature evaluation result includes a plurality of data type feature evaluation values ​​corresponding to a plurality of data type information in the multidimensional node data item. Exemplarily, the association parameter weight value is multiplied by the plurality of carbon footprint association parameters in the data item association analysis result respectively, to obtain a plurality of data type first feature evaluation values. The confidence parameter weight value is multiplied by the plurality of data type confidence parameters in the data item confidence analysis result respectively, to obtain a plurality of data type second feature evaluation values. The plurality of data type first feature evaluation values ​​are summed with the corresponding plurality of data type second feature evaluation values ​​to obtain a plurality of data type feature evaluation values.

[0076] S315. Obtain data item feature evaluation constraints, which include pre-set data type feature evaluation thresholds, determine whether the multidimensional node data item feature evaluation results satisfy the data item feature evaluation constraints, and obtain the multidimensional node data item determination results, which include whether each data type feature evaluation value in the multidimensional node data item feature evaluation results satisfies the data item feature evaluation constraints.

[0077] S316. Filter the multidimensional node data items based on the judgment results of the multidimensional node data items to obtain the node source factor data group, which includes the raw material acquisition node source factor group, the manufacturing and assembly node source factor group, the transportation node source factor group, the use node source factor group, and the recycling and processing node source factor group corresponding to the multi-level carbon footprint accounting node. Each node source factor group in the node source factor data group includes multiple data type information in the carbon footprint source factor data group corresponding to each carbon footprint accounting node. Exemplarily, the raw material acquisition node source factor group includes multiple data type information such as the material type, material quantity, material weight, energy emission, material carbon emission factor, and energy carbon emission factor corresponding to the raw material acquisition node. The manufacturing and assembly node source factor group includes multiple data type information such as the energy consumption, greenhouse gas emissions, greenhouse gas type, and gas warming potential value corresponding to the manufacturing and assembly node. The transportation node source factor group includes multiple data type information such as the quality of each module of the product corresponding to the transportation node, the transportation distance, and the carbon emission factor of the transportation tool. The use node source factor group includes multiple data types of information such as energy consumption, energy emission factor, and direct greenhouse gas emissions of each module of the product corresponding to the use node; the recycling node source factor group includes multiple data types of information such as energy consumption and direct greenhouse gas emissions corresponding to the recycling node. The above steps can obtain a reliable and comprehensive node source factor data group by analyzing the data types of multi-level carbon footprint accounting nodes, thereby improving the accuracy of carbon footprint accounting for each module of the product.

[0078] S4. Based on the multi-dimensional computing power allocation coefficient, the carbon footprint of multi-level carbon source data is calculated and corrected to obtain the multi-level corrected carbon footprint calculation results. The specific process is as follows:

[0079] S410, based on the node source factor data group, data collection is performed on each module of the product to obtain a carbon footprint data set, and after standardization processing, multi-level carbon source data is obtained, and the carbon footprint data set includes the raw material acquisition node source factor group, the manufacturing and assembly node source factor group, the transportation node source factor group, the use node source factor group, and the recycling node source factor group corresponding to the raw material acquisition node carbon footprint data, the manufacturing and assembly node carbon footprint data, the transportation node carbon footprint data, the use node carbon footprint data, and the recycling node carbon footprint data. Standardization processing refers to converting the data information in the carbon footprint data set into dimensionless pure values.

[0080] S420, input the multi-level carbon source data into the carbon footprint accounting platform, control the carbon footprint accounting platform to perform carbon footprint accounting on the multi-level carbon source data according to the multi-dimensional computing power allocation coefficient, and obtain the multi-level carbon footprint accounting results. The greater the computing power allocation coefficient information in the computing power allocation coefficient, the greater the ability of the carbon footprint accounting platform to process data on the carbon footprint accounting node corresponding to the computing power allocation coefficient information, the greater the amount of data of the carbon footprint data of the carbon footprint accounting node that the carbon footprint accounting platform can process, and the higher the efficiency and accuracy of the carbon footprint accounting platform to calculate and analyze the carbon footprint data of the carbon footprint accounting node. Exemplarily, the carbon footprint data of the raw material acquisition node includes multiple data information such as material type information, material quantity information, material weight information, energy emission information, material carbon emission factor information, and energy carbon emission factor information corresponding to the source factor group of the raw material acquisition node. The carbon footprint accounting platform can be Gabi software in the prior art, and Gabi software has the function of performing intelligent carbon footprint accounting on the input multi-level carbon source data according to the multi-dimensional computing power allocation coefficient. The multi-level carbon footprint calculation results include the total value of the carbon dioxide equivalent of multiple nodes corresponding to the multi-level carbon footprint calculation nodes. The larger the total value of the node carbon dioxide equivalent, the greater the carbon footprint contribution of each module of the product of the corresponding carbon footprint calculation node. The above steps use the carbon footprint calculation platform to efficiently calculate the carbon footprint of multi-level carbon source data, obtain multi-level carbon footprint calculation results, and improve the efficiency of carbon footprint calculation.

[0081] S430, inputting the multi-level carbon source data into the intelligent accounting analysis model to obtain a plurality of carbon footprint accounting data, wherein the plurality of carbon footprint accounting data include a plurality of total values ​​of dioxygen equivalent nodes corresponding to the multi-level carbon footprint accounting nodes output by the intelligent accounting analysis model.

[0082] S440, performing difference calculation on the multi-level carbon footprint calculation results and the multiple carbon footprint calculation data to obtain multiple carbon footprint calculation difference coefficients, wherein the multiple carbon footprint calculation difference coefficients include multiple difference information between the total values ​​of the carbon dioxide equivalent of multiple nodes in the multi-level carbon footprint calculation results and the corresponding multiple carbon footprint calculation data.

[0083] S450, determining whether a plurality of carbon footprint accounting difference coefficients satisfy a carbon footprint accounting difference interval, obtaining a plurality of abnormal accounting data groups, and correcting the multi-level carbon footprint accounting results based on the plurality of abnormal accounting data groups to obtain a multi-level corrected carbon footprint accounting result.

[0084] The carbon footprint accounting difference interval includes the preset carbon footprint accounting difference coefficient range information. If the carbon footprint accounting difference coefficient satisfies the carbon footprint accounting difference interval, the node dioxygen equivalent total value in the multi-level carbon footprint accounting result corresponding to the carbon footprint accounting difference coefficient is set as the node corrected dioxygen equivalent total value, and the node corrected dioxygen equivalent total value is added to the multi-level corrected carbon footprint accounting result; if the carbon footprint accounting difference coefficient does not meet the carbon footprint accounting difference interval, the node dioxygen equivalent total value and carbon footprint accounting data corresponding to the carbon footprint accounting difference coefficient are added to the abnormal accounting data group; the mean of all abnormal accounting data groups is calculated to obtain multiple node corrected dioxygen equivalent total values, and add them to the multi-level corrected carbon footprint accounting result. Multiple node corrected dioxygen equivalent total values ​​include multiple node dioxygen equivalent total values ​​in the multi-level carbon footprint accounting result corresponding to multiple carbon footprint accounting difference coefficients that meet the carbon footprint accounting difference interval, and / or multiple average values ​​of multiple abnormal accounting data groups corresponding to multiple carbon footprint accounting difference coefficients that do not meet the carbon footprint accounting difference interval.

[0085] Exemplarily, when constructing an intelligent accounting and analysis model, historical data queries are performed based on multi-level carbon source data to obtain multiple sets of construction data. Each set of construction data includes multi-level historical carbon source data and multiple historical carbon footprint accounting data information. 80% of the data information in the multiple sets of construction data is randomly divided into a training data set, and 20% of the data information is divided into a test data set. The training data set is continuously self-trained and learned to a convergence state to obtain an intelligent accounting and analysis model. The test data set is used as input information and input into the intelligent accounting and analysis model, and the parameters of the intelligent accounting and analysis model are updated and iteratively optimized through the test data set. The intelligent accounting and analysis model includes an input layer, a hidden layer, and an output layer. The intelligent accounting and analysis model has the function of performing intelligent carbon footprint calculation on the input multi-level carbon source data.

[0086] The above steps use the intelligent accounting analysis model to perform intelligent and efficient carbon footprint accounting on multi-level carbon source data, obtain multiple carbon footprint accounting data, and perform difference analysis on the multi-level carbon footprint accounting results based on them to obtain reliable multiple carbon footprint accounting difference coefficients, thereby adaptively correcting the multi-level carbon footprint accounting results according to the multiple carbon footprint accounting difference coefficients, thereby improving the accuracy of carbon footprint accounting of each module of the product.

[0087] S5. Generate a product carbon optimization plan based on the basic information of each module of the product and the multi-level modified carbon footprint calculation results, and optimize the supply chain carbon footprint of each module of the product. The specific process is as follows:

[0088] S510, summing up the total values ​​of the corrected dioxygen equivalent of multiple nodes in the multi-level corrected carbon footprint calculation results to obtain the total carbon footprint calculation result, and performing ratio calculations on the total values ​​of the corrected dioxygen equivalent of multiple nodes and the total carbon footprint calculation result to obtain multiple carbon footprint ratio characteristic coefficients. The larger the carbon footprint ratio characteristic coefficient, the greater the carbon footprint contribution of each module of the product of the carbon footprint calculation node corresponding to the carbon footprint ratio characteristic coefficient, and the greater the value of carbon footprint optimization of the carbon footprint calculation node.

[0089] S520. Marking a multi-level carbon footprint accounting node based on a plurality of carbon footprint ratio characteristic coefficients to obtain a multi-level carbon footprint node optimization value, wherein the multi-level carbon footprint node optimization value includes a multi-level carbon footprint accounting node and a plurality of carbon footprint ratio characteristic coefficients corresponding to the multi-level carbon footprint accounting node.

[0090] S530, setting the node source factor data group as the carbon footprint variable source data set.

[0091] S540. Obtain the basic information of each module of the product, including the structural composition, model specifications, component size parameters, performance parameters, material parameters and other data information of each module of the product, input the basic information of each module of the product, the carbon footprint variable source data set and the multi-level carbon footprint node optimization value into the carbon footprint optimization analysis model, and generate multiple product basic carbon optimization solutions.

[0092] When constructing a carbon footprint optimization analysis model, historical data queries are performed based on the multi-level carbon footprint node optimization value, basic information of each module of the product, and carbon footprint variable source data sets to obtain multiple sets of training data sets, each of which includes multi-level historical carbon footprint node optimization value, basic information of each module of the historical product, historical carbon footprint variable source data sets, and carbon optimization plans for each module of the historical product. The carbon footprint optimization analysis model can be obtained by continuously self-training and learning multiple sets of training data sets until convergence. The carbon footprint optimization analysis model includes an input layer, a hidden layer, and an output layer. The carbon footprint optimization analysis model has the function of intelligently matching the carbon optimization plans of products with the input multi-level carbon footprint node optimization value, basic information of each module of the product, and carbon footprint variable source data sets.

[0093] The above steps use the carbon footprint optimization analysis model to intelligently and efficiently match the product supply chain carbon optimization solutions with the optimization value of multi-level carbon footprint nodes, the basic information of each product module, and the carbon footprint variable source data set, and obtain multiple product basic carbon optimization solutions, laying a solid foundation for the subsequent screening of multiple product basic carbon optimization solutions.

[0094] S550. Select a product carbon optimization solution from multiple product basic carbon optimization solutions, and optimize the supply chain carbon footprint of each module of the product based on the product carbon optimization solution. The selection process is as follows:

[0095] Supply chain performance prediction is performed based on multiple product-based carbon optimization solutions to obtain multiple supply chain performance prediction feature values;

[0096] Carry out supply chain carbon footprint prediction based on multiple product-based carbon optimization solutions to obtain multiple supply chain carbon footprint prediction feature values;

[0097] Based on the mean calculation of multiple supply chain performance prediction characteristic values ​​and multiple supply chain carbon footprint prediction characteristic values, multiple scheme evaluation characteristic values ​​are obtained, and the product base carbon optimization scheme with the largest scheme evaluation characteristic value is taken as the product carbon optimization scheme.

[0098] The above steps screen multiple product-based carbon optimization solutions by performing supply chain performance prediction and supply chain carbon footprint prediction on multiple product-based carbon optimization solutions, obtain reliable product carbon optimization solutions, and improve the carbon footprint optimization quality of the product supply chain.

[0099] In summary, the method provided in this embodiment has the following advantages:

[0100] 1. By adopting a refined product splitting information model, the overall product is divided into modules, and the product is split into multiple independent modules to obtain a product system composed of multiple independent modules. By calculating the supply chain carbon footprint of each independent module of the product, the supply chain accounting accuracy of the overall product is improved.

[0101] 2. Obtain multi-level carbon footprint accounting nodes through the full life cycle information of the product supply chain; obtain multi-dimensional computing power allocation coefficients by marking the computing power allocation of multi-level carbon footprint accounting nodes; obtain multi-level carbon source data by collecting carbon footprint associated data of the product supply chain through multi-level carbon footprint accounting nodes; based on the multi-dimensional computing power allocation coefficient, perform carbon footprint accounting on multi-level carbon source data through the carbon footprint accounting platform to obtain multi-level carbon footprint accounting results; correct the multi-level carbon footprint accounting results through the intelligent accounting analysis model to obtain multi-level corrected carbon footprint accounting results; perform carbon footprint optimization analysis through the basic information of the product supply chain and the multi-level corrected carbon footprint accounting results to obtain the product carbon optimization plan, and optimize the carbon footprint of the product supply chain according to the product carbon optimization plan. By performing multi-dimensional carbon footprint accounting on the product supply chain, the accuracy of the carbon footprint accounting of the product supply chain is improved, and an adaptive and reliable carbon footprint optimization analysis of the product supply chain is performed according to the carbon footprint accounting results, which improves the quality of carbon footprint optimization of the product supply chain.

[0102] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0103] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A product supply chain carbon footprint accounting and optimization method, characterized in that: The following steps are involved: S1. Obtain product information, input a refined product information splitting model, and deconstruct the product into multiple modules, wherein the refined product information splitting model includes a top layer, a middle layer, and a bottom layer connected in sequence, wherein the top layer is used to identify the overall function of the product, the middle layer is used to divide the product into multiple units according to the principle of independence, each unit corresponds to a sub-function, and the bottom layer is used to merge units with associated sub-functions into one module, thereby finally completing product deconstruction; S2. Obtain the full life cycle information of the supply chain of each module of the product, obtain the multi-level carbon footprint accounting nodes, and perform cluster analysis on the carbon footprint accounting records to obtain the multi-dimensional computing power allocation coefficient; S3, obtaining a node source factor data group based on the multi-level carbon footprint accounting node, collecting and standardizing the carbon footprint data of each module of the product based on the node source factor data group, and obtaining multi-level carbon source data; S4. Based on the multi-dimensional computing power allocation coefficient, the carbon footprint of the multi-level carbon source data is calculated and corrected to obtain a multi-level corrected carbon footprint calculation result; S5. Generate a product carbon optimization plan based on the basic information of each module of the product and the multi-level corrected carbon footprint calculation results, and optimize the supply chain carbon footprint of each module of the product.

2. A product supply chain carbon footprint calculation and optimization method according to claim 1, characterized in that: In the middle layer, the product is divided into multiple modules through 3Dmax or SolidWorks software.

3. A product supply chain carbon footprint calculation and optimization method according to claim 1, characterized in that: The specific process of merging modules with associated sub-functions at the bottom layer is as follows: Obtain all modules and their corresponding sub-functions obtained by dividing the intermediate layer; Group all modules according to correlation influencing factors, including data call relationship, interface call relationship and business logic coupling; Merge modules in the same group and finally complete product deconstruction.

4. A product supply chain carbon footprint calculation and optimization method according to claim 1, characterized in that: The specific process of step S2 is as follows: S210, obtaining the full life cycle information of the supply chain of each module of the product and performing node identification to obtain a multi-level carbon footprint accounting node, wherein the multi-level carbon footprint accounting node includes a raw material acquisition node, a manufacturing and assembly node, a transportation node, a use node, and a recycling and processing node of each module of the product; S220, obtaining a carbon footprint accounting record, performing linear dimensionality reduction by singular value decomposition, and obtaining a standard carbon footprint accounting record, wherein the standard carbon footprint accounting record includes a plurality of historical standard dioxygen equivalent values ​​in the carbon footprint accounting record after dimensionality reduction processing; S230, performing cluster analysis on the standard carbon footprint accounting records based on the multi-level carbon footprint accounting nodes, classifying multiple historical standard carbon dioxide equivalent values ​​corresponding to the same carbon footprint accounting node in the standard carbon footprint accounting records into one category, and obtaining multi-dimensional standard carbon footprint accounting data, wherein the multi-dimensional standard carbon footprint accounting data includes multiple groups of standard carbon footprint accounting data, and each group of standard carbon footprint accounting data includes multiple historical standard carbon dioxide equivalent values ​​corresponding to each carbon footprint accounting node; S240: Calculate computing power characteristics based on the multi-dimensional standard carbon footprint accounting data to obtain a multi-dimensional computing power allocation coefficient.

5. A product supply chain carbon footprint calculation and optimization method according to claim 4, characterized in that: The specific process of step S240 is as follows: Performing data volume statistics on multiple groups of standard carbon footprint accounting data in the multi-dimensional standard carbon footprint accounting data to obtain multiple historical carbon footprint accounting data volumes corresponding to the multiple groups of standard carbon footprint accounting data; Adding up multiple historical carbon footprint accounting data volumes to obtain the total historical carbon footprint accounting data volume; The ratios of multiple historical carbon footprint accounting data volumes to the total historical carbon footprint accounting data volume are calculated to obtain a multi-dimensional computing power allocation coefficient.

6. A product supply chain carbon footprint calculation and optimization method according to claim 1, characterized in that: In step S3, the specific process of obtaining the node source factor data group based on the multi-level carbon footprint accounting node is as follows: S311. Based on the multi-level carbon footprint accounting nodes, obtain multi-dimensional node data items, wherein the multi-dimensional node data items include raw material acquisition node data item information, manufacturing and assembly node data item information, transportation node data item information, usage node data item information, and recycling and processing node data item information corresponding to the multi-level carbon footprint accounting nodes; S312, performing carbon footprint association analysis based on the multidimensional node data item to obtain a carbon footprint association analysis result, wherein the data item association analysis result includes a carbon footprint association parameter corresponding to each data type information in the multidimensional node data item; S313, obtaining a carbon source data record, wherein the carbon source data record includes historical carbon footprint associated data corresponding to each module of multiple historical products, and performing a carbon footprint confidence analysis on the multidimensional node data item based on the carbon source data record to obtain a data item confidence analysis result; S314, obtaining a data item analysis weight constraint condition, wherein the data item analysis weight constraint condition includes a preset association parameter weight value and a confidence parameter weight value, and based on the data item analysis weight constraint condition, performing a weighted calculation on the carbon footprint association analysis result and the data item confidence analysis result to obtain a multi-dimensional node data item feature evaluation result; S315, obtaining a data item feature evaluation constraint condition, wherein the data item feature evaluation constraint condition includes a preset data type feature evaluation threshold, determining whether the multidimensional node data item feature evaluation result satisfies the data item feature evaluation constraint condition, and obtaining a multidimensional node data item determination result; S316. Filter the multidimensional node data items based on the judgment results of the multidimensional node data items to obtain a node source factor data group, wherein the node source factor data group includes a raw material acquisition node source factor group, a manufacturing and assembly node source factor group, a transportation node source factor group, a usage node source factor group, and a recycling and processing node source factor group corresponding to the multi-level carbon footprint accounting node.

7. A product supply chain carbon footprint calculation and optimization method according to claim 6, characterized in that: In step S313, the specific process of carbon footprint confidence analysis is as follows: Based on the data type information in the multidimensional node data item, cluster analysis is performed on the multiple historical carbon footprint associated data in the carbon source data record, and the historical carbon footprint associated data corresponding to the same data type information are classified into one category to obtain multiple data type associated data groups, each data type associated data group including multiple historical carbon footprint associated data corresponding to each data type information in the multidimensional node data item; Performing data volume statistics on multiple data type-associated data groups respectively to obtain multiple data type-associated data volumes, and summing up the multiple data type-associated data volumes to obtain the total data volume associated with the data types; Ratios of the data volumes associated with multiple data types to the total data volumes associated with the data types are calculated respectively to obtain multiple data type confidence parameters corresponding to the multiple data type information in the multidimensional node data item, and add them to the data item confidence analysis results.

8. The method for calculating and optimizing carbon footprint of a product supply chain according to claim 1, characterized in that: The specific process in step S4 is as follows: S410, based on the node source factor data group, data collection is performed on each module of the product to obtain a carbon footprint data set, and after standardization processing, multi-level carbon source data is obtained, wherein the carbon footprint data set includes the raw material acquisition node source factor group, the manufacturing and assembly node source factor group, the transportation node source factor group, the use node source factor group, and the recycling node source factor group corresponding to the raw material acquisition node carbon footprint data, the manufacturing and assembly node carbon footprint data, the transportation node carbon footprint data, the use node carbon footprint data, and the recycling node carbon footprint data; S420, inputting the multi-level carbon source data into the carbon footprint calculation platform, and performing carbon footprint calculation on the multi-level carbon source data based on the multi-dimensional computing power allocation coefficient to obtain a multi-level carbon footprint calculation result; S430, inputting the multi-level carbon source data into an intelligent accounting analysis model to obtain a plurality of carbon footprint accounting data, wherein the plurality of carbon footprint accounting data include a plurality of total values ​​of dioxygen equivalent nodes corresponding to the multi-level carbon footprint accounting nodes output by the intelligent accounting analysis model; S440, performing difference calculation on the multi-level carbon footprint calculation result and the multiple carbon footprint calculation data to obtain multiple carbon footprint calculation difference coefficients, wherein the multiple carbon footprint calculation difference coefficients include multiple difference information between the total values ​​of the dioxygen equivalent of multiple nodes in the multi-level carbon footprint calculation result and the corresponding multiple carbon footprint calculation data; S450, determining whether the plurality of carbon footprint accounting difference coefficients satisfy the carbon footprint accounting difference interval, obtaining a plurality of abnormal accounting data groups, and correcting the multi-level carbon footprint accounting results based on the plurality of abnormal accounting data groups to obtain a multi-level corrected carbon footprint accounting result.

9. A product supply chain carbon footprint calculation and optimization method according to claim 8, characterized in that: In step S450, the carbon footprint calculation difference interval includes the preset carbon footprint calculation difference coefficient range information. If the carbon footprint calculation difference coefficient satisfies the carbon footprint calculation difference interval, the node dioxygen equivalent total value in the multi-level carbon footprint calculation result corresponding to the carbon footprint calculation difference coefficient is set as the node corrected dioxygen equivalent total value, and the node corrected dioxygen equivalent total value is added to the multi-level corrected carbon footprint calculation result; If the carbon footprint accounting difference coefficient does not meet the carbon footprint accounting difference range, the node dioxygen equivalent total value and carbon footprint accounting data corresponding to the carbon footprint accounting difference coefficient are added to the abnormal accounting data group; The mean of all abnormal accounting data groups is calculated to obtain the total value of the corrected dioxygen equivalent of multiple nodes, and it is added to the multi-level corrected carbon footprint accounting results.

10. A product supply chain carbon footprint calculation and optimization method according to claim 1, characterized in that: The specific process of step S5 is as follows: S510, summing up the total values ​​of the modified dioxygen equivalent of multiple nodes in the multi-level modified carbon footprint calculation result to obtain a total carbon footprint calculation result, and performing ratio calculation on the total values ​​of the modified dioxygen equivalent of multiple nodes and the total carbon footprint calculation result to obtain multiple carbon footprint ratio characteristic coefficients; S520, marking the multi-level carbon footprint accounting nodes based on the multiple carbon footprint ratio characteristic coefficients to obtain the multi-level carbon footprint node optimization value; S530, obtaining a carbon footprint variable source data set based on the node source factor data set; S540. Obtain basic information of each module of the product, including structural composition, model specifications, component size parameters, performance parameters, material parameters and other data information of each module of the product, input the basic information of each module of the product, the carbon footprint variable source data set and the multi-level carbon footprint node optimization value into the carbon footprint optimization analysis model, generate a product carbon optimization plan, and optimize the carbon footprint of the product supply chain based on the product carbon optimization plan.

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