Carbon footprint factor database construction method and device

Abstract

The invention discloses a carbon footprint factor database construction method and device, and the method comprises the steps: obtaining first background carbon footprint factor data based on a preset data selection rule; generating a component directory of the target product based on a predetermined bill of material or production process of the target product; acquiring corresponding activity level data based on each component information in the component directory of the target product, and generating a background information directory based on the activity level data; generating second background carbon footprint factor data based on the first background carbon footprint factor data and the background information directory; based on the activity level data, the background information directory and the second background carbon footprint factor data, constructing a carbon footprint factor database to determine the carbon emission; according to the method, the component-level carbon footprint factor database is constructed by integrating information such as product purchase data, a bill of materials or a production process flow, so that dynamic updating and standardized management of carbon emission factor data are realized.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint accounting technology, and in particular to a method and apparatus for constructing a carbon footprint factor database. Background Technology

[0002] Product carbon footprint (PCF) is the process of quantitatively calculating and assessing the greenhouse gas emissions generated by a product throughout its entire life cycle (including raw material acquisition, manufacturing, transportation and distribution, use, and disposal) based on the Life Cycle Assessment (LCA) methodology. The emission factor is the core foundational data for conducting PCF accounting and assessment, representing the greenhouse gas emissions per unit of activity and serving as a crucial basis for establishing carbon emission inventories and calculating product carbon footprints.

[0003] In existing technologies, carbon footprint factor data sources are scattered and the accounting process is complex. Manufacturing enterprises often find it difficult to obtain complete data in a timely and accurate manner during the implementation of carbon footprint accounting, resulting in significant uncertainty in the accounting results and affecting the accuracy and comparability of the evaluation.

[0004] Currently, carbon footprint factor data mainly comes from foreign databases or domestic public databases. Some companies directly use data from foreign carbon footprint factor databases for calculation, but due to differences in energy structure, production processes, equipment efficiency, and industrial chain structure among different countries, foreign databases cannot accurately reflect the actual situation of domestic manufacturing. Although several carbon footprint factor databases have been published domestically, their construction methods, data sources, and calculation methods are not uniform, and there is a lack of standardized data quality control and comparability evaluation mechanisms. Directly aggregating or mixing data from different sources will lead to problems such as high uncertainty in calculation results, poor comparability, and insufficient traceability.

[0005] Therefore, existing technologies are still insufficient to meet the needs of domestic manufacturing industries for high-precision, high-consistency, and high-traceability factor data in carbon footprint accounting.

[0006] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0007] This invention provides a method for constructing a carbon footprint factor database to achieve efficient, accurate, and traceable carbon footprint accounting for manufactured products. By integrating product procurement data, bills of materials, and production process information, a component-level carbon footprint factor database is constructed to achieve dynamic updates and standardized management of carbon emission factor data, thereby improving the scientific rigor, systematic nature, and industry applicability of carbon footprint accounting.

[0008] The method for constructing this carbon footprint factor database includes: Based on preset data selection rules, first background carbon footprint factor data are obtained; A component catalog of the target product is generated based on a pre-determined bill of materials or production process flow; wherein, the target product is determined based on pre-acquired product procurement data; Based on the component information in the component catalog of the target product, obtain the corresponding activity level data, and generate a background information catalog based on the activity level data; Based on the first background carbon footprint factor data and the background information directory, the second background carbon footprint factor data is generated. Based on the activity level data, the background information directory, and the second background carbon footprint factor data, a carbon footprint factor database is constructed to determine carbon emissions.

[0009] In some embodiments, obtaining corresponding activity level data based on component information in the component catalog of the target product, and generating a background information catalog based on the activity level data, includes: Obtain the activity level data corresponding to each component information in the component catalog of the target product; The activity level data corresponding to each component information are summarized and processed to generate a background information directory.

[0010] In some embodiments, generating second background carbon footprint factor data based on the first background carbon footprint factor data and the background information directory includes: Based on the number of data that meet the data selection rules in each of the first background carbon footprint factor data, the data quality level corresponding to the first background carbon footprint factor data is determined. The background information directory is matched with the first background carbon footprint factor data through field mapping, and the matched second background carbon footprint factor data is generated according to the data quality level.

[0011] In some embodiments, constructing a carbon footprint factor database based on the activity level data, the background information directory, and the second background carbon footprint factor data includes: Input the background information directory and the second background carbon footprint factor data into the initial carbon footprint factor database; Based on the activity level data and carbon footprint factor calculation parameters, the carbon footprint factor of the component is determined; The background information directory and the second background carbon footprint factor data are mapped to the carbon footprint factors of the component to obtain a carbon footprint factor database.

[0012] In some embodiments, the activity level data includes: raw material consumption, total transport weight of raw materials, total transport distance of raw materials, weight of raw materials purchased per unit batch, and energy consumption; the carbon footprint factor calculation parameters include: background carbon footprint factor of raw materials, carbon emission factor of transportation vehicles, energy consumption correction factor, and energy carbon emission factor; determining the carbon footprint factor of the component based on the activity level data and carbon footprint factor calculation parameters includes: The carbon emissions during the raw material acquisition stage are determined based on the raw material consumption and the background carbon footprint factor of the raw materials. The carbon emissions during the raw material transportation stage are determined based on the total transportation weight of the raw materials, the total transportation distance of the raw materials, the carbon emission factor of the transportation vehicle, the energy consumption correction coefficient, the raw material consumption, and the weight of raw materials purchased per unit batch. Based on the energy consumption and the energy carbon emission factor, determine the carbon emissions during the component production stage; The carbon footprint factor of the component is determined based on the carbon emissions during the raw material acquisition stage, the carbon emissions during the raw material transportation stage, and the carbon emissions during the component production stage.

[0013] In some embodiments, the product procurement data includes: a first product quantity and a first product amount. The step of determining the target product based on the pre-acquired product procurement data includes: The quantity and value of the first product are standardized to obtain the quantity and value of the second product. The product weight is determined based on the quantity and value of the second product. The comprehensive score of the product is determined based on the product weight, the quantity of the second product, the amount of the second product, and the preset target indicator quantity. Multiple target products are determined based on the comprehensive scores corresponding to each product.

[0014] In some embodiments, the standardization of the obtained first product quantity and first product amount to obtain the second product quantity and second product amount includes: The quantity of the second product is determined based on the quantity of the first product, the minimum quantity of the first product, and the maximum quantity of the first product. The amount of the second product is determined based on the amount of the first product, the minimum amount of the first product, and the maximum amount of the first product.

[0015] In some embodiments, determining the product weight based on the quantity and amount of the second product includes: Based on the quantity of the second product, the amount of the second product, and the quantity of product types, determine the probability value of the quantity of the second product or the amount of the second product; Based on the probability value and the quantity of the product type, determine the information entropy of the quantity of the second product or the amount of the second product; The product weight is determined based on the information entropy.

[0016] In some embodiments, determining multiple target products based on the comprehensive score corresponding to each of the products includes: The comprehensive scores corresponding to each product are sorted in a preset order; The comprehensive scores corresponding to each product ranked before the preset ranking are added together to obtain the first comprehensive score sum of each product ranked before the preset ranking; The cumulative contribution rate is obtained by comparing the sum of the first comprehensive scores with the sum of the second comprehensive scores of all the products. When the cumulative contribution rate is less than or equal to a preset percentage, the products ranked before the preset ranking are identified as target products.

[0017] In some embodiments, generating a component catalog of the target product based on a pre-determined bill of materials or production process flow includes: Based on the bill of materials or production process flow of each target product, the structure of the target product is analyzed to obtain the component information corresponding to each target product. The component information corresponding to each target product is summarized and processed to generate a component list of the target products.

[0018] In some embodiments, the step of performing structural analysis on the target products based on the bill of materials or production process flow of each target product to obtain the component information corresponding to each target product includes: Based on the functional attributes in the bill of materials and the preset component judgment characteristics, the target product is broken down into multiple functional units step by step. Functional units that meet the component determination characteristics are used as components to obtain component information corresponding to each target product.

[0019] In some embodiments, the step of performing structural analysis on the target products based on the bill of materials or production process flow of each target product to obtain the component information corresponding to each target product includes: According to the sequence of process steps in the production process flow, identify the input-output relationship of each process step and establish a process structure mapping relationship; Based on the process structure mapping relationship, the target product is disassembled to obtain the component information corresponding to the target product.

[0020] This invention also provides a carbon footprint factor database construction device to achieve efficient, accurate, and traceable carbon footprint accounting for manufactured products. By integrating product procurement data, bills of materials, and production process information, a component-level carbon footprint factor database is constructed to achieve dynamic updating and standardized management of carbon emission factor data, thereby improving the scientific rigor, systematic nature, and industry applicability of carbon footprint accounting.

[0021] The carbon footprint factor database construction device includes: The data acquisition module is used to acquire the first background carbon footprint factor data based on preset data selection rules; The component catalog generation module is used to generate a component catalog of the target product based on a pre-determined bill of materials or production process flow of the target product; wherein, the target product is determined based on pre-acquired product procurement data; The activity data processing module is used to obtain the corresponding activity level data based on the component information in the component catalog of the target product, and generate a background information catalog based on the activity level data. The background factor generation module is used to generate second background carbon footprint factor data based on the first background carbon footprint factor data and the background information directory. The database construction module is used to construct a carbon footprint factor database based on the activity level data, the background information directory, and the second background carbon footprint factor data, so as to determine carbon emissions.

[0022] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described carbon footprint factor database construction method.

[0023] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described carbon footprint factor database construction method.

[0024] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described carbon footprint factor database construction method.

[0025] The carbon footprint factor database construction method and apparatus provided in this invention analyzes procurement data to identify target products and establishes a component catalog based on the target products' bill of materials or production process flow. This achieves a refined decomposition from the product level to the component level, enhancing the structure and operability of the carbon footprint accounting process. By acquiring activity-level data such as raw material consumption, energy consumption, and transportation parameters during the component production stage, a background information catalog is constructed, providing high-quality, traceable basic data support for carbon footprint factor calculation. Based on a field mapping method, the background information catalog is associated and matched with the first background carbon footprint factor data, automatically filtering out factor data with priority data quality, ensuring the scientific nature and consistency of data selection. The method of this invention not only effectively shortens the overall process of carbon footprint accounting for manufactured products but also provides representative component-level carbon footprint factor data for different industries, thereby significantly improving the standardization and comparability of carbon footprint management results. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart illustrating a method for constructing a carbon footprint factor database in one embodiment of the present invention; Figure 2 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 3 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 4 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 5 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 6 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 7 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 8 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 9 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 10 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 11 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 12 This is a flowchart illustrating the carbon footprint factor database construction method in another embodiment of the present invention; Figure 13 This is a schematic diagram of the structure of the carbon footprint factor database construction device in an embodiment of the present invention; Figure 14 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and their descriptions are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. The acquisition, storage, use, and processing of data in the technical solutions of this application all comply with relevant laws and regulations. The user information in the embodiments of this application is obtained through legal and compliant means, and the acquisition, storage, use, and processing of user information have been authorized and agreed upon by the customer.

[0028] To facilitate understanding of the technical solution provided in this application, the relevant content of the technical solution in this application will be explained below.

[0029] This application aims to address the problems existing in the construction of carbon footprint factor databases, such as scattered data sources, inconsistent data quality, insufficient industry representativeness, and difficulty in achieving dynamic updates. Traditional product carbon footprint accounting methods generally rely on foreign databases or factor data with inconsistent sources and construction standards, making it difficult to accurately reflect the actual characteristics of domestic manufacturing in terms of energy structure, production processes, and transportation systems. This results in significant uncertainty and comparability bias in carbon footprint accounting results.

[0030] To address this, this application proposes a method for constructing a carbon footprint factor database. This method involves analyzing procurement data to identify target products and establishing a component catalog based on the bill of materials or production process flow of these target products. For each component in the catalog, data collection based on material and energy flow analysis is conducted to obtain activity-level data such as raw material consumption, energy consumption, and transportation parameters, generating a corresponding background information catalog. This background information catalog is matched with the acquired first background carbon footprint factor data through field mapping to obtain high-quality factor input. A nested iterative algorithm is employed to calculate and construct the component-level carbon footprint factors database, establishing field associations and dynamic iterative relationships between the first and second background carbon footprint factor data, thereby achieving continuous updating and automatic correction of the carbon footprint factor database. Through the above technical solution, this application effectively improves the systematization, standardization, and traceability of the carbon footprint factor database construction process, providing high-quality data support and accounting foundation for domestic manufacturing and carbon footprint management systems.

[0031] Example 1

[0032] This invention provides a method for constructing a carbon footprint factor database, such as... Figure 1 As shown, the method for constructing the carbon footprint factor database includes steps 101 to 105.

[0033] Step 101: Based on the preset data selection rules, obtain the first background carbon footprint factor data.

[0034] Step 102: Generate a component list for the target product based on the pre-determined bill of materials or production process flow. The target product is determined based on pre-acquired product procurement data.

[0035] Step 103: Obtain the corresponding activity level data based on the component information in the component catalog of the target product, and generate a background information catalog based on the activity level data.

[0036] Step 104: Generate second background carbon footprint factor data based on the first background carbon footprint factor data and the background information directory.

[0037] Step 105: Based on activity level data, background information directory and second background carbon footprint factor data, construct a carbon footprint factor database to determine carbon emissions.

[0038] According to the above embodiments, the carbon footprint factor database construction method provided by the present invention significantly improves the efficiency and accuracy of carbon footprint accounting. This method analyzes procurement data to identify target products and establishes a component catalog based on the target product's bill of materials or production process flow, achieving a refined decomposition from the product level to the component level, thus enhancing the structure and operability of the carbon footprint accounting process. By acquiring activity-level data such as raw material consumption, energy consumption, and transportation parameters during the component production stage, a background information catalog is constructed, providing high-quality, traceable basic data support for carbon footprint factor calculation. Based on a field mapping method, the background information catalog is associated and matched with the first background carbon footprint factor data, automatically filtering out factor data with priority data quality, ensuring the scientific nature and consistency of data selection. The method of the present invention not only effectively shortens the overall process of carbon footprint accounting for manufactured products but also provides representative component-level carbon footprint factor data for different industries, thereby significantly improving the standardization and comparability of carbon footprint management results.

[0039] In this embodiment of the invention, the first background emission factor data is obtained from authoritative data source organizations to ensure the data's authority and representativeness. These authoritative data source organizations include national-level government departments, research institutes, and universities.

[0040] The data selection rules are pre-defined to standardize the source and quality assessment of background carbon footprint factor data, specifically including: Rule 1: The data source for the selected background carbon footprint factor is measured data or official statistics released by the national competent authority.

[0041] Rule 2: The selected background carbon footprint factor data should accurately reflect the production process level of domestic bulk raw materials, energy industry chain and power structure characteristics, or include different vehicle types (including light, medium and heavy vehicles) of new energy vehicles such as diesel trucks, pure electric and hybrid vehicles that meet the national motor vehicle emission standards (National III to National VI).

[0042] Rule 3: The selected background carbon footprint factor data should fully reflect the regional characteristics and actual operation of the domestic energy industry chain and power structure.

[0043] Rule 4: The data structure and data format of the selected background carbon footprint factor data shall comply with the requirements of the EU's International Life Data System (ILCD) manual.

[0044] Rule 5: The selected background carbon footprint factor data must meet the requirements of completeness and traceability, and have a clear data source, applicable boundaries and accounting time.

[0045] In some embodiments, product procurement data includes: the quantity of a first product and the amount of the first product. For example... Figure 2 As shown, the steps for determining the target product based on pre-acquired product procurement data include steps 201 to 204.

[0046] Step 201: Standardize the obtained quantity and amount of the first product to obtain the quantity and amount of the second product.

[0047] Step 202: Determine the product weight w based on the quantity and value of the second product. j .

[0048] Step 203: Based on product weight w j The comprehensive score S of the product is determined by the quantity of the second product, the amount of the second product, and the preset target indicator quantity n. i .

[0049] Step 204: Based on the comprehensive score S corresponding to each product i Identify multiple target products.

[0050] According to the above embodiments, by introducing standardized processing and a multi-criteria decision-making mechanism in the product procurement data analysis stage, this invention can effectively improve the scientific rigor and objectivity of target product identification. Standardizing product quantity and value eliminates the impact of differences in dimensions and orders of magnitude on the analysis results, making various products comparable on the same scale. Determining product weights based on the principle of information entropy adaptively reflects the importance of product quantity and value to the overall procurement structure according to data distribution characteristics, thus avoiding subjective bias caused by manually setting weights. By calculating the comprehensive score of products, the relative contribution of each product in the procurement system can be fully quantified, and the main target products can be screened based on the cumulative contribution rate determination method, achieving automatic identification and prioritization of target product categories. This method not only improves the accuracy and stability of target product screening but also enhances the relevance and representativeness of the carbon footprint factor database construction process.

[0051] In some embodiments, such as Figure 3 As shown, step 201 includes steps 301 to 302.

[0052] Step 301: Determine the quantity of the second product based on the quantity of the first product, the minimum quantity of the first product, and the maximum quantity of the first product.

[0053] Step 302: Determine the amount of the second product based on the amount of the first product, the minimum amount of the first product, and the maximum amount of the first product.

[0054] In this embodiment of the invention, product procurement data or product distribution data of the target industry sector or target enterprise for the past three years are obtained. The product procurement data or product distribution data includes the quantity of multiple products and their corresponding monetary values.

[0055] To eliminate the dimensional differences in quantity and value among different products, it is necessary to standardize the product quantity and product value.

[0056] Specifically, the obtained product quantity is standardized using the following formula (1) to obtain the standardized product quantity:

[0057] The second product quantity represents the standardized product quantity, while the first product quantity represents the product quantity obtained from product procurement data or product distribution data.

[0058] The obtained product amount is standardized using the following formula (2) to obtain the standardized product amount:

[0059] The second product amount represents the standardized product amount, while the first product amount represents the product amount obtained from product procurement data or product distribution data.

[0060] According to the above embodiments, by standardizing the product quantity and product amount, the data of different products can be scaled in the same dimensions of quantity and amount, eliminating the impact of the difference in dimensions on the calculation results, thereby providing a unified and reliable data foundation for the subsequent calculation of the comprehensive product score and the identification of the target product.

[0061] In some embodiments, such as Figure 4 As shown, step 202 includes steps 401 to 403.

[0062] Step 401: Based on the quantity of the second product, the amount of the second product, and the quantity m of product types, determine the probability value of the quantity or amount of the second product. .

[0063] Step 402: Based on the probability value of the quantity or amount of the second product. Given the product type and quantity m, determine the information entropy E for the quantity or value of the second product. j .

[0064] Step 403: Information entropy E based on the quantity or value of the second product.j Determine product weight w j .

[0065] In this embodiment of the invention, the standardized product quantity, standardized product amount, and product type quantity m are substituted into the following formula (3) to calculate the probability value of the standardized quantity or standardized amount j of product i. .

[0066]

[0067] in, Let j represent the standardized quantity or standardized amount of product i, and m represent the quantity of product types.

[0068] The probability value of the standardized quantity or standardized amount j of the above product i. Substituting the product type and quantity m into the following formula (4), the information entropy E of the j-th target indicator is calculated. j The target indicators include: the quantity of standardized products and the value of standardized products.

[0069]

[0070] in, The probability value of the standardized quantity or standardized amount j of product i. , number of product types m.

[0071] The information entropy E of the j-th target indicator mentioned above j Substituting into the following formula (5), the product weight w of the j-th target indicator is calculated. j .

[0072]

[0073] Among them, E j The information entropy of the j-th target indicator is represented by the information entropy corresponding to the standardized product quantity or the standardized product amount.

[0074] According to the above embodiments, by introducing an information entropy model into the product data analysis process, this invention can objectively determine the weights of each target indicator based on the distribution characteristics of product quantity and product amount, avoiding subjective bias caused by manually setting weights. By calculating the probability distribution of standardized product quantity and product amount and their corresponding information entropy values, the uncertainty of different target indicators on the overall procurement structure can be reflected, allowing target indicators with larger distribution differences to automatically receive higher weights, thereby more accurately reflecting the influence of each target indicator in the target product identification process. The above method effectively improves the scientificity and objectivity of data weight allocation, providing reliable data support for subsequent comprehensive score calculation and target product screening.

[0075] In some embodiments, step 203 specifically includes: assigning the product weight w of the j-th target indicator calculated above to... j The number of target indicators n, and the standardized quantity or standardized amount of product i. Substitute into the following formula (6) to calculate the comprehensive score Si of product i.

[0076]

[0077] in, w represents the standardized quantity or standardized amount of product i. j Let represent the product weight of the j-th target indicator, and n represent the number of target indicators.

[0078] Based on the above embodiments, through the above calculations, a quantitative score reflecting the comprehensive influence of each product in the overall procurement data can be obtained by comprehensively considering the weights of multiple indicators such as product quantity and product value. This comprehensive score can objectively reflect the relative importance of different products in the procurement system, providing a quantitative basis for the subsequent selection and ranking of target products.

[0079] In some embodiments, such as Figure 5 As shown, step 204 includes steps 501 to 504.

[0080] Step 501: Calculate the overall score S for each product. i Sort according to the preset order.

[0081] Step 502: Accumulate the comprehensive score S corresponding to each product ranked before the preset ranking. i This yields the sum of the first comprehensive scores for all products before the preset ranking.

[0082] Step 503: Compare the sum of the first comprehensive scores with the sum of the second comprehensive scores of all products to obtain the cumulative contribution rate.

[0083] Step 504: When the cumulative contribution rate is less than or equal to a preset percentage, identify the products ranked higher than the preset percentage as target products. The sum of the first comprehensive scores is... The second comprehensive score is .

[0084] In this embodiment of the invention, the comprehensive score S for products 1 to n is... i Sort the products in descending order. Calculate the combined score S for products 1 to n. i Substituting into the following formula (7), the cumulative contribution rate is calculated:

[0085] in, This represents the total score of products 1 through i. express The total score of the comprehensive score.

[0086] When the calculated cumulative contribution rate is less than or equal to a preset threshold (e.g., 90%), the products corresponding to S1, S2, ..., Si are the target products.

[0087] According to the above embodiments, by introducing a cumulative contribution rate calculation after ranking products by their overall scores, this invention can objectively determine the range of target products based on quantitative indicators. This method automatically filters core product categories by comparing the cumulative percentage of product overall scores with a preset threshold, avoiding the subjectivity and uncertainty caused by relying on human experience. Using the cumulative contribution rate as a criterion effectively highlights products with a high contribution to the overall procurement structure, thereby improving the accuracy and stability of the target product identification process.

[0088] In some embodiments, when the acquired product procurement data or product distribution data contains multi-level directories, the processing flow of steps 202 to 204 described above needs to be repeated for each level of directory until the product in the lowest level directory is identified. After product identification is completed, the identified product is the final target product.

[0089] In some embodiments, such as Figure 6 As shown, step 102 includes steps 601 to 602.

[0090] Step 601: Based on the bill of materials or production process flow of each target product, perform structural analysis on the target product to obtain the component information corresponding to each target product.

[0091] Step 602: Summarize and process the component information corresponding to each target product to generate a component list for the target product.

[0092] According to the above embodiments, by introducing a structured decomposition and aggregation mechanism in the target product analysis stage, this invention can achieve systematic information extraction from the product level to the component level. Structural analysis of the target product based on the bill of materials or production process flow can accurately identify the constituent elements of the target product and their corresponding functional units, thereby establishing logical relationships and process mapping relationships between components. This process not only improves the completeness and transparency of product structural information but also provides finer-grained and clearer-boundary data support for subsequent carbon footprint factor calculations. By aggregating and generating a component directory from the analyzed component information, duplicate statistics and data omissions can be effectively avoided, ensuring the consistency and traceability of component information in subsequent calculation stages. This achieves a standardized conversion from product structure to component structure, significantly improving the standardization and scalability of the system in data management, carbon footprint analysis, and database construction.

[0093] In this embodiment of the invention, the components after structural analysis need to meet several criteria, including: independence, recyclability, simplicity, universality, and safety. Independence means that the component can be identified and separated independently without affecting the performance of adjacent components or the system; recyclability means that the component can support reuse, remanufacturing, or recycling; simplicity means that the component's material composition is singular; universality means that the component should be universal or substitutable in the manufacture of similar products; and safety means that the component can be safely disassembled physically without creating any safety hazards during disassembly.

[0094] After completing the structural analysis of the target products, the component information of each target product is summarized, duplicates are removed, and a Component Catalogue for Key Products is generated.

[0095] During the deduplication process, data analysis methods such as pivot tables can be used to identify and clean up duplicate components, but this invention does not limit this.

[0096] In some embodiments, such as Figure 7 As shown, step 601 includes steps 701 to 702.

[0097] Step 701: Based on the functional attributes in the bill of materials and the preset component judgment characteristics, the target product is broken down into multiple functional units step by step.

[0098] Step 702: Select the functional units that meet the component determination characteristics as components to obtain the component information corresponding to each target product.

[0099] In this embodiment of the invention, a production process flow chart and a bill of materials (BOM) for the target product are obtained. The BOM includes information such as component names, hierarchical relationships, component quantities, material types, component weights, and supply sources.

[0100] For example, taking motor products as an example, as shown in Table 1 below: Table 1 Bill of Materials for Motor Products

[0101] As shown in Table 1 above, this bill of materials defines a multi-level structural relationship for the motor product. The first level is the stator assembly, and the second level includes sub-components such as the core, coil layer, and insulation layer. Each level includes information such as component name, quantity, and material type. For example, the stator assembly is made of steel or copper, the coil layer is made of enameled wire, and the insulation layer is made of polyester film.

[0102] Based on the bill of materials of the target product, the product structure is analyzed by functional mapping. Each level of components is decomposed according to its functional attributes and hierarchical dependencies until the smallest functional unit is obtained, and this smallest functional unit is defined as a component.

[0103] In the above example, the stator assembly, coil layer and insulation layer can be regarded as independent component units, which can independently characterize the local performance of the product.

[0104] According to the above embodiments, the above steps enable a systematic structural analysis from the product level to the component level, providing a clear and complete structural foundation for subsequent component-level carbon footprint accounting, and ensuring the integrity, accuracy and traceability of the carbon emission accounting process.

[0105] In some embodiments, such as Figure 8 As shown, step 601 includes steps 801 to 802.

[0106] Step 801: Identify the input-output relationships of each process step according to the sequence of process steps in the production process flow, and establish a process structure mapping relationship.

[0107] Step 802: Deconstruct the target product based on the process structure mapping relationship to obtain the component information corresponding to the target product.

[0108] In this embodiment of the invention, when the bill of materials for the target product cannot be obtained, a reverse engineering approach can be used to physically disassemble the product step by step from top to bottom based on the production process flow chart of the target product until the smallest functional unit is obtained.

[0109] Specifically, a production process flow chart for the target product is obtained. This flow chart includes: process name, process sequence, input parameters, output parameters, raw materials, and the name of the semi-finished product or sub-component number corresponding to each process step. By analyzing the input-output relationships between each process step in the process flow, the energy flow and material flow of the target product at different processing stages are identified, thereby determining the output object of each process node.

[0110] For example, a typical process sequence for the target product includes: Material Preparation and Inspection → Metal Stamping and Forming Process → Heat Treatment Process → Machining Process → Welding and Assembly Process → Surface Treatment and Coating Process → Finished Product Inspection and Quality Verification Process.

[0111] In this process flow, the stamping process produces metal shell blanks, the machining process produces shaft or shell components, and the welding and assembly process produces modular assemblies. These outputs are the component units identified during reverse disassembly.

[0112] According to the above embodiments, by progressively identifying and mapping the outputs of each process node, a hierarchical physical structure of the target product can be established from top to bottom until components capable of independently representing functions are obtained. This method is applicable when a complete bill of materials is unavailable, and it enables systematic reconstruction and refined disassembly of the product structure based on production process information, thereby ensuring the systematic nature and accuracy of the component identification process.

[0113] In some embodiments, such as Figure 9 As shown, step 103 includes steps 901 to 902.

[0114] Step 901: Obtain the activity level data (ActivityData) corresponding to each component information in the component catalog of the target product.

[0115] Step 902: Summarize and process the activity level data corresponding to each component information to generate a background information directory.

[0116] In this embodiment of the invention, based on the component information contained in the component catalog of the target product, on-site surveys are conducted with the corresponding component manufacturing enterprises to obtain actual activity level data.

[0117] For example, for the same type of component, activity level data from no fewer than three manufacturers needs to be collected to ensure the representativeness and statistical reliability of the data. This activity level data includes key parameter information for the component production stage, specifically: raw material type, raw material consumption, energy consumption type, energy consumption, total transport weight of raw materials, total transport distance of raw materials, and weight of raw materials purchased per batch.

[0118] After data collection is completed, the component activity level data provided by different manufacturers are summarized, sorted, and duplicate items are processed to generate a background information catalogue covering the activity level data of all components in the component production stage.

[0119] In the process of deduplication, data analysis methods such as pivot tables, unique value matching, or cluster identification can be used to filter and clean up duplicate component data, and this invention does not limit this.

[0120] According to the above embodiments, by introducing a multi-source survey and data aggregation mechanism in the component-level data acquisition stage, this invention can significantly improve the representativeness, completeness, and traceability of carbon footprint accounting data. By aggregating and deduplicating component activity level data from different production enterprises, a high-quality background information directory covering the entire component production process can be formed. This method achieves standardized integration of activity level data from multiple enterprises and multiple stages, ensuring the scientific rigor and consistency of subsequent carbon footprint factor matching and calculation processes, thereby significantly improving the accuracy, reliability, and industry applicability of the component-level carbon footprint database.

[0121] In some embodiments, such as Figure 10 As shown, step 104 includes steps 1001 to 1002.

[0122] Step 1001: Based on the number of data that meet the data selection rules in each first background carbon footprint factor data, determine the data quality level corresponding to the first background carbon footprint factor data.

[0123] Step 1002: Match the background information directory with the first background carbon footprint factor data through field mapping, and generate the matched second background carbon footprint factor data according to the data quality level.

[0124] In this embodiment of the invention, the background carbon footprint factor data is graded in quality according to the number of background carbon footprint factor data that meet the preset data selection rules.

[0125] Specifically, when the background carbon footprint factor data meets all the data selection rules, its data quality level is defined as "Excellent". When the background carbon footprint factor data meets four data selection rules, its data quality level is defined as "Good". When the background carbon footprint factor data meets three data selection rules, its data quality level is defined as "Acceptable". When the background carbon footprint factor data meets fewer than two data selection rules, its data quality level is defined as "Poor".

[0126] After completing the above quality grading, the background carbon footprint factor data is associated with the component information in the background information directory through field matching. Matching is then performed according to the priority order of data quality levels to obtain the matched background carbon footprint factor data, which is then used as the first-level carbon footprint factor data. The priority order is: Excellent > Good > Satisfactory > Poor.

[0127] According to the above embodiments, the scientific nature and consistency of the factor data selection process are ensured through the above-mentioned grading and matching mechanism, thereby improving the accuracy and comparability of the subsequent carbon footprint factor calculation results.

[0128] In some embodiments, such as Figure 11 As shown, step 105 includes steps 1101 to 1102.

[0129] Step 1101: Input the background information directory and the second background carbon footprint factor data into the initial carbon footprint factor database.

[0130] Step 1102: Determine the carbon footprint factor (CF) of the component based on activity level data and carbon footprint factor calculation parameters. 组件 .

[0131] Step 1103: Combine the background information directory and the second background carbon footprint factor data with the component's carbon footprint factor CF. 组件 Field mapping is performed to obtain the carbon footprint factor database.

[0132] In this embodiment of the invention, the background information directory and the matched background carbon footprint factor data are input into the initial carbon footprint factor database. When inputting the initial carbon footprint factor database, it is necessary to note the data source, calculation scope, and calculation time of the background carbon footprint factor data.

[0133] Import the background information directory and its matched background carbon footprint factor data into the initial carbon footprint factor database. During the data import process, each matched background carbon footprint factor data needs to be annotated with information, including: data source, calculation scope, calculation time, and other metadata related to data quality, to ensure the traceability and consistency of the data in subsequent calls and updates.

[0134] According to the above embodiments, the above method can construct an initial carbon footprint factor database with complete information recording and dynamic management capabilities, providing reliable data support for subsequent component-level carbon footprint calculation and carbon footprint factor database iteration.

[0135] In some embodiments, activity level data includes: raw material consumption A 原材料 Total transport weight of raw materials B 原材料运输 The total transportation distance D of raw materials 原材料运输 The weight of raw materials purchased in a unit batch B 原材料采购批次 and energy consumption A 能源 The carbon footprint factor calculation parameters include: the background carbon footprint factor EF of the raw materials. 原材料 Carbon emission factor EF of transportation vehicles 运输 Energy consumption correction factor α and energy background carbon footprint factor EF 能源 .

[0136] like Figure 12 As shown, step 1102 includes steps 1201 to 1204.

[0137] Step 1201: Based on the raw material consumption A 原材料 and the background carbon footprint factor EF of raw materials 原材料 Determine the carbon emissions E during the raw material acquisition stage. 原材料获取 .

[0138] Step 1202: Based on the total transport weight B of the raw materials 原材料运输 The total transportation distance D of raw materials 原材料运输 Carbon emission factor EF of transportation vehicles 运输 Energy consumption correction factor α, raw material consumption A 原材料 and the weight of raw materials purchased in batches by unit B 原材料采购批次 Determine the carbon emissions E during the raw material transportation phase. 原材料运输 .

[0139] Step 1203: Based on energy consumption A 能源 and energy carbon emission factor EF 能源 Determine the carbon emissions E during the component production stage. 组件生产 .

[0140] Step 1204: Based on the carbon emissions E during the raw material acquisition stage 原材料获取 Carbon emissions during the raw material transportation phase E 原材料运输 Carbon emissions E during the component production stage 组件生产 Determine the carbon footprint factor (CF) of the component. 组件 .

[0141] In this embodiment of the invention, the scope of the component carbon footprint factor calculation includes: the raw material acquisition stage, the raw material transportation stage, and the component production stage.

[0142] The obtained raw material consumption A 原材料 and the background carbon footprint factor EF of raw materials 原材料 Substitute into the following formula (8) to calculate the carbon emissions E during the raw material acquisition stage. 原材料获取 .

[0143]

[0144] Among them, A 原材料 This indicates the amount of raw materials consumed during the production stage of a single functional unit component, expressed in kg / functional unit; EF 原材料 The background carbon footprint factor of the raw materials is expressed in kg CO2 eq / kg.

[0145] The total transport weight B of the acquired raw materials 原材料运输 The total transportation distance D of raw materials 原材料运输 Raw material consumption A 原材料 The weight of raw materials purchased in a unit batch B 原材料采购批次 Carbon emission factor EF of transportation vehicles 运输 Substituting the energy consumption correction factor α into the following formula (9), the carbon emissions E during the raw material transportation stage are calculated. 原材料运输 .

[0146]

[0147] Among them, B 原材料运输 This indicates the total transport weight of the raw materials, in kg; D 原材料运输 EF represents the total transportation distance of raw materials, in km; 运输 The carbon emission factor of the transportation vehicle is expressed in kg CO2 eq / kg / km; α is the energy consumption correction factor for the transportation vehicle based on the actual transport weight and actual transport distance; A 原材料 This indicates the amount of raw materials consumed during the production stage of a single functional unit component, expressed in kg / functional unit; B 原材料采购批次 This indicates the weight of raw materials purchased in a single batch.

[0148] The energy consumption A obtained 能源and energy carbon emission factor EF 能源 Substitute into the following formula (10) to calculate the carbon emissions E during the component production stage. 组件生产 .

[0149]

[0150] Among them, A 能源 EF represents the energy consumption during the component manufacturing stage. 能源 It is an energy carbon emission factor.

[0151] The calculated carbon emissions E during the feedstock acquisition phase 原材料获取 Carbon emissions during the raw material transportation phase E 原材料运输 Carbon emissions E during the component production stage 组件生产 Substituting into the following formula (11), the carbon footprint factor CF of the component is calculated. 组件 .

[0152]

[0153] Among them, E 原材料获取 This indicates the carbon emissions during the raw material acquisition stage, expressed in kg CO2 eq / functional unit; E 原材料运输 This indicates carbon emissions during the raw material transportation phase, expressed in kg CO2 eq / functional unit; E 组件生产 This indicates the carbon emissions during the component production stage, expressed in kg CO2 eq / functional unit.

[0154] According to the above embodiments, by establishing a carbon emission calculation model covering the stages of raw material acquisition, raw material transportation, and component production, this invention can achieve refined accounting of component-level carbon footprint. By correlating raw material consumption, transportation parameters, and energy consumption with corresponding carbon emission factors, the carbon emission contribution at each stage can be quantified, realizing the transparency of the staged identification and calculation process of emission sources. This method, by introducing energy consumption correction coefficients and unit batch procurement parameters, can effectively reflect the impact of different transportation modes, transportation distances, and raw material procurement batch differences on carbon emission results, thereby improving the accuracy and applicability of carbon emission accounting results. By summing the carbon emission amounts at each stage to calculate the component carbon footprint factor, the carbon emission accounting process is systematized, standardized, and traceable, providing a scientific basis and data support for constructing a high-precision carbon footprint factor database that conforms to industry characteristics.

[0155] In some embodiments, step 1103 specifically includes: The calculated component carbon footprint factor CF 组件The secondary carbon footprint factor data is input into the initial carbon footprint factor database. This initial carbon footprint factor database includes a background information directory, primary carbon footprint factor data, and secondary carbon footprint factor data.

[0156] By establishing field relationships in the carbon footprint factor database, the background information directory and primary carbon footprint factor data are mapped to secondary carbon footprint factor data, thus forming a complete carbon footprint factor database. During data entry, the component carbon footprint factor (CF) is analyzed. 组件 Information needs to be labeled, including metadata such as data source, accounting scope and accounting time, to ensure data traceability and consistency.

[0157] By establishing field-level relationships in the carbon footprint factor database, the secondary carbon footprint factor data can be synchronously iterated and dynamically updated along with the primary carbon footprint factor data, thereby improving the update efficiency of the carbon footprint database and the level of automation in data management.

[0158] The constructed carbon footprint factor database should have editing and input functions for component-level carbon footprint factor accounting, and support users to independently input activity level data. When users input activity level data, they need to submit corresponding supporting documents simultaneously to ensure the authenticity and verifiability of the data. Supporting documents include, but are not limited to: energy settlement invoices, transportation ledger information, material warehousing records, product bills of materials, and transportation mileage vouchers (such as mileage screenshots or vehicle driving records).

[0159] According to the above embodiments, the above method can realize the standardized entry and traceability management of activity level data, and ensure the data integrity, accuracy and traceability of the carbon footprint accounting process.

[0160] Example 2

[0161] The embodiments of the present invention are illustrated using the electrical equipment manufacturing industry as an example.

[0162] In some embodiments, since the State Grid Corporation of China is the main purchaser of electrical equipment, its procurement data can comprehensively reflect the production and supply status of the electrical equipment manufacturing industry. Therefore, this embodiment of the invention selects the procurement data of the State Grid Corporation of China for three consecutive years (e.g., 2020, 2021, and 2022) as the sample data source.

[0163] The purchase quantities and corresponding purchase amounts of different types of electrical equipment in the procurement data are standardized using formulas (1) and (2) to obtain standardized purchase quantities and standardized purchase amounts. The standardized purchase quantities and purchase amounts are then substituted into formulas (3) to (5) to calculate the purchase quantity weight and purchase amount weight of each type of electrical equipment.

[0164] Substituting the purchase quantity weight, purchase amount weight, standardized purchase quantity, and standardized purchase amount of each electrical equipment into formula (6), the comprehensive score of each electrical equipment is calculated. Further, the comprehensive score of each type of electrical equipment is calculated according to the following formula.

[0165] The comprehensive score for a certain type of electrical equipment is calculated as follows: (Quantity of standardized electrical equipment purchased × Weight of purchase quantity) + (Purchase amount of standardized electrical equipment × Weight of purchase amount).

[0166] The calculated comprehensive scores of various electrical equipment are sorted in descending order, and the cumulative contribution rate is calculated according to formula (7). When the cumulative contribution rate is less than or equal to the preset threshold (e.g., 90%), the corresponding electrical equipment type is determined as the target product with a greater impact on procurement.

[0167] For example, the target electrical equipment types determined by the above method include device materials, primary equipment, secondary equipment, and instruments and meters.

[0168] In some embodiments, the identified target electrical equipment type can be further subdivided according to the product structure to improve the accuracy and hierarchical representativeness of target identification. To this end, the above steps can be repeated for the selected target electrical equipment to perform stratified screening and comprehensive score calculation for the subdivided product categories.

[0169] For example, within the category of installation materials, cables, conductors, fittings, and insulators can be further subdivided and selected as target products. For the selected cable products, applying the above identification steps again can further subdivide the target products into control cables and power cables.

[0170] The target electrical equipment products obtained from the aforementioned multi-level screening are summarized and processed to form a set of target product types for the electrical equipment manufacturing industry.

[0171] In some embodiments, the production process flow diagram and bill of materials (BOM) of the target product are obtained, and the target product is structurally analyzed based on the functional mapping method, breaking it down level by level to the component level. After organizing the component information obtained from the breakdown, the names of each component are entered into a spreadsheet, and duplicate items are identified and cleaned up using pivot tables or unique value matching methods, thereby establishing a directory of electrical equipment components.

[0172] For example, taking a control cable as an example, its components can be disassembled into conductors, insulation materials, shielding materials, armor materials, and sheath materials. The components obtained from the above disassembly need to meet the requirements of independence, recyclability, simplicity, universality, and safety to ensure that the components can achieve functional independence, single material, reusability, and safe disassembly in the subsequent carbon footprint accounting process.

[0173] In some embodiments, on-site surveys are conducted at component manufacturers for each component in the electrical equipment component catalog to obtain activity level data required for carbon footprint accounting. For the same type of component, at least three manufacturers are surveyed to ensure data representativeness and statistical reliability.

[0174] During the research process, activity level data were collected during the component production stage, including: raw material types, raw material consumption, energy consumption types, energy consumption, raw material transportation methods, raw material transportation distances, and raw material transportation weights.

[0175] After data collection is completed, the activity level data provided by different component manufacturers are compiled into a spreadsheet, and duplicate items are identified and cleaned up using pivot tables or unique value matching methods to generate a background information catalogue.

[0176] In some embodiments, first background carbon footprint factor data is obtained from authoritative data source institutions and screened and evaluated for quality according to preset data selection rules. For each piece of first background carbon footprint factor data, a corresponding data quality label is assigned based on the number of times it meets the data selection rules. The data quality label includes "excellent", "good", "qualified" and "poor" levels.

[0177] The first background carbon footprint factor data is matched with the background information directory by field matching, and the best factor data is selected for association in order of priority of data quality level (excellent > good > qualified > poor).

[0178] The matched background information list is entered into the initial carbon footprint factor database as primary carbon footprint factor data. During the data entry process, each primary carbon footprint factor data needs to be labeled with information, including data source, calculation scope, calculation time, and other relevant metadata, to ensure the traceability and consistency of the data in subsequent carbon footprint calculation and update processes.

[0179] In some embodiments, the activity level data corresponding to each component in the electrical equipment component catalog are substituted into the aforementioned formulas (8) to (11) to calculate the carbon footprint factor of each component within the carbon footprint accounting scope. The carbon footprint accounting scope includes the raw material acquisition stage, the raw material transportation stage, and the component production stage.

[0180] The calculated carbon footprint factors for each component are entered into the initial carbon footprint factor database as secondary carbon footprint factor data. Through field matching, the secondary carbon footprint factor data is correlated with the primary carbon footprint factor data, thus forming a complete carbon footprint factor database.

[0181] During the data entry process, each secondary carbon footprint factor data needs to be annotated with detailed information, including the data source, calculation scope, calculation time and other relevant metadata information, to ensure the traceability and consistency of the data in the subsequent carbon footprint calculation, query and dynamic update process.

[0182] According to the above embodiments, the constructed carbon footprint factor database can provide standardized data support for product-level and component-level carbon emission accounting, achieving efficient, standardized, and traceable carbon emission calculation. This carbon footprint factor database can not only be used for enterprise-level carbon footprint accounting and product carbon label evaluation, but also provide scientific basis for industry authorities to conduct carbon emission intensity monitoring, carbon emission reduction path analysis, and green manufacturing policy formulation, thereby promoting the systematic construction of the carbon management system and low-carbon transformation of the equipment manufacturing industry.

[0183] This application provides a method for constructing a carbon footprint factor database, applied to the aforementioned carbon footprint factor database construction apparatus. This method and the apparatus described in one embodiment of this application are based on the same inventive concept and share a similar problem-solving principle. Therefore, the implementation of the method is the same as that of the apparatus described in one embodiment, and repetitions will not be repeated. The terms "unit" or "module" used below refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0184] like Figure 13 As shown, the carbon footprint factor database construction device 1300 includes: The data acquisition module 1301 is used to acquire the first background carbon footprint factor data based on preset data selection rules.

[0185] The component catalog generation module 1302 is used to generate a component catalog of the target product based on a pre-determined bill of materials or production process flow. The target product is determined based on pre-acquired product procurement data.

[0186] The activity data processing module 1303 is used to obtain the corresponding activity level data based on the information of each component in the component catalog of the target product, and generate a background information catalog based on the activity level data.

[0187] Background factor generation module 1304 is used to generate second background carbon footprint factor data based on first background carbon footprint factor data and background information directory.

[0188] Database construction module 1305 is used to construct a carbon footprint factor database based on activity level data, background information directory and second background carbon footprint factor data in order to determine carbon emissions.

[0189] In some embodiments, the activity data processing module includes: The Activity Data Acquisition submodule is used to acquire the activity level data corresponding to each component information in the component directory of the target product.

[0190] The Activity Data Summary Submodule is used to summarize and process the activity level data corresponding to each component information and generate a background information directory.

[0191] In some embodiments, the background factor generation module includes: The quality grading submodule is used to determine the data quality level corresponding to the first background carbon footprint factor data based on the number of data that meet the data selection rules in each first background carbon footprint factor data.

[0192] The field matching submodule is used to match the background information directory with the first background carbon footprint factor data through field mapping, and generate the matched second background carbon footprint factor data according to the data quality level.

[0193] In some embodiments, the database building module includes: The database input submodule is used to input the background information directory and the second background carbon footprint factor data into the initial carbon footprint factor database.

[0194] The carbon factor calculation submodule is used to determine the carbon footprint factor of a component based on activity level data and carbon footprint factor accounting parameters.

[0195] The field mapping submodule is used to perform field mapping processing on the background information directory and the second background carbon footprint factor data with the carbon footprint factors of the components to obtain the carbon footprint factor database.

[0196] In some embodiments, activity level data includes: raw material consumption, total transport weight of raw materials, total transport distance of raw materials, weight of raw materials per unit batch, and energy consumption. Carbon footprint factor calculation parameters include: background carbon footprint factor of raw materials, carbon emission factor of transport vehicles, energy consumption correction factor, and energy carbon emission factor. The carbon factor calculation submodule includes: The raw material stage calculation unit is used to determine the carbon emissions during the raw material acquisition stage based on the raw material consumption and the background carbon footprint factor of the raw materials.

[0197] The transportation phase calculation unit is used to determine the carbon emissions during the raw material transportation phase based on the total transportation weight of the raw materials, the total transportation distance of the raw materials, the carbon emission factor of the transportation vehicle, the energy consumption correction factor, the raw material consumption, and the weight of raw materials purchased per batch.

[0198] The production stage calculation unit is used to determine the carbon emissions during the component production stage based on energy consumption and energy carbon emission factors.

[0199] The component carbon factor generation unit is used to determine the carbon footprint factor of the component based on the carbon emissions during the raw material acquisition stage, the carbon emissions during the raw material transportation stage, and the carbon emissions during the component production stage.

[0200] In some embodiments, the product procurement data includes: the quantity of a first product and the amount of the first product, and further includes: the target product identification module includes: The standardization processing submodule is used to standardize the obtained quantity and amount of the first product to obtain the quantity and amount of the second product.

[0201] The weight calculation submodule is used to determine the product weight based on the quantity and amount of the second product.

[0202] The comprehensive score calculation submodule is used to determine the comprehensive score of a product based on the product weight, the quantity of the second product, the amount of the second product, and the preset target indicator quantity.

[0203] The target product screening submodule is used to determine multiple target products based on the comprehensive score of each product.

[0204] In some embodiments, the standardization processing submodule includes: A quantity standardization unit is used to determine the quantity of the second product based on the quantity of the first product, the minimum quantity of the first product, and the maximum quantity of the first product.

[0205] A standardized amount unit is used to determine the amount of the second product based on the amount of the first product, the minimum amount of the first product, and the maximum amount of the first product.

[0206] In some embodiments, the weight calculation submodule includes: The probability calculation unit is used to determine the probability value of the quantity or amount of the second product based on the quantity of the second product, the amount of the second product, and the quantity of product types.

[0207] The information entropy calculation unit is used to determine the information entropy of the quantity or amount of the second product based on the probability value and the quantity of product types.

[0208] The weight determination unit is used to determine product weights based on information entropy.

[0209] In some embodiments, the target product screening submodule includes: The score sorting unit is used to sort the comprehensive scores of each product according to a preset order.

[0210] The cumulative calculation unit is used to accumulate the comprehensive scores of each product before the preset ranking to obtain the first comprehensive score sum of each product before the preset ranking.

[0211] The contribution rate calculation unit is used to compare the sum of the first comprehensive scores with the sum of the second comprehensive scores of all products to obtain the cumulative contribution rate.

[0212] The target determination unit is used to identify the products ranked higher than the preset percentage as target products when the cumulative contribution rate is less than or equal to the preset percentage.

[0213] In some embodiments, the component directory generation module includes: The structural analysis submodule is used to perform structural analysis on target products based on the bill of materials or production process flow of each target product, and obtain the component information corresponding to each target product.

[0214] The information aggregation submodule is used to aggregate and process the component information corresponding to each target product and generate a component directory for the target product.

[0215] In some embodiments, the structure parsing submodule includes: The functional decomposition unit is used to break down the target product into multiple functional units step by step based on the functional attributes in the bill of materials and the preset component judgment characteristics.

[0216] The component identification unit is used to identify functional units that meet the component determination characteristics as components, and obtain the component information corresponding to each target product.

[0217] In some embodiments, the structure parsing submodule includes: The process analysis unit is used to identify the input-output relationships of each process step according to the sequence of process steps in the production process flow, and to establish process structure mapping relationships.

[0218] The structure mapping unit is used to decompose the target product based on the process structure mapping relationship to obtain the component information corresponding to the target product.

[0219] Figure 14 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention, such as... Figure 14 As shown, the computer device includes a processor 1401, a memory 1402, and a bus 1403.

[0220] The processor 1401 and the memory 1402 communicate with each other via the bus 1403.

[0221] The processor 1401 is used to call program instructions in the memory 1402 to execute the methods provided in the above-described method embodiments.

[0222] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described carbon footprint factor database construction method.

[0223] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described carbon footprint factor database construction method.

[0224] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0225] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0226] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0227] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0228] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0229] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of constructing a carbon footprint factor database, characterized by, The method comprises the following steps: Based on the preset data selection rule, the first background carbon footprint factor data is obtained; Based on the pre-determined bill of materials or production process of the target product, a component list of the target product is generated; wherein the target product is determined based on the pre-acquired product procurement data; Based on the component information in the component list of the target product, corresponding activity level data is obtained, and a background information list is generated based on the activity level data; Based on the first background carbon footprint factor data and the background information list, second background carbon footprint factor data is generated; Based on the activity level data, the background information list and the second background carbon footprint factor data, a carbon footprint factor database is constructed to determine the carbon emission.

2. The method of claim 1, wherein, Based on the component information in the component list of the target product, corresponding activity level data is obtained, and a background information list is generated based on the activity level data; The activity level data corresponding to the component information in the component list of the target product is obtained; The activity level data corresponding to each component information is summarized and processed to generate a background information list.

3. The method of claim 2, wherein, Based on the first background carbon footprint factor data and the background information list, second background carbon footprint factor data is generated; Based on the number of the first background carbon footprint factor data that meets the data selection rule, the data quality level corresponding to the first background carbon footprint factor data is determined; The background information list is matched with the first background carbon footprint factor data through field mapping, and the matched second background carbon footprint factor data is generated according to the data quality level.

4. The method of claim 3, wherein, Based on the activity level data, the background information list and the second background carbon footprint factor data, a carbon footprint factor database is constructed to determine the carbon emission. The background information list and the second background carbon footprint factor data are input into an initial carbon footprint factor database; Based on the activity level data and the carbon footprint factor accounting parameters, the carbon footprint factor of the component is determined; The background information list and the second background carbon footprint factor data are field-mapped with the carbon footprint factor of the component to obtain a carbon footprint factor database.

5. The method of claim 4, wherein, The activity level data includes raw material consumption, total transportation weight of raw materials, total transportation distance of raw materials, raw material weight per batch purchase and energy consumption; the carbon footprint factor accounting parameters include background carbon footprint factor of raw materials, carbon emission factor of transportation tools, energy consumption correction coefficient and energy carbon emission factor; based on the activity level data and the carbon footprint factor accounting parameters, the carbon footprint factor of the component is determined, comprising: According to the raw material consumption and the background carbon footprint factor of the raw materials, the carbon emission of the raw material acquisition stage is determined; According to the total transportation weight of the raw materials, the total transportation distance of the raw materials, the carbon emission factor of the transportation tools, the energy consumption correction coefficient, the raw material consumption and the raw material weight per batch purchase, the carbon emission of the raw material transportation stage is determined; According to the energy consumption and the energy carbon emission factor, the carbon emission of the component production stage is determined; The carbon footprint factor of the component is determined according to the carbon emission of the raw material acquisition stage, the carbon emission of the raw material transportation stage, and the carbon emission of the component production stage.

6. The method of claim 1, wherein, The product procurement data includes a first product quantity and a first product amount. The step of determining the target product based on the pre-acquired product procurement data includes: standardizing the obtained first product quantity and first product amount to obtain a second product quantity and a second product amount; determining a product weight according to the second product quantity and the second product amount; determining a comprehensive score of the product according to the product weight, the second product quantity, the second product amount, and a preset target index quantity; determining a plurality of target products based on the comprehensive scores corresponding to each product.

7. The method of claim 6, wherein, The step of standardizing the obtained first product quantity and first product amount to obtain a second product quantity and a second product amount includes: determining the second product quantity according to the first product quantity, a minimum value of the first product quantity, and a maximum value of the first product quantity; determining the second product amount according to the first product amount, a minimum value of the first product amount, and a maximum value of the first product amount.

8. The method of claim 6, wherein, The step of determining a product weight according to the second product quantity and the second product amount includes: determining a probability value of the second product quantity or the second product amount according to the second product quantity, the second product amount, and a product type quantity; determining an information entropy of the second product quantity or the second product amount according to the probability value and the product type quantity; determining the product weight based on the information entropy.

9. The method of claim 6, wherein, The step of determining a plurality of target products based on the comprehensive scores corresponding to each product includes: sorting the comprehensive scores corresponding to each product in a preset order; cumulatively adding the comprehensive scores corresponding to each product before a preset ranking to obtain a first comprehensive score sum of each product before the preset ranking; comparing the first comprehensive score sum with a second comprehensive score sum of all products to obtain a cumulative contribution rate; determining each product before the preset ranking as a target product when the cumulative contribution rate is less than or equal to a preset percentage.

10. The method of claim 1, wherein, The step of generating a component list of a target product based on a bill of materials or a production process flow of the target product determined in advance includes: performing structural analysis on the target product based on the bill of materials or the production process flow of each target product to obtain component information corresponding to each target product; performing summary processing on the component information corresponding to each target product to generate a component list of the target product.

11. The method of claim 10, wherein, The step of performing structural analysis on the target product based on the bill of materials or the production process flow of each target product to obtain component information corresponding to each target product includes: gradually splitting the target product into a plurality of functional units based on the functional attributes in the bill of materials and preset component determination characteristics; determining the functional units that meet the component determination characteristics as components to obtain the component information corresponding to each target product.

12. The method of claim 10, wherein, The target product is structurally analyzed based on the bill of materials or the production process flow of each target product, to obtain component information corresponding to each target product, including: According to the process step sequence in the production process flow, the input-output relationship of each process step is identified, and a process structure mapping relationship is established; Based on the process structure mapping relationship, the target product is disassembled to obtain the component information corresponding to the target product.

13. A carbon footprint factor database construction apparatus characterized by comprising: It includes: A data acquisition module for acquiring first background carbon footprint factor data based on a predetermined data selection rule; A component directory generation module for generating a component directory of a target product based on a pre-determined bill of materials or production process flow of the target product; wherein the target product is determined based on pre-acquired product procurement data; An activity data processing module for obtaining corresponding activity level data based on each component information in the component directory of the target product, and generating a background information directory based on the activity level data; A background factor generation module for generating second background carbon footprint factor data based on the first background carbon footprint factor data and the background information directory; A database construction module for constructing a carbon footprint factor database based on the activity level data, the background information directory and the second background carbon footprint factor data to determine the carbon emissions.

14. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method of any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the method of any one of claims 1 to 12.

16. A computer program product, characterised in that, The computer program product includes a computer program, and the computer program is executed by the processor to realize the method of any one of claims 1 to 12.

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