Method and system for constructing textile product carbon footprint database based on inventory data

Abstract

The present invention discloses a method and system for constructing a textile product carbon footprint database based on inventory data. The method solves the problem that the current localized, high-quality textile industry carbon footprint background database is missing, carbon footprint accounting relies on the international background database, and the accounting results are inaccurate and often overestimated. The present invention establishes an intelligent matching method to match the optimal emission factor for the material, performs carbon footprint accounting to obtain the carbon footprint data of the first textile product; merges the carbon footprint data of the first textile product of the same type to obtain the carbon footprint data of the second textile product; thereby constructing a textile product carbon footprint database; and establishes a database update and iteration method to continuously consolidate the localized textile product carbon footprint background database. The present invention improves the accuracy and efficiency of carbon footprint accounting results, enriches the textile industry carbon footprint database data, and the present invention helps to intelligently and conveniently form a localized, high-quality textile product industry carbon footprint database.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission, and in particular to a method and system for constructing a textile product carbon footprint database based on inventory data. Background Art

[0002] Due to the lack of a localized, high-quality textile industry carbon footprint database, textile products have to rely on international databases when calculating carbon footprints. This situation leads to poor accuracy of the calculation results, and the values ​​of the calculation results are often overestimated. For example, the invention with application number 202310565001.X generally uses conventional carbon emission factors rather than carbon emission factors in a specified area or time, or uses carbon footprint factors in international databases, which leads to the above problems.

[0003] Therefore, it is very important to establish a textile product carbon footprint database that conforms to the actual situation. The current product carbon footprint management system is still in its infancy, and there is no established textile product carbon footprint database. Therefore, it is urgent to study a method for constructing a textile product carbon footprint database to build a database to fill the gap in the domestic localized, high-quality textile product carbon footprint database. Summary of the invention

[0004] The present invention mainly solves the problem that the current textile industry lacks a carbon footprint background database, and carbon footprint accounting relies on an international background database, resulting in poor accuracy of accounting results. It provides a method and system for constructing a textile product carbon footprint database based on inventory data.

[0005] The above technical problem of the present invention is mainly solved by the following technical solution: A method for constructing a textile product carbon footprint database based on inventory data, comprising the following steps: Establish a classification system and linkage mechanism for textile products; Get bill of materials data for textile products; Set the evaluation index of the material emission factor, determine the matching degree of the evaluation index of each material of the textile product to calculate the recommended degree of the emission factor of the material, select the optimal emission factor of each material of the textile product according to the recommended degree of the emission factor, calculate the carbon footprint of the textile product, and obtain the carbon footprint data of the first textile product; The carbon footprint data of the first textile products of the same type are merged to calculate and obtain the carbon footprint data of the second textile products with regional characteristics; A textile product carbon footprint database is constructed based on the first and second textile product carbon footprint data.

[0006] The present invention constructs a textile product classification system with the supply chain as the entry point, and provides a classification basis for the construction of a textile product carbon footprint database. In the process of calculating the carbon footprint of textile products, the present invention adopts an intelligent matching method to obtain emission factors, so as to obtain the emission factors with the highest matching degree with materials in terms of geography, time and technology, thereby improving the accuracy and efficiency of the carbon footprint calculation results. By merging the carbon footprint data of textile products of the same type, the carbon footprint data of the textile product reflecting the average carbon footprint level of the region is obtained, and the carbon footprint database data is enriched, so that users can better and more comprehensively obtain the required carbon footprint data. The quality and transparency of the carbon footprint data of textile products are improved, and the carbon footprint calculation method of textile products based on inventory data is refined. The implementation of the present invention helps to form a localized, high-quality carbon footprint database for the textile product industry in an intelligent and convenient manner, and promote the green and low-carbon development of the textile industry.

[0007] As a preferred solution, the acquired textile product bill of materials data is subjected to field standardization, unit standardization, data missing management, abnormal data identification and processing operations.

[0008] Before calculating the carbon footprint of textile products, the input and output of each unit process are analyzed to obtain the raw materials, auxiliary materials and energy data related to the carbon footprint. Materials include input raw materials, auxiliary materials and consumed energy. Based on the analysis results, these material data are input into the corresponding bill of materials to obtain the bill of materials data. The bill of materials data must include the name of the material, material activity level data, material code, material production time, production location, material supplier and other information.

[0009] After obtaining the bill of materials data, the data needs to be preprocessed, including: Standardize data fields and establish standardized material data lists, including material names, material activity level data, material codes, material production time, production locations, material suppliers, and other information. After obtaining the material list data source, the obtained data can be mapped to the standardized material data list fields according to the preset field mapping relationship to achieve data field standardization.

[0010] Standardize data units, unify the data units of bill of materials data, and establish a conversion dictionary of commonly used units in the textile industry, covering raw material weight units (such as grams, kilograms, tons, etc.), area units (square meters, square yards, etc.), length units (centimeter, meter, kilometer, etc.), density units (grams per cubic centimeter, kilograms per cubic meter, etc.), energy units (joule, kilowatt-hour, and tons of steam, cubic meters of natural gas, etc. used by different companies), etc. In the data analysis process, according to this dictionary, through the predefined conversion function, the units of all data are unified, the weight unit is unified into kilograms, the area unit is unified into square meters, the length unit is unified into meters, the density unit is unified into kilograms per cubic meter, the heat (including steam) unit is unified into joules, the electricity unit is unified into kilowatt-hours, and the natural gas unit is unified into cubic meters.

[0011] Missing data governance refers to the governance of missing activity-level data.

[0012] For the management of missing activity level data, the present invention preferably sets two methods to handle missing activity level data, and selects one of the methods for processing with reference to the actual situation. One is based on input-output calculation. Except for a small amount of missing data in the process, other input and output materials are known, and the missing material data can be inferred according to the principle of conservation of materials. The second is analogy of similar processes. Find data from other batches or companies of the same type of textile products and similar production processes as a reference. Compare similar cases with complete activity level data with cases with missing data, analyze their differences in key factors such as production scale, product specifications, and equipment type, and then adjust them according to proportion or similarity.

[0013] Abnormal data identification and processing include two aspects: abnormal data identification and abnormal data processing.

[0014] Abnormal data identification, first, determines whether the input and output of the unit process conform to the law of conservation of materials. According to the law of conservation of materials, in the entire processing flow of textile products from raw material input to finished product output, check whether the logical relationship between the sum of the material input of each unit process and the final product quantity and waste generation is reasonable. If it does not conform to the law of conservation of materials, the data of this process is abnormal data. The second is to determine whether the activity level data is within a reasonable range. Based on the historical data of a large number of textile product manufacturers of the same type and specification, the activity level data of each process of textile products is statistically analyzed, and the mean (X), standard deviation (S) and other statistics of the activity level data are calculated. Reasonable upper and lower limits are set according to the results. When the activity level data reported by a company exceeds this range, it is marked as abnormal.

[0015] Abnormal data processing: For the identified abnormal activity level data, first trace the data source and verify the accuracy of the data with the enterprise. If it is confirmed that the data entry is wrong, make corrections based on the correct information. If the abnormal data is caused by special production conditions, mark the abnormal situation and consider its impact separately in the subsequent carbon footprint accounting analysis to avoid contamination of normal data.

[0016] As a preferred solution, the matching values ​​of the evaluation indicators of each material of the textile product are determined to calculate the recommended emission factor of the material, including: Determine the weight of each evaluation indicator according to the relative importance of each preset evaluation indicator; Set the evaluation benchmark and corresponding matching value of each evaluation indicator, and obtain the matching value of the evaluation indicator by comparing the emission factor data information with the evaluation benchmark; The recommended emission factor of the material is calculated based on the matching value of each evaluation indicator and the evaluation indicator weight.

[0017] The present invention determines material data information according to the input and output of the textile product unit process, obtains the activity level data of the unit process material and its corresponding emission factor, wherein the emission factor acquisition adopts an intelligent matching method to select the optimal emission factor from the textile product carbon footprint factor data set. The method firstly judges the material emission factor in the textile field according to the material code to determine whether to obtain the relevant emission factor from the local textile product factor data set or from other background emission factor data sets.

[0018] Specifically, obtain the list of materials of the textile products that need to be calculated, and determine whether the materials belong to the textile field; if so, select the emission factors corresponding to the material name from the local textile product emission factor data set. If there is no relevant emission factor corresponding to the material name in the local textile product factor data set, obtain the emission factor corresponding to the material name from other background emission factor databases; if not, select the emission factor corresponding to the material name from other background emission factor data sets. The selected emission factors include multiple ones.

[0019] The present invention calculates the carbon footprint of textile products, and the obtained calculation result is the carbon footprint factor of the textile products. These textile product carbon footprint factors are collected to form a local textile product carbon footprint factor data set.

[0020] Set evaluation indicators for material emission factors, including geographical matching, time matching, and technical matching.

[0021] Geographical match: Measures the degree of match between the source of the emission factor and the origin of the material.

[0022] Time matching: reflects the closeness between the emission factor data collection time and the material production time.

[0023] Technical matching degree: reflects the compatibility between emission factors and the production technology and process of material processing.

[0024] Use the analytic hierarchy process to determine the indicator weights. Preset the relative importance of each evaluation indicator, such as comparing each indicator pairwise through industry experts to determine their relative importance. For example, for geographical representation and temporal representation, experts use a 1-9 scale (1 means equal importance, 3 means slightly important, 5 means obviously important, 7 means strongly important, 9 means extremely important, and 2, 4, 6, and 8 are intermediate values) based on industry experience and the focus of current textile product carbon footprint accounting. If geographical representation is obviously more important than temporal representation, the corresponding element in the judgment matrix is ​​assigned a value of 5, while the element of temporal representation relative to geographical representation is assigned a value of 1 / 5. Similarly, a complete criterion layer judgment matrix A1 is constructed based on the preset relative importance of each evaluation indicator:

[0025] Calculate the maximum eigenvalue λ of the judgment matrix A1 max And the corresponding eigenvector W. After normalization, the eigenvector is the weight vector of each criterion layer index ν=[w1, w2, w3]=[0.649 0.250 0.101] T , where w1, w2, and w3 are the weights of geographical matching, time matching, and technical matching, respectively.

[0026] The evaluation benchmark and corresponding matching value of each evaluation indicator are set. The geographical matching degree takes the correspondence between the source of emission factors and the actual production place of materials as the core evaluation benchmark, and assigns corresponding matching values ​​according to different situations. The time matching degree takes the interval between the emission factor data collection time and the material production time as the key judgment benchmark and sets the corresponding matching value. The technical matching degree sets the matching value based on the data acquisition channel of the emission factor as the evaluation benchmark.

[0027] Obtain emission factor data, judge each evaluation index to obtain the corresponding matching value, and finally calculate the emission factor recommendation of the material in a weighted sum manner based on the matching value of each evaluation index and the evaluation index weight.

[0028] As a preferred solution, the optimal emission factors for each material of textile products are selected according to the recommended emission factors, including: Arrange each emission factor in descending order according to the recommendation degree, and select the emission factor with the highest recommendation degree as the optimal emission factor.

[0029] For the emission factors of the same materials, after calculating the recommended degree of the emission factors, the emission factors are arranged in descending order according to the recommended degree, and the emission factor with the highest recommended degree is selected as the optimal emission factor.

[0030] As a preferred option, a textile product classification system and association mechanism is established, including: Textile products are classified according to their entire life cycle process, supply chain paths are established for various types of textile products based on the production process, and textile products are coded and classified into corresponding categories of textile product catalogs.

[0031] Considering that the non-standard naming of textile products will lead to confusion in data storage, poor data traceability, inaccurate data associations, and other issues in the process of database construction, textile products are coded based on the statistical product classification catalog. Textile products are classified according to the whole life cycle process. The whole life cycle process is generally that fiber raw materials are made into fibers through planting, synthesis and regeneration, fibers are processed into yarns through spinning and dyeing and finishing, yarns are processed into fabrics through weaving and dyeing and finishing, and fabrics are sewn and finished. The process becomes a textile product. Therefore, in the database, textile products are divided into five categories: fiber raw materials, fibers, yarns, fabrics, and textile products. Product coding is performed according to the statistical product classification catalog. The statistical product catalog is divided into five layers, each layer is a 2-digit code, and each product is a 10-digit classification code. For example, the 10-digit statistical product classification code for dyed cotton cloth is: 1706040101, where 17 represents textile products, 1701 represents cotton, chemical fiber textiles and printing and dyeing products, 170106 represents printing and dyeing cloth, 17010602 represents dyed cloth, and 1701060201 represents dyed cotton cloth. The coded textile products are classified into the corresponding category of textile product catalog according to the classification. Based on the production process flow, the upstream and downstream relationships of each category of textile products are analyzed to establish the supply chain of textile products. For dyed cotton cloth 1701060201, the upstream cotton yarn product 17010201 is associated, and cotton yarn is associated with the upstream nylon fiber product 280301. The coded textile products are classified into the corresponding category catalog. By establishing such a supply chain relationship, the actual supply chain data or background factor data can be quickly matched for subsequent carbon footprint accounting, thereby improving the efficiency of carbon footprint accounting.

[0032] As a preferred solution, the carbon footprint data of textile products of the same type are combined to obtain the carbon footprint data of the second textile product, including: Select textile products of the same type; Calculate the weight of each textile product of the same type based on the product market share; Select materials with the same code from all bills of materials of the same type of textile products, merge the material activity level data, and obtain the merged bill of materials data; The optimal emission factor is selected based on the combined bill of materials data, and the carbon footprint data of the second textile product is obtained by calculating the carbon footprint of the textile product of this type.

[0033] After completing the carbon footprint calculation of a single textile product, the carbon footprint calculation result data, bill of materials data, emission factor data and related data are integrated to form the first textile product carbon footprint data. The related data includes basic information of textile products, life cycle modeling information, and unit process input and output information. In actual scenarios, the present invention often needs to merge the carbon footprint data of multiple products of the same type to reflect the average carbon footprint level of the textile products in the region.

[0034] Specifically, firstly, data acquisition is performed to obtain carbon footprint data of the first textile product of the same type. The same type means that the product code, geographic information and time information are consistent. The carbon footprint data of the textile product with the same product code, geographic information and time information is obtained.

[0035] Data screening: According to the average carbon footprint of the carbon footprint industry to be generated, representative carbon footprint data is selected from the first textile product carbon footprint data obtained. The representative condition setting is set according to the calculation requirements, and the selection range condition can be set. The carbon footprint data is verified and the above data preprocessing steps are used to supplement and improve the carbon footprint data.

[0036] Data desensitization: desensitize the information of textile product production units to prevent data leakage of production enterprises.

[0037] Data consolidation, including: Select the textile product information after screening and desensitization, including its bill of materials data, product output and other data.

[0038] The weight of each textile product of the same type is calculated based on the product market share.

[0039] Obtain the production information of the same type of textile products and determine the consolidation weight. The weight is estimated based on the market share of the product, weight = product output / total output of the production location where the product is produced.

[0040] Select materials with the same code from all bills of materials of the same type of textile products, merge the material activity level data, and obtain the merged bill of materials data.

[0041] Obtain the bill of materials data of the same type of textile products, select materials with the same code as the mergeable materials, group and merge the product bill of materials data in the data set, and for each group of bill of materials data, perform a weighted average of their activity level data.

[0042] The consolidated BOM data is obtained; the consolidated BOM data representing a region is formed.

[0043] The combined data is calculated, and the optimal emission factor is selected according to the combined bill of materials data, and the carbon footprint of this type of textile product is calculated to obtain the carbon footprint data of the second textile product.

[0044] Specifically, an intelligent matching method is used based on the merged bill of materials data to obtain the emission factor that has the highest matching degree between this type of textile product and each material after the merger. Carbon footprint accounting is performed through the textile product carbon footprint accounting steps based on the merged bill of materials data and the optimal emission factor to obtain the carbon footprint accounting data of textile products representing the region. The merged carbon footprint accounting data, inventory data, emission factor data and related data are integrated to form the second textile product carbon footprint data.

[0045] As a preferred solution, the carbon footprint accounting of textile products includes the following steps: Determine target textile products and system boundaries; According to the textile product processing steps, it is divided into several interrelated unit processes; Obtain the raw materials, auxiliary materials and energy consumption data of each unit process and establish a bill of materials; Establish a carbon footprint accounting model for textile products based on the life cycle assessment method; Input unit process bill of materials data to calculate the carbon footprint of textile products.

[0046] This plan selects the life cycle assessment method to calculate the carbon footprint of textile products, and establishes a textile product carbon footprint calculation model based on the textile product production process.

[0047] First, the system boundaries of textile products and their accounting are determined.

[0048] The system boundary of carbon footprint accounting for fiber products starts from animal husbandry (e.g., sheep and silkworm breeding), plant cultivation (e.g., cotton and flax cultivation), oil extraction, pulp production, etc. to fiber product manufacturing, packaging and storage.

[0049] The system boundary of yarn product carbon footprint accounting starts from the stage of obtaining fiber raw materials to the completion of yarn product manufacturing, packaging and storage.

[0050] The system boundary of carbon footprint accounting for fabric products starts from the stage of obtaining fiber raw materials to the completion of fabric product manufacturing, packaging and storage.

[0051] The system boundary of carbon footprint accounting for textile finished products (e.g., clothing, home textile products, industrial textiles) starts from the stage of obtaining fiber raw materials to the stage of manufacturing, packaging and storage of textile finished products.

[0052] According to the division of textile product processing procedures into multiple interrelated unit processes, textile product carbon footprint accounting is divided into multiple unit process carbon footprint accounting according to product processing procedures. The input and output of each unit process are analyzed to determine the consumption of raw materials, auxiliary materials and energy, that is, the consumption of materials. The carbon footprint accounting steps of each unit process include establishing a unit process carbon footprint calculation model, inputting the corresponding bill of materials data and the corresponding emission factor data, performing carbon footprint accounting on the unit process according to the unit process carbon footprint accounting model, and adding the unit process carbon footprint accounting data to obtain the final textile product carbon footprint accounting data.

[0053] Conduct data quality assessment on textile product carbon footprint accounting data. Obtain textile product carbon footprint accounting data that meets quality requirements and its quality assessment level, and enter and save these data into the carbon footprint database.

[0054] As a preferred solution, it also includes database updating, including: Obtain the basic factors of textile products, establish alternative association relationships for data update, select the basic factors that need data update, and perform update calculations based on the alternative association relationships; Obtain all industry factors that reference the underlying factor that needs to be replaced, count and sort the number of times the industry factors are referenced, and iteratively update the industry factors in order.

[0055] The construction method also includes a database update step, which includes updating the emission factors of the local textile product factor data set. Textile product emission factors can be divided into two categories. One is the basic factors, which mainly refer to various energy varieties and transportation factors used to calculate the carbon footprint of textile products, such as electricity emission factors, thermal emission factors, freight emission factors, etc. Changes in these basic factors will affect the carbon footprint of textile products. The other is the textile product industry factors, which mainly refer to specific textile product carbon footprint factors, such as various printing and dyeing products, and hemp spinning product carbon footprint factors.

[0056] For basic factors, obtain the basic factors, establish a list of basic factors based on the basic factors, and mark the time and version information of the basic factors.

[0057] Establish a basic factor substitution relationship, for example, use the 2024 electricity emission factor to replace the 2023 electricity emission factor.

[0058] When data update is required, a list of basic factors that need to be updated is selected from the local textile product emission factor data set, and the basic factors are updated and calculated based on the alternative association relationship.

[0059] For industry factors, after the underlying factor is updated, obtain the industry factor that references the underlying factor.

[0060] Traverse all industry factors that reference the basic factor, and count the number of times each industry factor references the basic factor by traversing all records.

[0061] The industry factors are arranged in ascending order by the number of citations, with the industry factors with the least citations arranged first. Considering that these industry factors do not depend on too many other factors, they are calculated first.

[0062] According to the sorted order, the industry factors are updated and calculated in turn. The industry factors with the least number of citations are calculated first and updated. As the calculation proceeds, the subsequent industry factors that rely on these calculated factors can also be gradually calculated and updated, thereby ensuring that the update and iterative calculation process of the entire emission factor data set is carried out in an orderly manner, and finally ensuring that the emission factors of textile products in the entire data set are updated.

[0063] A textile product carbon footprint database construction system based on inventory data, comprising: The emission factor optimization module sets the evaluation index of the material emission factor, determines the matching degree of the evaluation index of each material of the textile product to calculate the recommended degree of the emission factor of the material, and selects the optimal emission factor of each material of the textile product according to the recommended degree of the emission factor; The data merging module selects materials with the same code from all bills of materials of the same type of textile products, merges the material activity level data, and obtains the merged bill of materials data; A carbon footprint calculation module is used to establish a carbon footprint calculation model for each unit process based on the life cycle assessment method, input the unit process bill of materials data, calculate the carbon footprint of the textile product, and obtain the carbon footprint data of the first and second textile products; A database construction module is used to construct a textile product carbon footprint database based on the first and second textile product carbon footprint data.

[0064] The database system of the present invention can help enterprises to accurately collect carbon footprint inventory data, truly calculate product carbon footprints, help enterprises accurately grasp the carbon footprint level of textile products, and formulate effective emission reduction strategies.

[0065] As a preferred solution, the system further includes: The update module obtains the basic factors involved in textile products, establishes alternative association relationships for data updates, selects the basic factors that need data updates, and performs update calculations based on the alternative association relationships; obtains the industry factors that reference the basic factors, counts and sorts the number of references to the industry factors, and iteratively updates the industry factors in order.

[0066] Therefore, the advantages of the present invention are: 1. Database construction method In the process of carbon footprint accounting for textile products, the emission factors are obtained by priority recommendation method to obtain the emission factors with the best performance in terms of geography, time and technology, which improves the accuracy and efficiency of carbon footprint accounting results.

[0067] 2. Construct a textile product classification system based on the supply chain to provide a classification basis for the construction of a textile product carbon footprint database.

[0068] 3. By merging the carbon footprint data of the same type of textile products, we can obtain the carbon footprint data of the textile products that reflects the average carbon footprint level of the region, enrich the carbon footprint database data, and enable users to obtain the required carbon footprint data better and more comprehensively.

[0069] 4. The implementation of the present invention helps to form a localized, high-quality carbon footprint database for the textile product industry in an intelligent and convenient manner, and promotes the green and low-carbon development of the textile industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flow chart of the method for constructing a textile product carbon footprint database of the present invention.

[0071] Figure 2 It is a schematic diagram of the carbon footprint accounting process of textile products in the construction method of the present invention.

[0072] Figure 3 It is a flow chart of the material emission factor priority recommendation method of the present invention. DETAILED DESCRIPTION

[0073] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0074] Embodiment 1:

[0075] like Figures 1 to 3 As shown, this embodiment provides a method for constructing a textile product carbon footprint database based on inventory data, comprising the following steps: S1. Establish a classification system and linkage mechanism for textile products.

[0076] Textile products are classified according to their entire life cycle process, supply chain paths are established for various types of textile products based on the production process, and textile products are coded according to the statistical product classification catalog and classified into the corresponding category of textile product catalog.

[0077] The entire life cycle of textile products includes the acquisition of fiber raw materials, production stage, distribution and storage, use, and waste and recycling stages. The acquisition of fiber raw materials is mainly the production process of various types of fibers, including natural fibers, artificial fibers, and recycled fibers; the production stage is mainly the production and processing process of yarns, fabrics, and textile products; the distribution and storage, use, and waste and recycling stages are mainly for textile products. As a preferred solution of this embodiment, the establishment of a textile product classification system and association mechanism specifically includes: Establishment of textile product classification system Considering that the non-standard naming of textile products will lead to confusion in the data storage of the carbon footprint database, poor data traceability, inaccurate data associations, etc. during the database construction process, it is proposed to encode textile products based on the statistical product classification catalog. Textile products are classified according to the whole life cycle process. The whole life cycle process is generally that fiber raw materials are made into fibers through planting, synthesis and regeneration, fibers are processed into yarns through spinning and dyeing and finishing processes, yarns are processed into fabrics through weaving and dyeing and finishing processes, and fabrics are sewn and finished. Textile products are divided into five categories: fiber raw materials, fibers, yarns, fabrics and textile products. The product codes are carried out according to the statistical product classification catalog. The statistical product catalog is divided into five layers, each layer is a 2-digit code, and each product is a 10-digit classification code. For example, the 10-digit statistical product classification code for dyed cotton cloth is: 1706040101, where 17 represents textile products, 1701 represents cotton, chemical fiber textiles and printing and dyeing products, 170106 represents printed and dyed cloth, 17010602 represents dyed cloth, and 1701060201 represents dyed cotton cloth. The standardized database coding method helps to standardize the storage and processing of carbon footprint data, facilitate the rapid splicing of the entire life cycle of supply chain products, and the benchmarking and carbon reduction analysis of similar products from different companies.

[0078] Establishment of textile product association mechanism On the basis of the textile product classification system, the textile product supply chain association relationship is constructed based on the coding rules and the actual production process of textile products. According to the full life cycle process of textile products and the classification of textile products, the upstream and downstream relationships of various types of textile products are analyzed to establish the textile product supply chain. For example, for dyed cotton cloth 1701060201, the upstream cotton yarn product 17010201 is associated, and cotton yarn is associated with the upstream nylon fiber product 280301. By establishing such an association relationship, the subsequent carbon footprint accounting can quickly match the factor data, thereby improving the accuracy and efficiency of carbon footprint accounting.

[0079] S2. Carbon footprint calculation for textile products In the process of database construction, it is necessary to calculate the carbon footprint of each textile product. The carbon footprint calculation specifically includes: S21. Determine target textile product and system boundaries.

[0080] The system boundary of carbon footprint accounting for fiber products starts from animal husbandry (e.g., sheep and silkworm breeding), plant cultivation (e.g., cotton and flax cultivation), oil extraction, pulp production, etc. to fiber product manufacturing, packaging and storage.

[0081] The system boundary of yarn product carbon footprint accounting starts from the stage of obtaining fiber raw materials to the completion of yarn product manufacturing, packaging and storage.

[0082] The system boundary of carbon footprint accounting for fabric products starts from the stage of obtaining fiber raw materials to the completion of fabric product manufacturing, packaging and storage.

[0083] The system boundary of carbon footprint accounting for textile finished products (e.g., clothing, home textile products, industrial textiles) starts from the stage of obtaining fiber raw materials to the stage of manufacturing, packaging and storage of textile finished products.

[0084] S22. Divide the textile product processing procedures into multiple interrelated unit processes.

[0085] The carbon footprint accounting of textile products is to divide the product processing procedures into multiple unit processes and calculate their carbon footprint.

[0086] S23. Obtain the raw materials, auxiliary materials and energy consumption data of each unit process and establish a bill of materials. Materials include raw materials, auxiliary materials and energy. According to the material names obtained from the above analysis, obtain relevant material data from the data source and establish the bill of materials data. The bill of materials data must include the name of the material, material activity level data, material code, material production time, production location, material supplier and other information.

[0087] The bill of materials data is obtained from the data source, and the bill of materials data needs to be preprocessed before entering the model calculation. The preprocessing includes field standardization, unit standardization, data missing management, and abnormal data identification and processing.

[0088] Standardize data fields and establish standardized material data lists, including material names, material activity level data, material codes, material production time, production locations, material suppliers, and other information. After obtaining the material list data source, the obtained data can be mapped to the standardized material data list fields according to the preset field mapping relationship to achieve data field standardization.

[0089] Standardize data units, unify the data units of bill of materials data, and establish a conversion dictionary of commonly used units in the textile industry, covering raw material weight units (such as grams, kilograms, tons, etc.), area units (square meters, square yards, etc.), length units (centimeter, meter, kilometer, etc.), density units (grams per cubic centimeter, kilograms per cubic meter, etc.), energy units (joule, kilowatt-hour, and tons of steam, cubic meters of natural gas, etc. used by different companies), etc. In the data analysis process, according to this dictionary, through the predefined conversion function, the units of all data are unified, the weight unit is unified into kilograms, the area unit is unified into square meters, the length unit is unified into meters, the density unit is unified into kilograms per cubic meter, the heat (including steam) unit is unified into joules, the electricity unit is unified into kilowatt-hours, and the natural gas unit is unified into cubic meters.

[0090] Missing data governance refers to the governance of missing activity-level data.

[0091] For the management of missing activity level data, the present invention preferably sets two methods to handle missing activity level data, and selects one of the methods for processing with reference to the actual situation. One is based on input-output calculation. Except for a small amount of missing data in the process, other input and output materials are known, and the missing material data can be inferred according to the principle of conservation of materials. The second is analogy of similar processes. Find data from other batches or companies of the same type of textile products and similar production processes as a reference. Compare similar cases with complete activity level data with cases with missing data, analyze their differences in key factors such as production scale, product specifications, and equipment type, and then adjust them according to proportion or similarity.

[0092] Abnormal data identification and processing include two aspects: abnormal data identification and abnormal data processing.

[0093] Abnormal data identification, first, determines whether the input and output of the unit process conform to the law of conservation of materials. According to the law of conservation of materials, in the entire processing flow of textile products from raw material input to finished product output, check whether the logical relationship between the sum of the material input of each unit process and the final product quantity and waste generation is reasonable. If it does not conform to the law of conservation of materials, the data of this process is abnormal data. The second is to determine whether the activity level data is within a reasonable range. Based on the historical data of a large number of textile product manufacturers of the same type and specification, the activity level data of each process of textile products is statistically analyzed, and the mean (X), standard deviation (S) and other statistics of the activity level data are calculated. Reasonable upper and lower limits are set according to the results. When the activity level data reported by a company exceeds this range, it is marked as abnormal.

[0094] Abnormal data processing: For the identified abnormal activity level data, first trace the data source and verify the accuracy of the data with the enterprise. If it is confirmed that the data entry is wrong, make corrections based on the correct information. If the abnormal data is caused by special production conditions, mark the abnormal situation and consider its impact separately in the subsequent carbon footprint accounting analysis to avoid contamination of normal data.

[0095] In this step, the material data information is determined according to the input and output of the textile product unit process, and the activity level data of the unit process materials and their corresponding emission factors are obtained. The emission factor acquisition adopts an intelligent matching method to select the optimal emission factor from the acquired emission factors. The emission factor priority recommendation method includes the following steps: a. Identify the name, material code, material production time, production location, and material supplier information of the material to be accounted for.

[0096] b. Determine whether the material belongs to the textile field; The judgment condition is the first two digits of the material code, and whether the first two digits of the material code are "28", "17", or "18", which indicates that the code belongs to the textile field.

[0097] If yes, then select the relevant emission factors corresponding to the material name from the local textile product factor data set, which usually includes multiple. If there is no relevant emission factor corresponding to the material name in the local textile product factor data set, obtain the emission factor corresponding to the material name from other background emission factor databases.

[0098] If not, obtain the emission factor corresponding to the material name from other background emission factor datasets.

[0099] c. Set the evaluation index of material emission factors, determine the matching degree of evaluation index of each material of textile products to calculate the recommended degree of emission factors of materials. Specifically include: c1. Set the evaluation index of material emission factors; Evaluation indicators include geographical matching, time matching, and technical matching.

[0100] Geographical match: Measures the degree of match between the source of the emission factor and the origin of the material.

[0101] Time matching: reflects the closeness between the emission factor data collection time and the material production time.

[0102] Technical matching degree: reflects the compatibility between emission factors and the production technology and process of material processing.

[0103] c2. Determine the weight of each evaluation indicator according to the relative importance of each evaluation indicator; Use the analytic hierarchy process to determine the indicator weights. Preset the relative importance of each evaluation indicator, such as comparing each indicator pairwise through industry experts to determine their relative importance. For example, for geographical representation and temporal representation, experts use a 1-9 scale (1 means equal importance, 3 means slightly important, 5 means obviously important, 7 means strongly important, 9 means extremely important, and 2, 4, 6, and 8 are intermediate values) based on industry experience and the focus of current textile product carbon footprint accounting. If geographical representation is obviously more important than temporal representation, the corresponding element in the judgment matrix is ​​assigned a value of 5, while the element of temporal representation relative to geographical representation is assigned a value of 1 / 5. Similarly, a complete criterion layer judgment matrix A1 is constructed based on the preset relative importance of each evaluation indicator:

[0104] Calculate the maximum eigenvalue λ of the judgment matrix A1 max And the corresponding eigenvector W. After normalization, the eigenvector is the weight vector of each criterion layer index ν=[w1, w2, w3]=[0.649 0.250 0.101] T , where w1, w2, and w3 are the weights of geographical matching degree (GMD), temporal matching degree (TMD), and technical matching degree (TechMD), respectively.

[0105] c3. Set the evaluation benchmark and corresponding matching value of each evaluation indicator, and obtain the matching value of the evaluation indicator by comparing the emission factor data information with the evaluation benchmark; The evaluation benchmark and corresponding matching value of each evaluation indicator are set. The geographical matching degree takes the geographical location correspondence between the source of emission factors and the actual production place of materials as the core evaluation benchmark and assigns corresponding matching values ​​according to different situations. The time matching degree takes the interval between the emission factor data collection time and the material production time as the key judgment benchmark and sets the corresponding matching value. The technical matching degree sets the matching value based on the data acquisition channel of the emission factor as the evaluation benchmark. The matching values ​​of the evaluation indicators are shown in Table 1:

[0106] Table 1

[0107] c4. Calculate the recommended emission factor of the material based on the matching value of each evaluation indicator and the evaluation indicator weight.

[0108] Obtain emission factor data, judge each evaluation index and obtain the corresponding matching value. Finally, calculate the emission factor recommendation (RD) of the material in a weighted sum manner based on the matching value of each evaluation index and the evaluation index weight.

[0109] RD=(w1*GMD+w2*TMD+w3*TechMD) / 3 d. Select the optimal emission factor for each material of textile products based on the recommended emission factor.

[0110] Specifically, each emission factor is arranged in descending order according to the recommendation degree, and the emission factor with the highest recommendation degree is selected as the optimal emission factor.

[0111] S24. Establish a carbon footprint accounting model for textile products based on the life cycle assessment method.

[0112] The product carbon footprint calculation result is obtained by adding up the carbon footprint calculation results of each unit process. The unit process carbon footprint is divided into direct emission carbon footprint and indirect emission carbon footprint.

[0113] The carbon footprint accounting model for textile products is shown in the following formula: CF 纺织产品碳足迹 =∑E 单元过程,i / Q=∑(E 直接排放,i + E 间接排放,i ) / Q Where, CF 纺织产品碳足迹 Indicates the carbon footprint of textile products within the system boundary, in kgCO2 / kg. In this embodiment, the textile product functional unit is unified as "producing 1kg of 'a certain textile product'"; E 单元过程,i It represents the carbon emission generated by unit process i of textile product processing within the system boundary, in kgCO2; E 直接排放,i It represents the carbon emissions generated by direct emission sources of textile product processing unit process i within the system boundary, in kgCO2; E 间接排放,i It represents the carbon emissions generated by indirect emission sources of textile product processing unit process i within the system boundary, in kgCO2; Q represents the quantity converted to functional units within the system boundary, in kg.

[0114] For the calculation of direct carbon emissions of each unit process, it is necessary to consider the carbon emissions generated by animal physiological activities, energy combustion, chemical reactions and wastewater treatment. The calculation formula is as follows: E 直接排放,i =E 动物生理活动,i +E 能源燃烧,i +E 化学反应,i +E 废水处理,i In the formula, E 直接排放,i Represents the unit process of textile product processing within the system boundary i Carbon emissions from direct emission sources, in kgCO2; E动物生理活动,i Represents unit processes within the system boundary i Carbon emissions from animal physiological activities, in kgCO2; E 能源燃烧,i Represents unit processes within the system boundary i Carbon emissions from energy combustion, in kgCO2; E 化学反应,i Represents unit processes within the system boundary i Carbon emissions from chemical reactions, in kgCO2; E 废水处理,i Represents unit processes within the system boundary i Carbon emissions from wastewater treatment, in kgCO2.

[0115] Carbon emissions from animal physiological activities are calculated by multiplying the relevant activity level data with the corresponding emission factor. The calculation formula is as follows: E 动物生理活动 =E 肠道发酵 +E 粪便管理 = EF 肠道 *AD 动物数量 *GWP CH4 +EF 粪便CH4 *AD 挥发性固态 *GWP CH4 + EF 粪便N2O *AD 总氮含量 *GWP N2O In the formula, E 动物生理活动 It represents the carbon emissions generated by animal physiological activities within the system boundary, in kgCO2; E 肠道发酵 It indicates the carbon emissions generated by the activities during the animal's intestinal fermentation process, in kgCO2; E 粪便管理 It indicates the carbon emissions generated by activities during manure management, in kgCO2; EF 肠道 It represents the methane emission factor per unit animal produced during enteric fermentation; AD 动物数量 Activity level data representing the number of animals within the system boundaries; GWP CH4 represents the global warming potential of methane, which is 29.8; EF 粪便CH4 Express the methane emission factor per unit of volatile solids produced during manure management; AD 挥发性固态 Activity level data representing the total volatile solids in manure; EF粪便N2O Express the nitrous oxide emission factor produced per unit of total nitrogen content during manure management; AD 总氮含量 Activity level data indicating the total nitrogen content of feces; GWP N2O It represents the global warming potential of nitrous oxide, which is 298.

[0116] Carbon emissions from energy combustion are calculated by multiplying the relevant activity level data by the corresponding emission factor. The calculation formula is as follows: E 能源燃烧 =AD 能源 *EF 能源 In the formula, E 能源燃烧 Represents the carbon emissions generated by energy combustion within the system boundary, in kgCO2; AD 能源 Activity level data representing energy combustion within the system boundary, in units based on the quantified units of energy; EF 能源 Represents the emission factor for energy combustion.

[0117] Carbon emissions from chemical reactions are calculated by multiplying the relevant activity level data with the corresponding emission factor. The calculation formula is as follows: E 化学反应 =AD 化学品 *EF 化学品 In the formula, E 化学反应 Represents the carbon emissions produced by chemical reactions within the system boundary, in kgCO2; AD 化学品 Represents the activity level data of chemical reactions within the system boundary, and the unit is based on the quantitative unit of the chemical; EF 化学品 Represents the emission factor for chemical reactions.

[0118] Carbon emissions from wastewater treatment are calculated as follows: The industrial wastewater generated by textile and garment enterprises during the production process will produce methane after anaerobic treatment. The greenhouse gas emissions generated by wastewater treatment are calculated according to the following formula: E 废水处理 = E CH4 *GWP CH4 In the formula, E 废水处理 It represents the carbon emissions generated by wastewater treatment within the system boundary, in kgCO2; E CH4 It represents the amount of methane emitted from anaerobic wastewater treatment within the system boundary, in kg; GWPCH4 It represents the global warming potential of methane, which is 29.8.

[0119] For the calculation of indirect carbon emissions of each unit process, it is necessary to consider the carbon footprint of purchased energy and input raw materials and auxiliary materials. The calculation formula is as follows: E 间接排放,i =E 外购能源,i +E 原料 / 辅料,i =∑(AD 外购能源,ij *EF 外购能源,ij + AD 原料 / 辅料,ij *EF 原料 / 辅料,ij ) In the formula, E 间接排放,i It represents the indirect carbon emissions from textile product processing i within the system boundary, in kgCO2; E 外购能源,i It represents the carbon emissions in the production (including transmission) process of unit process i within the system boundary consuming purchased energy (such as electricity and heat), in kgCO2; E 原料 / 辅料,i It represents the greenhouse gas emissions generated in the production process of raw materials / auxiliary materials consumed by unit process i within the system boundary, in kgCO2; AD 外购能源,ij Activity level data representing purchased energy j (e.g. electricity, heat) consumed by unit process i within the system boundary, with the unit based on the quantified unit of purchased energy j; EF 外购能源,ij represents the emission factor of energy source j; AD 原料 / 辅料,ij Represents the activity level data of purchased raw materials / auxiliary materials j consumed by unit process i within the system boundary, and the unit is based on the quantitative unit of raw materials / auxiliary materials j; EF 原料 / 辅料,ij represents the emission factor of raw material / auxiliary material j.

[0120] The following example is used to illustrate steps S22-S24. Taking rayon printed and dyed fabric as an example, rayon printed and dyed fabric is divided into six unit processes: pre-treatment, traditional printing, steaming, washing, shaping and packaging according to the processing process. The carbon emissions of each unit process are then divided into direct emissions and indirect emissions. The direct emissions and indirect emissions are analyzed to obtain raw materials, auxiliary materials and energy data related to carbon footprint. The obtained data are shown in Table 2:

[0121] Table 2

[0122] S25. Input the unit process bill of materials data and calculate the carbon footprint of textile products.

[0123] Based on the above analysis, the bill of materials data of each material is obtained, and the data is input into the carbon footprint accounting model of textile products to perform carbon footprint accounting on textile products, and finally the carbon footprint accounting data of textile products is obtained.

[0124] S26. Conduct data quality assessment on carbon footprint accounting data.

[0125] The data quality is evaluated according to the data quality assessment model, and the evaluation result is output to determine whether the evaluation result DQR>4 is satisfied. If so, it indicates a poor quality result and it is necessary to return to step S23. If not, the carbon footprint accounting data is output.

[0126] A data quality assessment model is established to score the accuracy of activity level data and representativeness of emission factors for raw materials, auxiliary materials and energy input in the textile product processing process. Among them, the representativeness of emission factors includes time representativeness, regional representativeness and technical representativeness. The accuracy of activity level data mainly refers to the size of the gap between the data value and the true value. The consumption data of energy, raw materials, etc. in the textile product processing process should give priority to the first-hand data from the actual production statistical records of the production unit. The data quality evaluation table is shown in Table 3 below:

[0127] Table 3

[0128] According to the data quality rating of the activity level data and emission factors of the comprehensive materials in Table 3, the calculation formula for the overall data quality rating of a certain material is as follows: DQR=(A+TiR+TeR+GeR) / 4 Among them, A stands for accuracy, TiR stands for time representativeness, TeR stands for geographical representativeness, and GeR stands for technical representativeness.

[0129] The data quality of the final product's carbon footprint is obtained by taking the weighted average of all materials or energy required to produce the product.

[0130] Different DQR value ranges are used as the classification criteria, corresponding to different data quality levels: When the overall data quality rating (DQR) was ≤ 1.6, the overall data quality level was considered “excellent quality”; When 1.6< DQR≤ 2.0, the overall data quality level is “very good quality”; When 2.0< DQR≤ 3.0, the overall data quality level is “good quality”; When 3.0< DQR≤ 4.0, the overall data quality level is “fair quality”; When DQR>4, the overall data quality level is "poor quality".

[0131] Taking hydrogen peroxide in the pre-treatment unit of rayon printing and dyeing fabric as an example, assuming that the activity level data is obtained through calculation and the internal certification materials are complete, its accuracy ( A ) score is 2; the data collection time of hydrogen peroxide emission factor is less than 1 year ago and comes from the manufacturer's data, so its time representativeness ( TiR ) is scored as 1, and the geographical representation ( TeR ) is rated 1, and the technical representativeness ( G JZ ) score is 1. In summary, combined with the calculation formula of the data quality assessment model, the data quality DQR of hydrogen peroxide in the pre-treatment unit of rayon printing and dyeing fabrics is obtained to be 1.25, and the overall data quality level is recognized as "excellent quality".

[0132] After completing the carbon footprint calculation of a single textile product, the carbon footprint calculation result data, the bill of materials data, the emission factor data and related data are integrated to form the carbon footprint data of the first textile product. Multiple first textile product carbon footprint data are calculated and obtained through step S2. As the database data is improved, more first textile product carbon footprint data will be calculated and obtained. After completing the carbon footprint calculation of a single textile product, a single carbon footprint factor value based on the enterprise real scene data can be formed.

[0133] S3. Carbon footprint calculation of regional textile products In real-life scenarios, it is necessary to merge the carbon footprint data of multiple products of the same type to reflect the average carbon footprint level of the product in the region.

[0134] Specifically, merging the carbon footprint data of textile products of the same type to obtain the carbon footprint data of the second textile product includes the following steps: S31. Data acquisition: In step S2, the carbon footprint data of the first textile products are calculated and obtained, and the carbon footprint data of the first textile products of the same type are acquired. The same type means that the product code, geographic information and time information are consistent. The carbon footprint data of the textile products with the same product code, geographic information and time information are acquired.

[0135] For example, for data records with product code "1701060201" (dyed cotton cloth), production location "Hangzhou City" and production time "2024", these records belong to the same group of data that meets the screening criteria. All data records in the group that meet the screening criteria are combined to form a new data merged data set. This new data set contains information about products with consistent 10-digit product codes, consistent geography, and consistent time.

[0136] S32. Data screening: Select representative carbon footprint data from the first textile product carbon footprint data obtained according to the product carbon footprint to be generated to represent the average of a region. The representative condition setting is set according to the calculation requirements, and the selection range condition can be set. The carbon footprint data is verified, and the data preprocessing step in step S2 is used to supplement and improve the carbon footprint data.

[0137] S33. Data desensitization: desensitize the information of textile product production units to prevent data leakage of production enterprises.

[0138] S34. Data consolidation calculation, including: S341. Calculate the weight of each textile product of the same type based on the product market share.

[0139] Obtain the production information of these products in the data set and determine the combined weight. The weight is estimated based on the market share of the product, weight = product output / total output of the production location where the product is produced.

[0140] S342. Select materials with the same code from all bills of materials of the same type of textile products, merge the material activity level data, and obtain the merged bill of materials data.

[0141] Obtain the bill of materials data of the products in the data set, select materials with the same code as mergeable materials, group and merge the bill of materials data of the products in the data set, and for each group of bill of materials data, perform weighted average of their activity level data. Obtain the merged bill of materials data, and merge to form the bill of materials data representing a certain region.

[0142] S343. Intelligently match the optimal emission factor for the materials in the merged bill of materials data, calculate the carbon footprint of this type of textile products, and obtain the carbon footprint data of the second textile product.

[0143] Specifically, an intelligent matching method is used based on the merged bill of materials data to obtain the emission factor that has the highest matching degree between this type of textile product and each material after the merger. Carbon footprint accounting is performed through the textile product carbon footprint accounting steps based on the merged bill of materials data and the optimal emission factor to obtain the carbon footprint accounting data of textile products representing the region. The merged carbon footprint accounting data, inventory data, emission factor data and related data are integrated to form the second textile product carbon footprint data.

[0144] S4. Database construction: constructing a textile product carbon footprint database based on the first and second textile product carbon footprint data.

[0145] S5. Database update calculation.

[0146] Database updates include updating the emission factors of the textile product factor data set. Textile product emission factors can be divided into two categories. One is the basic factors, which mainly refer to various energy types and transportation factors used to calculate the carbon footprint of textile products, such as electricity emission factors, thermal emission factors, freight emission factors, etc. Changes in these basic factors will affect the carbon footprint of textile products. The other is the textile product industry factors, which mainly refer to specific textile product carbon footprint factors, such as various printing and dyeing products, and hemp spinning product carbon footprint factors.

[0147] Obtain the basic factors of textile products, establish alternative association relationships for data updates, select the basic factors that need data updates, and perform update calculations based on the alternative association relationships; specifically include: S51. For basic factors, obtain basic factors, establish a list of basic factors according to the basic factors, and mark the time and version information of the basic factors.

[0148] S52. Establish a basic factor substitution relationship, for example, use the 2024 electricity emission factor to replace the 2023 electricity emission factor.

[0149] S53. When data update is required, select the list of basic factors that need to be updated from the local textile product emission factor data set, and update the basic factors according to the substitution association relationship.

[0150] Obtain all industry factors that reference the basic factor, count and sort the number of times the industry factors are referenced, and iterate and update the industry factors in order. Specifically, it includes: S54. For industry factors, after the base factor is updated, obtain all industry factors that reference the base factor.

[0151] S55. Traverse all industry factors that reference the basic factor, and count the number of times each industry factor references the basic factor by traversing all records. For example, a single reference is a reference: a factor (such as an industry factor) directly uses the data or information of another factor (such as a basic factor) to perform its own calculations or determine its own value. There is a direct dependency between them, and there are no other factors in the middle. A secondary reference is a reference: a factor indirectly references another factor through other intermediate factors. There is at least one intermediate factor as a bridge, which connects the final factor with the initially referenced factor. The association relationship is relatively complex and involves more levels. By analogy, for multiple references, the association relationship is more complex and involves more levels.

[0152] S56. Arrange the industry factors in ascending order by the number of citations. The industry factors with the least number of citations are arranged first, considering that these industry factors do not depend on too many other factors and are calculated first.

[0153] S57. Update and calculate the industry factors in sequence according to the sorted order.

[0154] First, calculate the industry factors with the least number of citations and update them. As the calculation proceeds, the subsequent industry factors that rely on these calculated factors can also be gradually calculated and updated, thereby ensuring that the update and iterative calculation process of the entire emission factor data set is carried out in an orderly manner, and finally ensuring that the textile product emission factors of the entire data set are updated.

[0155] Embodiment 2:

[0156] This embodiment provides a textile product carbon footprint database construction system based on inventory data, which is used to implement the database construction method of embodiment 1. The system includes: A data acquisition module, which acquires the bill of materials data of textile products according to the product data list; The emission factor optimization module sets the evaluation index of the material emission factor, determines the matching degree of the evaluation index of each material of the textile product to calculate the recommended degree of the emission factor of the material, and selects the optimal emission factor of each material of the textile product according to the recommended degree of the emission factor; The data merging module selects the same materials of the same type of textile products, obtains the data of each material list, performs weighted average calculation, and obtains the merged list data; A carbon footprint calculation module is used to establish a carbon footprint calculation model for each unit process based on the life cycle assessment method, input the unit process bill of materials data, calculate the carbon footprint of the textile product, and obtain the carbon footprint data of the first and second textile products; A database construction module is used to construct a textile product carbon footprint database based on the first and second textile product carbon footprint data.

[0157] The update module obtains the basic factors involved in textile products, establishes alternative association relationships for data updates, selects the basic factors that need data updates, and performs update calculations based on the alternative association relationships; obtains the industry factors that reference the basic factors, counts and sorts the number of references to the industry factors, and iteratively updates the industry factors in order.

[0158] The database system of the present invention can help enterprises to accurately collect carbon footprint inventory data, truly calculate product carbon footprints, help enterprises accurately grasp the carbon footprint level of textile products, and formulate effective emission reduction strategies.

[0159] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A method for constructing a textile product carbon footprint database based on inventory data, characterized in that: The following steps are involved: Establish a classification system and linkage mechanism for textile products; Get bill of materials data for textile products; Set the evaluation index of the material emission factor, determine the matching degree of the evaluation index of each material of the textile product to calculate the recommended degree of the emission factor of the material, select the optimal emission factor of each material of the textile product according to the recommended degree of the emission factor, calculate the carbon footprint of the textile product, and obtain the carbon footprint data of the first textile product; The carbon footprint data of the first textile products of the same type are combined to calculate and obtain the carbon footprint data of the second textile products; A textile product carbon footprint database is constructed based on the first and second textile product carbon footprint data.

2. The method for constructing a textile product carbon footprint database based on inventory data according to claim 1 is characterized in that: Determine the matching value of the assessment index of each material of the textile product to calculate the recommended emission factor of the material, including: Determine the weight of each evaluation indicator according to the relative importance of each preset evaluation indicator; Set the evaluation benchmark and corresponding matching value of each evaluation indicator, and obtain the matching value of the evaluation indicator by comparing the emission factor data information with the evaluation benchmark; The recommended emission factor of the material is calculated based on the matching value of each evaluation indicator and the evaluation indicator weight.

3. The method for constructing a textile product carbon footprint database based on inventory data according to claim 2 is characterized in that: According to the emission factor recommendation, the optimal emission factor for each material of textile products is selected, including: Arrange each emission factor in descending order according to the recommendation degree, and select the emission factor with the highest recommendation degree as the optimal emission factor.

4. The method for constructing a textile product carbon footprint database based on inventory data according to any one of claims 1 to 3, characterized in that: Establish a textile product classification system and linkage mechanism, including: Textile products are classified according to their entire life cycle process, supply chains are established for various types of textile products based on the production process, and textile products are coded and classified into corresponding categories of textile product catalogs.

5. The method for constructing a textile product carbon footprint database based on inventory data according to any one of claims 1 to 3, characterized in that: The carbon footprint data of textile products of the same type are combined to obtain the second textile product carbon footprint data, including: Select textile products of the same type; Calculate the weight of each textile product of the same type based on the product market share; Select materials with the same code from all bills of materials of the same type of textile products, merge the material activity level data, and obtain the merged bill of materials data; The optimal emission factor is intelligently matched to the materials in the merged bill of materials data, and the carbon footprint of this type of textile products is calculated to obtain the carbon footprint data of the second textile product.

6. The method for constructing a textile product carbon footprint database based on inventory data according to claim 5 is characterized in that: Carbon footprint accounting for textile products includes the following steps: Determine target textile products and system boundaries; According to the textile product processing steps, it is divided into several interrelated unit processes; Obtain the raw materials, auxiliary materials and energy consumption data of each unit process and establish a bill of materials; Establish a carbon footprint accounting model for textile products based on the life cycle assessment method; Enter your bill of materials data to calculate the carbon footprint of your textile products.

7. The method for constructing a textile product carbon footprint database based on inventory data according to claim 1, characterized in that: Also includes database updates, including: Obtain the basic factors involved in textile products, establish alternative association relationships for data update, select the basic factors that need data update, and perform update calculations based on the alternative association relationships; Obtain the industry factors that cite the basic factor, count and sort the number of citations of the industry factors, and iteratively update and calculate the industry factors in order.

8. The method for constructing a textile product carbon footprint database based on inventory data according to claim 1 is characterized by: The acquired textile product bill of materials data is subjected to field standardization, unit standardization, data missing management, abnormal data identification and processing operations.

9. A textile product carbon footprint database construction system based on inventory data, implementing the method described in any one of claims 1 to 8, characterized in that: include: The emission factor optimization module sets the evaluation index of the material emission factor, determines the matching degree of the evaluation index of each material of the textile product to calculate the recommended degree of the emission factor of the material, and selects the optimal emission factor of each material of the textile product according to the recommended degree of the emission factor; The data merging module selects materials with the same code from all bills of materials of the same type of textile products, merges the material activity level data, and obtains the merged bill of materials data; The carbon footprint calculation module inputs the unit process bill of materials data, calculates the carbon footprint of the textile products, and obtains the first and second textile product carbon footprint data.

10. The textile product carbon footprint database construction system based on inventory data according to claim 9, characterized in that Also includes: A database construction module, constructing a textile product carbon footprint database based on the first and second textile product carbon footprint data; The update module obtains the basic factors involved in textile products, establishes alternative association relationships for data updates, selects the basic factors that need data updates, and performs update calculations based on the alternative association relationships; obtains the industry factors that reference the basic factors, counts and sorts the number of references to the industry factors, and iteratively updates the industry factors in order.

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