Copper ore mining and smelting product carbon footprint database construction method

Abstract

The invention relates to the technical field of carbon emission accounting, and discloses a copper mine mining and smelting product carbon footprint database construction method and system. The method comprises the following steps: collecting and preprocessing data; a multi-level data verification model is introduced, and material balance, element balance and energy balance verification of a base layer, water balance verification of a process layer, GWP calculation and carbon balance verification of an influence layer and global consistency verification of a comprehensive layer are carried out in sequence; performing quality scoring on the verified data by adopting a five-level scoring method based on an error range; and finally constructing a carbon footprint database. According to the method, through multi-level data verification and dynamic correction, the integrity and accuracy of the data are remarkably improved, accurate evaluation of the data quality is realized, and reliable data support is provided for fine management of carbon emission of enterprises.

Description

Technical Field

[0001] This application relates to the field of carbon emission accounting technology, and in particular to a method for constructing a carbon footprint database of copper mining and smelting products based on enterprise data. Background Technology

[0002] As an energy-intensive and emission-intensive industry, the copper mining and smelting sector accounts for a significant proportion of carbon emissions in the industrial sector. To achieve precise carbon emission management and effective emission reduction measures, establishing a scientific carbon footprint database has become an urgent need for the industry's development.

[0003] However, in practice, copper mining companies face numerous challenges when building carbon footprint databases. First, data collection by these companies often suffers from a "focus on primary processes while neglecting secondary ones." Companies typically focus only on data from major production stages, such as ore extraction, concentrate production, and energy consumption, while paying insufficient attention to data collection from secondary processes such as auxiliary material usage, equipment maintenance costs, and trace pollutant emissions. This results in significant information gaps in the database.

[0004] Secondly, existing data verification methods are too simplistic and one-sided. Most companies only use a single material balance or energy balance for verification, lacking a comprehensive verification mechanism that is multi-dimensional and multi-layered. This single verification method is unlikely to discover potential logical contradictions between data and cannot guarantee the systematicity and accuracy of the data. For example, even if the material balance meets the requirements, there may be a significant deviation between the water resource consumption data and the actual process requirements.

[0005] Third, enterprises generally lack systematic methods for data correction and supplementation. When data is missing or abnormal, they often resort to empirical estimations or simple averaging to fill in the gaps. This approach lacks scientific basis and may introduce greater errors. Especially when facing complex production processes and variable operating conditions, simple data correction methods cannot accurately reflect the actual situation.

[0006] Furthermore, enterprises' carbon footprint data quality assessment systems are inadequate. Most enterprises have not established unified data quality evaluation standards, making it impossible to quantitatively assess the reliability of their data. This makes it difficult for data users to judge the credibility of the data, affecting the accuracy and reliability of carbon footprint accounting results.

[0007] These problems severely restrict the sophistication of corporate carbon emission management. Companies struggle to conduct effective carbon emission analysis and forecasting based on incomplete or inaccurate data, making it even more difficult to formulate precise emission reduction strategies. Furthermore, the lack of a unified and standardized database construction methodology leads to inconsistent carbon accounting standards among companies within the industry, hindering horizontal benchmarking and experience sharing, thus impacting the overall low-carbon development process of the industry.

[0008] Therefore, there is an urgent need to develop a systematic and standardized method for constructing a carbon footprint database, which can ensure the integrity and accuracy of the data, support enterprises in carrying out precise carbon emission management, and promote the construction of an industry carbon accounting system. This is of great practical significance for improving the carbon emission management level of enterprises and promoting the green and low-carbon development of the industry. Summary of the Invention

[0009] The purpose of this application is to provide a method for constructing a carbon footprint database of copper mining and smelting products, so as to solve the problems mentioned in the background art.

[0010] This application discloses a method for constructing a carbon footprint database of copper mining and smelting products, including the following steps:

[0011] Data acquisition and preprocessing: Collect raw data from the copper mining and smelting process and preprocess the raw data to obtain preprocessed data;

[0012] Multi-level data verification and correction: A multi-level verification model is introduced into the preprocessed data in the data acquisition and preprocessing steps, including: At the basic level, material balance verification, elemental balance verification, and energy balance verification are carried out sequentially, and the data is supplemented and corrected according to the verification results to obtain material, elemental, and energy balance verification data; At the process level, water balance verification is carried out, and the material, elemental, and energy balance verification data obtained at the basic level are supplemented and corrected according to the verification results to obtain water resource balance verification data; At the impact level, the global warming potential (GWP) is calculated and carbon balance verification is carried out. The carbon emission data in the water resource balance verification data obtained at the process level is corrected by combining data clustering analysis and interquartile range (IQR) outlier detection methods to obtain carbon emission verification data; At the comprehensive level, global consistency checks and cross-time period and cross-stage correlation analyses are carried out to verify the verification data at each level to obtain multi-level verification and correction data;

[0013] Carbon footprint database construction: Based on the multi-level verification and correction data obtained in the multi-level data verification and correction steps, a five-level scoring method based on error range is used to score data quality. Combined with the data quality scoring results, a carbon footprint database for each stage of the copper mining and smelting product life cycle is constructed to achieve accurate quantification and evaluation of carbon emissions throughout the entire life cycle of copper mining and smelting products.

[0014] In a preferred embodiment, data acquisition and preprocessing further includes the following steps:

[0015] The research objectives and scope of the carbon footprint database are defined, and datasets of material inputs, product outputs and pollutant emissions in the copper mining and smelting process are identified. The collected raw data is preprocessed by classifying, cleaning, identifying missing values ​​and detecting error values ​​to obtain preprocessed data.

[0016] In a preferred embodiment, at the basic layer, material balance verification, elemental balance verification, and energy balance verification are carried out sequentially. Based on the verification results, the data are supplemented and corrected to obtain the material, elemental, and energy balance verification data, which further includes the following steps:

[0017] Material balance verification: Calculate the total amount of matter input and output of the system. If the difference between the two exceeds the preset error range, the corresponding data will be marked as material balance data to be corrected.

[0018] Element balance check: Calculate the input and output of key elements (such as copper). If the difference between the two exceeds the preset error range, the corresponding data is marked as element balance data to be corrected.

[0019] Energy balance check: Calculate the total energy input and total energy output of the system. If the difference between the two exceeds the preset error range, the corresponding data is marked as energy balance data to be corrected.

[0020] Data supplementation and correction: For the material balance data, element balance data, and energy balance data marked in the material balance verification step to the energy balance verification step, the data is corrected by using a supplementation method based on balance relationship and a multi-dimensional data supplementation strategy to obtain the corrected data.

[0021] Verification of verification results: Perform material balance verification, elemental balance verification, and energy balance verification on the corrected data in the data supplementation and correction steps. If all verification results are within the preset error range, the corrected data will be used as the material, elemental, and energy balance verification data; otherwise, return to the data supplementation and correction steps to continue data supplementation and correction.

[0022] In a preferred embodiment, at the process level, a water balance verification is performed, and the material, element, and energy balance verification data obtained at the basic level are supplemented and corrected based on the verification results to obtain water resource balance verification data. This further includes the following steps:

[0023] Water input calculation: Calculates the total amount of water input into the system, including the moisture content of raw materials, the amount of fresh water input, and the amount of circulating water;

[0024] Water output calculation: Calculate the total water output of the system, including the moisture in the product, wastewater discharge, evaporation loss and other losses;

[0025] Water balance check: Compare the total water input calculated in the water input calculation step with the total water output calculated in the water output calculation step. If the difference between the two exceeds the preset error range, the relevant data will be marked as water balance data to be corrected.

[0026] Data Supplementation and Correction: For the water balance data marked in the water balance verification step that needs correction, combined with the material, element, and energy balance verification data obtained in the basic layer, a supplementation method based on water balance relationships and a multi-dimensional data supplementation strategy are used to correct the data, resulting in corrected water balance data. The supplementation method based on balance relationships includes the following water balance equations: For the water balance of the overall production process: ∑Mfresh water = Mcompliant wastewater + Mloss; For the water balance of the wastewater treatment process: Mwastewater input = ∑Moutput wastewater from each stage = Mcompliant wastewater + Mreuse + Mloss; For the water balance of the i-th production unit: Mfresh wateri + Mreusei = Moutput wastewater to wastewater treatment stagei + Mon-site reusei + Mlossi; There is a cross-validation relationship between the water balance equations, and any link... Any anomalies in the water volume data can be reverse-checked using the water balance equations of other stages. These water balance equations serve as both a data supplement and verification tool. Furthermore, the multi-dimensional data supplementation strategy includes: using existing industry databases for comparison to identify abnormal data points; comparing the data with standard data from similar enterprises or authoritative databases; and adjusting data deviations based on the comparison results. When it is found that the water output from a certain process does not correspond to the water input in the wastewater treatment stage, the water output in the process needs to be corrected. A genetic algorithm is used to optimize the wastewater yield *wi* of each process, specifically including: initialization: representing the wastewater yield of each process as a gene, encoding it with real numbers, and constructing an initial population; fitness function: introducing the weight coefficient αi of each process, defining the fitness function f(w)=(∑αiwi·PT). 2 , where wi is the wastewater yield of the i-th process, P is the copper production, T is the target total water volume, αi is the weighting factor of the i-th process, and ∑αi=1, αi>0. The weighting factor αi is determined based on the importance of the process, data reliability, degree of environmental impact, and difficulty of wastewater treatment; Selection: roulette wheel selection method is used; Crossover: single-point crossover or multi-point crossover is used; Mutation: each gene of the offspring individuals is slightly modified, and the mutated value still needs to remain within the original range; Termination condition: the algorithm stops when the fitness value of the best individual in the population is less than a certain preset threshold; Output result: the individual with the smallest fitness value is output as the optimal combination of wastewater yields for each process;

[0027] Verification of verification results: The corrected water balance data from the data supplementation and correction steps are re-verified. If the verification result is within the preset error range, the corrected water balance data is used as the water resource balance verification data; otherwise, return to the data supplementation and correction steps to continue data supplementation and correction.

[0028] Consistency check: The water resource balance verification data obtained from the verification result step is cross-validated with the material, element and energy balance verification data obtained from the basic layer to ensure consistency among various types of data and obtain the final water resource balance verification data.

[0029] In a preferred embodiment, at the impact layer, the steps of calculating the global warming potential (GWP) and performing carbon balance verification, and correcting the carbon emission data in the water resource balance verification data obtained at the impact layer by combining data clustering analysis and interquartile range (IQR) outlier detection methods, to obtain the carbon emission verification data, further include the following steps:

[0030] GWP Calculation: Based on the water resource balance verification data obtained at the process level, the global warming potential (GWP) of various greenhouse gas emissions during copper mining and smelting is calculated to obtain GWP data.

[0031] Carbon balance verification: Calculate the total input and output of carbon elements in the system. If the difference between the two exceeds the preset error range, the relevant data will be marked as carbon balance data to be corrected.

[0032] Data clustering analysis: Cluster analysis is performed on the GWP data obtained from the GWP calculation steps to divide GWP data with similar characteristics into several cluster groups;

[0033] IQR outlier detection: For each cluster group obtained in the data clustering analysis step, outliers are detected using the interquartile range (IQR) method, and the detected outliers are marked as GWP data to be corrected;

[0034] Data Supplementation and Correction: For the carbon balance data to be corrected marked in the carbon balance verification step and the GWP data to be corrected marked in the IQR outlier detection step, combined with the material, element and energy balance verification data obtained in the basic layer and the water resource balance verification data obtained in the process layer, a supplementation method based on carbon balance relationship and a multi-dimensional data supplementation strategy are adopted to correct the data and obtain the corrected carbon emission data.

[0035] Verification of results: The carbon emission data obtained from the data supplementation and correction steps are subjected to carbon balance verification and GWP outlier detection again. If the carbon balance verification result is within the preset error range and the GWP data has no outliers, the corrected carbon emission data is used as the carbon emission verification data; otherwise, return to the data supplementation and correction steps to continue data supplementation and correction.

[0036] Consistency check: The carbon emission verification data obtained from the verification results step is cross-validated with the material, element and energy balance verification data obtained at the basic layer and the water resource balance verification data obtained at the process layer to ensure consistency among various types of data and obtain the final carbon emission verification data.

[0037] In a preferred embodiment, at the comprehensive layer, the steps of conducting global consistency checks and cross-time period and cross-process correlation analyses to verify the verification data at each level and obtain multi-level verification and correction data further include the following steps:

[0038] Data integration: Global consistency check of the material, element and energy balance verification data obtained from the basic layer and the water resource balance verification data obtained from the process layer: A global consistency check is performed on the dataset to be verified integrated in the data integration step to ensure that the data of different levels and types are consistent in value. If inconsistencies are found, the relevant data are marked as global data to be corrected.

[0039] Cross-time correlation analysis: Perform cross-time correlation analysis on the dataset to be validated integrated in the data integration step, compare the data change trends in different time periods, and if abnormal changes are found, mark the relevant data as time series data to be corrected;

[0040] Cross-process correlation analysis: Perform cross-process correlation analysis on the dataset to be verified by the data integration step, check the logical relationship between data in different production stages, and mark the relevant data as data of the stage to be corrected if logical contradictions are found.

[0041] Data Supplementation and Correction: For the global data, time-series data, and process data marked in the steps from global consistency check to cross-process correlation analysis that need to be corrected, the data supplementation and correction are carried out by multi-source data cross-validation and expert knowledge base assistance, so as to obtain the corrected comprehensive verification data.

[0042] Verification of comprehensive verification results: The corrected comprehensive verification data obtained from the data supplementation and correction steps are subjected to a global consistency check, cross-time period correlation analysis, and cross-stage correlation analysis again. If the global consistency check results meet the preset consistency conditions, the cross-time period correlation analysis results meet the preset temporal rationality conditions, and the cross-stage correlation analysis results meet the preset logical self-consistency conditions, then the corrected comprehensive verification data is used as the multi-level verification correction data; otherwise, return to the data supplementation and correction steps to continue data supplementation and correction.

[0043] Final dataset generation: The multi-level verification and correction data that have passed the verification step of the comprehensive verification results are integrated into the final dataset, which serves as the basis for the subsequent construction of the carbon footprint database.

[0044] In a preferred embodiment, the carbon footprint database construction step further includes the following steps:

[0045] Data quality scoring: A five-level scoring method based on error range is used to score the quality of the multi-level verification and correction data obtained from the multi-level data verification and correction steps. Specifically, this includes: determining scoring indicators: setting corresponding scoring indicators based on the verification results of material balance, elemental balance, energy balance, water balance, and carbon balance; setting error ranges: setting five error range levels for each scoring indicator, corresponding to scores of 1-5; calculating scores for each indicator: determining the score for each indicator based on the error values ​​of each indicator in the multi-level verification and correction data, and comparing them with the error range levels; and comprehensive scoring: calculating a weighted average of the scores for each indicator to obtain a comprehensive data quality score.

[0046] Data classification and organization: Based on the various stages of the copper mining and smelting product life cycle, the multi-level verification and correction data obtained from the multi-level data verification and correction steps are classified and organized to form a structured carbon footprint dataset.

[0047] Carbon footprint calculation model construction: Based on the structured carbon footprint dataset formed by data classification and organization steps, a carbon footprint calculation model for each stage of the copper mining and smelting product life cycle is constructed, including: determining functional units: selecting the corresponding functional units; establishing an emission factor library: collecting and organizing carbon emission factor data of various raw materials, energy and emissions; designing calculation formulas: designing corresponding carbon footprint calculation formulas for the carbon footprint accounting needs of each stage of the life cycle.

[0048] Database structure design: Design the logical and physical structure of the carbon footprint database, including data tables, fields, relationships, and indexes, to ensure the integrity, consistency, query efficiency, and scalability of the database;

[0049] Data import: The structured carbon footprint dataset formed by the data classification and organization steps, the data quality score results obtained by the data quality scoring steps, and the carbon footprint calculation model constructed by the carbon footprint calculation model construction steps are imported into the database according to the database structure design steps to form a carbon footprint database of copper mining and smelting products.

[0050] Database functionality implementation: Develop application functions for the carbon footprint database, including data query, statistical analysis, and report generation, to achieve accurate quantification, analysis, evaluation, and visualization of carbon emissions throughout the entire life cycle of copper mining and smelting products;

[0051] Database validation and optimization: The database is tested using sample data to verify its functionality, performance, and the accuracy of the calculation results. Based on the validation results, the database is optimized and improved to ensure its reliability, efficiency, and usability.

[0052] In a preferred embodiment, the preprocessing in the data acquisition and preprocessing also includes: data standardization: performing unit unification, format standardization, and numerical normalization on the acquired raw data to ensure data consistency and comparability.

[0053] In a preferred embodiment, the supplementation method based on the balance relationship includes: using the input and output data of adjacent processes, and according to the law of conservation of mass or the law of conservation of energy, to estimate the range of values ​​for the missing data.

[0054] In a preferred embodiment, the data clustering analysis employs the K-means algorithm, and the specific steps include: initializing K cluster centers; calculating the distance from each data point to each cluster center; assigning the data point to the nearest cluster center; recalculating the cluster centers; and repeating the above steps until the cluster centers no longer change or the maximum number of iterations is reached.

[0055] In a preferred embodiment, the multi-source data cross-validation method includes: using multiple data sources such as industry standard data, historical data, and data from similar enterprises to perform cross-validation on the data to be corrected. The validation content and indicators include:

[0056] Data consistency verification: Compare whether data from different data sources are consistent, calculate indicators such as data deviation or correlation coefficient, and assess the level of data consistency;

[0057] Data integrity verification: Check whether there are missing or outliers in the data to be corrected, calculate the missing or outlier rate, and assess the level of data integrity.

[0058] Data accuracy verification: Compare the data to be corrected with the standard or theoretical value, calculate the data error or confidence interval, and evaluate the accuracy level of the data;

[0059] Data timeliness verification: Analyze the timestamps or update frequency of the data to be corrected, calculate the indicators of data lag or expiration rate, and evaluate the timeliness level of the data;

[0060] Data correlation verification: Explore the correlation between the data to be corrected and other key parameters, calculate the correlation coefficient or regression coefficient, and assess the level of data correlation;

[0061] Based on the above cross-validation results, the credibility and usability of the data to be corrected are comprehensively judged, providing an objective basis for data supplementation and correction.

[0062] In a preferred embodiment, the database structure design further includes: designing a data partitioning strategy to partition and store data according to the time dimension or production process, thereby improving the query efficiency of large-scale data.

[0063] In a preferred embodiment, the database functionality implementation further includes: developing a data update and maintenance module to support the dynamic import of new data, the periodic archiving of historical data, and the management and tracing of data versions.

[0064] In a preferred embodiment, the following steps are also included:

[0065] Dynamic monitoring and early warning of carbon footprint: Based on the constructed carbon footprint database, a carbon emission trend analysis model and threshold early warning mechanism are designed to realize real-time monitoring and early warning of anomalies in the carbon footprint of copper mining and smelting products.

[0066] This application proposes a method for constructing a carbon footprint database of copper mining and smelting products. By introducing a multi-level data verification model, it achieves high-precision verification and dynamic correction of the data. Its technical effects are mainly reflected in the following aspects:

[0067] This significantly improves the data integrity of the carbon footprint database. During the data acquisition phase, this application comprehensively identified datasets including material inputs, product outputs, and pollutant emissions from the copper mining and smelting production process. Furthermore, systematic preprocessing filled in the gaps and errors in the original data, maximizing the integrity of the original data and laying a solid foundation for subsequent data verification and carbon footprint accounting.

[0068] This significantly improves the accuracy of the carbon footprint database. This application employs a multi-level verification model, conducting data verification at each level: from fundamental levels such as material balance, elemental balance, and energy balance, to the process level of water balance, and then to the impact level of carbon balance and GWP accounting. A comprehensive global verification is also performed at the integrated level. Targeted data correction methods are designed for each level, enabling precise identification and correction of data deviations, ensuring the effectiveness of dynamic data correction, and making the final carbon footprint data highly reliable.

[0069] This application achieves precise assessment and control of data quality. It constructs a five-level data quality scoring system based on error range. By setting scientific scoring indicators and thresholds, it quantitatively assesses carbon footprint data after multi-level verification, making the data quality status readily apparent. Based on the data quality assessment results, it can provide clear direction for improving carbon accounting models and dynamically updating data.

[0070] It supports enterprises in refined carbon emission management. Based on a copper mining and smelting product carbon footprint database with significantly improved data integrity, accuracy, and quality controllability, it enables enterprises to conduct precise carbon emission accounting, carbon footprint tracking, and full life-cycle carbon emission analysis. It can also integrate intelligent applications such as carbon emission prediction and early warning, thereby realizing the institutionalization, process-orientation, and refinement of carbon emission management, helping enterprises to scientifically formulate carbon reduction strategies and accelerate the low-carbon transformation.

[0071] This has promoted the construction of an industry carbon accounting system. The high-quality carbon footprint database for the copper mining and smelting industry constructed in this application can serve as the basic support for industry carbon accounting, providing data sources and calculation basis for establishing a unified and standardized industry carbon accounting system. In turn, it provides data support for industry carbon emission benchmark research, benchmarking against advanced enterprises, and analysis of industry emission reduction potential, thereby promoting energy conservation, emission reduction, and green development of the industry as a whole.

[0072] In summary, this application, by constructing a highly complete, accurate, and high-quality carbon footprint database of copper mining and smelting products, can significantly improve the carbon emission accounting level of enterprises and even the industry, support enterprises in implementing refined carbon emission management, promote the construction of the industry's carbon accounting system, and is of great significance to helping the steel industry save energy and reduce emissions and address climate change.

[0073] The specification of this application contains numerous technical features distributed across various technical solutions. Listing all possible combinations of these technical features (i.e., technical solutions) would make the specification excessively lengthy. To avoid this problem, the various technical features disclosed in the above-described invention, the various technical features disclosed in the following embodiments and examples, and the various technical features disclosed in the accompanying drawings can be freely combined to form various new technical solutions (all of which are considered to have been described in this specification), unless such a combination of technical features is technically infeasible. For example, one example discloses feature A+B+C, and another example discloses feature A+B+D+E. Features C and D are equivalent technical means that serve the same function, and technically only one needs to be used; they cannot be used simultaneously. Feature E can technically be combined with feature C. Therefore, the solution A+B+C+D should not be considered as described because it is technically infeasible, while the solution A+B+C+E should be considered as described. Attached Figure Description

[0074] Figure 1 This is a flowchart illustrating the method for constructing a carbon footprint database of copper mining and smelting products according to the first embodiment of this application.

[0075] Figure 2 This is a schematic diagram of data partitioning for copper mining and processing in an example of a method for constructing a carbon footprint database of copper mining and smelting products according to the first embodiment of this application.

[0076] Figure 3 This is a schematic diagram of a verification model in an example of a method for constructing a carbon footprint database of copper mining and smelting products according to the first embodiment of this application.

[0077] Figure 4This is a schematic diagram illustrating the splitting of total flue gas according to the rate ratio of dust collection flue gas and annular flue gas in an example of a method for constructing a carbon footprint database of copper mining and smelting products according to the first embodiment of this application.

[0078] Figure 5 This is a schematic diagram of the type and flow direction of water in the copper mining and processing process, as shown in an example of a method for constructing a carbon footprint database of copper mining and smelting products according to the first embodiment of this application. Detailed Implementation

[0079] In the following description, many technical details are presented to help the reader better understand this application. However, those skilled in the art will understand that the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments.

[0080] Explanation of some concepts:

[0081] Material balance refers to the state in which all input materials and output materials in a system or process are in equilibrium in terms of mass. For example, in copper mining, the total mass of input ore should be equal to the total mass of output copper concentrate, tailings, and other waste materials.

[0082] Energy balance refers to the equality of energy input and output in a system or process. For example, the energy provided by the energy consumed in the smelting process (such as electricity and coal) should be equal to the sum of the energy carried by the product and losses (such as heat dissipation losses and flue gas losses).

[0083] Water balance refers to the state in which the input of water (such as fresh water supply) is equal to the output of water (such as product moisture content, wastewater discharge, evaporation loss, etc.) in a system or process. For example, the amount of water input in mineral processing should be equal to the sum of the moisture carried by the product, the moisture carried by the tailings, and other losses.

[0084] Elemental balance refers to the material balance of a specific element, requiring that the total input of that element equals the total output. For example, in this application, the input and output of copper in each process should be balanced. The sources of copper include the copper content of raw materials, and the destinations include the copper content of products and the copper content of waste.

[0085] Carbon balance, a type of elemental balance, refers to the balance between the input and output of carbon. In this application, carbon balance verification requires calculating the carbon input from fossil fuels, raw materials, electricity, etc., as well as the carbon output from products, waste gas, waste residue, etc., to ensure that carbon is "not leaked or accumulated" throughout the entire production process.

[0086] An emission factor is a coefficient that converts the consumption of a certain raw material or fuel, or the output of a product, into greenhouse gas emissions. For example, the CO2 emission factor for coal combustion represents the CO2 emissions produced by burning a unit mass of coal. In this application, the emission factor is the basic data for carbon footprint accounting.

[0087] Global warming potential (GWP) is an indicator that measures the global warming potential of a particular greenhouse gas relative to CO2. The GWP of CO2 is defined as 1. For example, if the GWP of a gas is 10, it means that over a 100-year period, 1 kg of that gas causes radiative forcing equivalent to 10 kg of CO2. In this application, the sum of the products of each greenhouse gas emission and its GWP represents the total carbon emissions.

[0088] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0089] The first embodiment of this application relates to a method for constructing a carbon footprint database of copper mining and smelting products, the process of which is as follows: Figure 1 As shown, the method includes the following steps:

[0090] S1. Data Acquisition and Preprocessing: Collect raw data from the copper mining and smelting process and preprocess the raw data to obtain preprocessed data.

[0091] S2, Multi-level Data Validation and Correction: A multi-level validation model is introduced for the preprocessed data in S1.

[0092] Specifically, S2 further includes:

[0093] S21. At the basic level, conduct material balance verification, element balance verification, and energy balance verification in sequence, and supplement and correct the data according to the verification results to obtain material, element, and energy balance verification data.

[0094] S22. At the process level, water balance verification is carried out, and the material, element and energy balance verification data of S21 are supplemented and corrected based on the verification results to obtain water resource balance verification data.

[0095] S23. At the impact layer, calculate the global warming potential (GWP) and conduct carbon balance verification. Combine data clustering analysis and interquartile range (IQR) outlier detection method to correct the carbon emission data in the water resource balance verification data of S22 to obtain carbon emission verification data.

[0096] S24. At the comprehensive level, conduct global consistency checks and cross-time period and cross-process correlation analysis, verify the verification data of each level from S21 to S23, and obtain multi-level verification and correction data.

[0097] S3. Carbon Footprint Database Construction: Based on the multi-level verification and correction data obtained in step S2, a five-level scoring method based on error range is used to score data quality. Combined with the data quality scoring results, a carbon footprint database for each stage of the copper mining and smelting product life cycle is constructed to achieve accurate quantification and evaluation of carbon emissions throughout the entire life cycle of copper mining and smelting products.

[0098] Optionally, step S1 includes the following steps: defining the research objectives and scope of the carbon footprint database, identifying datasets such as material inputs, product outputs, and pollutant emissions in the copper mining and smelting process, and performing preprocessing such as classification, cleaning, missing value identification, and error value detection on the collected raw data to obtain preprocessed data.

[0099] Optionally, step S21 includes the following steps:

[0100] S211. Material balance verification: The system calculates the total amount of matter input and output. If the difference between the two exceeds the preset error range, the corresponding data is marked as material balance data to be corrected.

[0101] S212, Element Balance Check: Calculate the input and output quantities of key elements (such as copper). If the difference between the two exceeds the preset error range, the corresponding data will be marked as element balance data to be corrected.

[0102] S213. Energy balance check: Calculate the total energy input and output of the system. If the difference between the two exceeds the preset error range, the corresponding data will be marked as energy balance data to be corrected.

[0103] S214. Data Supplementation and Correction: For the material balance data, element balance data, and energy balance data marked in S211 to S213 that need to be corrected, a supplementation method based on balance relationships and a multi-dimensional data supplementation strategy are used to correct the data and obtain the corrected data.

[0104] S215 Verification of Verification Results: Perform material balance verification, elemental balance verification, and energy balance verification on the corrected data in S214 again. If all verification results are within the preset error range, the corrected data will be used as the material, elemental, and energy balance verification data; otherwise, return to S214 and continue to supplement and correct the data.

[0105] Optionally, step S22 includes the following steps:

[0106] S221. Water Input Calculation: Calculate the total water input in the system, including the moisture content of raw materials, the amount of fresh water input, and the amount of circulating water.

[0107] S222, Water Output Calculation: Calculate the total water output of the system, including the moisture in the product, wastewater discharge, evaporation loss and other losses.

[0108] S223. Water balance check: Compare the total water input calculated in S221 with the total water output calculated in S222. If the difference between the two exceeds the preset error range, the relevant data will be marked as water balance data to be corrected.

[0109] S224. Data Supplementation and Correction: For the water balance data marked in S223 that needs to be corrected, and in combination with the material, element and energy balance verification data obtained in step S21, the data is corrected using a supplementation method based on water balance relationship and a multi-dimensional data supplementation strategy to obtain the corrected water balance data.

[0110] S225 Verification of Verification Results: Perform water balance verification again on the corrected water balance data in S224. If the verification result is within the preset error range, the corrected water balance data will be used as the water resource balance verification data; otherwise, return to S224 and continue to supplement and correct the data.

[0111] S226. Consistency check: Cross-validate the water resource balance verification data obtained in S225 with the material, element and energy balance verification data obtained in step S21 to ensure consistency among various types of data and obtain the final water resource balance verification data.

[0112] Optionally, step S23 includes the following steps:

[0113] S231, GWP Calculation: Based on the water resource balance verification data obtained in step S22, calculate the global warming potential (GWP) of various greenhouse gas emissions during copper mining and smelting, and obtain GWP data.

[0114] S232. Carbon Balance Verification: Calculate the total input and output of carbon elements in the system. If the difference between the two exceeds the preset error range, the relevant data will be marked as carbon balance data to be corrected.

[0115] S233. Data Cluster Analysis: Perform cluster analysis on the GWP data calculated in step S231 to divide the GWP data with similar characteristics into several cluster groups.

[0116] S234, IQR outlier detection: For each cluster group obtained in step S233, outlier detection is performed using the interquartile range (IQR) method, and the detected outliers are marked as GWP data to be corrected.

[0117] S235. Data Supplementation and Correction: For the carbon balance data to be corrected marked in step S232 and the GWP data to be corrected marked in step S234, combined with the material, element and energy balance verification data obtained in step S21 and the water resource balance verification data obtained in step S22, the data is corrected using a supplementation method based on carbon balance relationship and a multi-dimensional data supplementation strategy to obtain the corrected carbon emission data.

[0118] S236. Verification of Verification Results: Perform carbon balance verification and GWP outlier detection again on the corrected carbon emission data obtained in step S235. If the carbon balance verification result is within the preset error range and there are no outliers in the GWP data, then use the corrected carbon emission data as the carbon emission verification data; otherwise, return to step S235 to continue data supplementation and correction.

[0119] S237. Consistency Check: Cross-validate the carbon emission verification data obtained in step S236 with the material, element and energy balance verification data obtained in step S21 and the water resource balance verification data obtained in step S22 to ensure consistency among various types of data and obtain the final carbon emission verification data.

[0120] Optionally, step S24 includes the following steps:

[0121] S241. Data Integration: Integrate the material, element and energy balance verification data obtained in step S21, the water resource balance verification data obtained in step S22, and the carbon emission verification data obtained in step S23 to form a dataset to be verified.

[0122] S242, Global Consistency Check: Perform a global consistency check on the dataset to be verified integrated in step S241 to ensure that the data at different levels and of different types are consistent in value. If inconsistencies are found, the relevant data will be marked as global data to be corrected.

[0123] S243. Cross-time period correlation analysis: Perform cross-time period correlation analysis on the dataset to be verified integrated in step S241, compare the data change trends in different time periods, and if abnormal changes are found, mark the relevant data as time series data to be corrected.

[0124] S244. Cross-process correlation analysis: Perform cross-process correlation analysis on the dataset to be verified integrated in step S241 to check the logical relationship between data in different production processes. If logical contradictions are found, the relevant data will be marked as data of the process to be corrected.

[0125] S245. Data Supplementation and Correction: For the global data, time-series data, and process data marked in steps S242 to S244 that need to be corrected, data supplementation and correction are carried out using methods such as multi-source data cross-validation and expert knowledge base assistance to obtain the corrected comprehensive verification data.

[0126] S246. Verification of Comprehensive Verification Results: Perform global consistency check, cross-time period correlation analysis, and cross-stage correlation analysis on the corrected comprehensive verification data obtained in step S245. If the global consistency check results meet the preset consistency conditions, the cross-time period correlation analysis results meet the preset temporal rationality conditions, and the cross-stage correlation analysis results meet the preset logical self-consistency conditions, then the corrected comprehensive verification data will be used as multi-level verification correction data; otherwise, return to step S245 to continue data supplementation and correction.

[0127] S247. Final Dataset Generation: The multi-level verification and correction data that passed the verification in step S246 are integrated into the final dataset, which serves as the basis for the subsequent construction of the carbon footprint database.

[0128] Optionally, step S3 includes the following steps:

[0129] S31. Data Quality Scoring: A five-level scoring method based on error range is used to score the quality of the multi-level verification and correction data obtained in step S2. Specifically, this includes: S311. Determining Scoring Indicators: Based on the verification results of material balance, elemental balance, energy balance, water balance, and carbon balance, corresponding scoring indicators are set; S312. Setting Error Ranges: Five error range levels are set for each scoring indicator, corresponding to scores of 1-5; S313. Calculating Scores for Each Indicator: Based on the error values ​​of each indicator in the multi-level verification and correction data, and in accordance with the error range levels, the score for each indicator is determined; S314. Comprehensive Scoring: The scores of each indicator are weighted and averaged to obtain a comprehensive data quality score.

[0130] S32. Data Classification and Organization: Based on the various stages of the copper mining and smelting product lifecycle, the multi-level verification and correction data obtained in step S2 are classified and organized to form a structured carbon footprint dataset.

[0131] S33. Carbon Footprint Calculation Model Construction: Based on the structured carbon footprint dataset formed in step S32, construct carbon footprint calculation models for each stage of the copper mining and smelting product lifecycle, including: S331. Determining Functional Units: Selecting appropriate functional units, such as "per ton of copper product"; S332. Establishing an Emission Factor Library: Collecting and organizing carbon emission factor data for various raw materials, energy sources, and emissions; S333. Designing Calculation Formulas: Designing corresponding carbon footprint calculation formulas for the carbon footprint accounting needs at each stage of the lifecycle.

[0132] S34. Database Structure Design: Design the logical and physical structure of the carbon footprint database, including data tables, fields, relationships, and indexes, to ensure the integrity, consistency, query efficiency, and scalability of the database.

[0133] S35. Data Import: Import the structured carbon footprint dataset formed in step S32, the data quality score obtained in step S31, and the carbon footprint calculation model constructed in step S33 into the database according to the database structure designed in step S34 to form a carbon footprint database for copper mining and smelting products.

[0134] S36. Database Function Implementation: Develop application functions for the carbon footprint database, including data query, statistical analysis, report generation, etc., to achieve accurate quantification, analysis and evaluation, and visualization of carbon emissions throughout the entire life cycle of copper mining and smelting products.

[0135] S37. Database Validation and Optimization: Test the database using sample data to verify its functionality, performance, and the accuracy of the calculation results. Optimize and improve the database based on the validation results to ensure its reliability, efficiency, and usability.

[0136] The above process will be further explained below.

[0137] Optionally, the preprocessing in step S1 may further include: data standardization processing: performing unit unification, format standardization and numerical normalization processing on the collected raw data to ensure data consistency and comparability.

[0138] Optionally, the supplementary method based on the balance relationship includes: using the input and output data of adjacent processes, and according to the law of conservation of mass or the law of conservation of energy, calculating the range of values ​​for the missing data.

[0139] Optionally, the data clustering analysis uses the K-means algorithm, and the specific steps include: initializing K cluster centers; calculating the distance from each data point to each cluster center; assigning the data point to the nearest cluster center; recalculating the cluster centers; repeating the above steps until the cluster centers no longer change or the maximum number of iterations is reached.

[0140] Optionally, the multi-source data cross-validation method includes: using multiple data sources such as industry standard data, historical data, and data from similar enterprises to cross-validate the data to be corrected. The verification content and indicators include: (1) Data consistency verification: comparing whether the data from different data sources are consistent, calculating indicators such as data deviation or correlation coefficient, and evaluating the consistency level of the data; (2) Data integrity verification: checking whether the data to be corrected has missing values ​​or outliers, calculating indicators such as data missing rate or outlier rate, and evaluating the integrity level of the data; (3) Data accuracy verification: comparing the data to be corrected with standard values ​​or theoretical values, calculating indicators such as data error or confidence interval, and evaluating the accuracy level of the data.

[0141] (4) Data timeliness verification: Analyze the timestamps or update frequency of the data to be corrected, calculate indicators such as data lag or expiration rate, and evaluate the timeliness level of the data; (5) Data correlation verification: Explore the correlation between the data to be corrected and other key parameters, calculate indicators such as correlation coefficient or regression coefficient, and evaluate the correlation level of the data. Based on the above cross-validation results, comprehensively judge the credibility and usability of the data to be corrected, and provide an objective basis for data supplementation and correction.

[0142] Optionally, the database structure design also includes: designing a data partitioning strategy to partition and store data according to the time dimension or production process, thereby improving the query efficiency of large-scale data.

[0143] Optionally, the database functionality implementation also includes: developing a data update and maintenance module to support the dynamic import of new data, the periodic archiving of historical data, and the management and traceability of data versions.

[0144] Optionally, the above method also includes the following steps: S4, dynamic monitoring and early warning of carbon footprint: based on the constructed carbon footprint database, design a carbon emission trend analysis model and a threshold early warning mechanism to realize real-time monitoring and early warning of anomalies in the carbon footprint of copper mining and smelting products.

[0145] It should be noted that the multi-level verification model constructed in this embodiment is significantly innovative in its overall data supplementation and inspection logic. This model adopts a systematic approach of "bottom-up, progressive layer-by-layer, and cross-validation," forming a complete data quality assurance system. At the most basic material level, the reliability of basic data is ensured through verification of three dimensions: matter balance, element balance, and energy balance. This multi-dimensional basic-level verification is not a simple parallel check, but rather a logically progressive relationship based on the laws of conservation of matter and energy. First, the accuracy of matter flow is ensured; then, element balance is verified based on the accurate matter flow; and finally, energy balance is verified on this basis. This progressive verification method gives data correction and supplementation a clear priority and direction.

[0146] At the process level, this application focuses on innovation and breakthroughs in water balance verification. Although water balance verification is an important component of Life Cycle Assessment (LCA) models, it often presents unique challenges in actual enterprise production. This is because enterprises typically do not conduct detailed quantitative statistics on water used in the production process, resulting in significant gaps or distortions in water flow and quantity data when constructing life cycle models. This situation makes water balance verification and data supplementation a particularly critical and challenging aspect of the modeling process.

[0147] To address this practical problem, this application innovatively links water balance verification with the verification results of the basic layer. First, through systematic analysis of the process, it identifies and quantifies various forms of water, including water carried over from raw materials, process water, and cooling water. Second, by tracing the migration and transformation processes of these different forms of water, it organically integrates water balance verification with material balance and energy balance. For example, the influence of material moisture content is considered in the material balance calculation, and the heat change of cooling water is considered in the energy balance calculation. This correlated verification mechanism not only compensates for the lack of water volume statistics in enterprises but also provides a reliable theoretical basis and calculation basis for supplementing water data.

[0148] This innovative water balance verification method has significant practical implications. It not only provides an effective way to conduct water balance accounting when enterprise data is incomplete, but also establishes a systematic water data quality control system. Through cross-validation with other balance verification methods, this method can effectively identify and correct abnormal data, significantly improving the reliability of water data in life cycle assessment models. Simultaneously, this method also provides reference and guidance for enterprises to improve water resource management and enhance data statistics.

[0149] At the impact level, this embodiment, for the first time, establishes a closed loop between the calculation of the Gross Power Wage (GWP) of carbon emissions and prior verification. By employing statistical methods such as data clustering and outlier detection, accurate verification of carbon emission data is achieved. This method does not simply involve numerical comparison, but rather establishes an intrinsic correlation between data points, forming a systematic carbon emission data quality control system. In particular, when anomalies are detected, the model can trace back to the verification results at the foundational and process layers, accurately pinpointing the source of the problem and making targeted corrections.

[0150] At the highest comprehensive level, this embodiment innovatively introduces global consistency checks and cross-dimensional correlation analysis. This is not a simple superposition of the aforementioned layers of verification, but rather achieves comprehensive data quality control by establishing a multi-dimensional data correlation network. For example, when an anomaly is detected in carbon emission data for a certain time period, the system automatically traces the material flow, energy flow, and water volume data for that period, and determines the root cause of the anomaly through multi-dimensional cross-validation. This three-dimensional verification system significantly improves data reliability.

[0151] Another major innovation of the model lies in its dynamic correction mechanism. Each level of verification is not a one-time event, but rather an iterative optimization process. When a problem is detected at a higher level, it can be fed back to a lower level for re-verification, forming a closed loop for continuous data quality improvement. Simultaneously, the model establishes a comprehensive data scoring mechanism, providing an objective basis for data quality management by quantitatively evaluating verification results. This data quality scoring system not only reflects the current reliability of the data but also guides the direction of improvement for subsequent data collection and management efforts.

[0152] This multi-level, multi-dimensional, and dynamically iterative verification model breaks through the limitations of traditional single-balance verification, achieving holistic control and continuous improvement of data quality. This model is not only applicable to the copper mining and smelting industry but can also be extended to other industrial sectors, providing new methodological support for industrial data quality management.

[0153] Technical effects:

[0154] The above embodiments propose a method for constructing a carbon footprint database of copper mining and smelting products based on enterprise data. By introducing a multi-level data verification model, high-precision verification and dynamic correction of the data are achieved. Its technical effects are mainly reflected in the following aspects:

[0155] This significantly improves the data integrity of the carbon footprint database. During the data acquisition phase, the above embodiments comprehensively identified datasets including material inputs, product outputs, and pollutant emissions from the copper mining and smelting production process. Furthermore, the system's preprocessing filled in any gaps and errors in the original data, maximizing the integrity of the original data and laying a solid foundation for subsequent data verification and carbon footprint accounting.

[0156] This significantly improves the accuracy of the carbon footprint database. The above embodiments employ a multi-level verification model, conducting data verification at each level, from fundamental levels such as material balance, elemental balance, and energy balance, to the process level of water balance, and then to the impact level of carbon balance and GWP accounting. A comprehensive global verification is also performed at the integrated level. Targeted data correction methods are designed for each level, enabling precise identification and correction of data deviations, ensuring the effectiveness of dynamic data correction, and making the final carbon footprint data highly reliable.

[0157] This approach enables precise assessment and control of data quality. The above embodiments construct a five-level data quality scoring system based on error range. By setting scientific scoring indicators and thresholds, it quantitatively evaluates carbon footprint data after multi-level verification, making the data quality status readily apparent. Based on the data quality assessment results, clear directions can be provided for improving carbon accounting models and dynamically updating data.

[0158] It supports enterprises in refined carbon emission management. Based on a copper mining and smelting product carbon footprint database with significantly improved data integrity, accuracy, and quality controllability, it enables enterprises to conduct precise carbon emission accounting, carbon footprint tracking, and full life-cycle carbon emission analysis. It can also integrate intelligent applications such as carbon emission prediction and early warning, thereby realizing the institutionalization, process-orientation, and refinement of carbon emission management, helping enterprises to scientifically formulate carbon reduction strategies and accelerate the low-carbon transformation.

[0159] This has promoted the construction of an industry carbon accounting system. The high-quality carbon footprint database for the copper mining and smelting industry constructed in the above embodiments can serve as the basic support for industry carbon accounting, providing data sources and calculation basis for establishing a unified and standardized industry carbon accounting system. In turn, it provides data support for industry carbon emission benchmark research, benchmarking against advanced enterprises, and analysis of industry emission reduction potential, thereby promoting energy conservation, emission reduction, and green development of the industry as a whole.

[0160] In summary, the above embodiments, by constructing a highly complete, accurate, and high-quality carbon footprint database of copper mining and smelting products, can significantly improve the carbon emission accounting level of enterprises and even the industry, support enterprises in implementing refined carbon emission management, promote the construction of the industry's carbon accounting system, and are of great significance to helping the steel industry save energy and reduce emissions and address climate change.

[0161] To better understand the technical solution of this application, a specific example is provided below. The details listed in this example are mainly for ease of understanding and are not intended to limit the scope of protection of this application.

[0162] This example proposes a method and system for constructing a carbon footprint database of copper mining and smelting products based on enterprise data. The aim is to provide an effective method for data verification and supplementation in building unit process datasets within the copper mining and smelting product carbon footprint database, ensuring the completeness and validity of the lifecycle inventory. Specifically, it includes the following steps:

[0163] 1. Data Acquisition and Preprocessing

[0164] 1.1 Define the research objectives and scope

[0165] Define the research objectives: First, clearly define the purpose of the research, such as assessing the environmental impact, resource consumption, or energy efficiency of copper mining and processing.

[0166] Defining the system boundaries: The evaluation scope includes the mining, beneficiation, smelting (including roughing and refining), and final product packaging and pre-shipment preparation stages of copper ore. It also includes the processes of recovering generated flue gas and smelting slag to produce products such as sulfuric acid and slag concentrate.

[0167] 1.2 Dataset Identification and Classification

[0168] Mining phase: Identify data on energy consumption (such as electricity and fuel oil), water consumption, waste generation (waste rock and tailings), chemical use (such as explosives and beneficiation agents), and greenhouse gas and other pollutant emissions involved in the mining process.

[0169] Mineral processing stage: Collect data on energy consumption, water consumption, chemical usage, tailings generation and disposal during mineral processing processes such as ore crushing, grinding and flotation.

[0170] Smelting stage: Collect environmental emission data for the roughing and refining processes, including energy consumption (mainly electricity and fuel), raw material consumption (such as flux, reducing agent, etc.), water resource consumption, waste gas (such as sulfur dioxide, nitrogen oxides, particulate matter), wastewater, and solid waste (such as slag and soot).

[0171] Supporting activities include oxygen production, wastewater treatment, and power generation processes. These activities also require the collection of corresponding datasets.

[0172] Data partitioning for copper mining and processing is shown in [link to data]. Figure 2 Data is partitioned according to commonly used products. The main process steps are: mine → copper concentrate, copper concentrate → anode copper, anode copper → cathode copper. Flue gas is recycled and reused within the plant as a recyclable material, and flue gas acid production is recorded as a separate dataset; smelting slag is also recycled and reused within the plant, and slag concentrate production is also recorded as a separate dataset.

[0173] 1.3 Identifying Missing Values ​​and Error Values

[0174] Based on the data list, further identify missing values ​​in the data. Missing values ​​may be due to incomplete data records or data loss. By identifying missing values, the blank spots in the data are clearly identified, and these can be supplemented and corrected in the next step of data validation and data supplementation.

[0175] 2. Data validation and data supplementation

[0176] To ensure data accuracy, this example introduces a multi-layered data validation model. This model verifies the rationality and consistency of the data by comprehensively validating various balances (such as material balance, energy balance, water balance, carbon balance, and economic balance). The validation model is as follows: Figure 3 As shown.

[0177] 2.1 Supplementary Emissions Data

[0178] For emission data that does not participate in material balance, energy balance, and water balance (such as air emissions and water body emissions), if there are missing data in the data list, you can refer to the "Second National Pollution Source Census Bulletin" or the average value of multiple relevant companies' environmental impact assessment reports to supplement it.

[0179] 2.2 Basic Layer Verification

[0180] After supplementing the emissions data, a basic layer verification is performed on the data list. In the multi-level data verification model, basic layer verification, as the first step in data processing, aims to ensure the core rationality and accuracy of the data. Basic layer verification mainly includes material balance verification and energy balance verification.

[0181] 2.2.1 Material balance verification

[0182] Material balance, as the most fundamental method of balance verification, should be given priority. If material balance cannot be achieved, subsequent balances may also be problematic. First, it is necessary to identify the various material flows during data acquisition and preprocessing. When building a copper ore dataset, it is necessary to identify raw materials (such as copper ore, copper concentrate, and copper slag), products (such as anode copper and cathode copper), by-products (such as electrolytic copper), and solid waste (such as various waste copper slags and acid production flue gas). A detailed analysis of these flow types is required to ensure that the quality of input and output matches within a reasonable error range.

[0183] • Calculation approach:

[0184] ① Determine the boundaries of the system and count the total mass of substances entering the system, including raw materials, etc.

[0185] ②The total mass of matter leaving the system in a statistical system, including product mass, by-product mass, and waste mass.

[0186] ③ Compare the total input and total output to determine the quality balance. If the difference between the input quality and the output quality is within an acceptable error range, the quality balance check is considered passed.

[0187] ·Main production process calculation formulas:

[0188]

[0189]

[0190] in,

[0191] M_total_input: Represents the total mass of all materials entering a certain process.

[0192] M_total_output: Represents the total mass of all materials output from this process.

[0193] δ represents the error range, which is optional and within ±5%.

[0194] δM: Represents the allowable error range

[0195] 2.2.2 Element Balance Verification

[0196] For copper mining and smelting datasets, the balance of copper must be considered. Element balance is a sub-item of mass balance, and special verification is required when the data list involves specific elements. The flow of elements should be specifically identified in the collected data. In the copper mining and smelting process, the flow of copper is: copper ore (0.4%-1%) → copper concentrate (20%-30%) → anode copper (99%) → cathode copper (99.9%). Ensuring that the mass of copper is conserved throughout this process is fundamental to guaranteeing the accuracy of the data list.

[0197] ●Calculation approach:

[0198] ① Select key elements and count the input quantities of elements in the system, including elements in the raw materials and other inputs.

[0199] ② The output quantity of elements in the statistical system, including elements in products and waste.

[0200] ③ Compare the total input and total output of each element to determine the element balance. If the difference between the total input and total output of each element is within an acceptable error range, the element balance check is considered successful.

[0201] ●Calculation formula

[0202]

[0203]

[0204] Where δ is the error range, which can be selected as ±5%.

[0205] 2.2.3 First-level judgment

[0206] The process involves verifying whether the mass balance or elemental balance check passes. If the mass balance or elemental balance check fails, a data correction procedure must be initiated immediately. The core purpose of the data correction procedure is to analyze and adjust the data to ensure that the corrected data meets the balance conditions. In the first round of verification, due to the presence of blank values, the balance conditions are largely not met; therefore, the main purpose of the first round is to fill in these blank values. If the mass balance or elemental balance check fails, the data correction procedure must be initiated immediately. The corrected data is then re-entered for mass balance verification. Only when the mass balance check passes can the next stage, energy balance verification, proceed.

[0207] 2.2.4 First Layer Supplement and Correction

[0208] In the replenishment and correction of material items, the first consideration is to replenish and correct based on the balance relationship. Based on the balance relationship, missing data is supplemented using input and output data from adjacent stages. For example, if product data for a reactor is missing, it can be inferred from the material flows upstream and downstream.

[0209] M 上(下)游物质i =M 下(上)游 ×X i

[0210] Where X i Let X be the proportion of upstream (downstream) substance i in the downstream (upstream) process. If all upstream and downstream substances are converted, then X... i =1.

[0211] (1) Material balance

[0212] In the anode copper dataset, flue gas is presented as a byproduct, and the unit is m³. 3 / h, and the corresponding working hours cannot be found in the data provided by the company. Since the flue gas is the raw material for sulfuric acid, the annual volume of flue gas produced can be calculated based on the law of conservation of mass. According to the data list, all the flue gas in the acid production process comes from the smelting process of producing anode copper. The emission time of the two types of flue gas is the same, so the total flue gas is split according to the rate ratio of dust collection flue gas and circulating flue gas (e.g., Figure 4 (As shown).

[0213]

[0214]

[0215] (2) Elemental balance

[0216] When dealing with substances containing key elements, missing data can be supplemented through elemental flow and elemental balance. In copper mining and processing, the flow of copper is as follows: copper ore (0.4%-1%) → copper concentrate (20%-30%) → anode copper (99%) → cathode copper (99.9%).

[0217]

[0218] Conservation of copper: M1×X1=M2×X2=M3×X3=M4×X4

[0219]

[0220] When M i When data is missing or needs correction, it can be done according to M. i-1 and M i+1 Replenish.

[0221] 2.2.5 Energy Balance Verification

[0222] After the material balance verification is passed, the energy balance verification is performed. Because energy balance depends on the flow and transformation of matter, only on the basis of a reasonable material balance can the energy input and output be accurately assessed. The core of energy balance verification is to analyze the energy input, output, and transformation within the system, ensuring that all energy flows remain balanced in each production stage. Energy balance verification not only focuses on the total energy input and output but also needs to consider the transformation and flow of different forms of energy (such as electrical energy, thermal energy, and chemical energy) in each subsystem.

[0223] ●Calculation approach:

[0224] ①Statistically calculate the input of various energy sources throughout the life cycle, including the energy contained in raw materials and the energy consumed during the production process.

[0225] ② Calculate the energy output of a product or service at each stage of its life cycle, including energy services provided during product use and energy that may be recovered during waste disposal.

[0226] ③ Compare the total input and total output energies to determine the energy balance. If the difference between the input and output energies is within an acceptable error range, the energy balance check is considered successful.

[0227] ●Calculation formula:

[0228] E 总输入 =E 上网电 +E 自产电

[0229] E 总输出 =E 使用电

[0230] |E 总输入 -E 总输出 |≤δE

[0231] Where δ is the error range, which can be selected as ±5%.

[0232] 2.2.6 Second-level judgment

[0233] The energy balance is checked to ensure it passes. If the check fails, the data correction procedure must be initiated immediately. The corrected data is then re-entered for energy verification. Only when the energy verification passes can the next process layer verification proceed.

[0234] 2.2.7 Second Layer Supplement and Correction

[0235] (1) Feedback from the enterprise: The balance verification results are fed back to the enterprise. Based on its actual operation and the accurate data collected by the monitoring equipment, the enterprise directly provides detailed information on energy consumption to ensure the accuracy and timeliness of data correction.

[0236] (2) Multi-dimensional data supplementation strategy: In the absence of direct enterprise data or when further verification of the integrity of enterprise data is required, industry average data, authoritative literature, and relevant data from environmental impact assessment (EIA) reports are used as supplements. These data sources are screened and compared, and their averages are taken or weighted using scientific algorithms, thereby effectively supplementing and verifying the original energy data and improving the comprehensiveness and reliability of the data.

[0237] 2.3 Process-level verification

[0238] 2.3.1 Water Balance Verification

[0239] Water balance verification mainly involves calculating and comparing the water volume entering and leaving the system to ensure water balance. During water balance verification, it's necessary to first identify the type and flow direction of water in the process. Note that circulating water is not included in the water balance verification. Currently, in my country, enterprises with advanced technology in this field consume approximately 8 tons of fresh water per ton of copper, with a water recycling rate of around 97%. Copper smelter wastewater is classified into three categories: ① Equipment and process circulating cooling water, which is the largest category, accounting for approximately 95% of the total water and wastewater used in copper smelters; ② Acidic wastewater containing heavy metals, which can be further divided into waste acid from the dilute acid purification process in sulfuric acid production, acidic wastewater from other production processes, workshop floor washing water, and initial rainwater from the plant area; ③ Domestic wastewater. Special attention needs to be paid to the balance between process output wastewater and wastewater treatment process input water during water balance verification.

[0240] • Calculation approach:

[0241] ① Input of water quantity in the statistical system, including moisture in raw materials and external water supply.

[0242] ② The output of water volume in the statistical system, including the moisture content in the product and wastewater discharge.

[0243] ③ Compare the total water input and total output to determine the water balance. If the difference between the total water input and total output is within an acceptable error range, the water balance check is considered successful.

[0244] · Calculation formula

[0245]

[0246] Where δ is the error range, which can be selected as ±5%.

[0247] 2.3.2 Third-level judgment

[0248] The water balance is checked to see if it passes. If the check fails, the data correction procedure must be initiated immediately. The corrected data is then re-entered for water balance verification. Only when the verification passes can the next process layer verification proceed. In the first round of verification, due to the presence of blank values, the balance conditions are basically not met. Therefore, the main purpose of the first round of verification is to fill in the blank values. The purpose of subsequent verifications is to correct the supplemented data.

[0249] 2.3.3 Third Layer Supplement and Correction

[0250] Water replenishment and correction are mainly based on balance relationships or existing industry data.

[0251] (1) Supplementation and correction based on water balance

[0252] In the copper mining and processing process, the type and direction of water flow are as follows: Figure 5 As shown.

[0253] For the overall production process:

[0254] ∑M 新水 =M 达标废水 +M 损失

[0255] For wastewater treatment:

[0256] M 废水处理的废水输入 =∑M 各环节输出废水 =M 达标废水 +M 回用量 +M 损失

[0257] For the i-th production unit:

[0258] M 新水i +M 回用水i =M 输出至废水处理环节废水i +M 当场回用量i +M 损失i

[0259] In other words, the supplementary method based on equilibrium relationships includes the following set of water balance equations:

[0260] For the water balance of the entire production process: ∑Mfresh water = Mcompliant wastewater + Mloss;

[0261] For the water balance in the wastewater treatment process: M_wastewater input for wastewater treatment = ∑M_wastewater output from each stage = M_compliant wastewater + M_reuse amount + M_loss;

[0262] For the water balance of the i-th production unit: Mfresh wateri + Mreclaimed wateri = Mwastewateri output to the wastewater treatment stage + Mon-site reusei + Mlossi;

[0263] Among them, there is a cross-validation relationship between the water balance equations. Any abnormality in the water volume data of any link can be reverse-checked through the water balance equations of other links. The water balance equation set also serves as a dual tool for data supplementation and data verification. When data is missing, it can be extrapolated based on other known data. When data verification is performed, it can be used to evaluate the rationality of the data.

[0264] Specifically, this example proposes a series of innovative solutions for the water balance verification method, significantly improving the accuracy and reliability of copper mining and smelting product lifecycle data. These innovations are mainly reflected in the following aspects:

[0265] First, this example creatively establishes a three-tiered water balance system: "overall-process-unit". At the overall level, the relationship ∑Mfresh water = Mcompliant wastewater + Mloss is used to calculate the overall water volume of the entire production process, ensuring the overall balance of water resource use at the factory level. At the process level, the equation Mwastewater input = ∑Moutput wastewater from each stage = Mcompliant wastewater + Mreuse + Mloss is used to accurately calculate the wastewater treatment process, unifying the wastewater generated in each production stage into the wastewater treatment system and detailing the wastewater's destination. At the unit level, the formula Mfresh wateri + Mreuse wateri = Mwastewater output to wastewater treatment stagei + Mon-site reusei + Mlossi is used to accurately calculate the water volume of a single production unit, considering multiple aspects such as fresh water input, reuse water use, wastewater generation, on-site reuse, and losses, achieving refined water volume management at the production unit level. This multi-tiered water balance system not only improves data accuracy but also provides more detailed data support for enterprise water resource management.

[0266] Secondly, this example systematically identifies and classifies the water flow during copper mining and smelting. Regarding fresh water supply, different types are considered, including industrial fresh water, domestic fresh water, and fire-fighting fresh water. For circulating water, it includes process circulating water, cooling circulating water, and equipment cooling water. For wastewater, it covers process wastewater (such as acidic wastewater containing heavy metals and slag flushing wastewater), cooling wastewater, domestic wastewater, and initial rainwater. Regarding losses, it statistically analyzes evaporation losses, leakage losses, and carryover losses. This comprehensive and detailed classification enables more accurate tracking and management of the use of various water resources.

[0267] Third, this example proposes a tiered water balance error control method. Based on the characteristics of different levels, corresponding error control standards are set: overall water balance error controlled within ±5%, process water balance error controlled within ±3%, and unit water balance error controlled within ±2%. Simultaneously, this example systematically analyzes the sources of error, including measurement error, statistical error, and loss estimation error, and proposes targeted measures to reduce these errors. Measurement error is reduced by calibrating and standardizing metering equipment such as water meters; statistical error is reduced by standardized data collection and recording methods; and loss estimation error is reduced by combining empirical formulas and measured data.

[0268] Fourth, this example innovatively combines water balance verification with other balance verifications. Regarding material balance, the impact of moisture content in materials on water balance is considered, treating water as a crucial component of material flow. Regarding energy balance, energy transfer in the cooling water system is taken into account, incorporating water temperature changes into the energy balance calculation. Regarding carbon balance, carbon emissions during wastewater treatment are considered, integrating the carbon reduction effects of water-saving measures into the evaluation. This multi-dimensional balance verification method ensures the systematic nature and accuracy of the data.

[0269] Fifth, this example establishes a closed-loop management mechanism for water balance data verification. In the data acquisition phase, multiple methods are combined, including automated online monitoring, regular manual data recording, and laboratory data analysis. In the data verification phase, checks are conducted on rationality, completeness, and accuracy. In the data correction phase, outlier identification, missing value supplementation, and error correction are performed. In the continuous improvement phase, management levels are continuously enhanced through regular evaluation, problem diagnosis, and optimization measures. This closed-loop management mechanism ensures continuous improvement in the quality of water balance data.

[0270] By implementing the innovative methods described above, this example not only significantly improves the accuracy and reliability of water balance verification, providing a scientific basis for enterprise water resource management, but also possesses strong practicality and promotional value. This method can be extended to other similar industrial processes, promoting the overall improvement of water resource management in the industry and contributing to water conservation, emission reduction, and sustainable development in the industrial sector.

[0271] (2) Supplementing and correcting based on the database

[0272] Utilize existing industry databases for comparison to identify outlier data points. Compare the data with standard data from similar companies or authoritative databases to identify anomalies. Adjust data deviations based on the comparison results. For example, in water balance verification, it was found that the water output from each process did not match the water input from the wastewater treatment stage, requiring correction of the water output in the process. This can be supplemented by industry literature.

[0273]

[0274] ∑w i ×M 铜 =T 废水

[0275] Using a genetic algorithm, the optimal w is obtained. i The specific steps for retrieving the value are as follows:

[0276] A. Initialization

[0277] ① Encoding: First, represent the wastewater yield of each process as a gene (or a part of a chromosome). Since the wastewater yield is a continuous value within a range, real number encoding can be used. Assume there are n processes, and the wastewater yield of each process is represented as w. i (i = 1, 2, ..., n), and each w i ∈[a i ,b i The copper production is P.

[0278] ② Initial Population: An initial population is randomly generated. Each individual in the population is a potential solution, i.e., a specific combination of wastewater yields from all processes. The population size is P, and each individual can be represented as a vector w = (w1, w2, ...).

[0279] B. Fitness Function

[0280] The fitness function is used to evaluate the quality of each individual. Here, the fitness function can be defined as the absolute value or square of the difference between the sum of the products of all process wastewater yields and copper production and the target value T (for simplicity, the square can be used):

[0281]

[0282] The goal is to find the w that minimizes f(w).

[0283] C. Choice

[0284] Roulette wheel selection: Individuals are selected from w(i) to enter the next generation of the population w(i+1). The higher the fitness value (i.e., the closer it is to the target value), the greater the probability of being selected.

[0285] D. Cross

[0286] Single-point crossover or multi-point crossover: Randomly select a pair of individuals as parents, then cut at one or more points, exchange gene segments after the cut point, and generate two new offspring individuals.

[0287] E. Mutation

[0288] Real number variation: This involves making small, probabilistic changes to each gene in offspring individuals (i.e., the wastewater yield of each process step) to increase population diversity. The changed values ​​must remain within the original range.

[0289] w i '=w i +Δcdot(b i -a i cdotrand()

[0290] Where Δ is the variable length, and rand() is a random number between [-1, 1].

[0291] F. Termination Conditions

[0292] Fitness threshold: The algorithm stops when the fitness value of the best individual in the population is less than a certain preset threshold.

[0293] G. Output Results

[0294] After the algorithm finishes, it outputs the individual with the smallest fitness value, which is the optimal combination of wastewater production rates for all processes.

[0295] In other words, the K-means algorithm also includes introducing a weighting coefficient 'a' for each process. i That is, the fitness function includes the weighting factor a for each process. i Specifically:

[0296]

[0297] Among them, w i Let P be the wastewater yield of the i-th process, P be the copper production, T be the target total wastewater volume, and a be the total wastewater yield. i Let be the weight factor for the i-th process, and ∑a i =1, a i >0; the weighting factor a i The determination is based on the importance of the process, the reliability of the data, the degree of environmental impact, and the difficulty of wastewater treatment.

[0298] Specifically, this example innovatively introduces a genetic algorithm into the water balance verification method to solve the problem of optimizing wastewater yield calculation. This method not only provides a novel approach to data supplementation and verification but also significantly improves the accuracy and reliability of the calculation results.

[0299] In actual production processes, the wastewater yield of different processes often fluctuates within a certain range, posing a challenge to water balance verification. To address this issue, this paper proposes an optimization method based on a genetic algorithm. This method first defines the wastewater yield of each process as a gene, and these genes collectively constitute a chromosome, representing a possible solution. Considering the actual physical meaning of the wastewater yield, the wastewater yield *wi* of each process is constrained within a reasonable range [ai, bi]. This encoding method ensures both the practicality of the calculation results and fully considers the actual constraints of the production process.

[0300] In the algorithm implementation, this example designs a sophisticated fitness function. This function calculates the squared difference between the sum of the products of wastewater yield and copper production across all processes and the target total wastewater volume, used to evaluate the merits of each candidate solution. This evaluation method not only considers the requirements of water balance but also takes into account the actual situation of the production process, making the optimization results more practically meaningful. To simplify the calculation process and improve algorithm efficiency, this example chooses to use the squared difference as the evaluation index, which ensures computational accuracy while also improving the algorithm's convergence speed.

[0301] In the population evolution process, this example employs a roulette wheel selection strategy, giving individuals with higher fitness values ​​(i.e., wastewater yield combinations closer to the target value) a greater chance of being retained in the next generation. During the crossover operation, new offspring individuals are generated by randomly selecting and recombining parts of the genes of parent individuals, thus producing new wastewater yield combinations. This approach effectively explores the solution space and increases the probability of finding the optimal solution.

[0302] To further enhance the algorithm's exploration capabilities, this example introduces an innovative approach to the mutation operation. The wastewater yield for each process is adjusted slightly and randomly, with the adjustment magnitude controlled by the variable asynchronous length Δ. Simultaneously, interval constraints ensure that the mutated value remains within a reasonable range. Specifically, the mutated wastewater yield wi' is calculated using the formula wi'=wi+Δ(bi-ai)rand(), where rand() is a random number between [-1,1]. This design ensures population diversity while avoiding the generation of unreasonable solutions.

[0303] The algorithm's termination condition also reflects the innovative thinking in this example. The algorithm stops iterating when the fitness value of the best individual in the population is less than a preset threshold. This termination condition ensures both the accuracy of the calculation results and avoids unnecessary waste of computational resources. Finally, the individual with the smallest fitness value is output, representing the optimal combination of wastewater yields for all processes.

[0304] This water balance verification method based on genetic algorithms has significant advantages. First, it can automatically search for the optimal solution, obtaining wastewater yield combinations that meet the constraints without manual intervention. Second, through a population evolution mechanism, it can effectively avoid getting trapped in local optima. Third, each step of the algorithm fully considers practical engineering constraints, ensuring that the calculation results have practical application value. Finally, this method has good scalability, capable of handling water balance verification under existing operating conditions and easily adapting to new requirements brought about by process improvements.

[0305] 2.4 Impact Layer Verification

[0306] In the context of addressing climate change and assessing greenhouse gas emissions, carbon balance verification is of great significance. It is even more essential in building a carbon footprint database. Global warming potential (GWP) values ​​can serve as an indicator for carbon verification. GWP values ​​measure the contribution of different greenhouse gases to global warming; optionally, carbon dioxide is used as the baseline (CO2 has a GWP value of 1).

[0307] 2.4.1 GWP Value Verification

[0308] (1) Data preprocessing and cluster analysis

[0309] Data cleaning: First, the raw material and energy consumption data in the database are cleaned to remove duplicates, handle missing values, and ensure the consistency of data format.

[0310] Feature extraction: Based on the analysis requirements, key features are extracted from the data, such as material type, energy consumption type, and emissions. These features will be used for subsequent cluster analysis.

[0311] Clustering Algorithm Application: A suitable clustering algorithm is employed to perform cluster analysis on the data in the database based on extracted features, in order to identify data groups with similar GWP (Global Placement) characteristics. The clustering results should clearly reflect the similarities and differences between different data items.

[0312] (2) GWP anomaly detection based on IQR

[0313] Calculate the IQR (Interquartile Range): For each clustered data set, calculate the IQR of its Global Power of Stratification (GWP) value, which is the difference between the third quartile (Q3) and the first quartile (Q1). The IQR is a robust indicator of data dispersion and is suitable for identifying outliers.

[0314] Set an outlier threshold: Based on the IQR value, set a reasonable outlier threshold (optional, a multiple of the IQR, such as 1.5 times or 3 times the IQR). This threshold will be used to determine whether the GWP value falls within the outlier range.

[0315] GWP Anomaly Detection: The GWP value of each data item is compared with a set anomaly threshold. If the GWP value of a data item exceeds the threshold range, it is marked as an anomaly and considered an item to be inspected.

[0316] 2.4.2 Fourth-level judgment: GWP value verification

[0317] In-depth analysis: For data items marked as outliers, a thorough review of the original data and recalculation were conducted. Special attention was paid to potential misclassifications during clustering and possible biases introduced during IQR calculation.

[0318] Expert verification: Experts in relevant fields are invited to participate in the analysis and verification of anomalies to ensure the accuracy and reliability of the anomalies.

[0319] Data feedback and correction: Based on the analysis results, the original data is corrected as necessary, and cluster analysis and IQR calculation are performed again to ensure the accuracy and reasonableness of the GWP value.

[0320] 2.5 Comprehensive Layer Verification

[0321] Building upon the first three levels of verification, the comprehensive layer verification combines all the data to perform global, consistency, and correlation checks.

[0322] 2.5.1 System-wide consistency check

[0323] Taking into account the balance of multiple factors such as matter, energy, water, and carbon, the system verifies the matching and consistency of all data streams. Cross-validation of different verification methods further improves data accuracy.

[0324] Verification passed: All data streams are consistent across the entire system, and the data enters the final update stage of the database.

[0325] Validation failed: Examine the validation results of the first two levels to identify cross-level errors or omissions. Use multi-level data association analysis to assess the relationships between data and identify the causes of inconsistencies.

[0326] Data correction method: Conduct a system-wide data review, re-verify data consistency, and adjust the data to conform to system logic. Perform consistency verification again after correction.

[0327] 2.5.2 Cross-time period and cross-process verification

[0328] Perform time series analysis on the data and verify the flow across different production stages to ensure the temporal continuity of the data and the logical consistency between stages. Especially for long-term or multi-stage production processes, cross-time period and cross-stage verification can identify potential data anomalies or errors.

[0329] Validation passed: The data is consistent across time series and different production stages, and is ready to be entered into the database.

[0330] Validation failed: Analyzing the temporal continuity of the data and the logic between processes may reveal issues such as delayed data recording or incorrect recording of batch changes. Use time series analysis or production process flow verification to identify the problem.

[0331] Data correction methods: Correct or supplement missing data in the time series or between stages to ensure logical consistency of the data. Perform cross-time period and cross-stage verification again.

[0332] 2.6 Data Scoring

[0333] In data validation, δ represents the error range, which is optional and can be within ±5%. When δ > 5%, data correction and supplementation are required; when δ < 5%, the error requirement is considered met, and the data list is complete and usable. Finally, the final output data can be scored based on the δ value output at each level, thus outputting the quality of the data list along with its output.

[0334] An example scoring table is as follows:

[0335] Error range Score 0%≤δ≤1% 5 1%<δ≤2% 4 2%<δ≤3% 3 3%<δ≤4% 2 4%<δ≤5% 1

[0336] The output data quality assessment is: {Level 1 validation score, Level 2 validation score, Level 3 validation score, Level 4 validation score} = Total score. By analyzing the scores for each data item, the quality of each data category can be clearly determined; higher scores indicate better data quality.

[0337] For example, if Company A's production data list, after verification, yields an output evaluation of {5, 4, 3, 4} = 16, it indicates that the input and output data for matter and energy in the data list are of high quality; while the water data is relatively good, it contains some errors; and the final calculated GWP value is very close to the expectation, indicating that the greenhouse gas emission estimates in the data list are relatively accurate and meet industry standards and expectations. A final score of 16 indicates that the data has high reliability in life cycle assessment and can reasonably reflect the product's carbon footprint.

[0338] In the above example, by introducing a multi-level data verification model, high-precision data verification and dynamic correction were achieved, significantly improving the data integrity and accuracy of the carbon footprint database. Its technical effect lies in its ability to comprehensively identify and correct data anomalies and omissions in the production process, ensuring more reliable lifecycle analysis results, supporting enterprises in accurately assessing and optimizing carbon emissions, and achieving effective environmental management and sustainable development goals.

[0339] All documents mentioned in this application are considered to be incorporated in their entirety into the disclosure of this application so that they can serve as a basis for modifications if necessary. Furthermore, it should be understood that after reading the foregoing disclosure of this application, those skilled in the art can make various alterations or modifications to this application, and these equivalent forms also fall within the scope of protection claimed in this application.

Claims

1. A method for constructing a carbon footprint database of copper mining and smelting products, characterized in that, Includes the following steps: Data acquisition and preprocessing: Collect raw data from the copper mining and smelting process and preprocess the raw data to obtain preprocessed data; Multi-level data verification and correction: A multi-level verification model is introduced into the preprocessed data in the data acquisition and preprocessing steps, including: At the basic level, material balance verification, elemental balance verification, and energy balance verification are carried out sequentially, and the data is supplemented and corrected according to the verification results to obtain material, elemental, and energy balance verification data; At the process level, water balance verification is carried out, and the material, elemental, and energy balance verification data obtained at the basic level are supplemented and corrected according to the verification results to obtain water resource balance verification data; At the impact level, the global warming potential (GWP) is calculated and carbon balance verification is carried out. The carbon emission data in the water resource balance verification data obtained at the process level is corrected by combining data clustering analysis and interquartile range (IQR) outlier detection methods to obtain carbon emission verification data; At the comprehensive level, global consistency checks and cross-time period and cross-stage correlation analyses are carried out to verify the verification data at each level to obtain multi-level verification and correction data; Carbon footprint database construction: Based on the multi-level verification and correction data obtained in the multi-level data verification and correction steps, a five-level scoring method based on error range is used to score data quality. Combined with the data quality scoring results, a carbon footprint database for each stage of the copper mining and smelting product life cycle is constructed to achieve accurate quantification and evaluation of carbon emissions throughout the entire life cycle of copper mining and smelting products.

2. The method as described in claim 1, characterized in that, Data acquisition and preprocessing further include the following steps: The research objectives and scope of the carbon footprint database are defined, and datasets of material inputs, product outputs and pollutant emissions in the copper mining and smelting process are identified. The collected raw data is preprocessed by classifying, cleaning, identifying missing values ​​and detecting error values ​​to obtain preprocessed data.

3. The method as described in claim 1, characterized in that, At the basic level, material balance verification, elemental balance verification, and energy balance verification are carried out sequentially. Based on the verification results, the data are supplemented and corrected to obtain the material, elemental, and energy balance verification data, which further includes the following steps: Material balance verification: Calculate the total amount of matter input and output of the system. If the difference between the two exceeds the preset error range, the corresponding data will be marked as material balance data to be corrected. Element balance check: Calculate the input and output of key elements (such as copper). If the difference between the two exceeds the preset error range, the corresponding data is marked as element balance data to be corrected. Energy balance check: Calculate the total energy input and total energy output of the system. If the difference between the two exceeds the preset error range, the corresponding data is marked as energy balance data to be corrected. Data supplementation and correction: For the material balance data, element balance data, and energy balance data marked in the material balance verification step to the energy balance verification step, the data is corrected by using a supplementation method based on balance relationship and a multi-dimensional data supplementation strategy to obtain the corrected data. Verification of verification results: Perform material balance verification, elemental balance verification, and energy balance verification on the corrected data in the data supplementation and correction steps. If all verification results are within the preset error range, the corrected data will be used as the material, elemental, and energy balance verification data; otherwise, return to the data supplementation and correction steps to continue data supplementation and correction.

4. The method as described in claim 1, characterized in that, At the process level, water balance verification is carried out, and the material, element, and energy balance verification data obtained at the basic level are supplemented and corrected based on the verification results to obtain water resource balance verification data. This further includes the following steps: Water input calculation: Calculates the total amount of water input into the system, including the moisture content of raw materials, the amount of fresh water input, and the amount of circulating water; Water output calculation: Calculate the total water output of the system, including the moisture in the product, wastewater discharge, evaporation loss and other losses; Water balance check: Compare the total water input calculated in the water input calculation step with the total water output calculated in the water output calculation step. If the difference between the two exceeds the preset error range, the relevant data will be marked as water balance data to be corrected. Data Supplementation and Correction: For the water balance data marked as needing correction in the water balance verification step, combined with the material, element, and energy balance verification data obtained in the basic layer, a supplementation method based on water balance relationships and a multi-dimensional data supplementation strategy are used to correct the data, resulting in corrected water balance data. The supplementation method based on balance relationships includes the following water balance equations: For the water balance of the overall production process: ∑Mfresh water = Mcompliant wastewater + Mloss; For the water balance of the wastewater treatment process: Mwastewater input from wastewater treatment = ∑Moutput wastewater from each stage = Mcompliant wastewater + Mreuse The water balance for the i-th production unit is calculated as follows: Mfresh wateri + Mreclaimed wateri = Mwastewateri output to the wastewater treatment stage + Mon-site reusei + Mlossi. The water balance equations are cross-validated; any anomaly in water volume data at any stage can be reverse-checked using the water balance equations of other stages. The water balance equation set serves as both a data supplement and a data verification tool. Furthermore, the multi-dimensional data supplementation strategy includes: using existing industry databases for comparison, identifying abnormal data points, and comparing the data with benchmarks from similar enterprises or authoritative databases. The data is compared with the standard data, and the data deviation is adjusted according to the comparison results. When it is found that the water output of a certain process does not correspond to the water input of the wastewater treatment stage, the water output of the process needs to be corrected. A genetic algorithm is used to optimize the wastewater yield wi of each process, which includes: initialization: the wastewater yield of each process is represented as a gene, encoded with real numbers, and an initial population is constructed; fitness function: the weight coefficient αi of each process is introduced, and the fitness function f(w)=(∑αiwi·PT)2 is defined, where wi is the wastewater yield of the i-th process, P is the copper production, and T is the target total water. αi is the weight factor of the i-th process, and ∑αi=1, αi>0. The weight factor αi is determined based on the importance of the process, data reliability, degree of environmental impact, and difficulty of wastewater treatment. Selection: roulette wheel selection method is used. Crossover: single-point crossover or multi-point crossover is used. Mutation: each gene of the offspring individuals is slightly modified, and the value after mutation must still remain within the original range. Termination condition: the algorithm stops when the fitness value of the best individual in the population is less than a certain preset threshold. Output result: the individual with the smallest fitness value is output as the optimal combination of wastewater yield for each process. Verification of verification results: The corrected water balance data from the data supplementation and correction steps are re-verified. If the verification result is within the preset error range, the corrected water balance data is used as the water resource balance verification data; otherwise, return to the data supplementation and correction steps to continue data supplementation and correction. Consistency check: The water resource balance verification data obtained from the verification result step is cross-validated with the material, element and energy balance verification data obtained from the basic layer to ensure consistency among various types of data and obtain the final water resource balance verification data.

5. The method as described in claim 1, characterized in that, At the impact layer, the Global Warming Potential (GWP) is calculated and a carbon balance verification is performed. The carbon emission data in the water resource balance verification data obtained at the impact layer is corrected using data clustering analysis and the interquartile range (IQR) outlier detection method. The process of obtaining the carbon emission verification data further includes the following steps: GWP Calculation: Based on the water resource balance verification data obtained at the process level, the global warming potential (GWP) of various greenhouse gas emissions during copper mining and smelting is calculated to obtain GWP data. Carbon balance verification: Calculate the total input and output of carbon elements in the system. If the difference between the two exceeds the preset error range, the relevant data will be marked as carbon balance data to be corrected. Data clustering analysis: Cluster analysis is performed on the GWP data obtained from the GWP calculation steps to divide GWP data with similar characteristics into several cluster groups; IQR outlier detection: For each cluster group obtained in the data clustering analysis step, outliers are detected using the interquartile range (IQR) method, and the detected outliers are marked as GWP data to be corrected; Data Supplementation and Correction: For the carbon balance data to be corrected marked in the carbon balance verification step and the GWP data to be corrected marked in the IQR outlier detection step, combined with the material, element and energy balance verification data obtained in the basic layer and the water resource balance verification data obtained in the process layer, a supplementation method based on carbon balance relationship and a multi-dimensional data supplementation strategy are adopted to correct the data and obtain the corrected carbon emission data. Verification of results: The carbon emission data obtained from the data supplementation and correction steps are subjected to carbon balance verification and GWP outlier detection again. If the carbon balance verification result is within the preset error range and the GWP data has no outliers, the corrected carbon emission data is used as the carbon emission verification data; otherwise, return to the data supplementation and correction steps to continue data supplementation and correction. Consistency check: The carbon emission verification data obtained from the verification results step is cross-validated with the material, element and energy balance verification data obtained at the basic layer and the water resource balance verification data obtained at the process layer to ensure consistency among various types of data and obtain the final carbon emission verification data.

6. The method as described in claim 1, characterized in that, At the comprehensive level, the steps of conducting global consistency checks and cross-time period and cross-process correlation analysis to verify the verification data at each level and obtain multi-level verification and correction data further include the following steps: Data integration: Global consistency check of the material, element and energy balance verification data obtained from the basic layer and the water resource balance verification data obtained from the process layer: A global consistency check is performed on the dataset to be verified integrated in the data integration step to ensure that the data of different levels and types are consistent in value. If inconsistencies are found, the relevant data are marked as global data to be corrected. Cross-time correlation analysis: Perform cross-time correlation analysis on the dataset to be validated integrated in the data integration step, compare the data change trends in different time periods, and if abnormal changes are found, mark the relevant data as time series data to be corrected; Cross-process correlation analysis: Perform cross-process correlation analysis on the dataset to be verified by the data integration step, check the logical relationship between data in different production stages, and mark the relevant data as data of the stage to be corrected if logical contradictions are found. Data Supplementation and Correction: For the global data, time-series data, and process data marked in the steps from global consistency check to cross-process correlation analysis that need to be corrected, the data supplementation and correction are carried out by multi-source data cross-validation and expert knowledge base assistance, so as to obtain the corrected comprehensive verification data. Verification of comprehensive verification results: The corrected comprehensive verification data obtained from the data supplementation and correction steps are subjected to a global consistency check, cross-time period correlation analysis, and cross-stage correlation analysis again. If the global consistency check results meet the preset consistency conditions, the cross-time period correlation analysis results meet the preset temporal rationality conditions, and the cross-stage correlation analysis results meet the preset logical self-consistency conditions, then the corrected comprehensive verification data is used as the multi-level verification correction data; otherwise, return to the data supplementation and correction steps to continue data supplementation and correction. Final dataset generation: The multi-level verification and correction data that have passed the verification step of the comprehensive verification results are integrated into the final dataset, which serves as the basis for the subsequent construction of the carbon footprint database.

7. The method as described in claim 1, characterized in that, The carbon footprint database construction process further includes the following steps: Data quality scoring: A five-level scoring method based on error range is used to score the quality of the multi-level verification and correction data obtained from the multi-level data verification and correction steps. Specifically, this includes: determining scoring indicators: setting corresponding scoring indicators based on the verification results of material balance, elemental balance, energy balance, water balance, and carbon balance; setting error ranges: setting five error range levels for each scoring indicator, corresponding to scores of 1-5; calculating scores for each indicator: determining the score for each indicator based on the error values ​​of each indicator in the multi-level verification and correction data, and comparing them with the error range levels; and comprehensive scoring: calculating a weighted average of the scores for each indicator to obtain a comprehensive data quality score. Data classification and organization: Based on the various stages of the copper mining and smelting product life cycle, the multi-level verification and correction data obtained from the multi-level data verification and correction steps are classified and organized to form a structured carbon footprint dataset. Carbon footprint calculation model construction: Based on the structured carbon footprint dataset formed by data classification and organization steps, a carbon footprint calculation model for each stage of the copper mining and smelting product life cycle is constructed, including: determining functional units: selecting the corresponding functional units; establishing an emission factor library: collecting and organizing carbon emission factor data of various raw materials, energy and emissions; designing calculation formulas: designing corresponding carbon footprint calculation formulas for the carbon footprint accounting needs of each stage of the life cycle. Database structure design: Design the logical and physical structure of the carbon footprint database, including data tables, fields, relationships, and indexes, to ensure the integrity, consistency, query efficiency, and scalability of the database; Data import: The structured carbon footprint dataset formed by the data classification and organization steps, the data quality score results obtained by the data quality scoring steps, and the carbon footprint calculation model constructed by the carbon footprint calculation model construction steps are imported into the database according to the database structure design steps to form a carbon footprint database of copper mining and smelting products. Database functionality implementation: Develop application functions for the carbon footprint database, including data query, statistical analysis, and report generation, to achieve accurate quantification, analysis, evaluation, and visualization of carbon emissions throughout the entire life cycle of copper mining and smelting products; Database validation and optimization: The database is tested using sample data to verify its functionality, performance, and the accuracy of the calculation results. Based on the validation results, the database is optimized and improved to ensure its reliability, efficiency, and usability.

8. The method as described in claim 1, characterized in that, The preprocessing in the data acquisition and preprocessing also includes: data standardization: the collected raw data is standardized in terms of units, format, and numerical values ​​to ensure data consistency and comparability.

9. The method as described in claim 3, characterized in that, The supplementary method based on the balance relationship includes: using the input and output data of adjacent processes, and according to the law of conservation of mass or the law of conservation of energy, calculating the range of values ​​for the missing data.

10. The method as described in claim 5, characterized in that, The data clustering analysis uses the K-means algorithm, and the specific steps include: initializing K cluster centers; calculating the distance from each data point to each cluster center; assigning the data point to the nearest cluster center; recalculating the cluster centers; repeating the above steps until the cluster centers no longer change or the maximum number of iterations is reached.

11. The method as described in claim 6, characterized in that, The multi-source data cross-validation method includes: using multiple data sources such as industry standard data, historical data, and data from similar enterprises to perform cross-validation on the data to be corrected. The validation content and indicators include: Data consistency verification: Compare whether data from different data sources are consistent, calculate indicators such as data deviation or correlation coefficient, and assess the level of data consistency; Data integrity verification: Check whether there are missing or outliers in the data to be corrected, calculate the missing or outlier rate, and assess the level of data integrity. Data accuracy verification: Compare the data to be corrected with the standard or theoretical value, calculate the data error or confidence interval, and evaluate the accuracy level of the data; Data timeliness verification: Analyze the timestamps or update frequency of the data to be corrected, calculate the indicators of data lag or expiration rate, and evaluate the timeliness level of the data; Data correlation verification: Explore the correlation between the data to be corrected and other key parameters, calculate the correlation coefficient or regression coefficient, and assess the level of data correlation; Based on the above cross-validation results, the credibility and usability of the data to be corrected are comprehensively judged, providing an objective basis for data supplementation and correction.

12. The method as described in claim 7, characterized in that, The database structure design also includes: designing a data partitioning strategy, partitioning and storing data according to time dimension or production process, and improving the query efficiency of large-scale data.

13. The method as described in claim 7, characterized in that, The database functionality also includes: developing a data update and maintenance module to support the dynamic import of new data, the periodic archiving of historical data, and the management and tracing of data versions.

14. The method as described in claim 1, characterized in that, It also includes the following steps: Dynamic monitoring and early warning of carbon footprint: Based on the constructed carbon footprint database, a carbon emission trend analysis model and threshold early warning mechanism are designed to realize real-time monitoring and early warning of anomalies in the carbon footprint of copper mining and smelting products.

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