A product carbon footprint accounting data quality dynamic evaluation and correction method and system

Abstract

The present application relates to the technical field of carbon footprint data quality assessment, and particularly relates to a product carbon footprint accounting data quality dynamic assessment and correction method and system. The method first constructs a multi-dimensional quality scoring model and obtains a comprehensive quality score; based on the sensitivity contribution rate of each activity data to the total carbon footprint, a dynamic penalty coefficient is generated, and the data corresponding to the dynamic penalty coefficient exceeding the preset threshold is marked as key inferior data; according to the comprehensive quality score and the sensitivity level, a hierarchical correction strategy is performed on the key inferior data; after data correction, the accounting model is re-run, the comprehensive quality score and the total carbon footprint are updated, and iteration is repeated until the dynamic penalty coefficient of all key inferior data is lower than the threshold or the uncertainty of the overall accounting result is reduced to the target range. Through dynamic assessment and hierarchical correction, the present application realizes closed-loop management of carbon footprint data quality, effectively supporting carbon label certification and green supply chain decision-making.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint data quality assessment technology, and in particular to a method and system for dynamic assessment and correction of product carbon footprint accounting data quality. Background Technology

[0002] The scientific validity and reliability of Product Carbon Footprint (PCF) calculation results fundamentally depend on the quality of the input data. However, in current enterprise accounting practices, data quality management faces significant challenges:

[0003] First, the data sources are mixed and of varying quality. The accounting data typically combines actual enterprise data (primary data), estimated data from suppliers (secondary data), and average data from general databases (tertiary data). Existing methods often treat all data equally, lacking a refined quality scoring mechanism, resulting in low-quality data lowering the overall credibility of the results.

[0004] Second, there is a lack of real-time early warning. Traditional accounting typically performs uncertainty analysis only after the calculations are completed. If key data quality is found to be too low at this point, it needs to be re-collected, resulting in rework. There is a lack of a dynamic closed-loop system that can assess data quality in real time during the accounting process and automatically trigger correction mechanisms.

[0005] Third, the weighting is unreasonable and ignores differences in sensitivity. Existing quality assessment methods typically use a uniform scoring standard for all materials. In reality, the data quality of certain high-carbon emission links (such as electrolytic aluminum and cement clinker) has a significant impact on the final result, while the impact of certain auxiliary materials (such as packaging nails) is negligible. Traditional "egalitarian" quality assessment fails to guide resources to focus on key emission reduction links.

[0006] Fourth, there is a lack of corrective measures. Even when low-quality data is identified, existing systems lack automated corrective strategies (such as automatically replacing it with high-precision database factors or triggering supplier data collection tasks), resulting in problematic data remaining unresolved for extended periods.

[0007] Therefore, there is an urgent need in this field for a carbon footprint data quality management technology that can quantify data quality in multiple dimensions, dynamically weight based on sensitivity, and has the ability to automatically correct closed loops. Summary of the Invention

[0008] The present invention aims to at least partially solve one of the technical problems in the related art.

[0009] Therefore, the first objective of this invention is to propose a method for dynamic evaluation and correction of product carbon footprint accounting data quality, comprising the following steps: S1. Construct a multi-dimensional quality scoring model to quantify and score each activity's data from six dimensions: geographical representativeness, temporal representativeness, technical representativeness, completeness, traceability, and consistency, and obtain a comprehensive quality score. S2, based on the sensitivity contribution rate of each activity data to the total carbon footprint, dynamically weight the comprehensive quality score to generate a dynamic penalty coefficient, and mark the data corresponding to the dynamic penalty coefficient that exceeds the preset threshold as key poor data; S3, based on the comprehensive quality score and sensitivity level, implement a graded correction strategy for the key poor data, including automatically replacing it with matching factors in an authoritative database, triggering a supplier data collection task, or directly accepting it; S4, after data correction, rerun the accounting model, update the overall quality score and total carbon footprint, repeat the iteration until the dynamic penalty coefficient of all key poor data is lower than the threshold or the uncertainty of the overall accounting result is reduced to the target range, and output a quality report containing the quality score of each data point and correction records.

[0010] In one embodiment of the present invention, S1 includes: S11. Calculate the geographical representativeness score based on the matching degree between the data origin and the accounting object origin; calculate the temporal representativeness score based on the proximity between the data collection time and the accounting period; calculate the technical representativeness score based on the matching degree between the production process and the technical level; evaluate the completeness of the data coverage to obtain the integrity score; and calculate the traceability score based on the strength of the supporting documents for the data source. S12, evaluate the longitudinal comparability of data in time series and the consistency of statistical standards across time series to obtain a consistency score; S13. The scores of the above six dimensions are weighted and summed based on preset weights, and the overall quality score is calculated using the weighted method.

[0011] In one embodiment of the present invention, S2 includes: S21, calculate the contribution percentage of each activity data point to the total carbon footprint using the following formula: , in This is the d-th activity data. That is the corresponding emission factor. It is the total carbon footprint; S22, calculate the dynamic penalty coefficient, the calculation formula is: , in This is a risk amplification factor. For the overall quality score; S23. Compare the dynamic penalty coefficient with the preset threshold. If the threshold is exceeded, mark the data as critical poor data.

[0012] In one embodiment of the present invention, step S3, which involves implementing a graded correction strategy for the critical poor-quality data based on the overall quality score and sensitivity level, includes: S31, When the overall quality score is lower than the first threshold, automatically retrieve the default factor that best matches the region and technology from the authoritative database to replace the original data and mark it as the estimated value; S32, when the overall quality score is between the first threshold and the second threshold, a data collection task order is automatically generated and pushed to the corresponding supplier, requiring them to supplement the measured data or supporting documents within a time limit, and the calculation result is marked as provisional. S33. When the overall quality score is higher than the second threshold, the data is directly accepted for calculation, and the confidence interval is recorded for uncertainty analysis.

[0013] In one embodiment of the present invention, S4 includes: S41, automatically rerun the accounting model after data correction, and update the overall quality score and total carbon footprint for each activity data; S42, determine whether the dynamic penalty coefficients of all key poor-quality data are lower than the preset threshold, or whether the uncertainty of the overall accounting result has been reduced to the target range; S43. If the condition is not met, return to step S2 to continue iterating. If the condition is met, output a data quality report containing the quality score of each data point, correction records, and the final uncertainty interval.

[0014] In one embodiment of the present invention, the risk amplification factor The system's default constant parameter is set to 2.0.

[0015] In one embodiment of the present invention, the formula for calculating the overall quality score using the weighted method in step S13 is as follows:

[0016] in This represents the overall quality score of the d-th activity data point. This represents the geographical representativeness score, which is assigned based on the degree of matching between the data origin and the origin of the accounting object. This represents the time representativeness score, which is assigned a value based on the proximity of the data collection time to the accounting period. Indicates the score for technical representativeness. Indicates the completeness score. This represents the traceability score, which is assigned a value based on the strength of the supporting documentation for the data source. Indicates the consistency score. to These are the weight coefficients for the corresponding dimensions.

[0017] To achieve the above objectives, a second aspect of the present invention provides a dynamic evaluation and correction system for the quality of product carbon footprint accounting data, comprising: The six-dimensional benchmark scoring module is used to build a multi-dimensional quality scoring model, which quantifies and scores each activity's data from six dimensions: geographical representativeness, temporal representativeness, technical representativeness, completeness, traceability, and consistency, to obtain a comprehensive quality score. The sensitive weighted inferior value identification module is used to dynamically weight the comprehensive quality score based on the sensitivity contribution rate of each activity data to the total carbon footprint, generate a dynamic penalty coefficient, and mark the data corresponding to the dynamic penalty coefficient that exceeds the preset threshold as key inferior data. The graded differentiation correction module is used to implement graded correction strategies for the key poor data based on the comprehensive quality score and sensitivity level, including automatically replacing it with matching factors in an authoritative database, triggering supplier data collection tasks, or directly accepting it. The iterative accounting report module is used to rerun the accounting model after data correction, update the comprehensive quality score and total carbon footprint, repeat the iteration until the dynamic penalty coefficient of all key poor data is lower than the threshold or the uncertainty of the overall accounting result is reduced to the target range, and output a quality report containing the quality score of each data point and correction records.

[0018] In one embodiment of the present invention, the sensitive weighted inferiority screening module is used for: The contribution of each activity's data to the total carbon footprint is calculated using the following formula: , in This is the d-th activity data. That is the corresponding emission factor. It is the total carbon footprint; The dynamic penalty coefficient is calculated using the following formula: , in This is a risk amplification factor. For the overall quality score; The dynamic penalty coefficient is compared with a preset threshold. If the threshold is exceeded, the data is marked as critical poor data.

[0019] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0020] The method, system, and storage medium of this invention, through dynamic evaluation and graded correction, achieve closed-loop management of carbon footprint data quality, ensuring the scientificity and credibility of the accounting results, and effectively supporting carbon label certification and green supply chain decision-making.

[0021] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for dynamic evaluation and correction of product carbon footprint accounting data quality according to an embodiment of the present invention; Figure 2 This is a structural diagram of a product carbon footprint accounting data quality dynamic assessment and correction system according to an embodiment of the present invention. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] The following describes, with reference to the accompanying drawings, a method and system for dynamic evaluation and correction of product carbon footprint accounting data quality according to an embodiment of the present invention.

[0026] Example 1 like Figure 1 As shown, this invention proposes a method for dynamic evaluation and correction of product carbon footprint accounting data quality.

[0027] The method includes four core steps: construction of a multidimensional quality scoring model, dynamic weighting of sensitivity, execution of graded correction strategies, and iterative optimization closed loop.

[0028] Step 1: Construction of a multidimensional quality scoring model Define a comprehensive quality score Qd (0-100) for a single data point d, which is quantified based on five core dimensions: Representativeness (R) Geographic representativeness (Rgeo): The degree of matching between the data origin and the accounting object origin (same factory area = 100, same province = 80, same country = 60, cross-border = 40).

[0029] Time representativeness (Rtime): The proximity of the data collection time to the accounting period (current year = 100, within 1 year = 80, within 3 years = 50, greater than 3 years = 20).

[0030] Rtech (Technological Representativeness): The degree of matching between production process and technological level (same process = 100, similar process = 70, industry average = 50).

[0031] Completeness (I): Whether the data coverage is complete (e.g., whether it includes all losses, whether it covers the entire lifecycle stage).

[0032] Traceability (T): Whether the data source is supported by vouchers (actual invoices / documents = 100, supplier stamp confirmation = 80, verbal inquiry = 40, database default = 20).

[0033] Consistency (C): Whether the data logic is self-consistent (e.g., whether the energy consumption to output ratio is within a reasonable range).

[0034] The weighted calculation formula is:

[0035] in: This represents the overall quality score of the d-th activity data point, ranging from 0 to 100, and is used to quantify the credibility level of the data in carbon footprint accounting. The score represents the geographical representativeness score, which is assigned based on the spatial matching degree between the data collection location and the actual production location of the product. The score is 100 for the same factory area, 80 for the same province, 60 for the same country, and 40 for the same country. The time representativeness score is assigned based on the time interval between the data collection time and the current accounting cycle: 100 for data collected in the current year, 80 for data collected within the past year, 50 for data collected within the past three years, and 20 for data collected more than three years ago. The score represents the technical representativeness score, which is assigned based on the consistency between the production process and technical level reflected in the data and the accounting object. The same process is 100, similar process is 70, and the industry average is 50. The integrity score is used to assess the completeness of data coverage, including indicators such as whether production losses are included and whether specific stages of the entire life cycle are covered. The traceability score is assigned based on the strength of the supporting documentation for the data source. 100 is awarded for having a verified invoice or original document, 80 for supplier stamp confirmation, 40 for verbal inquiry records, and 20 for direct database references (default value). The data consistency score is used to assess the longitudinal comparability of activity data over time (i.e., whether the statistical methods and boundary ranges are consistent between the current period and historical periods) and the horizontal consistency of statistical standards between different product lines. If the statistical standards are completely consistent and there are no changes in methodology, the score is 100; if there are changes but they have been retrospectively adjusted according to the standards, the score is 90; if there are minor differences in statistical standards but they do not affect the overall trend, the score is 70; if there are significant changes in statistical methods and they are not adjusted, or if there are conflicts in the same data collection rules between different production lines, the score is 40 or below.

[0036] Step 2: Sensitivity dynamic weighting mechanism.

[0037] Introducing a sensitivity-weighted correction factor Linking data quality to its contribution to the total carbon footprint: Sensitivity analysis: Calculate data for each activity. Total carbon footprint Contribution percentage ,in, This corresponds to the d-th activity data. The emission factors.

[0038] Dynamic penalty coefficient: Apply a larger penalty weight to data that is highly sensitive (high emissions) and of low quality.

[0039] The calculation formula is: .

[0040] in: This represents the dynamic penalty coefficient for the d-th data point, used to amplify the risk signal of highly sensitive and low-quality data. When this coefficient exceeds a preset threshold, the system will forcibly trigger the data correction process. This represents the risk amplification factor, a system-preset constant parameter (usually set to 2.0), used to adjust the intensity of the influence of sensitivity on the final penalty coefficient; This represents the sensitivity contribution rate of the d-th activity data point to the total carbon footprint; This represents the overall quality score of the d-th data point (values ​​range from 0 to 100). This item represents the quality defect rate of the data. The lower the quality, the larger the value of this item, which in turn leads to a penalty coefficient. Significantly increased.

[0041] Key Defective Data Judgment: If If the data exceeds a set threshold θ (e.g., 1.5), it will be marked as "critical poor quality data" and forced into the correction process.

[0042] Step 3: Execute the graded correction strategy.

[0043] Based on quality score Based on the sensitivity level, the system automatically executes a three-level correction strategy: Level L1 (Low score / critical data, Q<60): Automatic replacement. The system automatically retrieves the best-matching default factor in terms of region and technology from authoritative databases (such as CLCD, Ecoinvent) to replace the original data and marks it as an "estimated value," while highlighting the uncertainty in the report.

[0044] Level L2 (Medium / Important Data, 60 ≤ Q < 80): Task Triggered. The system automatically generates a "Data Acquisition Task Order" and pushes it to the corresponding supplier, requiring them to supplement the measured data or supporting documents within a specified period. Before the data is completed, the calculation result is marked as "Tentative".

[0045] Level L3 (High-scoring / General data, Q ≥ 80): Directly accepted. The data is directly used in the calculation, but the system records its confidence interval for the final uncertainty analysis.

[0046] Step 4: Iteratively optimize the closed loop. Recalculation: After data correction (replacement or supplementation), the system automatically reruns the accounting model and updates it. and Convergence check: Repeat the above process until all key data are collected. If the value is below the threshold, or if the uncertainty of the overall accounting result is reduced to the target range (e.g., within ±10%).

[0047] Quality report generation: Outputs a detailed data quality report containing quality scores for each data point, correction records, and the final uncertainty interval, for review by third-party verification agencies.

[0048] Example 2 To provide a detailed explanation of the methods proposed in this invention, specific embodiments are used to further illustrate the methods below.

[0049] In the accounting for a certain electronic product, the electricity consumption of chip manufacturing (accounting for 40% of total carbon emissions) uses the industry average from 3 years ago. =45), while the plastic casing (accounting for 2% of total carbon emissions) uses the measured value from last month ( =90).

[0050] Assessment: Sensitivity of chip data =0.4, low quality, calculated as follows (Approaching the threshold); if γ is increased or the quality is lower, an alarm will be triggered.

[0051] Correction: The system determines that the chip data is "critical poor quality data" and automatically selects the latest power combination factor for the region from the database to replace it, or sends a data request to the chip supplier.

[0052] Result: After correction, the chip data quality score improved to 85, the uncertainty of the overall accounting results decreased from ±25% to ±8%, and the credibility of the report was greatly improved.

[0053] As a specific implementation method, the system for implementing the above method should include: Data quality scoring engine: Built-in multi-dimensional scoring algorithm and weight configuration interface.

[0054] Sensitivity analysis module: Calculates the contribution of each data stream to the overall result in real time.

[0055] Intelligent Correction Controller: Performs automatic replacement, task distribution, and status tracking.

[0056] Authoritative database interface: Connects to mainstream LCA databases and provides alternative factor search services.

[0057] Example 3 like Figure 2 As shown, this invention proposes a dynamic evaluation and correction system 10 for product carbon footprint accounting data quality, comprising: The six-dimensional benchmark scoring module 100 is used to construct a multi-dimensional quality scoring model, which quantifies and scores each activity's data from six dimensions: geographical representativeness, temporal representativeness, technical representativeness, completeness, traceability, and consistency, to obtain a comprehensive quality score. The sensitive weighted inferior value identification module 200 is used to dynamically weight the comprehensive quality score based on the sensitivity contribution rate of each activity data to the total carbon footprint, generate a dynamic penalty coefficient, and mark the data corresponding to the dynamic penalty coefficient that exceeds a preset threshold as key inferior data. The graded differentiation correction module 300 is used to perform graded correction strategies on the key poor data based on the comprehensive quality score and sensitivity level, including automatically replacing it with matching factors in an authoritative database, triggering supplier data collection tasks, or directly accepting it. The iterative accounting report module 400 is used to rerun the accounting model after data correction, update the comprehensive quality score and total carbon footprint, repeat the iteration until the dynamic penalty coefficient of all key poor data is lower than the threshold or the uncertainty of the overall accounting result is reduced to the target range, and output a quality report containing the quality score of each data point and correction records.

[0058] Furthermore, the sensitive weighted inferiority screening module is used for: The contribution of each activity's data to the total carbon footprint is calculated using the following formula: , in This is the d-th activity data. That is the corresponding emission factor. It is the total carbon footprint; The dynamic penalty coefficient is calculated using the following formula: , in This is a risk amplification factor. For the overall quality score; The dynamic penalty coefficient is compared with a preset threshold. If the threshold is exceeded, the data is marked as critical poor data.

[0059] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for dynamic evaluation and correction of the quality of carbon footprint accounting data for products.

[0060] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0061] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for dynamic evaluation and correction of product carbon footprint accounting data quality, characterized in that, Includes the following steps: S1. Construct a multi-dimensional quality scoring model to quantify and score each activity's data from six dimensions: geographical representativeness, temporal representativeness, technical representativeness, completeness, traceability, and consistency, and obtain a comprehensive quality score. S2, based on the sensitivity contribution rate of each activity data to the total carbon footprint, dynamically weight the comprehensive quality score to generate a dynamic penalty coefficient, and mark the data corresponding to the dynamic penalty coefficient that exceeds the preset threshold as key poor data; S3, based on the comprehensive quality score and sensitivity level, implement a graded correction strategy for the key poor data, including automatically replacing it with matching factors in an authoritative database, triggering a supplier data collection task, or directly accepting it; S4, after data correction, rerun the accounting model, update the overall quality score and total carbon footprint, repeat the iteration until the dynamic penalty coefficient of all key poor data is lower than the threshold or the uncertainty of the overall accounting result is reduced to the target range, and output a quality report containing the quality score of each data point and correction records.

2. The method as described in claim 1, characterized in that, S1 includes: S11. Calculate the geographical representativeness score based on the matching degree between the data origin and the accounting object origin; calculate the temporal representativeness score based on the proximity between the data collection time and the accounting period; calculate the technical representativeness score based on the matching degree between the production process and the technical level; evaluate the completeness of the data coverage to obtain the integrity score; and calculate the traceability score based on the strength of the supporting documents for the data source. S12, evaluate the longitudinal comparability of data in time series and the consistency of statistical standards across time series to obtain a consistency score; S13. The scores of the above six dimensions are weighted and summed based on preset weights, and the overall quality score is calculated using the weighted method.

3. The method as described in claim 1, characterized in that, S2 includes: S21, calculate the contribution percentage of each activity data point to the total carbon footprint using the following formula: ， in This is the d-th activity data. That is the corresponding emission factor. It is the total carbon footprint; S22, calculate the dynamic penalty coefficient, the calculation formula is: ， in This is a risk amplification factor. For the overall quality score; S23. Compare the dynamic penalty coefficient with the preset threshold. If the threshold is exceeded, mark the data as critical poor data.

4. The method as described in claim 1, characterized in that, Step S3, which involves implementing a graded correction strategy for the key poor-quality data based on the overall quality score and sensitivity level, includes: S31, When the overall quality score is lower than the first threshold, automatically retrieve the default factor that best matches the region and technology from the authoritative database to replace the original data and mark it as the estimated value; S32, when the overall quality score is between the first threshold and the second threshold, a data collection task order is automatically generated and pushed to the corresponding supplier, requiring them to supplement the measured data or supporting documents within a time limit, and the calculation result is marked as provisional. S33. When the overall quality score is higher than the second threshold, the data is directly accepted for calculation, and the confidence interval is recorded for uncertainty analysis.

5. The method as described in claim 1, characterized in that, S4 includes: S41, automatically rerun the accounting model after data correction, and update the overall quality score and total carbon footprint for each activity data; S42, determine whether the dynamic penalty coefficients of all key poor-quality data are lower than the preset threshold, or whether the uncertainty of the overall accounting result has been reduced to the target range; S43. If the condition is not met, return to step S2 to continue iterating. If the condition is met, output a data quality report containing the quality score of each data point, correction records, and the final uncertainty interval.

6. The method as described in claim 3, characterized in that, The risk amplification factor The system's default constant parameter is set to 2.

0.

7. The method as described in claim 2, characterized in that, The formula for calculating the overall quality score using the weighted method in step S13 is as follows: in This represents the overall quality score of the d-th activity data point. This represents the geographical representativeness score, which is assigned based on the degree of matching between the data origin and the origin of the accounting object. This represents the time representativeness score, which is assigned a value based on the proximity of the data collection time to the accounting period. Indicates the score for technical representativeness. Indicates the completeness score. This represents the traceability score, which is assigned a value based on the strength of the supporting documentation for the data source. Indicates the consistency score. to These are the weight coefficients for the corresponding dimensions.

8. A dynamic evaluation and correction system for the quality of product carbon footprint accounting data, characterized in that, include: The six-dimensional benchmark scoring module is used to build a multi-dimensional quality scoring model, which quantifies and scores each activity's data from six dimensions: geographical representativeness, temporal representativeness, technical representativeness, completeness, traceability, and consistency, to obtain a comprehensive quality score. The sensitive weighted inferior value identification module is used to dynamically weight the comprehensive quality score based on the sensitivity contribution rate of each activity data to the total carbon footprint, generate a dynamic penalty coefficient, and mark the data corresponding to the dynamic penalty coefficient that exceeds the preset threshold as key inferior data. The graded differentiation correction module is used to implement graded correction strategies for the key poor data based on the comprehensive quality score and sensitivity level, including automatically replacing it with matching factors in an authoritative database, triggering supplier data collection tasks, or directly accepting it. The iterative accounting report module is used to rerun the accounting model after data correction, update the comprehensive quality score and total carbon footprint, repeat the iteration until the dynamic penalty coefficient of all key poor data is lower than the threshold or the uncertainty of the overall accounting result is reduced to the target range, and output a quality report containing the quality score of each data point and correction records.

9. The system as described in claim 8, characterized in that, The sensitivity-weighted inferiority screening module is used for: The contribution of each activity's data to the total carbon footprint is calculated using the following formula: ， in This is the d-th activity data. That is the corresponding emission factor. It is the total carbon footprint; The dynamic penalty coefficient is calculated using the following formula: ， in This is a risk amplification factor. For the overall quality score; The dynamic penalty coefficient is compared with a preset threshold. If the threshold is exceeded, the data is marked as critical poor data.

10. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as claimed in any one of claims 1-7.

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