Cloud platform-based product carbon labeling management system and method

Abstract

The application discloses a product carbon mark management system and method based on a cloud platform, and relates to the field of environmental information management and green supply chain.The method comprises the following steps: collecting carbon emission data of each stage from the whole life cycle of a product, generating standardized emission data through nonlinear normalization; generating stage carbon synergy factors based on node collaborative analysis and sensitivity evaluation, and constructing a cross-enterprise carbon synergy matrix; calculating stage carbon impact factors and damping coefficients to generate comprehensive stage emission factors; calculating cumulative sensitivity accordingly to form a whole life cycle carbon footprint; identifying abnormal stages and dynamically correcting in combination with historical data to generate carbon mark stability; and finally generating a carbon mark and storing it through a blockchain to realize a cloud platform management closed loop.Through efficient collection and standardization of whole life cycle carbon emission data, multi-layer collaborative analysis and dynamic correction, and credible storage and management closed loop of the carbon mark, the accuracy, reliability and traceability of the product carbon mark are realized.

Description

Technical Field

[0001] This invention relates to the field of environmental information management and green supply chain, specifically to a product carbon labeling management system and method based on a cloud platform. Background Technology

[0002] With increasing global environmental awareness and the gradual implementation of carbon reduction policies, enterprises have a growing need to manage carbon emissions throughout the entire product lifecycle. Product carbon labeling, as an important indicator for measuring a product's environmental impact, reflects the carbon emissions throughout its entire lifecycle, including production, transportation, storage, use, and recycling. It provides consumers with a reference and assists enterprises in implementing green management and sustainable development strategies. However, existing product carbon labeling management methods have several shortcomings.

[0003] First, data collection suffers from fragmentation and insufficient standardization. Traditional methods rely on manual labor or simple information systems to obtain carbon emission data, resulting in a single data source and a lack of comprehensive coverage of all stages of production, transportation, warehousing, and recycling. Furthermore, data from different sources vary significantly in format, accuracy, and timeliness, making it difficult to standardize carbon emission calculations and accurately reflect the true carbon footprint of products. Second, there is insufficient analysis of emissions relationships within stages and across enterprises. Existing technologies typically focus only on the carbon emissions of a single enterprise or process, failing to fully consider the interactions between emission nodes within the lifecycle stages and the synergistic relationships between different enterprise stages. Therefore, it is difficult to comprehensively assess the overall carbon emission impact in complex supply chains.

[0004] Furthermore, traditional carbon labeling assessment methods lack dynamic correction capabilities, relying primarily on static calculations. They fail to incorporate historical emission data and stage-specific sensitivity for real-time adjustments, making it difficult to promptly identify abnormal emission phases or address emission fluctuations, resulting in a lack of continuity and reliability in carbon labeling results. More importantly, existing methods typically store carbon labeling data within internal enterprise systems or centralized databases, lacking reliable evidence storage and management loop mechanisms. This makes it impossible to guarantee data immutability and traceability, and hinders unified management across enterprises or platforms.

[0005] Therefore, there is an urgent need to provide a product carbon labeling management method and system that can efficiently collect, standardize, analyze, dynamically correct, and reliably store carbon emission data throughout the product lifecycle in a cloud platform environment, in order to meet the requirements of enterprises, regulatory agencies, and consumers for the accuracy, transparency, and traceability of carbon emission information. Summary of the Invention

[0006] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a product carbon labeling management system and method based on a cloud platform to solve the above-mentioned technical problems.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a product carbon labeling management method based on a cloud platform, comprising:

[0008] Carbon emission-related data are collected from all stages of the product lifecycle, including real-time sensor data, enterprise resource planning system data, and third-party environmental monitoring data from the production, transportation, storage, and recycling stages. Standardized emission data are generated through non-linear normalization.

[0009] Based on standardized emission data, collaborative analysis and sensitivity assessment of emission nodes within a stage are conducted to generate stage carbon synergy factors.

[0010] Based on the carbon synergy factors at each stage, the emission relationships between different enterprise stages are analyzed to generate a cross-enterprise carbon synergy matrix;

[0011] Based on standardized emission data within the phase, phase carbon synergy factors and cross-enterprise carbon synergy matrices, the phase carbon impact factor and phase damping coefficient are calculated. Combined with the emission node sensitivity within the phase, inter-node synergy and inter-enterprise phase relationships, a comprehensive phase emission factor is generated.

[0012] Based on the cumulative sensitivity calculated by the comprehensive stage emission factor, stage emissions are correlated with the entire life cycle sequence to generate the carbon footprint of the product throughout its entire life cycle.

[0013] By combining historical emission data and cumulative sensitivity analysis of the carbon footprint throughout the entire life cycle, the emission patterns of each life cycle stage are compared and trended to identify abnormal emission stages. Dynamic correction is achieved by adjusting the emission data of abnormal stages and reassessing the cumulative sensitivity of the stages, while generating carbon label stability.

[0014] The final product carbon label is generated based on the full life cycle carbon footprint, the corrected cumulative sensitivity, and the carbon label stability. The carbon label is then uploaded to the blockchain system for notarization, and the notarization result is returned to the cloud platform, completing the carbon label management closed loop.

[0015] The present invention is further configured such that generating standardized emission data through nonlinear normalization includes:

[0016] In the process of collecting carbon emission-related data from the entire product life cycle, real-time sensor data, enterprise resource planning system data, and third-party environmental monitoring data from the production, transportation, storage, and recycling stages are cleaned and converted into a structured emission data set.

[0017] An initial emission intensity index is generated for each record in the structured emission dataset. The initial emission intensity index is constructed by using stage sensitivity weights, nonlinear amplification coefficients and external calibration factors. At the same time, the expression of anomalous emission records is enhanced by interval mapping.

[0018] The initial emission intensity index is subjected to nonlinear normalization. During the nonlinear normalization process, a stage normalization adjustment index and a damping adjustment factor are introduced to generate standardized emission data.

[0019] The present invention is further configured such that the carbon synergy factor based on the standardized emission data generation stage includes:

[0020] During the life cycle phase, emission nodes within the phase are defined, and collaborative relationships between nodes are established. Nodes within the phase are associated through collaborative relationships to characterize the interactions between nodes.

[0021] Sensitivity assessment is performed on each emission node within the stage, node sensitivity is calculated, and the sensitivity characteristics of the node in the stage emission structure are characterized.

[0022] Based on a comprehensive analysis of node sensitivity and inter-node synergy, a stage carbon synergy factor is generated.

[0023] The present invention is further configured such that generating a cross-enterprise carbon synergy matrix based on carbon synergy factors at each stage includes:

[0024] Between life cycle stages, carbon synergy factors of each enterprise stage are collected, enterprise set and enterprise stage set are defined, and the correlation between enterprise stages is established.

[0025] The correlation between enterprise stages is analyzed, and the degree of correlation between enterprise stages is determined based on the differences in carbon synergy factors and nonlinear adjustment parameters, and an inter-enterprise stage correlation matrix is ​​constructed.

[0026] Based on the inter-firm stage correlation matrix, the stage correlation relationships within the firm set are comprehensively aggregated to generate a cross-firm carbon synergy matrix, which represents the emission synergy relationship between different firm stages.

[0027] The present invention is further configured such that the calculation of the carbon impact factor and the stage damping coefficient includes:

[0028] The standardized emission data and node coordination relationships of each emission node within the stage are analyzed to generate individual node impact coefficients;

[0029] Based on the nonlinear integration of the individual node impact coefficient and the stage carbon synergy factor, a stage carbon impact factor is generated, which characterizes the overall impact of the emission nodes within the stage on the stage emissions.

[0030] By combining the cross-enterprise carbon synergy matrix and the nonlinear adjustment coefficient, the stage damping coefficient is calculated to reflect the effect of cross-enterprise synergy on suppressing stage carbon shocks.

[0031] The present invention is further configured such that generating the integrated stage emission factor includes:

[0032] The sensitivity of stage nodes is collaboratively corrected, and the node sensitivity is nonlinearly fused with the collaborative relationship of nodes within the stage.

[0033] The modified node sensitivity, stage carbon impact factor and stage damping coefficient are nonlinearly integrated to generate a stage comprehensive emission factor, which reflects the comprehensive impact of stage emissions under node sensitivity, node synergy and cross-enterprise synergy.

[0034] The comprehensive emission factors of each stage within the life cycle are summarized in sequence to form a set of comprehensive emission factors for the entire life cycle stage.

[0035] The present invention is further configured such that the calculation of cumulative sensitivity and generation of life-cycle carbon footprint based on the comprehensive stage emission factor includes:

[0036] Cumulative sensitivity analysis of comprehensive emission factors at each stage of the life cycle is conducted, and stage carbon synergy factors and cross-enterprise synergy relationships are combined to generate the cumulative sensitivity of each stage.

[0037] Based on the life cycle stages, the cumulative sensitivity of each stage is sequentially correlated, and continuous influence between stages is formed through non-linear recursive accumulation.

[0038] Introducing stage time weights to adjust cumulative sensitivity reflects the duration and relative importance of each stage in the lifecycle;

[0039] The adjusted cumulative sensitivity of each stage is integrated in the order of the product life cycle to generate the carbon footprint of the entire product life cycle, forming a continuous and traceable full life cycle emission assessment result.

[0040] The present invention is further configured such that the analysis of the life-cycle carbon footprint by combining historical emission data and cumulative sensitivity includes:

[0041] Deviation analysis is performed on the comprehensive emission factors at each stage of the life cycle and historical emission data to generate stage emission deviation values, reflecting the degree of stage emission anomalies;

[0042] Based on the comparison between the stage emission deviation value and the abnormal threshold, the abnormal emission stage is identified and the stage that needs to be dynamically corrected is marked;

[0043] Dynamic corrections are made for abnormal phases by adjusting the phase comprehensive emission factor in combination with the phase cumulative sensitivity, and a corrected phase emission factor is generated.

[0044] The stage cumulative sensitivity is reassessed based on the revised stage emission factor, forming a revised cumulative sensitivity set;

[0045] Based on the differences in comprehensive emission factors before and after the correction, a carbon label stability is generated, which reflects the consistency and continuity of the carbon footprint throughout the entire life cycle before and after the correction.

[0046] The present invention is further configured such that generating the final product carbon label based on the full life cycle carbon footprint, the corrected cumulative sensitivity, and the carbon label stability includes:

[0047] A preliminary carbon label is generated by nonlinearly fusing the cumulative sensitivity of each stage of the life cycle with the carbon footprint of the entire life cycle.

[0048] The initial carbon label is revised based on the carbon label stability to form the final product carbon label;

[0049] Generate a blockchain evidence hash based on the final product carbon label, product unique identifier and evidence storage timestamp, and upload the blockchain evidence hash to the blockchain system.

[0050] It receives blockchain-based evidence confirmation information and returns the confirmation results to the cloud platform, thus realizing a closed loop for carbon labeling management.

[0051] This invention also provides a cloud-based product carbon labeling management system, the system comprising:

[0052] Data Acquisition and Standardization Module: Collects carbon emission-related data from all stages of the product lifecycle, including real-time sensor data from production, transportation, warehousing, and recycling stages, enterprise resource planning system data, and third-party environmental monitoring data, and generates standardized emission data through non-linear normalization;

[0053] Phase Co-processing Analysis Module: Based on standardized emission data, this module performs co-processing analysis and sensitivity assessment of emission nodes within a phase, generating phase carbon co-processing factors.

[0054] Cross-enterprise correlation module: Analyzes the emission relationships between different enterprise stages based on carbon synergy factors at each stage, and generates a cross-enterprise carbon synergy matrix;

[0055] Phase Integrated Adjustment Module: Based on standardized emission data within the phase, phase carbon synergy factors, and cross-enterprise carbon synergy matrices, the phase carbon impact factor and phase damping coefficient are calculated. Combined with the sensitivity of emission nodes within the phase, synergy between nodes, and inter-enterprise phase relationships, a comprehensive phase emission factor is generated.

[0056] Lifecycle correlation module: Based on the comprehensive stage emission factor, the cumulative sensitivity is calculated, and the stage emissions are correlated with the entire life cycle sequence to generate the product's full life cycle carbon footprint;

[0057] Anomaly Monitoring and Dynamic Response Module: Combines historical emission data and cumulative sensitivity analysis to analyze the carbon footprint throughout the entire life cycle, compares and analyzes emission patterns at each stage of the life cycle, identifies abnormal emission stages, and completes dynamic correction by adjusting emission data at abnormal stages and reassessing the cumulative sensitivity of the stages, while generating carbon label stability.

[0058] Carbon Label Storage and Closed-Loop Module: Based on the full life cycle carbon footprint and the corrected cumulative sensitivity and carbon label stability, the final product carbon label is generated, the carbon label is uploaded to the blockchain system for storage, and the storage result is returned to the cloud platform to complete the carbon label management closed loop.

[0059] This invention provides a cloud-based product carbon labeling management system and method. The method collects carbon emission-related data from all stages of the product's lifecycle, including real-time sensor data, enterprise resource planning system data, and third-party environmental monitoring data from production, transportation, storage, and recycling stages. Standardized emission data is generated through nonlinear normalization. Based on the standardized emission data, collaborative analysis and sensitivity assessment of emission nodes within each stage are performed to generate stage carbon synergy factors. The emission relationships between different enterprise stages are analyzed based on the stage carbon synergy factors to generate a cross-enterprise carbon synergy matrix. The stage carbon impact factor and stage damping coefficient are calculated based on the standardized emission data within each stage, the stage carbon synergy factors, and the cross-enterprise carbon synergy matrix, combined with the sensitivity of emission nodes within each stage. Inter-node collaboration and inter-enterprise stage relationships generate a comprehensive stage emission factor; based on the comprehensive stage emission factor, cumulative sensitivity is calculated, and stage emissions are correlated with the entire life cycle sequence to generate the product's full life cycle carbon footprint; combining historical emission data and cumulative sensitivity analysis of the full life cycle carbon footprint, emission patterns at each life cycle stage are compared and trend analyzed to identify abnormal emission stages, and dynamic correction is achieved by adjusting the emission data of abnormal stages and reassessing the stage cumulative sensitivity, while simultaneously generating carbon label stability; based on the full life cycle carbon footprint, the corrected cumulative sensitivity, and carbon label stability, a final product carbon label is generated, the carbon label is uploaded to the blockchain system for notarization, and the notarization result is returned to the cloud platform, completing the carbon label management closed loop. The beneficial effects include:

[0060] 1. Efficient data acquisition and standardized processing: By collecting carbon emission-related data from all stages of the product lifecycle, including real-time sensor data, enterprise resource planning system data, and third-party environmental monitoring data during production, transportation, warehousing, and recycling, and generating standardized emission data through nonlinear normalization, comprehensive coverage and unified standardized processing of carbon emission data are achieved, improving data acquisition efficiency and accuracy, and providing a reliable foundation for subsequent collaborative analysis and full lifecycle carbon footprint calculation.

[0061] 2. Multi-layered collaborative analysis and dynamic correction capabilities: Based on standardized emission data, collaborative analysis and sensitivity assessment are performed on emission nodes within a stage to generate stage-specific carbon synergy factors. Furthermore, a cross-enterprise carbon synergy matrix is ​​constructed to achieve comprehensive analysis of the relationship between emissions within and across stages. Simultaneously, the entire life-cycle carbon footprint is dynamically corrected using historical emission data and cumulative sensitivity, and carbon labeling stability is generated to ensure the continuity, reliability, and traceability of carbon labeling results.

[0062] 3. Trusted storage and management closed loop for carbon labels: The final product carbon label is generated based on the full life cycle carbon footprint, the corrected cumulative sensitivity, and the stability of the carbon label. The label is stored on the blockchain and the storage results are returned to the cloud platform, forming a complete carbon label management closed loop. This achieves the immutability, traceability, and transparency of carbon label data, meeting the trust requirements of enterprises, regulatory agencies, and consumers for carbon emission information.

[0063] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0065] Figure 1 A flowchart illustrating a cloud platform-based product carbon labeling management method as an exemplary embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram illustrating the structure of a cloud-based product carbon labeling management system, which is an exemplary embodiment of the present invention. Detailed Implementation

[0067] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0068] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0069] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0070] Example 1:

[0071] Cloud-based product carbon labeling management methods, such as Figure 1 As shown, it includes:

[0072] Carbon emission-related data are collected from all stages of the product lifecycle, including real-time sensor data, enterprise resource planning system data, and third-party environmental monitoring data from the production, transportation, storage, and recycling stages. Standardized emission data are generated through non-linear normalization.

[0073] Based on standardized emission data, collaborative analysis and sensitivity assessment of emission nodes within a stage are conducted to generate stage carbon synergy factors.

[0074] Based on the carbon synergy factors at each stage, the emission relationships between different enterprise stages are analyzed to generate a cross-enterprise carbon synergy matrix;

[0075] Based on standardized emission data within the phase, phase carbon synergy factors and cross-enterprise carbon synergy matrices, the phase carbon impact factor and phase damping coefficient are calculated. Combined with the emission node sensitivity within the phase, inter-node synergy and inter-enterprise phase relationships, a comprehensive phase emission factor is generated.

[0076] Based on the cumulative sensitivity calculated by the comprehensive stage emission factor, stage emissions are correlated with the entire life cycle sequence to generate the carbon footprint of the product throughout its entire life cycle.

[0077] By combining historical emission data and cumulative sensitivity analysis of the carbon footprint throughout the entire life cycle, the emission patterns of each life cycle stage are compared and trended to identify abnormal emission stages. Dynamic correction is achieved by adjusting the emission data of abnormal stages and reassessing the cumulative sensitivity of the stages, while generating carbon label stability.

[0078] The final product carbon label is generated based on the full life cycle carbon footprint, the corrected cumulative sensitivity, and the carbon label stability. The carbon label is then uploaded to the blockchain system for notarization, and the notarization result is returned to the cloud platform, completing the carbon label management closed loop.

[0079] The present invention is further configured such that generating standardized emission data through nonlinear normalization includes:

[0080] In the process of collecting carbon emission-related data from all stages of a product's lifecycle, real-time sensor data, enterprise resource planning system data, and third-party environmental monitoring data from the production, transportation, storage, and recycling stages are uniformly cleaned and converted into a structured emission data set; specifically, a lifecycle stage set is defined. These correspond to the production, transportation, warehousing, and recycling stages, respectively. For any stage... Emissions-related records were collected in parallel from three sources: embedded sensor data. Enterprise Resource Planning Data Third-party monitoring data Through heterogeneous data cleaning and format standardization operators Unify multi-source data into a structured record set. Embedded sensor data Data is collected through embedded monitoring devices installed in production equipment, transport vehicles, storage facilities, and recycling equipment, specifically including fuel consumption (such as natural gas, diesel, and electricity consumption) and greenhouse gas concentrations. , , Real-time concentration of oxygen, flue gas flow rate, exhaust gas temperature, particulate matter emission rate, equipment operating time and power consumption, mileage, instantaneous fuel flow rate, and load during transportation; Enterprise Resource Planning (ERP) data. Data is collected through enterprise ERP systems (including subsystems for production planning, supply chain, and logistics scheduling), specifically including product production batches, process flows, raw material consumption, transportation batch information, transportation methods (road, rail, sea, air), warehousing energy consumption (cold chain power consumption, ventilation energy consumption, lighting energy consumption), recycling operation lists (dismantling processes, classification methods, recycling volume), equipment maintenance and scrapping cycle data; and third-party monitoring data. Data is collected through external independent testing agencies and industry certification platforms, specifically including interim emission testing reports. , , O, fluoride emissions), regional carbon intensity limits, national and international standards (ISO 14064, PAS 2050, etc.), environmental baseline emission factors (such as carbon emissions per unit of electricity consumption, regional energy carbon intensity), compliance calibration factors and historical comparison data, and structured record sets. The calculation formula is: ,in, Implement a series of deterministic data engineering steps: timestamp synchronization (nearest neighbor interpolation based on a unified clock reference), unit standardization (converting energy consumption and emissions to a unified unit), time window resampling (compressing or expanding record density according to a phased sampling strategy), missing value imputation (based on local polynomial interpolation or quantile band imputation based on adjacent periods), and overflow truncation based on quantile constraints for each record. These are comparable original observations or derived indices at the same numerical scale;

[0081] An initial emission intensity index is generated for each record in the structured emission dataset. This initial emission intensity index is constructed using stage sensitivity weights, nonlinear amplification factors, and external calibration factors. Furthermore, interval mapping is used to enhance the representation of anomalous emission records. Specifically, for the structured dataset... Each record Initial emission intensity is constructed based on stage characteristics. The formula for calculating the initial emission intensity index is as follows: It employs a superposition of power-law amplification terms and mapping enhancement terms to simultaneously reflect high-value amplification and anomaly sensitivity. , Representation phase The Emission intensity of the records This represents the stage sensitivity weight, reflecting the strength of the response of that life cycle stage to numerical values ​​in the energy and emission structure. Its function is to amplify or compress the original magnitude, and its value ranges from (0,1]. This is the stage nonlinear amplification factor, which is adjusted by power law to improve the model's ability to distinguish high emission records. Its value range is [1,5]. This is an external calibration factor, derived from third-party monitoring correction coefficients, with a value range of [0,1]. For interval mapping operators, the input value Projecting onto a specified dynamic interval to enhance outlier sensitivity; interval mapping operator. Piecewise convergent polynomial mappings are used to enhance anomalous responses, typically defined as: ,parameter These are the boundaries of the low and high dynamic ranges, respectively. This is the convergence rate factor of the mapping. Through power To achieve nonlinear amplification of medium to high values, Used to characterize the energy consumption sensitivity of this stage. For external calibration terms, interval mapping is used. For observations that exceed the normal range, a convergent transformation is performed to enhance anomaly detection capabilities, while simultaneously incorporating weights from third-party correction information. ;

[0082] The initial emission intensity index is subjected to nonlinear normalization. During the nonlinear normalization process, a stage normalization adjustment index and a damping adjustment factor are introduced to generate standardized emission data. Specifically, for the initial intensity... Nonlinear normalization is performed to balance the amplification effect of high values ​​with damping control of extreme values, and standardized emission data is output. The calculation formula is: ,in, Represents standardized emission data. The normalized nonlinear adjustment exponent is defined as the stage-normalized exponent, with a value range of (0,2]. This is the damping adjustment factor, with a value range of [0,1]. For nonlinear damping operators, it is defined as: ,in This is the stage coupling coefficient, with a value range of (0,1]. The suppression coefficient has a value range of [0.1, 10]. pass Controlling the emphasis on high-intensity nonlinearity, Through damping operator When the strength is too high, progressive suppression is provided to avoid single-point extreme values ​​dominating subsequent synergy calculations. The damping operator internally implements the principle of gradual change in strength using a bounded fractional form. The inhibitory effect tends to the upper bound as it increases; parameters Adjusting the coupling strength, By setting a suppression rate, a two-layer nonlinear function structure is used to amplify high emission signals while performing bounded suppression of extreme values, so that standardized data can have numerical stability and cross-source comparability while retaining high signal distinctiveness.

[0083] The present invention is further configured such that the carbon synergy factor based on the standardized emission data generation stage includes:

[0084] During the lifecycle phase, emission nodes within the phase are defined, and collaborative relationships between nodes are established. Nodes within a phase are associated through these collaborative relationships to characterize their interactions. Specifically, for the lifecycle phase... The set of emission nodes within is defined as ,in, Representation phase The Each emission node is used to construct a collaborative correlation matrix between stage nodes. , representing the collaborative relationship between nodes, is defined as: ,in, They are nodes and Standardized emission data, It is a nonlinear cooperative mapping operator, employing a nonlinear interaction function within each stage. To coordinate the amplification index, the amplification intensity of high amplitude pairs is controlled, and its function is to enhance the identification of common high emission nodes. The value range is... , This is the internal damping coefficient within the stage, controlling phase difference sensitivity. Its function is to suppress inflated scores for highly dissimilar node pairs. Its value range is... , Applying a power-law amplification to the product of node pairs aims to amplify the synergistic effect of node pairs simultaneously at high emission levels. Used to control sensitivity to high-value pairs. The node value difference increases linearly to achieve damping, preventing node pairs with huge emission patterns from generating false high coordination scores. The coordination correlation matrix between stage nodes, by simultaneously considering amplitude coupling and similarity damping, obtains a more refined characterization of the real coordination relationship within the stage, reducing misjudgments caused by single high values ​​or numerical differences.

[0085] Sensitivity assessments are performed on each emission node within a stage, calculating node sensitivity to characterize the sensitivity characteristics of each node within the stage's emission structure. Specifically, this assessment is tailored to the stage. Each emission node With its standardized emissions As a fundamental variable, an expression combining sensitivity nonlinear amplification and cooperative suppression is introduced to calculate the nodal sensitivity coefficient. The node sensitivity coefficient, which measures the potential contribution of a node to overall emission fluctuations during a given period, is calculated using the following formula: ,in, This is a sensitivity nonlinear index that adjusts the amplification of high values, thereby enhancing the significance of high emission nodes. Its value range is [1,3]. This is the collaborative weight adjustment coefficient, which quantifies the influence of the collaborative association matrix between nodes and incorporates it into the suppression term. Its function is to avoid the artificially high or low sensitivity of a single node in the collaborative dense region. Its value ranges from [0.1, 1]. For nodes The sum of the inter-node collaborative association matrices with other nodes. Through power Nonlinear amplification of single-node emission levels gives higher weight to high-emission nodes in sensitivity metrics. Sum of the collaborative association matrices between nodes and other nodes according to coefficients In summary, this structure is designed to suppress excessively high sensitivity values ​​generated by individual nodes in a strong cooperative network. It achieves higher sensitivity evaluation for "high-emission and isolated" nodes, while moderately suppressing nodes that are "high-emission but covered by a strong cooperative network".

[0086] Based on a comprehensive analysis of node sensitivity and inter-node synergy, a stage-specific carbon synergy factor is generated. Specifically, based on node sensitivity... Based on this, the inter-node collaborative correlation matrix is ​​fused in a nonlinear superposition form to obtain the stage-level carbon synergy factor. This factor aims to characterize the overall synergy strength and sensitivity distribution of nodes within a stage, reflecting both the existence of highly sensitive nodes and the amplification effect of the synergy structure between nodes. The formula for calculating the stage-level carbon synergy factor is as follows: ,in, The stage-level collaboration enhancement coefficient controls the amplification effect of node collaboration on stage-level factors, balancing network density and numerical stability. Its value ranges from [0.1, 1]. Represents a node The nonlinear superposition of synergistic effects with other nodes reflects the transformation of multiple weak synergistic effects into strong synergistic effects. The stage-level carbon synergistic factor integrates node-level sensitivity and network synergy in a nonlinear superposition manner to obtain a comprehensive measure of the emission structure within the stage. This quantitative result highlights the influence of key nodes and reflects the overall amplification effect brought about by the synergy of the node group.

[0087] The present invention is further configured such that generating a cross-enterprise carbon synergy matrix based on carbon synergy factors at each stage includes:

[0088] Between lifecycle stages, carbon synergy factors at each enterprise stage are collected, defining enterprise sets and enterprise stage sets, and establishing relationships between enterprise stages; specifically, the set of participating enterprises within the lifecycle is defined as... Each enterprise Set of stage nodes Obtain the stage carbon synergy factor for each enterprise stage. ,in Characterizing enterprises The The comprehensive characteristics of internal coordination and sensitivity at each stage;

[0089] The correlation between enterprise stages is analyzed, and the degree of correlation between enterprise stages is determined based on the differences in carbon synergy factors and nonlinear adjustment parameters at each stage, thus constructing an inter-stage correlation matrix between enterprises; specifically, for any two enterprise stages... and The correlation between the two is calculated by using the phase synergy factor as the amplitude basis and incorporating a nonlinear mapping with differential damping. This mapping simultaneously amplifies the coupling effect of highly synergistic segments and suppresses pairings with large differences in synergy, forming an inter-firm stage correlation matrix. Used to quantify enterprises stage With enterprises stage Emission synergy, ,in, It is a non-linear synergistic index. For cross-enterprise damping coefficients, The differences in carbon synergistic factors between stages reflect the inconsistency in emission patterns across stages;

[0090] Based on the inter-firm stage correlation matrix, the stage correlation relationships within the firm set are comprehensively aggregated to generate a cross-firm carbon synergy matrix, which characterizes the emission synergy relationships between different firm stages. Specifically, based on the correlation matrix... The internal stage relationships of enterprises are non-linearly superimposed according to the enterprise-to-enterprise (enterprise-to-block) method to form a second-order matrix describing the strength of inter-enterprise collaboration. For any enterprise-to-enterprise pair... Define block aggregation value ,in The cross-enterprise synergy enhancement coefficient is represented by all block values, which together form a cross-enterprise carbon synergy matrix. By non-linear aggregation from stage-to-level to enterprise-to-level, a high-resolution quantification of cross-enterprise collaborative relationships is formed.

[0091] The present invention is further configured such that the calculation of the carbon impact factor and the stage damping coefficient includes:

[0092] The standardized emission data and inter-node coordination relationships of each emission node within a stage are analyzed to generate an individual node impact coefficient. Specifically, each life cycle stage consists of several emission nodes. Each node not only has its own emissions but is also constrained by the inter-node coordination relationships within the stage. To quantify the impact of a single node within a stage, an individual node impact coefficient is introduced for each emission node within the stage. Calculate the individual impact coefficient of the node ,in, It is a non-linear exponent. The nodal cooperative damping coefficient, The impact of molecular amplification node emissions on the stage. Suppress excessive impact from highly collaborative nodes by combining inter-node collaboration relationships;

[0093] A stage carbon impact factor is generated by nonlinearly integrating the individual node impact coefficients and the stage carbon synergy factor. This factor characterizes the overall impact of emission nodes within a stage on stage emissions. Specifically, the individual node impacts are not linearly additive but exhibit a synergistic superposition effect. Therefore, a nonlinear product structure is introduced to integrate all node impacts with the stage carbon synergy factor, thus integrating the individual node impacts within the stage to calculate the stage carbon impact factor. The nonlinear product reflects the superposition effect of nodal impacts within a stage. The stage-based impact enhancement factor represents the source stage emission sensitivity.

[0094] By combining the cross-enterprise carbon synergy matrix and nonlinear adjustment coefficients, a stage damping coefficient is calculated to reflect the suppressive effect of cross-enterprise synergy on stage carbon shocks. Specifically, stages do not exist in isolation but are influenced by cross-enterprise emission synergy. To suppress excessive shocks caused by strong cross-enterprise correlations, a stage damping coefficient is introduced and calculated in conjunction with cross-enterprise emission synergy. , For cross-enterprise damping adjustment coefficient, The cumulative results of the cross-enterprise carbon collaboration matrix are weighted, and the more collaboration relationships there are, the stronger the damping, which can alleviate the excessive expansion of emissions in a certain stage caused by strong cross-enterprise linkages and ensure the fairness and robustness of the carbon footprint.

[0095] The present invention is further configured such that generating the integrated stage emission factor includes:

[0096] A collaborative correction is performed on the sensitivity of stage nodes by nonlinearly fusing node sensitivity with the collaborative relationships between nodes within the stage. Specifically, node sensitivity cannot be used in isolation; it needs to be corrected in conjunction with the collaborative effects between nodes. Through a nonlinear product structure, the collaborative relationships are embedded into the sensitivity correction, combining stage node sensitivity with the collaborative relationships within the stage to generate corrected sensitivity. Integrating node sensitivity and inter-node synergy, The synergistic enhancement coefficient;

[0097] The modified node sensitivity, stage carbon impact factor, and stage damping coefficient are nonlinearly integrated to generate a stage-level comprehensive emission factor, reflecting the combined impact of stage emissions under node sensitivity, node synergy, and cross-enterprise synergy. Specifically, the modified node sensitivity, stage carbon impact factor, and stage damping coefficient are nonlinearly integrated to generate a stage-level comprehensive emission factor, calculated using the following formula: ,in, The impact of carbon shock during the scale-up phase , Nonlinear superposition node sensitivity and co-correction and damping coefficient Multiplication achieves a balance between internal shocks and cross-enterprise collaboration, comprehensively reflects the multidimensional impact mechanism of stage emissions, and enables accurate characterization of stage emission behavior;

[0098] The comprehensive emission factors for each stage within the life cycle are summarized sequentially to form a set of comprehensive emission factors for the entire life cycle stage. Specifically, the life cycle consists of multiple sequential stages, and the comprehensive emission factors for each stage need to be combined sequentially to form a set of comprehensive emission factors for the entire life cycle stage. .

[0099] The present invention is further configured such that the calculation of cumulative sensitivity and generation of life-cycle carbon footprint based on the comprehensive stage emission factor includes:

[0100] Cumulative sensitivity analysis is performed on the comprehensive emission factors at each stage of the life cycle, combining stage carbon synergy factors and cross-enterprise synergy relationships to generate the cumulative sensitivity for each stage. Specifically, emissions at each stage of the life cycle are not only affected by their own emission factors but also by synergistic interference from other stages and constraints from cross-enterprise relationships. To avoid ignoring correlations through isolated calculations of single stages, a nonlinear ratio structure is used to construct the cumulative sensitivity, calculated using the following formula: ,in, The nonlinear sensitivity of emission factors during the amplification phase; Used to adjust the denominator of the amplification intensity calculation in conjunction with emission factors from other stages. Stage carbon synergistic factor and cross-enterprise collaboration matrix This reflects the interference and coordination relationships between different stages. To accumulate adjustment coefficients across stages, quantify the actual sensitivity of stage emissions in the global life cycle, and avoid data distortion in single stages;

[0101] The cumulative sensitivity of each stage is sequentially correlated according to the life cycle stage order, forming a continuous influence between stages through non-linear recursive accumulation. Specifically, the life cycle stages are ordered, and subsequent stages often inherit the emission effects of previous stages. To characterize this sequential effect, a recursive product structure is introduced, extending the cumulative sensitivity of previous stages to the current stage. Based on the life cycle order, the cumulative sensitivity of each stage is non-linearly recursively accumulated. The recursive product form extends the sensitivity impact of previous stages to the current stage, reflecting the sequential relationship of the life cycle. The cumulative impact coefficient of the preceding stages reflects the sequentiality of the life cycle and the temporal dependence between stages, ensuring that the carbon footprint calculation conforms to the dynamic evolution law;

[0102] A time weighting factor is introduced to adjust the cumulative sensitivity, reflecting the duration and relative importance of each stage in the lifecycle. Specifically, the duration and importance of different stages in the lifecycle are inconsistent, requiring adjustment through a time weighting factor to make the stage sensitivity more closely reflect the true contribution. The adjustment formula is as follows: Among them, the weighting coefficient Reflecting the proportion or criticality of a stage in its life cycle, and the overall emission factor of that stage. Nonlinear combination enables dynamic adjustment, improving the fairness and accuracy of carbon footprint calculation and avoiding bias in results due to short-term high emissions or long-term low emissions.

[0103] The adjusted cumulative sensitivities of each stage are integrated in lifecycle order to generate the product's full lifecycle carbon footprint, forming a continuous and traceable full lifecycle emission assessment result. Specifically, the full lifecycle carbon footprint requires integrating the adjusted cumulative values ​​of all stages into a unified indicator, and using a non-linear multiplication method to enhance the continuity and correlation between stages. The formula for calculating the product's full lifecycle carbon footprint is as follows: This enables a complete closed-loop calculation from stage emissions to the carbon footprint throughout the entire life cycle, ensuring the traceability and continuity of the results.

[0104] The present invention is further configured such that the analysis of the life-cycle carbon footprint by combining historical emission data and cumulative sensitivity includes:

[0105] Deviation analysis is performed on the comprehensive emission factors at each stage of the life cycle compared with historical emission data to generate stage emission deviation values, reflecting the degree of emission anomalies at each stage; specifically, the actual comprehensive emission factors at each stage of the life cycle are... Compared with historical emission baseline data In contrast, aberration signals are amplified using a deviation metric formula. To improve the detection accuracy of sensitive phases, a phase anomaly sensitivity coefficient is introduced. and the carbon footprint throughout the entire life cycle With stage cumulative sensitivity The nonlinear amplification effect is calculated using the following formula: in, This represents the degree of deviation from historical emission patterns during a given period; a larger value indicates a greater difference between the emissions during that period and historical patterns. Due to the difference between the stage and the historical benchmark, This is the sensitivity coefficient for stage-specific anomalies, used to control the amplification of differences. It is the interaction factor between the total life cycle carbon footprint and the stage-accumulated sensitivity, reflecting the amplification effect of anomalies in the overall carbon footprint;

[0106] By comparing the deviation values ​​of stage emissions with the abnormal threshold, abnormal emission stages are identified, and stages that require dynamic correction are marked; specifically, this is done by setting abnormal thresholds. Determine the deviation value of the stage. Whether the threshold is exceeded, thus identifying whether the stage is an abnormal stage, is determined by the following formula: ,in, This indicates that the current stage is abnormal and requires correction. This indicates that emissions are normal and will not trigger corrections. The anomaly detection threshold set for the system;

[0107] Dynamic corrections are made for abnormal stages by adjusting the stage's comprehensive emission factor in conjunction with the stage's cumulative sensitivity, resulting in a corrected stage emission factor. Specifically, when a stage is identified as abnormal, the correction impact coefficient is used. and stage cumulative sensitivity For stage emission factors Dynamic corrections are made to mitigate its abnormal effects. The calculation formula is as follows: ,in, This is the revised stage-integrated emission factor. To correct the influence coefficient and control the correction intensity, The cumulative sensitivity of each stage to the entire life cycle is determined by the magnitude of the correction for abnormal stages, which is proportional to their importance. This enables precise mitigation of abnormal stages and avoids distortion of the overall carbon footprint due to anomalies in a single stage.

[0108] The stage cumulative sensitivity is reassessed based on the revised stage emission factors, forming a revised cumulative sensitivity set; specifically, the revised emission factors... This will change the impact of the stage over the entire lifecycle, therefore the revised cumulative sensitivity needs to be recalculated. The calculation formula is: ,in, To accumulate sensitivity for the corrected stage, This is the sensitivity correction coefficient, which adjusts the effect of the correction factor on sensitivity. This reflects the relative effect of the correction magnitude on cumulative sensitivity;

[0109] Based on the differences in comprehensive emission factors before and after the correction, a carbon label stability is generated to reflect the consistency and continuity of the life-cycle carbon footprint before and after the correction. Specifically, the carbon label stability is used to measure the consistency of emission factors before and after the correction. This is achieved by accumulating the correction magnitude across all stages and combining it with stage stability adjustment coefficients. This forms a global stability index, calculated using the following formula: ,in, For carbon labeling stability, the closer the value is to 1, the higher the consistency before and after correction. This is the stage stability adjustment coefficient, which adjusts the effect of the correction magnitude on stability. By multiplying across stages, stability is ensured to reflect the consistency of corrections throughout the entire life cycle. Global stability indicators guarantee the continuity and traceability of carbon labeling for the final product, thereby enhancing the credibility and robustness of the label.

[0110] The present invention is further configured such that generating the final product carbon label based on the full life cycle carbon footprint, the corrected cumulative sensitivity, and the carbon label stability includes:

[0111] The cumulative sensitivity of each stage of the life cycle is nonlinearly fused with the total life cycle carbon footprint to generate a preliminary carbon label; specifically, the life cycle carbon footprint... It measures total global emissions, but the impact of different stages on the final carbon label is uneven, therefore it is necessary to incorporate the corrected stage-cumulative sensitivity. With stage weight coefficient By integrating and constructing a preliminary carbon labeling formula, the importance of each stage can be amplified or suppressed. The preliminary carbon labeling calculation formula is as follows: ,in, This is a preliminary carbon labeling system and does not yet consider stability corrections. For the entire life cycle carbon footprint, To accumulate sensitivity for the corrected stage, This is the stage weighting coefficient; the larger the value, the stronger the impact of that stage on carbon labeling.

[0112] The initial carbon label is revised based on its stability to form the final product carbon label; specifically, the initial carbon label... It only reflects total emissions and stage sensitivity, but not the consistency before and after the correction. The carbon labeling stability index (Stab) reflects the impact of the correction on consistency, so an adjustment factor needs to be introduced. The initial label is revised to generate the final product carbon label, calculated using the following formula: ,in, For the final carbon label, For carbon labeling stability, This is a stability adjustment coefficient, which controls the strength of the stability effect in the correction process;

[0113] A blockchain notarization hash is generated based on the final product's carbon label, unique product identifier, and notarization timestamp, and then uploaded to the blockchain system. Specifically, to ensure the immutability of the carbon label, the final carbon label needs to be... Unique Product Identifier and evidence timestamp Perform cryptographic hash calculations to generate blockchain evidence hash values. The calculation formula is: ,in, This is the hash value for evidence storage, used for on-chain storage on the blockchain. For a secure hash algorithm, output a fixed-length 512-bit string. For the final carbon label, As a unique identifier for the product, This serves as a timestamp for evidence storage, recording the moment the blockchain evidence was generated. This indicates a string concatenation operation. The blockchain-stored hash value ensures the tamper-proof and unique nature of the final carbon identifier, providing a reliable basis for subsequent blockchain verification.

[0114] The system receives blockchain-based notarization confirmation information and returns the confirmation result to the cloud platform, thus achieving a closed loop in carbon labeling management. Specifically, the generated notarization hash value is uploaded to the blockchain network, where it is verified and recorded by blockchain nodes, generating the block height and confirmation information. After receiving the confirmation information, the cloud platform completes the carbon labeling management loop and the data transmission link. Blockchain Evidence Storage Returning to the point, on-chain notarization enables transparency and immutability of carbon labeling, forming a complete cloud-blockchain closed-loop mechanism.

[0115] Example 2:

[0116] Please see Figure 2 This exemplary cloud-based product carbon labeling management system includes:

[0117] Data Acquisition and Standardization Module: Collects carbon emission-related data from all stages of the product lifecycle, including real-time sensor data from production, transportation, warehousing, and recycling stages, enterprise resource planning system data, and third-party environmental monitoring data, and generates standardized emission data through non-linear normalization;

[0118] Phase Co-processing Analysis Module: Based on standardized emission data, this module performs co-processing analysis and sensitivity assessment of emission nodes within a phase, generating phase carbon co-processing factors.

[0119] Cross-enterprise correlation module: Analyzes the emission relationships between different enterprise stages based on carbon synergy factors at each stage, and generates a cross-enterprise carbon synergy matrix;

[0120] Phase Integrated Adjustment Module: Based on standardized emission data within the phase, phase carbon synergy factors, and cross-enterprise carbon synergy matrices, the phase carbon impact factor and phase damping coefficient are calculated. Combined with the sensitivity of emission nodes within the phase, synergy between nodes, and inter-enterprise phase relationships, a comprehensive phase emission factor is generated.

[0121] Lifecycle correlation module: Based on the comprehensive stage emission factor, the cumulative sensitivity is calculated, and the stage emissions are correlated with the entire life cycle sequence to generate the product's full life cycle carbon footprint;

[0122] Anomaly Monitoring and Dynamic Response Module: Combines historical emission data and cumulative sensitivity analysis to analyze the carbon footprint throughout the entire life cycle, compares and analyzes emission patterns at each stage of the life cycle, identifies abnormal emission stages, and completes dynamic correction by adjusting emission data at abnormal stages and reassessing the cumulative sensitivity of the stages, while generating carbon label stability.

[0123] Carbon Label Storage and Closed-Loop Module: Based on the full life cycle carbon footprint and the corrected cumulative sensitivity and carbon label stability, the final product carbon label is generated, the carbon label is uploaded to the blockchain system for storage, and the storage result is returned to the cloud platform to complete the carbon label management closed loop.

[0124] It should be noted that the cloud-based product carbon labeling management system and the cloud-based product carbon labeling management method provided in the above embodiments belong to the same concept. The specific methods of execution of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the cloud-based product carbon labeling management system provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0125] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A product carbon labeling management method based on a cloud platform, characterized in that, include: Carbon emission-related data are collected from all stages of the product lifecycle, including real-time sensor data, enterprise resource planning system data, and third-party environmental monitoring data from the production, transportation, storage, and recycling stages. Standardized emission data are generated through non-linear normalization. Based on standardized emission data, collaborative analysis and sensitivity assessment of emission nodes within a stage are conducted to generate stage carbon synergy factors. Based on the carbon synergy factors at each stage, the emission relationships between different enterprise stages are analyzed to generate a cross-enterprise carbon synergy matrix; Based on standardized emission data within the phase, phase carbon synergy factors and cross-enterprise carbon synergy matrices, the phase carbon impact factor and phase damping coefficient are calculated. Combined with the emission node sensitivity within the phase, inter-node synergy and inter-enterprise phase relationships, a comprehensive phase emission factor is generated. Based on the cumulative sensitivity calculated by the comprehensive stage emission factor, stage emissions are correlated with the entire life cycle sequence to generate the carbon footprint of the product throughout its entire life cycle. By combining historical emission data and cumulative sensitivity analysis of the carbon footprint throughout the entire life cycle, the emission patterns of each life cycle stage are compared and trended to identify abnormal emission stages. Dynamic correction is achieved by adjusting the emission data of abnormal stages and reassessing the cumulative sensitivity of the stages, while generating carbon label stability. The final product carbon label is generated based on the full life cycle carbon footprint, the corrected cumulative sensitivity, and the carbon label stability. The carbon label is then uploaded to the blockchain system for notarization, and the notarization result is returned to the cloud platform, completing the carbon label management closed loop.

2. The product carbon labeling management method based on a cloud platform according to claim 1, characterized in that, Generating standardized emission data through nonlinear normalization includes: In the process of collecting carbon emission-related data from the entire product life cycle, real-time sensor data, enterprise resource planning system data, and third-party environmental monitoring data from the production, transportation, storage, and recycling stages are cleaned and converted into a structured emission data set. An initial emission intensity index is generated for each record in the structured emission dataset. The initial emission intensity index is constructed by using stage sensitivity weights, nonlinear amplification coefficients and external calibration factors. At the same time, the expression of anomalous emission records is enhanced by interval mapping. The initial emission intensity index is subjected to nonlinear normalization. During the nonlinear normalization process, a stage normalization adjustment index and a damping adjustment factor are introduced to generate standardized emission data.

3. The product carbon labeling management method based on a cloud platform according to claim 1, characterized in that, Carbon synergistic factors based on the standardized emissions data generation stage include: During the life cycle phase, emission nodes within the phase are defined, and collaborative relationships between nodes are established. Nodes within the phase are associated through collaborative relationships to characterize the interactions between nodes. Sensitivity assessment is performed on each emission node within the stage, node sensitivity is calculated, and the sensitivity characteristics of the node in the stage emission structure are characterized. Based on a comprehensive analysis of node sensitivity and inter-node synergy, a stage carbon synergy factor is generated.

4. The product carbon labeling management method based on a cloud platform according to claim 3, characterized in that, A cross-enterprise carbon synergy matrix is ​​generated based on carbon synergy factors at each stage, including: Between life cycle stages, carbon synergy factors of each enterprise stage are collected, enterprise set and enterprise stage set are defined, and the correlation between enterprise stages is established. The correlation between enterprise stages is analyzed, and the degree of correlation between enterprise stages is determined based on the differences in carbon synergy factors and nonlinear adjustment parameters, and an inter-enterprise stage correlation matrix is ​​constructed. Based on the inter-firm stage correlation matrix, the stage correlation relationships within the firm set are comprehensively aggregated to generate a cross-firm carbon synergy matrix, which represents the emission synergy relationship between different firm stages.

5. The product carbon labeling management method based on a cloud platform according to claim 1, characterized in that, The calculation of the stage carbon impact factor and stage damping coefficient includes: The standardized emission data and node coordination relationships of each emission node within the stage are analyzed to generate individual node impact coefficients; Based on the nonlinear integration of the individual node impact coefficient and the stage carbon synergy factor, a stage carbon impact factor is generated, which characterizes the overall impact of the emission nodes within the stage on the stage emissions. By combining the cross-enterprise carbon synergy matrix and the nonlinear adjustment coefficient, the stage damping coefficient is calculated to reflect the effect of cross-enterprise synergy on suppressing stage carbon shocks.

6. The product carbon labeling management method based on a cloud platform according to claim 5, characterized in that, Generate integrated stage emission factors, including: The sensitivity of stage nodes is collaboratively corrected, and the node sensitivity is nonlinearly fused with the collaborative relationship of nodes within the stage. The modified node sensitivity, stage carbon impact factor and stage damping coefficient are nonlinearly integrated to generate a stage comprehensive emission factor, which reflects the comprehensive impact of stage emissions under node sensitivity, node synergy and cross-enterprise synergy. The comprehensive emission factors of each stage within the life cycle are summarized in sequence to form a set of comprehensive emission factors for the entire life cycle stage.

7. The product carbon labeling management method based on a cloud platform according to claim 1, characterized in that, The cumulative sensitivity and life-cycle carbon footprint calculated based on the comprehensive stage emission factor include: Cumulative sensitivity analysis of comprehensive emission factors at each stage of the life cycle is conducted, and stage carbon synergy factors and cross-enterprise synergy relationships are combined to generate the cumulative sensitivity of each stage. Based on the life cycle stages, the cumulative sensitivity of each stage is sequentially correlated, and continuous influence between stages is formed through non-linear recursive accumulation. Introducing stage time weights to adjust cumulative sensitivity reflects the duration and relative importance of each stage in the lifecycle; The adjusted cumulative sensitivity of each stage is integrated in the order of the product life cycle to generate the carbon footprint of the entire product life cycle, forming a continuous and traceable full life cycle emission assessment result.

8. The product carbon labeling management method based on a cloud platform according to claim 7, characterized in that, The life-cycle carbon footprint analysis, combining historical emissions data and cumulative sensitivity, includes: Deviation analysis is performed on the comprehensive emission factors at each stage of the life cycle and historical emission data to generate stage emission deviation values, reflecting the degree of stage emission anomalies; Based on the comparison between the stage emission deviation value and the abnormal threshold, the abnormal emission stage is identified and the stage that needs to be dynamically corrected is marked; Dynamic corrections are made for abnormal phases by adjusting the phase comprehensive emission factor in combination with the phase cumulative sensitivity, and a corrected phase emission factor is generated. The stage cumulative sensitivity is reassessed based on the revised stage emission factor, forming a revised cumulative sensitivity set; Based on the differences in comprehensive emission factors before and after the correction, a carbon label stability is generated, which reflects the consistency and continuity of the carbon footprint throughout the entire life cycle before and after the correction.

9. The product carbon labeling management method based on a cloud platform according to claim 1, characterized in that, The final product carbon label is generated based on the full lifecycle carbon footprint, the corrected cumulative sensitivity, and the carbon label stability, including: A preliminary carbon label is generated by nonlinearly fusing the cumulative sensitivity of each stage of the life cycle with the carbon footprint of the entire life cycle. The initial carbon label is revised based on the carbon label stability to form the final product carbon label; Generate a blockchain evidence hash based on the final product carbon label, product unique identifier and evidence storage timestamp, and upload the blockchain evidence hash to the blockchain system. It receives blockchain-based evidence confirmation information and returns the confirmation results to the cloud platform, thus realizing a closed loop for carbon labeling management.

10. A cloud-based product carbon labeling management system, used to implement the cloud-based product carbon labeling management method according to any one of claims 1-9, characterized in that, include: Data Acquisition and Standardization Module: Collects carbon emission-related data from all stages of the product lifecycle, including real-time sensor data from production, transportation, warehousing, and recycling stages, enterprise resource planning system data, and third-party environmental monitoring data, and generates standardized emission data through non-linear normalization; Phase Co-processing Analysis Module: Based on standardized emission data, this module performs co-processing analysis and sensitivity assessment of emission nodes within a phase, generating phase carbon co-processing factors. Cross-enterprise correlation module: Analyzes the emission relationships between different enterprise stages based on carbon synergy factors at each stage, and generates a cross-enterprise carbon synergy matrix; Phase Integrated Adjustment Module: Based on standardized emission data within the phase, phase carbon synergy factors, and cross-enterprise carbon synergy matrices, the phase carbon impact factor and phase damping coefficient are calculated. Combined with the sensitivity of emission nodes within the phase, synergy between nodes, and inter-enterprise phase relationships, a comprehensive phase emission factor is generated. Lifecycle correlation module: Based on the comprehensive stage emission factor, the cumulative sensitivity is calculated, and the stage emissions are correlated with the entire life cycle sequence to generate the product's full life cycle carbon footprint; Anomaly Monitoring and Dynamic Response Module: Combines historical emission data and cumulative sensitivity analysis to analyze the carbon footprint throughout the entire life cycle, compares and analyzes emission patterns at each stage of the life cycle, identifies abnormal emission stages, and completes dynamic correction by adjusting emission data at abnormal stages and reassessing the cumulative sensitivity of the stages, while generating carbon label stability. Carbon Label Storage and Closed-Loop Module: Based on the full life cycle carbon footprint and the corrected cumulative sensitivity and carbon label stability, the final product carbon label is generated, the carbon label is uploaded to the blockchain system for storage, and the storage result is returned to the cloud platform to complete the carbon label management closed loop.

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