A method, system, device, and readable storage medium for calculating the carbon footprint of consumer products based on their entire life cycle.

Abstract

This invention discloses a method, system, device, and readable storage medium for calculating the carbon footprint of consumer products based on their entire lifecycle. It relates to the field of carbon footprint accounting technology and includes: collecting activity data from the consumer product production process; dividing the process into cycle stages to obtain corresponding carbon emission factor data; analyzing the carbon emission intensity values ​​of different cycle stages and calculating the contribution ratio of each cycle stage; defining an inter-stage data flow matrix; performing consistency verification based on the inter-stage data flow matrix; if the verification is inconsistent, calculating the difference in carbon emission intensity; and sequentially checking the contribution ratio of different activities. The method of this invention reveals the nonlinear relationship between coupling points and the system as a whole through inter-stage data flow matrix balance analysis. By sorting the set of key coupling points, it establishes an efficient and coordinated emission reduction path, ultimately forming a set of key coupling points based on activities with high contribution rates.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint accounting technology, and in particular to a method, system, device and readable storage medium for carbon footprint accounting of consumer products based on the entire life cycle. Background Technology

[0002] The production, transportation, sales, use, and recycling of consumer goods span the entire life cycle. Their complex supply chain network and multi-stage resource flow pose significant challenges to carbon emission accounting. In existing technologies, typical carbon footprint calculation methods are mainly based on life cycle assessment methodologies to achieve quantitative assessment of various environmental impact factors in the process from production to disposal of consumer goods. These methods typically employ process modeling or input-output modeling to divide the multiple life cycle stages of consumer goods into several independent or related modules and calculate the carbon emission intensity of each module separately.

[0003] While the above methods can achieve basic statistics on carbon emissions from consumer products, traditional carbon footprint accounting models are relatively weak in analyzing the coupling relationship between resource flows and carbon intensity at different stages of the life cycle. They are unable to dynamically capture the deep-seated impact of resource flows on carbon emissions, making overall optimization difficult. In addition, they are difficult to accurately identify key nodes that contribute significantly to carbon emissions and often use average allocation or experience-based threshold setting for emission source management, which cannot meet the optimization needs of complex, multi-stage, continuous operations of consumer products. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method, system, device, and readable storage medium for calculating the carbon footprint of consumer products based on the entire life cycle. This addresses the shortcomings of traditional carbon footprint accounting models in analyzing the coupling relationship between resource flow and carbon intensity at different stages of the life cycle. These models struggle to dynamically capture the deep-seated impact of resource flow on carbon emissions, making overall optimization difficult. Furthermore, they are unable to accurately identify key nodes that significantly contribute to carbon emissions and often rely on average allocation or experience-based threshold setting for emission source management, which fails to meet the optimization needs of complex, multi-stage, continuous operations of consumer products.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for calculating the carbon footprint of consumer products based on their entire life cycle, comprising:

[0008] Collect activity data from the consumer goods production process, divide the cycle into stages to obtain corresponding carbon emission factor data, analyze the carbon emission intensity values ​​of different cycles and calculate the contribution ratio of each cycle stage, and define the data flow matrix between stages.

[0009] Consistency verification is performed based on the inter-stage data flow matrix. If the verification is inconsistent, the carbon emission intensity difference is calculated, the contribution ratio of different activities is checked sequentially, the carbon emission intensity data marked as abnormal activities is smoothed and corrected, and the corrected emission intensity is updated and output.

[0010] Based on the actual data units of different activities in the cycle phase, define the activity scale factor matrix, calculate the rate of change, sort them to form a set of key nodes, calculate the contribution rate of carbon intensity correlation values ​​for the updated inter-phase data flow matrix data, and sort them to form a set of key coupling points.

[0011] Based on energy consumption data from different cycle stages, energy flow intensity is analyzed and energy efficiency within a stage is calculated based on carbon emission intensity. An inter-stage energy flow vector model is constructed for energy flow allocation, and the weights of the energy flow allocation are adjusted based on the set of key coupling points.

[0012] Construct an optimization function, referencing the set of key nodes as guidance, update the energy flow intensity, calculate the total energy flow intensity distribution value for all cycle stages, update the energy flow distribution weight between stages, calculate the energy flow ratio of different stages, define the optimization objective function for iterative optimization, and determine the total optimized energy flow for each cycle stage.

[0013] Based on the energy flow threshold, the corresponding periodic stages of the total energy flow are screened, marked as key coupling paths, and the periodic stage activities that need to be optimized for energy flow are identified.

[0014] As a preferred embodiment of the consumer product carbon footprint accounting method based on the entire life cycle described in this invention, the steps include: dividing the cycle into stages to obtain corresponding carbon emission factor data, analyzing the carbon emission intensity values ​​of different cycles and calculating the contribution ratio of each cycle stage, and defining an inter-stage data flow matrix, including...

[0015] Based on the entire life cycle of consumer products, the basic activity data is divided into cycle stages, including production, transportation, sales, use and recycling. For each cycle stage, the specific activities and activity data of the consumer product in different cycle stages are defined, and the corresponding carbon emission factor data is obtained from the carbon emission factor library.

[0016] Actual carbon emissions are calculated based on activity data and unit emission factors, and the total actual carbon emissions at different life cycle stages are statistically analyzed as the stage intensity value of the life cycle. The contribution ratio of the corresponding life cycle stage intensity value is calculated based on activity data.

[0017] The data flow intensity is determined by combining the carbon emission factor with the resource flow at different cycle stages. The correlation of carbon intensity at different cycle stages is analyzed, and the data flow matrix between stages is defined.

[0018] As a preferred embodiment of the consumer product carbon footprint accounting method based on the entire life cycle described in this invention, the following steps are included: consistency verification is performed based on the inter-stage data flow matrix; if inconsistencies are found, the carbon emission intensity difference is calculated; the contribution ratio of different activities is checked sequentially; carbon emission intensity data marked as abnormal activities are smoothed and corrected; and the corrected emission intensity is updated and output.

[0019] Consistency verification is performed based on the inter-stage data flow matrix to verify whether the stage strength values ​​of all stages are consistent with the sum of all flow strengths in the inter-stage flow matrix.

[0020] If the verification is consistent, the data flow is conserved; if the verification is inconsistent, the verification is deemed to have failed. The periodic phase that caused the data inconsistency is located, and the carbon emission intensity difference is calculated. Within the periodic phase, the contribution ratio of different activities is checked sequentially. If the actual emission intensity of an activity deviates from the theoretical ratio, the activity is marked as an abnormal activity.

[0021] The carbon emission intensity data marked as anomalous activity is smoothed and corrected, the corrected emission intensity is updated and the corrected emission intensity of all cycle stages is combined into an emission intensity set;

[0022] The data was re-verified based on the revised emission intensity, and the inter-stage data flow matrix data was updated.

[0023] As a preferred embodiment of the consumer product carbon footprint accounting method based on the entire life cycle described in this invention, the following steps are taken: Based on the actual data units of different activities in different life cycle stages, an activity scale factor matrix is ​​defined, the rate of change is calculated, and the data are sorted to form a key node set. For the updated inter-stage data flow matrix data, the contribution rate of carbon intensity correlation values ​​is calculated, and the data are sorted to form a key coupling point set, including...

[0024] Based on the emission intensity set, we observe the changes in the modified emission intensity at different stages under different activity scales, and define the activity scale factor matrix based on the actual data units of different activities in the cycle stage.

[0025] The rate of change of the activity scale factor of different activities is calculated, and the contribution of the activity scale factor of different activities to the emission intensity of different cycle stages is analyzed. The activity scale with the largest rate of change in the activity scale factor matrix is ​​integrated and the critical point is marked, and sorted to form a set of key nodes.

[0026] By using the updated inter-stage data flow matrix, the proportion of carbon intensity correlation values ​​among all inter-stage carbon intensity correlation values ​​is calculated as the contribution rate. The sum of the mean and twice the standard deviation of the contribution rate based on historical data is used as the coupling point threshold. Activity points with carbon intensity correlation values ​​whose contribution rate is greater than the coupling point threshold are sorted to form a set of key coupling points.

[0027] As a preferred embodiment of the consumer product carbon footprint accounting method based on the entire life cycle described in this invention, the steps include: analyzing energy flow intensity based on energy consumption data from different life cycle stages and calculating energy consumption efficiency within each stage based on carbon emission intensity; constructing an inter-stage energy flow vector model for energy flow allocation; and adjusting the weights of the energy flow allocation based on a set of key coupling points.

[0028] Based on energy consumption data from different cycle stages, analyze energy flow intensity and calculate energy efficiency within each stage in conjunction with carbon emission intensity.

[0029] Construct an inter-stage energy flow vector model, define the energy flow allocation between every two stages based on the correlation factor, and adjust the weights of the energy flow allocation based on the set of key coupling points.

[0030] As a preferred embodiment of the consumer product carbon footprint accounting method based on the entire life cycle described in this invention, the construction of the optimization function, using a set of key nodes as guidance, and updating the energy flow intensity, includes:

[0031] The optimization objective is to set the optimal carbon emission efficiency, define the optimization function, and introduce the energy consumption efficiency within the stage. The energy flow intensity is updated again based on the rate of change of the critical point corresponding to different cycle stages, while ensuring the conservation of total energy after redistribution.

[0032] As a preferred embodiment of the consumer product carbon footprint accounting method based on the entire life cycle described in this invention, the following steps are taken: The total energy flow intensity allocation values ​​for all cycle stages are statistically analyzed, and the energy flow allocation weights between stages are updated. The energy flow ratios for different stages are statistically analyzed, an optimization objective function is defined for iterative optimization, and the optimized total energy flow for each cycle stage is determined, including...

[0033] The total energy flow intensity allocation value for all cycle stages is calculated, and the energy flow allocation weight between stages is updated. The adjustment value of the energy flow intensity between stages in each cycle is based on the stage carbon emission intensity after energy efficiency optimization.

[0034] Based on the updated total energy flow, the energy flow ratio at different stages is statistically analyzed. Combined with the optimized unit energy flow emission intensity, an optimization objective function is defined. Iterative optimization is performed using the gradient descent method to determine the optimized total energy flow for each cycle stage.

[0035] As a preferred embodiment of the consumer product carbon footprint accounting method based on the entire life cycle described in this invention, the step of filtering the cycle stage corresponding to the total energy flow based on the energy flow threshold and marking it as a key coupling path includes:

[0036] Using the sum of the historical mean and standard deviation of total energy flow as the energy flow threshold, the periodic stages corresponding to total energy flow that are greater than or equal to the energy flow threshold are marked as critical coupling paths.

[0037] As a preferred embodiment of the consumer product carbon footprint accounting method based on the entire life cycle described in this invention, the step of determining the cycle-stage activities requiring energy flow optimization includes:

[0038] Based on the key coupling paths, the carbon emission values ​​between cycle stages are statistically analyzed, and the cycle stage activities that require energy flow optimization are determined based on the key coupling paths.

[0039] As a preferred embodiment of the consumer product carbon footprint accounting method based on the entire life cycle described in this invention, the collection of consumer product production process activity data includes:

[0040] Basic activity data is obtained from the production process of consumer goods, and a traceable carbon emission factor database is constructed based on the activity data and the corresponding carbon emission factor data.

[0041] Secondly, this invention provides a consumer product carbon footprint accounting system based on the entire life cycle, including:

[0042] The cycle phase segmentation module collects activity data in the consumer goods production process, divides the cycle phases and obtains the corresponding carbon emission factors to realize the construction of basic data.

[0043] The carbon emission intensity analysis module analyzes the carbon emission intensity values ​​at different cycle stages, calculates the contribution ratio of each stage, and establishes a data flow matrix between stages.

[0044] The outlier correction module performs consistency verification based on the inter-stage data flow matrix and smoothly corrects for differences in carbon emission intensity and marked anomalous activities.

[0045] The key set module defines the activity scale factor matrix, calculates the rate of change and sorts them to generate a key node set. Based on the updated data flow matrix data, it calculates the contribution rate of carbon intensity correlation values ​​and sorts them to generate a key coupling point set.

[0046] The phase-based energy efficiency analysis module analyzes the energy flow intensity of the cycle phase and calculates the phase-based energy efficiency by combining carbon emission data. It constructs an energy flow vector model and adjusts the energy flow allocation weights by combining the set of key coupling points.

[0047] The critical node guidance module, based on the optimization function and referring to the set of critical nodes, updates the energy flow intensity of the cycle stage, calculates the energy flow intensity allocation value of all cycle stages, and updates the weight allocation between stages.

[0048] The activity optimization module filters key coupling paths using energy flow thresholds to identify key activity items for optimization during specific periods.

[0049] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the consumer product carbon footprint accounting method based on the entire life cycle as described in the first aspect of the present invention.

[0050] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the consumer product carbon footprint accounting method based on the entire life cycle as described in the first aspect of the present invention.

[0051] The beneficial effects of this invention are as follows: by analyzing the data flow matrix balance between stages, the nonlinear relationship between coupling points and the system as a whole is revealed; by sorting the set of key coupling points, an efficient collaborative emission reduction path is established; based on the activity points with high contribution rates, a set of key coupling points is finally formed; by integrating energy flow and carbon emission data into a multi-level technical solution, a systematic capability for collaborative emission reduction throughout the entire life cycle is formed; the combination of energy flow allocation and energy consumption efficiency analysis supplements the single-dimensional optimization scheme for carbon emissions; and the pertinence and effectiveness of the entire process are improved from the perspective of collaborative management of energy and carbon. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the 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.

[0053] Figure 1 This is a flowchart illustrating the carbon footprint accounting method for consumer products based on the entire life cycle in Example 1.

[0054] Figure 2 This is a schematic diagram of the structure of the consumer product carbon footprint accounting system based on the entire life cycle in Example 1. Detailed Implementation

[0055] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0056] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0058] Example 1, referring to Figures 1 to 2 This is the first embodiment of the present invention, which provides a method for calculating the carbon footprint of consumer products based on their entire life cycle, including the following steps:

[0059] Preferably, data on consumer goods production process activities are collected, including:

[0060] Basic activity data is obtained from the production process of consumer goods (such as data on material production consumption, transportation consumption, sales consumption, usage consumption, and recycling consumption). Based on this activity data and corresponding carbon emission factor data, a traceable carbon emission factor database is constructed, where the unit of carbon emission factor is... / ton indicates the amount of carbon dioxide emissions generated per ton of cargo.

[0061] Furthermore, the cycle is divided into stages to obtain corresponding carbon emission factor data, the intensity values ​​of carbon emission stages in different cycles are analyzed and the contribution ratio of each stage is calculated, and an inter-stage data flow matrix is ​​defined, including...

[0062] Based on the entire lifecycle of consumer products, basic activity data is divided into cycle stages, including production, transportation, sales, use, and recycling. For each defined cycle stage, the specific activities and activity data of the consumer product in different cycle stages are identified (e.g., electricity consumption data in the transportation stage). Corresponding carbon emission factor data is then obtained from a carbon emission factor database, represented as follows:

[0063]

[0064] in, This represents the set of activity data for the i-th period. This represents the m-th activity data in the i-th cycle phase. Let m represent the emission factor of the m-th activity unit in the i-th cycle phase, where m is the total number;

[0065] Actual carbon emissions are calculated based on activity data and unit emission factors, and the total actual carbon emissions at different life cycle stages are statistically analyzed as the stage intensity value of the life cycle. The contribution ratio of the corresponding life cycle stage intensity value is calculated based on activity data.

[0066] Based on carbon emission factors and resource flows at different cycle stages (e.g., the amount of raw materials that need to be transported from the production stage to the sales stage), data flow intensity is determined. The correlation of carbon intensity at different cycle stages is analyzed, and an inter-stage data flow matrix is ​​defined, represented as follows:

[0067]

[0068]

[0069]

[0070] in, This indicates the data flow intensity from i to j during the periodic phase. Let represent the amount of resource flow from stage i to stage j in a cycle, and n represent the total number of stages in a cycle. This represents the amount of resource flow from stage i to stage k in a cycle. This represents the stage intensity value of stage i in the cycle. This represents the correlation value of carbon intensity between phases i and j in the cycle. This represents the data flow matrix between stages.

[0071] By combining activity datasets with unit emission factors, carbon emission data for each stage of the life cycle is determined in a more refined manner, thereby improving the accuracy of carbon emission accounting. The activity datasets clarify the specific activities at each stage of the life cycle (such as energy consumption in the production stage, fuel use in the transportation stage, and electricity consumption in the sales stage), refining the sources of carbon emissions in the life cycle of consumer products. Combined with unit carbon emission factors (such as emission factors for energy and fuel), the carbon emission contribution of individual activities is quantified.

[0072] The intensity value of each stage of the life cycle is determined by the sum of the carbon emissions of each activity. By the contribution ratio of each activity, the activity that contributes the most to the carbon emission intensity in each stage can be intuitively identified. For example, the transportation stage may become the main source of emissions due to its high fuel consumption.

[0073] By systematically correlating carbon intensity between cyclical stages through a data flow matrix, the problem of carbon emission transmission from resource flow between stages is solved. It clarifies how resource flow in different stages affects carbon emissions. For example, high-intensity correlation points can be identified at nodes with high resource flow, which helps to accurately find optimizable coupling correlation points.

[0074] By combining the correlation values ​​of resource flow and carbon intensity, the carbon footprint differences of resource flow paths are displayed. For example, optimizing the switch from land transportation to sea transportation intuitively quantifies the emission reduction effect of optimization on carbon emissions.

[0075] By balancing the data flow matrix between stages, the nonlinear relationship between coupling points and the system as a whole is revealed. The introduction of the data flow matrix effectively solves the problem of the separation between single-stage carbon emission accounting and cross-stage correlation operations. The identification of coupling points reveals the hidden connections behind the system behavior in a single stage. By combining stage intensity values, resource flow, and data flow matrix, a multi-dimensional data cross-analysis capability is formed.

[0076] Example 2, refer to Figures 1 to 2 This is the second embodiment of the present invention, which provides a method for calculating the carbon footprint of consumer products based on their entire life cycle, including the following steps:

[0077] Preferably, consistency verification is performed based on the inter-stage data flow matrix. If inconsistencies are found, the carbon emission intensity difference is calculated, the contribution ratio of different activities is checked sequentially, the carbon emission intensity data marked as anomalous activities is smoothed and corrected, and the corrected emission intensity is updated and output, including...

[0078] Consistency verification is performed based on the inter-stage data flow matrix. It verifies whether the sum of the stage strength values ​​of all stages is consistent with the sum of the flow strength values ​​of all stages in the inter-stage flow matrix. If the sum of the flow strength values ​​of all stages in the inter-stage flow matrix is ​​the same as the sum of the stage strength values ​​of all stages, the verification is considered consistent. If the sum of the flow strength values ​​of all stages in the inter-stage flow matrix is ​​different from the sum of the stage strength values ​​of all stages, the verification is considered inconsistent. This is expressed as follows:

[0079]

[0080]

[0081] in, This represents the sum of the stage strength values ​​across all stages. This represents the sum of the stage strength values ​​for all stages:

[0082] If the verification is consistent, the data flow is considered to be conserved; if the verification is inconsistent, the verification is considered to have failed. The periodic phase causing the data inconsistency is located, and the carbon emission intensity difference is calculated. Within the periodic phase, the contribution ratio of different activities is checked sequentially. If the actual emission intensity of an activity deviates from the theoretical ratio, the activity is marked as an anomalous activity, as shown below:

[0083]

[0084]

[0085]

[0086] in, This represents the difference in carbon emission intensity during the i-th cycle phase. This represents the actual emission intensity of the o-th activity in the i-th cycle phase. This represents the unit carbon emission factor of the o-th activity in the i-th cycle phase. This represents the activity data for the 0th activity in the ith period. This represents the deviation of the carbon emission intensity of the o-th activity in the i-th cycle phase. This represents the contribution of the o-th activity in the i-th cycle phase;

[0087] The carbon emission intensity data marked as anomalous activity is smoothed and corrected, the corrected emission intensity is updated, and the corrected emission intensity of all period periods is combined into an emission intensity set, represented as:

[0088]

[0089] in, This represents the adjusted emission intensity of the o-th activity in the i-th cycle phase;

[0090] The data was re-verified based on the revised emission intensity, and the inter-stage data flow matrix data was updated.

[0091] Consistency verification is performed through inter-stage data flow matrices to ensure the integrity and data closure of carbon emission accounting modeling. By calculating carbon emission intensity differences, the source of inconsistency can be located and abnormal stages can be accurately marked. By constructing a difference calculation model, the difference value can be accurately quantified. By checking the contribution ratio of activities within a stage in turn, abnormal activities can be finely screened, improving the granularity and depth of data verification. The granularity of anomaly checking is refined from the stage level to the activity level, which not only improves the accuracy of anomaly location but also reduces the information loss that may be caused by the traditional coarse analysis based on stages.

[0092] By smoothing and correcting abnormal activity data, the continuity of the output emission intensity set and the availability of re-verification are ensured. The smoothing and correction mechanism corrects the actual deviation of carbon emission intensity without deleting the dynamic range information of the original data, which greatly reduces the information loss that may occur during data processing.

[0093] By re-verifying and correcting emission intensity and inter-stage data flow matrices, and dynamically adjusting and updating data accuracy, the data flow intensity can be continuously aligned with actual operations. This solves the problem of cumulative error amplification caused by stage deviations in the whole life cycle accounting. By combining abnormal activity marking with data flow matrix updates, multi-level data governance with vertical depth and horizontal breadth is reflected. The two-level data governance method can not only identify problems, but also generate effective improvement measures, making it of practical significance for the dynamic adjustment of the whole system plan. By dynamically correcting the carbon emission intensity set, the ability to make quantitative decisions on differences in the whole life cycle stages is enhanced. This not only ensures the reliability of intermediate operations, but also introduces a precise sparsity strategy in the final output (i.e., enhances the focused management of high emission activities).

[0094] Furthermore, based on the actual data units of different activities in the cycle phase, an activity scale factor matrix is ​​defined, the rate of change is calculated, and the resulting sequence is used to form a set of key nodes. For the updated inter-phase data flow matrix, the contribution rate of carbon intensity correlation values ​​is calculated, and the resulting sequence is used to form a set of key coupling points, including...

[0095] Based on the emission intensity set, we observe the changes in modified emission intensity at different stages under different activity scales. Based on the actual data units of different activities in the cycle stage, we define the activity scale factor matrix, which is expressed as:

[0096]

[0097] in, Represents the activity size factor matrix. This represents a cycle phase with a total number of n and an activity scale factor with a total number of Q, such as the material consumption in the transportation phase.

[0098] The rate of change of activity scale factors for different activities is calculated to analyze the contribution of these factors to emission intensity at different cycle stages. Activities with the largest rate of change in the activity scale factor matrix are integrated and critical points are marked, then sorted to form a set of key nodes, represented as follows:

[0099]

[0100]

[0101]

[0102] in, This represents the total carbon emission intensity in the i-th cycle stage under a given activity scale factor matrix. This represents the initial carbon emission intensity under the baseline activity scale in the i-th cycle phase. This represents the sensitivity coefficient of the 0th activity in the ith period, obtained through regression analysis of emissions per unit activity. This represents the activity scale factor for the o activities in the i-th cycle phase. This represents the baseline rate of change at the 0th activity point. The activity scale factor, representing the activity point with the largest rate of change, is used to mark critical points;

[0103] By using the updated inter-stage data flow matrix, the proportion of carbon intensity correlation values ​​among all inter-stage carbon intensity correlation values ​​is calculated as the contribution rate. The sum of the mean and twice the standard deviation of the contribution rate based on historical data is used as the coupling point threshold. Activity points with carbon intensity correlation values ​​whose contribution rate is greater than the coupling point threshold are sorted to form a set of key coupling points.

[0104] By constructing an activity scale factor matrix, activity data is unified across different spatiotemporal scales, enhancing the cross-stage comparison capability of the analysis. This enables quantitative analysis of activity data of different granularities at each stage within a unified framework, laying the foundation for subsequent rate of change calculation and critical point labeling. By calculating the rate of change of the contribution of activity scale factors to the emission intensity of each stage, dynamic optimization priorities among activities are formed, providing a refined assessment of the impact of activity scale changes on emission dynamics. This allows highly sensitive activities to be prioritized, reducing blind or ineffective operations in resource optimization.

[0105] By forming a set of key nodes, the focus on high-emission activities within the optimization phase is optimized. The critical point marking based on the rate of change calculation ensures that the set of key nodes not only includes activities with high emission intensity, but also focuses on the responsiveness of emission intensity to changes in the scale of activities, providing a more dynamic basis for optimization decisions.

[0106] By updating the inter-stage data flow matrix, the carbon intensity coupling relationship between the cycle stages is identified, and the selection of resource flow paths is optimized. The inter-stage data flow matrix combines resource flow and accurately identifies carbon intensity correlation, dynamically mapping the emission transmission impact between stages to the resource flow path. The calculation of carbon intensity correlation value combines the cycle stage intensity and resource flow, ensuring that not only the emission intensity of a single stage is considered, but also its transmission effect on upstream or downstream stages is captured.

[0107] By sorting the set of key coupling points, an efficient collaborative emission reduction path can be established. Based on the activity points with high contribution rates, the set of key coupling points is finally formed, which can determine the design logic of the priority emission reduction path. For example, based on the resource flow path, highly correlated coupling points can be addressed first, thereby forming a collaborative emission reduction scheme between stages.

[0108] Furthermore, based on energy consumption data from different cycle stages, energy flow intensity is analyzed and energy efficiency within each stage is calculated based on carbon emission intensity. An inter-stage energy flow vector model is constructed for energy flow allocation, and the weights of the energy flow allocation are adjusted based on a set of key coupling points, including...

[0109] Based on energy consumption data from different cycle phases, energy flow intensity is analyzed, and energy efficiency within each phase is calculated by combining it with carbon emission intensity, as expressed below:

[0110]

[0111]

[0112] in, This represents the energy flow intensity during the i-th period. This represents the average power of o activities in the i-th cycle phase. This represents the duration of activity o in the i-th cycle phase. This represents the energy flow intensity of activity o in the i-th cycle phase. This represents the energy efficiency of o activities in the i-th cycle phase. This represents the adjusted emission intensity of the o-th activity in the i-th cycle phase;

[0113] Construct an inter-stage energy flow vector model, adjust the energy flow allocation weights based on the set of key coupling points, define the energy flow allocation between every two stages, and adjust the energy flow allocation weights based on the set of key coupling points (ensuring priority optimization of energy flow allocation to key coupling nodes), expressed as:

[0114]

[0115]

[0116] in, This represents the total energy flow from the i-th cycle stage to the j-th cycle stage. This represents the weight adjustment factor for key coupling points. This represents the correlation value of carbon intensity between the i-th cycle stage and the k-th cycle stage. This represents the energy flow ratio from the i-th cycle stage to the j-th cycle stage, through... Calculated.

[0117] By gradually accumulating the energy flow intensity of the cycle phase and the energy flow intensity of the activity, an intuitive distribution of phase energy consumption is generated. The total energy flow intensity of the cycle phase is calculated by accumulating the average power and duration data of each activity within the phase, which fills the gap in the static description of the original data in energy consumption analysis and forms a dynamic and detailed energy consumption distribution map. The decomposition and accumulation of the activity energy flow intensity supports the comprehensive tracking of multi-level energy consumption sources and helps to discover the energy consumption patterns of specific activities.

[0118] By calculating energy consumption efficiency within a stage, energy consumption level is correlated with carbon emission intensity to accurately assess stage performance. The energy consumption efficiency within a stage is calculated by combining energy flow intensity and carbon emission intensity, revealing the energy resource utilization efficiency corresponding to unit emission in each stage. Energy consumption efficiency analysis breaks the limitations of traditional separate accounting of energy consumption and carbon emissions, enabling process optimization to balance the trade-off between the goals of minimizing energy consumption and carbon emissions.

[0119] By using the inter-stage energy flow vector model to calculate energy allocation, the energy migration flow between different cycle stages was quantified, filling the data gap in the life cycle analysis of inter-stage flow differences. Energy flow allocation was optimized by adjusting the weights of key coupling points, and a resource allocation priority mechanism with key nodes as the core was constructed. The key coupling point weight adjustment factor directly binds energy flow allocation to key carbon-related nodes. By adjusting the weights, more reasonable energy allocation is prioritized for highly correlated stages and paths.

[0120] By dynamically mapping carbon intensity correlation values ​​across stages, flow energy consumption and emission intensity are managed in a unified manner. The carbon intensity correlation values ​​provide a dynamic adjustment basis in the calculation of energy flow ratios and allocations, enabling cross-stage energy optimization to not only rely on energy consumption structure but also consider the carbon footprint of each stage.

[0121] By using a coupling model of energy flow ratio and correlation factors, the dynamic feedback of energy flow distribution throughout the entire life cycle is quantified. The energy flow ratio model, through the intervention of correlation factors, transforms energy consumption analysis into a traceable dynamic feedback mechanism. By adjusting the weights of energy flow vectors and key coupling points between stages, an energy optimization collaboration network is constructed. This network not only cares about the energy allocation of local paths but also relies on the collaborative effect of the global network, enabling the multi-path optimization objectives in complex systems to have more synergy.

[0122] By integrating multi-level technical solutions for energy flow and carbon emission data, a systematic capability for coordinated emission reduction throughout the entire life cycle is formed. The combination of energy flow allocation and energy efficiency analysis complements the single-dimensional optimization scheme for carbon emissions. From the perspective of coordinated management of energy and carbon, the pertinence and effectiveness of the entire process are improved. Based on dynamic energy and static carbon emissions, the coupling relationship between each stage and path of the life cycle is dynamically visualized through the integration of vector models and weight adjustment factors.

[0123] Example 3, referring to Figures 1 to 2 This is the third embodiment of the present invention, which provides a method for calculating the carbon footprint of consumer products based on their entire life cycle, including the following steps:

[0124] Preferably, an optimization function is constructed, referencing the set of key nodes as guidance, to update the energy flow intensity, including...

[0125] Setting optimal carbon emission efficiency as the optimization objective, an optimization function is defined, and in-stage energy consumption efficiency is introduced. Referring to the set of critical nodes and using the rate of change corresponding to the critical points of different cycle stages as guidance, the energy flow intensity is updated, ensuring the total energy is conserved after redistribution. This is expressed as:

[0126]

[0127]

[0128]

[0129] in, This represents the optimization objective value for the i-th cycle stage. This represents the allocated value of the active energy flow intensity. This represents the average energy efficiency within a given period. This indicates the adjustment step size in the i-th period. This represents the rate of change in the i-th period. This represents the baseline step size, determined based on historical experience.

[0130] By setting a function with optimal carbon emission efficiency as the optimization objective, energy consumption and carbon emission management are clearly quantified, forming a multi-dimensional optimization constraint system. The optimization objective function effectively balances the relationship between energy consumption efficiency and carbon emission intensity at different stages, establishing a dynamic, two-way interactive objective constraint system.

[0131] By introducing intra-stage energy efficiency, the boundary of carbon emission efficiency optimization is dynamically adjusted to achieve high-efficiency resource allocation. Intra-stage energy efficiency, as an important component of the optimization function, reflects the effectiveness of current energy utilization and directly constrains the resource allocation ratio during the optimization process. By referencing the rate of change, the critical point guidance of the key node set is incorporated into the optimization objective, enhancing the execution priority of optimization. The redistribution of activity energy flow intensity, combined with the constraint of total energy conservation, makes the adjustments after each optimization more in line with actual resource needs, ensuring that the overall system always follows the law of balanced resource utilization.

[0132] Furthermore, the total energy flow intensity allocation values ​​for all cycle stages are statistically analyzed, and the energy flow allocation weights between stages are updated. The energy flow ratios for different stages are statistically analyzed, and an optimization objective function is defined for iterative optimization to determine the optimized total energy flow for each cycle stage, including...

[0133] The total energy flow intensity allocation values ​​for all cycle stages are calculated, and the energy flow allocation weights between stages are updated. The adjustment value of the energy flow intensity between stages in each cycle is based on the stage carbon emission intensity after energy efficiency optimization, expressed as:

[0134]

[0135] in, This indicates the updated total energy flow. This represents the allocation value of the total energy flux intensity in the i-th cycle phase. This represents the maximum periodicity optimization target value. This represents the optimized value in the i-th cycle stage;

[0136] Based on the updated total energy flow, the energy flow ratios at different stages are statistically analyzed. Combined with the optimized unit energy flow emission intensity, an optimization objective function is defined. Iterative optimization is performed using gradient descent to determine the optimized total energy flow for each cycle stage, minimizing the overall lifecycle carbon emission cost. The objective is to achieve unit energy flow emission intensity between stages, while dynamically adjusting the coupling relationship. This is expressed as:

[0137]

[0138]

[0139]

[0140]

[0141] in, This represents the energy flow ratio from stage i to stage j in the cycle. This represents the unit energy flow emission intensity from period i to j. and This represents the energy flow ratio between time steps t+1 and t. This indicates that the optimization compensation is achieved by updating the coupling relationship between each stage through gradient descent, and the iteration stops when the update change is not significant.

[0142] By statistically analyzing and updating the total energy flow intensity allocation value for each cycle stage, the overall optimization and key reconfiguration of resource allocation are achieved. This enables the scheme to coordinate resource allocation across the entire cycle stage, avoiding the imbalance problem caused by traditional one-sided optimization that only applies to some cycle stages. At the same time, it improves the resource utilization efficiency throughout the entire life cycle. By combining the energy flow ratio of each stage with the emission intensity per unit energy flow, the synergistic relationship between system energy consumption and emissions is quantified. By combining the emission intensity per unit energy flow, energy consumption is directly coupled with emission costs, reflecting the precise impact of resource flow on carbon footprint. This synergistic design is better than the traditional single-layer optimization model, as it can simultaneously track the contradictory changes between energy consumption dynamics and emission optimization.

[0143] By setting an optimization objective function and using gradient descent to iteratively update it, dynamic adjustment and optimal solution seeking are achieved. The optimization compensation mechanism dynamically adjusts the resource coupling relationship between cycle stages, improving the sensitivity and adaptability of optimization. The optimization compensation mechanism ensures that energy consumption allocation adjustments are closer to actual data changes, and establishes sensitive control rules for resource reallocation.

[0144] Example 4, refer to Figures 1 to 2 This is the fourth embodiment of the present invention, which provides a method for calculating the carbon footprint of consumer products based on their entire life cycle, including the following steps:

[0145] Preferably, based on the energy flow threshold, the corresponding periodic stage of the total energy flow is selected and marked as a critical coupling path, including:

[0146] Using the sum of the historical mean and standard deviation of total energy flow as the energy flow threshold, the periodic stages corresponding to total energy flow that are greater than or equal to the energy flow threshold are marked as critical coupling paths.

[0147] By pre-screening energy flow thresholds, key coupling paths in the life cycle can be accurately located, reducing the analytical complexity of resource input. By using the carbon emission values ​​between statistical periods of key coupling paths, energy consumption path optimization and carbon emission reduction can be coordinated and unified, intuitively linking energy flow intensity and carbon emission costs. The goal of energy flow optimization can be directly mapped to emission reduction management, enabling energy flow optimization operations to focus not only on energy consumption but also on its environmental impact.

[0148] Furthermore, identify the cycle phase activities that require energy flow optimization, including:

[0149] Based on the key coupling paths, the carbon emission values ​​between cycle stages are statistically analyzed, and the cycle stage activities that require energy flow optimization are determined based on the key coupling paths.

[0150] By further refining the activities within each cycle stage based on the key coupling path, the optimization tasks for each stage are broken down from the path level to the activity level, thus locking in specific optimization objectives. For example, for high-energy-consuming activities in the optimization path, the corresponding energy flow allocation is directly adjusted. The combination of critical path marking and activity item optimization forms a closed-loop system for optimization tasks, further strengthening the synergy between paths and activities, and enabling the priority logic of resource allocation to run through the entire process from path selection to activity task execution.

[0151] This embodiment also provides a consumer product carbon footprint accounting system based on the entire life cycle, including,

[0152] The cycle phase segmentation module collects activity data in the consumer goods production process, divides the cycle phases and obtains the corresponding carbon emission factors to realize the construction of basic data.

[0153] The carbon emission intensity analysis module analyzes the carbon emission intensity values ​​at different cycle stages, calculates the contribution ratio of each stage, and establishes a data flow matrix between stages.

[0154] The outlier correction module performs consistency verification based on the inter-stage data flow matrix and smoothly corrects for differences in carbon emission intensity and marked anomalous activities.

[0155] The key set module defines the activity scale factor matrix, calculates the rate of change and sorts them to generate a key node set. Based on the updated data flow matrix data, it calculates the contribution rate of carbon intensity correlation values ​​and sorts them to generate a key coupling point set.

[0156] The phase-based energy efficiency analysis module analyzes the energy flow intensity of the cycle phase and calculates the phase-based energy efficiency by combining carbon emission data. It constructs an energy flow vector model and adjusts the energy flow allocation weights by combining the set of key coupling points.

[0157] The critical node guidance module, based on the optimization function and referring to the set of critical nodes, updates the energy flow intensity of the cycle stage, calculates the energy flow intensity allocation value of all cycle stages, and updates the weight allocation between stages.

[0158] The activity optimization module filters key coupling paths using energy flow thresholds to identify key activity items for optimization during specific periods.

[0159] This embodiment also provides a computer device applicable to the consumer product carbon footprint accounting method based on the entire life cycle, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the consumer product carbon footprint accounting method based on the entire life cycle as proposed in the above embodiment.

[0160] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0161] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the consumer product carbon footprint accounting method based on the entire life cycle as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0162] In summary, this invention reveals the nonlinear relationship between coupling points and the overall system through inter-stage data flow matrix balancing analysis. By sorting the set of key coupling points, an efficient collaborative emission reduction path is established. Based on activity points with high contribution rates, a set of key coupling points is ultimately formed. By integrating energy flow and carbon emission data into a multi-level technical solution, a systematic capability for collaborative emission reduction throughout the entire life cycle is formed. The combination of energy flow allocation and energy consumption efficiency analysis supplements the single-dimensional optimization scheme for carbon emissions, improving the pertinence and effectiveness of the entire process from the perspective of collaborative management of energy and carbon.

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A full life cycle based accounting method for carbon footprint of consumer goods, characterized in that, Comprise: Collecting consumer product production process activity data, dividing the life cycle stages to obtain corresponding carbon emission factor data, calculating actual carbon emission data, and counting the total actual carbon emission of different life cycle stages as the stage intensity value, and calculating the contribution proportion of the corresponding life cycle stage intensity value according to the activity data, determining the data flow intensity combined with the resource flow amount of different cycle stages, analyzing the carbon intensity correlation of different cycle stages, and defining the inter-stage data flow matrix; Based on the consistency verification of the inter-stage data flow matrix, if the verification is inconsistent, calculate the carbon emission intensity difference, and sequentially check the contribution proportion of different activities within the cycle stage, if there is a deviation between the actual emission intensity and the theoretical emission intensity of the activity, smooth the carbon emission intensity data marked as abnormal activity, update the output corrected emission intensity, and form an emission intensity set by combining the corrected emission intensity of all cycle stages; Based on the actual data unit of different activities in the cycle stage, based on the emission intensity set, observe the change of the corrected emission intensity of different stages under different activity scales, define the activity scale factor matrix based on the actual data unit of different activities in the cycle stage, calculate the change rate based on the activity scale factor of different activities, analyze the contribution change of the activity scale factor of different activities to the emission intensity of different cycle stages, integrate and mark the critical point according to the activity scale with the largest change rate in the activity scale factor matrix, and sort to form a key node set, re-verify based on the corrected emission intensity, and update the inter-stage data flow matrix data, calculate the proportion of carbon intensity correlation value in all stages as the contribution rate through the updated inter-stage data flow matrix data, and sort to form a key coupling point set; According to the energy consumption data of different cycle stages, analyze the energy flow intensity and calculate the energy consumption efficiency within the stage based on the carbon emission intensity, construct an inter-stage energy flow vector model, define the energy flow allocation between each two stages according to the correlation factor, and adjust the energy flow allocation based on the key coupling point set; Set the optimal carbon emission efficiency as the optimization target, define the optimization function, and introduce the energy consumption efficiency within the stage, refer to the change rate corresponding to the critical point of different cycle stages according to the key node set as a guide, update the energy flow intensity again, and ensure the total energy conservation after the redistribution, count the allocation value of the total energy flow intensity of all cycle stages, and update the inter-stage energy flow allocation weight, wherein the adjustment value of the energy flow intensity between each cycle stage is the stage carbon emission intensity after energy efficiency optimization, according to the updated energy flow total amount, count the energy flow proportion of different stages, and combine the unit energy flow emission intensity after optimization to define the optimization objective function, and through gradient descent method for iterative optimization, determine the optimized energy flow total amount of each cycle stage, and minimize the overall carbon emission cost of the life cycle; According to the energy flow threshold, screen the cycle stage corresponding to the energy flow total amount, mark as the key coupling path, and determine the cycle stage activity item that needs to be optimized. The performing period stage division obtains corresponding carbon emission factor data, analyzes carbon emission stage intensity values of different periods, and calculates contribution proportions of period stages, defines an inter-stage data flow matrix, including, Period stage division is performed on base activity data in the whole life cycle of a consumer product, including production, transportation, sales, use, and recycling. For each divided period stage, specific activities in different period stages of the consumer product and activity data of the consumer product are determined, and corresponding carbon emission factor data are obtained from a carbon emission factor library; Actual carbon emission data are calculated according to activity data and unit emission factors, and actual carbon emission totals of different life cycle stages are counted as stage intensity values, and the contribution proportions of the corresponding life cycle stage intensity values are calculated according to activity data; Based on carbon emission factors and resource flow amounts of different period stages, data flow intensity is determined, carbon intensity correlation of different period stages is analyzed based on stage intensity values, and an inter-stage data flow matrix is defined according to carbon intensity correlation values of different period stages; The activity scale factor matrix is defined based on actual data units of different activities in the period stage, change rate calculation is performed, a key node set is sorted, the contribution rate of the carbon intensity correlation value of the updated inter-stage data flow matrix data is calculated, and a key coupling point set is sorted, including, Based on the emission intensity set, the change of the corrected emission intensity of different stages under different activity scales is observed, and the activity scale factor matrix is defined based on actual data units of different activities in the period stage; Change rate calculation is performed based on the activity scale factor of different activities, the contribution change of the activity scale factor of different activities to the emission intensity of different period stages is analyzed, the critical point is integrated and marked according to the activity scale with the largest change rate in the activity scale factor matrix, and a key node set is sorted; The proportion of the carbon intensity correlation value in all inter-stage carbon intensity correlation values is calculated as the contribution rate through the updated inter-stage data flow matrix data, the sum of the mean value and twice the standard deviation of the contribution rate of the historical data is taken as the coupling point threshold value, and the activity points of the carbon intensity correlation value with a contribution rate greater than the coupling point threshold value are sorted to form a key coupling point set; According to the energy consumption data of different period stages, energy flow intensity is analyzed, and intra-stage energy consumption efficiency is calculated based on carbon emission intensity, a inter-stage energy flow vector model is constructed for energy flow allocation, and weight adjustment is performed on the energy flow allocation based on the key coupling point set, including, According to the energy consumption data of different period stages, energy flow intensity is analyzed, and intra-stage energy consumption efficiency is calculated based on carbon emission intensity; The inter-stage energy flow vector model is constructed, which is defined by a formula and represented as: wherein, represents the total amount of energy flow from the i-th cycle phase to the j-th cycle phase, represents the key coupling point weight adjustment factor, represents the energy flow intensity of the i-th cycle phase, represents the energy flow proportion from the i-th cycle phase to the j-th cycle phase; Energy flow allocation between every two stages is defined according to the correlation factor, and weight adjustment is performed on the energy flow allocation based on the key coupling point set, which is represented as: wherein, represents the carbon intensity linkage value of the ith cycle phase to the kth cycle phase; Carbon emission efficiency optimization is set as the optimization target, an optimization function is defined, intra-stage energy consumption efficiency is introduced, the change rate corresponding to the critical point of different period stages is taken as a guide according to the key node set, energy flow intensity is updated again, and the total energy is conserved after the redistribution, and the specific process is represented as: wherein, represents the optimization target value of the i-th cycle phase, represents the distribution value of the activity energy flow intensity, represents the average value of the in-phase energy consumption efficiency, represents the adjustment step of the i-th cycle phase, represents the change rate of the i-th cycle phase, represents the reference step, determined based on historical experience, represents the energy flow intensity of the o-th activity in the i-th cycle phase, represents the in-phase energy consumption efficiency of the o-th activity in the i-th cycle phase, represents the corrected emission intensity of the o-th activity in the i-th cycle phase; The distribution values of the total energy flow intensity of all cycle stages are counted, and the inter-stage energy flow distribution weight is updated, the energy flow proportion of different stages is counted, an optimization objective function is defined for iterative optimization, and the optimized total energy flow of each cycle stage is determined, including The distribution values of the total energy flow intensity of all cycle stages are counted, and the inter-stage energy flow distribution weight is updated, wherein the adjustment value of the energy flow intensity between each cycle stage is based on the stage carbon emission intensity after energy efficiency optimization; According to the updated total energy flow, the energy flow proportion of different stages is counted, and combined with the optimized unit energy flow emission intensity, the overall life cycle carbon emission cost is minimized, the unit energy flow emission intensity between stages is taken as the target, an optimization objective function is defined, iterative optimization is performed by gradient descent method, and the optimized total energy flow of each cycle stage is determined.

2. The full life cycle based carbon footprint accounting method for consumer goods according to claim 1, characterized in that: The consistency verification is performed based on the inter-stage data flow matrix, if the verification is inconsistent, the carbon emission intensity difference is calculated, the contribution proportion of different activities is sequentially checked, the carbon emission intensity data marked as abnormal activities is smoothed and corrected, and the output corrected emission intensity is updated, including The consistency verification is performed based on the inter-stage data flow matrix, and whether the sum of all flow intensities of the inter-stage flow matrix is consistent with the stage intensity value of all stages is verified; If the verification is consistent, the data flow conservation is judged, if the verification is inconsistent, the verification fails, the cycle stage causing the data inconsistency is located, the carbon emission intensity difference is calculated, and the contribution proportion of different activities in the cycle stage is sequentially checked, if the actual emission intensity of the activity deviates from the theoretical emission intensity, the activity is marked as an abnormal activity; The carbon emission intensity data marked as abnormal activities is smoothed and corrected, the output corrected emission intensity is updated, and the corrected emission intensities of all cycle stages form an emission intensity set.

3. The full life cycle based carbon footprint accounting method for consumer goods according to claim 1, wherein: The cycle stage corresponding to the energy flow total amount is screened according to the energy flow threshold value, and is marked as a key coupling path, including The sum of the mean and standard deviation of the historical energy flow total amount is taken as the energy flow threshold value, the cycle stage corresponding to the energy flow total amount greater than or equal to the energy flow threshold value is marked as a key coupling path.

4. The full life cycle based carbon footprint accounting method for consumer goods according to claim 1, characterized in that: The cycle stage activity item that needs to be optimized is determined, including According to the key coupling path, the carbon emission value between cycle stages is counted, and the cycle stage activity item that needs to be optimized is determined according to the key coupling path.

5. The full life cycle based carbon footprint accounting method for consumer goods according to claim 1, wherein: The consumption product production process activity data is collected, including The basic activity data is obtained from the production process of the consumption product, and the traceable carbon emission factor library is constructed according to the activity data and the corresponding carbon emission factor data.

6. A full life cycle based carbon footprint accounting system for consumer goods based on the full life cycle based carbon footprint accounting method of any one of claims 1-5. including The cycle stage division module collects the activity data in the production process of the consumption product, divides the cycle stages, and obtains the corresponding carbon emission factor to realize the construction of basic data; The carbon emission intensity analysis module calculates the actual carbon emission data, counts the total amount of actual carbon emission of different life cycle stages as the stage intensity value of the life cycle, calculates the contribution proportion of the corresponding life cycle stage intensity value according to the activity data, determines the data flow intensity combined with the resource flow amount of different cycle stages, analyzes the carbon intensity correlation of different cycle stages, and defines the inter-stage data flow matrix. An outlier correction module performs consistency verification based on the inter-stage data flow matrix, and performs smoothing correction on the carbon emission intensity difference and marked abnormal activities; A key set module observes the changes of the corrected emission intensity of different stages under different activity scales based on the emission intensity set, defines an activity scale factor matrix based on the actual data units of different activities in the period stage, calculates the change rate based on the activity scale factor of different activities, analyzes the contribution change of the activity scale factor of different activities to the emission intensity of different period stages, integrates and marks the critical points according to the activity scale with the largest change rate in the activity scale factor matrix, sorts to form a key node set, calculates the proportion of the carbon intensity correlation value in all inter-stage carbon intensity correlation values as the contribution rate through the updated inter-stage data flow matrix data, and sorts to form a key coupling point set; An intra-stage energy consumption efficiency analysis module analyzes the energy flow intensity of the period stage and calculates the intra-stage energy consumption efficiency in combination with the carbon emission data, constructs an inter-stage energy flow vector model, defines the energy flow distribution between each two stages according to the correlation factor, and adjusts the energy flow distribution based on the key coupling point set; A key node guiding module sets the optimal carbon emission efficiency as an optimization target, defines an optimization function, and introduces the intra-stage energy consumption efficiency, refers to the key node set according to the change rate corresponding to the critical point of different period stages as a guide, updates the energy flow intensity, and ensures the total energy conservation after the redistribution, counts the distribution value of the total energy flow intensity of all period stages, and updates the inter-stage energy flow distribution weight, wherein the adjustment value of the energy flow intensity between each period stage is the stage carbon emission intensity after the energy efficiency optimization; An activity item optimization module filters the key coupling path through the energy flow threshold, and locates the period stage activity item for key optimization; The corresponding carbon emission factor data is obtained by performing period stage division, the carbon emission stage intensity values of different periods are analyzed and the contribution proportions of the period stages are calculated, and the inter-stage data flow matrix is defined, including, The basic activity data is divided into period stages based on the whole life cycle of the consumer goods, including production, transportation, sales, use and recycling, the specific activities of the consumer goods in different period stages and the activity data of the consumer goods are determined for each divided period stage, and the corresponding carbon emission factor data is obtained from the carbon emission factor library; The actual carbon emission data is calculated according to the activity data and the unit emission factor, and the total actual carbon emission amount of different life cycle stages is counted as the life cycle stage intensity value, and the contribution proportion of the corresponding life cycle stage intensity value is calculated according to the activity data; The data flow intensity is determined based on the carbon emission factor and the resource flow amount of different period stages, the carbon intensity correlation of different period stages is analyzed in combination with the stage intensity value, and the inter-stage data flow matrix is defined according to the carbon intensity correlation value of different period stages; The activity scale factor matrix is defined based on the actual data units of different activities in the period stage, the change rate is calculated, the key node set is sorted, the contribution rate of the carbon intensity correlation value is calculated based on the updated inter-stage data flow matrix data, and the key coupling point set is sorted, including, Based on the emission intensity set, the changes of the corrected emission intensity of different stages under different activity scales are observed, and the activity scale factor matrix is defined based on the actual data units of different activities in the cycle stage; Based on the activity scale factor of different activities, the change rate is calculated, the contribution change of the activity scale factor of different activities to the emission intensity of different cycle stages is analyzed, the critical point is integrated and marked according to the activity scale with the largest change rate in the activity scale factor matrix, and the key node set is sorted; Through the updated inter-stage data flow matrix data, the proportion of the carbon intensity correlation value in all inter-stage carbon intensity correlation values is calculated as the contribution rate, the sum of the mean value and twice the standard deviation of the contribution rate based on the historical data is taken as the coupling point threshold value, and the activity points with the carbon intensity correlation value greater than the coupling point threshold value are sorted to form the key coupling point set; According to the energy consumption data of different cycle stages, the energy flow intensity is analyzed, and the energy consumption efficiency in the stage is calculated based on the carbon emission intensity, the inter-stage energy flow vector model is constructed for energy flow allocation, and the energy flow allocation is adjusted based on the key coupling point set, including, According to the energy consumption data of different cycle stages, the energy flow intensity is analyzed, and the energy consumption efficiency in the stage is calculated based on the carbon emission intensity; The inter-stage energy flow vector model is constructed, which is defined by formula and represented as: wherein, represents the total amount of energy flow from the i-th cycle phase to the j-th cycle phase, represents the key coupling point weight adjustment factor, represents the energy flow intensity of the i-th cycle phase, represents the energy flow proportion from the i-th cycle phase to the j-th cycle phase; The energy flow allocation between every two stages is defined according to the correlation factor, and the energy flow allocation is adjusted based on the key coupling point set, which is represented as: wherein, represents the carbon intensity linkage value of the ith cycle phase to the kth cycle phase; The carbon emission efficiency is set as the optimization target, the optimization function is defined, the energy consumption efficiency in the stage is introduced, the change rate corresponding to the critical point of different cycle stages is taken as a guide according to the key node set, the energy flow intensity is updated, and the total energy is conserved after the redistribution, and the specific process is represented as: wherein, represents an optimization target value of the i-th cycle phase, represents an allocation value of the activity energy flow intensity, represents an average value of the in-phase energy consumption efficiency, represents an adjustment step length of the i-th cycle phase, represents a change rate of the i-th cycle phase, represents a reference step length, determined based on historical experience, represents an energy flow intensity of o activities in the i-th cycle phase, represents an in-phase energy consumption efficiency of o activities in the i-th cycle phase; The distribution value of the total energy flow intensity of all cycle stages is counted, and the inter-stage energy flow allocation weight is updated, the energy flow proportion of different stages is counted, the optimization objective function is defined for iterative optimization, and the optimized total energy flow of each cycle stage is determined, including, The distribution value of the total energy flow intensity of all cycle stages is counted, and the inter-stage energy flow allocation weight is updated, the adjustment value of the energy flow intensity between each cycle stage is based on the stage carbon emission intensity optimized based on energy efficiency; According to the updated energy flow total amount, the energy flow proportion of different stages is counted, and the unit energy flow emission intensity after optimization is combined to minimize the overall carbon emission cost of the life cycle, the unit energy flow emission intensity between stages is taken as the target, the optimization objective function is defined, the gradient descent method is used for iterative optimization, and the optimized total energy flow of each cycle stage is determined. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the full life cycle based consumption product carbon footprint accounting method of any one of claims 1-5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the full life cycle based consumption product carbon footprint accounting method of any one of claims 1-5.

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