Consumer product carbon footprint accounting method, system and device based on full life cycle and readable storage medium thereof

Abstract

The invention discloses a consumer goods carbon footprint accounting method, system and device based on a full life cycle and a readable storage medium thereof, and relates to the technical field of carbon footprint accounting, and the method comprises the steps: collecting consumer goods production process activity data, carrying out the cycle stage division to obtain corresponding carbon emission factor data, and carrying out the calculation of the carbon emission factor data; carbon emission stage intensity values of different periods are analyzed, contribution proportions of period stages are calculated, an inter-stage data flow matrix is defined, consistency verification is carried out based on the inter-stage data flow matrix, if verification is not consistent, carbon emission intensity differences are calculated, and contribution proportions of different activities are sequentially checked. According to the method, the nonlinear relation between coupling points and the whole system is revealed through inter-stage data flow matrix balance analysis, an efficient collaborative emission reduction path is established through sorting of key coupling point sets, and the key coupling point sets are finally formed based on active points 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. 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

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides a method and system 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 profound 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. This approach fails to meet the optimization needs of complex, multi-stage, continuous operations of consumer products.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: 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: 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. 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; Based on the actual data units of different activities in the periodic stage, an activity scale factor matrix is defined, a change rate is calculated, a key node set is sorted, the inter-stage data flow matrix data is updated, a carbon intensity correlation value contribution rate is calculated, and a key coupling point set is sorted. According to the energy consumption data of different periodic stages, the energy flow intensity is analyzed, the energy consumption efficiency in the stage is calculated based on the carbon emission intensity, the inter-stage energy flow vector model is constructed to perform energy flow distribution, and the energy flow distribution is adjusted based on the key coupling point set. An optimization function is constructed, the key node set is referred to as a guide, the energy flow intensity is updated, the total energy flow intensity distribution value of all periodic stages is calculated, and the inter-stage energy flow distribution weight is updated, the energy flow proportion of different stages is calculated, the optimization target function is defined for iterative optimization, and the optimized total energy flow of each periodic stage pair is determined. According to the energy flow threshold, the energy flow total amount corresponding to the periodic stage pair is screened, which is marked as a key coupling path, and the periodic stage activity item that needs to be optimized is determined.

[0006] As a preferred scheme of the full life cycle-based consumer carbon footprint accounting method, wherein: the periodic stage division is performed to obtain corresponding carbon emission factor data, the carbon emission stage intensity values of different periods are analyzed, and the contribution proportion of the periodic stage is calculated, and the inter-stage data flow matrix is defined, including, The full life cycle of the consumer is based on the periodic stage division of the basic activity data, including production, transportation, sales, use and recycling, for each divided periodic stage, the specific activities of different periodic stages of the consumer and the activity data of the consumer are determined, and the corresponding carbon emission factor data is obtained from the carbon emission factor library; According to the activity data and the unit emission factor, the actual carbon emission data is calculated, and the actual carbon emission total of different life cycle stages is calculated 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; Based on the carbon emission factor, the data flow intensity is determined according to the resource flow amount of different periodic stages, the carbon intensity correlation of different periodic stages is analyzed, and the inter-stage data flow matrix is defined.

[0007] As a preferred scheme of the full life cycle based carbon footprint accounting method of consumer goods, wherein: the consistency verification based on the inter-stage data flow matrix is performed, 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 corrected emission intensity is updated, including, The consistency verification based on the inter-stage data flow matrix is performed to verify whether the stage intensity values of all stages are consistent with the sum of all flow intensities of the inter-stage flow matrix. If the verification is consistent, it is determined that the data flow is conserved, if the verification is inconsistent, it is determined that the verification fails, the period stage causing the data inconsistency is located, the carbon emission intensity difference is calculated, and the contribution proportion of different activities in the period stage is sequentially checked, if the actual emission intensity of the activity deviates from the theoretical proportion, the activity is marked as an abnormal activity. The carbon emission intensity data marked as abnormal activities is smoothed and corrected, the corrected emission intensity is updated, and the corrected emission intensity of all period stages is combined to form an emission intensity set. The corrected emission intensity is re-verified, and the inter-stage data flow matrix data is updated.

[0008] As a preferred scheme of the full life cycle based carbon footprint accounting method of consumer goods, wherein: the actual data unit of different activities in the period stage is defined to define an activity scale factor matrix, 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 change of the corrected emission intensity of different stages under different activity scales is observed, the actual data unit of different activities in the period stage is defined to define an activity scale factor matrix. The change rate is calculated 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 the key node set is sorted. The proportion of the carbon intensity correlation value in all stages is calculated as the contribution rate based on 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 with the carbon intensity correlation value greater than the coupling point threshold value are sorted to form the key coupling point set.

[0009] As a preferred scheme of the full life cycle based carbon footprint accounting method of consumer goods, wherein: the energy flow intensity is analyzed according to the energy consumption data of different period stages, the energy consumption efficiency in the stage is calculated based on the carbon emission intensity, the energy flow vector model between stages is constructed, the energy flow distribution is adjusted based on the key coupling point set, including, The energy flow intensity is analyzed according to the energy consumption data of different period stages, and the energy consumption efficiency in the stage is calculated in combination with the carbon emission intensity; The energy flow vector model between stages is constructed, the energy flow distribution between each two stages is defined according to the correlation factor, and the energy flow distribution is adjusted based on the key coupling point set.

[0010] As a preferred scheme of the full life cycle based carbon footprint accounting method of consumer goods, wherein: the optimization function is constructed, the key node set is referred to as a guide to update the energy flow intensity, including, The optimization target of optimal carbon emission efficiency is set, the optimization function is defined, the energy consumption efficiency in the stage is introduced, the key node set is referred to as a guide according to the change rate corresponding to the critical point of different period stages to update the energy flow intensity, and the total energy conservation after the redistribution is ensured.

[0011] As a preferred scheme of the full life cycle based carbon footprint accounting method of consumer goods, wherein: the total energy flow intensity distribution value of all period stages is counted, the energy flow distribution weight between stages is updated, the energy flow proportion of different stages is counted, the optimization target function is defined for iterative optimization, and the optimized total energy flow of each period stage pair is determined, including, The total energy flow intensity distribution value of all period stages is counted, and the energy flow distribution weight between stages is updated, wherein the adjustment value of the energy flow intensity between each period stage is the stage carbon emission intensity after the energy efficiency optimization; The energy flow proportion of different stages is counted according to the updated total energy flow, the optimization target function is defined in combination with the optimized unit energy flow emission intensity, the iterative optimization is carried out by the gradient descent method, and the optimized total energy flow of each period stage pair is determined.

[0012] As a preferred scheme of the full life cycle based carbon footprint accounting method of consumer goods, wherein: the period stage pair corresponding to the total energy flow is screened according to the energy flow threshold value, and is marked as a key coupling path, including, The sum of the mean and the standard deviation of the total energy flow in history is taken as the energy flow threshold value, the period stage pair corresponding to the total energy flow greater than or equal to the energy flow threshold value is marked as a key coupling path.

[0013] As a preferred scheme of the full life cycle based carbon footprint accounting method of consumer goods, wherein: the period stage activity item that needs to be optimized is determined, including, 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.

[0014] 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: 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.

[0015] Secondly, this invention provides a consumer product carbon footprint accounting system based on the entire life cycle, including: 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. 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. 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. The key set module defines the activity scale factor matrix, calculates the rate of change and sorts it to generate a key node set. Based on the updated data flow matrix data, it calculates the contribution rate of carbon intensity correlation value and sorts it to generate a key coupling point set. 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. 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 inter-stage allocation weights. The activity optimization module filters key coupling paths using energy flow thresholds to identify key activity items for optimization during specific periods.

[0016] 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.

[0017] 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.

[0018] The application has the advantages that: through inter-stage data flow matrix balance analysis, the nonlinear relationship between the coupling point and the whole system is revealed, through the sorting of the key coupling point set, an efficient collaborative emission reduction path is established, based on the high contribution rate of the activity point, the key coupling point set is finally formed, through the integration of the multi-level technical scheme of energy flow and carbon emission data, the systematic ability of whole life cycle collaborative emission reduction is formed, the combination of energy flow allocation and energy efficiency analysis supplements the single-dimensional optimization scheme of carbon emission, and the whole process is improved in pertinence and effectiveness from the collaborative management of energy and carbon. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Fig. 1 The flowchart of the whole life cycle-based carbon footprint accounting method of the consumer product in embodiment 1.

[0021] Fig. 2 The structural diagram of the whole life cycle-based carbon footprint accounting system of the consumer product in embodiment 1. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0023] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0024] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0025] Embodiment 1, refer to Figs. 1-2 , as the first embodiment of the present application, the embodiment provides a whole life cycle-based carbon footprint accounting method of consumer product, including the following steps: Preferably, the activity data of the consumer product production process is collected, including, The basic activity data is obtained from the production process of the consumer goods, including relevant activity data such as material production consumption, transportation consumption, sales consumption, use consumption, and recycling consumption, and a traceable carbon emission factor library is constructed according to the activity data and corresponding carbon emission factor data, wherein the unit of the carbon emission factor is / ton, representing the carbon dioxide emissions per ton of goods.

[0026] Further, the corresponding carbon emission factor data is obtained by dividing the period stage, the carbon emission stage intensity value of different periods is analyzed and the contribution proportion of the period stage is calculated, and the inter-stage data flow matrix is defined, including, The basic activity data is divided into production, transportation, sales, use, and recycling based on the whole life cycle of the consumer goods, the specific activities of different period stages of the consumer goods and the activity data of the consumer goods (such as the power consumption data of the transportation stage) are determined for each divided period stage, and the corresponding carbon emission factor data is obtained from the carbon emission factor library, which is represented as: ; Wherein, represents the activity data set of the i-th period stage, represents the m-th activity data of the i-th period stage, represents the m-th activity unit emission factor of the i-th period stage, wherein m is the total number; The actual carbon emission data is calculated according to the activity data and the unit emission factor, and the total amount of actual carbon emission of different life cycle stages is calculated as the stage intensity value of the life cycle, 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 (such as the amount of raw materials transported from the production stage to the sales stage), the carbon intensity correlation of different period stages is analyzed, and the inter-stage data flow matrix is defined, which is represented as: ; ; ; Wherein, represents the data flow intensity from period stage i to j, represents the resource flow amount from period stage i to j, and n represents the total number of period stages, represents the resource flow amount from period stage i to k, represents the stage intensity value of period stage i, represents the carbon intensity correlation value between period stages i and j, represents the inter-stage data flow matrix.

[0027] By combining the activity data set with the unit emission factor, the carbon emission data of the refined life cycle stage is determined, the accuracy of carbon emission accounting is improved, the activity data set clearly specifies the specific activities of each stage of the life cycle (such as energy consumption in the production stage, fuel use in the transportation stage, and power consumption in the sales stage), refines the source items of carbon emissions in the life cycle of consumer goods, and quantifies the carbon emission contribution of individual activities by combining the unit carbon emission factor (such as the emission factor of energy and fuel); The intensity value of the life cycle stage is determined by the cumulative sum of the carbon emissions of each activity. Through the contribution proportion of each activity, the activity that contributes most to the carbon emission intensity in each stage can be intuitively identified. For example, the transportation stage may become the main emission source due to high oil consumption; Through systematic carbon intensity correlation between stages by data flow matrix, the transmission problem of carbon emission caused by resource flow between stages is solved, and how resource flow between different stages affects carbon emission is clearly specified. For example, high-intensity correlation points can be identified at nodes with high resource flow, which helps to accurately find the coupling points that can be optimized; By combining resource flow and carbon intensity correlation value, the carbon footprint difference of resource flow path is displayed, such as switching from land transportation to sea transportation, which intuitively quantifies the emission reduction effect of optimization on carbon emission; Through inter-stage data flow matrix balance analysis, the non-linear relationship between coupling points and the whole system is revealed. The introduction of data flow matrix effectively solves the problem of fragmentation between single-stage carbon emission accounting and cross-stage correlation operation. The identification of coupling points reveals the hidden connection behind the behavior of individual stage systems. Through the combination of stage intensity value, resource flow amount and data flow matrix, multi-dimensional data cross-analysis capability is formed.

[0028] Embodiment 2, refer to Figs. 1-2 For the second embodiment of the present application, the embodiment provides a full life cycle-based carbon footprint accounting method for consumer goods, comprising the following steps: Preferably, 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 of the marked abnormal activities is smoothed and corrected, and the corrected emission intensity is updated and output, including, Consistency verification is performed based on the inter-stage data flow matrix. Whether the sum of the stage intensity values of all stages is consistent with the sum of all flow intensities of the inter-stage flow matrix is verified. If the sum of all flow intensities of the inter-stage flow matrix is the same as the sum of the stage intensity values of all stages, it is judged that the verification is consistent. If the sum of all flow intensities of the inter-stage flow matrix is different from the sum of the stage intensity values of all stages, it is judged that the verification is inconsistent, which is expressed as: ; ; wherein, denotes the sum of the stage intensity values of all stages, denotes the sum of the stage intensity values of all stages: If the verification is consistent, it is judged that the data flow is consistent, if the verification is inconsistent, it is judged that the verification fails, the period stage causing the data inconsistency is located, and the carbon emission intensity difference is calculated, and the contribution proportion of the activities in the period stage is checked in turn, if the actual emission intensity of the activity deviates from the theoretical proportion, the activity is marked as an abnormal activity, which is represented as: ; ; ; wherein, denotes the carbon emission intensity difference of the i th period stage, denotes the actual emission intensity of the o th activity in the i th period stage, denotes the unit carbon emission factor of the o th activity in the i th period stage, denotes the activity data of the o th activity in the i th period stage, denotes the carbon emission intensity deviation value of the o th activity in the i th period stage, denotes the contribution of the o th activity in the i th period stage; The carbon emission intensity data marked as abnormal activity is smoothed and corrected, the output corrected emission intensity is updated, and the corrected emission intensity of all period stages is combined to form an emission intensity set, which is represented as: ; wherein, denotes the corrected emission intensity of the o th activity in the i th period stage; Based on the corrected emission intensity, re-verification is carried out, and the inter-stage data flow matrix data is updated.

[0029] The consistency verification is carried out through the inter-stage data flow matrix, the integrity of the carbon emission accounting modeling and the data closed loop are ensured, the inconsistent source is located by calculating the carbon emission intensity difference, the abnormal stage is accurately marked, the difference value is accurately quantified by constructing the difference calculation model, the abnormal activity is finely selected by checking the contribution proportion of the activities in the stage in turn, the granularity of the abnormal check is refined from the stage level to the activity level, which not only improves the accuracy of the abnormal positioning, but also reduces the information loss that may be caused by the traditional extensive analysis in the stage unit; The smoothing correction mechanism corrects the actual deviated carbon emission intensity while not deleting the dynamic range information of the original data, greatly reducing the information loss that may occur in the data processing process, by smoothing and correcting the abnormal activity data, ensuring the continuity and revalidation availability of the output emission intensity set; By revalidating the emission intensity and inter-stage data flow matrix, the data accuracy is dynamically adjusted and updated, so that the data flow intensity can be continuously aligned with the actual operation, thereby solving the problem of cumulative error expansion caused by stage deviation in full life cycle accounting. Through the combination of abnormal activity marking and data flow matrix updating, multi-level data management in vertical depth and horizontal breadth is realized. The double-level data management method not only finds problems, but also generates effective improvement measures, which has practical significance for dynamic adjustment of the whole system scheme. Through dynamic correction of the carbon emission intensity set, the difference between the stages in the whole cycle is quantitatively decided, which not only ensures the reliability of the intermediate link operation, but also introduces accurate sparsification strategy in the final output (i.e. enhances the focus management of high-emission activities).

[0030] Further, based on the actual data units of different activities in the cycle stage, an activity scale factor matrix is defined, the change rate is calculated, and a key node set is sorted. The contribution rate of the updated inter-stage data flow matrix data to the carbon intensity correlation value is calculated, and a key coupling point set is sorted, including, Based on the emission intensity set, the change of the corrected emission intensity in different activities under different activity scales in different stages is observed, and based on the actual data units of different activities in the cycle stage, an activity scale factor matrix is defined, which is represented as: ; Wherein, represents the activity scale factor matrix, represents the total number of n cycle stages, and the total number of Q activity scale factors, such as the material consumption of the transportation 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 marked according to the activity scale with the largest change rate in the activity scale factor matrix, and a key node set is sorted, which is represented as: ; ; ; Wherein, represents the total carbon emission intensity of the i th cycle stage under the given activity scale factor matrix, represents the initial carbon emission intensity under the reference activity scale of the i th cycle stage, sensitivity coefficient of the oth activity in the ith cycle stage, obtained by regression analysis of the unit activity on the emission, activity scale factor of the oth activity in the ith cycle stage, change rate of the oth activity point, activity scale factor of the activity point with the largest change rate, used to mark the critical point; By updating the 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, and the sum of the mean value and twice the standard deviation of the contribution rate based on historical data is taken as the coupling point threshold. The activity points of the carbon intensity correlation values greater than the coupling point threshold are sorted to form a key coupling point set.

[0031] By constructing the activity scale factor matrix, the activity data is unified at different temporal and spatial scales, the cross-stage comparison ability of the analysis is enhanced, the activity data of different granularities in each stage can be quantitatively analyzed in a unified framework, and the foundation is laid for subsequent change rate calculation and critical point marking. Through the calculation of the change rate of the activity scale factor on the contribution of the stage emission intensity, a dynamic optimization priority among activities is formed, which provides a fine evaluation of the emission dynamics of the activity scale change, so that high-sensitivity activities can be preferentially marked, and the blindness or invalid operation of resource optimization is reduced; By forming the key node set, the focusing degree of high-emission activities within the stage is optimized, and the critical point marking based on the change rate calculation makes the key node set not only contain activities with high emission intensity, but also pay attention to the responsiveness of emission intensity to activity scale change, providing a more dynamic basis for optimization decision-making; By updating the inter-stage data flow matrix, the carbon intensity coupling relationship between the cycle stages is identified, and the resource flow path selection is optimized. The inter-stage data flow matrix not only combines the resource flow, but also accurately determines the carbon intensity correlation. The dynamic mapping of the emission transmission effect between stages to the resource flow path ensures that not only the single-stage emission intensity is concerned, but also the transmission effect on the upstream or downstream stage is captured; By sorting the key coupling point set, an efficient and collaborative emission reduction path is established. Based on the activity points with high contribution rate, the key coupling point set is finally formed, which can determine the design logic of the priority emission reduction path, such as solving high correlation coupling points according to the resource flow path, and then forming an inter-stage collaborative emission reduction scheme.

[0032] Further, according to the energy consumption data of different cycle stages, the energy flow intensity is analyzed, the energy consumption efficiency within 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 combined with the carbon emission intensity, which is represented as: ; ; wherein, represents the energy flow intensity of the i-th cycle stage, represents the average power of the o-th activity in the i-th cycle stage, represents the duration of the o-th activity in the i-th cycle stage, represents the energy flow intensity of the o-th activity in the i-th cycle stage, represents the energy consumption efficiency in the stage of the o-th activity in the i-th cycle stage, represents the corrected emission intensity of the o-th activity in the i-th cycle stage; A stage-to-stage energy flow vector model is constructed, the energy flow distribution between each two stages is defined according to the correlation factor, and the energy flow distribution is weighted based on the set of key coupling points, (to ensure that the energy flow distribution of the key coupling nodes is optimized first), which is represented as: ; ; wherein, represents the total energy flow from the i-th cycle stage to the j-th cycle stage, represents the key coupling point weight adjustment factor, represents the carbon intensity correlation value between the i-th cycle stage and the k-th cycle stage, represents the energy flow ratio from the i-th cycle stage to the j-th cycle stage, which is calculated by .

[0033] Through the step-by-step accumulation calculation of cycle stage energy flow intensity and activity energy flow intensity, the intuitive distribution of stage energy consumption is generated. The total energy flow intensity of the cycle stage is calculated by accumulating the average power and duration data of each activity in the stage, which fills the gap of static description of original data in energy consumption analysis, forms a dynamic and detailed energy consumption distribution chart, and supports comprehensive tracking of multi-level energy consumption sources through decomposition and accumulation of activity energy flow intensity, which helps to find the energy consumption mode of specific activities; Through the calculation of energy consumption efficiency in the stage, the energy consumption level is associated with the carbon emission intensity, and the stage performance is accurately evaluated. The energy consumption efficiency in the stage is calculated by combining the energy flow intensity and the carbon emission intensity, which reveals the utilization efficiency of energy resources corresponding to unit emission in each stage. Energy consumption efficiency analysis breaks through the limitations of traditional energy consumption and carbon emission accounting, enabling process optimization to balance the trade-off between energy consumption and carbon emission minimization goals; The energy flow between different period stages is quantified through the energy distribution calculation by the inter-stage energy flow vector model, the data gap of the flow difference between stages in the life cycle analysis is filled, the energy flow distribution is optimized through the key coupling point weight adjustment, a resource allocation priority mechanism is constructed with the key node as the core, the key coupling point weight adjustment factor directly binds the energy flow distribution to the key carbon associated node, and by adjusting the weight, more reasonable energy distribution is configured for the stages and paths with high correlation; The flow energy consumption and emission intensity are uniformly managed through the dynamic cross-stage mapping of the carbon intensity correlation value, the carbon intensity correlation value provides a dynamic adjustment basis in the energy flow proportion and distribution calculation, so that the cross-stage energy optimization not only depends on the energy consumption structure, but also considers the carbon footprint of each stage The energy flow distribution dynamic feedback of the whole life cycle is quantified through the coupling model of the energy flow proportion and the correlation factor, the energy consumption analysis becomes a traceable dynamic feedback mechanism through the intervention of the correlation factor, and the energy optimization cooperation network is constructed through the inter-stage energy flow vector and the key coupling point weight adjustment. The network not only concerns the energy distribution of the local path, but also depends on the cooperation effect of the global network, so that the multi-path optimization target in the complex system can have more synergy; Through the multi-level technical scheme integrating the energy flow and the carbon emission data, the systematic ability of the whole life cycle collaborative emission reduction is formed, the combination of the energy flow distribution and the energy consumption efficiency analysis supplements the single-dimensional optimization scheme of the carbon emission, the whole process is improved in pertinence and effectiveness from the collaborative management of energy and carbon, and on the basis of the dynamic energy and the static carbon emission, the coupling relationship between the stages and the paths of the life cycle is dynamically visualized through the integration of the vector model and the weight adjustment factor.

[0034] Embodiment 3, refer to Figs. 1-2 For the third embodiment of the present application, the embodiment provides a kind of carbon footprint accounting method of consumer goods based on whole life cycle, comprising the following steps: Preferably, an optimization function is constructed, a key node set is referred to as a guide, energy flow intensity is updated, including, Set carbon emission efficiency optimum as optimization target, define optimization function, and introduce intra-stage energy consumption efficiency, refer to key node set according to the change rate corresponding to different period stages critical point as a guide, update energy flow intensity, and ensure that the total energy is conserved after redistribution, expressed as: ; ; ; Wherein, represent the optimization target value of the i-th period stage, represent the distribution value of activity energy flow intensity, represents the average value of energy consumption efficiency in the stage, represents the adjustment step of the i-th cycle stage, represents the change rate of the i-th cycle stage, represents the reference step, which is determined based on historical experience.

[0035] By setting the function optimization goal as the optimal carbon emission efficiency, the energy consumption and carbon emission management are quantified, a multi-dimensional optimization constraint system is formed, and the objective function optimizes the relationship between energy consumption efficiency and carbon emission intensity in different stages. A dynamic and two-way interactive target constraint system is established. By introducing the energy consumption efficiency in the stage, the boundary of carbon emission efficiency optimization is dynamically adjusted, and efficient resource allocation is realized. The energy consumption efficiency in the stage is an important component of the optimization function, which reflects the effectiveness of current energy consumption, and directly constrains the resource allocation ratio in the optimization process. By referring to the change rate, the critical points of the key node set are introduced into the optimization target, enhancing the execution priority of optimization. The redistribution of active energy flow intensity combined with the constraint of total energy conservation makes the adjustment after each optimization more in line with actual resource demand, ensuring that the overall system level always follows the balanced utilization rule of resources.

[0036] Further, the total energy flow intensity distribution value of all cycle stages is counted, and the inter-stage energy flow distribution weight is updated. The energy flow ratio of different stages is counted, and the optimization target function is defined for iterative optimization to determine the total energy flow after optimization of each cycle stage pair, including, The total energy flow intensity distribution value of all cycle stages is counted, and the inter-stage energy flow distribution weight is updated. The adjustment value of the energy flow intensity between each cycle stage is based on the carbon emission intensity of the stage after energy efficiency optimization, which is represented as: ; Where, represents the updated total energy flow, represents the distribution value of the total energy flow intensity of the i-th cycle stage, represents the maximum cycle stage optimization target value, represents the optimization value of the i-th cycle stage; According to the updated total energy flow, the energy flow ratio of different stages is counted, and the optimization target function is defined by combining the unit energy flow emission intensity after optimization. The gradient descent method is used for iterative optimization to determine the total energy flow after optimization of each cycle stage pair, and the life cycle carbon emission cost is minimized. The unit energy flow emission intensity between stages is targeted, and the coupling relationship is dynamically adjusted, which is represented as: ; ; ; ; wherein, represents the energy flow ratio of the cycle stage i to j, represents the unit energy flow emission intensity of the cycle stage i to j, and represents the energy flow ratio value of the time step t+1 and t, represents the optimization compensation, updating the coupling relationship between each stage by gradient descent, and stopping iteration when the change is not obvious; Through the statistics and update of the total energy flow intensity distribution value of the cycle stage, the overall optimization and key reconfiguration of resource allocation are realized, so that the scheme can overall plan the resource allocation of the whole cycle stage, avoid the imbalance problem caused by the traditional one-sided optimization only acting on part of the cycle stage, and improve the resource utilization efficiency of the whole life cycle. By combining the stage energy flow ratio and the unit energy flow emission intensity, the synergistic relationship between energy consumption and emission is quantified, the energy consumption is directly coupled with the emission cost by combining the unit energy flow emission intensity, and the exact influence of resource flow on carbon footprint is reflected. This synergistic design is better than the traditional single-layer optimization model, which can simultaneously track the contradiction between energy consumption dynamics and emission optimization; Through setting the optimization objective function and using gradient descent method for iterative update, dynamic adjustment and optimal solution seeking are realized. Through the optimization compensation mechanism, the resource coupling relationship between the cycle stages is dynamically adjusted, the sensitivity and adaptability of the optimization are improved, and the optimization compensation mechanism ensures that the energy consumption allocation adjustment is closer to the actual data change, and establishes a sensitive regulation rule for resource reallocation.

[0037] Embodiment 4, with reference to Figs. 1-2 , the fourth embodiment of the present application provides a life cycle-based carbon footprint accounting method for consumer goods, comprising the following steps: Preferably, the energy flow total corresponding to the cycle stage pair is screened according to the energy flow threshold value, and is marked as a key coupling path, including, The sum of the mean and the standard deviation of the historical energy flow total is taken as the energy flow threshold value, and the energy flow total corresponding to the cycle stage pair greater than or equal to the energy flow threshold value is marked as a key coupling path.

[0038] Through the pre-screening of the energy flow threshold value, the life cycle key coupling path is accurately located, the analysis complexity of resource input is reduced, the carbon emission value between the cycle stages is statistically obtained through the key coupling path, the energy consumption path optimization and carbon emission reduction are coordinated and unified, the energy flow intensity and carbon emission cost are directly linked, the goal of energy flow optimization is directly mapped to emission reduction management, and the energy flow optimization operation not only pays attention to energy consumption but also pays attention to the environmental impact caused thereby.

[0039] Further, the cycle stage activity item that needs to be subjected to energy flow optimization is determined, including, According to the key coupling path, the carbon emission value between the statistical cycle stages is counted, and the cycle stage activity item needing to be subjected to energy flow optimization is determined according to the key coupling path.

[0040] By further refining the activity item in the cycle stage according to the key coupling path, the optimization task of each stage is refined from the path level to the activity level, and the specific optimization target is locked. For example, for the high-energy consumption activity in the optimization path, the corresponding energy flow allocation is directly adjusted, the key path marking and the activity item optimization are combined to form a closed-loop system of the optimization task, and the synergistic effect between the path and the activity further strengthens, so that the priority logic of resource allocation can run through the whole process from path selection to activity task execution.

[0041] The embodiment also provides a consumer product carbon footprint accounting system based on a whole life cycle, comprising, A cycle stage division module collects activity data in a consumer product production process, divides cycle stages, and obtains corresponding carbon emission factors, and realizes basic data construction; A carbon emission intensity analysis module analyzes carbon emission intensity values of different cycle stages, calculates stage contribution proportions, and establishes an inter-stage data flow matrix; An abnormal value correction module performs consistency verification based on the inter-stage data flow matrix, and smoothes and corrects carbon emission intensity differences and marked abnormal activities; A key set module defines an activity scale factor matrix, calculates a change rate and sorts to generate a key node set, calculates a carbon intensity correlation value contribution rate based on updated data flow matrix data, and sorts to generate a key coupling point set; An intra-stage energy consumption efficiency analysis module analyzes energy flow intensity of a cycle stage, calculates intra-stage energy consumption efficiency in combination with carbon emission data, constructs an energy flow vector model, and adjusts energy flow allocation weights in combination with the key coupling point set; A key node guiding module updates the energy flow intensity of the cycle stage based on an optimization function and referring to the key node set, counts energy flow intensity allocation values of all cycle stages, and updates inter-stage allocation weights; An activity item optimization module screens a key coupling path through an energy flow threshold, and locates a cycle stage activity item needing to be subjected to key optimization.

[0042] The embodiment also provides a computer device suitable for the consumer product carbon footprint accounting method based on a whole life cycle, comprising 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, so as to realize the consumer product carbon footprint accounting method based on a whole life cycle as proposed in the above embodiment.

[0043] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved by WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0044] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to implement the method for calculating the carbon footprint of a consumer product based on a whole life cycle as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk.

[0045] In summary, the present application discloses the nonlinear relationship between the coupling points and the whole system through the inter-stage data flow matrix balance analysis, establishes an efficient collaborative emission reduction path through the sorting of the key coupling point set, forms the key coupling point set based on the activity points with high contribution rate, forms the systematic ability of whole life cycle collaborative emission reduction through the multi-level technical scheme integrating the energy flow and carbon emission data, and supplements the single-dimensional optimization scheme of carbon emission through the combination of energy flow allocation and energy efficiency analysis, thereby improving the pertinence and effectiveness of the whole process from the perspective of collaborative management of energy and carbon.

[0046] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for calculating the carbon footprint of consumer products based on their entire life cycle, characterized in that, include: 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. 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. 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. 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. 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. Based on the energy flow threshold, the corresponding periodic phase pairs of total energy flow are screened, marked as key coupling paths, and the periodic phase activities that need to be optimized for energy flow are determined.

2. The method for calculating the carbon footprint of consumer products based on the entire life cycle as described in claim 1, characterized in that: The process involves dividing the cycle into stages to obtain corresponding carbon emission factor data, analyzing the intensity values ​​of carbon emission stages in different cycles and calculating the contribution ratio of each stage, and defining an inter-stage data flow matrix. include, 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. 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. 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.

3. The method for calculating the carbon footprint of consumer products based on the entire life cycle as described in claim 2, characterized in that: The consistency verification based on the inter-stage data flow matrix is ​​performed. 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 are smoothed and corrected, and the output corrected emission intensity is updated, including... 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. 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. 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; The data was re-verified based on the revised emission intensity, and the inter-stage data flow matrix data was updated.

4. The method for calculating the carbon footprint of consumer products based on the entire life cycle as described in claim 3, characterized in that: 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 data are sorted to form a set of key nodes. For the updated inter-phase data flow matrix data, the contribution rate of carbon intensity correlation values ​​is calculated, and the data is sorted to form a set of key coupling points, including... 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. 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. 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.

5. The method for calculating the carbon footprint of consumer products based on the entire life cycle as described in claim 4, characterized in that: The process involves analyzing energy flow intensity based on energy consumption data from different cycle stages and calculating energy efficiency within each stage based on carbon emission intensity. An inter-stage energy flow vector model is then constructed for energy flow allocation. Weight adjustments to the energy flow allocation are made based on a set of key coupling points. 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. 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.

6. The method for calculating the carbon footprint of consumer products based on the entire life cycle as described in claim 5, characterized in that: The constructed optimization function, guided by the set of key nodes, updates the energy flow intensity. include, 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.

7. The method for calculating the carbon footprint of consumer products based on the entire life cycle as described in claim 6, characterized in that: The process involves calculating the total energy flow intensity distribution across all cycle stages, updating the energy flow distribution weights between stages, calculating the energy flow ratios of different stages, defining an optimization objective function for iterative optimization, and determining the optimized total energy flow for each cycle stage, including... 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. 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.

8. The method for calculating the carbon footprint of consumer products based on the entire life cycle as described in claim 7, characterized in that: The step of filtering the total energy flow corresponding to the periodic stage based on the energy flow threshold and marking them as key coupling paths includes: Based on the sum of the mean and standard deviation of the historical total energy flow as the energy flow threshold, the periodic phase pairs corresponding to the total energy flow that are greater than or equal to the energy flow threshold are marked as critical coupling paths.

9. The method for calculating the carbon footprint of consumer products based on the entire life cycle as described in claim 8, characterized in that: The process of identifying the cycle phase activities requiring energy flow optimization includes, 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.

10. The method for calculating the carbon footprint of consumer products based on the entire life cycle as described in claim 2, characterized in that: The data collected pertains to the activities in the consumer goods production process. include, 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.

11. A consumer product carbon footprint accounting system based on the entire life cycle, based on the consumer product carbon footprint accounting method based on the entire life cycle as described in any one of claims 1 to 10, characterized in that: include, 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. 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. 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. The key set module defines the activity scale factor matrix, calculates the rate of change and sorts it to generate a key node set. Based on the updated data flow matrix data, it calculates the contribution rate of carbon intensity correlation value and sorts it to generate a key coupling point set. 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. 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 inter-stage allocation weights. The activity optimization module filters key coupling paths using energy flow thresholds to identify key activity items for optimization during specific periods.

12. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the consumer product carbon footprint accounting method based on the entire life cycle as described in any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the consumer product carbon footprint accounting method based on the entire life cycle as described in any one of claims 1 to 10.

Images