Power equipment product carbon footprint uncertainty analysis method and system based on AHP-PD

Abstract

The invention discloses an electric power equipment product carbon footprint uncertainty analysis method and system based on AHP-PD, and belongs to the technical field of product carbon footprint evaluation and uncertainty analysis. The method comprises the following steps: constructing a carbon footprint uncertainty hierarchical structure model comprising a target layer, a criterion layer and an index layer; constructing a judgment matrix based on the hierarchical structure model; calculating a weight set W and a local weight set Wi; synthesizing a global weight set omega according to the weight set W and the local weight set Wi; the uncertainty of the carbon footprint source is quantified; obtaining probability distribution of the total uncertainty of the carbon footprint of the electric power equipment product; and adopting the statistical characteristics of the probability distribution as the uncertainty analysis result of the carbon footprint in the full life cycle of the electric power equipment product. According to the method, the final result is in a probability form (such as a confidence interval), the real situation can be better reflected compared with a single determined value, and risk decision is supported.

Description

Technical Field

[0001] This invention relates to the field of product carbon footprint assessment and uncertainty analysis technology, and more specifically, to a method and system for carbon footprint uncertainty analysis of power equipment products based on AHP-PD. Background Technology

[0002] Advancing the "dual carbon" goals places higher demands on the green and low-carbon development of power equipment. Accurately calculating its carbon footprint is fundamental to achieving this goal. However, calculating the carbon footprint of power equipment throughout its entire lifecycle involves numerous stages, from raw material acquisition to end-of-life recycling, and the data sources are diverse, often exhibiting inherent uncertainties.

[0003] For example, energy carbon emission factors fluctuate geographically and temporally; energy and material consumption in production processes deviate from theoretical values; and emissions from processes such as SF6 leakage, an insulating gas, are difficult to monitor accurately.

[0004] Most current mainstream carbon footprint accounting methods employ deterministic approaches, providing a single, definitive value. This method cannot quantify the reliability of the results, nor can it identify the key factors that have the greatest impact on the overall uncertainty, thus making it difficult to guide companies in accurately improving data quality and optimizing emission reduction measures. Summary of the Invention

[0005] To address the above problems, this invention proposes a method for analyzing the carbon footprint uncertainty of power equipment products based on AHP-PD, comprising:

[0006] Identify the carbon footprint sources at each stage of the entire life cycle of power equipment products, and construct a hierarchical structure model of carbon footprint uncertainty, including: target layer, criterion layer and indicator layer;

[0007] Based on the hierarchical model, a judgment matrix is ​​constructed for each element of the criterion layer relative to the target layer and for each element of the indicator layer relative to the corresponding criterion layer.

[0008] Calculate the eigenvectors of the judgment matrix and perform a consistency check on the eigenvectors. After passing the check, determine the weight set W of each element of the criterion layer relative to the target layer and the local weight set Wi of each element of the index layer relative to the corresponding criterion layer based on the eigenvectors.

[0009] Based on the weight set W and the local weight set Wi, synthesize the global weight set ω of all elements of the index layer relative to the target layer;

[0010] For each carbon footprint source in the global weight set ω, the uncertainty distribution type and parameters are defined, and the uncertainty of the carbon footprint source is quantified based on the uncertainty distribution type and parameters.

[0011] Based on Monte Carlo simulation, the uncertainty of the quantified carbon footprint sources is sampled and propagated to obtain the probability distribution of the total uncertainty of the carbon footprint of power equipment products.

[0012] The statistical characteristics of the probability distribution are obtained, and the statistical characteristics of the probability distribution are used as the uncertainty analysis results of the carbon footprint of power equipment products throughout their entire life cycle.

[0013] Optionally, the target layer includes: the total uncertainty of the carbon footprint of power equipment products; the criteria layer includes at least: uncertainty of energy supply, uncertainty of energy consumption, uncertainty of process emissions, and uncertainty of key links; the indicator layer includes: specific attributable processes under the criteria layer.

[0014] Optionally, based on the hierarchical structure model, a judgment matrix is ​​constructed for each element of the criterion layer relative to the target layer and for each element of the indicator layer relative to the corresponding criterion layer, including:

[0015] Using the 1-9 scale method, the importance of each element within the same level relative to a certain criterion at the next higher level is compared pairwise to construct a judgment matrix A. The calculation formula is as follows:

[0016] A = (a ij ) n×n

[0017] Among them, a ij The scale representing the relative importance of elements i and j with respect to the previous level criterion, and satisfying a ij >0,a ji =1 / a ij ,a ii =1.

[0018] Optionally, the eigenvectors of the judgment matrix can be calculated based on the sum-product method or the square root method.

[0019] Optionally, a consistency check is performed on the feature vector, including:

[0020] Calculate the consistency index CI, query the average random consistency index RI, and calculate the consistency ratio CR;

[0021] The formula for calculating the consistency index (CI) is as follows:

[0022] CI=(λ max -n) / (n - 1)

[0023] The formula for calculating the conformity ratio (CR) is:

[0024] CR = CI / RI

[0025] When CR < 0.1, the consistency of the matrix is ​​verified.

[0026] Where, λ max To determine the largest eigenvalue of the matrix, n is the order of the matrix.

[0027] Optionally, the formula for calculating the global weight set ω is as follows:

[0028] ω = Wi·W.

[0029] Optionally, probability distributions or fuzzy numbers can be used to quantify the uncertainty of carbon footprint sources;

[0030] The probability distribution includes: normal distribution, log-normal distribution, or triangular distribution.

[0031] Optional, fuzzy number, including: trapezoidal fuzzy number or triangular fuzzy number.

[0032] Optionally, based on Monte Carlo simulations, sampling and propagation calculations are performed to account for the uncertainty of the quantified carbon footprint sources, including:

[0033] Set the number of iterations N for the Monte Carlo simulation;

[0034] For each iteration k (k = 1, 2, ..., N):

[0035] Based on the determined uncertainty distribution of each carbon footprint source, random sampling is performed to obtain a set of specific carbon footprint values.

[0036] Calculate the total product carbon footprint (CF) under this iteration. k The calculation formula is:

[0037]

[0038] Where, ω i Let i be the global weight of the i-th carbon footprint source. Let be the sampled value of the i-th carbon footprint source in the k-th iteration;

[0039] Collect all CFs obtained from N iterations. k The value represents the probability distribution of the total uncertainty of the carbon footprint of power equipment products.

[0040] Optional statistical characteristics, including at least: mean, standard deviation, and confidence interval.

[0041] Furthermore, this invention also proposes an uncertainty analysis system for the carbon footprint of power equipment products based on AHP-PD, comprising:

[0042] An initial unit is used to identify the carbon footprint sources at each stage of the entire life cycle of power equipment products, and to construct a carbon footprint uncertainty hierarchical structure model including a target layer, a criterion layer, and an indicator layer; based on the hierarchical structure model, a judgment matrix is ​​constructed for each element of the criterion layer relative to the target layer and for each element of the indicator layer relative to the corresponding criterion layer.

[0043] The calculation unit is used to calculate the eigenvectors of the judgment matrix and perform a consistency check on the eigenvectors. After passing the check, it determines the weight set W of each element of the criterion layer relative to the target layer and the local weight set Wi of each element of the index layer relative to the corresponding criterion layer based on the eigenvectors. Based on the weight set W and the local weight set Wi, it synthesizes the global weight set ω of all elements of the index layer relative to the target layer.

[0044] The output unit is used to determine the uncertainty distribution type and parameters of each carbon footprint source in the global weight set ω, and to quantify the uncertainty of the carbon footprint source according to the uncertainty distribution type and parameters; based on Monte Carlo simulation, the quantified uncertainty of the carbon footprint source is sampled and propagated to obtain the probability distribution of the total uncertainty of the carbon footprint of the power equipment product; the statistical characteristics of the probability distribution are obtained, and the statistical characteristics of the probability distribution are used as the uncertainty analysis result of the carbon footprint of the power equipment product throughout its entire life cycle.

[0045] Optionally, the target layer includes: the total uncertainty of the carbon footprint of power equipment products; the criteria layer includes at least: uncertainty of energy supply, uncertainty of energy consumption, uncertainty of process emissions, and uncertainty of key links; the indicator layer includes: specific attributable processes under the criteria layer.

[0046] Optionally, based on the hierarchical structure model, a judgment matrix is ​​constructed for each element of the criterion layer relative to the target layer and for each element of the indicator layer relative to the corresponding criterion layer, including:

[0047] Using the 1-9 scale method, the importance of each element within the same level relative to a certain criterion at the next higher level is compared pairwise to construct a judgment matrix A. The calculation formula is as follows:

[0048] A = (a ij ) n×n

[0049] Among them, a ij The scale representing the relative importance of elements i and j with respect to the previous level criterion, and satisfying a ij >0,a ji =1 / a ij ,a ii =1.

[0050] Optionally, the eigenvectors of the judgment matrix can be calculated based on the sum-product method or the square root method.

[0051] Optionally, a consistency check is performed on the feature vector, including:

[0052] Calculate the consistency index CI, query the average random consistency index RI, and calculate the consistency ratio CR;

[0053] The formula for calculating the consistency index (CI) is as follows:

[0054] CI=(λ max -n) / (n - 1)

[0055] The formula for calculating the conformity ratio (CR) is:

[0056] CR = CI / RI

[0057] When CR < 0.1, the consistency of the matrix is ​​verified.

[0058] Where, λ max To determine the largest eigenvalue of the matrix, n is the order of the matrix.

[0059] Optionally, the formula for calculating the global weight set ω is as follows:

[0060] ω = Wi·W.

[0061] Optionally, the uncertainty of carbon footprint sources can be quantified using probability distributions or fuzzy numbers;

[0062] The probability distribution includes: normal distribution, log-normal distribution, or triangular distribution.

[0063] Optional, fuzzy number, including: trapezoidal fuzzy number or triangular fuzzy number.

[0064] Optionally, based on Monte Carlo simulations, sampling and propagation calculations are performed to account for the uncertainty of the quantified carbon footprint sources, including:

[0065] Set the number of iterations N for the Monte Carlo simulation;

[0066] For each iteration k (k = 1, 2, ..., N):

[0067] Based on the determined uncertainty distribution of each carbon footprint source, random sampling is performed to obtain a set of specific carbon footprint values.

[0068] Calculate the total product carbon footprint (CF) under this iteration. k The calculation formula is:

[0069]

[0070] Where, ω i Let i be the global weight of the i-th carbon footprint source. Let be the sampled value of the i-th carbon footprint source in the k-th iteration;

[0071] Collect all CFs obtained from N iterations. k The value represents the probability distribution of the total uncertainty of the carbon footprint of power equipment products.

[0072] Optional statistical characteristics, including at least: mean, standard deviation, and confidence interval.

[0073] In another aspect, the present invention also provides a computing device, comprising: one or more processors;

[0074] A processor is used to execute one or more programs;

[0075] When the one or more programs are executed by the one or more processors, the method described above is implemented.

[0076] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method described above.

[0077] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0078] This invention provides a method for carbon footprint uncertainty analysis of power equipment products based on AHP-PD, comprising: identifying carbon footprint sources at each stage of the power equipment product's entire life cycle; constructing a hierarchical structure model of carbon footprint uncertainty including a target layer, a criterion layer, and an indicator layer; based on the hierarchical structure model, constructing judgment matrices for each element of the criterion layer relative to the target layer and each element of the indicator layer relative to the corresponding criterion layer; calculating the eigenvectors of the judgment matrices and performing a consistency check on the eigenvectors; after passing the check, determining the weight set W of each element of the criterion layer relative to the target layer and the weight set W of each element of the indicator layer relative to the corresponding criterion layer based on the eigenvectors. The method involves: obtaining a local weight set Wi; synthesizing a global weight set ω for all elements of the index layer relative to the target layer based on the weight set W and the local weight set Wi; quantifying the uncertainty of each carbon footprint source in the global weight set ω according to the uncertainty distribution type and parameters; sampling and propagation calculations of the quantified carbon footprint source uncertainty based on Monte Carlo simulation to obtain the probability distribution of the total uncertainty of the carbon footprint of the power equipment product; obtaining the statistical characteristics of the probability distribution and using them as the uncertainty analysis result of the carbon footprint of the power equipment product throughout its entire life cycle. This invention makes the final result a probabilistic form (such as a confidence interval), which reflects the true situation better than a single deterministic value and supports risk decision-making. Attached Figure Description

[0079] Figure 1 This is a flowchart of the method of the present invention;

[0080] Figure 2 This is a schematic diagram of the structure of the method model of the present invention;

[0081] Figure 3 This is a structural diagram of the system of the present invention. Detailed Implementation

[0082] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0083] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0084] Example 1:

[0085] This invention proposes an uncertainty analysis method S100 for the carbon footprint of power equipment products based on AHP-PD, such as... Figure 1 As shown, it includes:

[0086] S101 identifies the carbon footprint sources at each stage of the entire life cycle of power equipment products and constructs a hierarchical structure model of carbon footprint uncertainty, including: target layer, criterion layer and indicator layer.

[0087] S102, Based on the hierarchical structure model, construct the judgment matrix of each element of the criterion layer relative to the target layer and the judgment matrix of each element of the index layer relative to the corresponding criterion layer.

[0088] S103, calculate the eigenvector of the judgment matrix and perform a consistency check on the eigenvector. After passing the check, determine the weight set W of each element of the criterion layer relative to the target layer and the local weight set Wi of each element of the index layer relative to the corresponding criterion layer based on the eigenvector.

[0089] S104, Based on the weight set W and the local weight set Wi, synthesize the global weight set ω of all elements of the index layer relative to the target layer;

[0090] S105, for each carbon footprint source in the global weight set ω, the uncertainty distribution type and parameters are determined, and the uncertainty of the carbon footprint source is quantified according to the uncertainty distribution type and parameters;

[0091] S106, based on Monte Carlo simulation, samples and propagates the uncertainty of the quantified carbon footprint sources to obtain the probability distribution of the total uncertainty of the carbon footprint of power equipment products.

[0092] S107, Obtain the statistical characteristics of the probability distribution, and use the statistical characteristics of the probability distribution as the uncertainty analysis result of the carbon footprint of the power equipment product throughout its entire life cycle.

[0093] The target layer includes: the total uncertainty of the carbon footprint of power equipment products; the criteria layer includes at least: uncertainty of energy supply, uncertainty of energy consumption, uncertainty of process emissions, and uncertainty of key links; the indicator layer includes: specific attributable processes under the criteria layer.

[0094] Specifically, based on the hierarchical structure model, the construction of judgment matrices for each element of the criterion layer relative to the target layer and for each element of the indicator layer relative to the corresponding criterion layer includes:

[0095] Using the 1-9 scale method, the importance of each element within the same level relative to a certain criterion at the next higher level is compared pairwise to construct a judgment matrix A. The calculation formula is as follows:

[0096] A = (a ij ) n×n

[0097] Among them, a ij The scale representing the relative importance of elements i and j with respect to the previous level criterion, and satisfying a ij >0,a ji =1 / a ij ,a ii =1.

[0098] Among them, the eigenvectors of the judgment matrix are calculated based on the sum-product method or the square root method.

[0099] The consistency check of the feature vectors includes:

[0100] Calculate the consistency index CI, query the average random consistency index RI, and calculate the consistency ratio CR;

[0101] The formula for calculating the consistency index (CI) is as follows:

[0102] CI=(λ max -n) / (n-1)

[0103] The formula for calculating the conformity ratio (CR) is:

[0104] CR = CI / RI

[0105] When CR < 0.1, the consistency of the matrix is ​​verified.

[0106] Where, λ max To determine the largest eigenvalue of the matrix, n is the order of the matrix.

[0107] The formula for calculating the global weight set ω is as follows:

[0108] ω = Wi·W.

[0109] Among them, probability distributions or fuzzy numbers quantify the uncertainty of carbon footprint sources;

[0110] The probability distribution includes: normal distribution, log-normal distribution, or triangular distribution.

[0111] Among them, fuzzy numbers include trapezoidal fuzzy numbers or triangular fuzzy numbers.

[0112] Among these, based on Monte Carlo simulations, sampling and propagation calculations are performed to address the uncertainty of quantified carbon footprint sources, including:

[0113] Set the number of iterations N for the Monte Carlo simulation;

[0114] For each iteration k (k = 1, 2, ..., N):

[0115] Based on the determined uncertainty distribution of each carbon footprint source, random sampling is performed to obtain a set of specific carbon footprint values.

[0116] Calculate the total product carbon footprint (CF) under this iteration. k The calculation formula is:

[0117]

[0118] Where, ω i Let i be the global weight of the i-th carbon footprint source. Let be the sampled value of the i-th carbon footprint source in the k-th iteration;

[0119] Collect all CFs obtained from N iterations. k The value represents the probability distribution of the total uncertainty of the carbon footprint of power equipment products.

[0120] The statistical characteristics include at least the mean, standard deviation, and confidence interval.

[0121] The following description is based on another embodiment of the method of the present invention:

[0122] The specific implementation steps include:

[0123] S1: Identify the carbon footprint sources at each stage of the entire life cycle of power equipment products, and construct a carbon footprint uncertainty hierarchical structure model including a target layer, a criterion layer, and an indicator layer; wherein, the target layer is the total uncertainty of the product carbon footprint, the criterion layer includes at least the uncertainty of energy supply, the uncertainty of energy consumption, the uncertainty of process emissions, and the uncertainty of key links, and the indicator layer consists of specific attributable processes under the criterion layer;

[0124] S2: Based on the hierarchical structure model, construct the judgment matrix of each element of the criterion layer relative to the target layer and the judgment matrix of each element of the indicator layer relative to the corresponding criterion layer.

[0125] S3: Calculate the eigenvectors of each judgment matrix and perform a consistency check. Determine the weight set W of each element in the criterion layer and the local weight set W of each element in the index layer relative to the corresponding criterion layer using the eigenvectors.i ;

[0126] S4: The global weight set ω of all elements in the synthetic index layer relative to the target layer, calculated as: ω = W i ·W;

[0127] S5: For each carbon footprint source in the indicator layer, determine its uncertainty distribution type and parameters, and quantify it using probability distribution or fuzzy numbers;

[0128] S6: Based on Monte Carlo simulation, sample and propagate the uncertainties of all carbon footprint sources quantified in step S5 to obtain the probability distribution of the total uncertainty of the product's carbon footprint.

[0129] S7: Use the statistical characteristics of the probability distribution as the result of uncertainty analysis. The statistical characteristics include at least the mean, standard deviation, and confidence interval.

[0130] Specifically, the criterion layer mentioned in step S1 includes:

[0131] Energy supply uncertainty stems from fluctuations and data lags in the average carbon emission factor of the power grid, fuel calorific value, and carbon emission coefficient.

[0132] Energy consumption uncertainty stems from the deviation between the actual energy consumption of manufacturing and testing equipment and the rated or theoretically estimated values.

[0133] Uncertainty in process emissions stems from non-energy-driven direct greenhouse gas emissions, including leakage of insulating gas SF6 and emissions of welding shielding gases.

[0134] Uncertainty in key areas stems from areas that contribute significantly to the overall carbon footprint or have significantly different data quality, manifested as measurement errors in activity level data, missing data, or data based on industry averages.

[0135] The method for constructing the judgment matrix in step S2 is as follows: using the 1-9 scaling method, a judgment matrix A = (a ij ) n×n

[0136] In step S3, the consistency index CI = (λ) is calculated. max -n) / (n-1) and consistency ratio CR=CI / RI. When CR<0.1, the consistency of the judgment matrix is ​​considered acceptable.

[0137] In step S5, for carbon footprint sources with sufficient data, the uncertainty is quantified using a normal distribution, a log-normal distribution, or a triangular distribution; for carbon footprint sources with insufficient data, the uncertainty is quantified using a trapezoidal fuzzy number or a triangular fuzzy number.

[0138] In step S6, the specific steps for sampling and propagation calculation include: setting the number of iterations N; for each iteration k, sampling is performed... And calculate Collect all CF k The value forms the probability distribution of the total uncertainty.

[0139] The following description is based on another embodiment of the method of the present invention:

[0140] The specific implementation steps include:

[0141] S1: Identify the carbon footprint sources at each stage of the entire life cycle of power equipment products, and construct a hierarchical structure model of carbon footprint uncertainty that includes target layer, criterion layer and indicator layer;

[0142] The target layer is the total uncertainty of the product's carbon footprint, the criterion layer includes at least the uncertainty of energy supply, the uncertainty of energy consumption, the uncertainty of process emissions, and the uncertainty of key links, and the indicator layer consists of specific attributable processes under the criterion layer.

[0143] S2: Based on the hierarchical structure model, construct the judgment matrix of each element of the criterion layer relative to the target layer and the judgment matrix of each element of the indicator layer relative to the corresponding criterion layer.

[0144] S3: Calculate the eigenvectors of each judgment matrix and perform a consistency check. Determine the weight set W of each element in the criterion layer and the local weight set W of each element in the index layer relative to the corresponding criterion layer using the eigenvectors. i ;

[0145] S4: The global weight set ω of all elements in the synthetic index layer relative to the target layer, calculated as: ω = W i ·W;

[0146] S5: For each carbon footprint source in the indicator layer, determine its uncertainty distribution type and parameters, and quantify it using probability distribution or fuzzy numbers;

[0147] S6: Based on Monte Carlo simulation, sample and propagate the uncertainties of all carbon footprint sources quantified in step S5 to obtain the probability distribution of the total uncertainty of the product's carbon footprint.

[0148] S7: Use the statistical characteristics of the probability distribution as the result of uncertainty analysis. The statistical characteristics include at least the mean, standard deviation, and confidence interval.

[0149] Specifically, the criterion layer mentioned in step S1 includes:

[0150] Energy supply uncertainty stems from fluctuations and data lags in the average carbon emission factor of the power grid, fuel calorific value, and carbon emission coefficient.

[0151] Energy consumption uncertainty stems from the deviation between the actual energy consumption of manufacturing and testing equipment and the rated or theoretically estimated values.

[0152] Uncertainty in process emissions stems from non-energy-driven direct greenhouse gas emissions, including leakage of insulating gas SF6 and emissions of welding shielding gases.

[0153] Uncertainty in key areas stems from areas that contribute significantly to the overall carbon footprint or have significantly different data quality, manifested as measurement errors in activity level data, missing data, or data based on industry averages.

[0154] The method for constructing the judgment matrix in step S2 is as follows: using the 1-9 scale method, the importance of each element within the same level relative to a certain criterion in the next higher level is compared pairwise to construct the judgment matrix A.

[0155] A = (a ij ) n×n (1)

[0156] Among them, a ij The scale representing the relative importance of elements i and j with respect to the previous level criterion, and satisfying a ij >0,a ji =1 / a ij ,a ii =1.

[0157] In step S3, the method for calculating the eigenvector of the judgment matrix is ​​either the sum-product method or the square root method; the steps of the consistency test include calculating the consistency index CI, querying the average random consistency index RI, and calculating the consistency ratio CR.

[0158] The formula for calculating the consistency index (CI) is as follows:

[0159] CI=(λ max -n) / (n-1) (2)

[0160] The formula for calculating the conformity ratio (CR) is:

[0161] CR = CI / RI (3)

[0162] When CR < 0.1, the consistency of the judgment matrix is ​​considered acceptable; where λ max To determine the largest eigenvalue of the matrix, n is the order of the matrix.

[0163] According to the method of claim 1, in step S5,

[0164] For carbon footprint sources with sufficient data and clear statistical characteristics, their uncertainty is quantified using probability distributions, including normal distributions, log-normal distributions, or triangular distributions.

[0165] For carbon footprint sources that lack data and rely on expert experience, their uncertainty is quantified using trapezoidal fuzzy numbers (A,B,C,D) or triangular fuzzy numbers (A,B,C), where A and D are the lower and upper bounds of the fuzzy number, respectively, and B and C are the ranges of the most likely values.

[0166] According to the method described in claim 5, when the uncertainty of the carbon footprint source is quantified using fuzzy quantification, before performing Monte Carlo simulation in step S6, the fuzzy numbers need to be converted into a clear probability distribution, and the conversion method is the α-cut set method.

[0167] The specific steps for sampling and propagation calculations described in step S6 are as follows:

[0168] S61: Set the number of iterations N for the Monte Carlo simulation;

[0169] S62: For each iteration k (k = 1, 2, ..., N):

[0170] a) Based on the uncertainty distribution of each carbon footprint source determined in step S5, random sampling is performed to obtain a set of specific carbon footprint values.

[0171] b) Calculate the total product carbon footprint (CF) for this iteration. k The calculation formula is:

[0172]

[0173] Where, ω i Let i be the global weight of the i-th carbon footprint source. Let be the sampled value of the i-th carbon footprint source in the k-th iteration;

[0174] S63: Collect all CFs obtained from N iterations of calculation. k The value forms the probability distribution of the total uncertainty of the product's carbon footprint.

[0175] This embodiment takes a 126kV GIS switchgear as the analysis object, and the system boundary is "cradle to gate", covering the stages of raw material acquisition, component manufacturing and product assembly. This embodiment strictly follows the step sequence of claim 1.

[0176] S1: Constructing a hierarchical model of carbon footprint uncertainty:

[0177] Identify the carbon footprint sources throughout the product's entire lifecycle and construct a hierarchical model, such as... Figure 2Specifically, it includes:

[0178] Target layer: Total uncertainty of carbon footprint of 126kV GIS products.

[0179] Criteria Level: Includes uncertainty in energy supply (B1), uncertainty in energy consumption (B2), uncertainty in process emissions (B3), and uncertainty in key aspects (B4).

[0180] Indicator layer: The specific attributable processes under the criteria layer, totaling 17 items, are detailed below:

[0181] Energy supply uncertainty indicators include: C 11 Purchased electricity carbon emission factor, C 12 Natural gas carbon emission factor, C 13 Diesel carbon emission coefficient.

[0182] Energy consumption uncertainty indicators include: C 21 Power consumption for smelting aluminum alloy casing, C 22 Power consumption for shell machining, C 23 Power consumption of epoxy resin casting and curing, C 24 Energy consumption in assembly workshop, C 25 Power consumption during factory testing.

[0183] Uncertainty indicators for process emissions include: C 31 SF6 gas filling leakage rate, C 32 SF6 gas annual leakage rate, C 33 Welding shielding gas emission factor, C 34 Surface treatment solvents evaporate and are emitted.

[0184] Key uncertainty indicators include: C 41 Carbon footprint of epoxy resin insulation components, C 42 Carbon footprint of copper material in main conductive circuit, C 43 Carbon footprint of sealing rubber materials, C 44 Carbon footprint of core casting blank, C 45 Product transportation distance and mode.

[0185] S2: Construct the judgment matrix:

[0186] Based on the hierarchical structure model, domain experts were invited to construct a judgment matrix using the 1-9 scaling method.

[0187] Construct the judgment matrix A of the criterion layer (B) relative to the target layer (A).

[0188] Construct judgment matrices B1-B4 for each index layer element relative to its corresponding criterion layer.

[0189] S3: Calculate weights and perform consistency checks:

[0190] Based on the judgment matrix constructed by S2, the eigenvectors (i.e. weights) of each judgment matrix are calculated and a consistency check is performed.

[0191] The weights of the criterion layer (B) relative to the target layer (A) are calculated, and the results are shown in Table 1.

[0192] Table 1

[0193] A <![CDATA[B1]]> <![CDATA[B2]]> <![CDATA[B3]]> <![CDATA[B4]]> Weight (W) <![CDATA[B1]]> 1 1 / 2 1 / 3 2 0.166 <![CDATA[B2]]> 2 1 1 / 2 3 0.293 <![CDATA[B3]]> 3 2 1 4 0.467 <![CDATA[B4]]> 1 / 2 1 / 3 1 / 4 1 0.074

[0194] Consistency test result: λ max =4.045, CI=0.015, RI=0.89, CR=0.017<0.1, passing the consistency test;

[0195] 2. Weight calculation of the indicator layer relative to the criterion layer:

[0196] a) The weights of each indicator under energy supply uncertainty (B1) are calculated, and the results are shown in Table 2:

[0197] Table 2

[0198] <![CDATA[B1]]> <![CDATA[C 11 ]]> <![CDATA[C 12 ]]> <![CDATA[C 13 ]]> <![CDATA[Local weight (W1)]]> <![CDATA[C 11 ]]> 1 3 5 0.637 <![CDATA[C 12 ]]> 1 / 3 1 3 0.258 <![CDATA[C 13 ]]> 1 / 5 1 / 3 1 0.105

[0199] Consistency test result: λ max =3.039, CI=0.019, RI=0.58, CR=0.033<0.1, passing the consistency test.

[0200] b) The weights of each indicator under energy consumption uncertainty (B2) are calculated, and the results are shown in Table 3:

[0201] Table 3

[0202] <![CDATA[B2]]> <![CDATA[C 21 ]]> <![CDATA[C 22 ]]> <![CDATA[C 23 ]]> <![CDATA[C 24 ]]> <![CDATA[C 25 ]]> <![CDATA[Local weight (W2)]]> <![CDATA[C 21 ]]> 1 2 3 4 5 0.416 <![CDATA[C 22 ]]> 1 / 2 1 2 3 4 0.277 <![CDATA[C 23 ]]> 1 / 3 1 / 2 1 2 3 0.177 <![CDATA[C 24 ]]> 1 / 4 1 / 3 1 / 2 1 2 0.102 <![CDATA[C 25 ]]> 1 / 5 1 / 4 1 / 3 1 / 2 1 0.058

[0203] Consistency test result: λ max =5.126, CI=0.032, RI=1.12, CR=0.028<0.1, passing the consistency test.

[0204] c) The weights of each indicator under the uncertainty of process emissions (B3) are calculated, and the results are shown in Table 4:

[0205] Table 4

[0206] <![CDATA[B3]]> <![CDATA[C 31 ]]> <![CDATA[C 32 ]]> <![CDATA[C 32 ]]> <![CDATA[C 32 ]]> <![CDATA[Local weight (W3)]]> <![CDATA[C 31 ]]> 1 1 / 2 4 3 0.300 <![CDATA[C 32 ]]> 2 1 5 4 0.482 <![CDATA[C 33 ]]> 1 / 4 1 / 5 1 1 / 2 0.080 <![CDATA[C 34 ]]> 1 / 3 1 / 4 2 1 0.138

[0207] Consistency test result: λ max =4.177, CI=0.059, RI=0.90, CR=0.066<0.1, passing the consistency test.

[0208] d) The weights of each indicator under the uncertainty of key links (B4) are calculated, and the results are shown in Table 5:

[0209] Table 5

[0210]

[0211] Consistency test result: λ max =5.087, CI=0.022, RI=1.12, CR=0.019<0.1, passing the consistency test;

[0212] S4: Synthesize global weights:

[0213] The global weight set ω of all elements in the synthetic index layer relative to the target layer. The calculation formula is: ω = W i ×W, where W is the set of weights for the criterion layer, W i This represents the local weight set of each indicator under its corresponding criterion layer.

[0214] Based on the complete weight set obtained from S3 calculation, the calculation results of the synthesized global weights are shown in Table 6:

[0215] Table 6

[0216]

[0217] S5: Uncertainty Quantification

[0218] For each carbon footprint source in the indicator layer, its uncertainty distribution type and parameters are determined based on its data type, source, and availability, and then quantified using probability distributions or fuzzy numbers. The quantification results are shown in Table 7.

[0219] Table 7

[0220]

[0221]

[0222]

[0223] Uncertainty Quantification Explanation:

[0224] 1. Criteria for selecting distribution type:

[0225] Normal distribution: suitable for parameters with sufficient historical data and symmetrical distribution (such as C). 11 C 22 C 24 C 42 );

[0226] Triangular distribution: suitable for parameters with limited data but whose minimum, most likely, and maximum values ​​can be estimated (such as C). 13 C 21 C 25 C 34 C 45 );

[0227] Uniform distribution: suitable for parameters where only the range of values ​​is known but the distribution pattern is lacking (such as C). 12 C 23 C 33 C 41 C 44 );

[0228] Beta distribution: suitable for describing parameters with variable distribution patterns within a bounded interval, especially suitable for describing proportions or ratios (such as the leakage rates of C31 and C32);

[0229] Triangular fuzzy number: suitable for parameters where data is severely lacking and judgment mainly relies on expert experience (such as C). 43 );

[0230] 2. Parameter determination method:

[0231] For parameters with monitoring data (such as C) 21 C 22 (etc.), determined based on the statistical characteristics of historical data;

[0232] For database parameters (such as C) 11 C 12 (etc.), determined based on the range and variation of values ​​in different databases;

[0233] For process parameters (such as C) 31 C 32 (etc.), determined based on the allowable range of process standards and actual operating data;

[0234] For parameters lacking data (such as C) 43 (This was determined based on expert interviews and industry research.)

[0235] 3. Fuzzy number processing:

[0236] For the triangular fuzzy numbers used in C43, before performing Monte Carlo simulation in S6, the α-cut method will be used to transform them into a clear probability distribution so that they can be sampled together with other probability distributions.

[0237] S6: Monte Carlo Simulation and Uncertainty Propagation;

[0238] Based on the uncertainties of all carbon footprint sources quantified in S5, Monte Carlo simulation is used to perform random sampling and uncertainty propagation calculations to obtain the probability distribution of the total uncertainty of the product's carbon footprint.

[0239] 1. Set the simulation parameters;

[0240] Number of simulation iterations: N = 50,000. This number is sufficient to ensure the statistical stability of the output results.

[0241] Random number seed: Set to a fixed value (e.g., 12345) to ensure the reproducibility of the results.

[0242] 2. Perform simulation iterations;

[0243] For each iteration k (where k = 1, 2, ..., 50,000), perform the following steps:

[0244] a) Random sampling;

[0245] Based on the uncertainty distribution type and parameters set for each carbon footprint source (C11 to C45) in S5, independent random sampling is performed to obtain the specific values ​​of each carbon footprint source in this iteration.

[0246] b) Calculate the total carbon footprint for a single iteration;

[0247] Based on a set of specific values ​​obtained from sampling Combining the activity level data corresponding to each indicator A i (For example: power consumption, material consumption, transportation distance, etc.), calculate the total carbon footprint (CF) of the product under this iteration. k The calculation formula is as follows:

[0248]

[0249] in

[0250] n = 17, representing the total number of carbon footprint sources in the indicator layer.

[0251] A i This represents the activity level data (fixed value) for the i-th indicator.

[0252] This is the sampled value of the i-th index in the k-th iteration.

[0253] 3. Forming a total uncertainty probability distribution;

[0254] Collect the total product carbon footprint {CF} calculated from all N = 50000 iterations. 1 ,CF 2 ,…,CF 50000This forms the probability distribution of the total uncertainty of the product's carbon footprint. This distribution is represented by a set of 50,000 data points, fully describing the possible range of values ​​for the total carbon footprint and their corresponding probabilities.

[0255] 4. Key Implementation Details:

[0256] Fuzzy number processing: For index C 43 (Carbon footprint of sealing rubber material), before sampling, its triangular fuzzy numbers were transformed into a clear probability distribution using the α-cut method.

[0257] Correlation handling: In this embodiment, it is assumed that the uncertainties of each carbon footprint source are independent of each other. If strong known correlations exist in actual analysis (e.g., multiple energy consumptions are related to output), a correlation structure should be established during the sampling process (e.g., using a Copula function) to more realistically reflect the uncertainty.

[0258] Computational efficiency: Large-scale iterative calculations are performed using software tools with efficient matrix operations and random sampling capabilities, such as Python (with NumPy and Pandas libraries) or MATLAB.

[0259] Step S7: Result Analysis and Specific Output:

[0260] Based on the probability distribution of the total carbon footprint value CFk of 50,000 products obtained from S6, its statistical characteristics are used as the final uncertainty analysis result and further interpreted.

[0261] 1. Calculate statistical characteristics;

[0262] Statistical analysis of 50,000 carbon footprint data points (CFk) from the simulation output yielded the following key statistical characteristics:

[0263] Average carbon footprint: 15.8 tons of CO2e;

[0264] Carbon footprint standard deviation: 1.2 tons of CO2e;

[0265] 95% confidence interval: [13.6, 18.3] tons of CO2e;

[0266] Coefficient of variation: 7.6%;

[0267] 2. Analysis of the contribution (sensitivity) of uncertainty;

[0268] The Spearman rank correlation coefficient method was used to analyze the correlation between each input parameter (i.e., the sampled value sequence of the 17 index layer carbon footprint sources) and the output result (total carbon footprint CFk sequence) to quantify the contribution of each uncertainty source to the total uncertainty. The analysis results are shown in Table 8:

[0269] Table 8

[0270]

[0271] 3. Results interpretation and decision support;

[0272] a) Result reliability assessment:

[0273] Traditional deterministic methods yield a single carbon footprint of 15.8 tons, while this method reveals a 95% probability that the true value will fall within the range of 13.6 to 18.3 tons. This provides important risk information for users of carbon footprint claims, indicating a potential bias of ±14.9% in the results.

[0274] b) Identification of key sources of uncertainty:

[0275] Sensitivity analysis clearly indicates that SF6 operational leakage (C 32 ), grid factor (C) 11 ) and power consumption for aluminum shell smelting (C 21 These three factors are the top three sources of uncertainty, contributing over 50% of the total uncertainty. This provides a clear direction for enterprises to accurately improve data quality and optimize their carbon management strategies.

[0276] c) Recommendations for tiered management:

[0277] Level 1 Control (Contribution > 10%): Enterprises should prioritize investment in SF6 sealing technology and online monitoring systems to reduce C 32 The uncertainty; at the same time, the grid factor (C) used must be clearly stated in the carbon footprint report. 11 ) and its uncertainties.

[0278] Level 2 control (contribution 5%-10%): Control over aluminum alloy smelting (C 21 Energy consumption monitoring and optimization of the process are carried out, and the use of epoxy resin (C) is sought. 41 Suppliers provide more accurate primary data.

[0279] Level 3 control (contribution < 5%): For links with low contribution (such as C) 42 C 22 C 34 (etc.) can maintain the current data management strategy.

[0280] The beneficial effects of this invention are as follows:

[0281] Systematic: The unique uncertainties of power equipment are systematically identified through a hierarchical model, resulting in a clear structure.

[0282] Scientific rigor: Combining qualitative judgment (AHP) with quantitative simulation (Monte Carlo) ensures that the analysis results are both comprehensive and objective.

[0283] Guiding: It can accurately identify key uncertainties and guide enterprises to prioritize improvements and optimize resource allocation.

[0284] Practicality: The final result is in probabilistic form (such as confidence intervals), which reflects the true situation better than a single definite value and supports risk decision-making.

[0285] Example 2:

[0286] This invention also proposes an AHP-PD-based carbon footprint uncertainty analysis system 200 for power equipment products, such as... Figure 3 As shown, it includes:

[0287] Initial unit 201 is used to identify the carbon footprint sources at each stage of the entire life cycle of power equipment products and construct a carbon footprint uncertainty hierarchical structure model including: target layer, criterion layer and indicator layer; based on the hierarchical structure model, construct the judgment matrix of each element of the criterion layer relative to the target layer and each element of the indicator layer relative to the corresponding criterion layer.

[0288] The calculation unit 202 is used to calculate the eigenvector of the judgment matrix and perform a consistency check on the eigenvector. After passing the check, it determines the weight set W of each element of the criterion layer relative to the target layer and the local weight set Wi of each element of the index layer relative to the corresponding criterion layer based on the eigenvector. Based on the weight set W and the local weight set Wi, it synthesizes the global weight set ω of all elements of the index layer relative to the target layer.

[0289] Output unit 203 is used to determine the uncertainty distribution type and parameters of each carbon footprint source in the global weight set ω, and to quantify the uncertainty of the carbon footprint source according to the uncertainty distribution type and parameters; based on Monte Carlo simulation, it performs sampling and propagation calculations on the quantified uncertainty of the carbon footprint source to obtain the probability distribution of the total uncertainty of the carbon footprint of the power equipment product; it obtains the statistical characteristics of the probability distribution, and uses the statistical characteristics of the probability distribution as the uncertainty analysis result of the carbon footprint of the power equipment product throughout its entire life cycle.

[0290] The target layer includes: the total uncertainty of the carbon footprint of power equipment products; the criteria layer includes at least: uncertainty of energy supply, uncertainty of energy consumption, uncertainty of process emissions, and uncertainty of key links; the indicator layer includes: specific attributable processes under the criteria layer.

[0291] Specifically, based on the hierarchical structure model, the construction of judgment matrices for each element of the criterion layer relative to the target layer and for each element of the indicator layer relative to the corresponding criterion layer includes:

[0292] Using the 1-9 scale method, the importance of each element within the same level relative to a certain criterion at the next higher level is compared pairwise to construct a judgment matrix A. The calculation formula is as follows:

[0293] A = (a ij ) n×n

[0294] Among them, a ij The scale representing the relative importance of elements i and j with respect to the previous level criterion, and satisfying a ij >0,a ji =1 / a ij ,a ii =1.

[0295] Among them, the eigenvectors of the judgment matrix are calculated based on the sum-product method or the square root method.

[0296] The consistency check of the feature vectors includes:

[0297] Calculate the consistency index CI, query the average random consistency index RI, and calculate the consistency ratio CR;

[0298] The formula for calculating the consistency index (CI) is as follows:

[0299] CI=(λ max -n) / (n-1)

[0300] The formula for calculating the conformity ratio (CR) is:

[0301] CR = CI / RI

[0302] When CR < 0.1, the consistency of the matrix is ​​verified.

[0303] Where, λ max To determine the largest eigenvalue of the matrix, n is the order of the matrix.

[0304] Optionally, the formula for calculating the global weight set ω is as follows:

[0305] ω = Wi·W.

[0306] Among them, the uncertainty of carbon footprint sources is quantified by probability distribution or fuzzy numbers;

[0307] The probability distribution includes: normal distribution, log-normal distribution, or triangular distribution.

[0308] Among them, fuzzy numbers include trapezoidal fuzzy numbers or triangular fuzzy numbers.

[0309] Optionally, based on Monte Carlo simulations, sampling and propagation calculations are performed to account for the uncertainty of the quantified carbon footprint sources, including:

[0310] Set the number of iterations N for the Monte Carlo simulation;

[0311] For each iteration k (k = 1, 2, ..., N):

[0312] Based on the determined uncertainty distribution of each carbon footprint source, random sampling is performed to obtain a set of specific carbon footprint values.

[0313] Calculate the total product carbon footprint (CF) under this iteration. k The calculation formula is:

[0314]

[0315] Where, ω i Let i be the global weight of the i-th carbon footprint source. Let be the sampled value of the i-th carbon footprint source in the k-th iteration;

[0316] Collect all CFs obtained from N iterations. k The value represents the probability distribution of the total uncertainty of the carbon footprint of power equipment products.

[0317] The statistical characteristics include at least the mean, standard deviation, and confidence interval.

[0318] The method of this invention makes the final result a probabilistic form (such as a confidence interval), which reflects the true situation better than a single deterministic value and supports risk decision-making.

[0319] Example 3:

[0320] Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby implementing the steps of the methods in the above embodiments.

[0321] Example 4:

[0322] Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiments.

[0323] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0324] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0325] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0326] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0327] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0328] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for analyzing the uncertainty of carbon footprint of power equipment products based on AHP-PD, characterized in that, include: Identify the carbon footprint sources at each stage of the entire life cycle of power equipment products, and construct a hierarchical structure model of carbon footprint uncertainty, including: target layer, criterion layer and indicator layer; Based on the hierarchical model, a judgment matrix is ​​constructed for each element of the criterion layer relative to the target layer and for each element of the indicator layer relative to the corresponding criterion layer. Calculate the eigenvectors of the judgment matrix and perform a consistency check on the eigenvectors. After passing the check, determine the weight set W of each element of the criterion layer relative to the target layer and the local weight set Wi of each element of the index layer relative to the corresponding criterion layer based on the eigenvectors. Based on the weight set W and the local weight set Wi, synthesize the global weight set ω of all elements of the index layer relative to the target layer; For each carbon footprint source in the global weight set ω, the uncertainty distribution type and parameters are defined, and the uncertainty of the carbon footprint source is quantified based on the uncertainty distribution type and parameters. Based on Monte Carlo simulation, the uncertainty of the quantified carbon footprint sources is sampled and propagated to obtain the probability distribution of the total uncertainty of the carbon footprint of power equipment products. The statistical characteristics of the probability distribution are obtained, and the statistical characteristics of the probability distribution are used as the uncertainty analysis results of the carbon footprint of power equipment products throughout their entire life cycle.

2. The method for analyzing the uncertainty of carbon footprint of power equipment products according to claim 1, characterized in that, The target layer includes: the total uncertainty of the carbon footprint of power equipment products; the criteria layer includes at least: uncertainty of energy supply, uncertainty of energy consumption, uncertainty of process emissions, and uncertainty of key links; the indicator layer includes: specific attributable processes under the criteria layer.

3. The method for uncertainty analysis of carbon footprint of power equipment products according to claim 1, characterized in that, The construction of the judgment matrix based on the hierarchical structure model, whereby each element of the criterion layer is relative to the target layer and each element of the indicator layer is relative to the corresponding criterion layer, includes: Using the 1-9 scale method, the importance of each element within the same level relative to a certain criterion at the next higher level is compared pairwise to construct a judgment matrix A. The calculation formula is as follows: A=(a ij ) n×n Among them, a ij The scale representing the relative importance of elements i and j with respect to the previous level criterion, and satisfying a ij >0,a ji =1 / a ij ,a ii =1.

4. The method for uncertainty analysis of carbon footprint of power equipment products according to claim 1, characterized in that, The eigenvectors of the judgment matrix are calculated based on the sum-product method or the square root method.

5. The method for uncertainty analysis of carbon footprint of power equipment products according to claim 1, characterized in that, The consistency check of the feature vector includes: Calculate the consistency index CI, query the average random consistency index RI, and calculate the consistency ratio CR; The formula for calculating the consistency index (CI) is as follows: CI=(λ max -n) / (n-1) The formula for calculating the conformity ratio (CR) is: CR = CI / RI When CR < 0.1, the consistency of the matrix is ​​verified. Where, λ max To determine the largest eigenvalue of the matrix, n is the order of the matrix.

6. The method for analyzing the uncertainty of carbon footprint of power equipment products according to claim 1, characterized in that, The formula for calculating the global weight set ω is as follows: ω = Wi·W.

7. The method for uncertainty analysis of carbon footprint of power equipment products according to claim 1, characterized in that, The uncertainty of carbon footprint sources is quantified by probability distribution or fuzzy numbers; The probability distribution includes: normal distribution, log-normal distribution, or triangular distribution.

8. The method for uncertainty analysis of carbon footprint of power equipment products according to claim 1, characterized in that, The fuzzy numbers include: trapezoidal fuzzy numbers or triangular fuzzy numbers.

9. The method for uncertainty analysis of carbon footprint of power equipment products according to claim 1, characterized in that, The Monte Carlo simulation-based sampling and propagation calculations for the uncertainty of quantified carbon footprint sources include: Set the number of iterations N for the Monte Carlo simulation; For each iteration k (k = 1, 2, ..., N): Based on the determined uncertainty distribution of each carbon footprint source, random sampling is performed to obtain a set of specific carbon footprint values. Calculate the total product carbon footprint (CF) under this iteration. k The calculation formula is: Where, ω i Let i be the global weight of the i-th carbon footprint source. Let be the sampled value of the i-th carbon footprint source in the k-th iteration; Collect all CFs obtained from N iterations. k The value represents the probability distribution of the total uncertainty of the carbon footprint of power equipment products.

10. The method for uncertainty analysis of carbon footprint of power equipment products according to claim 1, characterized in that, The statistical characteristics include at least the mean, standard deviation, and confidence interval.

11. A carbon footprint uncertainty analysis system for power equipment products based on AHP-PD, characterized in that, include: An initial unit is used to identify the carbon footprint sources at each stage of the entire life cycle of power equipment products, and to construct a carbon footprint uncertainty hierarchical structure model including a target layer, a criterion layer, and an indicator layer; based on the hierarchical structure model, a judgment matrix is ​​constructed for each element of the criterion layer relative to the target layer and for each element of the indicator layer relative to the corresponding criterion layer. The calculation unit is used to calculate the eigenvectors of the judgment matrix and perform a consistency check on the eigenvectors. After passing the check, it determines the weight set W of each element of the criterion layer relative to the target layer and the local weight set Wi of each element of the index layer relative to the corresponding criterion layer based on the eigenvectors. Based on the weight set W and the local weight set Wi, it synthesizes the global weight set ω of all elements of the index layer relative to the target layer. The output unit is used to determine the uncertainty distribution type and parameters of each carbon footprint source in the global weight set ω, and to quantify the uncertainty of the carbon footprint source according to the uncertainty distribution type and parameters; based on Monte Carlo simulation, the quantified uncertainty of the carbon footprint source is sampled and propagated to obtain the probability distribution of the total uncertainty of the carbon footprint of the power equipment product; the statistical characteristics of the probability distribution are obtained, and the statistical characteristics of the probability distribution are used as the uncertainty analysis result of the carbon footprint of the power equipment product throughout its entire life cycle.

12. The carbon footprint uncertainty analysis system for power equipment products according to claim 11, characterized in that, The target layer includes: the total uncertainty of the carbon footprint of power equipment products; the criteria layer includes at least: uncertainty of energy supply, uncertainty of energy consumption, uncertainty of process emissions, and uncertainty of key links; the indicator layer includes: specific attributable processes under the criteria layer.

13. The carbon footprint uncertainty analysis system for power equipment products according to claim 11, characterized in that, The construction of the judgment matrix based on the hierarchical structure model, whereby each element of the criterion layer is relative to the target layer and each element of the indicator layer is relative to the corresponding criterion layer, includes: Using the 1-9 scale method, the importance of each element within the same level relative to a certain criterion at the next higher level is compared pairwise to construct a judgment matrix A. The calculation formula is as follows: A=(a ij ) n×n Among them, a ij The scale representing the relative importance of elements i and j with respect to the previous level criterion, and satisfying a ij >0,a ji =1 / a ij ,a ii =1.

14. The carbon footprint uncertainty analysis system for power equipment products according to claim 11, characterized in that, The eigenvectors of the judgment matrix are calculated based on the sum-product method or the square root method.

15. The carbon footprint uncertainty analysis system for power equipment products according to claim 11, characterized in that, The consistency check of the feature vector includes: Calculate the consistency index CI, query the average random consistency index RI, and calculate the consistency ratio CR; The formula for calculating the consistency index (CI) is as follows: CI=(λ max -n) / (n - 1) The formula for calculating the conformity ratio (CR) is: CR = CI / RI When CR < 0.1, the consistency of the matrix is ​​verified. Where, λ max To determine the largest eigenvalue of the matrix, n is the order of the matrix.

16. The carbon footprint uncertainty analysis system for power equipment products according to claim 11, characterized in that, The formula for calculating the global weight set ω is as follows: ω = Wi·W.

17. The carbon footprint uncertainty analysis system for power equipment products according to claim 11, characterized in that, The uncertainty of carbon footprint sources is quantified by probability distribution or fuzzy numbers; The probability distribution includes: normal distribution, log-normal distribution, or triangular distribution.

18. The carbon footprint uncertainty analysis system for power equipment products according to claim 11, characterized in that, The fuzzy numbers include: trapezoidal fuzzy numbers or triangular fuzzy numbers.

19. The carbon footprint uncertainty analysis system for power equipment products according to claim 11, characterized in that, The Monte Carlo simulation-based sampling and propagation calculations for the uncertainty of quantified carbon footprint sources include: Set the number of iterations N for the Monte Carlo simulation; For each iteration k (k = 1, 2, ..., N): Based on the determined uncertainty distribution of each carbon footprint source, random sampling is performed to obtain a set of specific carbon footprint values. Calculate the total product carbon footprint (CF) under this iteration. k The calculation formula is: Where, ω i Let i be the global weight of the i-th carbon footprint source. Let be the sampled value of the i-th carbon footprint source in the k-th iteration; Collect all CFs obtained from N iterations. k The value represents the probability distribution of the total uncertainty of the carbon footprint of power equipment products.

20. The carbon footprint uncertainty analysis system for power equipment products according to claim 11, characterized in that, The statistical characteristics include at least the mean, standard deviation, and confidence interval.

21. A computer device, characterized in that, include: One or more processors; A processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method described in any one of claims 1-10 is implemented.

22. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the method as described in any one of claims 1-10.

Images