Electrical equipment product carbon footprint path optimization method and system considering uncertainty

Abstract

This invention discloses a method and system for optimizing the carbon footprint path of electrical equipment products considering uncertainties. The method includes: acquiring sampled values ​​of inventory data for the entire lifecycle of the electrical equipment product to be optimized; using an established carbon footprint accounting model, calculating the carbon footprint accounting value of each entity node based on the sampled values; sampling each inventory data multiple times based on a predefined probability distribution, and calculating the carbon footprint output value of the entity node corresponding to each sampling using the carbon footprint accounting model to determine the coefficient of variation and carbon emission placement confidence interval of the entity node; determining the optimal carbon footprint path with the goal of minimizing the overall benefits of the entire lifecycle carbon footprint and uncertainties, and using the carbon footprint accounting value of each entity node within its corresponding carbon emission placement confidence interval as a constraint. This method and system reduce carbon emissions throughout the product supply chain lifecycle, achieving low-carbon management of the product supply chain.

Description

Technical Field

[0001] This invention relates to the field of power information technology, and more specifically, to a method and system for optimizing the carbon footprint of electrical equipment products considering uncertainties. Background Technology

[0002] Against the backdrop of the global "dual-carbon" strategy and increasingly stringent international trade carbon footprint management, the full life-cycle carbon footprint management of electrical equipment, as a core support for the green transformation of the power industry, has become a key issue for industry development. Current technological advancements have yielded some progress: breakthroughs have been achieved in digitalization and blockchain technologies based on Life Cycle Assessment (LCA); artificial intelligence technology is also gradually being applied to carbon footprint assessment, improving accounting efficiency and accuracy through multi-source data fusion. These technologies provide basic support for carbon footprint measurement, but specific optimization solutions have not yet been developed to address the complexity and uncertainty of the electrical equipment supply chain.

[0003] Current technologies still have shortcomings, and uncertainties have not been systematically integrated. The electrical equipment supply chain faces multiple variables such as mineral resource shortages, logistics fluctuations, and policy adjustments. Current models are mostly based on static parameter modeling and fail to quantify the impact of these dynamic fluctuations on carbon footprint. At the same time, carbon reduction path optimization lacks clarity and specificity. Existing solutions mostly remain at the level of macro-level guidance and have not formed quantitative optimization paths that can be implemented in each link. For example, the emission reduction priorities and specific implementation strategies of key nodes such as raw material procurement, production processes, and logistics are unclear. Data barriers across suppliers and links lead to a disconnect between carbon footprint accounting and path optimization. Traditional methods are difficult to achieve dynamic coordination of carbon flow, logistics, and costs across the entire chain. Optimization solutions lack practicality and dynamic adjustment capabilities and cannot adapt to real-time changes in the supply chain. Summary of the Invention

[0004] To address the technical problems in existing technologies where uncertainties in the carbon footprint path optimization of electrical equipment products are not systematically integrated, making it difficult to achieve dynamic coordination of carbon flow, logistics, and costs across the entire supply chain, and where optimization schemes lack practicality and dynamic adjustment capabilities, thus failing to adapt to real-time changes in the supply chain, this invention proposes a method and system for optimizing the carbon footprint path of electrical equipment products that considers uncertainties.

[0005] According to one aspect of the present invention, a method for optimizing the carbon footprint of electrical equipment products considering uncertainties is provided, the method comprising: Obtain sampled values ​​of the inventory data for the entire lifecycle of the electrical equipment product to be optimized, wherein the entire lifecycle includes several stages from cradle to grave for the electrical equipment product to be optimized, and each stage includes at least one entity node; A carbon footprint accounting model for the entire life cycle of electrical equipment products, established through life cycle assessment, is used to calculate the carbon footprint accounting value of each entity node at each stage of the electrical equipment product to be optimized, based on the sampled values. Based on the predefined probability distribution of each list data, each list data is sampled multiple times. After calculating the carbon footprint output value of each entity node in each stage corresponding to each sampling through the full life cycle carbon footprint accounting model of electrical equipment products, the coefficient of variation of the uncertainty of the carbon footprint result of each entity node in each stage and the corresponding carbon emission placement confidence interval are calculated based on the carbon footprint output value. With the goal of minimizing the overall benefits of the carbon footprint and uncertainty throughout the entire life cycle, and with the carbon footprint accounting value of each entity node within its corresponding carbon emission placement confidence interval as a constraint, an improved Dijkstra algorithm is used to determine the optimal carbon footprint path based on the carbon footprint emission value of each entity node, the coefficient of variation, and the carbon emission placement confidence interval.

[0006] According to another aspect of the present invention, a carbon footprint path optimization system for electrical equipment products considering uncertainties is provided, the system comprising: The data acquisition unit is used to acquire sampled values ​​of the inventory data of the electrical equipment product to be optimized throughout its entire life cycle, wherein the entire life cycle includes several stages of the electrical equipment product to be optimized from cradle to grave, and each stage includes at least one entity node. The carbon footprint accounting unit is used to calculate the carbon footprint accounting value of each entity node of each stage of the electrical equipment product to be optimized based on the sampled values, using a full life cycle carbon footprint accounting model of electrical equipment products established through life cycle assessment methods. The uncertainty analysis unit is used to sample each list data multiple times based on the probability distribution of each list data in a predefined manner, and calculate the carbon footprint output value of each entity node in each stage corresponding to each sampling through the full life cycle carbon footprint accounting model of the electrical equipment product. Then, it calculates the coefficient of variation of the uncertainty of the carbon footprint result of each entity node in each stage and the corresponding carbon emission placement confidence interval based on the carbon footprint output value. The path optimization unit is used to determine the optimal carbon footprint path with the goal of minimizing the overall benefits of the carbon footprint and uncertainty throughout the entire life cycle, and with the carbon footprint calculation value of each entity node within its corresponding carbon emission placement confidence interval as a constraint. The improved Dijkstra algorithm is used to determine the optimal carbon footprint path based on the carbon footprint emission value of each entity node, the coefficient of variation, and the carbon emission placement confidence interval.

[0007] Based on another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods for optimizing the carbon footprint path of electrical equipment products taking into account uncertainties.

[0008] According to another aspect of the present invention, the present invention provides an electronic device, comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement any one of the steps in the method for optimizing the carbon footprint of electrical equipment products considering uncertainties.

[0009] This invention provides a method and system for optimizing the carbon footprint path of electrical equipment products considering uncertainties. The method includes: acquiring sampled values ​​of inventory data for the entire lifecycle of the electrical equipment product to be optimized; using a lifecycle assessment-based carbon footprint accounting model for the entire lifecycle of the electrical equipment product, calculating the carbon footprint accounting value for each entity node at each stage of the product to be optimized based on the sampled values; sampling each inventory data multiple times based on a predefined probability distribution, and calculating the carbon footprint output value for each entity node at each stage corresponding to each sampling using the carbon footprint accounting model; calculating the coefficient of variation of the uncertainty of the carbon footprint result for each entity node at each stage and the corresponding carbon emission placement confidence interval based on the carbon footprint output value; using the goal of minimizing the overall benefit of the entire lifecycle carbon footprint and uncertainty, and with the carbon footprint accounting value of each entity node within its corresponding carbon emission placement confidence interval as a constraint, employing an improved Dijkstra algorithm to determine the optimal carbon footprint path based on the carbon footprint emission value of each entity node, the coefficient of variation, and the carbon emission placement confidence interval. The proposed method and system are divided into five stages and provide precise quantitative formulas, achieving refined and standardized carbon footprint accounting for the entire life cycle of electrical equipment products, solving the problem of vagueness in traditional accounting. By introducing the Monte Carlo method, various uncertainties such as parameters and models are systematically quantified, overcoming the limitations of existing static modeling in adapting to the dynamic fluctuations of the supply chain. By adopting the improved Dijkstra algorithm, carbon emissions are used as path weights to reconstruct the entire industrial chain network, which can output low-carbon optimization paths that are segmented and feasible, effectively addressing the pain points of traditional solutions lacking specificity and practicality. Attached Figure Description

[0010] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures: Figure 1A flowchart of a method for optimizing the carbon footprint of electrical equipment products considering uncertainties according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the full life cycle network of electrical equipment products according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a path optimization system for the carbon footprint of electrical equipment products that takes into account uncertainties, according to an embodiment of the present invention. Detailed Implementation

[0011] 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.

[0012] 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.

[0013] Figure 1 This is a flowchart illustrating a method for optimizing the carbon footprint of electrical equipment products considering uncertainties, according to an embodiment of the present invention. Figure 1 As shown, the carbon footprint path optimization method 100 for electrical equipment products that takes into account uncertainties, as described in this preferred embodiment, starts from step 101.

[0014] In step 101, sampled values ​​of the inventory data of the electrical equipment product to be optimized throughout its entire life cycle are obtained. The entire life cycle includes several stages of the electrical equipment product to be optimized from cradle to grave, and each stage includes at least one entity node.

[0015] Figure 2 This is a schematic diagram of the structure of the entire life cycle network of electrical equipment products according to an embodiment of the present invention. Figure 2 As shown, in this preferred embodiment, the full life-cycle network structure of the electrical equipment product starts with raw material mining and is divided into five stages: raw material acquisition, manufacturing, transportation, use, and recycling. Each stage includes several entity nodes; specifically, raw material mining is taken as the root node. The raw material acquisition stage includes three raw material supplier nodes, namely... The manufacturing phase includes two transformer manufacturer nodes, namely... and The transportation phase includes two logistics and transportation provider nodes, namely... and The usage phase includes two types of end-user nodes, namely... and The recycling and processing phase includes two recycling and processing unit nodes, namely... and .

[0016] In step 102, a carbon footprint accounting model for the entire life cycle of electrical equipment products, established through a life cycle assessment method, is used to calculate the carbon footprint accounting value of each entity node at each stage of the electrical equipment product to be optimized, based on the sampled values.

[0017] Preferably, a life-cycle carbon footprint accounting model for electrical equipment products, established through a life-cycle assessment method, is used. Based on the sampled values, the carbon footprint accounting values ​​for each entity node at each stage of the electrical equipment product to be optimized are calculated. Specifically, the life-cycle carbon footprint accounting model for electrical equipment products divides the entire life cycle of the electrical equipment product into five stages: raw material acquisition, manufacturing, transportation, use, and recycling. For the raw material acquisition stage, the carbon footprint calculation formula is as follows: In the formula, This is the carbon footprint accounting value for the raw material acquisition stage. For the first Physical quantity of similar materials; For the first The physical quantity of energy-like substances; For the first Carbon footprint coefficient of raw materials for similar materials; For the first Carbon footprint coefficient of energy types; Material utilization rate during the raw material acquisition stage; For the manufacturing stage, the carbon footprint calculation formula is as follows: In the formula, This is the carbon footprint accounting value for the manufacturing stage. The first phase consumed during the manufacturing stage Energy consumption For the first emission Physical quantities of greenhouse gases For the first Emission factors of various energy sources For the first Global warming potential of gases; Energy utilization rate during the production and manufacturing stage; For the transportation phase, the carbon footprint calculation formula is as follows: In the formula, The carbon footprint accounting value for the transportation phase. and The first of each of the transportation The quality and distance of similar products For the transportation of the first Carbon emission factors of transportation vehicles for this type of product; For the usage phase, its carbon footprint calculation formula is: In the formula, The carbon footprint accounting value during the usage phase. This represents the average daily power consumption during product operation. This represents the average daily operating time. Local electricity emission factor; For the recycling and processing stage, the carbon footprint calculation formula is as follows: In the formula, This is the carbon footprint accounting value for the recycling and processing stage. For the first step in the recycling process Energy consumption For the first Carbon emission coefficient of energy type The product can be recycled and used for the first time. Material quantity of similar raw materials For the first Carbon footprint coefficient of similar materials.

[0018] Life Cycle Assessment (LCA) is a bottom-up process analysis method that covers the entire lifecycle from raw material acquisition to product use and disposal—a "cradle-to-grave" assessment of greenhouse gas emissions. Electrical equipment products include core technological equipment for the generation, transmission, distribution, conversion, control, and protection of electrical energy, encompassing energy conversion, power distribution control, measurement and protection, and control execution equipment. A comprehensive lifecycle assessment can generally be categorized as follows: Figure 2 The product lifecycle is divided into five stages. By analyzing these five stages, the carbon footprint calculation formulas for each stage can be derived. By adding the carbon footprint calculation values ​​for each of the five stages, the carbon footprint calculation value for the entire product lifecycle can be obtained.

[0019] This preferred embodiment constructs a carbon footprint accounting formula for electrical equipment products in five stages using the life cycle assessment (LCA) method, which realizes the refinement and standardization of carbon footprint accounting and solves the problem of the general and vague traditional accounting.

[0020] In step 103, the Monte Carlo algorithm is used to sample each list data multiple times based on the predefined probability distribution of each list data. After calculating the carbon footprint output value of each entity node in each stage corresponding to each sampling through the full life cycle carbon footprint accounting model of electrical equipment products, the carbon emission placement confidence interval of each entity node in each stage describing the uncertainty of the carbon footprint accounting result is calculated based on the carbon footprint output value.

[0021] Preferably, based on a predefined probability distribution of each inventory data, multiple samples are taken from each inventory data. After calculating the carbon footprint output value for each entity node at each stage corresponding to each sampling using the full life-cycle carbon footprint accounting model for electrical equipment products, the coefficient of variation of the uncertainty of the carbon footprint result for each entity node at each stage and the corresponding carbon emission placement confidence interval are calculated based on the carbon footprint output value, including: Based on the sources of uncertainty in the inventory data, they are categorized into parameter uncertainty, scenario uncertainty, and model uncertainty, with their corresponding probability distributions defined as normal distribution, triangular distribution, and uniform distribution, respectively. The hierarchical Monte Carlo algorithm is used to sample each inventory data multiple times according to the probability distribution corresponding to its uncertain source classification, and the carbon footprint output value of each entity node in each stage corresponding to each sampling is calculated through the full life cycle carbon footprint accounting model of electrical equipment products. Based on the carbon footprint output value, calculate the coefficient of variation of the uncertainty of the carbon footprint result for each entity node in each stage and the corresponding carbon emission placement confidence interval, where: The average carbon footprint of each entity node is determined based on the carbon footprint output value of each entity node at each stage. The calculation formula is as follows: In the formula, n represents the total number of times the inventory data of the corresponding entity node is sampled. The output value of the carbon footprint calculated using the inventory data from the i-th sampling; The standard deviation of the corresponding entity node is determined based on the carbon footprint output value of each entity node in each stage and the average carbon footprint of its corresponding entity node. The calculation formula is as follows: The coefficient of variation for each entity node is determined based on its average carbon footprint and standard deviation. The calculation formula is as follows: The upper and lower limits of the carbon emission placement confidence interval for each entity node are determined based on the average carbon footprint and coefficient of variation of that entity node. The calculation formulas are as follows: In the formula, and These are the lower and upper limits of the 95% carbon emission placement range for this entity node, respectively.

[0022] In this preferred embodiment, the probability distribution of the defined inventory data is determined based on the classification of the sources of uncertainty in the inventory data. Inventory data originating from parameter uncertainty is fitted with a normal distribution; inventory data originating from scenario uncertainty is fitted with a triangular distribution; and inventory data originating from model uncertainty is fitted with a uniform distribution. When using the Monte Carlo simulation method for repeated calculations with multiple sampling, the number of samplings is generally set to more than 1000. The Monte Carlo output is a confidence interval with probabilistic characteristics. The range of values ​​under the 95% confidence interval is determined by comprehensively utilizing the average, standard deviation, and coefficient of variation calculated based on multiple carbon footprint output values ​​to describe the carbon footprint uncertainty results. Monte Carlo simulation overcomes the limitation of existing static modeling in adapting to the dynamic fluctuations of the supply chain.

[0023] In step 104, with the goal of minimizing the overall benefits of the carbon footprint and uncertainty throughout the entire life cycle, and with the carbon footprint calculation value of each entity node within its corresponding carbon emission placement confidence interval as a constraint, the improved Dijkstra algorithm is used to determine the optimal carbon footprint path based on the carbon footprint emission value of each entity node, the coefficient of variation, and the carbon emission placement confidence interval.

[0024] Preferably, step 1 involves defining a directed weighted graph of the network structure for the entire lifecycle of electrical equipment products. Among them, vertex set middle, As the root node, it corresponds to the starting point of raw material mining in the entire life cycle of the proposed electrical equipment products; Corresponding to entity nodes at each stage of the entire lifecycle; directed edge set Representative node arrive A collection of upstream and downstream relationships. It is a connection node and The directed edges conform to the pre-process constraints of the electrical equipment product manufacturing to be optimized, and are unidirectional directed edges that only allow from upstream to downstream in the life cycle; each edge It has dual attributes, including carbon emissions. and uncertainty Its values ​​are respectively the node The carbon footprint accounting value and coefficient of variation; Based on edge Dual attributes construct edge The comprehensive benefits are expressed as follows: In the formula, and These represent the carbon footprint and uncertainty of the baseline path, respectively, using the industry average. and Let be the weighting coefficient, satisfying ; Weight set Represents the node arrive The comprehensive benefits of considering the uncertainty of carbon footprint; Step 2: Initialize the overall benefits and node set of the path, where: , , In the formula, root node The overall benefit of the location is set to 0. and From the root node To node and nodes The cumulative comprehensive benefits, without covering all physical nodes of the entire life cycle of the electrical equipment product to be optimized, The initial value is set to infinity; set A is the set of determined nodes in the optimal carbon footprint path, and the initial elements only contain the root node. ,gather To determine the set of nodes to be traversed for the optimal carbon footprint path, the initial elements include nodes excluding the root node. All entity nodes outside; Step 3, for the set For all nodes to be traversed, calculate the value starting from the root node. The upstream nodes already determined in the optimal carbon footprint path of set A To the node The cumulative comprehensive benefit is minimized, and the carbon emission value range constraint verification is performed based on the carbon emission placement range to ensure the upstream node. To the node The carbon footprint accounting values ​​satisfy the constraints of their carbon emission confidence intervals, set A and set B. The update formula is: In the formula, It is the node from the previous stage selected in set U. Nodes to the next stage The carbon footprint accounting value meets the constraints, and The smallest node; Step 4, when =+∞, then the root node If no feasible supply chain path remains for the remaining nodes in set U, the algorithm terminates, indicating no optimal carbon footprint path. If set U is empty, or all nodes in the recycling and processing stage have been traversed, proceed to step 5; otherwise, return to step 3 to continue iterating. Step 5: Backtrack from the nodes in the garbage collection phase of set A to the root node in reverse order. Output the optimal carbon footprint path that takes into account uncertainties.

[0025] This preferred embodiment is based on Figure 2 Taking the network structure of the entire life cycle of electrical equipment products shown in the figure as an example, a directed weighted graph is constructed. Then the vertex set V = Directed edge set Each directed edge is unidirectional, satisfying the process constraints throughout the entire lifecycle from one stage to the next adjacent stage, such as from node... The starting directed edge only leads to the node. and nodes of and From node The starting directed edge only leads to the node. and nodes of and The optimal carbon footprint path can be obtained by iteratively solving using the improved Dijkstra algorithm described in this preferred embodiment: root node →No. 1 Low-Carbon Emission Raw Material Supplier (Using recycled silicon steel and low-carbon copper) → No. 1 Clean Energy Manufacturer (100% Green Electricity Supply in the Factory Area) → No.1 Low-Carbon Logistics Provider (New energy trunk line transportation) → Industrial end users (high proportion of green electricity consumption in distribution network) → No. 1 High Recovery Rate Recycling and Processing Unit Using this method, entity node 2, which has lower overall efficiency in the production and manufacturing stage but whose carbon footprint accounting value is not within its 95% carbon emission placement confidence interval, was effectively excluded, fully demonstrating the accuracy of this method.

[0026] Figure 3 This is a schematic diagram of a path optimization system for the carbon footprint of electrical equipment products that takes into account uncertainties, according to an embodiment of the present invention. Figure 3 As shown, the carbon footprint path optimization system 200 for electrical equipment products that considers uncertainties according to this preferred embodiment includes: The data acquisition unit 201 is used to acquire sampled values ​​of the inventory data of the electrical equipment product to be optimized throughout its entire life cycle, wherein the entire life cycle includes several stages of the electrical equipment product to be optimized from cradle to grave, and each stage includes at least one entity node. The carbon footprint accounting unit 202 is used to calculate the carbon footprint accounting value of each entity node of each stage of the electrical equipment product to be optimized based on the sampled values ​​using a full life cycle carbon footprint accounting model of electrical equipment products established through life cycle assessment methods. Uncertainty analysis unit 203 is used to sample each list data multiple times based on the probability distribution of each list data in a predefined manner, and calculate the carbon footprint output value of each entity node in each stage corresponding to each sampling through the full life cycle carbon footprint accounting model of electrical equipment products. Then, it calculates the coefficient of variation of the uncertainty of the carbon footprint result of each entity node in each stage and the corresponding carbon emission placement confidence interval based on the carbon footprint output value. The path optimization unit 204 is used to determine the optimal carbon footprint path with the goal of minimizing the overall benefits of the carbon footprint and uncertainty throughout the entire life cycle, and with the carbon footprint calculation value of each entity node within its corresponding carbon emission placement confidence interval as a constraint. It employs an improved Dijkstra algorithm to determine the optimal carbon footprint path based on the carbon footprint emission value of each entity node, the coefficient of variation, and the carbon emission placement confidence interval.

[0027] The uncertainty-considered path optimization system 200 for electrical equipment products in an embodiment of the present invention corresponds to the uncertainty-considered path optimization method 100 for electrical equipment products in another embodiment of the present invention, and will not be described again here.

[0028] Based on another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods for optimizing the carbon footprint path of electrical equipment products taking into account uncertainties.

[0029] According to another aspect of the present invention, the present invention provides an electronic device, comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement any one of the steps in the method for optimizing the carbon footprint of electrical equipment products considering uncertainties.

[0030] The present invention has been described with reference to a few embodiments. However, it will be apparent to those skilled in the art that other embodiments besides those disclosed above fall equivalently within the scope of the present invention.

[0031] Generally, all terms used in this invention are interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed, unless explicitly stated otherwise.

[0032] 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 embodied 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for optimizing the carbon footprint of electrical equipment products considering uncertainties, characterized in that, The method includes: Obtain sampled values ​​of the inventory data for the entire lifecycle of the electrical equipment product to be optimized, wherein the entire lifecycle includes several stages from cradle to grave for the electrical equipment product to be optimized, and each stage includes at least one entity node; A carbon footprint accounting model for the entire life cycle of electrical equipment products, established through life cycle assessment, is used to calculate the carbon footprint accounting value of each entity node at each stage of the electrical equipment product to be optimized, based on the sampled values. Based on the predefined probability distribution of each list data, each list data is sampled multiple times. After calculating the carbon footprint output value of each entity node in each stage corresponding to each sampling through the full life cycle carbon footprint accounting model of electrical equipment products, the coefficient of variation of the uncertainty of the carbon footprint result of each entity node in each stage and the corresponding carbon emission placement confidence interval are calculated based on the carbon footprint output value. With the goal of minimizing the overall benefits of the carbon footprint and uncertainty throughout the entire life cycle, and with the carbon footprint accounting value of each entity node within its corresponding carbon emission placement confidence interval as a constraint, an improved Dijkstra algorithm is used to determine the optimal carbon footprint path based on the carbon footprint emission value of each entity node, the coefficient of variation, and the carbon emission placement confidence interval.

2. The method according to claim 1, characterized in that, A life-cycle carbon footprint accounting model for electrical equipment products, established using a life-cycle assessment method, is adopted. Based on the sampled values, the carbon footprint accounting values ​​for each entity node in each stage of the electrical equipment product to be optimized are calculated. The life-cycle carbon footprint accounting model for electrical equipment products divides the entire life cycle of the electrical equipment product into five stages: raw material acquisition, manufacturing, transportation, use, and recycling. Specifically: For the raw material acquisition stage, the carbon footprint calculation formula is as follows: In the formula, This is the carbon footprint accounting value for the raw material acquisition stage. For the first Physical quantity of similar materials; For the first The physical quantity of energy-like substances; For the first Carbon footprint coefficient of raw materials for similar materials; For the first Carbon footprint coefficient of energy types; For the utilization rate of electrical equipment materials in the raw material acquisition stage; For the manufacturing stage, the carbon footprint calculation formula is as follows: In the formula, This is the carbon footprint accounting value for the manufacturing stage. The first phase consumed during the manufacturing stage Energy consumption For the first emission Physical quantities of greenhouse gases For the first Emission factors of various energy sources For the first Global warming potential of gases; Energy utilization rate during the production and manufacturing stage; For the transportation phase, the carbon footprint calculation formula is as follows: In the formula, The carbon footprint accounting value for the transportation phase. and The first of each of the transportation The quality and distance of similar products For the transportation of the first Carbon emission factors of transportation vehicles for this type of product; For the usage phase, its carbon footprint calculation formula is: In the formula, The carbon footprint accounting value during the usage phase. This represents the average daily power consumption during product operation. This represents the average daily operating time. Local electricity emission factor; For the recycling and processing stage, the carbon footprint calculation formula is as follows: In the formula, This is the carbon footprint accounting value for the recycling and processing stage. For the first step in the recycling process Energy consumption For the first Carbon emission coefficient of energy type The product can be recycled and used for the first time. Material quantity of similar raw materials For the first Carbon footprint coefficient of similar materials.

3. The method according to claim 1, characterized in that, Based on a predefined probability distribution for each inventory data point, multiple samples are taken from each inventory data point. The carbon footprint output value for each entity node at each stage corresponding to each sampling is calculated using the full life-cycle carbon footprint accounting model for electrical equipment products. Then, based on the carbon footprint output value, the coefficient of variation of the uncertainty of the carbon footprint result for each entity node at each stage and the corresponding carbon emission placement confidence interval are calculated, including: Based on the sources of uncertainty in the inventory data, they are categorized into parameter uncertainty, scenario uncertainty, and model uncertainty, with their corresponding probability distributions defined as normal distribution, triangular distribution, and uniform distribution, respectively. The hierarchical Monte Carlo algorithm is used to sample each inventory data multiple times according to the probability distribution corresponding to its uncertain source classification, and the carbon footprint output value of each entity node in each stage corresponding to each sampling is calculated through the full life cycle carbon footprint accounting model of electrical equipment products. Based on the carbon footprint output value, calculate the coefficient of variation of the uncertainty of the carbon footprint result for each entity node in each stage and the corresponding carbon emission placement confidence interval, where: The average carbon footprint of each entity node is determined based on the carbon footprint output value of each entity node at each stage. The calculation formula is as follows: In the formula, n represents the total number of times the inventory data of the corresponding entity node is sampled. The output value of the carbon footprint calculated using the inventory data from the i-th sampling; The standard deviation of the corresponding entity node is determined based on the carbon footprint output value of each entity node in each stage and the average carbon footprint of its corresponding entity node. The calculation formula is as follows: The coefficient of variation for each entity node is determined based on its average carbon footprint and standard deviation. The calculation formula is as follows: The upper and lower limits of the carbon emission placement confidence interval for each entity node are determined based on the average carbon footprint and coefficient of variation of that entity node. The calculation formulas are as follows: In the formula, and These are the lower and upper limits of the 95% carbon emission placement range for this entity node, respectively.

4. The method according to claim 1, characterized in that, With the objective of minimizing the overall benefits of the carbon footprint and uncertainty throughout the entire life cycle, and using the carbon footprint calculation value of each entity node within its corresponding carbon emission placement confidence interval as a constraint, an improved Dijkstra algorithm is employed to determine the optimal carbon footprint path based on the carbon footprint emission value of each entity node, the coefficient of variation, and the carbon emission placement confidence interval. This includes: Step 1: Define a directed weighted graph of the network structure for the entire lifecycle of electrical equipment products. Among them, vertex set middle, As the root node, it corresponds to the starting point of raw material mining in the entire life cycle of the proposed electrical equipment products; Corresponding to entity nodes at each stage of the entire lifecycle; directed edge set Representative node arrive A collection of upstream and downstream relationships. It is a connection node and The directed edges conform to the pre-process constraints of the electrical equipment product manufacturing to be optimized, and are unidirectional directed edges that only allow from upstream to downstream in the life cycle; each edge It has dual attributes, including carbon emissions. and uncertainty Its values ​​are respectively the node The carbon footprint accounting value and coefficient of variation; Based on edge Dual attributes construct edge The comprehensive benefits are expressed as follows: In the formula, and These represent the carbon footprint and uncertainty of the baseline path, respectively, using the industry average. and Let be the weighting coefficient, satisfying ; Weight set Represents the node arrive The comprehensive benefits of considering the uncertainty of carbon footprint; Step 2: Initialize the overall benefits and node set of the path, where: ， ， In the formula, root node The overall benefit of the location is set to 0. and From the root node To node and nodes The cumulative comprehensive benefits, without covering all physical nodes of the entire life cycle of the electrical equipment product to be optimized, The initial value is set to infinity; set A is the set of determined nodes in the optimal carbon footprint path, and the initial elements only contain the root node. ,gather To determine the set of nodes to be traversed for the optimal carbon footprint path, the initial elements include nodes excluding the root node. All entity nodes outside; Step 3, for the set For all nodes to be traversed, calculate the value starting from the root node. The upstream nodes already determined in the optimal carbon footprint path of set A To the node The cumulative comprehensive benefit is minimized, and the carbon emission value range constraint verification is performed based on the carbon emission placement range to ensure the upstream node. To the node The carbon footprint accounting values ​​satisfy the constraints of their carbon emission confidence intervals, set A and set B. The update formula is: In the formula, It is the node from the previous stage selected in set U. Nodes to the next stage The carbon footprint accounting value meets the constraints, and The smallest node; Step 4, when =+∞, then the root node If no feasible supply chain path remains for the remaining nodes in set U, the algorithm terminates, indicating no optimal carbon footprint path. If set U is empty, or all nodes in the recycling and processing stage have been traversed, proceed to step 5; otherwise, return to step 3 to continue iterating. Step 5: Backtrack from the nodes in the garbage collection phase of set A to the root node in reverse order. Output the optimal carbon footprint path that takes into account uncertainties.

5. A path optimization system for the carbon footprint of electrical equipment products considering uncertainties, characterized in that, The system includes: The data acquisition unit is used to acquire sampled values ​​of the inventory data of the electrical equipment product to be optimized throughout its entire life cycle, wherein the entire life cycle includes several stages of the electrical equipment product to be optimized from cradle to grave, and each stage includes at least one entity node. The carbon footprint accounting unit is used to calculate the carbon footprint accounting value of each entity node of each stage of the electrical equipment product to be optimized based on the sampled values, using a full life cycle carbon footprint accounting model of electrical equipment products established through life cycle assessment methods. The uncertainty analysis unit is used to sample each list data multiple times based on the probability distribution of each list data in a predefined manner, and calculate the carbon footprint output value of each entity node in each stage corresponding to each sampling through the full life cycle carbon footprint accounting model of the electrical equipment product. Then, it calculates the coefficient of variation of the uncertainty of the carbon footprint result of each entity node in each stage and the corresponding carbon emission placement confidence interval based on the carbon footprint output value. The path optimization unit is used to determine the optimal carbon footprint path with the goal of minimizing the overall benefits of the carbon footprint and uncertainty throughout the entire life cycle, and with the carbon footprint calculation value of each entity node within its corresponding carbon emission placement confidence interval as a constraint. The improved Dijkstra algorithm is used to determine the optimal carbon footprint path based on the carbon footprint emission value of each entity node, the coefficient of variation, and the carbon emission placement confidence interval.

6. The system according to claim 5, characterized in that, The carbon footprint accounting unit adopts a life-cycle carbon footprint accounting model for electrical equipment products established through life-cycle assessment methods. Based on the sampled values, it calculates the carbon footprint accounting values ​​for each entity node at each stage of the electrical equipment product to be optimized. Specifically, the life-cycle carbon footprint accounting model for electrical equipment products divides the entire life cycle of the electrical equipment product into five stages: raw material acquisition, manufacturing, transportation, use, and recycling. For the raw material acquisition stage, the carbon footprint calculation formula is as follows: In the formula, This is the carbon footprint accounting value for the raw material acquisition stage. For the first Physical quantity of similar materials; For the first The physical quantity of energy-like substances; For the first Carbon footprint coefficient of raw materials for similar materials; For the first Carbon footprint coefficient of energy types; Material utilization rate during the raw material acquisition stage; For the manufacturing stage, the carbon footprint calculation formula is as follows: In the formula, This is the carbon footprint accounting value for the manufacturing stage. The first phase consumed during the manufacturing stage Energy consumption For the first emission Physical quantities of greenhouse gases For the first Emission factors of various energy sources For the first Global warming potential of gases; Energy utilization rate during the production and manufacturing stage; For the transportation phase, the carbon footprint calculation formula is as follows: In the formula, The carbon footprint accounting value for the transportation phase. and The first of each of the transportation The quality and distance of similar products For the transportation of the first Carbon emission factors of transportation vehicles for this type of product; For the usage phase, its carbon footprint calculation formula is: In the formula, The carbon footprint accounting value during the usage phase. This represents the average daily power consumption during product operation. This represents the average daily operating time. Local electricity emission factor; For the recycling and processing stage, the carbon footprint calculation formula is as follows: In the formula, This is the carbon footprint accounting value for the recycling and processing stage. For the first step in the recycling process Energy consumption For the first Carbon emission coefficient of energy type The product can be recycled and used for the first time. Material quantity of similar raw materials For the first Carbon footprint coefficient of similar materials.

7. The system according to claim 5, characterized in that, The uncertainty analysis unit, based on a predefined probability distribution of each inventory data point, samples each inventory data point multiple times. After calculating the carbon footprint output value for each entity node at each stage corresponding to each sampling using the electrical equipment product lifecycle carbon footprint accounting model, it calculates the coefficient of variation of the uncertainty of the carbon footprint result for each entity node at each stage and the corresponding carbon emission placement confidence interval based on the carbon footprint output value, including: Based on the sources of uncertainty in the inventory data, they are categorized into parameter uncertainty, scenario uncertainty, and model uncertainty, with their corresponding probability distributions defined as normal distribution, triangular distribution, and uniform distribution, respectively. The hierarchical Monte Carlo algorithm is used to sample each inventory data multiple times according to the probability distribution corresponding to its uncertain source classification, and the carbon footprint output value of each entity node in each stage corresponding to each sampling is calculated through the full life cycle carbon footprint accounting model of electrical equipment products. Based on the carbon footprint output value, calculate the coefficient of variation of the uncertainty of the carbon footprint result for each entity node in each stage and the corresponding carbon emission placement confidence interval, where: The average carbon footprint of each entity node is determined based on the carbon footprint output value of each entity node at each stage. The calculation formula is as follows: In the formula, n represents the total number of times the inventory data of the corresponding entity node is sampled. The output value of the carbon footprint calculated using the inventory data from the i-th sampling; The standard deviation of the corresponding entity node is determined based on the carbon footprint output value of each entity node in each stage and the average carbon footprint of its corresponding entity node. The calculation formula is as follows: The coefficient of variation for each entity node is determined based on its average carbon footprint and standard deviation. The calculation formula is as follows: The upper and lower limits of the carbon emission placement confidence interval for each entity node are determined based on the average carbon footprint and coefficient of variation of that entity node. The calculation formulas are as follows: In the formula, and These are the lower and upper limits of the 95% carbon emission placement range for this entity node, respectively.

8. The system according to claim 5, characterized in that, The path optimization unit aims to minimize the overall benefits of the carbon footprint and uncertainty throughout the entire life cycle. It uses the carbon footprint calculation value of each entity node within its corresponding carbon emission placement confidence interval as a constraint. An improved Dijkstra algorithm is employed to determine the optimal carbon footprint path based on the carbon footprint emission value of each entity node, the coefficient of variation, and the carbon emission placement confidence interval. This includes: Step 1: Define a directed weighted graph of the network structure for the entire lifecycle of electrical equipment products. Among them, vertex set middle, As the root node, it corresponds to the starting point of raw material mining in the entire life cycle of the proposed electrical equipment products; Corresponding to entity nodes at each stage of the entire lifecycle; directed edge set Representative node arrive A collection of upstream and downstream relationships. It is a connection node and The directed edges conform to the pre-process constraints of the electrical equipment product manufacturing to be optimized, and are unidirectional directed edges that only allow from upstream to downstream in the life cycle; each edge It has dual attributes, including carbon emissions. and uncertainty Its values ​​are respectively the node The carbon footprint accounting value and coefficient of variation; Based on edge Dual attributes construct edge The comprehensive benefits are expressed as follows: In the formula, and These represent the carbon footprint and uncertainty of the baseline path, respectively, using the industry average. and Let be the weighting coefficient, satisfying ; Weight set Represents the node arrive The comprehensive benefits of considering the uncertainty of carbon footprint; Step 2: Initialize the overall benefits and node set of the path, where: ， ， In the formula, root node The overall benefit of the location is set to 0. and From the root node To node and nodes The cumulative comprehensive benefits, without covering all physical nodes of the entire life cycle of the electrical equipment product to be optimized, The initial value is set to infinity; set A is the set of determined nodes in the optimal carbon footprint path, and the initial elements only contain the root node. ,gather To determine the set of nodes to be traversed for the optimal carbon footprint path, the initial elements include nodes excluding the root node. All entity nodes outside; Step 3, for the set For all nodes to be traversed, calculate the value starting from the root node. The upstream nodes already determined in the optimal carbon footprint path of set A To the node The cumulative comprehensive benefit is minimized, and the carbon emission value range constraint verification is performed based on the carbon emission placement range to ensure the upstream node. To the node The carbon footprint accounting values ​​satisfy the constraints of their carbon emission confidence intervals, set A and set B. The update formula is: In the formula, It is the node from the previous stage selected in set U. Nodes to the next stage The carbon footprint accounting value meets the constraints, and The smallest node; Step 4, when =+∞, then the root node If no feasible supply chain path remains for the remaining nodes in set U, the algorithm terminates, indicating no optimal carbon footprint path. If set U is empty, or all nodes in the recycling and processing stage have been traversed, proceed to step 5; otherwise, return to step 3 to continue iterating. Step 5: Backtrack from the nodes in the garbage collection phase of set A to the root node in reverse order. Output the optimal carbon footprint path that takes into account uncertainties.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-4.

10. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the steps of the method according to any one of claims 1-4.

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