A product full production cycle carbon footprint traceability evaluation method and system

Abstract

The application provides a product full production cycle carbon footprint traceability evaluation method and system, relates to the technical field of carbon footprint traceability, and comprises the following steps: obtaining production activity data of each link in the full life cycle of a product, constructing a directed weighted network model, performing path traversal, extracting a traversal path with a path carbon intensity meeting a preset path carbon intensity condition as a first carbon flow path, obtaining node net carbon emissions of each node, and determining the apportionment of each node to scope three emissions; performing matching on production activity data of each node corresponding link and emission parameters in a carbon emission database, obtaining link carbon emission intensity of each link, and determining link carbon emission of each link based on the link carbon emission intensity and the node net carbon emissions. The application can identify a small-flow high-carbon emission intensity path, improve the scope three emission accounting accuracy to a level that can be traced to a specific supply chain node through a double-layer mapping relationship between microcosmic accounting at a link level and macroscopic traceability at a node level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon footprint tracing, and in particular to a product full production cycle carbon footprint tracing and evaluation method and system. BACKGROUND

[0002] Currently, the method for tracing and evaluating the product full life cycle carbon footprint is mainly based on the life cycle assessment standard and the greenhouse gas accounting system protocol. When accounting for scope 3 emissions, the apportionment method and the estimation method are commonly used methods. The estimation method estimates emissions based on industry average data. The proportion of the material purchased by the enterprise from the supplier to the total output of the supplier is the basis for calculating the apportionment amount of scope 3 emissions by the apportionment method, and the industry average emission data instead of the actual emission data of the supplier is the core way of the estimation method. In the absence of detailed data of the supply chain, the above methods can complete the accounting of scope 3 emissions.

[0003] However, the material proportion is the basis for apportioning scope 3 emissions by the apportionment method, and the actual carbon flow and carbon emission intensity of different supply chain paths are not considered by the apportionment method. The supply chain path with small flow but high carbon emission intensity cannot be identified by the apportionment method. The use of industry average data instead of actual emission data is the way used by the estimation method. In the case of complex supply chain structure and involving multi-level supply relationship, the tracing ability of the specific carbon flow transmission path is the lack of the estimation method.

[0004] Therefore, in terms of identifying high carbon emission intensity paths and achieving accurate tracing of scope 3 emissions, the existing product full life cycle carbon footprint tracing and evaluation method has deficiencies. SUMMARY

[0005] The present application provides a product full production cycle carbon footprint tracing and evaluation method and system to solve the problem that the existing product carbon footprint tracing and evaluation method cannot identify high carbon intensity paths, the apportionment of scope 3 emissions lacks a quantitative tracing method based on path carbon intensity and node carbon contribution, and the carbon emission accounting at the link level is not associated with the carbon flow tracing at the node level.

[0006] In a first aspect, the present application provides a product full production cycle carbon footprint tracing and evaluation method, comprising: obtaining production activity data of each link of the product full life cycle, and constructing a directed weighted network model according to the production activity data;

[0007] Performing path traversal on the directed weighted network model, extracting a traversal path with path carbon intensity satisfying a preset path carbon intensity condition as a first carbon flow path, obtaining node net carbon emission amounts of each node on the first carbon flow path, and determining the apportionment amounts of each node to scope 3 emissions according to the node net carbon emission amounts;

[0008] The production activity data and emission parameters in a carbon emission database corresponding to each node pair of each link on the first carbon flow path are matched to obtain link carbon emission intensity of each link, and link carbon emission of each link is determined based on the link carbon emission intensity and the node net carbon emission;

[0009] The apportioned amount and the link carbon emission are integrated to build a product carbon footprint trace list.

[0010] Optionally, in a possible implementation manner of the first aspect, the building of the directed weighted network model according to the production activity data comprises:

[0011] A first edge weight value is assigned to a directed edge of the directed weighted network model according to a material flow amount in the production activity data;

[0012] A second edge weight value is assigned to the directed edge of the directed weighted network model according to an energy consumption amount in the production activity data;

[0013] A node in the directed weighted network model represents a link entity in a product life cycle,

[0014] The directed edge represents a material flow relationship or an energy transmission relationship between the link entities.

[0015] Optionally, in a possible implementation manner of the first aspect, the performing of the path traversal on the directed weighted network model comprises:

[0016] A plurality of forward traversal paths are obtained by traversing from a starting node of the directed weighted network model to a terminal node in a forward direction of the directed edge;

[0017] A plurality of reverse traversal paths are obtained by traversing from the terminal node of the directed weighted network model to the starting node in a reverse direction of the directed edge;

[0018] The forward traversal paths and the reverse traversal paths are compared in node sequence to identify a path with consistent node sequence in the forward traversal paths and the reverse traversal paths as a bidirectional consistent path;

[0019] An edge weight value ratio of the first edge weight value and the second edge weight value of each directed edge on the bidirectional consistent path is calculated;

[0020] A directed edge with the edge weight value ratio greater than that of an adjacent directed edge on the bidirectional consistent path is identified, and the bidirectional consistent path containing the identified directed edge is extracted as the first carbon flow path.

[0021] Optionally, in a possible implementation manner of the first aspect, the traversed path satisfying the preset path carbon intensity condition is taken as a first carbon flow path, including:

[0022] Identifying path coincident nodes in the first carbon flow paths;

[0023] Grouping the first carbon flow paths containing the same path coincident nodes into a path verification group;

[0024] Calculating path carbon intensities of the first carbon flow paths in the path verification group, calculating a representative value and a dispersion of path carbon intensities in the path verification group;

[0025] Identifying the first carbon flow path in which the path carbon intensity deviates from the representative value of path carbon intensities in the path verification group by an amount greater than the dispersion of path carbon intensities in the path verification group;

[0026] Obtaining adjacent nodes of the path coincident nodes in the first carbon flow path in which the path carbon intensity deviates from the representative value of path carbon intensities in the path verification group by an amount greater than the dispersion of path carbon intensities in the path verification group, and supplementing the adjacent nodes to the first carbon flow path in which the path carbon intensity deviates from the representative value of path carbon intensities in the path verification group by an amount greater than the dispersion of path carbon intensities in the path verification group;

[0027] Updating the carbon emission parameter of the path coincident nodes in the directed and weighted network model based on the first carbon flow path after supplementing the adjacent nodes.

[0028] Optionally, in a possible implementation manner of the first aspect, the obtaining of the node net carbon emission amount of each node on the first carbon flow path includes:

[0029] Obtaining a node in-degree and a node out-degree of each node on the first carbon flow path, the node in-degree representing a number of the directed edges pointing to the node, and the node out-degree representing a number of the directed edges pointing from the node;

[0030] If the node in-degree is zero and the node out-degree is greater than zero, obtaining a node direct carbon emission amount of the node as the node net carbon emission amount of the node;

[0031] If the node in-degree is one and the node out-degree is one, obtaining the node direct carbon emission amount of the node, obtaining a node net carbon emission amount of an upstream node of the node, performing accumulation on the node net carbon emission amount of the upstream node to obtain a node upstream cumulative carbon emission amount of the node, and calculating the node net carbon emission amount of the node based on the node direct carbon emission amount of the node and the node upstream cumulative carbon emission amount of the node;

[0032] If the node in-degree is greater than 1, the node direct carbon emission of the node and the node upstream cumulative carbon emission of the node are obtained, the first edge weight of each directed edge pointing to the node is obtained, a node convergence adjustment amount of the node is calculated based on the first edge weight of each directed edge pointing to the node, and the node net carbon emission of the node is calculated based on the node direct carbon emission of the node, the node upstream cumulative carbon emission of the node, and the node convergence adjustment amount of the node.

[0033] Optionally, in a possible implementation manner of the first aspect, the determining the range three emission allocation of each node based on the node net carbon emission comprises:

[0034] calculating a path carbon emission representative value of the first carbon flow path based on the node net carbon emission of each node on the first carbon flow path;

[0035] calculating a node carbon emission deviation amount of the node net carbon emission of each node on the first carbon flow path and the path carbon emission representative value;

[0036] if the node carbon emission deviation amount is positive, obtaining a node position serial number of the node in the first carbon flow path, and calculating the range three emission allocation of the node based on the node net carbon emission of the node, the node carbon emission deviation amount of the node, and the node position serial number of the node;

[0037] if the node carbon emission deviation amount is negative, calculating the range three emission allocation of the node based on the node net carbon emission of the node, the node carbon emission deviation amount of the node, the node in-degree of the node, and the node out-degree of the node;

[0038] if the node carbon emission deviation amount is zero, obtaining a node material output of the node, and calculating the range three emission allocation of the node based on the node net carbon emission of the node and the node material output of the node.

[0039] Optionally, in a possible implementation manner of the first aspect, after the determining the range three emission allocation of each node based on the node net carbon emission, the method further comprises:

[0040] performing aggregation on the range three emission allocation of all nodes on the first carbon flow path to obtain a first carbon flow path total allocation;

[0041] projecting the first carbon flow path total allocation to the traversal path of the directed weighted network model that is not extracted as the first carbon flow path;

[0042] calculating a carbon emission consistency parameter of the traversal path not extracted as the first carbon flow path and the first carbon flow path at the path coincidence node;

[0043] if the carbon emission consistency parameter is less than a preset consistency parameter condition, identifying the path coincidence node whose carbon emission consistency parameter is less than the preset consistency parameter condition;

[0044] performing correction on the apportioned amount of the identified path coincidence node;

[0045] recomputing the intra-group path carbon intensity representative value and the intra-group path carbon intensity dispersion of the path verification group based on the corrected apportioned amount.

[0046] Optionally, in a possible implementation manner of the first aspect, the matching of the production activity data of each node corresponding link on the first carbon flow path with the emission parameter in the carbon emission database comprises:

[0047] performing dimension reduction processing on the production activity data to extract a first characteristic variable in the production activity data affecting carbon emission;

[0048] based on the first characteristic variable, identifying data in the production activity data having a feature correlation degree less than other production activity data;

[0049] performing elimination on the identified data;

[0050] obtaining the emission parameter matched with each node corresponding link on the first carbon flow path from the carbon emission database.

[0051] Optionally, in a possible implementation manner of the first aspect, the obtaining of the emission parameter matched with each node corresponding link on the first carbon flow path from the carbon emission database comprises:

[0052] obtaining time attributes, geographical attributes and industry attributes of each node corresponding link on the first carbon flow path;

[0053] retrieving the emission parameter matched with each node corresponding link on the first carbon flow path from the carbon emission database according to the time attributes, the geographical attributes and the industry attributes of each node corresponding link on the first carbon flow path;

[0054] obtaining activity level data in the production activity data of each node corresponding link on the first carbon flow path;

[0055] Based on the activity level data of each node corresponding to the link on the first carbon flow path and the matched emission parameter, the link carbon emission intensity of each node corresponding to the link on the first carbon flow path is calculated.

[0056] Optionally, in a possible implementation manner of the first aspect, the integrating the allocation amount and the link carbon emission amount to construct a product carbon footprint trace list comprises:

[0057] constructing a decision matrix comprising a carbon emission intensity index, an energy efficiency index, a resource recycling rate index, and a supply chain carbon footprint transparency index;

[0058] respectively assigning an index weight to the carbon emission intensity index, the energy efficiency index, the resource recycling rate index, and the supply chain carbon footprint transparency index in the decision matrix;

[0059] inputting data in the product carbon footprint trace list into the decision matrix, and calculating a product carbon efficiency evaluation result based on the index weight.

[0060] Optionally, in a possible implementation manner of the first aspect, the calculating the product carbon efficiency evaluation result based on the index weight comprises:

[0061] generating a product carbon footprint visual trace map based on the product carbon footprint trace list and the product carbon efficiency evaluation result;

[0062] performing sorting on the link carbon emission amounts of the nodes on the first carbon flow path according to numerical values;

[0063] obtaining nodes located in a front preset number as carbon emission key nodes;

[0064] generating a product digital carbon label based on the product carbon footprint trace list, the product carbon efficiency evaluation result, and the carbon emission key nodes.

[0065] Optionally, in a possible implementation manner of the first aspect, the obtaining production activity data of each link in a product life cycle comprises:

[0066] acquiring the production activity data of a product raw material acquisition stage, a production and manufacturing stage, a logistics and transportation stage, a product use stage, and a waste recycling stage through an application program interface and an Internet of Things device;

[0067] performing standardization processing on the acquired production activity data;

[0068] determining a node set and an edge set of the directed weighted network model based on a product bill of materials and a process flow diagram.

[0069] In a second aspect of the present application, a product full production cycle carbon footprint traceability evaluation system is provided, comprising: an instruction acquisition module, configured to acquire a scheduling instruction of a superior scheduling center and extract a level identifier of the scheduling instruction;

[0070] a mode control module, configured to determine a current regulation mode according to the level identifier, and if the current regulation mode is a preset mode, freeze a coordination state variable of a current edge agent and close a first communication interface between the current edge agent and other edge agents;

[0071] an error analysis module, configured to acquire feedback data generated by the current edge agent after a preset stable time, determine an execution error according to the feedback data and target data in the scheduling instruction, and construct an error time sequence model according to the execution error;

[0072] a parameter adjustment module, configured to determine the execution error based on the error time sequence model, and if the execution error is an abnormal execution error, acquire a first reference decision from a preset decision set and adjust a first decision parameter of the current edge agent according to the first reference decision.

[0073] The product full production cycle carbon footprint traceability evaluation method and system provided by the present application have the following beneficial effects:

[0074] 1. The present application can identify small-flow high-carbon emission intensity paths in the supply chain by screening the first carbon flow path based on path carbon intensity, overcoming the limitation of traditional methods that only focus on high-carbon emission total path while ignoring carbon emission intensity. By distinguishing between the newly added carbon emissions of the supply chain node itself and the carbon emissions passed from the upstream, the present application overcomes the problem that traditional methods cannot quantify the actual contribution of the node in carbon flow transmission;

[0075] 2. The present application establishes a double-layer mapping relationship between micro-accounting at the link level and macro-tracing at the node level by establishing link carbon emission intensity, and cooperatively accounts for direct carbon emissions at the link level and indirect carbon emissions between nodes, solving the problem that link carbon emission accounting and carbon flow path tracing are disconnected in traditional methods. BRIEF DESCRIPTION OF DRAWINGS

[0076] Figure 1 is a flowchart of a product full production cycle carbon footprint traceability evaluation method provided by an embodiment of the present application;

[0077] Figure 2 is an application environment diagram of a product full production cycle carbon footprint traceability evaluation method provided by an embodiment of the present application;

[0078] Figure 3 is a structural diagram of a product full production cycle carbon footprint traceability evaluation system provided by an embodiment of the present application;

[0079] Figure 4 is a hardware structure schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0081] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0082] Referring to Figure 1 is a flowchart of a product full production cycle carbon footprint traceability evaluation method provided by an embodiment of the present application, Figure 1 The execution subject of the method shown in the figure can be a software and / or hardware device. The execution subject of the present application can include but is not limited to at least one of the following: a user device, a network device, and the like. The user device can include but is not limited to a computer, a smart phone, a personal digital assistant (PDA), and the above-mentioned electronic devices, and the like. The network device can include but is not limited to a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of computers or network servers based on cloud computing. Cloud computing is a kind of distributed computing, which is a super virtual computer composed of a group of loosely coupled computers. The present embodiment does not make any limitation.

[0083] Referring to Figure 2 , Figure 2 is an application environment diagram of the product full life cycle carbon footprint traceability evaluation method of the present application. As Figure 2As shown, the entire application environment is composed of a data collection layer, a network transmission layer, a central control system, and a multi-terminal output layer. In the data collection layer, production activity data is collected from the product raw material acquisition stage, the production and manufacturing stage, the logistics transportation stage, the product use stage, and the waste recycling stage through Internet of Things devices and API interfaces. The production activity data collected in each stage is aggregated to the carbon footprint traceability evaluation central control system through the network transmission layer composed of 5G base stations, cloud server clusters, and edge computing gateways. The central control system includes four core processing modules and two support modules. The model construction module performs directed weighted network model construction, the traceability allocation module performs carbon flow path identification and scope three emission allocation, the carbon emission accounting module performs carbon emission parameter matching and accounting, and the data integration module performs multi-dimensional carbon efficiency comprehensive evaluation. The support modules include a carbon emission database system and an output module. The data processing process is sequentially executed in the order of the model construction module, the traceability allocation module, and the carbon emission accounting module. The output result of the carbon emission accounting module is transmitted to the output module, and the data integration module performs product carbon efficiency evaluation based on the data of the carbon emission database system. The central control system realizes the continuous improvement of data quality through a feedback optimization mechanism. Specifically, the path verification result of the traceability allocation module is fed back to the carbon emission database system to update and optimize the carbon emission parameters in the database, and the optimized database parameters further support the carbon efficiency evaluation work of the fourth module. Finally, the product carbon footprint traceability list, the visualized traceability map, and the digital carbon label generated by the output module are displayed to users through various terminals such as PC, mobile terminal, and visualized large screen, providing data support for enterprise carbon management decision-making and external carbon information disclosure.

[0084] The product full production cycle carbon footprint traceability evaluation method provided in this embodiment includes steps 100 to 400, specifically as follows:

[0085] Step 100: Obtain production activity data of each link of the product life cycle, and construct a directed weighted network model according to the production activity data.

[0086] In order to realize accurate traceability and evaluation of the product full production cycle carbon footprint, the present application first needs to construct a data model that can completely reflect the whole process from raw material acquisition to waste recycling of the product, i.e. a directed weighted network model, which not only covers the production activity data of each link of the product life cycle, but also accurately describes the material flow relationship and energy transmission relationship between each link, laying a foundation for carbon flow path identification and carbon emission allocation.

[0087] In some embodiments, step 100 includes step 110 and step 120:

[0088] Step 110: Obtain production activity data of each link of the product life cycle.

[0089] Specifically, step 110 specifically includes steps 111 to 113:

[0090] Step 111: Collect production activity data of product raw material acquisition stage, production manufacturing stage, logistics transportation stage, product use stage and waste recycling stage through application program interface with Internet of Things devices.

[0091] Among them, the production activity data includes material consumption, energy use, process parameters and the like in the whole process from product raw material acquisition, production manufacturing, logistics transportation, product use to waste recycling.

[0092] Among them, the application program interface is a standardized communication interface between external data sources, used to obtain production activity data from business systems such as enterprise resource planning systems, manufacturing execution systems, logistics management systems, etc. The Internet of Things devices are intelligent sensors and data collection devices deployed in the production site, including smart meters, flow meters, temperature sensors, pressure sensors, GPS locators, etc., used to monitor and upload physical parameters in the production process in real time.

[0093] Step 112: Perform standardized processing on the collected production activity data.

[0094] It should be noted that since the production activity data of each link of the product life cycle comes from different systems and devices, these production activity data have significant differences in format, unit, precision, time granularity, etc., which are typical heterogeneous data. The traditional carbon footprint accounting method directly uses the heterogeneous data without processing, resulting in the problem of incomparable data and large deviation of calculation results, so it is necessary to perform standardized processing on the collected production activity data.

[0095] Perform format uniformity processing on the production activity data, convert all production activity data to a unified data format, and define a standard data field structure. For example, perform unit conversion processing on the production activity data, and convert all material quantities in the production activity data to mass units and energy consumption to energy units. Perform time alignment processing on the production activity data, convert all timestamps of the production activity data to a standard time format, and unify the time granularity.

[0096] Further, perform missing value processing and abnormal value detection and processing on the production activity data. For production activity data with missing values, interpolation method is used for completion; for production activity data points that deviate significantly from the normal range, mark them as abnormal data and perform review or correction.

[0097] Step 113: Determine the node set and edge set of the directed weighted network model based on the product bill of materials and process flow diagram.

[0098] It should be noted that the core data structure used by the present application to describe the carbon flow path of the whole life cycle of a product is a directed weighted network model. The directed weighted network model is composed of a node set and an edge set, the nodes represent entities in the life cycle of a product, and the edges represent material flow relationships or energy transmission relationships between the entities.

[0099] The product bill of materials is a hierarchical list describing the composition of a product. The process flow chart is a graphical document describing the production process of a product. The node set is a set of all nodes in the directed weighted network model, and the edge set is a set of all directed edges in the directed weighted network model.

[0100] The initial node set of the directed weighted network model is determined based on the product bill of materials. The product bill of materials is parsed, and raw materials, parts, intermediate products, finished products, and other material entities in the product bill of materials are extracted, and each material entity is mapped to a node in the directed weighted network model.

[0101] The node set of the directed weighted network model is expanded based on the process flow chart. The process flow chart is parsed, and process nodes in the process flow chart are extracted, and each process node is also mapped to a node in the directed weighted network model, so that the node set of the directed weighted network model contains both material entity nodes and process node nodes. The process node is a processing process node in the process flow chart.

[0102] The edge set of the directed weighted network model is determined based on the material flow relationship in the product bill of materials and the process flow chart. The assembly relationship between upper and lower materials in the product bill of materials and the input-output relationship of raw materials inputting a process, process outputting an intermediate product, and intermediate product inputting a next process in the process flow chart are extracted. In the directed weighted network model, if material A flows to material B, a directed edge is established between node A and node B, and the direction of the directed edge points from node A to node B.

[0103] Step 120: constructing a directed weighted network model according to production activity data.

[0104] Specifically, step 120 includes steps A1 and A2:

[0105] Step A1: assigning a first edge weight to the directed edge of the directed weighted network model according to the material flow amount in the production activity data.

[0106] The material flow amount data corresponding to each directed edge in the directed weighted network model is extracted from the production activity data. For the directed edge from node A to node B in the directed weighted network model, the amount of material transported from node A to node B is found from the production activity data.

[0107] For example, for the directed edge from the aluminum ingot node to the aluminum alloy cast node, the mass of the aluminum ingot input to the casting process is extracted from the production activity data of the production manufacturing stage; for the directed edge from the notebook computer whole node to the land transportation node, the number of transported notebook computers and the mass of a single notebook computer are extracted from the production activity data of the logistics transportation stage, and the total mass of transportation is calculated.

[0108] Unit conversion is performed on the material flow quantity data, and all material flow quantity data is converted into mass units. For material flow quantity data expressed in units such as pieces, volume, etc., conversion is performed according to the mass or density of a single piece of material.

[0109] The extracted and converted material flow quantity data is used as the first edge weight of the corresponding directed edge in the directed weighted network model.

[0110] For directed edges that exist in the directed weighted network model but are not recorded in the production activity data, the usage relationship in the product bill of materials is used to perform calculation. For example, the product bill of materials specifies that each notebook computer requires one motherboard, and if the production activity data records the production of 1000 notebook computers but does not directly record the usage of the motherboard, the material flow quantity of the motherboard is calculated to be 1000, and the first edge weight is calculated according to the mass of a single motherboard.

[0111] Through the above steps, the first edge weight based on material flow quantity is assigned to all directed edges in the directed weighted network model.

[0112] Step A2: Assign the second edge weight to the directed edges of the directed weighted network model according to the energy consumption in the production activity data.

[0113] The energy consumption data corresponding to each directed edge in the directed weighted network model is extracted from the production activity data. For a directed edge from node A to node B in the directed weighted network model, the directed edge represents the process of material flowing from node A to node B, and the energy consumption of the process of material flowing from node A to node B is extracted from the production activity data.

[0114] It can be understood that for the directed edges of the production manufacturing stage, the energy consumption comes from the energy consumption data of the process equipment; for the directed edges of the logistics transportation stage, the energy consumption comes from the fuel consumption data of the transportation tool; for the directed edges of the product use stage, the energy consumption comes from the energy consumption data during the use of the product.

[0115] Unit conversion is performed on the energy consumption data, and all energy consumption data is converted into energy units. Different types of energy have different calorific values, and conversion is performed according to the standard calorific value conversion table.

[0116] The extracted and converted energy consumption data is taken as the second edge weight, and is assigned to the corresponding directed edge in the directed weighted network model.

[0117] For the directed edges that exist in the directed weighted network model but are not directly recorded in the production activity data, estimation is performed according to industry energy consumption benchmark data. For example, for some small suppliers that do not install energy monitoring equipment, energy consumption data cannot be directly obtained, and the energy consumption is estimated according to the product of the industry average energy consumption level of the process and the material flow amount.

[0118] It should be noted that the nodes in the directed weighted network model represent the link entities in the product life cycle. The link entities include raw material suppliers, raw material mining or planting nodes, raw material storage nodes, various processing process nodes, intermediate product nodes, finished product nodes, transportation link nodes, distribution storage nodes, product use nodes, waste recycling nodes, and renewable material nodes. Each node has a unique node identifier in the directed weighted network model, which is used to accurately locate the node in subsequent path traversal and carbon emission calculation.

[0119] The directed edge represents the material flow relationship or energy transfer relationship between the link entities. The material flow relationship refers to the directional flow of materials from one link entity to another link entity, such as the flow of raw materials from suppliers to production processes, the flow of intermediate products from upstream processes to downstream processes, and the flow of finished products from production bases to distribution centers. In this application, the directed edge is mainly used to describe the material flow relationship, and the energy consumption is taken as an accompanying attribute in the material flow process, which is reflected by the second edge weight.

[0120] Preferably, in step 120, node attributes can also be assigned to the nodes of the directed weighted network model. The node attributes include node type, node geographical location, node time attribute, node belonging enterprise or organization identifier, etc. The node attributes play an important role in subsequent carbon emission parameter matching and carbon footprint tracing, such as the node geographical location for matching the regional power grid carbon emission factor, and the node time attribute for matching the emission parameters of the corresponding year.

[0121] Step 200: performing path traversal on the directed weighted network model, extracting a traversal path with a path carbon intensity satisfying a preset path carbon intensity condition as a first carbon flow path, obtaining the node net carbon emission amount of each node on the first carbon flow path, and determining the apportionment of the scope three emissions of each node according to the node net carbon emission amount.

[0122] In some embodiments, step 200 includes steps 210 to 240:

[0123] Step 210: performing path traversal on the directed weighted network model.

[0124] In the present application, all possible material flow paths from raw material acquisition to product waste recycling are found by performing path traversal on the directed weighted network model. Traditional methods only perform one-way traversal, which easily misses the key path in a complex supply chain network. Therefore, the present application uses a two-way traversal combined with edge weight ratio analysis method to accurately identify the carbon flow path with high coupling of material flow and energy consumption.

[0125] Specifically, step 210 specifically includes steps 211 to 215:

[0126] Step 211: traversing from the starting node of the directed weighted network model to the terminal node along the positive direction of the directed edge to obtain multiple positive traversal paths.

[0127] Wherein, the starting node is a node with zero in-degree in the directed weighted network model, representing the starting point of the product life cycle, including raw material supplier nodes, raw material mining or planting nodes, etc. The terminal node is a node with zero out-degree in the directed weighted network model, representing the end point of the product life cycle, including finished product nodes, product use nodes, waste recycling nodes, and regenerated material nodes. The positive traversal path is a node sequence starting from the starting node, passing through several intermediate nodes along the direction of the directed edge, and finally reaching the terminal node.

[0128] Starting from the starting node of the directed weighted network model, perform depth-first traversal or breadth-first traversal along the positive direction of the directed edge, record all nodes passed in the traversal process, and form the positive traversal path. During the traversal process, when the terminal node is reached, the current path is saved as a complete positive traversal path, and then backtracking to the last branch node, continue to traverse other branches until all possible paths are traversed. Through the above traversal, multiple positive traversal paths are obtained.

[0129] Step 212: traversing from the terminal node of the directed weighted network model to the starting node along the reverse direction of the directed edge to obtain multiple reverse traversal paths.

[0130] Reverse traversal is to verify the integrity and accuracy of the positive traversal path. Traditional methods only perform forward traversal, which easily misses some paths or produces incorrect paths in a complex supply chain network. The present application can backtrack from the terminal node to the starting node through reverse traversal to verify the connectivity of the path.

[0131] Wherein, the reverse traversal path is a node sequence starting from the terminal node, passing through several intermediate nodes along the reverse direction of the directed edge, and finally reaching the starting node.

[0132] Starting from the terminal node of the directed weighted network model, performing depth-first traversal or breadth-first traversal in the reverse direction of the directed edge, recording all nodes passed in the traversal process to form a reverse traversal path. In the traversal process, when the starting node is reached, the current path is saved as a complete reverse traversal path, and then backtracking to the previous branch node to continue traversing other branches until all possible paths are traversed. Through the above traversal, multiple reverse traversal paths are obtained.

[0133] Step 213: Comparing the node sequence of the forward traversal path with the reverse traversal path, identifying the path with the same node sequence in the forward traversal path and the reverse traversal path as the bidirectional consistent path.

[0134] It should be noted that in the directed weighted network model, due to the existence of multiple starting nodes and terminal nodes, as well as multiple branch convergence structures, forward traversal may produce some incomplete or incorrect paths. By comparing the node sequence of the forward traversal path with the reverse traversal path, the truly connected and complete path can be identified.

[0135] Wherein, the node sequence is a set of nodes in the path arranged in the traversal order. The bidirectional consistent path is a path with the same node sequence in the forward traversal path and the reverse traversal path, indicating that there is a complete and connected material flow path from the starting node to the terminal node.

[0136] For each forward traversal path, the node sequence of the forward traversal path is extracted; for each reverse traversal path, the node sequence of the reverse traversal path is reversed to obtain the forward node sequence corresponding to the reverse traversal path. The node sequence of the forward traversal path and the forward node sequence corresponding to the reverse traversal path are compared node by node, if the number of nodes in the two node sequences is the same and the node identifiers at each position are completely consistent, the forward traversal path and the reverse traversal path are identified as the same path, and this path is taken as the bidirectional consistent path.

[0137] It can be understood that only the bidirectional consistent path is the complete material flow path that really exists in the directed weighted network model, and the bidirectional consistent path can effectively exclude the incorrect paths or incomplete paths generated in the traversal process.

[0138] Step 214: Calculating the edge weight ratio of the first edge weight and the second edge weight of each directed edge in the bidirectional consistent path.

[0139] The larger the edge weight ratio is, the more energy is consumed per unit of material flow, the higher the carbon emission intensity is, and the more important the directed edge is in carbon footprint tracing.

[0140] Step 215: identifying a directed edge with an edge weight ratio value greater than an edge weight ratio value of an adjacent directed edge on the bidirectional consistent path, and extracting the bidirectional consistent path containing the identified directed edge as the first carbon flow path.

[0141] It should be noted that not all bidirectional consistent paths are key carbon flow paths. In the product life cycle, the energy consumption intensity of some links may be significantly higher than that of other links, and these high-energy consumption links are the main contributors to carbon footprint. The traditional method lacks a screening mechanism for carbon flow paths, treating all paths equally, resulting in high computational complexity and lack of emphasis. The present application identifies directed edges with energy consumption intensity higher than adjacent links through edge weight ratio analysis, thereby extracting key carbon flow paths.

[0142] wherein the adjacent directed edge is an upstream directed edge or a downstream directed edge directly connected to the current directed edge on the bidirectional consistent path. The first carbon flow path is a bidirectional consistent path containing a directed edge with energy consumption intensity significantly higher than adjacent links, representing a key carbon flow path in the product life cycle.

[0143] For each directed edge on the bidirectional consistent path, the edge weight ratio value of the directed edge is obtained, and the edge weight ratio values of the upstream adjacent directed edge and the downstream adjacent directed edge of the directed edge are obtained. If the edge weight ratio value of the directed edge is greater than the edge weight ratio value of the upstream adjacent directed edge, or the edge weight ratio value of the directed edge is greater than the edge weight ratio value of the downstream adjacent directed edge, the directed edge is identified as a high-energy consumption directed edge. The bidirectional consistent path containing at least one high-energy consumption directed edge is extracted as the first carbon flow path.

[0144] It is not difficult to understand that the first carbon flow path represents a key path with high coupling of material flow and energy consumption and significant carbon emission intensity in the product life cycle, which is the focus of carbon footprint traceability evaluation.

[0145] Step 220: extracting a traversal path with a path carbon intensity satisfying a preset path carbon intensity condition as the first carbon flow path.

[0146] Due to the complexity of the supply chain network, the same node may be shared by multiple first carbon flow paths, and the carbon emission data of these paths at the shared node may be inconsistent. The traditional method lacks a verification mechanism for path carbon intensity, directly using unverified path data, resulting in insufficient reliability of carbon footprint accounting results. The present application can identify and correct abnormal paths through carbon intensity analysis of the path verification group, ensuring the accuracy of the first carbon flow path.

[0147] Specifically, step 220 includes steps 221 to 226:

[0148] Step 221: identifying a path coincidence node in the multiple first carbon flow paths.

[0149] wherein, path coincidence node is a node that is commonly contained by multiple first carbon flow paths, representing the intersection point of different carbon flow paths in the supply chain network.

[0150] All first carbon flow paths are traversed, and the number of occurrences of each node in multiple first carbon flow paths is counted. If a node occurs in two or more first carbon flow paths, the node is identified as a path coincidence node.

[0151] Step 222: Grouping first carbon flow paths containing the same path coincidence node into a path verification group.

[0152] wherein, path verification group is a set of first carbon flow paths containing the same path coincidence node, used for performing carbon intensity comparison analysis on these first carbon flow paths.

[0153] For each path coincidence node, all first carbon flow paths containing the path coincidence node are grouped into the same path verification group. If a first carbon flow path contains multiple path coincidence nodes, the first carbon flow path can be simultaneously grouped into multiple path verification groups.

[0154] Step 223: Calculating the path carbon intensity of each first carbon flow path in the path verification group, and calculating the representative value and dispersion of the path carbon intensity within the path verification group.

[0155] wherein, path carbon intensity is the sum of the second edge weight values of all directed edges in the first carbon flow path, divided by the sum of the first edge weight values of all directed edges, representing the average energy consumption corresponding to unit material flow on the first carbon flow path. The representative value of the path carbon intensity within the group is the average or median of the path carbon intensity of each first carbon flow path in the path verification group, representing the central tendency of the path carbon intensity within the path verification group. The dispersion of the path carbon intensity within the group is the standard deviation or interquartile range of the path carbon intensity of each first carbon flow path in the path verification group, representing the dispersion of the path carbon intensity within the path verification group.

[0156] For each first carbon flow path in the path verification group, the sum of the second edge weight values of all directed edges in the first carbon flow path is calculated to obtain the total energy consumption of the first carbon flow path; the sum of the first edge weight values of all directed edges in the first carbon flow path is calculated to obtain the total material flow of the first carbon flow path; the ratio of the total energy consumption of the first carbon flow path to the total material flow of the first carbon flow path is taken as the path carbon intensity of the first carbon flow path.

[0157] The path carbon intensity of all first carbon flow paths in the path verification group is averaged to obtain the representative value of the path carbon intensity within the path verification group. The standard deviation of the path carbon intensity of all first carbon flow paths in the path verification group is calculated to obtain the dispersion of the path carbon intensity within the path verification group.

[0158] Step 224: identifying a first carbon flow path whose path carbon intensity deviates from the path carbon intensity representative value of the path verification group by an amount greater than the intra-group path carbon intensity dispersion.

[0159] Wherein, the path carbon intensity deviation amount is the absolute value of the difference between the path carbon intensity of the first carbon flow path and the path carbon intensity representative value of the path verification group, indicating the degree to which the path carbon intensity of the first carbon flow path deviates from the trend in the path verification group.

[0160] For each first carbon flow path in the path verification group, the difference between the path carbon intensity of the first carbon flow path and the path carbon intensity representative value of the path verification group is calculated, and the absolute value of the difference is taken as the path carbon intensity deviation amount of the first carbon flow path. If the path carbon intensity deviation amount of the first carbon flow path is greater than the intra-group path carbon intensity dispersion of the path verification group, the first carbon flow path is identified as an abnormal path.

[0161] It can be understood that the first carbon flow path whose path carbon intensity deviation amount is greater than the intra-group path carbon intensity dispersion has a path carbon intensity that deviates significantly from the normal level of the path verification group, which may be caused by inaccurate carbon emission data at the path coincidence node or missing links in path identification.

[0162] Step 225: obtaining adjacent nodes of the path coincidence node in the first carbon flow path whose path carbon intensity deviation amount is greater than the intra-group path carbon intensity dispersion, and supplementing the adjacent nodes to the first carbon flow path whose path carbon intensity deviation amount is greater than the intra-group path carbon intensity dispersion.

[0163] Wherein, the adjacent nodes are nodes directly connected to the path coincidence node through directed edges, including upstream nodes pointing to the path coincidence node and downstream nodes pointed to by the path coincidence node.

[0164] For the identified abnormal first carbon flow path, the adjacent nodes of the path coincidence node in the abnormal first carbon flow path are searched in the directed weighted network model. It is checked whether the adjacent nodes are already included in the abnormal first carbon flow path. If the adjacent nodes are not included in the abnormal first carbon flow path, the adjacent nodes are inserted into the abnormal first carbon flow path at the corresponding positions of the path coincidence node, forming a supplemented first carbon flow path.

[0165] For example, if an abnormal first carbon flow path is: node A→node B→node C→node E, and the path coincidence node is node C, it is found through searching that the adjacent nodes of node C include node D, and node D is not included in the abnormal first carbon flow path. Node D is supplemented to the abnormal first carbon flow path, forming a supplemented first carbon flow path: node A→node B→node C→node D→node E.

[0166] Step 226: updating the carbon emission parameter of the path coincidence node in the directed weighted network model based on the first carbon flow path after supplementing the adjacent nodes.

[0167] The carbon emission parameter is a carbon emission related attribute of the node, including the direct carbon emission amount of the node, the energy consumption amount of the node, and the like.

[0168] The carbon emission parameter of the path coincidence node is recalculated based on the first carbon flow path after supplementing the adjacent nodes. Specifically, the carbon emission amount of the path coincidence node is redistributed according to the material flow amount and the energy consumption amount of the upstream node and the downstream node of the path coincidence node on the supplemented first carbon flow path. The updated carbon emission parameter is assigned to the path coincidence node in the directed weighted network model to replace the original carbon emission parameter.

[0169] Step 230: obtaining the node net carbon emission amount of each node on the first carbon flow path.

[0170] Specifically, step 230 specifically includes steps 231 to 234:

[0171] Step 231: obtaining the node in-degree and the node out-degree of each node on the first carbon flow path. The node in-degree represents the number of directed edges pointing to the node, and the node out-degree represents the number of directed edges pointing from the node.

[0172] For each node on the first carbon flow path, the number of directed edges pointing to the node in the directed weighted network model is counted as the node in-degree, and the number of directed edges pointing from the node in the directed weighted network model is counted as the node out-degree.

[0173] Step 232: if the node in-degree is zero and the node out-degree is greater than zero, obtaining the node direct carbon emission amount of the node as the node net carbon emission amount of the node.

[0174] The node with the node in-degree of zero and the node out-degree greater than zero is a starting node, which has no upstream node, and its carbon emission is completely from the direct carbon emission of the node itself.

[0175] The node direct carbon emission amount is the direct carbon emission generated by the activity of the node itself, excluding the carbon emission delivered by the upstream node. The node net carbon emission amount is the actual carbon emission contribution of the node in the product life cycle, including the node direct carbon emission amount and the carbon emission delivered by the upstream node.

[0176] For the node with the node in-degree of zero and the node out-degree greater than zero, the node direct carbon emission amount of the node is obtained from the production activity data or the carbon emission database, and the node direct carbon emission amount of the node is taken as the node net carbon emission amount of the node.

[0177] Step 233: If the node in-degree is one and the node out-degree is one, obtaining the node direct carbon emission of the node, traversing the upstream nodes of the node to obtain the node net carbon emission of the upstream nodes, performing accumulation on the node net carbon emission of the upstream nodes to obtain the node upstream cumulative carbon emission of the node, and calculating the node net carbon emission of the node based on the node direct carbon emission of the node and the node upstream cumulative carbon emission of the node.

[0178] The node with the node in-degree of one and the node out-degree of one is a single-chain node, and the single-chain node has only one upstream node. The carbon emission of the single-chain node includes the direct carbon emission of the node itself and the carbon emission transmitted by the upstream node.

[0179] The upstream node is a node that is directed to the current node by a directed edge in the directed and weighted network model. The node upstream cumulative carbon emission is the sum of the node net carbon emissions of all upstream nodes of the node, representing the carbon emission transmitted by the upstream nodes to the current node.

[0180] For the node with the node in-degree of one and the node out-degree of one, the node direct carbon emission of the node is obtained from the production activity data or the carbon emission database. The upstream nodes of the node are found by traversing upwards along the first carbon flow path, and the node net carbon emission of the upstream nodes is obtained. If the node net carbon emission of the upstream node has not been calculated, the node net carbon emission of the upstream node is calculated recursively. The node net carbon emissions of all upstream nodes of the node are accumulated to obtain the node upstream cumulative carbon emission of the node. The node direct carbon emission of the node and the node upstream cumulative carbon emission of the node are summed to obtain the node net carbon emission of the node.

[0181] Step 234: If the node in-degree is greater than one, obtaining the node direct carbon emission of the node and the node upstream cumulative carbon emission of the node, obtaining the first edge weight of each directed edge directed to the node, calculating the node convergence adjustment of the node based on the first edge weight of each directed edge directed to the node, and calculating the node net carbon emission of the node based on the node direct carbon emission of the node, the node upstream cumulative carbon emission of the node, and the node convergence adjustment of the node.

[0182] It should be noted that the node with the node in-degree greater than one is a convergence node, and the convergence node has multiple upstream nodes. Multiple material flows converge at the convergence node. In the traditional method, when calculating the carbon emission of the convergence node, all upstream node carbon emissions are simply added, ignoring the difference in the flow amount of the material flow of different upstream nodes, resulting in unreasonable carbon emission distribution. The node convergence adjustment is used in the present application to perform weighted distribution on the upstream carbon emission based on the flow amount of the material flow of each upstream directed edge, so as to more accurately calculate the net carbon emission of the convergence node.

[0183] The node convergence adjustment amount is an adjustment coefficient calculated based on the first edge weight of each directed edge pointing to the node, and is used to correct the node upstream cumulative carbon emission amount of the convergence node, so that it reflects the difference in carbon emission contribution of different material flows.

[0184] For a node with a node in-degree greater than 1, the node direct carbon emission amount of the node is obtained from the production activity data or the carbon emission database. All upstream nodes of the node are traversed to obtain the node net carbon emission amount of each upstream node, and the node net carbon emission amount of each upstream node is summed to obtain the node upstream cumulative carbon emission amount of the node. The first edge weight of each directed edge pointing to the node is obtained, and the proportion of the first edge weight of each directed edge to the sum of the first edge weights of all directed edges pointing to the node is calculated as the material flow contribution proportion of each directed edge. The node net carbon emission amount of each upstream node is multiplied by the material flow contribution proportion of the corresponding directed edge to obtain the carbon emission amount of each upstream node allocated to the current node. The carbon emission amounts of each upstream node allocated to the current node are summed to obtain the node convergence adjustment amount of the node. The node direct carbon emission amount of the node and the node convergence adjustment amount of the node are summed to obtain the node net carbon emission amount of the node.

[0185] It can be understood that through the node convergence adjustment amount, the upstream carbon emission of the convergence node can be reasonably allocated according to the contribution proportion of each upstream material flow, avoiding the problem of unclear carbon emission responsibility caused by simple accumulation.

[0186] Step 240: determining the allocation amount of each node to the scope three emission according to the node net carbon emission amount.

[0187] Specifically, step 240 specifically includes steps 241 to 245:

[0188] Step 241: calculating a path carbon emission representative value of the first carbon flow path based on the node net carbon emission amount of each node on the first carbon flow path.

[0189] The path carbon emission representative value is the average value or the median of the node net carbon emission amount of each node on the first carbon flow path, and represents the concentration trend of the node carbon emission on the first carbon flow path.

[0190] The node net carbon emission amounts of all nodes on the first carbon flow path are summed, and the ratio of the sum to the total number of nodes on the first carbon flow path is taken as the path carbon emission representative value of the first carbon flow path.

[0191] Step 242: calculating the node carbon emission deviation amount of the node net carbon emission amount of each node on the first carbon flow path from the path carbon emission representative value.

[0192] The node carbon emission deviation of a node is the difference between the node net carbon emission of the node and the path carbon emission representative value of the first carbon flow path, and represents the deviation of the carbon emission level of the node from the average level of the path.

[0193] For each node on the first carbon flow path, the node net carbon emission of the node is subtracted from the path carbon emission representative value of the first carbon flow path to obtain the node carbon emission deviation of the node. A positive node carbon emission deviation indicates that the carbon emission of the node is higher than the average level of the path, a negative node carbon emission deviation indicates that the carbon emission of the node is lower than the average level of the path, and a zero node carbon emission deviation indicates that the carbon emission of the node is consistent with the average level of the path.

[0194] Step 243: If the node carbon emission deviation is positive, the node position sequence number of the node in the first carbon flow path is obtained, and the allocation amount of the node to the scope three emissions is calculated based on the node net carbon emission of the node, the node carbon emission deviation of the node, and the node position sequence number of the node.

[0195] The node with a positive node carbon emission deviation is a high-carbon emission node, which is the main contributor to the carbon footprint and should bear more responsibility for the scope three emissions. At the same time, the position of the node in the first carbon flow path also affects its responsibility for the scope three emissions. The carbon emission of a node located upstream will be transmitted to the downstream, and its influence on the overall carbon footprint is greater.

[0196] The node position sequence number is the sequence number of the node arranged in the first carbon flow path in the traversal order, and the node position sequence number of the starting node is 1, which is incremented in turn. The allocation amount is the responsibility allocation amount of the node to the scope three emissions.

[0197] For the node with a positive node carbon emission deviation, the node position sequence number of the node in the first carbon flow path is obtained. The product of the node net carbon emission of the node and the node carbon emission deviation of the node is calculated, and the ratio of the product to the path carbon emission representative value of the first carbon flow path is multiplied by the weight coefficient of the node position sequence number to obtain the allocation amount of the node to the scope three emissions. The weight coefficient of the node position sequence number decreases with the node position sequence number, indicating that the nodes closer to the upstream bear higher allocation responsibilities.

[0198] Step 244: If the node carbon emission deviation is negative, the allocation amount of the node to the scope three emissions is calculated based on the node net carbon emission of the node, the node carbon emission deviation of the node, the node in-degree of the node, and the node out-degree of the node.

[0199] The node with a negative node carbon emission deviation is a low-carbon emission node, and the carbon emission of the low-carbon emission node is lower than the average level of the path, and its responsibility for the scope three emissions is relatively small. However, the topological position (node in-degree and node out-degree) of the low-carbon emission node in the supply chain network still affects its allocation amount.

[0200] For the node whose node carbon emission deviation is negative, the node's share of scope 3 emissions is calculated based on the node's net carbon emission, the absolute value of the node's carbon emission deviation, the node's in-degree and the node's out-degree. Specifically, the node's net carbon emission is multiplied by the ratio of the absolute value of the node's carbon emission deviation and the representative value of the path carbon emission of the first carbon flow path, and then multiplied by the normalization coefficient of the sum of the node's in-degree and the node's out-degree, to obtain the node's share of scope 3 emissions.

[0201] Step 245: If the node carbon emission deviation is zero, the node's node material output is obtained, and the node's share of scope 3 emissions is calculated based on the node's net carbon emission and the node's material output.

[0202] The node whose node carbon emission deviation is zero is an average carbon emission node, and the carbon emission of the average carbon emission node is consistent with the average level of the path. For the average carbon emission node, the allocation is performed based on the material output of the node, which can reflect the actual contribution of the node in the material flow.

[0203] The node material output is the total amount of material output by the node to the downstream node, which is obtained by summing the first edge weight of each directed edge pointing to the node.

[0204] For the node whose node carbon emission deviation is zero, the node's node material output is obtained from the production activity data, or calculated by summing the first edge weight of each directed edge pointing to the node. The node's unit material carbon emission intensity is obtained by dividing the node's net carbon emission by the node's material output, and the node's share of scope 3 emissions is obtained by multiplying the node's unit material carbon emission intensity by the node's material output.

[0205] In some embodiments, after determining the share of scope 3 emissions of each node according to the node's net carbon emission in step 240, steps B1 to B5 are further included:

[0206] Step B1: The shares of all nodes on the first carbon flow path are aggregated to obtain the total share of the first carbon flow path.

[0207] Step B2: The total share of the first carbon flow path is projected to the traversal paths in the directed weighted network model that are not extracted as the first carbon flow path.

[0208] Step B3: The carbon emission consistency parameter of the traversal paths that are not extracted as the first carbon flow path and the first carbon flow path at the path coincidence node is calculated.

[0209] Step B4: If the carbon emission consistency parameter is less than the preset consistency parameter condition, identify the path coincidence nodes with the carbon emission consistency parameter less than the preset consistency parameter condition. If the carbon emission consistency parameter at a certain path coincidence node is less than the preset consistency parameter condition, it indicates that there is a significant difference in the carbon emission data of the path coincidence node on the first carbon flow path and the traversal path not extracted as the first carbon flow path, which may be due to the deviation in the allocation amount calculation of the first carbon flow path, or the traversal path not extracted as the first carbon flow path is actually a key carbon flow path but is missed.

[0210] Identify the path coincidence nodes with the carbon emission consistency parameter less than the preset consistency parameter condition, and mark these path coincidence nodes as to-be-corrected nodes.

[0211] Step B5: Perform correction on the allocation amount of the identified path coincidence nodes, and recalculate the in-group path carbon intensity representative value and the in-group path carbon intensity dispersion of the path verification group based on the corrected allocation amount.

[0212] It should be noted that in the directed weighted network model, in addition to the first carbon flow path, there are other traversal paths not extracted as the first carbon flow path. Although these traversal paths are not key carbon flow paths, they may still have an impact on the product carbon footprint. By projecting the total allocation amount of the first carbon flow path to the traversal paths not extracted as the first carbon flow path, it can be verified whether the allocation amount of the first carbon flow path is reasonable, and whether there is a missed key path.

[0213] The carbon emission consistency parameter is the consistency degree of the carbon emission data of the traversal path not extracted as the first carbon flow path and the first carbon flow path at the path coincidence node. It is obtained by calculating the relative value of the difference in carbon emission amount of the two paths at the path coincidence node.

[0214] For the identified path coincidence nodes, the allocation amount of the path coincidence node on the first carbon flow path is corrected according to the projected allocation amount on the traversal path not extracted as the first carbon flow path. The correction method is to perform weighted average on the allocation amount of the path coincidence node on the first carbon flow path and the projected allocation amount of the path coincidence node on the traversal path not extracted as the first carbon flow path. Based on the corrected allocation amount, the in-group path carbon intensity representative value and the in-group path carbon intensity dispersion of the path verification group containing the path coincidence node are recalculated to ensure the carbon intensity consistency of each first carbon flow path in the path verification group.

[0215] Step 300: Perform matching on the production activity data of each node corresponding link and the emission parameters in the carbon emission database to obtain the link carbon emission intensity of each link, and determine the link carbon emission amount of each link based on the link carbon emission intensity and the node net carbon emission amount.

[0216] In some embodiments, step 300 comprises step 310:

[0217] Step 310: performing matching on the production activity data of the corresponding links of each node on the first carbon flow path and the emission parameters in the carbon emission database.

[0218] Specifically, step 310 comprises steps 311 to 314:

[0219] Step 311: performing dimensionality reduction processing on the production activity data to extract first characteristic variables in the production activity data that affect carbon emissions.

[0220] The first characteristic variables are characteristics in the production activity data that have a strong correlation with carbon emissions, including energy consumption, energy type, material type, and production process.

[0221] The principal component analysis dimensionality reduction processing is performed on the production activity data to calculate the contribution of each characteristic in the production activity data to carbon emissions. Characteristics with a contribution greater than a preset contribution threshold are selected as the first characteristic variables. The first characteristic variables usually include energy consumption, energy type (such as electricity, coal, and natural gas), material type (such as steel, plastic, and chemicals), and production process type, which directly affect carbon emissions.

[0222] Step 312: based on the first characteristic variables, identifying data in the production activity data that has a feature correlation degree less than other production activity data.

[0223] The feature correlation degree is the correlation strength between a data point in the production activity data and the first characteristic variables, which is obtained by calculating the projection distance or correlation coefficient of the data point in the first characteristic variable space.

[0224] For each data point in the production activity data, the feature correlation degree of the data point and the first characteristic variables is calculated. The feature correlation degrees of all data points in the production activity data are sorted to identify data points with a feature correlation degree in the last preset percentage. If the feature correlation degree of a data point is less than a preset multiple of the feature correlation degrees of other data points in the production activity data, the data point is identified as low-correlation data.

[0225] Step 313: performing elimination on the identified data.

[0226] Step 314: obtaining emission parameters matching the corresponding links of each node on the first carbon flow path from the carbon emission database.

[0227] The carbon emission database contains emission parameters for different industries, different regions, and different time periods. The emission parameters are key coefficients for converting activity level data into carbon emissions.

[0228] The carbon emission database is a database for storing emission parameters of various activities, including a national greenhouse gas inventory database, a life cycle assessment database, an industry carbon emission factor database, etc. The emission parameter is the carbon emission of a unit activity level, such as a carbon emission factor of a unit kilowatt-hour of electricity, a carbon emission factor of a unit kilogram of steel production, etc.

[0229] Further, step 314 specifically includes steps C1 to C4:

[0230] Step C1: Obtain the time attribute, geographical attribute and industry attribute of the corresponding link of each node on the first carbon flow path.

[0231] Step C2: According to the time attribute, geographical attribute and industry attribute of the corresponding link of each node on the first carbon flow path, retrieve the emission parameter matched with the corresponding link of each node on the first carbon flow path from the carbon emission database.

[0232] Step C3: Obtain the activity level data in the production activity data of the corresponding link of each node on the first carbon flow path.

[0233] Step C4: Based on the activity level data of the corresponding link of each node on the first carbon flow path and the matched emission parameter, calculate the link carbon emission intensity of the corresponding link of each node on the first carbon flow path.

[0234] From the node attributes assigned to the nodes in step 100, the time attribute, geographical attribute and industry attribute of the corresponding link of each node on the first carbon flow path are extracted. If the node attributes do not include the time attribute, geographical attribute and industry attribute, they are obtained from the production activity data or enterprise basic information.

[0235] The activity level data is the activity intensity or scale of the corresponding link of the node, including energy consumption, material consumption, product output, etc. The link carbon emission intensity is the ratio of the carbon emission of the corresponding link of the node to the activity level data, indicating the carbon emission corresponding to a unit activity level.

[0236] For each node on the first carbon flow path, the product of the activity level data of the corresponding link of the node and the matched emission parameter is calculated as the preliminary carbon emission of the corresponding link of the node. The ratio of the preliminary carbon emission of the corresponding link of the node to the activity level data of the corresponding link of the node is taken as the link carbon emission intensity of the corresponding link of the node.

[0237] Step 400: Integrate the allocation amount and the link carbon emission to build a product carbon footprint traceability list, and generate a product carbon efficiency evaluation result according to the product carbon footprint traceability list.

[0238] In some embodiments, step 400 of integrating the allocation amount and the link carbon emission to build a product carbon footprint traceability list specifically includes steps D1 to D4:

[0239] Step D1: constructing a decision matrix comprising a carbon emission intensity index, an energy efficiency index, a resource recycling utilization rate index, and a supply chain carbon footprint transparency index.

[0240] The decision matrix is a matrix structure comprising multiple evaluation indexes, used for multi-dimensional comprehensive evaluation of product carbon efficiency. The carbon emission intensity index is the carbon emission corresponding to a unit function or unit value of the product. The energy efficiency index is the ratio of energy output to energy input in the product life cycle. The resource recycling utilization rate index is the proportion of recycled resources in the total resource consumption in the product life cycle. The supply chain carbon footprint transparency index is the completeness and traceability degree of carbon emission data at each link of the product supply chain.

[0241] The rows of the decision matrix represent the products or product schemes to be evaluated, and the columns of the decision matrix represent the carbon emission intensity index, the energy efficiency index, the resource recycling utilization rate index, and the supply chain carbon footprint transparency index. The calculated values of each index are filled into the corresponding positions of the decision matrix.

[0242] Step D2: assigning index weights to the carbon emission intensity index, the energy efficiency index, the resource recycling utilization rate index, and the supply chain carbon footprint transparency index in the decision matrix.

[0243] The index weight is the weight coefficient of each evaluation index in the comprehensive evaluation, and the sum of the index weights is 1.

[0244] The index weights of each index are determined based on the analytic hierarchy process or expert scoring method. For products that focus on carbon emission reduction, the index weight of the carbon emission intensity index can be set to a higher value, such as 0.4. For products that focus on circular economy, the index weight of the resource recycling utilization rate index can be set to a higher value, such as 0.3. The index weights of the energy efficiency index and the supply chain carbon footprint transparency index are set according to actual needs.

[0245] The determined index weights are assigned to the corresponding index columns in the decision matrix to form a weighted decision matrix.

[0246] Step D3: inputting the data in the product carbon footprint traceability list into the decision matrix and calculating the product carbon efficiency evaluation result based on the index weights.

[0247] Further, step D3 specifically includes steps D31 to D33:

[0248] Step D31: based on the index weights of each index in the decision matrix, calculating the comprehensive scores of the product in the carbon emission intensity index, the energy efficiency index, the resource recycling utilization rate index, and the supply chain carbon footprint transparency index, and obtaining the product carbon efficiency evaluation result.

[0249] The normalized values of each index in the decision matrix are multiplied by the corresponding index weight to obtain the weighted score of each index. The weighted scores of each index are summed to obtain the comprehensive score of the product in the carbon emission intensity index, the energy efficiency index, the resource recycling utilization rate index, and the supply chain carbon footprint transparency index. The comprehensive score is the product carbon efficiency evaluation result.

[0250] The normalized values of each index in the decision matrix are multiplied by the corresponding index weight to obtain the weighted score of each index. The weighted scores of each index are summed to obtain the comprehensive score of the product in the carbon emission intensity index, the energy efficiency index, the resource recycling utilization rate index, and the supply chain carbon footprint transparency index. The comprehensive score is the product carbon efficiency evaluation result.

[0251] The numerical range of the product carbon efficiency evaluation result is usually 0 to 1 or 0 to 100, and the larger the value, the better the product carbon efficiency performance.

[0252] Step D32: Based on the product carbon footprint traceability list and the product carbon efficiency evaluation result, a product carbon footprint visualization traceability map is generated.

[0253] Step D33: The link carbon emissions of each node on the first carbon flow path are sorted according to the numerical value.

[0254] Step D34: The nodes ranked in the front of the pre-set number are obtained as the carbon emission key nodes.

[0255] Step D35: Based on the product carbon footprint traceability list, the product carbon efficiency evaluation result, and the carbon emission key nodes, a product digital carbon label is generated.

[0256] Based on the directed weighted network model constructed in step 100, the first carbon flow path is highlighted in the map. The link carbon emissions of each node are mapped to the visual attributes of the node, such as displaying the node with higher link carbon emissions as a larger circle or darker color. The product carbon efficiency evaluation result is marked in the map, and the scores of each index in the decision matrix are displayed. Among them, the carbon emission key nodes are the nodes ranked in the front of the pre-set number on the first carbon flow path, representing the key links that contribute most to the product carbon footprint. The pre-set number is set according to actual needs, such as the top 5 nodes or the top 10 nodes.

[0257] On the basis of the above steps, the present application further includes the following embodiments:

[0258] Referring to Figure 3 is a structural schematic diagram of a product full production cycle carbon footprint traceability evaluation system provided by an embodiment of the present application. The data processing system of the product full production cycle carbon footprint traceability evaluation system includes:

[0259] a model construction module, configured to acquire production activity data of each link in a product life cycle, and construct a directed and weighted network model according to the production activity data;

[0260] a traceable apportionment module, configured to perform path traversal on the directed and weighted network model, extract a traversal path with a path carbon intensity satisfying a preset path carbon intensity condition as a first carbon flow path, acquire node net carbon emission of each node on the first carbon flow path, and determine an apportionment amount of the range III emission of each node according to the node net carbon emission;

[0261] a carbon emission accounting module, configured to perform matching on production activity data of each link corresponding to each node on the first carbon flow path and emission parameters in a carbon emission database, acquire link carbon emission intensity of each link, and determine link carbon emission of each link based on the link carbon emission intensity and the node net carbon emission;

[0262] a data integration module, configured to perform integration on the apportionment amount and the link carbon emission to construct a product carbon footprint traceable list.

[0263] Figure 3 The apparatus of the embodiments shown can be used to perform the steps in the method embodiments shown, and the implementation principles and technical effects are similar, which will not be described here again. Figure 1 The steps in the method embodiments shown can be used to perform the steps in the method embodiments shown, and the implementation principles and technical effects are similar, which will not be described here again.

[0264] Referring to Figure 4 is a hardware structure schematic diagram of an electronic device provided by the embodiments of the present application. The electronic device 60 comprises a processor 61, a memory 62 and a computer program; wherein

[0265] The memory 62 is configured to store the computer program, and the memory can also be a flash memory. The computer program is, for example, an application program, a functional module and the like for implementing the above method.

[0266] The processor 61 is configured to execute the computer program stored in the memory to implement each step of the device in the above method. For details, please refer to the related description in the above method embodiments.

[0267] Optionally, the memory 62 can be independent or integrated with the processor 61.

[0268] When the memory 62 is a device independent of the processor 61, the device can further comprise:

[0269] The bus 63 is configured to connect the memory 62 and the processor 61.

[0270] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for tracing and assessing the carbon footprint of a product throughout its entire production lifecycle, characterized in that, include: Acquire production activity data for each stage of the product's entire lifecycle, and construct a directed weighted network model based on the production activity data; A path traversal is performed on the directed weighted network model to extract the traversed paths whose path carbon intensity satisfies the preset path carbon intensity condition as the first carbon flow path. The net carbon emissions of each node on the first carbon flow path are obtained, and the allocation of the emissions of each node on the first carbon flow path to the third range is determined based on the net carbon emissions of the nodes. The path carbon intensity is the ratio of the sum of the weights of the second edges of all directed edges on the first carbon flow path to the sum of the weights of the first edges of all directed edges, which represents the average energy consumption corresponding to a unit material flow on the first carbon flow path. Matching the production activity data of each node in the first carbon flow path with the emission parameters in the carbon emission database includes: Dimensionality reduction processing is performed on the production activity data to extract the first feature variable affecting carbon emissions from the production activity data; Based on the first feature variable, identify data in the production activity data whose feature correlation with the first feature variable is less than that of other production activity data; The identified data is then removed; Obtaining emission parameters from the carbon emission database that match the corresponding links at each node in the first carbon flow path includes: Obtain the time attributes, geographical attributes, and industry attributes of each node corresponding to the first carbon flow path; Based on the time attribute, geographical attribute, and industry attribute of each node in the first carbon flow path, the emission parameters that match the corresponding links of each node in the first carbon flow path are retrieved from the carbon emission database. Obtain the activity level data from the production activity data of each node corresponding to the first carbon flow path; Based on the activity level data and the matching emission parameters of each node corresponding to the first carbon flow path, the stage carbon emission intensity of each node corresponding to the first carbon flow path is calculated. The carbon emission intensity of each stage is obtained, and the carbon emission amount of each stage is determined based on the carbon emission intensity of the stage and the net carbon emission of the node. The carbon emission intensity of the stage is the ratio of the carbon emission of the corresponding stage of the node to the activity level data. The activity level data is the activity intensity or scale of the corresponding stage of the node, including energy consumption, material consumption, and product output. The allocated amount and the carbon emissions of the process are integrated to construct a product carbon footprint traceability list.

2. The method according to claim 1, characterized in that, The construction of the directed weighted network model based on the production activity data includes: The first edge weight is assigned to the directed edge of the directed weighted network model based on the material flow rate in the production activity data. The directed edges of the directed weighted network model are assigned second edge weights based on the energy consumption data in the production activity data. The nodes in the directed weighted network model represent entities at various stages in the product lifecycle; The directed edges represent the material flow relationship or energy transfer relationship between the entities in the process.

3. The method according to claim 2, characterized in that, The path traversal performed on the directed weighted network model includes: From the starting node of the directed weighted network model, traverse along the positive direction of the directed edges to the terminal node to obtain multiple positive traversal paths; From the terminal node of the directed weighted network model, traverse in the reverse direction of the directed edges to the starting node to obtain multiple reverse traversal paths; The node sequences of the forward traversal path and the reverse traversal path are compared, and the path in which the node sequences of the forward traversal path and the reverse traversal path are consistent is identified as a bidirectional consistent path. Calculate the ratio of the first edge weight to the second edge weight of each directed edge on the bidirectional consistent path; Identify directed edges on the bidirectional consistent path whose edge weight ratio is greater than the edge weight ratio of adjacent directed edges, and extract the bidirectional consistent path containing the identified directed edges as the first carbon flow path.

4. The method according to any one of claims 1 to 3, characterized in that, The traversal path whose carbon intensity satisfies the preset path carbon intensity condition is used as the first carbon flow path, including: Identify overlapping nodes in multiple first carbon flow paths; The first carbon flow path containing the same overlapping nodes of the path is assigned to the path verification group. Calculate the path carbon intensity of each first carbon flow path within the path verification group, and calculate the representative value of the path carbon intensity within the group and the dispersion of the path carbon intensity within the group. Identify the first carbon flow path whose carbon intensity deviation from the representative value of the carbon intensity of the path within the path verification group is greater than the dispersion of the carbon intensity of the path within the group. Obtain the adjacent nodes of the overlapping nodes in the first carbon flow path where the path carbon intensity deviation is greater than the path carbon intensity dispersion within the group, and add the adjacent nodes to the first carbon flow path where the path carbon intensity deviation is greater than the path carbon intensity dispersion within the group. The carbon emission parameters of the overlapping nodes in the directed weighted network model are updated based on the first carbon flow path after supplementing the adjacent nodes.

5. The method according to claim 4, characterized in that, The step of obtaining the net carbon emissions of each node on the first carbon flow path includes: Obtain the in-degree and out-degree of each node on the first carbon flow path; the in-degree represents the number of directed edges pointing to the node, and the out-degree represents the number of directed edges pointing from the node. If the in-degree of a node is zero and the out-degree of a node is greater than zero, then the direct carbon emissions of the node are obtained as the net carbon emissions of the node. If the node has an in-degree of 1 and an out-degree of 1, then obtain the direct carbon emissions of the node, traverse the upstream nodes of the node to obtain the net carbon emissions of the upstream nodes, accumulate the net carbon emissions of the upstream nodes to obtain the cumulative carbon emissions of the upstream nodes of the node, and calculate the net carbon emissions of the node based on the direct carbon emissions of the node and the cumulative carbon emissions of the upstream nodes of the node. If the in-degree of the node is greater than 1, then obtain the node's direct carbon emissions and the cumulative carbon emissions of the node's upstream neighbors. Obtain the first edge weight of each directed edge pointing to the node. Calculate the node's aggregation adjustment based on the first edge weight of each directed edge pointing to the node. Calculate the node's net carbon emissions based on the node's direct carbon emissions, the cumulative carbon emissions of the node's upstream neighbors, and the node aggregation adjustment.

6. The method according to claim 1, characterized in that, The determination of each node's share of emissions in Scope 3 based on the node's net carbon emissions includes: The path carbon emission representative value of the first carbon flow path is calculated based on the net carbon emissions of each node on the first carbon flow path. Calculate the deviation of the node's net carbon emissions from the representative value of carbon emissions along the first carbon flow path. If the node carbon emission deviation is positive, then obtain the node position number of the node in the first carbon flow path, and calculate the share of the node's emissions in range three based on the node's net carbon emissions, the node carbon emission deviation, and the node position number. If the node carbon emission deviation is negative, the node's share of the emissions in range 3 is calculated based on the node's net carbon emissions, node carbon emission deviation, node in-degree, and node out-degree. If the node's carbon emission deviation is zero, obtain the node's material output. Based on the node's net carbon emissions and material output, calculate the node's share of emissions in range three.

7. The method according to claim 6, characterized in that, After determining the allocation of each node's share of Scope 3 emissions based on the node's net carbon emissions, the following is also included: The allocation of all nodes on the first carbon flow path is summarized to obtain the total allocation of the first carbon flow path; The total allocation of the first carbon flow path is projected onto the traversal paths in the directed weighted network model that were not extracted as the first carbon flow path; Calculate the carbon emission consistency parameters at the nodes where the traversal paths that were not extracted as the first carbon flow path overlap with the first carbon flow path. If the carbon emission consistency parameter is less than the preset consistency parameter condition, identify the overlapping nodes of the path where the carbon emission consistency parameter is less than the preset consistency parameter condition; The allocation of the identified path overlap nodes is corrected, and the representative value of the path carbon intensity within the group and the dispersion of the path carbon intensity within the group are recalculated based on the corrected allocation.

8. The method according to claim 1, characterized in that, The process of integrating the allocated amount with the carbon emissions from the process to construct a product carbon footprint traceability list includes: Construct a decision matrix that includes carbon emission intensity indicators, energy efficiency indicators, resource recycling rate indicators, and supply chain carbon footprint transparency indicators; The carbon emission intensity index, energy efficiency index, resource recycling rate index, and supply chain carbon footprint transparency index in the decision matrix are each assigned an index weight. The data from the product carbon footprint traceability list is input into the decision matrix, and the product carbon efficiency evaluation result is calculated based on the indicator weights.

9. The method according to claim 8, characterized in that, The calculation of the product carbon efficiency evaluation result based on the index weights includes: Based on the weights of each indicator in the decision matrix, the comprehensive scores of the product in terms of carbon emission intensity, energy efficiency, resource recycling rate, and supply chain carbon footprint transparency are calculated to obtain the product carbon efficiency evaluation results. Based on the product carbon footprint traceability list and product carbon efficiency evaluation results, a visual traceability map of the product carbon footprint is generated. The carbon emissions of each node in the first carbon flow path are sorted according to their numerical values. The nodes that rank at the top of a preset number are identified as key nodes for carbon emissions. Digital carbon labels for products are generated based on the product carbon footprint traceability list, product carbon efficiency evaluation results, and key carbon emission nodes.

10. The method according to claim 1, characterized in that, The acquisition of production activity data at each stage of the product's entire lifecycle includes: Data on production activities, including raw material acquisition, manufacturing, logistics, product use, and waste recycling, are collected through application programming interfaces (APIs) and IoT devices. The collected production activity data is subjected to standardization processing; Based on the product bill of materials and process flow diagram, the set of nodes and the set of edges of the directed weighted network model are determined.

11. A product full-production-cycle carbon footprint traceability and assessment system, employing the product full-production-cycle carbon footprint traceability and assessment method as described in any one of claims 1 to 10, characterized in that, include: The model building module is used to acquire production activity data at each stage of the product's entire life cycle and to build a directed weighted network model based on the production activity data. The traceability allocation module is used to perform path traversal on the directed weighted network model, extract the traversed paths whose path carbon intensity meets the preset path carbon intensity conditions as the first carbon flow path, obtain the net carbon emissions of each node on the first carbon flow path, and determine the allocation amount of each node on the first carbon flow path for the emissions of the third range based on the net carbon emissions of the nodes; the path carbon intensity is the ratio of the sum of the weights of the second edges of all directed edges on the first carbon flow path to the sum of the weights of the first edges of all directed edges, representing the average energy consumption corresponding to a unit material flow on the first carbon flow path. The carbon emission accounting module is used to match the production activity data of each node in the first carbon flow path with the emission parameters in the carbon emission database, including: Dimensionality reduction processing is performed on the production activity data to extract the first feature variable affecting carbon emissions from the production activity data; Based on the first feature variable, identify data in the production activity data whose feature correlation with the first feature variable is less than that of other production activity data; The identified data is then removed; Obtaining emission parameters from the carbon emission database that match the corresponding links at each node in the first carbon flow path includes: Obtain the time attributes, geographical attributes, and industry attributes of each node corresponding to the first carbon flow path; Based on the time attribute, geographical attribute, and industry attribute of each node in the first carbon flow path, the emission parameters that match the corresponding links of each node in the first carbon flow path are retrieved from the carbon emission database. Obtain the activity level data from the production activity data of each node corresponding to the first carbon flow path; Based on the activity level data and the matching emission parameters of each node corresponding to the first carbon flow path, the stage carbon emission intensity of each node corresponding to the first carbon flow path is calculated. The carbon emission intensity of each stage is obtained, and the carbon emission amount of each stage is determined based on the carbon emission intensity of the stage and the net carbon emission of the node. The carbon emission intensity of the stage is the ratio of the carbon emission of the corresponding stage of the node to the activity level data. The activity level data is the activity intensity or scale of the corresponding stage of the node, including energy consumption, material consumption, and product output. The data integration module is used to integrate the allocated amount with the carbon emissions of the process to construct a product carbon footprint traceability list.

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