Carbon footprint tracking method and device, storage medium and computer program product

Abstract

The invention discloses a carbon footprint tracking method and device, a storage medium and a computer program product, and relates to the technical field of carbon emission accounting, and the method comprises the steps: obtaining a carbon footprint evaluation target inputted by a user, and screening a to-be-tracked core product according to the carbon footprint evaluation target; drawing a life cycle boundary diagram according to the supply chain hierarchy diagram of the to-be-tracked core product, and marking high-emission key nodes in the life cycle boundary diagram; collecting carbon emission activity data and emission factors of each node in the life cycle boundary diagram; and performing carbon footprint calculation according to the carbon emission activity data and the emission factor to obtain a carbon footprint calculation result, and generating a carbon footprint tracking report according to the carbon footprint calculation result and the high-emission key node. The accuracy of carbon footprint tracking is improved by drawing the life cycle boundary diagram, so that emission reduction can be performed in a targeted manner according to the carbon footprint tracking report.

Description

Technical Field

[0001] This application relates to the field of carbon emission accounting technology, and in particular to carbon footprint tracking methods, devices, storage media and computer program products. Background Art

[0002] Existing reports typically only list total emissions and the emission percentage of each stage, without directly linking the data to the node locations on the life cycle boundary map or the flow of materials and energy. Users must manually interpret the report data by referring to the boundary map, resulting in: unclear spatial locations of key high-emission nodes; and a vague mapping relationship between emission data and actual supply chain operations, making it difficult to guide targeted emission reductions. Users cannot directly obtain the specific emissions and percentages of each key node from the report, nor the correspondence between key nodes and the life cycle boundary map, leading to a lack of precise targeting in the formulation of emission reduction measures.

[0003] Therefore, how to improve the accuracy of carbon footprint tracking and carry out targeted emission reductions based on carbon footprint tracking reports has become a technical problem that this application urgently needs to solve.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a carbon footprint tracking method, device, storage medium, and computer program product, which aims to solve the technical problem of how to improve the accuracy of carbon footprint tracking and carry out targeted emission reduction based on the carbon footprint tracking report.

[0006] To achieve the above objectives, this application proposes a carbon footprint tracking method, the method comprising: Obtain the carbon footprint assessment target input by the user, and filter the core products to be tracked based on the carbon footprint assessment target; Draw a life cycle boundary diagram based on the supply chain hierarchy diagram of the core product to be tracked, and mark the key high-emission nodes in the life cycle boundary diagram. Collect carbon emission activity data and emission factors for each node in the life cycle boundary diagram; Carbon footprint calculation is performed based on the carbon emission activity data and the emission factors to obtain carbon footprint calculation results. A carbon footprint tracking report is then generated based on the carbon footprint calculation results and the high emission key nodes.

[0007] In one embodiment, the step of drawing a lifecycle boundary diagram based on the supply chain hierarchy diagram of the core product to be tracked, and marking high-emission key nodes in the lifecycle boundary diagram, includes: Extract preliminary supplier data and physical process links related to the core product to be tracked from the supply chain hierarchy diagram of the core product to be tracked; Referring to the greenhouse gas accounting system framework, the physical process links are classified into standardized life cycles, and a life cycle boundary diagram is drawn based on the standardized life cycle. The life cycle boundary diagram uses arrows to indicate the flow of materials and energy, and marks key data dimensions in each node of the life cycle boundary diagram. The emission potential value of each node in the life cycle boundary diagram is calculated based on the preliminary data from the supplier, and high emission critical nodes are marked in the life cycle boundary diagram based on the emission potential value.

[0008] In one embodiment, the step of collecting carbon emission activity data and emission factors of each node in the life cycle boundary diagram includes: Based on the material and energy flow and key data dimensions of each node in the life cycle boundary diagram, the carbon emission activity data type and emission factor type of each node are defined. Initial carbon emission activity data and initial emission factors are collected in a tiered manner according to the carbon emission activity data type and the emission factor type. Logical verification and cross-verification are performed on the initial carbon emission activity data and the initial emission factor to obtain the carbon emission activity data and emission factor.

[0009] In one embodiment, the specific steps for calculating the carbon footprint based on the carbon emission activity data and the emission factors to obtain the carbon footprint calculation result include: Set the emission percentage of each node in the lifecycle boundary diagram; Carbon footprint calculation results are obtained by calculating the emission percentage, carbon emission activity data, and emission factors.

[0010] In one embodiment, after the step of calculating the carbon footprint based on the emission ratio, the carbon emission activity data, and the emission factor to obtain the carbon footprint calculation result, the method further includes: The life cycle boundary diagram is adjusted based on the pre-generated uncertainty analysis report, and the collection requirements for carbon emission activity data and emission factors are supplemented at each node of the life cycle boundary diagram. The carbon emission activity data and emission factors of each node in the adjusted life cycle boundary map are re-collected, and the following steps are returned: carbon footprint calculation is performed based on the emission ratio, the carbon emission activity data, and the emission factors to obtain the carbon footprint calculation result.

[0011] In one embodiment, the step of adjusting the lifecycle boundary diagram based on a pre-generated uncertainty analysis report further includes: Identify the sources of uncertainty in the carbon emission activity data and the emission factors, and assess the weight of the impact of the sources of uncertainty on the carbon footprint calculation results using a parameter adjustment method; The carbon emission activity data and highly sensitive parameters in the emission factors are selected based on the influence weights. The highly sensitive parameters are randomly sampled using the Monte Carlo simulation method, and the probability distribution and confidence interval of the carbon footprint calculation results are generated. The uncertainty propagation coefficient of each node in the life cycle boundary diagram is calculated based on the probability distribution, the confidence interval, and the emission percentage. Based on the uncertainty propagation coefficient, the error contribution rate and uncertainty value of each node are calculated. Uncertainty assessment is performed based on the error contribution rate and uncertainty value of each node, and an uncertainty analysis report is generated.

[0012] In one embodiment, the step of generating a carbon footprint tracking report based on the carbon footprint calculation results and the high-emission critical nodes includes: Based on the carbon footprint calculation results, the core data dimensions are identified, and the key high-emission nodes are used as the basis for dividing the core chapters. A report chapter framework is generated based on the core data dimensions and the core chapter division criteria, and a correspondence is established between the marked positions of the high-emission key nodes in the life cycle boundary diagram and the report chapter framework. The carbon footprint tracking report is generated by visually coupling the correspondence, the report chapter framework, and the life cycle boundary diagram.

[0013] In addition, to achieve the above objectives, this application also proposes a carbon footprint tracking device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the carbon footprint tracking method as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the carbon footprint tracking method described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the carbon footprint tracking method described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: The system obtains the carbon footprint assessment target input by the user, and filters the core products to be tracked based on the carbon footprint assessment target; it draws a life cycle boundary diagram based on the supply chain hierarchy diagram of the core products to be tracked, and marks the high-emission key nodes in the life cycle boundary diagram; it collects carbon emission activity data and emission factors of each node in the life cycle boundary diagram; it calculates the carbon footprint based on the carbon emission activity data and the emission factors, obtains the carbon footprint calculation result, and generates a carbon footprint tracking report based on the carbon footprint calculation result and the high-emission key nodes. First, by "screening core products to be tracked based on carbon footprint assessment targets," interference from non-core product data is avoided, ensuring a direct match between the tracking targets and user needs, thus improving the targeting and accuracy of data collection from the source. Second, by "drawing a lifecycle boundary map based on the supply chain hierarchy and marking high-emission key nodes," the tracking scope is clearly defined through the supply chain hierarchy (avoiding omissions or redundancy caused by ambiguous boundaries), and the marking of high-emission nodes identifies key emission reduction priorities, providing precise anchors for subsequent data collection and report generation. Third, by "collecting carbon emission activity data and emission factors for each node," a one-to-one correspondence between data and boundary map nodes is achieved, ensuring the accuracy of calculation inputs and avoiding errors caused by data mismatch with links in traditional methods. Finally, by "generating a report by combining carbon footprint calculation results and high-emission key nodes," the report is directly linked to specific emission nodes (such as high-emission links marked on the boundary map), allowing users to intuitively identify emission reduction priorities and achieve a closed loop of "calculation results - key nodes - emission reduction measures," thereby simultaneously achieving the technical effects of improving the accuracy of carbon footprint tracking and targeted emission reduction. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the carbon footprint tracking method of this application. Figure 2 This is a flowchart illustrating the third embodiment of the carbon footprint tracking method of this application. Figure 3 This is a flowchart illustrating the fourth embodiment of the carbon footprint tracking method of this application. Figure 4 This is a flowchart illustrating the fifth embodiment of the carbon footprint tracking method of this application. Figure 5 This is a schematic diagram of the module structure of the carbon footprint tracking device according to an embodiment of this application; Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the carbon footprint tracking method in this application embodiment.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: obtain the carbon footprint assessment target input by the user, and screen the core products to be tracked according to the carbon footprint assessment target; draw a life cycle boundary diagram according to the supply chain hierarchy diagram of the core products to be tracked, and mark the high emission key nodes in the life cycle boundary diagram; collect carbon emission activity data and emission factors of each node in the life cycle boundary diagram; calculate the carbon footprint according to the carbon emission activity data and the emission factors to obtain the carbon footprint calculation result, and generate a carbon footprint tracking report according to the carbon footprint calculation result and the high emission key nodes.

[0024] This application's embodiments take into account that: existing reports typically only list total emissions and the emission percentage of each stage, without directly linking the data to the node locations and material and energy flows on the life cycle boundary map. Users need to manually interpret the report data by referring to the boundary map, resulting in: unclear spatial locations of key high-emission nodes; and a vague mapping relationship between emission data and actual supply chain operations, making it difficult to guide targeted emission reductions. Users cannot directly obtain the specific emissions and percentages of each key node from the report, as well as the correspondence between key nodes and the life cycle boundary map, leading to a lack of precise targeting in the formulation of emission reduction measures.

[0025] Therefore, this application provides a solution. First, by "screening core products to be tracked based on carbon footprint assessment targets," interference from non-core product data is avoided, ensuring that the tracking targets directly match user needs, thus improving the targeting and accuracy of data collection from the source. Second, by "drawing a lifecycle boundary map based on the supply chain hierarchy and marking high-emission key nodes," the tracking scope is clearly defined through the supply chain hierarchy (avoiding omissions or redundancy caused by ambiguous boundaries), and the marking of high-emission nodes identifies key emission reduction areas, providing precise anchors for subsequent data collection and report generation. Third, by "collecting carbon emission activity data and emission factors for each node," a one-to-one correspondence between data and boundary map nodes is achieved, ensuring the accuracy of calculation inputs and avoiding errors caused by data mismatch with links in traditional methods. Finally, by "generating a report by combining carbon footprint calculation results and high-emission key nodes," the report is directly linked to specific emission nodes (such as high-emission links marked on the boundary map), allowing users to intuitively locate emission reduction priorities and achieve a closed loop of "calculation results - key nodes - emission reduction measures," thereby simultaneously achieving the technical effects of improving carbon footprint tracking accuracy and targeted emission reduction.

[0026] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or carbon footprint tracking system capable of performing the above functions. The following description uses carbon footprint tracking as an example to illustrate this embodiment and the subsequent embodiments.

[0027] Based on this, embodiments of this application provide a carbon footprint tracking method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the carbon footprint tracking method of this application.

[0028] In this embodiment, the carbon footprint tracking method includes steps S10 to S40: Step S10: Obtain the carbon footprint assessment target input by the user, and filter the core products to be tracked based on the carbon footprint assessment target; "Carbon footprint assessment objectives" refers to the set of all constraints entered by the user in the system interface to define the scope and depth of this carbon footprint accounting. The form can be text description, drop-down menu selection, uploaded file or structured JSON, and must include at least six dimensions: product category, functional unit, geographical scope, time period, accuracy level and regulatory purpose. "Core products to be tracked" refers to a single product or product family that, according to established rules, is the most contributing to the carbon footprint assessment target, has the best data availability, and is the most representative for subsequent supply chain emission reduction decisions among all candidate products.

[0029] After obtaining the carbon footprint assessment target, the system can use a large language model to convert the natural language or form information in the carbon footprint assessment target input by the user into a computable target feature vector. Then, it calls the metadata in the product database and performs cosine similarity matching with the feature vector to select the core products to be tracked with the highest matching degree to the target.

[0030] Through this screening mechanism, the system can quickly eliminate distracting items that are irrelevant or have low relevance to the evaluation objectives from massive amounts of product data, significantly reducing the complexity of subsequent data collection and calculation, while ensuring that the final report focuses on the emission reduction targets that users are truly concerned about.

[0031] For example, in one specific embodiment, the user inputs a carbon footprint assessment target of "exporting smartphone lithium batteries to Region A in 2024, requiring compliance with the secondary carbon footprint threshold of mobile phone lithium battery regulations in Region A". The system selects "smartphone lithium batteries" and "Region A" as key searches to retrieve the core products to be tracked.

[0032] Step S20: Draw a life cycle boundary diagram based on the supply chain hierarchy diagram of the core product to be tracked, and mark the high-emission key nodes in the life cycle boundary diagram; A “supply chain hierarchy graph” refers to a topological structure that describes the entire process of a core product to be tracked, from raw material mining, component manufacturing, complete machine assembly, logistics and distribution to final disposal, in the form of a directed acyclic graph. Nodes represent physical process links, and edges represent the flow relationships of materials or energy. "Lifecycle boundary diagram" refers to a graphical representation of the supply chain hierarchy as a standardized lifecycle stage (raw materials, manufacturing, transportation, use, and end-of-life treatment) within the framework of the Greenhouse Gas Accounting System (GHG Protocol). The diagram uses arrowed lines to mark the flow of materials and energy, and provides key data dimensions (such as input material name, energy type, process parameters, and geographical coordinates) next to each node in the form of labels. “High-emission critical nodes” refer to nodes that are identified by the system in the life cycle boundary map as having significant emission potential and whose weight of impact on the overall carbon footprint exceeds a preset threshold. They are usually visually enhanced by red highlighting or flashing icons.

[0033] The system first extracts the physical process links directly related to the core product to be tracked from the supply chain hierarchy diagram, eliminating non-physical nodes that only provide administrative or financial services. Then, it uses a standardized lifecycle template for stage classification, generating a draft of the lifecycle boundary diagram. Next, based on preliminary supplier data (such as production volume, energy consumption, and emission factor range estimates), the system calculates the emission potential value for each node and marks high-emission critical nodes on the lifecycle boundary diagram using color gradients or heatmaps, allowing users to easily identify emission reduction priorities in a graphical interface. In one possible implementation, the system allows users to switch between different accounting boundaries and automatically recalculates the emission potential value and updates the distribution of high-emission critical nodes after each switch.

[0034] Additionally, it should be noted that the life cycle boundary diagram supports multiple interactive methods: it can be automatically generated from structured data extracted from the enterprise ERP system, or users can manually correct the node positions and connections through a drag-and-drop graphical editor; the threshold for high-emission critical nodes can be set using the system's default 80 / 20 Pareto principle, or users can define their own percentage or absolute emission values.

[0035] For example, in one specific implementation, for the core product to be tracked: the lithium battery for smartphone model B, all physical process steps involved in manufacturing this model of lithium battery are retrieved from the enterprise's ERP system and standardized and categorized to form a lifecycle boundary map. Based on the energy consumption and emission factors pre-filled by the supplier, the system estimates that the emission potential values ​​of the three nodes "14nm wafer manufacturing", "high-nickel cathode material sintering", and "air transport to the distribution center in region A" account for 74% of the total footprint. Therefore, these three nodes are automatically marked as high-emission critical nodes.

[0036] Step S30: Collect carbon emission activity data and emission factors for each node in the life cycle boundary diagram; "Carbon emission activity data" refers to the quantifiable activities directly related to greenhouse gas emissions at each node of the life cycle boundary diagram, including but not limited to energy consumption, material input, output, transportation distance, and waste disposal. "Emission factor" refers to the coefficient required to convert the above activity data into carbon dioxide equivalent. Its source can be IPCC default value, national or regional database, industry association dataset, supplier measured value or third-party verified value.

[0037] The system first defines the types of carbon emission activity data and emission factor types to be collected based on the material and energy flow and key data dimensions of each node in the lifecycle boundary diagram, and sends them to the data acquisition agent in JSON Schema format. Subsequently, the data acquisition agent collects the initial carbon emission activity data and initial emission factors in a tiered manner: Level 1 is automatically extracted from the enterprise's internal system, Level 2 is connected to the upstream information system of the supply chain via API, Level 3 obtains offline files via email, and Level 4 triggers manual entry or on-site review. All initial data are confirmed as the final carbon emission activity data and emission factors only after undergoing logical verification (range check, unit conversion, time consistency) and cross-verification (comparison of multi-source data at the same node and trend analysis with historical data).

[0038] Through tiered data collection and dual verification mechanisms, the system significantly improves data completeness and accuracy, laying a solid foundation for subsequent carbon footprint calculations. In one possible implementation, for upstream nodes in the supply chain that cannot yet provide measured emission factors, the system allows default values ​​to be used as placeholders and marked with an uncertainty level, which will then be automatically updated once the supplier provides the measured values.

[0039] Step S40: Calculate the carbon footprint based on the carbon emission activity data and the emission factors to obtain the carbon footprint calculation result, and generate a carbon footprint tracking report based on the carbon footprint calculation result and the high emission key nodes.

[0040] "Carbon footprint calculation results" refers to the total emissions of the core product to be tracked throughout its entire life cycle, expressed in carbon dioxide equivalents, and the detailed distribution of these emissions at each node. "Carbon footprint tracking report" refers to an interactive document that can be directly used for decision support, generated by visually coupling the carbon footprint calculation results with key high-emission nodes.

[0041] The system first performs carbon footprint calculations based on the emission share, carbon emission activity data, and emission factors of each node in the life cycle boundary diagram. It uses a product model of emission = activity data × emission factor to calculate the single-point emission for each node, then accumulates the emissions by stage to obtain the total emission. Subsequently, the system uses high-emission key nodes as the core basis for dividing the report into chapters, establishing a one-to-one hyperlink relationship between the report's chapter framework and the node markings in the life cycle boundary diagram. Finally, the system uses a visualization engine to integrate the life cycle boundary diagram, emission values, node details, and emission reduction recommendations into a single page using heatmaps, Sankey diagrams, waterfall diagrams, and other formats, generating a carbon footprint tracking report.

[0042] This embodiment provides a carbon footprint tracking method. First, by "screening core products to be tracked based on carbon footprint assessment targets," interference from non-core product data is avoided, ensuring that the tracking targets directly match user needs and improving the targeting and accuracy of data collection from the source. Second, by "drawing a life cycle boundary map based on the supply chain hierarchy and marking high-emission key nodes," the tracking scope is clearly defined through the supply chain hierarchy (avoiding omissions or redundancy caused by ambiguous boundaries), and the marking of high-emission nodes identifies key emission reduction areas, providing precise anchors for subsequent data collection and report generation. Third, by "collecting carbon emission activity data and emission factors for each node," a one-to-one correspondence between data and boundary map nodes is achieved, ensuring the accuracy of calculation inputs and avoiding errors caused by data mismatch with links in traditional methods. Finally, by "generating a report by combining carbon footprint calculation results and high-emission key nodes," the report is directly associated with specific emission nodes (such as high-emission links marked on the boundary map), allowing users to intuitively locate emission reduction priorities and achieve a closed loop of "calculation results - key nodes - emission reduction measures," thereby simultaneously achieving the technical effects of improving the accuracy of carbon footprint tracking and targeted emission reduction.

[0043] In one feasible implementation, step S20 may include steps S21 to S23: Step S21: Extract preliminary supplier data and physical process links related to the core product to be tracked from the supply chain hierarchy diagram of the core product to be tracked. "Physical process links" refer to observable operational units in the real world where the conversion, transportation, storage, and processing of matter or energy occur, such as aluminum ingot smelting, injection molding, land transportation and distribution, and wastewater treatment; "related" means that the physical process link plays an irreplaceable role in the realization of the function of the core product to be tracked, and if the link is missing, the product cannot complete its intended function; "preliminary supplier data" refers to raw energy consumption, output, emission factor range, etc., provided by upstream enterprises in the supply chain through questionnaires, EDI, API, or offline documents, which have not been finally verified.

[0044] The system scans the supply chain hierarchy from top to bottom, using the final assembly node of the product as the root and searching along the direction of material and energy flow, retaining all reachable nodes; then it removes non-physical nodes that only provide financial, administrative, and information services, thereby obtaining a set of physical process links directly related to the core product to be tracked.

[0045] Through this extraction mechanism, the system can lock the accounting boundary onto the physical activities that truly contribute to the product's carbon footprint, avoiding redundant calculations caused by boundary expansion, while ensuring the completeness and accuracy of subsequent lifecycle boundary map drawing.

[0046] In one possible implementation, the system allows users to manually add or delete nodes via an interactive canvas to address sudden changes in the supply chain or special scenario requirements.

[0047] Step S22: Referring to the greenhouse gas accounting system framework, the physical process links are classified into standardized life cycles, and a life cycle boundary diagram is drawn based on the standardized life cycle; wherein, the life cycle boundary diagram uses arrows to indicate the flow of materials and energy, and key data dimensions are marked in each node of the life cycle boundary diagram; "Standardized life cycle" refers to mapping physical process links to a unified life cycle stage division according to the specific provisions of the greenhouse gas accounting system. It usually includes five major stages: raw material acquisition, production and manufacturing, transportation and distribution, usage stage, and end-of-life treatment. "Life cycle boundary diagram" refers to a logical relationship diagram that graphically presents the above five major stages and their internal nodes. Arrows indicate the flow of materials and energy, and key data dimension labels are embedded in the nodes. "Key data dimension" refers to the set of quantifiable information that must be recorded at each node, including at least the input material name, input energy type, process parameters, geographical coordinates, timestamp, and data quality level.

[0048] The system first categorizes the extracted physical process steps into standardized lifecycle stages according to the specific rules of the greenhouse gas accounting system. Then, it uses a graphics engine to automatically generate a lifecycle boundary diagram, with arrow thickness proportional to flow rate and node color dynamically changing according to data quality level. Key data dimensions are displayed inside the nodes as collapsible labels, allowing users to click to view details.

[0049] Additionally, it should be noted that the lifecycle boundary diagram is stored in vector graphics format, supporting infinite scaling and layered display; the arrow types include unidirectional solid lines (main material flow), unidirectional dashed lines (auxiliary energy flow), and bidirectional arrows (circulation and recycling flow).

[0050] Step S23: Calculate the emission potential value of each node in the life cycle boundary diagram based on the preliminary data of the supplier, and mark the high emission key nodes in the life cycle boundary diagram based on the emission potential value.

[0051] "Emission potential value" refers to the estimated carbon emissions of a single node based on preliminary supplier data using a simplified algorithm, typically expressed in kgCO2e or as a percentage. Since the preliminary supplier data includes activity data and emission factor ranges provided by the supplier, the emission potential value for each node in the life cycle boundary diagram is calculated based on this data. When the emission potential value exceeds a percentage or absolute threshold set by the system or user, the node is determined to be a high-emission critical node.

[0052] Specifically, the system first performs random sampling of activity data and emission factors for each node. Each random sampling result is used to calculate the single potential value using the formula: Emission Potential Value = Activity Data × Emission Factor. Repeated sampling and statistical analysis are performed to form a probability distribution and confidence interval for the emission potential values. The distribution value is then compared with the overall lifecycle total emission estimate. Nodes exceeding a preset threshold (e.g., 10%) are marked as high-emission critical nodes and displayed on the lifecycle boundary map with red highlighting, enlarged icons, or flashing animations. Through early potential estimation and visual annotation, the system helps users identify the maximum emission reduction opportunities before data collection, significantly improving the targeting of subsequent data collection and the efficiency of resource allocation. In one possible implementation, users can dynamically adjust the threshold or switch to the Pareto 80 / 20 rule, and the system recalculates and updates the high-emission critical nodes in real time.

[0053] In one feasible implementation, step S40 may include steps S41 to S43: Step S41: Based on the carbon footprint calculation results, clarify the core data dimensions and use the high-emission key nodes as the basis for dividing the core chapters; "Core data dimensions" refer to the set of data that must be highlighted in the report and are most valuable for decision-making, including emissions, percentages, uncertainty ranges, data quality levels, and improvement potential scores.

[0054] The system first analyzes the carbon footprint calculation results, selecting the top 80% of emissions as core data dimensions. Then, using high-emission key nodes as anchor points, the report is divided into several core chapters, each focusing on a single high-emission key node, ensuring users can directly pinpoint the largest emission source. This node-centric chapter division enables rapid mapping from data to decision-making, avoiding the pitfalls of traditional reports that are "data-heavy and lack focus." In one possible implementation, the system supports user-defined chapter numbers or a "hotspot-improvement" two-column layout.

[0055] Step S42: Generate a report chapter framework based on the core data dimensions and the core chapter division criteria, and establish the correspondence between the marked positions of the high-emission key nodes in the life cycle boundary diagram and the report chapter framework; "Report chapter framework" refers to the structured document skeleton that includes titles, page numbers, chart placeholders, and text description templates; "correspondence" refers to the bidirectional hyperlink mapping between the coordinates and IDs of high-emission key nodes in the life cycle boundary diagram and the anchor points of the report chapters.

[0056] The system first reads the core data dimensions and chapter division criteria, automatically generating an XML framework containing a table of contents, thumbnails, and jump links. Then, it establishes a hash mapping table between graphical identifiers and chapter anchors for each high-emission key node, ensuring that users can jump to the corresponding chapter by clicking a node on the lifecycle boundary diagram, and vice versa. Through this mapping mechanism, the system achieves seamless interaction between graphics and text, allowing users to switch freely between visual and textual modes without manual searching.

[0057] Step S43: Visually couple the correspondence, the report chapter framework, and the life cycle boundary diagram to generate a carbon footprint tracking report.

[0058] "Visualization" refers to using technologies such as graphic rendering, interactive dashboards, and dynamic charts to integrate the corresponding relationships, report chapter framework, and life cycle boundary diagram into the same user interface; "Carbon Footprint Tracking Report" refers to the final digital report delivered to the user that is interactive, exportable, and auditable.

[0059] The system invokes the visualization engine, first rendering the lifecycle boundary map as the base map on the central canvas, with node colors grading from high to low according to emissions; then, clickable chapter bubbles are superimposed above each high-emission key node, displaying the chapter title and emission percentage within the bubble; the right sidebar synchronously loads the detailed text, data tables, and improvement suggestions for the corresponding chapter; users can dynamically observe emission changes under different scenarios through interactive operations such as zooming, filtering, and timeline playback.

[0060] Based on the first embodiment of this application, a second embodiment of this application is proposed. In the second embodiment of this application, content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter.

[0061] In this embodiment, step S30, which involves collecting carbon emission activity data and emission factors for each node in the life cycle boundary diagram, may include steps S311 to S313: Step S311: Define the carbon emission activity data type and emission factor type of each node based on the material and energy flow direction and key data dimensions of each node in the life cycle boundary diagram; "Carbon emission activity type" refers to the activity quantity category derived from material and energy flow and key data dimensions for subsequent carbon footprint calculation, such as electricity consumption, natural gas consumption, raw material input, and transportation mileage; "emission factor type" refers to the coefficient category that must be used to convert the above activity quantities into carbon dioxide equivalent, such as grid emission factor, natural gas lower heating value emission factor, raw material production emission factor, and transportation mode emission factor.

[0062] The system automatically generates corresponding metadata templates for carbon emission activity data types and emission factor types by reading the material and energy flow and key data dimensions of each node in the life cycle boundary diagram, thereby ensuring the relevance and completeness of subsequent data collection.

[0063] Step S312: Collect initial carbon emission activity data and initial emission factors according to the carbon emission activity data type and the emission factor type. "Tiered data collection" refers to the system dividing data collection into four levels based on the reliability, real-time nature, and degree of automation of the data source: Level 1 is automatic extraction from the enterprise's internal system, Level 2 is through API connection to the upstream information system of the supply chain, Level 3 is through obtaining offline files via email, and Level 4 is triggered by manual filling or on-site review.

[0064] "Initial carbon emission activity data" refers to unverified raw activity values ​​directly derived from the four levels mentioned above; "Initial emission factor" refers to unverified raw coefficient values ​​directly derived from databases, literature, or supplier statements.

[0065] Based on the defined data types and emission factor types of carbon emission activities at each node, the system automatically matches the most appropriate data collection level: Level 1 extraction is used for real-time meter readings, and Level 3 email extraction is used for quarterly energy consumption reports from suppliers. Through this tiered data collection strategy, the system significantly reduces data collection costs and time while ensuring comprehensive data coverage.

[0066] Step S313: Perform logical verification and cross-verification on the initial carbon emission activity data and the initial emission factor to obtain carbon emission activity data and emission factor.

[0067] "Logical verification" refers to checking the numerical range, units, temporal consistency, and physical rationality of initial carbon emission activity data and initial emission factors; "cross-verification" refers to comparing multi-source data at the same node horizontally, or performing longitudinal trend analysis between current data and historical data from the same period, to identify abnormal fluctuations. Through this dual verification mechanism, the system significantly reduces the data error rate and improves the reliability of carbon footprint calculation.

[0068] In this embodiment, by using the material and energy flow and key data dimensions of each node in the life cycle boundary diagram as a benchmark, the system first accurately defines the types of carbon emission activity data and emission factor types, then collects initial data according to the reliability level, and performs dual quality control through logical verification and cross-verification, finally outputting carbon emission activity data and emission factors that can be directly used for calculation, thereby eliminating mismatch and error at the data source level and laying a solid data foundation for subsequent high-reliability carbon footprint calculation.

[0069] Based on the first and / or second embodiments of this application, a third embodiment of this application is proposed. In the third embodiment of this application, content that is the same as or similar to the first and / or second embodiments described above can be referred to the above description and will not be repeated hereafter.

[0070] Based on this, please refer to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of this application, as shown below. Figure 2 As shown, S30, which calculates the carbon footprint based on the carbon emission activity data and the emission factors, and obtains the carbon footprint calculation result, may include steps S321 to S322: Step S321: Set the emission percentage of each node in the life cycle boundary diagram; "Emissions percentage" refers to the weight percentage that the system pre-sets for each node in the life cycle boundary diagram. This weight percentage is used to allocate the initial estimate of overall emissions during the first carbon footprint calculation. The purpose of setting the emission percentage for each node in the life cycle boundary diagram is to provide a weighting basis for subsequent uncertainty propagation calculations and emission reduction priority ranking, while also enabling report readers to intuitively identify high-emission stages.

[0071] In one possible implementation, the system uses the node emission distribution of similar products in history as the initial weight; in another possible implementation, the system allows users to manually adjust the emission percentage by dragging a slider and updates the carbon footprint calculation results and visualization charts in real time.

[0072] Step S322: Calculate the carbon footprint based on the emission ratio, the carbon emission activity data, and the emission factor to obtain the carbon footprint calculation result.

[0073] Carbon footprint calculation refers to the process of multiplying the carbon emission activity data of each node with the corresponding emission factor, summing them according to the emission ratio, and then converting them into carbon dioxide equivalent based on global warming potential. The carbon footprint calculation result refers to the carbon emissions of the entire life cycle based on the functional unit of the product (such as each laptop computer or each kilogram of steel).

[0074] Furthermore, it should be noted that the purpose of calculating the carbon footprint based on emission percentages, carbon emission activity data, and emission factors is to transform validated input data into output indicators with decision-making significance, providing a quantitative basis for generating tracking reports and formulating emission reduction measures. In one possible implementation, the system uses matrix operations to complete the calculation of all nodes at once and avoids duplicate calculations through a caching mechanism.

[0075] In this embodiment, an emission percentage reflecting the importance of emissions is set for each node in the life cycle boundary diagram. This weight, along with verified carbon emission activity data and emission factors, is then simultaneously substituted into the carbon footprint calculation model to output a carbon footprint calculation result that combines node-level weights with overall results. This allows users to obtain the total carbon emissions at the product level and immediately identify the relative contribution of each node in the overall picture, providing a quantitative basis for emission reduction priorities and resource allocation decisions.

[0076] Based on the third embodiment of this application, a fourth embodiment of this application is proposed. In the fourth embodiment of this application, the same or similar contents as those in the third embodiment can be referred to the above description, and will not be repeated hereafter.

[0077] Based on this, please refer to Figure 3 , Figure 3 This is a schematic flowchart of the fourth embodiment of this application, as shown below. Figure 3 As shown, after step S32, which calculates the carbon footprint based on the emission ratio, the carbon emission activity data, and the emission factor to obtain the carbon footprint calculation result, steps A321 to A322 may also be included: Step A321: Adjust the life cycle boundary diagram according to the pre-generated uncertainty analysis report, and supplement the collection requirements of carbon emission activity data and emission factors at each node of the life cycle boundary diagram; Uncertainty analysis reports refer to structured documents generated through Monte Carlo simulations, including probability distributions, confidence intervals, and error contribution rates; data collection requirements refer to metadata such as data accuracy levels, sample sizes, measurement methods, and data update frequencies added at each node of the lifecycle boundary diagram.

[0078] Additionally, it should be noted that the purpose of adjusting the lifecycle boundary diagram based on the pre-generated uncertainty analysis report is to: quantify the sources of error, reverse-optimize the data collection strategy, improve the overall data quality and the credibility of the calculation results, and achieve closed-loop management of "using data to improve data".

[0079] In one possible implementation, the system visualizes the error contribution rate of each node on the lifecycle boundary map by color intensity, and the user can click on the red high error node to bring up a list of collection requirements; in another possible implementation, the system automatically generates API instructions to push new collection requirements to the enterprise LCA data management platform, triggering a new round of data collection workflow.

[0080] Step A322: Re-collect carbon emission activity data and emission factors for each node in the adjusted life cycle boundary map, and return to step: Calculate the carbon footprint based on the emission ratio, the carbon emission activity data, and the emission factors to obtain the carbon footprint calculation result.

[0081] The adjusted lifecycle boundary diagram refers to an updated flowchart with added or modified collection requirements, data accuracy levels, and node splitting / merging markers. Additionally, it should be noted that the purpose of re-collecting and returning carbon footprint calculation results using the adjusted lifecycle boundary diagram is to replace the original low-reliability data with higher-quality or finer-grained data, thereby iteratively reducing overall uncertainty and ultimately making the carbon footprint calculation results more stable and reliable.

[0082] In one possible implementation, the system enables version control, retaining snapshots of the lifecycle boundary graph before and after each adjustment, allowing users to compare the improvement effects.

[0083] In this embodiment, the dynamic adjustment of the life cycle boundary map is driven by the pre-generated uncertainty analysis report. At each high error node, higher precision or finer granular data collection requirements are added. Then, the updated boundary map is used to re-collect and replace the original data, and the carbon footprint calculation is performed again to form a closed loop for optimizing the carbon footprint calculation. This continuously compresses the overall uncertainty and makes the final carbon footprint calculation result more stable and reliable.

[0084] Based on the third and / or fourth embodiments of this application, a fifth embodiment of this application is proposed. In the fifth embodiment of this application, the same or similar content as the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0085] Based on this, please refer to Figure 4 , Figure 4 This is a schematic flowchart of the fifth embodiment of this application. Figure 4 As shown, before step S321 of adjusting the lifecycle boundary diagram based on the pre-generated uncertainty analysis report, steps S01 to S05 are also included: Step S01: Identify the sources of uncertainty in the carbon emission activity data and the emission factors, and evaluate the weight of the impact of the sources of uncertainty on the carbon footprint calculation results using the parameter adjustment method; Uncertainty sources refer to the root causes of deviations in carbon emission activity data or emission factors, including measurement errors, database differences, insufficient spatiotemporal representativeness, variations in technical routes, and insufficient data timeliness; parameter adjustment method refers to the sensitivity perturbation of a single parameter by a step size of ±10% or ±20% to observe its impact on the final carbon footprint calculation result; influence weight refers to the proportion of contribution of a certain uncertainty source to the variance of the total result.

[0086] In addition, it should be noted that the purpose of identifying sources of uncertainty and assessing their impact weights is to provide a quantitative basis for subsequent screening of highly sensitive parameters and Monte Carlo simulations, and to avoid the waste of computational resources caused by indiscriminate sampling of all parameters.

[0087] The system first scans all carbon emission activity data (such as electricity consumption and diesel consumption) and all emission factors (such as grid emission factors and diesel emission factors), listing the potential sources of bias: measurement instrument errors, database age differences, insufficient regional representativeness, differences in technological approaches, and insufficient data timeliness. Then, using a "parameter adjustment method," each parameter is perturbed by a step size of ±10% or ±20%. The carbon footprint result is recalculated after each perturbation, and the magnitude of the change is recorded to assess the "weight of influence" of this uncertainty source on the final result.

[0088] Step S02: Filter the carbon emission activity data and the highly sensitive parameters in the emission factors according to the influence weights; Highly sensitive parameters refer to carbon emission activity data or emission factors whose influence weights exceed a preset threshold. The purpose of selecting highly sensitive parameters based on their influence weights is to concentrate computational resources on quantifying the uncertainty of a small number of key parameters, thereby improving the efficiency of Monte Carlo simulations.

[0089] Sort all parameters by influence weight from high to low, set a threshold (e.g., weight > 0.05) as the "high sensitivity" threshold, and automatically include parameters above the threshold in the next step of key analysis, while the remaining low sensitivity parameters keep a fixed value in subsequent sampling to save computing power.

[0090] Step S03: The Monte Carlo simulation method is used to randomly sample the highly sensitive parameters and generate the probability distribution and confidence interval of the carbon footprint calculation results; Monte Carlo simulation refers to repeatedly running carbon footprint calculations by randomly sampling a large number of highly sensitive parameter values ​​to obtain the probability distribution of the carbon footprint calculation results. The probability distribution refers to the statistical curve of the frequency of the results, usually represented by a histogram or kernel density plot. The confidence interval refers to the probability that the result falls within a certain interval, such as the 95% confidence interval.

[0091] Additionally, it should be noted that the purpose of using Monte Carlo simulation is to transform static point estimates into dynamic probabilistic descriptions, enabling users to quantify risks and formulate robust emission reduction strategies.

[0092] For highly sensitive parameters, the system generates random numbers based on their actual distribution characteristics (normal, triangular, uniform, etc.) and performs sampling. Each sampling calls the carbon footprint calculation formula to obtain a result. After all the processes are completed, a probability distribution curve of the carbon footprint calculation results is generated, and a 95% confidence interval is provided.

[0093] Step S04: Calculate the uncertainty propagation coefficient of each node in the life cycle boundary diagram based on the probability distribution, the confidence interval, and the emission percentage; The uncertainty propagation coefficient is a scaling factor between node-level uncertainties and the total system uncertainty. It characterizes how errors are propagated from nodes to the overall outcome. Its mathematical expression is: Uncertainty transmission coefficient = Emission share × (Standard deviation of node parameters / Total standard deviation of carbon footprint calculation results) Specifically, a system-level probability distribution is extracted from the probability distribution of the Monte Carlo results, and the total standard deviation of the system, i.e. the total standard deviation of the carbon footprint calculation results, is calculated. For each node in the life cycle boundary diagram, the carbon emission sample sequence of that node is extracted using the same batch of Monte Carlo samples, and the node-level standard deviation is calculated.

[0094] In addition, it should be noted that the purpose of calculating the uncertainty transmission coefficient based on probability distribution, confidence interval and emission ratio is to quantify the amplification or reduction effect of each node on the overall uncertainty, and to lay the foundation for the calculation of error contribution rate.

[0095] Step S05: Calculate the error contribution rate and uncertainty value of each node based on the uncertainty propagation coefficient, perform uncertainty assessment based on the error contribution rate and uncertainty value of each node, and generate an uncertainty analysis report.

[0096] The node error contribution rate refers to the percentage contribution of a single node's uncertainty to the overall variance; the uncertainty value refers to the standard deviation or coefficient of variation of node-level carbon emissions. The specific calculation logic for the node error contribution rate is as follows: sum the squares of the uncertainty transmission coefficients of all nodes, and then use the percentage of the squared transmission coefficient of each node as its error contribution rate (i.e., contribution rate = (node ​​uncertainty transmission coefficient)). 2 ) / (Uncertainty propagation coefficients at all nodes) 2 sum)).

[0097] The specific calculation logic for uncertain values ​​includes the calculation logic for the standard deviation of node-level carbon emissions and the calculation logic for the coefficient of variation. Specifically: Node-level standard deviation σ=

[0098] in, This represents the node carbon emission value from a single sampling. Let N be the mean of the sample sequence and N be the number of samples.

[0099] Coefficient of variation CV= ×100% Where σ is the node standard deviation, This is the mean of the sample sequence.

[0100] The node uncertainty value is quantified by combining the sample sequence generated by Monte Carlo simulation with the standard deviation (or coefficient of variation) to determine the dispersion of the node's carbon emission results, directly reflecting the reliability of the node's data. The higher the value, the greater the potential impact of parameter fluctuations at that node on the carbon footprint calculation results, and the more important it is to prioritize reducing uncertainty through data acquisition optimization (such as increasing sample size and improving measurement accuracy).

[0101] In this embodiment, the sources of uncertainty in carbon emission activity data and emission factors are first systematically identified, and the influence weight of each source on the carbon footprint calculation results is quantified by parameter adjustment method. Then, highly sensitive parameters are screened for Monte Carlo random sampling to obtain the probability distribution and confidence interval of the results. Subsequently, the uncertainty transmission coefficient and error contribution rate at the node level are calculated, and finally, an uncertainty analysis report containing visual ranking and improvement suggestions is generated. This allows users to accurately locate and prioritize improvements to the links that contribute the most to the overall uncertainty, thereby achieving a simultaneous improvement in data quality and decision robustness at the management level.

[0102] This application also provides a carbon footprint tracking device, please refer to... Figure 5 The carbon footprint tracking device includes: The core product screening module 10 is used to obtain the carbon footprint assessment target input by the user and screen the core products to be tracked according to the carbon footprint assessment target. Boundary diagram drawing module 20 is used to draw a life cycle boundary diagram based on the supply chain hierarchy diagram of the core product to be tracked, and mark high-emission key nodes in the life cycle boundary diagram. The data acquisition module 30 is used to collect carbon emission activity data and emission factors of each node in the life cycle boundary diagram; The report generation module 40 is used to calculate the carbon footprint based on the carbon emission activity data and the emission factors, obtain the carbon footprint calculation result, and generate a carbon footprint tracking report based on the carbon footprint calculation result and the high emission key nodes.

[0103] The carbon footprint tracking device provided in this application, employing the carbon footprint tracking method in the above embodiments, can solve the technical problem of carbon footprint tracking. Compared with the prior art, the beneficial effects of the carbon footprint tracking device provided in this application are the same as those of the carbon footprint tracking method provided in the above embodiments, and other technical features in the carbon footprint tracking device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0104] This application provides a carbon footprint tracking device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the carbon footprint tracking method in Embodiment 1 above.

[0105] The following is for reference. Figure 6 The diagram illustrates a structural schematic suitable for implementing the carbon footprint tracking device in the embodiments of this application. The carbon footprint tracking device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The carbon footprint tracking device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0106] like Figure 6 As shown, the carbon footprint tracking device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the carbon footprint tracking device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the carbon footprint tracking device to communicate wirelessly or wiredly with other devices to exchange data. While the figures show carbon footprint tracking devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0107] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0108] The carbon footprint tracking device provided in this application, employing the carbon footprint tracking method described in the above embodiments, can solve the technical problem of carbon footprint tracking. Compared with the prior art, the beneficial effects of the carbon footprint tracking device provided in this application are the same as those of the carbon footprint tracking method provided in the above embodiments, and other technical features of the carbon footprint tracking device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0109] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0110] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0111] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the carbon footprint tracking method in the above embodiments.

[0112] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0113] The aforementioned computer-readable storage medium may be included in the carbon footprint tracking device; or it may exist independently and not assembled into the carbon footprint tracking device.

[0114] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the carbon footprint tracking device, cause the carbon footprint tracking device to: acquire a carbon footprint assessment target input by the user; filter core products to be tracked based on the carbon footprint assessment target; draw a life cycle boundary diagram based on the supply chain hierarchy diagram of the core products to be tracked; mark high-emission key nodes in the life cycle boundary diagram; collect carbon emission activity data and emission factors of each node in the life cycle boundary diagram; perform carbon footprint calculation based on the carbon emission activity data and the emission factors to obtain a carbon footprint calculation result; and generate a carbon footprint tracking report based on the carbon footprint calculation result and the high-emission key nodes.

[0115] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0116] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0117] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0118] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described carbon footprint tracking method, thereby solving the technical problem of carbon footprint tracking. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the carbon footprint tracking method provided in the above embodiments, and will not be repeated here.

[0119] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the carbon footprint tracking method described above.

[0120] The computer program product provided in this application can solve the technical problem of carbon footprint tracking. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the carbon footprint tracking method provided in the above embodiments, and will not be repeated here.

[0121] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A carbon footprint tracking method, characterized in that, The carbon footprint tracking method includes: Obtain the carbon footprint assessment target input by the user, and filter the core products to be tracked based on the carbon footprint assessment target; Draw a life cycle boundary diagram based on the supply chain hierarchy diagram of the core product to be tracked, and mark the key high-emission nodes in the life cycle boundary diagram. Collect carbon emission activity data and emission factors for each node in the life cycle boundary diagram; Carbon footprint calculation is performed based on the carbon emission activity data and the emission factors to obtain carbon footprint calculation results. A carbon footprint tracking report is then generated based on the carbon footprint calculation results and the high emission key nodes.

2. The carbon footprint tracking method as described in claim 1, characterized in that, The step of drawing a lifecycle boundary diagram based on the supply chain hierarchy diagram of the core product to be tracked, and marking high-emission key nodes in the lifecycle boundary diagram, includes: Extract preliminary supplier data and physical process links related to the core product to be tracked from the supply chain hierarchy diagram of the core product to be tracked; Referring to the greenhouse gas accounting system framework, the physical process links are classified into standardized life cycles, and a life cycle boundary diagram is drawn based on the standardized life cycle. The life cycle boundary diagram uses arrows to indicate the flow of materials and energy, and marks key data dimensions in each node of the life cycle boundary diagram. The emission potential value of each node in the life cycle boundary diagram is calculated based on the preliminary data from the supplier, and high emission critical nodes are marked in the life cycle boundary diagram based on the emission potential value.

3. The carbon footprint tracking method as described in claim 1, characterized in that, The steps for collecting carbon emission activity data and emission factors at each node in the life cycle boundary diagram include: Based on the material and energy flow and key data dimensions of each node in the life cycle boundary diagram, the carbon emission activity data type and emission factor type of each node are defined. Initial carbon emission activity data and initial emission factors are collected in a tiered manner according to the carbon emission activity data type and the emission factor type. Logical verification and cross-verification are performed on the initial carbon emission activity data and the initial emission factor to obtain the carbon emission activity data and emission factor.

4. The carbon footprint tracking method as described in claim 1, characterized in that, The step of calculating the carbon footprint based on the carbon emission activity data and the emission factors to obtain the carbon footprint calculation result includes: Set the emission percentage of each node in the lifecycle boundary diagram; Carbon footprint calculation results are obtained by calculating the emission percentage, carbon emission activity data, and emission factors.

5. The carbon footprint tracking method as described in claim 4, characterized in that, After the step of calculating the carbon footprint based on the emission ratio, the carbon emission activity data, and the emission factor to obtain the carbon footprint calculation result, the method further includes: The life cycle boundary diagram is adjusted based on the pre-generated uncertainty analysis report, and the collection requirements for carbon emission activity data and emission factors are supplemented at each node of the life cycle boundary diagram. The carbon emission activity data and emission factors of each node in the adjusted life cycle boundary map are re-collected, and the following steps are returned: carbon footprint calculation is performed based on the emission ratio, the carbon emission activity data, and the emission factors to obtain the carbon footprint calculation result.

6. The carbon footprint tracking method as described in claim 5, characterized in that, Prior to the step of adjusting the lifecycle boundary diagram based on the pre-generated uncertainty analysis report, the following also applies: Identify the sources of uncertainty in the carbon emission activity data and the emission factors, and assess the weight of the impact of the sources of uncertainty on the carbon footprint calculation results using a parameter adjustment method; The carbon emission activity data and highly sensitive parameters in the emission factors are selected based on the influence weights. The highly sensitive parameters are randomly sampled using the Monte Carlo simulation method, and the probability distribution and confidence interval of the carbon footprint calculation results are generated. The uncertainty propagation coefficient of each node in the life cycle boundary diagram is calculated based on the probability distribution, the confidence interval, and the emission percentage. Based on the uncertainty propagation coefficient, the error contribution rate and uncertainty value of each node are calculated. Uncertainty assessment is performed based on the error contribution rate and uncertainty value of each node, and an uncertainty analysis report is generated.

7. The carbon footprint tracking method as described in claim 1, characterized in that, The step of generating a carbon footprint tracking report based on the carbon footprint calculation results and the high-emission critical nodes includes: Based on the carbon footprint calculation results, the core data dimensions are identified, and the key high-emission nodes are used as the basis for dividing the core chapters. A report chapter framework is generated based on the core data dimensions and the core chapter division criteria, and a correspondence is established between the marked positions of the high-emission key nodes in the life cycle boundary diagram and the report chapter framework. The carbon footprint tracking report is generated by visually coupling the correspondence, the report chapter framework, and the life cycle boundary diagram.

8. A carbon footprint tracking device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the carbon footprint tracking method as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the carbon footprint tracking method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the carbon footprint tracking method as described in any one of claims 1 to 7.

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