Enterprise value chain carbon emission data processing method, system, equipment and medium

Abstract

The invention provides an enterprise value chain carbon emission data processing method, system and device and a medium, and the method comprises the steps: integrating an external carbon emission parameter and operation activity data of an internal business system, building an incidence relation of different data source fields, generating a data set with a unified identifier, and carrying out the processing of the data set; the method comprises the steps that supplier data are acquired, whether the supplier data contain a carbon emission value or not is identified, if the supplier data contain the carbon emission value, the carbon emission value is directly used as an actual carbon emission value, otherwise, a theoretical carbon emission value is calculated through a material structure of a target commodity and a dynamic coefficient library, and finally a visual report of carbon emission distribution is generated based on the actual or theoretical carbon emission value. According to the method, automatic treatment and accurate checking of enterprise total value chain carbon emission data are realized, the data accuracy and management efficiency are remarkably improved, and the method is suitable for enterprise carbon footprint accounting and supply chain emission reduction strategy making under the carbon neutralization target.

Description

Technical Field

[0001] This invention relates to the field of carbon emission technology, and in particular to a method, system, equipment and medium for processing carbon emission data in an enterprise value chain. Background Technology

[0002] Under the current global trend towards carbon neutrality, managing carbon emissions (Scope 3) across the enterprise value chain has become a core challenge in emission reduction efforts. Existing technological solutions have significant limitations: First, integrating multi-source data is difficult. External parameters relied upon for carbon emission calculations (such as third-party coefficient libraries), internal operational data (such as procurement, production, and logistics records), and manually supplemented data are scattered across heterogeneous systems. Due to semantic differences in fields, cross-platform data fragmentation occurs, requiring significant manual cleaning and alignment. Second, supplier data application is inefficient. Although some suppliers can provide actual carbon emission values, insufficient feedback rates and inconsistent formats make them difficult to use directly. When data is missing, traditional methods can only use static theoretical coefficients for calculation, failing to adapt to the dynamic hierarchical relationships of product material structures.

[0003] Therefore, existing carbon emission reports are mostly offline composites, unable to dynamically link multi-dimensional information such as suppliers, material categories, and transportation routes, and also unable to pinpoint carbon emission hotspots in real time. This makes it difficult for companies to accurately quantify emissions and even more difficult to effectively formulate emission reduction strategies. Summary of the Invention

[0004] This invention provides a method, system, device, and medium for processing carbon emission data in the enterprise value chain, in order to solve the technical problems of existing carbon emission reports that cannot dynamically link multi-dimensional information such as suppliers, material categories, and transportation routes, and are also difficult to locate carbon emission hotspots in real time.

[0005] This application provides a method for processing carbon emission data in an enterprise value chain. The method includes: collecting raw data for calculating the carbon emissions of a target product, the raw data including carbon emission parameters from an external data platform and operational activity data from an internal business system; establishing relationships between different source fields in the raw data to generate a cross-system data set with a unified identifier; identifying supplier data in the data set; if the supplier data includes carbon emission values, then determining the carbon emission values ​​as the actual carbon emissions; if the supplier data does not include carbon emission values, then calculating the corresponding theoretical carbon emissions based on the carbon footprint topology of the target product, the carbon footprint topology being used to define all material components contained in the target product, the hierarchical relationships between the material components, and the quantified composition ratios; and generating a carbon emission distribution visualization report for the target product based on the actual carbon emissions or the theoretical carbon emissions.

[0006] In one embodiment of this application, the step of calculating the corresponding theoretical carbon emissions based on the carbon footprint topology of the target product includes: extracting element behavior data of corresponding levels from the internal business system according to the material hierarchy of the target product, wherein the element behavior data includes element identification information and corresponding quantitative values ​​of each value chain element; matching the element identification information with a preset carbon emission coefficient library to obtain the target carbon emission coefficient corresponding to each element identification; calculating the quantitative values ​​of each value chain element and the corresponding target carbon emission coefficient to obtain the carbon emissions of various elements; and summing the carbon emissions of all single-type elements associated with the target product to generate the theoretical carbon emissions.

[0007] In one embodiment of this application, obtaining the target carbon emission coefficient corresponding to each element identifier includes: if the current element identifier has a matching unit carbon emission coefficient in the carbon emission coefficient library, then the matching unit emission coefficient is determined as the target carbon emission coefficient; if the current element identifier does not have a matching unit carbon emission coefficient in the carbon emission coefficient library, then the substitution coefficient with the highest similarity is matched according to the element description text features, and the substitution coefficient is determined as the target carbon emission coefficient.

[0008] In one embodiment of this application, establishing associations between fields from different sources in the original data to generate a cross-system data set with a unified identifier includes: performing standardization processing on the field names in the original data, converting field names from different source systems into identifier fields of a unified format according to preset mapping rules; establishing a correspondence between the material number and supplier number, product weight unit, and transportation distance fields by associating purchase order data, shipping document data, and product design data in the original data according to the material number of the target product; performing anomaly identification on the original data, and generating a manual review mark if the weight value is less than a preset first threshold, the transportation distance exceeds a preset second threshold, or a key field is empty, so as to perform data review based on the manual review mark; and performing data extraction, transformation, and loading processes on the data that has undergone the field association processing and anomaly identification according to a preset cycle to output a data set containing identifier fields of a unified format.

[0009] In one embodiment of this application, the collection of raw data for calculating the carbon emissions of a target commodity includes at least one of the following: obtaining the carbon emission parameters from an external data platform, wherein the carbon emission parameters include the unit carbon emission coefficient of the material, transportation distance parameters, and travel activity carbon emission coefficients; extracting the business activity data from an internal business system, wherein the business activity data includes material weight information in purchase orders, transportation mode information in product shipment records, and commuting information in employee attendance records; and receiving supplementary data through a human-machine interface, wherein the supplementary data includes carbon emission values ​​not provided by the supplier and product energy consumption test results issued by a third-party institution.

[0010] In one embodiment of this application, generating a carbon emission distribution visualization report for the target product includes: extracting the calculation results of the actual and theoretical carbon emissions associated with the target product; aggregating carbon emission data based on preset dimensions to generate a multi-level visualization chart, wherein the dimensions include value chain element types, emission stages, and geographical regions; the multi-level structure includes: a first-level view showing the total carbon emission distribution of the target product throughout its entire product lifecycle; a second-level view showing the carbon emission proportion of each emission stage; and a third-level view showing the ranking of carbon emission contributions of different value chain elements within each emission stage; wherein the emission stages include raw material acquisition, manufacturing, transportation and warehousing, product use, and end-of-life disposal; and outputting an interactive report containing the visualization chart, wherein the interactive report supports dynamic comparative analysis based on geographical regions or time periods.

[0011] In one embodiment of this application, after generating the carbon emission distribution visualization report of the target commodity, the method further includes: based on the carbon emission contribution ranking results identified in the third-level view of the visualization report, locating the target value chain element whose carbon emission intensity exceeds a preset third threshold; for the target value chain element, associating it with the corresponding original data source system and performing data verification; if the verification finds data anomalies, generating a data calibration task and pushing it to the responsible party for manual processing; if the data verification passes, starting the emission reduction strategy simulation engine, outputting the optimization scheme and expected carbon reduction amount for the high-intensity value chain element; updating the optimization scheme and expected carbon reduction amount to the interactive report, and triggering an external blockchain node to perform encrypted storage.

[0012] This application provides a corporate value chain carbon emission data processing system, comprising: a data acquisition module for collecting raw data for calculating the carbon emissions of a target product, including carbon emission parameters from an external data platform and operational activity data from an internal business system; a data association engine for establishing relationships between different source fields in the raw data and generating a cross-system data set with a unified identifier; a carbon emission calculation module for identifying supplier data in the data set; if the supplier data includes carbon emission values, the carbon emission values ​​are determined as the actual carbon emissions; if the supplier data does not include carbon emission values, the corresponding theoretical carbon emissions are calculated based on the carbon footprint topology of the target product, wherein the carbon footprint topology defines all material components contained in the target product, the hierarchical relationships between the material components, and the quantified composition ratios; and a visualization report engine for generating a visualization report on the carbon emission distribution of the target product based on the actual or theoretical carbon emissions.

[0013] This application provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device enables the enterprise value chain carbon emission data processing method described above.

[0014] This application provides a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the enterprise value chain carbon emission data processing method described above.

[0015] The beneficial effects of this invention are as follows: The enterprise value chain carbon emission data processing method proposed in this invention automatically collects external carbon emission parameters, internal business activity data, and manually supplemented data, and establishes cross-system field relationships based on intelligent semantic alignment, eliminating multi-source heterogeneous data silos and effectively improving the usability of raw data. When identifying supplier data, the actual carbon emission values ​​returned by the supplier are used first. If data is missing, the theoretical carbon emission is dynamically calculated layer by layer through the product material structure. This approach breaks through the limitations of relying on static coefficients in the traditional model, thereby achieving simultaneous optimization of the calculation coverage and accuracy of carbon emission data. Finally, a multi-dimensional carbon emission distribution visualization report is generated based on a unified data set, which locates carbon emission hotspots in real time at the supplier, material category, and transportation route dimensions, providing a more favorable basis for enterprises to make precise emission reduction decisions. This solution replaces manual intervention with full-chain automated management, not only meeting the requirements of international carbon accounting standards, but also systematically solving the problem of data fragmentation and visualization disconnect in Range 3 carbon emissions. Attached Figure Description

[0016] 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. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram:

[0018] Figure 1 This is a schematic diagram illustrating the implementation environment of an enterprise value chain carbon emission data processing method, as shown in an exemplary embodiment of this application.

[0019] Figure 2 This is a flowchart illustrating an exemplary embodiment of a corporate value chain carbon emission data processing method.

[0020] Figure 3 This is a block diagram illustrating an exemplary embodiment of an enterprise value chain carbon emission data processing system.

[0021] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0022] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0023] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0024] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0025] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the implementation environment of a corporate value chain carbon emission data processing method provided in an embodiment of the present invention.

[0026] like Figure 1 As shown, the implementation environment for the enterprise value chain carbon emission data processing method includes a data acquisition module 101 and a computer device 102.

[0027] The enterprise-localized data acquisition module 101 integrates raw data through a three-way channel: an external platform adapter automatically acquires international standard carbon footprint parameters and geographical transportation information; an internal system parser connects in real time to business databases such as procurement, production, and logistics to extract details of business activities; and a manual calibration interface supports structured input of third-party reports and missing data. All data, after unified identification mapping and anomaly filtering, is transmitted to cloud computing devices via an encrypted link to ensure cross-system field semantic consistency and eliminate data silos.

[0028] Computer Equipment 102 operates on a three-layer engine-driven processing flow: the data governance engine constructs standardized datasets using knowledge graphs, dynamically linking key fields such as purchase order numbers and material codes; the dual-mode calculation engine prioritizes actual carbon emission values ​​returned by suppliers, and when these are missing, it performs theoretical calculations layer by layer based on the product's material structure, intelligently matching a dynamic coefficient library; the visualization decision engine compiles and generates multi-dimensional carbon emission distribution reports, identifying supplier contributions, material hotspots, and analyzing transportation routes. After edge nodes execute feedback commands, new data triggers cloud-based recalibration and report optimization, forming a closed loop of "collection-computation-visualization-feedback." At the hardware level, industrial-grade data security is achieved through edge computing nodes, while the cloud computing cluster provides accelerated knowledge reasoning and coefficient matching capabilities, systematically solving the data fragmentation problem of range 3 carbon emissions.

[0029] In existing technologies, enterprise value chain carbon emission management faces challenges such as difficulties in integrating multi-source data and inefficient application of supplier data. Carbon emission calculation requires the integration of external parameters, internal operational data, and manually supplemented data, but this data is scattered across heterogeneous systems, and semantic differences in fields lead to data fragmentation across platforms. Supplier-provided carbon emission data suffers from low feedback rates and inconsistent formats. When data is missing, traditional methods use static theoretical coefficients for calculation, failing to reflect the actual material structure of the product. This results in carbon emission reports that are mostly offline patchwork results, making it difficult to dynamically correlate multi-dimensional information and accurately pinpoint carbon emission hotspots. For example, a manufacturing company needs to calculate the carbon emissions of a certain product, but due to inconsistent data fields between the procurement and logistics systems, it cannot automatically correlate material weight and transportation distance. Furthermore, key suppliers fail to provide carbon emission data, causing the calculated results to deviate from reality.

[0030] To address the aforementioned issues, this application proposes a method for processing carbon emission data across enterprise value chains. The method includes: collecting raw data for calculating the carbon emissions of a target product, including carbon emission parameters from an external data platform, operational data from internal business systems, and supplementary data from human-machine interfaces; establishing relationships between fields from different sources within the raw data to generate a cross-system data set with a unified identifier; identifying supplier data within the data set; if the supplier data includes carbon emission values, these values ​​are determined as the actual carbon emissions; if the supplier data does not include carbon emission values, the corresponding theoretical carbon emissions are calculated based on the carbon footprint topology of the target product; and generating a visualization report of the carbon emission distribution of the target product based on the actual or theoretical carbon emissions.

[0031] The raw data refers to all the basic data required to calculate carbon emissions. This can be achieved through API interfaces connecting to external platforms, database connections to internal systems, and manual data entry interfaces receiving supplementary data, covering key information such as material coefficients and transportation parameters. The relationships refer to the logical correspondence between data fields in different systems. This can be achieved by standardizing field names and converting field identifiers using preset mapping rules. For example, mapping "material code" in the procurement system and "product number" in the logistics system to "material ID". The dataset refers to the integrated dataset after association processing. This can be achieved by using ETL tools to perform data cleaning and transformation processes, forming structured data storage containing uniformly formatted identifier fields. The carbon footprint topology refers to the network model of the hierarchical relationships between product materials. This can be achieved using BOM parsing and supply chain mapping technologies, reflecting the complete flow path from raw materials and components to finished products.

[0032] Specifically, when calculating the carbon emissions of an electronic product, the system first obtains the unit carbon emission coefficient for metal materials from an external platform, extracts component weight data from the purchase order in the internal ERP system, and supplements it with energy consumption values ​​for packaging materials obtained from third-party testing via a human-machine interface. Next, the "component number" in the procurement system and the "part code" in the logistics system are uniformly converted into "material IDs," establishing a correlation with transportation distance and supplier IDs. When an anomaly is detected in the weight data of a batch of battery components, it is automatically marked for manual review. If a supplier has provided actual carbon emission measurement data for the battery production process, this actual value is directly used. For plastic casing suppliers that have not provided data, the system analyzes the hierarchical relationship of casing raw materials according to the product BOM, matching the carbon emission coefficient of the injection molding process for calculation. Finally, the carbon emission data from each stage is aggregated according to raw material acquisition, manufacturing, and other stages, generating interactive visual charts that display the carbon emission percentage and key emission sources at each stage.

[0033] Understandably, through the above technical solutions, this application solves the problem of difficult multi-source data integration, realizes automatic correlation and anomaly detection of cross-system data, and reduces the workload of manual data cleaning. By prioritizing the use of actual supplier data combined with dynamic theoretical calculations, the accuracy of carbon emission quantification is improved. The generated interactive visualization report supports multi-dimensional data analysis, helping enterprises quickly locate high-emission links and providing data support for optimizing supply chain carbon management.

[0034] Figure 2 This is a flowchart illustrating an exemplary embodiment of the enterprise value chain carbon emission data processing method of this application.

[0035] like Figure 2 As shown, in an exemplary embodiment, the enterprise value chain carbon emission data processing method includes at least steps S210 to S240, which are described in detail below:

[0036] Step S210: Collect raw data for calculating the carbon emissions of the target product. The raw data includes carbon emission parameters from an external data platform, operational data from an internal business system, and supplementary data from a human-machine interface.

[0037] In one embodiment of this application, the collection of raw data for calculating the carbon emissions of the target commodity includes: obtaining carbon emission parameters from an external data platform, wherein the carbon emission parameters include the unit carbon emission coefficient of the material, transportation distance parameters, and travel activity carbon emission coefficient; extracting operational activity data from an internal business system, wherein the operational activity data includes material weight information in purchase orders, transportation mode information in product shipment records, and commuting information in employee attendance records; and receiving supplementary data through a human-machine interface, wherein the supplementary data includes carbon emission values ​​not provided by the supplier and product energy consumption test results issued by a third-party organization.

[0038] In one specific embodiment of this application, the method of collecting raw data for calculating the carbon emissions of a target commodity is further proposed, including obtaining carbon emission parameters from an external data platform, extracting operational activity data from an internal business system, and receiving supplementary data through a human-machine interface.

[0039] The external data platform refers to third-party data sources used to obtain standardized carbon emission parameters. This can be achieved through API interface integration to ensure the authority and consistency of basic data such as material unit emission factors and transportation distance parameters. The internal business system refers to the enterprise's internal business management systems for procurement, production, and logistics. This can be achieved through database queries or middleware integration to extract structured data such as material weight and transportation methods from purchase orders, avoiding errors from manual data entry. The human-machine interface is a supplementary channel for receiving unstructured or missing data. This can be achieved through web forms or file uploads to fill gaps in carbon emission figures not provided by suppliers and third-party test results.

[0040] Specifically, a multi-dimensional data collection system is constructed, implementing source-specific and targeted collection of external parameters, internal operational data, and manually supplemented data. The external data platform obtains basic data such as unit carbon emission coefficients and transportation parameters for materials through standardized interfaces, resolving cross-platform data semantic differences. Internal business systems extract operational activity data such as material weight and transportation methods from purchase orders through automated integration mechanisms, ensuring the real-time nature and accuracy of data acquisition. The human-machine interface receives carbon emission values ​​not provided by suppliers and third-party test results through an interactive input interface, supplementing data gaps not covered by automatic system collection. Through the collaborative collection of these three data sources, a unified input covering all data types is formed, eliminating data fragmentation between heterogeneous systems.

[0041] Understandably, through the above technical solution, this application solves the problem of difficult multi-source data integration, achieving unified collection of external parameters, internal operational data, and manually supplemented data, ensuring the integrity and consistency of the data required for carbon emission calculation. Automated interface integration reduces manual data processing costs, and human-machine interface supplements missing data, improving the rate of data return and utilization from suppliers, thus providing a reliable data foundation for subsequent carbon emission calculations.

[0042] Step S220: Establish the relationships between fields from different sources in the original data to generate a cross-system data set with a unified identifier.

[0043] In one embodiment of this application, establishing associations between fields from different sources in the original data to generate a cross-system data set with a unified identifier includes: performing standardization processing on field names in the original data, converting field names from different source systems into identifier fields of a unified format according to preset mapping rules; associating purchase order data, shipping document data, and product design data in the original data according to the material number of the target product, establishing a correspondence between the material number and the supplier number, product weight unit, and transportation distance fields; performing anomaly identification on the original data, and generating a manual review mark if the weight value is less than a preset first threshold, the transportation distance exceeds a preset second threshold, or a key field is empty, so as to perform data review based on the manual review mark; and performing data extraction, transformation, and loading processes on the data that has undergone field association processing and anomaly identification according to a preset cycle, and outputting a data set containing identifier fields of a unified format.

[0044] In one specific implementation of this application, the field name standardization process refers to aligning the semantics of fields in heterogeneous systems through a pre-defined mapping table. This can be achieved using regular expression matching or natural language processing techniques, such as mapping "prod_weight" to "material weight," eliminating naming differences between different systems. The material number association mechanism uses this unique identifier as an index for data association, which can be implemented using database foreign key constraints or graph database node connections. For example, it can horizontally associate supplier information in a purchase order with path parameters in a transport document using the material number. The anomaly identification rules refer to setting numerical validity thresholds based on business logic, including a pre-defined first threshold and a pre-defined second threshold. This can be implemented using a rule engine or machine learning model, such as setting a maximum transport distance of 5000 kilometers to automatically mark data exceeding the limit and trigger a review process. The data extraction, transformation, and loading process refers to periodically performing ETL operations, which can be implemented using Apache NiFi or Informatica tools. For example, incremental data synchronization can be performed daily at midnight to ensure the timeliness of the data set. The preset first threshold and the preset second threshold are both set values ​​obtained based on actual production experience. Their values ​​are usually specific values ​​greater than or equal to 0. This application does not impose any restrictions on their setting method or specific values.

[0045] Specifically, during the data integration process, the first step is to unify field names from different sources into standardized identifiers using mapping rules. For example, "product net weight" in the internal system and "net_weight" on the external platform are uniformly mapped to "material weight". Next, based on the core identifier of the material number, supplier numbers scattered in purchase orders, transportation distances in shipping documents, and weight units in product design data are associated to form a cross-system data relationship network. Subsequently, through preset anomaly detection rules, data that violates business logic, such as negative weight values ​​or excessively long transportation distances, is automatically flagged. For example, when a record with a transportation distance of 10,000 kilometers is detected, a review flag is generated, and the data is paused from entering the calculation process. Finally, through a timed ETL process, the standardized and anomaly-detected data is loaded into the target database, forming a data set with unified identifiers and controllable quality.

[0046] Understandably, through the above technical solutions, this application has achieved automated integration and quality control of multi-source data, and solved the problem of data fragmentation caused by differences in field semantics. For example, it links data that were originally scattered in the procurement system and logistics system into a complete data chain through a unified identifier; it reduces the interference of erroneous data on subsequent carbon emission calculations through anomaly identification rules, such as automatically intercepting abnormal transportation distance data to avoid distortion of calculation results; and it ensures the continuous updating capability of the data set through periodic ETL processes, such as synchronizing newly added supplier data daily to support dynamic analysis needs.

[0047] Step S230: Identify supplier data in the dataset. If the supplier data includes carbon emission values, then determine the carbon emission values ​​as the actual carbon emissions. If the supplier data does not include carbon emission values, then calculate the corresponding theoretical carbon emissions based on the carbon footprint topology of the target product.

[0048] In one embodiment of this application, calculating the corresponding theoretical carbon emissions based on the carbon footprint topology of a target product includes: extracting element behavior data of corresponding levels from the internal business system according to the material hierarchy of the target product, wherein the element behavior data includes element identification information and corresponding quantitative values ​​of value chain elements; matching the element identification information with a preset carbon emission coefficient library to obtain the target carbon emission coefficient corresponding to each element identification; calculating the quantitative values ​​of each value chain element and the corresponding target carbon emission coefficient to obtain the carbon emissions of various elements; and summing the carbon emissions of all single-type elements associated with the target product to generate the theoretical carbon emissions.

[0049] In one specific embodiment of this application, the material hierarchy refers to the multi-level composition structure of the target commodity in the supply chain, from raw materials to finished products. This can be implemented using the parent-child node relationship in a bill of materials database to ensure the completeness and hierarchical accuracy of the extracted element behavior data. Element behavior data refers to quantitative indicators reflecting the production, transportation, and other stages of materials at each level. This can be achieved by parsing the material weight field in purchase orders and the distance field in transportation documents, transforming abstract material relationships into calculable carbon emission baseline data. The carbon emission coefficient library is a database storing the unit carbon emission values ​​corresponding to different materials. This can be constructed by combining industry standard coefficients with historical data training models, used to dynamically match element identification information to adapt to supply chain changes. The carbon emission amount of a single element refers to the carbon emission value generated by a single material in a specific stage. This can be calculated by multiplying the quantitative value by the target carbon emission coefficient, enabling independent accounting of the carbon emission contribution of each stage.

[0050] Specifically, the material hierarchy is expanded level by level through database queries, for example, from the finished product layer down to the component layer and the raw material layer. After the element behavior data corresponding to each level is extracted, the element identification information is matched in real time with the carbon emission coefficient database. If a standard coefficient exists, it is directly used; if it is missing, a similarity matching algorithm is triggered to obtain a substitute coefficient. The quantified value of each element is multiplied by its matched carbon emission coefficient to generate the carbon emission data for that level. The calculation results of all levels are accumulated along the supply chain path to form the theoretical total carbon emissions covering the entire life cycle.

[0051] Understandably, through the above technical solution, this application resolves the issue of carbon emission calculation bias caused by missing supplier data, achieving adaptive matching of dynamic material hierarchy relationships. By decomposing the supply chain structure step by step and dynamically calling carbon emission coefficients, it ensures that theoretical calculations cover all stages of the product's entire lifecycle, improving the accuracy of calculation results and adaptability to supply chain changes. Simultaneously, independently calculating carbon emissions at each stage provides data support for subsequent optimization of high-emission stages.

[0052] In one embodiment of this application, obtaining the target carbon emission coefficient corresponding to each element identifier includes: if the current element identifier has a matching unit carbon emission coefficient in the carbon emission coefficient database, then the matching unit emission coefficient is determined as the target carbon emission coefficient; if the current element identifier does not have a matching unit carbon emission coefficient in the carbon emission coefficient database, then the substitution coefficient with the highest similarity is matched according to the feature of the element description text, and the substitution coefficient is determined as the target carbon emission coefficient.

[0053] In one specific embodiment of this application, the element identifier refers to a code or symbol used to uniquely identify value chain elements. Specifically, it can be implemented using material numbers, process codes, or supplier IDs, and is used to quickly retrieve the corresponding carbon emission coefficient in structured data. The unit carbon emission coefficient refers to the carbon emission parameter corresponding to a unit quantified value, and can be implemented using kilograms of CO2 equivalent per kilogram of material or ton-kilometers of transportation emission coefficients, and is used to convert operational activity data into carbon emissions. The substitution coefficient refers to an approximate carbon emission coefficient obtained through semantic similarity matching. Specifically, it can be implemented by using a natural language processing model to extract text feature vectors and calculate cosine similarity, and is used to maintain the continuity of the calculation process when there is no matching element identifier.

[0054] Specifically, when an element identifier cannot be matched with a corresponding parameter in the carbon emission coefficient database, the system automatically extracts the descriptive text features of that element, such as material name, production process description, or supplier classification information. A pre-trained text embedding model converts the descriptive text into a high-dimensional vector, and the system retrieves the existing coefficient record in the coefficient database that is closest to the current vector space. During similarity calculation, priority is given to matching coefficient entries with the same industry classification or physical attributes to ensure the consistency of the physical meaning of the substitution coefficients. Therefore, even in scenarios where the supplier has not provided carbon emission data or the newly added material has not been entered into the coefficient database, interpretable theoretical carbon emission calculation results can still be dynamically generated.

[0055] Understandably, through the above technical solution, this application solves the problem of calculation interruption caused by carbon emission coefficient matching failure, avoiding the time delay and subjective error of manually supplementing coefficients. In scenarios where supplier data formats are chaotic or historical data is lacking for new materials, it can automatically select alternative coefficients with similar physical properties, ensuring the integrity of the theoretical carbon emission calculation process and the traceability of the results.

[0056] Step S240: Generate a visualization report on the carbon emission distribution of the target product based on the actual or theoretical carbon emissions.

[0057] In one embodiment of this application, generating a visualization report on the carbon emission distribution of a target product includes: extracting the calculation results of the actual and theoretical carbon emissions associated with the target product; aggregating carbon emission data based on preset dimensions to generate a multi-level visualization chart, the dimensions of which include value chain element types, emission stages, and geographical regions; the multi-level structure includes: a first-level view showing the distribution of the target product's total carbon emissions throughout its entire product lifecycle; a second-level view showing the carbon emission proportion of each emission stage; and a third-level view showing the ranking of the carbon emission contributions of different value chain elements within each emission stage; the emission stages include raw material acquisition, production and manufacturing, transportation and warehousing, product use, and end-of-life disposal; and outputting an interactive report containing visualization charts, the interactive report supporting dynamic comparative analysis based on geographical regions or time periods.

[0058] In one specific embodiment of this application, the multi-level structured visualization chart refers to a progressive analysis interface consisting of three view levels. This can be achieved by using a data visualization engine to construct the linkage relationships and drill-down paths between views, realizing the analysis of carbon emission distribution from macro to micro levels through a hierarchical penetration mechanism. The preset dimension refers to a predefined direction for data aggregation and analysis, which can be achieved by using multi-dimensional modeling techniques in a data warehouse to classify and store carbon emission data, supporting multi-factor correlation analysis through dimension combinations. The interactive report refers to a visualization interface with dynamic response capabilities, which can be implemented by developing chart interaction components using a front-end framework, triggering data queries and view updates through a parameter passing mechanism.

[0059] Specifically, after data calculations are completed, the actual and theoretical carbon emission calculation results are extracted to the visualization engine. The data is aggregated according to three dimensions: value chain element type, emission stage, and geographic region, forming a multidimensional dataset. The first-level view displays the total carbon emission proportion of each stage of the entire life cycle in a pie chart format. Clicking on a specific stage block triggers a second-level view displaying the emission proportions of different element types within that stage. The third-level view displays the carbon emission contribution of specific elements in sorted order using bar charts; for example, in the transportation and warehousing stage, it displays the carbon emission values ​​for each transportation route. The interactive report includes a built-in timeline control and map component. When users select different quarters or check regional ranges, the system automatically refreshes the view data and generates comparative analysis results.

[0060] Understandably, through the aforementioned technical solutions, this application achieves multi-dimensional dynamic analysis of the carbon emission distribution of target commodities, enabling enterprises to quickly identify high-emission stages and specific elements. For example, when analyzing an electronic product, the third-level view can show that the carbon emission proportion of the rare metal smelting stage in its raw material acquisition exceeds a preset value, providing data support for enterprises to optimize their supply chains. The interactive comparison function can compare the differences in transportation carbon emissions from factories in different regions, assisting in the formulation of regionalized emission reduction strategies.

[0061] In one embodiment of this application, after generating a visualization report on the carbon emission distribution of the target product, the method further includes: locating target value chain elements whose carbon emission intensity exceeds a preset third threshold based on the carbon emission contribution ranking results identified in the third-level view of the visualization report; for the located target value chain elements, associating them with the corresponding original data source system and performing data verification; if the verification finds data anomalies, generating a data calibration task and pushing it to the responsible party for manual processing; if the data verification passes, starting the emission reduction strategy simulation engine, outputting the optimization scheme and expected carbon reduction amount for the high-intensity value chain elements; updating the optimization scheme and expected carbon reduction amount to the interactive report, and triggering an external blockchain node to perform encrypted storage. The third threshold varies depending on the product or production process, and higher values ​​are usually determined based on relevant industry standards or other specifications. This application does not limit its specific value or method.

[0062] In one specific embodiment of this application, the third-level view refers to a view that displays the carbon emission contribution ranking of different value chain elements within each emission stage through drill-down. This can be implemented using the drill-down function in a data visualization tool to focus on key issue nodes in specific emission stages. Data verification refers to the consistency check of the original data associated with the positioning elements. This can be achieved by calling the API interface of the data source system to obtain the original records, thus eliminating misjudgments caused by data errors. The emission reduction strategy simulation engine refers to an algorithm module that generates optimization schemes based on historical data and industry benchmarks. This can be achieved by using machine learning models to predict the carbon reduction effects of different emission reduction measures, used to automatically output feasible solutions. Blockchain encrypted storage refers to writing the optimization process data into a distributed ledger. This can be achieved by using a consortium blockchain's smart contract to execute hash value on-chain operations, ensuring the data is tamper-proof.

[0063] Specifically, after generating the visualization report, the third-level view quickly identifies elements exceeding carbon emission intensity limits by ranking contributions, such as anomalies in a supplier's transportation emissions. By linking raw data from the procurement and logistics systems, it verifies whether parameters such as transportation distance and vehicle type match the initial calculations. If null values ​​are found in the transportation distance field, a calibration task is generated to notify the procurement department to supplement the data; if the data is correct, the emission reduction model is invoked to simulate the carbon reduction after replacing trucks with electric ones or optimizing routes. Finally, the optimization plan is embedded in the report, and the plan parameters and verification records are encrypted and stored on the blockchain, forming a complete and auditable chain of evidence.

[0064] It is understandable that, through the above technical solutions, this application solves the problems in the prior art of being unable to dynamically locate carbon emission exceeding factors, lacking automated data verification mechanisms, and having poor traceability of optimization schemes. It achieves accurate identification of high emission factors, rapid calibration of abnormal data, and reliable evidence storage of emission reduction strategies, thereby improving the accuracy and auditability of corporate carbon management decisions.

[0065] Finally, it should be emphasized that, in conjunction with the foregoing embodiments, the enterprise value chain carbon emission data processing method proposed in this application solves the difficulties in cross-platform integration caused by the dispersion of enterprise carbon emission data, and realizes the automated association of external parameters, internal data, and manually supplemented information. It improves the calculation accuracy in scenarios where supplier data is missing, and adapts to changes in product structure by dynamically analyzing material hierarchy relationships. The generated interactive report supports filtering transportation route carbon emission data by geographical region, providing a visual analysis basis for enterprises to locate high-emission links.

[0066] Figure 3 This is a block diagram illustrating an exemplary embodiment of a corporate value chain carbon emission data processing system. The system can be applied to... Figure 1 The system is shown in the implementation environment. This system can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the system is applicable.

[0067] like Figure 3 As shown, the exemplary enterprise value chain carbon emission data processing system includes: a data acquisition module 310, a data association engine 320, a carbon emission calculation module 330, and a visualization report engine 340.

[0068] The data acquisition module 310 is used to collect raw data for calculating the carbon emissions of the target product, including carbon emission parameters from external data platforms, operational data from internal business systems, and supplementary data from human-machine interfaces; the data association engine 320 is used to establish relationships between fields from different sources in the raw data and generate a cross-system data set with a unified identifier; the carbon emission calculation module 330 is used to identify supplier data in the data set, and if it contains carbon emission values, it is used as the actual carbon emission amount; if the supplier data does not contain carbon emission values, the theoretical carbon emission amount is calculated based on the carbon footprint topology of the target product; and the visualization report engine 340 is used to generate a visualization report on the carbon emission distribution of the target product based on the actual or theoretical carbon emission amount.

[0069] In one specific embodiment of this application, the data acquisition module refers to a component that integrates heterogeneous data from multiple sources. Specifically, it can be implemented by using API interfaces to connect to external platforms, ETL tools to extract data from internal systems, and manual data entry interfaces to receive supplementary data. This module solves the problem of data silos across multiple systems through a unified data entry point. The data association engine refers to a processor that implements cross-system field mapping. Specifically, it can be implemented by using semantic parsing algorithms to standardize field names, establishing relational database indexes based on material numbers, and anomaly detection rule bases to identify data errors. This engine eliminates semantic differences between fields through unified identifiers. The carbon emission calculation module refers to a computational unit that dynamically processes supplier data. Specifically, it can be implemented using a supplier data format converter, a carbon footprint topology parser, and a hierarchical element carbon emission accumulation algorithm. This module improves data utilization by prioritizing a combination of actual values ​​and dynamic theoretical calculations. The visualization report engine refers to a tool that generates interactive analytical charts. Specifically, it can be implemented using multi-dimensional data aggregation models, hierarchical drill-down chart components, and dynamic comparative analysis interfaces. This engine supports emission hotspot location through multi-dimensional view linkage.

[0070] Specifically, the data acquisition module obtains the carbon emission coefficient per unit of materials from an external platform, extracts material weight information from purchase orders from the internal system, and receives supplementary data from suppliers through a human-machine interface. The data association engine performs standardization processing on purchase order numbers and shipping document numbers, establishes a correlation with product design data, detects abnormal weight values, and generates verification markers. When supplier data is missing, the carbon emission calculation module analyzes the material hierarchy of the target product, matches dynamic parameters in the carbon emission coefficient library level by level, and accumulates the carbon emissions of each level of elements to generate a theoretical value. The visualization reporting engine aggregates actual and theoretical values ​​according to raw material acquisition and manufacturing stages, generating an interactive heatmap that can be drilled down to the transportation path dimension.

[0071] It should be noted that the enterprise value chain carbon emission data processing system and the enterprise value chain carbon emission data processing method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the enterprise value chain carbon emission data processing system provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0072] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the enterprise value chain carbon emission data processing method provided in the above embodiments.

[0073] Figure 4 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 4 The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0074] like Figure 4 As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from Storage Unit 408 into Random Access Memory (RAM) 403, such as performing the methods described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0075] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0076] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs various functions defined in the system of this application.

[0077] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0078] 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. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains 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 a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, 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.

[0079] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0080] Another aspect of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the enterprise value chain carbon emission data processing method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0081] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the enterprise value chain carbon emission data processing method provided in the various embodiments described above.

[0082] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for processing carbon emission data in an enterprise value chain, characterized in that, The method includes: Collect raw data for calculating the carbon emissions of the target product, including carbon emission parameters from an external data platform and operational data from an internal business system; Establish the relationships between fields from different sources in the original data to generate a cross-system data set with a unified identifier; The supplier data in the dataset is identified. If the supplier data includes carbon emission values, the carbon emission values ​​are determined as the actual carbon emissions. If the supplier data does not include carbon emission values, the corresponding theoretical carbon emissions are calculated based on the carbon footprint topology of the target product. The carbon footprint topology is used to define all material components contained in the target product, the hierarchical relationship between each material component, and the quantitative composition ratio. Based on the actual carbon emissions or the theoretical carbon emissions, a visualization report on the carbon emission distribution of the target product is generated.

2. The enterprise value chain carbon emission data processing method according to claim 1, characterized in that, The calculation of the corresponding theoretical carbon emissions based on the carbon footprint topology of the target commodity includes: Based on the material hierarchy of the target product, the corresponding element behavior data is extracted from the internal business system level by level. The element behavior data includes the element identification information and corresponding quantitative value of each value chain element. Based on the element identification information, a preset carbon emission coefficient library is matched to obtain the target carbon emission coefficient corresponding to each element identification. The quantitative values ​​of each value chain element and the corresponding target carbon emission coefficients are calculated to obtain the carbon emissions of each element. The theoretical carbon emissions are generated by summing up the carbon emissions of all single-category elements associated with the target commodity.

3. The enterprise value chain carbon emission data processing method according to claim 2, characterized in that, The acquisition of the target carbon emission coefficient corresponding to each element identifier includes: If the current element identifier has a matching unit carbon emission coefficient in the carbon emission coefficient library, then the matching unit emission coefficient will be determined as the target carbon emission coefficient. If the current element identifier does not have a matching unit carbon emission coefficient in the carbon emission coefficient database, then the substitution coefficient with the highest similarity is matched based on the feature description text characteristics, and the substitution coefficient is determined as the target carbon emission coefficient.

4. The enterprise value chain carbon emission data processing method according to claim 1, characterized in that, Establish relationships between fields from different sources in the original data to generate a cross-system data set with a unified identifier, including: The field names in the original data are standardized by converting them into identifier fields of a uniform format according to preset mapping rules. Based on the material number of the target product, the purchase order data, shipping document data and product design data are associated in the original data to establish a correspondence between the material number and the supplier number, product weight unit and transportation distance fields; The original data is subjected to anomaly identification. If the weight value is less than a preset first threshold, the transportation distance exceeds a preset second threshold, or the key field is empty, a manual review mark is generated to perform data review based on the manual review mark. For the data that has undergone the aforementioned field association processing and anomaly identification, a data extraction, transformation, and loading process is executed based on a preset cycle, outputting a data set containing a uniformly formatted identifier field.

5. The enterprise value chain carbon emission data processing method according to claim 1, characterized in that, Collect raw data used to calculate the carbon emissions of the target commodity, including at least one of the following: The carbon emission parameters are obtained from an external data platform, wherein the carbon emission parameters include the unit carbon emission factor of the material, the transportation distance parameter, and the carbon emission factor of the travel activity. The operational activity data is extracted from the internal business system, including material weight information in purchase orders, transportation method information in product shipment records, and commuting information in employee attendance records. Supplementary data is received through a human-machine interface. This supplementary data includes carbon emission figures not provided by the supplier and product energy consumption test results issued by a third-party organization.

6. The enterprise value chain carbon emission data processing method according to any one of claims 1-5, characterized in that, Generate a visualization report on the carbon emission distribution of the target product, including: Extract the calculation results of the actual and theoretical carbon emissions associated with the target product; Based on preset dimensions, carbon emission data is aggregated to generate multi-level visual charts. These dimensions include value chain element types, emission stages, and geographical regions. The multi-level structure includes: a first-level view showing the distribution of the total carbon emissions of the target product throughout its entire life cycle; a second-level view showing the carbon emission proportion of each emission stage; and a third-level view showing the ranking of the carbon emission contributions of different value chain elements within each emission stage. The emission stages include raw material acquisition, production and manufacturing, transportation and warehousing, product use, and end-of-life disposal. The output includes an interactive report containing the visualization charts, which supports dynamic comparative analysis based on geographic regions or time periods.

7. The enterprise value chain carbon emission data processing method according to claim 6, characterized in that, After generating the carbon emission distribution visualization report for the target product, the process also includes: Based on the carbon emission contribution ranking results identified in the third-level view of the visualization report, target value chain elements whose carbon emission intensity exceeds a preset third threshold are located. For the target value chain elements, associate them with the corresponding original data source systems and perform data verification; If the verification finds data anomalies, a data calibration task is generated and pushed to the responsible party for manual processing. If the data verification is successful, the emission reduction strategy simulation engine will be activated to output the optimization scheme and expected carbon reduction of high-intensity value chain elements. The optimization plan and expected carbon reduction are updated in the interactive report, and an external blockchain node is triggered to perform encrypted storage.

8. A carbon emission data processing system for an enterprise value chain, characterized in that, The system includes: The data acquisition module is used to collect raw data for calculating the carbon emissions of the target product, including carbon emission parameters from external data platforms and operational activity data from internal business systems. The data association engine is used to establish relationships between fields from different sources in the original data, and generate cross-system data sets with a unified identifier. The carbon emission calculation module is used to identify supplier data in the dataset. If the supplier data includes carbon emission values, the carbon emission values ​​are determined as the actual carbon emission amount. If the supplier data does not include carbon emission values, the corresponding theoretical carbon emission amount is calculated based on the carbon footprint topology of the target product. The carbon footprint topology is used to define all material components contained in the target product, the hierarchical relationship between each material component, and the quantitative composition ratio. A visualization reporting engine for generating visualization reports on the carbon emission distribution of a target product based on actual or theoretical carbon emissions.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the enterprise value chain carbon emission data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the enterprise value chain carbon emission data processing method according to any one of claims 1 to 7.

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