Supplier green dynamic evaluation method and system based on carbon footprint data driving

Abstract

The application discloses a supplier green dynamic evaluation method and system based on carbon footprint data driving, and relates to the technical field of environmental protection and low carbon, wherein the method comprises the following steps: obtaining effective data of a supplier and green certification data of an industry to which the supplier belongs; performing carbon footprint accounting based on the effective data and the green certification data, obtaining carbon performance results of the supplier, and updating green evaluation information of the supplier according to the carbon performance results; listening to a target event message, refreshing the green evaluation information based on the target event message; and calculating a comprehensive green score of the supplier based on the target event message and the effective data. The scheme can improve the data updating timeliness and industry adaptation accuracy of the supplier green evaluation, timely respond to carbon data disclosure policy requirements, and thus help enterprises improve the green risk management level of a supply chain.

Description

Technical Field

[0001] This application relates to the field of environmental protection and low-carbon technology, and in particular to a supplier green dynamic evaluation method and system based on carbon footprint data. Background Technology

[0002] With the comprehensive advancement of the "dual carbon" goals, supplier carbon emission data has become a core element of enterprise supply chain risk management, directly impacting the green compliance and sustainable development capabilities of the supply chain. Currently, a supplier environmental scoring system based on Excel templates is widely used. This system calculates scores in batches after manually importing data, but it has significant inherent flaws: First, the data update cycle is as long as one quarter, making it difficult to meet the rigid requirements of policies such as the EU Carbon Border Adjustment Mechanism (CBAM) for real-time carbon data disclosure, leading to compliance risks for enterprises. Second, the carbon accounting module and the scoring module are physically isolated, requiring manual export / import of intermediate files, causing a lag in carbon data transmission and failing to support timely business decisions. Third, the use of fixed form configurations lacks dynamic adaptability to the differentiated green certification standards (such as ISO 14064, PAS 2050, and China Green Product Certification) of different industries such as electronics manufacturing, chemicals, and textiles, limiting its applicability. Another technology introduces message queues to update scores, but its event triggering relies solely on periodic scanning and lacks a two-way data contract with the carbon accounting subsystem. This means it can only push accounting results forward, lacking a mechanism for retrospectively tracing missing parameters. Consequently, when the carbon accounting system returns a "default value," the scoring engine cannot effectively identify data anomalies or initiate targeted remedial processes, ultimately affecting the accuracy and reliability of the evaluation results and failing to meet enterprises' needs for refined and dynamic supplier green evaluations. Summary of the Invention

[0003] The purpose of this application is to provide a supplier green dynamic evaluation method and system based on carbon footprint data, which aims to solve the technical problems of lagging data updates and poor industry adaptability in the existing supplier environmental scoring methods.

[0004] To achieve the above objectives, this application provides a supplier green dynamic evaluation method driven by carbon footprint data, the method comprising: Obtain valid data on suppliers and green certification data for the industries to which suppliers belong; Based on the valid data and the green certification data, carbon footprint calculation is performed to obtain the supplier's carbon performance results, and the supplier's green evaluation information is updated according to the carbon performance results. Listen for target event messages, and refresh the green evaluation information based on the target event messages; wherein, the target event messages include supplementary recording completion events based on the carbon footprint accounting and certification update events based on the preset industry knowledge base; Based on the target event message and the valid data, calculate the supplier's overall green score.

[0005] In one implementation, obtaining valid data of the supplier and green certification data of the supplier's industry includes: Obtain the supplier's raw data, which includes industry codes and product category codes; The original data is subjected to three levels of verification to obtain the valid data, and the valid data is stored in a preset relational database; Based on the industry codes in the valid data, a set of green certification standards associated with the supplier's industry is queried from a preset graph database; Based on the aforementioned set of green certification standards, the front-end form of the supplier's industry is dynamically rendered and adapted to obtain the green certification data of the supplier's industry.

[0006] In one embodiment, the three-level verification includes format verification, logical relationship verification between data, and external credit verification API verification.

[0007] In one implementation, based on the target event message and the valid data, a supplier's overall green score is calculated, including: In response to the target event message, a preset dynamic scoring engine is invoked, and the supplier's comprehensive green score is calculated by combining the core data in the event message with the valid data stored in the relational database. The core data includes carbon performance results, green certification data, and data freshness.

[0008] In one embodiment, the formula for calculating the comprehensive green score is: , In the formula, For carbon performance results; Data for green certification; For data freshness; These are the weighting coefficients for carbon performance results; The weighting coefficients for green certification data; This is the weighting coefficient for data freshness.

[0009] In one embodiment, based on the valid data and the green certification data, carbon footprint calculation is performed to obtain the supplier's carbon performance results, and the supplier's green evaluation information is updated according to the carbon performance results, including: Based on the valid data and the green certification data, the preset carbon footprint accounting API is invoked to interact with the preset carbon footprint accounting subsystem. Based on the results of the data interaction, missing carbon accounting parameters are identified, and a task to supplement the traceability information is generated. The task of supplementing the data is sent to the supplier's carbon performance management system. Once the data supplementation is completed, the complete data is synchronized to the carbon footprint accounting subsystem.

[0010] In one embodiment, the method further includes: Based on the comprehensive green score, the supplier's green evaluation result is output; The green evaluation results include a comprehensive score dashboard, supplier rankings, and procurement recommendation levels.

[0011] In one embodiment, the procurement recommendation levels include a first level, a second level, and a third level, where the first level is priority cooperation, the second level is continuous observation, and the third level is suspension of cooperation.

[0012] Furthermore, this application also provides a supplier green dynamic evaluation system driven by carbon footprint data, the system comprising: The data acquisition module is used to acquire valid data from suppliers and green certification data for the industries to which suppliers belong. The carbon footprint accounting interaction module is used to perform carbon footprint accounting based on the valid data and the green certification data, obtain the supplier's carbon performance results, and update the supplier's green evaluation information according to the carbon performance results. An event listening module is used to listen for target event messages and refresh the green evaluation information based on the target event messages; wherein, the target event messages include supplementary recording completion events based on the carbon footprint accounting and certification update events based on the preset industry knowledge base; The comprehensive scoring module is used to calculate the supplier's comprehensive green score based on the target event message and the valid data.

[0013] In one embodiment, the system further includes a result output module, which outputs the supplier's green evaluation result based on the comprehensive green score; wherein the green evaluation result includes a comprehensive score dashboard, supplier ranking, and procurement recommendation level.

[0014] The above-mentioned technical solution of this application has at least the following beneficial technical effects: The technical solution of this application obtains valid supplier data and green certification data of the relevant industry, updates green evaluation information by combining carbon footprint accounting results, and refreshes data and calculates comprehensive scores by monitoring target event messages such as completion of supplementary data entry and certification updates. This can improve the timeliness of supplier green evaluation data updates and the accuracy of industry adaptation, respond to carbon data disclosure policy requirements in a timely manner, and help enterprises improve the level of green risk management in the supply chain. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating an embodiment of the carbon footprint data-driven supplier green dynamic evaluation method provided in this application. Figure 2 This is a flowchart illustrating another embodiment of the carbon footprint data-driven supplier green dynamic evaluation method provided in this application; Figure 3 A block diagram of an embodiment of the carbon footprint data-driven supplier green dynamic evaluation system provided in this application; Figure 4 A block diagram of another embodiment of the carbon footprint data-driven supplier green dynamic evaluation system provided in this application; Figure 5 This application provides an architecture diagram of a specific embodiment of a supplier green dynamic evaluation system driven by carbon footprint data; Figure 6 This application provides a flowchart illustrating a specific embodiment of a carbon footprint data-driven supplier green dynamic evaluation method. Figure 7 This application provides a schematic diagram of the industry-adaptive module of a specific embodiment of a carbon footprint data-driven supplier green dynamic evaluation system. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of this application. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0017] The embodiments described in this application are only some, not all, of the embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments described herein without inventive effort are within the scope of protection of this application.

[0018] With the comprehensive advancement of the "dual carbon" goals, supplier carbon emission data has become a core element of enterprise supply chain risk management, directly impacting the green compliance and sustainable development capabilities of the supply chain. Currently, a supplier environmental scoring system based on Excel templates is widely used. This system calculates scores in batches after manually importing data, but it has significant inherent flaws: First, the data update cycle is as long as one quarter, making it difficult to meet the rigid requirements of policies such as the EU Carbon Border Adjustment Mechanism (CBAM) for real-time carbon data disclosure, leading to compliance risks for enterprises. Second, the carbon accounting module and the scoring module are physically isolated, requiring manual export / import of intermediate files, causing a lag in carbon data transmission and failing to support timely business decisions. Third, the use of fixed form configurations lacks dynamic adaptability to the differentiated green certification standards (such as ISO 14064, PAS 2050, and China Green Product Certification) of different industries such as electronics manufacturing, chemicals, and textiles, limiting its applicability. Another technology introduces message queues to update scores, but its event triggering relies solely on periodic scanning and lacks a two-way data contract with the carbon accounting subsystem. This means it can only push accounting results forward, lacking a mechanism for retrospectively tracing missing parameters. Consequently, when the carbon accounting system returns a "default value," the scoring engine cannot effectively identify data anomalies or initiate targeted remedial processes, ultimately affecting the accuracy and reliability of the evaluation results and failing to meet enterprises' needs for refined and dynamic supplier green evaluations.

[0019] To address the aforementioned technical issues, this application provides a supplier green dynamic evaluation method driven by carbon footprint data.

[0020] In one embodiment of this application, please refer to Figure 1 The carbon footprint data-driven supplier green dynamic evaluation method includes the following steps: Step S1: Obtain valid data from the supplier and green certification data for the supplier's industry. In one embodiment, step S1 includes the following specific steps: Step S11: Obtain the supplier's raw data, which includes industry codes and product category codes. In this step S11, the system receives raw data imported by the supplier through a standardized Excel template, containing preset fields such as the unified social credit code, industry code, and product category code. Alternatively, data can be manually entered through a step-by-step guided front-end interface. Simultaneously, an OCR component is used to recognize key fields in the PDF carbon emission report, achieving an accuracy rate of over 98.5%. This multi-channel data collection approach broadens the data acquisition channels, ensuring the comprehensiveness of the raw data and reducing manual data entry workload and errors.

[0021] Step S12: Perform three-level verification on the original data to obtain valid data, and store the valid data in a preset relational database. In one embodiment, the three-level verification includes format verification, logical relationship verification between data, and external credit verification API verification. In this step S12, the first-level verification verifies the format standardization of fields such as year and unified social credit code according to preset rules to ensure compliance with data entry requirements; the second-level verification verifies the logical relationship between industry codes and corresponding certification qualifications, report numbers, etc., through a rule engine. For example, when a specific industry obtains corresponding green certification, it needs to submit relevant report numbers; the third-level verification calls the public interface of the National Enterprise Credit Information Publicity System to verify whether the supplier enterprise status is normal. Through three-level verification and screening, invalid and erroneous data are effectively eliminated to ensure that the valid data stored in the relational database is true and compliant.

[0022] Step S13: Based on the industry code in the valid data, query the set of green certification standards associated with the supplier's industry from the preset graph database. In this step S13, based on the industry code in the valid data (such as the specific industry corresponding to "C26"), query the green certification standards corresponding to the industry in the preset graph database to obtain a set of suitable certification standards such as ISO 14064 and China Green Factory. By accurately matching industry certification standards, a basis is provided for subsequent form rendering, and the industry relevance of the evaluation is improved.

[0023] Step S14: Based on the set of green certification standards, dynamically render and adapt the front-end form to the supplier's industry to obtain the green certification data for that industry. In step S14, the retrieved set of green certification standards is transformed into structured data that the front-end can recognize. The front-end automatically generates a supplementary data collection form specific to that industry according to preset rendering rules, without the need for manual modification of form templates or adjustment of system configurations. By dynamically adapting to industry form requirements, the system meets the green certification data collection needs of different industries, enhancing the industry adaptability flexibility of the system.

[0024] Step S2: Based on valid data and green certification data, perform carbon footprint calculation to obtain the supplier's carbon performance results, and update the supplier's green evaluation information according to the carbon performance results. In one embodiment, step S2 includes the following specific steps: Step S21: Based on valid data and green certification data, call the preset carbon footprint accounting API to interact with the preset carbon footprint accounting subsystem. In this step S21, based on valid data and green certification data, use a secure and encrypted network protocol to call the interface of the carbon footprint accounting subsystem. After identity verification, key information such as supplier identification and product category code is transmitted to achieve secure data transmission and interaction. The security and stability of data interaction are ensured through standardized interface communication.

[0025] Step S22: Based on the results of data interaction, identify missing carbon accounting parameters and generate a task to supplement the information to be traced. In this step S22, when the carbon accounting subsystem returns a response result that the carbon emission value is empty or that the parameter is missing, it automatically generates a supplementary task instruction containing the supplier identifier, the name of the missing parameter, and the supplementary deadline (72 hours after the current time). By accurately identifying the missing data and triggering the supplementary process, the integrity of the carbon accounting data is ensured.

[0026] Step S23: The supplementary data entry task is sent to the supplier's carbon performance management system. After the supplementary data entry is completed, the complete data is synchronized to the carbon footprint accounting subsystem. In this step S23, the generated supplementary data entry task instruction is stored in the green evaluation information database of the carbon performance management system, and the task is sent out through the system interface. After the supplier completes the supplementary data entry of the missing parameters, the complete carbon accounting-related data is synchronized to the carbon footprint accounting subsystem, forming a data supplementary data entry closed loop. Through the closed-loop management of task issuance and data synchronization, the timely improvement of carbon accounting data is ensured, thereby improving the accuracy of carbon performance results.

[0027] Step S3: Monitor target event messages and refresh green evaluation information based on them. Target event messages include completion events for supplementary data entry based on the carbon footprint accounting and certification update events from the preset industry knowledge base. In step S3, a message subscription model is used to monitor carbon data update events from the carbon footprint accounting subsystem and certification standard update events from the industry knowledge base. Received event messages are encrypted and verified to prevent tampering. Upon receiving a valid message, the green evaluation information in the carbon performance management system is refreshed in real time. This event-driven real-time data refresh mechanism ensures that system data remains synchronized with carbon accounting results and industry certification standard updates, improving data timeliness.

[0028] Step S4: Calculate the supplier's overall green score based on the target event message and valid data. In one embodiment, step S4 includes the following specific steps: In response to the target event message, invoke a preset dynamic scoring engine, and combine the core data in the event message with the valid data stored in the relational database to calculate the supplier's overall green score; wherein, the core data includes carbon performance results, green certification data, and data freshness. Specifically, the formula for calculating the overall green score is: , In the formula, For carbon performance results; Data for green certification; For data freshness; These are the weighting coefficients for carbon performance results; The weighting coefficients for green certification data; This is the weighting coefficient for data freshness.

[0029] In step S4, in response to the target event message, the real-time computing engine deployed on the cluster server is invoked. This engine combines the carbon performance results, green certification compliance rate (the ratio of the number of certifications obtained to the number that should be obtained), and data freshness (a correlation indicator between the most recent data update time and the current time) from the event message with preset weighting coefficients. Take 0.5, Take 0.3, Substituting 0.2 into the formula for calculating the comprehensive green score yields the comprehensive green score. Under thousands of concurrent requests, 99% of the calculation request latency is controlled within 200 milliseconds. Through a real-time calculation engine and scientific weight allocation, the score is calculated quickly and accurately, providing data support for the evaluation results output.

[0030] In one implementation, please refer to Figure 2 The method also includes the following steps: Step S5: Based on the comprehensive green score, output the supplier's green evaluation results. These results include a comprehensive score dashboard, supplier rankings, and procurement recommendation levels. In one embodiment, the procurement recommendation levels include Level 1, Level 2, and Level 3, where Level 1 indicates priority cooperation, Level 2 indicates continued observation, and Level 3 indicates suspension of cooperation. In Step S5, a visual comprehensive score display dashboard, supplier green level rankings categorized by industry, and procurement recommendations for the three levels (priority cooperation, continued observation, and suspension of cooperation) are generated based on the comprehensive green score. The generated report highlights rows with data errors in red, bolds key field names, and includes specific information correction guidelines. Through intuitive visualization and clear procurement guidance, this helps companies quickly grasp the green level of their suppliers and improve the efficiency of supply chain green risk management decisions.

[0031] In addition, this application also provides a supplier green dynamic evaluation system driven by carbon footprint data. Please refer to [link / reference]. Figure 3 The system includes: The data acquisition module is used to acquire valid data from suppliers and green certification data for their respective industries. By efficiently collecting multi-dimensional supplier data and accurately matching it to industry green certification standards, this module ensures the integrity, compliance, and industry suitability of the evaluation data, providing reliable data support for subsequent carbon footprint calculation and comprehensive scoring. Specifically, the data acquisition module can be an industrial-grade server, an intelligent data acquisition terminal, or an edge computing gateway; no restrictions are placed on its implementation.

[0032] The carbon footprint accounting interaction module is used to calculate the carbon footprint based on valid data and green certification data, obtain the supplier's carbon performance results, and update the supplier's green evaluation information according to the carbon performance results. This module establishes a secure and efficient interaction mechanism with the carbon footprint accounting subsystem and forms a closed loop for missing parameter supplementation, ensuring the accuracy and completeness of the carbon performance results and improving the real-time performance and credibility of the supplier's green evaluation information. Specifically, the carbon footprint accounting interaction module can be a high-performance gateway device, an encrypted communication module, or a data interaction terminal; no restrictions are placed here.

[0033] The event monitoring module is used to monitor target event messages and refresh the green evaluation information based on these messages. These target event messages include completion events for supplementary data entry based on the carbon footprint calculation and certification update events from a preset industry knowledge base. By capturing carbon data updates and industry certification standard change events in real time and synchronously refreshing system data, this module ensures that the evaluation data remains consistent with the latest developments, thus improving the timeliness and dynamic adaptability of supplier green evaluations. Specifically, the event monitoring module can be a network event monitor, an intelligent message receiving terminal, or a real-time data synchronization server; no restrictions are placed on this.

[0034] The comprehensive scoring module calculates a supplier's overall green score based on target event messages and valid data. By rapidly responding to event messages and scientifically calculating the comprehensive score using multi-dimensional indicators, this module enables accurate quantitative evaluation of a supplier's green level, providing data-driven decision support for enterprise supply chain green risk management. Specifically, the comprehensive scoring module can be a multi-core computing server, a real-time data processing terminal, or a high-performance computing cluster node; no restrictions are placed on its implementation.

[0035] In one implementation, please refer to Figure 4 The system also includes a results output module, which outputs green evaluation results for suppliers based on a comprehensive green score. These results include a comprehensive score dashboard, supplier rankings, and procurement recommendation levels. This module, by visually presenting the evaluation results and providing clear procurement recommendations, helps companies quickly understand the differences in green levels among suppliers, thereby improving the efficiency and accuracy of green decision-making and cooperation within the supply chain. Specifically, the results output module can be an industrial-grade display terminal, a smart report printing device, or a visual data dashboard server; no restrictions are placed on its implementation.

[0036] Please see Figures 5 to 6 In one specific embodiment, taking the green evaluation process of a supplier in the electronics manufacturing industry (Unified Social Credit Code: 91110000MA00XXXXXX, Industry Code C26, Product Category Code C261) as an example, the specific implementation process of this application is explained in detail: System deployment basics: Please refer to Figure 5 In this embodiment, the system adopts a microservice architecture deployed on a Kubernetes cluster in an enterprise private cloud. Each module implements API routing through Spring Cloud Gateway, and the service registry is Nacos. The graph database associated with the industry adaptation module uses Neo4j, storing three types of nodes: Industry, Certification, and ProductCategory, along with their corresponding relationships. The dynamic scoring engine is deployed based on the Flink real-time computing framework, with Redis Pub / Sub used for event listening to ensure timely response. Implementation process: Please refer to Figure 6 First, suppliers import raw data using a standardized Excel template, including fields such as the Unified Social Credit Code, industry code C26, product category code C261, ISO 14064 certification (yes), and product carbon footprint report number. The data acquisition module (using an industrial-grade server) simultaneously uses an OCR component to recognize the supplier's submitted PDF carbon emission report, extracting key fields for data entry. Second, the data undergoes a three-level verification engine: Level 1 verification confirms the compliance of the "Year 2024" and "Unified Social Credit Code" formats; Level 2 verification, determined by the Drools rule engine, forces verification that the product carbon footprint report number is not empty due to the industry code being C26 and ISO 14064 certification being "yes," thus passing the verification; Level 3 verification calls the National Enterprise Credit Information Publicity System API to confirm the enterprise status is NORMAL, and valid data is stored in a relational database. Subsequently, the industry adaptation module... Figure 7The logic uses industry code C26 as an index and matches relevant certification standards in the Neo4j graph database using Cypher statements, returning two core certifications: ISO 14064 and PAS 2050. The front end automatically and dynamically renders industry-specific supplementary forms to complete the green certification data collection. Then, the carbon footprint accounting interaction module (using a high-performance gateway device) calls the carbon footprint accounting subsystem API via HTTPS protocol, carrying a Bearer token in the request header and passing in information such as supplier identifier and product category code. Upon receiving the response, it finds that the carbon_emission_value field is empty and the status_code is MISSING_PARAMETER. The system automatically generates a supplementary data entry task instruction, specifying that the missing parameter is "carbon emission intensity data," and the deadline for supplementary data entry is the current time + 72 hours. This instruction is then sent to the enterprise performance management system via REST API. After the supplier completes the supplementary data entry, the carbon footprint accounting subsystem returns the complete carbon performance result (carbon emission intensity: 0.8t CO2 / 10,000 yuan of output value), and the system synchronously updates the supplier's green evaluation information to the database. Next, the event listening module (using a network event monitor) continuously subscribes to the `carbon_events` channel. Upon capturing the `carbon_data_updated` event (including supplier identifier, update timestamp, and carbon performance results) published by the carbon footprint accounting subsystem, it immediately triggers the dynamic scoring engine. The engine calls a Flink real-time calculation job, combining the valid data stored in the relational database (100% green certification compliance rate, i.e., 2 certifications obtained / 2 certifications should be obtained) and data freshness (last update within 12 hours), and calculates a score of 92.5 using the formula: "Comprehensive Green Score = 0.5 × Carbon Performance Result Conversion Value + 0.3 × Green Certification Compliance Rate + 0.2 × Data Freshness". The entire calculation process is completed within 187ms, meeting the ≤200ms response requirement. Finally, the results output module (using a visual data dashboard server) generates three major evaluation results based on the comprehensive score: First, a comprehensive score dashboard that intuitively displays the supplier's carbon performance, certification compliance rate, and data freshness indicators, as well as the total score; second, an industry classification ranking, placing the supplier in the electronics manufacturing industry (C26) at number 8; and third, a procurement recommendation level, which, due to a score ≥90, is determined to be at the first level (priority cooperation), and an HTML report is generated with no erroneous data, only an optimization guideline stating "It is recommended to continue to maintain the effectiveness of ISO 14064 certification". This application aims to protect a supplier green dynamic evaluation method and system based on carbon footprint data. The technical solution of this application obtains valid supplier data and green certification data of the industry, updates green evaluation information by combining carbon footprint accounting results, and refreshes data and calculates comprehensive scores by listening to target event messages such as completion of supplementary data entry and certification update. This can improve the timeliness of supplier green evaluation data updates and the accuracy of industry adaptation, respond to carbon data disclosure policy requirements in a timely manner, and help enterprises improve the level of green risk management in the supply chain.

[0037] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of this application and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of this application should be included within the protection scope of this application. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A supplier green dynamic evaluation method driven by carbon footprint data, characterized in that, include: Obtain valid data on suppliers and green certification data for the industries to which suppliers belong; Based on the valid data and the green certification data, carbon footprint calculation is performed to obtain the supplier's carbon performance results, and the supplier's green evaluation information is updated according to the carbon performance results. Listen for target event messages, and refresh the green evaluation information based on the target event messages; wherein, the target event messages include supplementary recording completion events based on the carbon footprint accounting and certification update events based on the preset industry knowledge base; Based on the target event message and the valid data, calculate the supplier's overall green score.

2. The supplier green dynamic evaluation method based on carbon footprint data as described in claim 1, characterized in that, Obtain valid data from suppliers and green certification data for the supplier's industry, including: Obtain the supplier's raw data, which includes industry codes and product category codes; The original data is subjected to three levels of verification to obtain the valid data, and the valid data is stored in a preset relational database; Based on the industry codes in the valid data, a set of green certification standards associated with the supplier's industry is queried from a preset graph database; Based on the aforementioned set of green certification standards, the front-end form of the supplier's industry is dynamically rendered and adapted to obtain the green certification data of the supplier's industry.

3. The supplier green dynamic evaluation method based on carbon footprint data as described in claim 2, characterized in that, The three-level verification includes format verification, logical relationship verification between data, and external credit verification API verification.

4. The supplier green dynamic evaluation method based on carbon footprint data as described in claim 2, characterized in that, Based on the target event message and the valid data, calculate the supplier's overall green score, including: In response to the target event message, a preset dynamic scoring engine is invoked, and the supplier's comprehensive green score is calculated by combining the core data in the event message with the valid data stored in the relational database. The core data includes carbon performance results, green certification data, and data freshness.

5. The supplier green dynamic evaluation method based on carbon footprint data as described in claim 4, characterized in that, The formula for calculating the overall green score is as follows: ， In the formula, For carbon performance results; Data for green certification; For data freshness; These are the weighting coefficients for carbon performance results; The weighting coefficients for green certification data; This is the weighting coefficient for data freshness.

6. The supplier green dynamic evaluation method based on carbon footprint data driven according to any one of claims 1 to 5, characterized in that, Based on the valid data and the green certification data, carbon footprint calculation is performed to obtain the supplier's carbon performance results, and the supplier's green evaluation information is updated according to the carbon performance results, including: Based on the valid data and the green certification data, the preset carbon footprint accounting API is invoked to interact with the preset carbon footprint accounting subsystem. Based on the results of the data interaction, missing carbon accounting parameters are identified, and a task to supplement the traceability information is generated. The task of supplementing the data is sent to the supplier's carbon performance management system. Once the data supplementation is completed, the complete data is synchronized to the carbon footprint accounting subsystem.

7. The supplier green dynamic evaluation method based on carbon footprint data driven according to any one of claims 1 to 5, characterized in that, The method further includes: Based on the comprehensive green score, the supplier's green evaluation result is output; The green evaluation results include a comprehensive score dashboard, supplier rankings, and procurement recommendation levels.

8. The supplier green dynamic evaluation method based on carbon footprint data as described in claim 7, characterized in that, The procurement recommendation levels include Level 1, Level 2, and Level 3. Level 1 is priority for cooperation, Level 2 is continuous observation, and Level 3 is suspension of cooperation.

9. A supplier green dynamic evaluation system driven by carbon footprint data, characterized in that, The system includes: The data acquisition module is used to acquire valid data from suppliers and green certification data for the industries to which suppliers belong. The carbon footprint accounting interaction module is used to perform carbon footprint accounting based on the valid data and the green certification data, obtain the supplier's carbon performance results, and update the supplier's green evaluation information according to the carbon performance results. An event listening module is used to listen for target event messages and refresh the green evaluation information based on the target event messages; wherein, the target event messages include supplementary recording completion events based on the carbon footprint accounting and certification update events based on the preset industry knowledge base; The comprehensive scoring module is used to calculate the supplier's comprehensive green score based on the target event message and the valid data.

10. The supplier green dynamic evaluation system based on carbon footprint data as described in claim 9, characterized in that, The system also includes a result output module, which outputs the green evaluation results of suppliers based on the comprehensive green score; wherein the green evaluation results include a comprehensive score dashboard, supplier ranking, and procurement recommendation level.

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