Enterprise green transformation data processing method, device, equipment, medium and product

Abstract

The application provides an enterprise green transformation data processing method, device, equipment, medium and product, and relates to the technical field of energy. The method is applied to an electronic device and includes the following steps: a first interface is called to obtain a plurality of evaluation index data of enterprise green transformation, the plurality of evaluation index data including first category data and second category data; a plurality of pre-configured quantification functions are called to perform standard quantification processing on the plurality of evaluation index data, to obtain a first evaluation value corresponding to each evaluation data, the quantification function being related to a relative benchmark sub-function and / or an internal index sub-function; the quantification function is also related to a normalization sub-function, so as to convert the plurality of evaluation index data into a standardized result that can be compared under a unified scale; and the plurality of first evaluation values corresponding to the plurality of evaluation index data are subjected to weighted processing, to obtain a second evaluation value, the second evaluation value being used to evaluate the effect of enterprise green transformation. The application can improve the accuracy and efficiency of enterprise green transformation data processing.

Description

Technical Field

[0001] This application relates to the field of energy technology, and in particular to a data processing method, apparatus, equipment, medium and product for enterprise green transformation. Background Technology

[0002] Currently, green transformation has become a core issue in industrial development. Enterprises need to process information related to their green transformation in order to accurately assess its effectiveness and meet regulatory compliance requirements.

[0003] Current technologies primarily rely on single numerical indicators for processing enterprise green transformation data, resulting in low data processing accuracy. Therefore, how to process enterprise green transformation data to achieve comparable and traceable comprehensive assessments has become an urgent technical problem to be solved. Summary of the Invention

[0004] This application provides a method and apparatus for assessing enterprise green transformation, which can improve the accuracy and efficiency of enterprise green transformation data processing and assessment.

[0005] Firstly, this application provides a data processing method for enterprise green transformation. This method is applied to an electronic device and includes: calling a first interface to obtain multiple evaluation indicator data for enterprise green transformation. The multiple evaluation indicator data includes first category data and second category data. The first category data is comparable across different enterprises based on a benchmark value, while the second category data does not have a benchmark value across different enterprises; calling multiple pre-configured quantization functions to perform standardized quantization processing on the multiple evaluation indicator data to obtain a first evaluation value corresponding to each evaluation data point. The quantization function is related to a relative benchmark sub-function and / or an internal indicator sub-function. The relative benchmark sub-function represents the degree of influence of the relative difference between the evaluation indicator data and the benchmark value on the first evaluation value, and the internal indicator sub-function represents the degree of influence of internal enterprise indicators on the first evaluation value. The internal enterprise indicators are determined based on the evaluation indicator data. The quantization function is also related to a normalization sub-function to transform the multiple evaluation indicator data into a standardized result comparable under a unified scale; and weighting the multiple first evaluation values ​​corresponding to the multiple evaluation indicator data to obtain a second evaluation value, which is used to evaluate the effectiveness of enterprise green transformation.

[0006] In one possible embodiment, when the first category of data and / or the second category of data have time attributes, the corresponding quantization function is also related to a time decay sub-function; wherein, the time decay sub-function is related to a pre-configured time decay coefficient and the time difference data of the evaluation index data; the time decay coefficient is used to control the decay rate of the time decay sub-function, and the time decay sub-function indicates that the timeliness weight of the evaluation index data decays exponentially with the change of the time decay coefficient and the time difference data.

[0007] In one possible embodiment, the first category of data includes at least one of the following: environmental product declarations, carbon footprint reports, carbon inventory reports, and EU carbon border adjustment mechanism product data.

[0008] The first quantification function corresponding to the Environmental Product Declaration and Carbon Footprint Report is related to the first sub-function, the first indicator function, the pre-configured carbon footprint averaging adjustment coefficient, and the reward coefficient. The first sub-function is related to the first relative benchmark sub-function, the pre-configured carbon footprint adjustment coefficient, and the time decay sub-function. The first relative benchmark sub-function represents the relative difference between the carbon footprint result and the carbon footprint benchmark value. The first indicator function is used to identify and count the number of products whose carbon footprint results are lower than the carbon footprint benchmark value. The first sub-function indicates that the output of the first relative benchmark sub-function has an exponential decay relationship with the first assessment value of the Environmental Product Declaration and Carbon Footprint Report, and the timeliness weight of the Environmental Product Declaration and Carbon Footprint Report has an exponential decay relationship with the time difference data. The reward coefficient is used to adjust the reward weight corresponding to products with excess emission reductions. The carbon footprint adjustment coefficient is used to adjust the degree of influence of the output of the first relative benchmark sub-function on the output of the first sub-function. The carbon footprint averaging adjustment coefficient is used to adjust the degree of influence of the output of the first sub-function on the first assessment value. The first quantification function indicates that the output of the first sub-function is positively correlated with the first assessment value, and indicates that the number of products with excess emission reductions counted by the first indicator function has an exponential positive correlation with the first assessment value under the effect of the reward coefficient.

[0009] The second quantification function corresponding to the carbon inventory report is related to the time decay sub-function, the pre-configured carbon inventory adjustment coefficient, and the second relative benchmark sub-function. The second relative benchmark sub-function represents the relative difference between the enterprise's carbon emissions per unit of output and the benchmark value of carbon emissions per unit of output. The carbon inventory adjustment coefficient is used to adjust the degree of influence of the output of the second relative benchmark sub-function on the first assessment value. The second quantification function indicates that the output of the second relative benchmark sub-function has an exponential decay relationship with the first assessment value of the carbon inventory report, and the timeliness weight of the carbon inventory report has an exponential decay relationship with the time difference data.

[0010] The third quantification function corresponding to the EU carbon border adjustment mechanism product data is related to the pre-configured EU carbon border adjustment mechanism product adjustment coefficient and the third relative benchmark sub-function. The third relative benchmark sub-function represents the relative difference between the carbon emissions of the EU carbon border adjustment mechanism product and the benchmark value of the EU carbon border adjustment mechanism product carbon emissions. The EU carbon border adjustment mechanism product adjustment coefficient is used to adjust the degree of influence of the output of the third relative benchmark sub-function on the first assessment value. The third quantification function indicates that the output of the third relative benchmark sub-function has an exponential decay relationship with the first assessment value of the EU carbon border adjustment mechanism product data.

[0011] In one possible embodiment, the second category of data includes at least one of the following: environmental and social governance reports, renewable energy use data, material use data, carbon asset data, corporate green transformation-related budget data, published patent data, corporate project data, and corporate management data.

[0012] The fourth quantification function corresponding to the environmental and social governance report is related to the pre-configured environmental and social governance report adjustment coefficient and the first internal indicator sub-function. The first internal indicator sub-function represents the rating information indicator of the environmental and social governance report, the environmental and social governance report adjustment coefficient is used to adjust the degree of influence of the output result of the first internal indicator sub-function on the first evaluation value, and the fourth quantification function indicates that the first evaluation value corresponding to the environmental and social governance report is positively correlated with the rating information indicator.

[0013] The fifth quantification function corresponding to renewable energy usage data is related to the second internal indicator sub-function, the pre-configured renewable energy adjustment coefficient, the second indicator function, the first weight, and the external green electricity sales coefficient. The second internal indicator sub-function represents the structural information of the green electricity usage rate indicator and the non-green electricity usage rate indicator. The second indicator function is used to identify whether the enterprise has renewable energy usage data. The renewable energy adjustment coefficient is used to adjust the impact of the output of the second internal indicator sub-function on the first evaluation value. The first weight is used to distinguish the impact of the green electricity usage rate indicator, the green electricity external sales indicator, and the non-green electricity usage rate indicator on the first evaluation value. The external green electricity sales coefficient is used to control whether the weight coefficient of the green electricity external sales indicator is applied. The fifth quantification function indicates that the green electricity usage rate indicator, the green electricity external sales indicator, and the non-green electricity usage indicator are positively correlated with the first evaluation value.

[0014] The sixth quantification function corresponding to the material usage data is related to the third internal indicator subfunction, the fourth internal indicator subfunction, the pre-configured first material adjustment coefficient, the pre-configured second material adjustment coefficient, the third indicator function, the fourth indicator function, and the second weight. Specifically, the third internal indicator subfunction represents the structural information of the recycled material utilization rate index and the circular material utilization rate index; the fourth internal indicator subfunction represents the structural information of the solid waste output ratio index, the air pollutant output ratio index, and the water pollutant output ratio index; the third indicator function is used to identify whether the enterprise has recycled or circular material usage data; the fourth indicator function is used to identify whether the enterprise has waste or pollutant output data; the first material adjustment coefficient is used to adjust the influence of the output result of the third internal indicator subfunction on the first evaluation value; and the second material adjustment coefficient is used to adjust the output of the fourth internal indicator subfunction. The results affect the degree of influence of the first assessment value; the second weight is used to distinguish the degree of influence of the material utilization rate index and the waste output ratio index on the first assessment value. The material utilization rate index includes the recycled material utilization rate index and the circular material utilization rate index. The waste output ratio index includes the solid waste output ratio index, the air pollutant output ratio index and the water pollutant output ratio index; the sixth quantification function indicates that the recycled material utilization rate index and the circular material utilization rate index are positively correlated with the first assessment value, and the solid waste output ratio index, the air pollutant output ratio index, and the water pollutant output ratio index are negatively correlated with the first assessment value.

[0015] The seventh quantitative function corresponding to the funding budget data is related to the fifth internal indicator sub-function and the pre-configured funding budget adjustment coefficient; among them, the fifth internal indicator sub-function represents the funding budget ratio indicator; the funding budget adjustment coefficient is used to adjust the degree of influence of the output result of the fifth internal indicator sub-function on the first evaluation value; the seventh quantitative function indicates that the funding budget ratio indicator is positively correlated with the first evaluation value.

[0016] The eighth quantification function corresponding to the carbon asset data is related to the sixth internal indicator subfunction, the pre-configured carbon asset adjustment coefficient, the time decay subfunction, and the certification status adjustment coefficient. Among them, the sixth internal indicator subfunction represents the carbon asset coverage ratio of the enterprise's carbon quota, certified voluntary emission reduction quota, and uncertified voluntary emission reduction quota relative to the enterprise's carbon emissions within a preset period. The carbon asset adjustment coefficient is used to adjust the degree of influence of the output result of the sixth internal indicator subfunction on the first assessment value. The certification status adjustment coefficient is used to distinguish the degree of influence of certified voluntary emission reduction quota and uncertified voluntary emission reduction quota on the first assessment value. The time decay subfunction indicates that the time weight of the carbon asset data has an exponential decay relationship with the time difference data. The eighth quantification function indicates that the carbon asset coverage ratio is positively correlated with the first assessment value.

[0017] The ninth quantification function corresponding to the publicly disclosed patent data is related to the seventh internal indicator subfunction, the pre-configured patent adjustment coefficient, and the time decay subfunction. Among them, the seventh internal indicator subfunction represents the score index corresponding to each patent status of the enterprise; the patent adjustment coefficient is used to adjust the degree of influence of the output result of the seventh internal indicator subfunction on the first evaluation value; the time decay subfunction indicates that the timeliness weight of the publicly disclosed patent data has an exponential decay relationship with the time difference data; and the ninth quantification function indicates that the score index corresponding to the patent status has a positive correlation with the first evaluation value.

[0018] The tenth quantification function corresponding to the enterprise project data is related to the eighth internal indicator subfunction, the time decay subfunction, the pre-configured project adjustment coefficient, the project type coefficient, and the project status coefficient. Among them, the eighth internal indicator subfunction represents the expected emission reduction ratio of each project of the enterprise within the preset period, and the expected emission reduction ratio represents the ratio of the expected emission reduction of the project to the enterprise's carbon emissions. The project adjustment coefficient is used to adjust the degree of influence of the output result of the eighth internal indicator subfunction on the first evaluation value. The project type coefficient is used to distinguish the degree of influence of different types of projects on the first evaluation value, and the project status coefficient is used to distinguish the degree of influence of different status projects on the first evaluation value. The time decay subfunction indicates that the timeliness weight of the enterprise project data and the time difference data have an exponential decay relationship. The tenth quantification function indicates that the expected emission reduction ratio of the project is positively correlated with the first evaluation value.

[0019] The eleventh quantitative function corresponding to the enterprise management data is related to the ninth internal indicator subfunction and the management type coefficient. Among them, the ninth internal indicator subfunction represents the execution status score indicators of the enterprise's internal carbon incentive management system, dedicated sustainability or carbon management department system, and sustainability or carbon management system. The management type coefficient is used to distinguish the degree of influence of different types of management systems on the first evaluation value. The eleventh quantitative function shows that the execution status score indicators of various management systems within the enterprise are positively correlated with the first evaluation value.

[0020] In one possible embodiment, the method further includes: calling a first interface to obtain the proportion of transaction amounts between each purchasing enterprise and each supplier enterprise within a preset period; determining an influence matrix based on the proportion of transaction amounts between each purchasing enterprise and each supplier enterprise; constructing a Leontief inverse matrix based on the influence matrix to obtain a propagation matrix, wherein the Leontief inverse matrix represents the degree of supply chain influence experienced by each purchasing enterprise through each supplier enterprise; determining a second evaluation matrix based on the propagation matrix and the first evaluation matrix, wherein the first evaluation matrix represents the second evaluation value of each purchasing enterprise; and the second evaluation matrix represents the third evaluation value of each purchasing enterprise, wherein the third evaluation value is used to evaluate the green transformation effect of the supply chain of each purchasing enterprise.

[0021] In one possible embodiment, the method further includes: determining a third evaluation matrix based on a first evaluation matrix, an influence matrix, and a propagation matrix, the third evaluation matrix including a fourth evaluation value; or, determining a fourth evaluation value based on elements of a second evaluation matrix and elements of an influence matrix, the fourth evaluation value being used to evaluate the green transformation effect of each enterprise affected by the supply chain.

[0022] In one possible embodiment, the method further includes: acquiring external data related to enterprise green transformation; extracting entities, entity attributes, and inter-entity relationships from the external data to obtain a knowledge graph; wherein the entities in the knowledge graph include enterprise green transformation entities and calculation result entities, and the enterprise green transformation entities include multiple evaluation indicator data; the inter-entity relationships in the knowledge graph include supply relationships and procurement relationships, and the supplier enterprise and the purchaser enterprise are determined through supply relationships and procurement relationships; and updating the calculation result entity based on at least one of a first evaluation value, a second evaluation value, a third evaluation value, and a fourth evaluation value.

[0023] In one possible embodiment, the method further includes: when multiple duplicate entities are obtained after entity extraction, matching the preset discrimination keys of the duplicate entities based on a matching strategy to obtain a matching result; the matching strategy includes precise matching and / or fuzzy matching based on similarity; when the matching result indicates that the preset discrimination keys of multiple duplicate entities match, triggering a preset verification strategy.

[0024] In one possible embodiment, the method further includes: if the second evaluation value is lower than a preset threshold, performing reasoning based on a knowledge graph to identify the root cause entity that caused the second evaluation value to be lower than the preset threshold.

[0025] In one possible embodiment, the method further includes acquiring external benchmark data related to the enterprise's green transformation; acquiring external data and external benchmark data related to the enterprise's green transformation includes: calling a second interface to call a corresponding data adapter to perform: connecting to the data source of the external data and external benchmark data, capturing the external data and external benchmark data, parsing the external data and external benchmark data, and verifying the external data and external benchmark data to obtain the external data and external benchmark data; wherein, the interface encapsulates interfaces of multiple data adapters, and the multiple data adapters are used to process external data and external benchmark data from different data sources; the external benchmark data includes at least one of the following: carbon footprint benchmark value, carbon emissions per unit of output benchmark value, and carbon emissions per product benchmark value of the EU carbon border adjustment mechanism.

[0026] In one possible embodiment, the method further includes: updating associated data related to the target data in response to a data update event of the target data, wherein the target data includes at least one of the following: external data, external benchmark data, and quantization function.

[0027] Secondly, this application provides a data processing device for enterprise green transformation, which is applied to an electronic device. The device includes: a first quantization module, used to call a first interface to obtain multiple evaluation indicator data for enterprise green transformation. The multiple evaluation indicator data includes first category data and second category data. The first category data is comparable across different enterprises based on a benchmark value, while the second category data does not have a benchmark value across different enterprises. The first quantization module is also used to call multiple pre-configured quantization functions to perform standard quantization processing on the multiple evaluation indicator data to obtain a first evaluation value corresponding to each evaluation data. The quantization function is related to a relative benchmark sub-function and / or an internal indicator sub-function. The relative benchmark sub-function represents the degree of influence of the relative difference between the evaluation indicator data and the benchmark value on the first evaluation value, and the internal indicator sub-function represents the degree of influence of the enterprise's internal indicators on the first evaluation value. The enterprise's internal indicators are determined based on the evaluation indicator data. The quantization function is also related to a normalization sub-function to transform the multiple evaluation indicator data into a standardized result that is comparable under a unified scale. The first quantization module is also used to perform weighted processing on the multiple first evaluation values ​​corresponding to the multiple evaluation indicator data to obtain a second evaluation value, which is used to evaluate the effectiveness of enterprise green transformation.

[0028] Thirdly, this application provides an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method as described in any of the first aspects.

[0029] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any of the first aspects.

[0030] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method of any one of the first aspects.

[0031] In this embodiment, the electronic device calls a first interface to obtain multiple evaluation indicator data for the enterprise's green transformation. These multiple evaluation indicator data can reflect the effectiveness of the enterprise's green transformation from multiple dimensions, including a first category of data that is comparable across different enterprises based on benchmark values, and a second category of data for which no benchmark values ​​exist across different enterprises.

[0032] Next, the electronic device invokes multiple pre-configured quantification functions to perform standardized quantification processing on multiple evaluation indicator data. The quantification function corresponding to the first category of data is related to the relative benchmark sub-function. The relative benchmark sub-function represents the degree of influence of the relative difference between the evaluation indicator data and the benchmark value on the first evaluation value. This allows for the uniform processing of evaluation indicator data across different companies based on the benchmark value. The quantification function corresponding to the second category of data is related to the internal indicator sub-function. The internal indicator sub-function represents the degree of influence of the company's internal indicators on the first evaluation value. These internal indicators are determined based on the evaluation indicator data, allowing for the uniform processing of evaluation indicator data from various companies through internal indicators.

[0033] Building upon this, the quantization function is also related to the normalization sub-function, which is used to transform the first evaluation value corresponding to each evaluation indicator data to a uniform scale. Therefore, the first evaluation value corresponding to each evaluation indicator data is a standardized result that can be compared under a uniform scale.

[0034] Finally, by weighting multiple first evaluation values ​​corresponding to multiple evaluation index data, the electronic device can comprehensively determine the second evaluation value based on the first evaluation values ​​that are comparable under a unified scale.

[0035] In summary, the enterprise green transformation data processing method of this application embodiment can achieve automated enterprise green transformation data processing by calling the first interface, calling the quantization function, and weighted processing. Furthermore, the above operations can improve the accuracy of enterprise green transformation data processing and accurately evaluate the effectiveness of enterprise green transformation. Attached Figure Description

[0036] 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.

[0037] Figure 1 This is a schematic diagram illustrating a scenario of the enterprise green transformation data processing method according to an embodiment of this application.

[0038] Figure 2 This is a flowchart of the enterprise green transformation data processing method according to an embodiment of this application;

[0039] Figure 3 A flowchart illustrating a data processing method for enterprise green transformation according to yet another embodiment of this application;

[0040] Figure 4 This is a schematic diagram of an enterprise green transformation data processing device according to an embodiment of this application;

[0041] Figure 5 This is a schematic diagram of the system architecture of the enterprise green transformation data processing method, apparatus and electronic equipment according to an embodiment of this application;

[0042] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0043] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0044] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0045] The following will first explain the technical terms involved in the embodiments of this application.

[0046] Enterprise green transformation: Enterprises aim to achieve ecological protection, energy conservation and carbon reduction, and efficient resource utilization by systematically and comprehensively upgrading and transforming their production methods, energy structure, processes, technology and equipment, business management, and supply chain system. This involves shifting from high-energy-consuming, high-emission, and extensive operations to a low-carbon, clean, intelligent, circular, and sustainable development model, while simultaneously ensuring compliant operation, cost reduction and efficiency improvement, and long-term green value enhancement throughout the entire process.

[0047] Knowledge graph: A graph-based data structure consisting of nodes (entities) and edges (relationships), designed to objectively and realistically reflect various entities, concepts and their relationships in the objective world through the connection of nodes and edges.

[0048] Supply chain: The entire network of a company's products from raw material procurement and manufacturing to delivery to consumers, involving the coordination of logistics, information flow and capital flow, and is usually composed of multiple corporate entities working together.

[0049] The related technologies have the following limitations:

[0050] 1. Lack of data quantification standards. Enterprises undergoing green transformation involve various types of green transformation actions, and the indicators to be examined for these different actions vary significantly. Comparisons are often impossible not only between enterprises but also between different green transformation actions within a single enterprise. This makes it difficult to aggregate enterprise green transformation actions, hindering enterprises from assessing their actual overall performance in green transformation and from clearly demonstrating their efforts and results to other companies in the supply chain.

[0051] 2. Lack of supply chain collaboration assessment. Related technologies mostly focus on the green transformation of individual enterprises or only on data transfer between enterprises, failing to link the results of enterprise green transformation with the overall green transformation of the supply chain. This makes it impossible to quantify the transmission effect of enterprise green transformation on the upstream and downstream of the supply chain, resulting in a situation where supply chain green transformation work is "single-point advancement, lacking collaboration."

[0052] 3. Difficulty in integrating heterogeneous data from multiple sources. The data generated by various types of green transformation initiatives come from diverse sources, are scattered, and lack uniformity in standards, formats, and key data content. This makes it difficult to establish systematic connections, form a comprehensive view, and effectively build a data foundation for further processing.

[0053] 4. Lack of dynamic monitoring and tracking of enterprises' continuous transformation process. Related technologies are usually based on carbon emission data at a static point in time for assessment, which cannot capture the historical trend of green transformation behavior of enterprises in the process of technological upgrading, process improvement, supply chain collaboration, etc. This static assessment model not only limits the enthusiasm of enterprises for green transformation, but also makes it difficult to form a forward-looking plan and coordination for green transformation of the supply chain.

[0054] Based on this, embodiments of this application provide a method, apparatus, device, medium, and product for processing enterprise green transformation data. By integrating multi-source heterogeneous data into a structured knowledge graph and combining it with quantification functions that correspond one-to-one with each evaluation indicator data, the application accurately and comprehensively determines the evaluation values ​​used to assess the effectiveness of enterprise green transformation. Through a supply chain influence propagation algorithm, the application accurately assesses the green transformation effectiveness of the entire supply chain in which the enterprise operates. Therefore, embodiments of this application can achieve automatic and accurate enterprise green transformation data processing.

[0055] Figure 1 This is a schematic diagram illustrating a scenario of the enterprise green transformation data processing method according to an embodiment of this application.

[0056] like Figure 1 As shown, the scenario includes: user 1 and electronic device 2. Electronic device 2 can be a terminal device or a server. For example, electronic device 2 responds to a user triggering a button to execute the enterprise green transformation data processing method of this application embodiment.

[0057] In one possible embodiment, the electronic device 2 may include a display, the display interface of which may display the aforementioned trigger button and related data of the enterprise green transformation data processing method of this application embodiment.

[0058] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0059] Figure 2 This is a flowchart illustrating a data processing method for enterprise green transformation according to an embodiment of this application. The data processing method for enterprise green transformation according to an embodiment of this application can be... Figure 1 The electronic device 2 in the middle executes.

[0060] like Figure 2 As shown, the enterprise green transformation data processing method of this application embodiment includes steps S201 to S203.

[0061] S201. Call the first interface to obtain multiple assessment indicator data for the enterprise's green transformation.

[0062] The first interface is used to access data from multiple assessment indicators for the company's green transformation. Combined with... Figure 1 For example, multiple evaluation metric data can be pre-stored in electronic device 2, which is configured with a first interface. Of course, multiple evaluation metric data can also be stored in a different electronic device than electronic device 2, which will not be elaborated upon here.

[0063] The assessment indicators include data in two categories: Category I and Category II. Category I data is comparable across different companies based on benchmark values. For example, Category I data is comparable based on benchmark values ​​set by authoritative institutions or industry standards. In contrast, while Category II data reflects the effectiveness of a company's green transformation, there are no benchmark values ​​across different companies, making it difficult to conduct unified comparisons of Category II data across different companies.

[0064] S202. Call multiple pre-configured quantization functions to perform standard quantization processing on multiple evaluation index data to obtain the first evaluation value corresponding to each evaluation data.

[0065] The quantization function is related to the relative benchmark subfunction and / or the internal index subfunction.

[0066] The relative benchmark subfunction represents the degree of influence of the relative difference between the evaluation index data and the benchmark value on the first evaluation value. The relative benchmark subfunction is used to quantify the first category data based on the benchmark value.

[0067] The internal indicator subfunction represents the degree of influence of the company's internal indicators on the first evaluation value. These internal indicators are determined based on the evaluation indicator data. The internal indicator subfunction is used to determine the company's internal indicators from the second category of data, and then quantify the second category of data based on these internal indicators.

[0068] The quantization function is also related to the normalization subfunction to transform multiple evaluation index data into standardized results that can be compared on a uniform scale.

[0069] S203. Weight the multiple first evaluation values ​​corresponding to multiple evaluation indicator data to obtain a second evaluation value. The second evaluation value is used to evaluate the effectiveness of the enterprise's green transformation.

[0070] In this embodiment, the electronic device calls a first interface to obtain multiple evaluation indicator data for the enterprise's green transformation. These multiple evaluation indicator data can reflect the effectiveness of the enterprise's green transformation from multiple dimensions, including a first category of data that is comparable across different enterprises based on benchmark values, and a second category of data for which no benchmark values ​​exist across different enterprises.

[0071] Next, the electronic device invokes multiple pre-configured quantification functions to perform standardized quantification processing on multiple evaluation indicator data. The quantification function corresponding to the first category of data is related to the relative benchmark sub-function. The relative benchmark sub-function represents the degree of influence of the relative difference between the evaluation indicator data and the benchmark value on the first evaluation value. This allows for the uniform processing of evaluation indicator data across different companies based on the benchmark value. The quantification function corresponding to the second category of data is related to the internal indicator sub-function. The internal indicator sub-function represents the degree of influence of the company's internal indicators on the first evaluation value. These internal indicators are determined based on the evaluation indicator data, allowing for the uniform processing of evaluation indicator data from various companies through internal indicators.

[0072] Building upon this, the quantization function is also related to the normalization sub-function, which is used to transform the first evaluation value corresponding to each evaluation indicator data to a uniform scale. Therefore, the first evaluation value corresponding to each evaluation indicator data is a standardized result that can be compared under a uniform scale.

[0073] Finally, by weighting multiple first evaluation values ​​corresponding to multiple evaluation index data, the electronic device can comprehensively determine the second evaluation value based on the first evaluation values ​​that are comparable under a unified scale.

[0074] In summary, the enterprise green transformation data processing method of this application embodiment can achieve automated enterprise green transformation data processing by calling the first interface, calling the quantization function, and weighted processing. Furthermore, the above operations can improve the accuracy of enterprise green transformation data processing and accurately evaluate the effectiveness of enterprise green transformation.

[0075] In one possible embodiment, if the first category of data and / or the second category of data has a time attribute, the corresponding quantization function is also related to a time decay subfunction.

[0076] The time decay sub-function is related to the pre-configured time decay coefficient and the time difference data of the evaluation index data. The time decay coefficient is used to control the decay rate of the time decay sub-function, and the time decay sub-function indicates that the timeliness weight of the evaluation index data decays exponentially with the change of the time decay coefficient and the time difference data.

[0077] The following formula (1) represents the time decay subfunction of one embodiment.

[0078] (1)

[0079] In this embodiment of the application, the i-th evaluation indicator data after sorting multiple evaluation indicator data is used as an example for explanation.

[0080] : The time difference data for the i-th evaluation indicator. When , where represents the timeliness weight of the i-th evaluation indicator data as the latest data.

[0081] Time decay coefficient. It can be pre-configured to the default value of 0.8, or other non-negative numbers.

[0082] : Timeliness weight of the i-th evaluation indicator data.

[0083] : The natural logarithm to the base e.

[0084] It should be noted that the above formula (1) is only an example, for example, except for Besides functions, other exponential functions, logarithmic functions, power functions, etc., can also be used.

[0085] In this embodiment, when the first category of data and / or the second category of data has a time attribute, the corresponding quantization function is also related to a time decay sub-function. This time decay sub-function is related to a pre-configured time decay coefficient and the time difference data of the evaluation indicator data. The time decay coefficient controls the decay rate of the time decay sub-function to adapt to the "freshness" requirements of the evaluation indicator data in different business scenarios. The time decay sub-function indicates that the timeliness weight of the evaluation indicator data decays exponentially with the changes in the time decay coefficient and the time difference data. This exponential decay relationship is continuously and smoothly changing, making the quantization process of the first category of data and / or the second category of data with time attributes more stable.

[0086] Therefore, the time decay sub-function described above can weaken the influence of "old" first-category data and / or second-category data, and avoid the impact of outdated or invalid first-category data and / or second-category data on the accuracy of the first evaluation value.

[0087] In one possible embodiment, the first category of data includes at least one of the following: environmental product declarations, carbon footprint reports, carbon inventory reports, and EU carbon border adjustment mechanism product data.

[0088] Environmental Product Declaration (EPD): A standardized document based on life cycle assessment that quantifies the environmental impact of a company's products (such as resource consumption and emissions). For example, the EPD of a flooring material specifies the water consumption and greenhouse gas emissions during its production process.

[0089] Carbon footprint report: Focuses on the total greenhouse gas emissions of a company's products throughout their entire lifecycle (or a specific stage). For example, a carbon footprint report for a bottle of beverage covers carbon emissions from raw material preparation, production and storage, packaging and transportation, and waste disposal. Typically, the EPD report will include information related to the beverage's carbon footprint.

[0090] The first quantification function corresponding to the environmental product declaration and carbon footprint report is related to the first sub-function, the first indicator function, the pre-configured carbon footprint average adjustment coefficient, and the reward coefficient.

[0091] The first sub-function is related to the first relative benchmark sub-function, the pre-configured carbon footprint adjustment coefficient, and the time decay sub-function. The first relative benchmark sub-function represents the relative difference between the carbon footprint result and the carbon footprint benchmark value. The first indicator function is used to identify and count the number of products whose carbon footprint results are lower than the carbon footprint benchmark value. The first sub-function indicates that the output result of the first relative benchmark sub-function has an exponential decay relationship with the first assessment value of the Environmental Product Declaration and the Carbon Footprint Report, and the timeliness weight of the Environmental Product Declaration and the Carbon Footprint Report has an exponential decay relationship with the time difference data. The reward coefficient is used to adjust the reward weight corresponding to the excess emission reduction products. The carbon footprint adjustment coefficient is used to adjust the degree of influence of the output result of the first relative benchmark sub-function on the output result of the first sub-function. The carbon footprint average adjustment coefficient is used to adjust the degree of influence of the output result of the first sub-function on the first assessment value. The first quantification function indicates that the output result of the first sub-function is positively correlated with the first assessment value, and indicates that the number of excess emission reduction products counted by the first indicator function has an exponential positive correlation with the first assessment value under the action of the reward coefficient.

[0092] The following formula (2) represents the first quantization function of one embodiment.

[0093] (2)

[0094] Carbon footprint average adjustment factor The default value is 2.5, or another non-negative number.

[0095] The number of environmental product declarations and carbon footprint reports.

[0096] , Reward coefficient The default value for b is 1.02, or another non-negative number. The default value for b is 0.5, or another non-negative number.

[0097] First indicator function. Satisfies the condition. Returns 1 if the condition is met, otherwise returns 0.

[0098] : Carbon footprint result of the i-th product, kgCO2e / functional unit or declared unit.

[0099] : The baseline carbon footprint of the i-th product, kgCO2e / functional unit or declared unit.

[0100] : Normalization subfunction, specifically the hyperbolic tangent function.

[0101] In this embodiment, the example is the i-th product after the enterprise has produced multiple products and sorted them.

[0102] : The initial quantification value of the EPD and carbon footprint report of the i-th product. The following formula (3) is an example of a calculation formula for the initial quantification value of the EPD and carbon footprint report of the i-th product, that is, the following formula (3) is a specific example of the first sub-function.

[0103] (3)

[0104] The first relative benchmark subfunction. It should be noted that... This indicates that the i-th product absorbs or stores carbon during its life cycle, meaning that the net carbon footprint of this product is negative (i.e., a carbon sequestration product). In this case, the first evaluation value of the carbon sequestration product is only related to the time decay function.

[0105] Carbon footprint adjustment factor The default value is 1.5, or another non-negative number. In this embodiment, the carbon footprint adjustment factor is used to adjust the degree of influence of the relative difference between the carbon footprint result of a single product and the carbon footprint benchmark value on the initial quantification value of the product. The carbon footprint average adjustment factor is used to adjust the degree of influence of the average of the initial assessment values ​​of all products of the enterprise on the first assessment value.

[0106] Initial quantification values ​​for environmental product statements and carbon footprint reports. The difference between the corresponding calculation year and the end year of the environmental product declaration and carbon footprint report for the i-th product.

[0107] In this embodiment, the first relative benchmark sub-function represents the carbon footprint reduction effect by the relative difference between the carbon footprint result and the carbon footprint benchmark value. The carbon footprint adjustment coefficient allows for flexible adjustment of the impact of the carbon footprint reduction effect on the initial quantification value, adapting to the varying difficulties in carbon footprint reduction for different enterprise products. The time decay sub-function accurately models the exponential decay relationship between the timeliness weight of environmental product declarations and carbon footprint reports and the time difference data, preventing outdated or invalid environmental product declarations and carbon footprint reports from affecting the accuracy of the initial quantification value. Therefore, the first sub-function determines the positive correlation between the carbon footprint reduction effect and the initial quantification value, accurately modeling and encouraging enterprise emission reduction.

[0108] The first indicator function can be used to identify and count the number of products whose carbon footprint results are lower than the carbon footprint benchmark. The reward coefficient is used to adjust the reward weight corresponding to products with excess emission reduction.

[0109] In this embodiment, the first quantification function is related to the first sub-function, the first indicator function, and the pre-configured carbon footprint equalization adjustment coefficient and reward coefficient, which enables the first quantification function to accurately model and encourage products to exceed emission reduction targets.

[0110] Combining the examples of formulas (2) and (3), the first quantization function indicates that the average value of the output of the first sub-function is positively correlated with the first evaluation value.

[0111] It should be noted that formulas (2) and (3) above, as well as the various formulas below, are merely examples and not limitations. For example, in formula (2), The exponential function can be replaced by the sigmoid function. In formula (3), the exponential function can be replaced by a piecewise function, a power function, etc. Other formula transformations similar to formula (2) or (3) in the following text are similar and will not be described in detail here.

[0112] The second quantification function corresponding to the carbon inventory report is related to the time decay sub-function, the pre-configured carbon inventory adjustment coefficient, and the second relative benchmark sub-function. The carbon inventory report is a written report generated by an enterprise after sorting out, statistically analyzing, and calculating the carbon emissions generated by its own production and operation activities within a preset period.

[0113] The second relative benchmark sub-function represents the relative difference between the enterprise's carbon emissions per unit of output and the benchmark value of carbon emissions per unit of output. The carbon inventory adjustment coefficient is used to adjust the degree of influence of the output of the second relative benchmark sub-function on the first assessment value. The second quantification function indicates that the output of the second relative benchmark sub-function has an exponential decay relationship with the first assessment value of the carbon inventory report, and the timeliness weight of the carbon inventory report has an exponential decay relationship with the time difference data.

[0114] The following formula (4) represents the second quantization function of one embodiment.

[0115] (4)

[0116] Carbon emissions per unit of output value of an enterprise, expressed in tCO2e / ten thousand yuan.

[0117] : Benchmark value for carbon emissions per unit of output value, in tCO2e / ten thousand yuan.

[0118] : Second relative benchmark subfunction.

[0119] Carbon inventory adjustment factor, Pre-configured as default value , or other non-negative numbers.

[0120] The first assessment value in the carbon inventory report The difference between the corresponding calculation year and the end year of the cycle of the most recent carbon inventory report.

[0121] In formula (4), This is the normalization subfunction.

[0122] The following formula (5) represents the carbon emissions per unit of enterprise output. Example of the calculation formula.

[0123] (5)

[0124] The carbon emissions of a company in a given year are expressed in tCO2e. Carbon inventory reports are published annually with only one annual value; quarterly reports are published quarterly with four quarterly values, and the company's annual carbon emissions are the sum of these four quarterly values; similarly, carbon inventory reports can be published monthly.

[0125] The annual output value of the enterprise is expressed in ten thousand yuan. If there is no entity with an annual output value but a carbon inventory report, It can be pre-configured to a default value of 0.05 or other non-negative numbers.

[0126] In this embodiment, the second relative benchmark sub-function represents the overall carbon emission reduction effect of an enterprise by the relative difference between the enterprise's carbon emission per unit of output and the benchmark value of carbon emission per unit of output. The carbon inventory adjustment coefficient allows for flexible adjustment of the impact of the overall carbon emission reduction effect on the quantified value of the carbon inventory report, adapting to the carbon emission reduction difficulties of enterprises in different industries and of different sizes. The time decay sub-function accurately models the exponential decay relationship between the timeliness weight of the carbon inventory report and the time difference data, avoiding the impact of outdated or invalid carbon inventory report data on the accuracy of the quantified value of the carbon inventory report. The second quantification function thus determined can represent the positive correlation between the overall carbon emission reduction effect of an enterprise and the quantified value of the carbon inventory report, accurately modeling and encouraging enterprises to reduce carbon emissions per unit of output.

[0127] The third quantification function corresponding to the EU carbon border adjustment mechanism product data is related to the pre-configured EU carbon border adjustment mechanism product adjustment coefficient and the third relative benchmark sub-function.

[0128] EU Carbon Border Adjustment Mechanism (CBAM) Product Data: This indicates the high-carbon-emission imported goods covered by the EU CBAM, including products from industries such as steel, cement, and aluminum. Specific product subcategories are identified using Combined Nomenclature (CN), a code used for CBAM product classification and customs declaration. For example, aluminum ingots exported from China to the EU must have their carbon emissions calculated according to CBAM rules and require the purchase of corresponding certificates.

[0129] The third relative benchmark sub-function represents the relative difference between the carbon emissions of products under the EU carbon border adjustment mechanism and the benchmark value of carbon emissions of products under the EU carbon border adjustment mechanism. The adjustment coefficient of the products under the EU carbon border adjustment mechanism is used to adjust the degree of influence of the output of the third relative benchmark sub-function on the first assessment value. The third quantification function indicates that the output of the third relative benchmark sub-function has an exponential decay relationship with the first assessment value of the EU carbon border adjustment mechanism product data.

[0130] For example, when the enterprise whose product belongs to the EU Carbon Border Adjustment Mechanism does not belong to the steel, aluminum, cement, hydrogen, or fertilizer industries, the default value of the third quantification function is 1. When the enterprise whose product belongs to the EU Carbon Border Adjustment Mechanism belongs to the above five industries but is exempt, the default value of the third quantification function is 1. When the enterprise whose product belongs to the EU Carbon Border Adjustment Mechanism belongs to the above five industries but is not exempt, the value of the third quantification function is determined by the following formula (6):

[0131] (6)

[0132] In this embodiment, the example is the i-th EU carbon border adjustment mechanism product after a company has produced multiple EU carbon border adjustment mechanism products in a sorted order.

[0133] : Carbon emissions of the i-th EU carbon border adjustment mechanism product, in tCO2e / t, where tCO2e / t represents tons of carbon dioxide equivalent per ton of product, that is, how many tons of carbon dioxide equivalent are generated for each ton of EU carbon border adjustment mechanism product produced.

[0134] The benchmark value for carbon emissions of products under the EU Carbon Border Adjustment Mechanism is expressed in tCO2e / t.

[0135] : Third relative benchmark subfunction. In the example of formula (6), the third quantization function indicates that the average value of the output of the third relative benchmark subfunction has an exponential decay relationship with the first assessment value of the EU carbon border adjustment mechanism product data.

[0136] The EU carbon border adjustment mechanism product adjustment factor. The default value is 1, or another non-negative number.

[0137] : The number of products produced by the company under the EU carbon border adjustment mechanism in the same year.

[0138] In formula (6), This is the normalization subfunction.

[0139] In this embodiment, the third relative benchmark sub-function represents the emission reduction effect of EU carbon border adjustment mechanism (EBRM) products by comparing the relative difference between the carbon emissions of EBRM products and the benchmark value of EBRM products. The EBRM product adjustment coefficient allows for flexible adjustment of the impact of the emission reduction effect of EBRM products on the first assessment value, adapting to differences in emission reduction difficulty and accounting rules across different industries and categories of EBRM products. The resulting third quantitative function represents the positive correlation between the emission reduction effect of EBRM products and the first assessment value, accurately modeling and encouraging enterprises to reduce carbon emissions of products covered by the EBRM.

[0140] In summary, the embodiments of this application use the first to the third quantification functions to accurately quantify the environmental product declaration, carbon inventory report, and EU carbon border adjustment mechanism product data based on corresponding benchmark values.

[0141] In one possible embodiment, the second category of data includes at least one of the following: environmental and social governance reports, renewable energy use data, material use data, carbon asset data, corporate green transformation-related budget data, published patent data, corporate project data, and corporate management data.

[0142] The fourth quantitative function corresponding to the environmental and social governance report is related to the pre-configured environmental and social governance report adjustment coefficient and the first internal indicator sub-function.

[0143] Environmental, Social, and Governance (ESG) Reports: Non-financial reports by companies disclosing their environmental, social, and governance performance. For example, a company might publish an annual ESG report showcasing its emissions reduction achievements, employee rights policies, and board diversity data.

[0144] Among them, the first internal indicator sub-function represents the rating information indicator of the environmental and social governance report, the environmental and social governance report adjustment coefficient is used to adjust the degree of influence of the output result of the first internal indicator sub-function on the first evaluation value, and the fourth quantification function indicates that the first evaluation value corresponding to the environmental and social governance report is positively correlated with the rating information indicator.

[0145] The following formula (7) represents the fourth quantization function in one embodiment.

[0146] (7)

[0147] The rating information indicators mapped from the environmental and social governance report. The mapping rule for the rating information indicators is as follows: the ranking of the rating system in the environmental and social governance report is mapped to the rating information indicators in this embodiment. The levels, from highest to lowest, are 1, 2, 3, ... For levels represented by tiers (e.g., A, B, C), the mapping proceeds from highest to lowest according to the tier ranking; for levels represented by scores, the total score is divided into 10 equal tiers from highest to lowest, and the mapping proceeds from the highest tier to the lowest tier. ≥8. When an environmental and social governance report exists but lacks a ranking or cannot be mapped to rating information indicators, then... =8.

[0148] Adjustment coefficient for environmental and social governance report The default value is 0.1, or another non-negative number.

[0149] The number of different rating systems for environmental and social governance reports within the same time period. When environmental and social governance reports exist but lack a ranking or cannot be mapped to rating information indicators, let... =1; if =0, considered as no environmental and social governance report, 0.

[0150] In formula (7), This is the first internal index subfunction. The normalization subfunction is defined as follows. The natural base function can be replaced by the natural logarithm function, the exponential function can be replaced by the power function, etc. The embodiments of this application do not specifically limit formula (7). Other similar formulas in the following text are similar to the transformations of formula (7), and will not be repeated here.

[0151] In this embodiment, the first internal indicator sub-function intuitively reflects the non-financial performance level of an enterprise's environmental, social, and governance (ESG) performance through the rating information indicators of the ESG report. The influence of the rating information indicators on the first assessment value can be flexibly adjusted using the ESG report adjustment coefficient to adapt to differences in enterprise ESG levels and assessment needs. Through the fourth quantification function with an exponential decay relationship, the correlation between the first assessment value corresponding to the ESG report and the rating information indicators can be accurately modeled, achieving precise quantification of the enterprise's non-financial performance. The fourth quantification function thus determined can represent the positive correlation between the rating information indicators of the ESG report and the first assessment value, accurately modeling and encouraging enterprises to improve their ESG performance.

[0152] Based on this, the embodiments of this application establish explicit fallback rules for special cases in the rating information indicator mapping process, specifically including when... When ≥8, let =8. When an ESG report exists but lacks a ranking and cannot be mapped to rating information indicators, =1 and When =0, let 0, which can effectively avoid the problem of quantization failure caused by missing ratings and abnormal mapping, and improve the robustness and practicality of the fourth quantization function.

[0153] The fifth quantification function corresponding to the renewable energy usage data is related to the second internal index sub-function, the pre-configured renewable energy adjustment coefficient, the second indicator function, the first weight, and the external green electricity sales coefficient.

[0154] Renewable energy usage data: This indicates the renewable energy usage of an enterprise within a preset period, resulting from its own production and operation activities.

[0155] Among them, the second internal indicator sub-function represents the structural information of the green electricity usage rate indicator and the non-green electricity usage rate indicator, and the second indicator function is used to identify whether the enterprise has renewable energy usage data.

[0156] The renewable energy adjustment coefficient is used to adjust the degree of influence of the output of the second internal indicator sub-function on the first evaluation value. The first weight is used to distinguish the degree of influence of the green electricity utilization rate indicator, the green electricity sales indicator, and the non-green electricity utilization rate indicator on the first evaluation value. The green electricity sales coefficient is used to control whether the weight coefficient of the green electricity sales indicator is applied. The fifth quantification function indicates that the green electricity utilization rate indicator, the green electricity sales indicator, and the non-green electricity utilization rate indicator are positively correlated with the first evaluation value.

[0157] The following formula (8) represents the fifth quantization function of one embodiment.

[0158] (8)

[0159] Green electricity utilization rate indicator, dimensionless, range [0,1], 0 is taken when unavailable.

[0160] : Non-green electricity utilization rate indicator, dimensionless, range [0,1], 0 is taken when it cannot be obtained.

[0161] The second indicator function satisfies the condition. Returns 1 if the condition is met, otherwise returns 0.

[0162] Renewable energy adjustment coefficient The default value is 1, or another non-negative number.

[0163] , , First weight, , The weighting of the green electricity utilization rate indicator. Pre-configured as default value ; Indicates green electricity sales indicators. Pre-configured as default value ; This indicates the weight of the non-green electricity usage rate indicator. Pre-configured as default value .

[0164] The weighting coefficient for the green electricity sales indicator is 1 when the status is yes and 0 when the status is no.

[0165] In formula (8), This is the second internal index subfunction. This is the normalization subfunction.

[0166] In this embodiment, the second internal indicator sub-function represents the overall level of a company's renewable energy use through a weighted structure of green electricity utilization rate, non-green electricity utilization rate, and green electricity sales. The renewable energy adjustment coefficient allows for flexible adjustment of the impact of the overall renewable energy utilization level on the first assessment value, adapting to differences in energy structure and green electricity usage ratios among different companies. The first weight distinguishes the impact of the green electricity utilization rate, green electricity sales, and non-green electricity utilization rate on the first assessment value, while the green electricity sales coefficient flexibly controls whether the green electricity sales indicator participates in the weighted calculation. The second indicator function effectively identifies whether a company has valid renewable energy usage data, preventing invalid or missing data from interfering with the first assessment value. The exponential normalization sub-function accurately models the mapping relationship between the company's energy structure indicators and the first assessment value, achieving stable quantification of renewable energy usage data. The resulting fifth quantification function represents the positive correlation between the green electricity utilization rate, green electricity sales, and non-green electricity utilization indicators and the first assessment value, accurately modeling and encouraging companies to increase their green electricity usage ratio and optimize their energy consumption structure.

[0167] The sixth quantification function corresponding to the material usage data is related to the third internal index subfunction, the fourth internal index subfunction, the pre-configured first material adjustment coefficient, the pre-configured second material adjustment coefficient, the third indicator function, the fourth indicator function, and the second weight.

[0168] Material usage data: This indicates the amount of recycled or circular materials used and waste generated by an enterprise within a preset period due to its own production and operation activities.

[0169] The third internal indicator subfunction represents the structural information of the recycled material utilization rate indicator and the circular material utilization rate indicator; the fourth internal indicator subfunction represents the structural information of the solid waste output ratio indicator, the air pollutant output ratio indicator, and the water pollutant output ratio indicator; the third indicator function is used to identify whether the enterprise has data on the use of recycled or circular materials; the fourth indicator function is used to identify whether the enterprise has data on the output of waste or pollutants; the first material adjustment coefficient is used to adjust the degree of influence of the output of the third internal indicator subfunction on the first evaluation value; and the second material adjustment coefficient is used to adjust the output of the fourth internal indicator subfunction. The results affect the degree of influence of the first assessment value; the second weight is used to distinguish the degree of influence of the material utilization rate index and the waste output ratio index on the first assessment value. The material utilization rate index includes the recycled material utilization rate index and the circular material utilization rate index. The waste output ratio index includes the solid waste output ratio index, the air pollutant output ratio index and the water pollutant output ratio index; the sixth quantification function indicates that the recycled material utilization rate index and the circular material utilization rate index are positively correlated with the first assessment value, and the solid waste output ratio index, the air pollutant output ratio index, and the water pollutant output ratio index are negatively correlated with the first assessment value.

[0170] The following formula (9) represents the sixth quantization function of one embodiment.

[0171] (9)

[0172] : Recycled material utilization rate index, mass ratio, dimensionless, range [0,1], 0 if unavailable.

[0173] : Recycling material utilization rate index, mass ratio, dimensionless, range [0,1], 0 if not available.

[0174] Solid waste output ratio index, mass ratio, dimensionless, taken as 0 when unavailable.

[0175] : Air pollutant output ratio index, mass ratio, dimensionless, taken as 0 when unavailable.

[0176] : Water pollutant output ratio index, mass ratio, dimensionless, taken as 0 when unavailable.

[0177] The third indicator function satisfies the condition. Returns 1 if the condition is met, otherwise returns 0.

[0178] The fourth indicator function satisfies the condition. Returns 1 if the condition is met, otherwise returns 0.

[0179] , Second weight, This indicates the weight of the material utilization rate indicator. Can be preconfigured as default value or other non-negative numbers; This indicates the weight of the waste-to-output ratio indicator. Can be preconfigured as default value , or other non-negative numbers.

[0180] , These are the first material adjustment coefficient and the second material adjustment coefficient, respectively. , Configurable to default value 0.5 and , or other non-negative numbers.

[0181] : Number of effective waste production ratios, with a value of , The number of non-zero elements in the array; if all of them are 0, then let... To prevent the denominator from being 0.

[0182] In formula (9), the third internal index sub-function is: The fourth internal index subfunction is The normalization sub-functions include and .

[0183] In this embodiment, the third internal indicator sub-function represents the comprehensive level of material recycling by the enterprise through the structural information of the recycled material utilization rate indicator and the circular material utilization rate indicator. The fourth internal indicator sub-function represents the comprehensive level of waste and pollutant emissions by the enterprise through the structural information of the solid waste output ratio indicator, the air pollutant output ratio indicator, and the water pollutant output ratio indicator. The first material adjustment coefficient can flexibly adjust the degree of influence of the material recycling level on the first assessment value, and the second material adjustment coefficient can flexibly adjust the degree of influence of the waste and pollutant emission level on the first assessment value, to adapt to the differences in material use and emission control among different industries and enterprises. The third indicator function can effectively identify whether the enterprise has recycled or circular material use data, and the fourth indicator function can effectively identify whether the enterprise has waste or pollutant output data, avoiding invalid or missing data in the material use data from interfering with the first assessment value. The second weight can distinguish the degree of influence of the material utilization rate indicator and the waste output ratio indicator on the first assessment value, realizing differentiated assessment of the material utilization rate indicator and the waste output ratio indicator. By using a normalized subfunction in exponential form, the mapping relationship between material usage, emission indicators, and the first assessment value can be accurately modeled, achieving stable quantification of material usage data. The resulting sixth quantification function indicates a positive correlation between the recycled material utilization rate and the circular material utilization rate and the first assessment value, while the solid waste output ratio, air pollutant output ratio, and water pollutant output ratio are negatively correlated with the first assessment value. This accurate modeling encourages enterprises to increase the proportion of recycled / circular materials used and reduce waste and pollutant emissions.

[0184] The seventh quantitative function corresponding to the funding budget data is related to the fifth internal indicator sub-function and the pre-configured funding budget adjustment coefficient.

[0185] Budgetary data: indicates the financial investment companies make in green transformation.

[0186] Among them, the fifth internal indicator sub-function represents the proportion of funding budget; the funding budget adjustment coefficient is used to adjust the degree of influence of the output of the fifth internal indicator sub-function on the first evaluation value; the seventh quantitative function indicates that the proportion of funding budget is positively correlated with the first evaluation value.

[0187] The following formula (10) represents the seventh quantization function of one embodiment.

[0188] (10)

[0189] The capital budget ratio indicator is the proportion of a company's green transformation-related capital budget to its total budget. It can be used to quantify the intensity of a company's capital investment in green transformation. It is dimensionless and can take a value of [0,1]. When it is not available, it can take a value of 0.

[0190] : Budget adjustment coefficient, used to adjust the proportion of budget to the first assessment value ( The extent of the impact, The default value is 3, or another non-negative number.

[0191] In formula (10), This is the fifth internal index subfunction. This is the normalization subfunction.

[0192] In this embodiment, the fifth internal indicator sub-function intuitively reflects the company's investment in green transformation through the capital budget ratio indicator. The capital budget adjustment coefficient allows for flexible adjustment of the impact of the capital budget ratio indicator on the first assessment value, adapting to differences in the scale of green transformation investment among companies of different industries and sizes. The normalization sub-function accurately models the correlation between the capital budget ratio indicator and the first assessment value, constraining capital investment to a reasonable quantitative range and ensuring the stability and comparability of the first assessment value. The resulting seventh quantitative function indicates a positive correlation between the capital budget ratio indicator and the first assessment value; that is, the higher the capital budget ratio, the higher the first assessment value, thus accurately modeling and encouraging companies to increase their investment in green transformation.

[0193] The eighth quantification function corresponding to carbon asset data is related to the sixth internal indicator subfunction, the pre-configured carbon asset adjustment coefficient, the time decay subfunction, and the verification status adjustment coefficient.

[0194] Carbon asset data: Equity assets that companies acquire through emissions reduction actions or carbon trading, such as carbon allowances and carbon credits. For example, a company sells 100,000 tons of saved carbon allowances on the carbon market and generates revenue.

[0195] Among them, the sixth internal indicator sub-function represents the carbon asset coverage ratio of the enterprise's carbon quota, certified voluntary emission reduction quota, and uncertified voluntary emission reduction quota relative to the enterprise's carbon emissions within a preset period; the carbon asset adjustment coefficient is used to adjust the degree of influence of the output of the sixth internal indicator sub-function on the first assessment value; the certification status adjustment coefficient is used to distinguish the degree of influence of certified voluntary emission reduction quota and uncertified voluntary emission reduction quota on the first assessment value; the time decay sub-function indicates that the timeliness weight of carbon asset data and the time difference data have an exponential decay relationship; and the eighth quantification function indicates that the carbon asset coverage ratio has a positive correlation with the first assessment value.

[0196] The following formula (11) represents the eighth quantization function of one embodiment.

[0197] (11)

[0198] The enterprise carbon emission allowance index for a pre-defined period represents the total amount of carbon emission allowances allocated to the enterprise within a specific accounting period. It reflects the scale of legally held carbon emission rights held by the enterprise, and is expressed in tCO2e. For key emission-controlled industries such as power generation, steel, cement, and aluminum, a value of 0 is used when data is unavailable. For enterprises in other industries, when data is missing, the default value is the enterprise carbon emissions ∑E for that period. i .

[0199] This refers to a company's certified voluntary emission reductions within a pre-defined period. It represents the company's completed nationally certified voluntary emission reductions held and not cancelled within a specific accounting period. Certified voluntary emission reductions are compliant emission reductions verified and approved by the authorities, and can be used for carbon compliance and offsetting carbon emissions. They are one of the company's important carbon assets, measured in tCO2e. If data is unavailable, the value is 0.

[0200] Uncertified voluntary emission reductions within a certain preset period: This refers to the voluntary emission reductions held by an enterprise within a certain accounting period but which have not yet been certified by the state. These emission reductions have not passed official verification and do not have full compliance effect. Therefore, their weight in quantification is usually lower than that of certified voluntary emission reductions. The unit is tCO2e. When data is unavailable, the value is 0.

[0201] Carbon emissions for a company within a predetermined period represent the total greenhouse gas emissions calculated by the company according to carbon inventory rules within a specific accounting period. This indicator serves as the benchmark for calculating the relative level of carbon assets, and the unit is tCO2e. If the company does not have a carbon inventory reporting entity or , The default value is 0.1 or other non-negative numbers.

[0202] Taking the preset period as years as an example, Benchmark values ​​for carbon assets The difference between the corresponding calculation year and the year and the most recent carbon asset data before that year.

[0203] , : Verification status adjustment factor Used to adjust the impact of certified voluntary emission reduction targets on the first assessment value. The default value is 1 or another non-negative number. Used to adjust the impact of unverified voluntary emission reductions on the first assessment value. The default value is 0.5 or another non-negative number.

[0204] Carbon asset adjustment factor The default value is 1.5 or another non-negative number.

[0205] In formula (11), This is the sixth internal index subfunction. This is the normalization function.

[0206] In this embodiment, the sixth internal indicator subfunction represents the comprehensive carbon asset holding level and compliance capability of an enterprise by using the carbon asset coverage ratio of the enterprise's carbon quota indicators, certified voluntary emission reduction indicators, and uncertified voluntary emission reduction indicators relative to the enterprise's carbon emissions within a preset period. The carbon asset adjustment coefficient allows for flexible adjustment of the impact of carbon assets on the first assessment value. The certified adjustment coefficient and the uncertified adjustment coefficient distinguish the value difference between certified and uncertified voluntary emission reductions, adapting to differences in carbon asset structure and carbon compliance capability among different enterprises. The time decay subfunction accurately models the time-dependent weighting of carbon asset data and the exponential decay relationship between time-difference data, preventing outdated or invalid carbon asset data from affecting the accuracy of the first assessment value. The normalization subfunction maps carbon assets to a preset numerical range, achieving stable, standardized quantification of carbon asset data. The resulting eighth quantification function represents the positive correlation between carbon asset coverage ratio and the first assessment value, accurately modeling and encouraging enterprises to improve their carbon assets.

[0207] The ninth quantization function corresponding to the publicly disclosed patent data is related to the seventh internal index subfunction, the pre-configured patent adjustment coefficient, and the time decay subfunction.

[0208] Publicly available patent data indicates a company's technological innovation capabilities in green transformation.

[0209] Among them, the seventh internal indicator sub-function represents the score indicator corresponding to each patent status of the enterprise; the patent adjustment coefficient is used to adjust the degree of influence of the output result of the seventh internal indicator sub-function on the first evaluation value; the time decay sub-function indicates that the timeliness weight of the published patent data and the time difference data have an exponential decay relationship; the ninth quantification function indicates that the score indicator corresponding to the patent status has a positive correlation with the first evaluation value.

[0210] The following formula (12) represents the ninth quantization function of one embodiment.

[0211] (12)

[0212] Among them, publicly available patent data refers to publicly available patent data related to carbon quantification, green transformation, energy conservation, and efficiency improvement. When a company has no patent data related to green transformation in the calculation year of the first evaluation value corresponding to the publicly available patent data and in previous years, the value is 0.

[0213] The score corresponds to the i-th patent status of the enterprise. If the patent status is under examination, the score is 0.7; if the patent status is effective, the score is 1; if the patent status is invalid / rejected / withdrawn, the score is 0. The above values ​​can be configured to other non-negative numbers.

[0214] The first assessment value corresponding to the publicly disclosed patent data. The difference between the corresponding calculation year and the application date of publicly available patent data related to green transformation of various enterprises in that year and before.

[0215] Patent adjustment factor, default 1. Can be configured to other non-negative numbers.

[0216] In formula (12), This is the seventh internal index subfunction. This is the normalization subfunction.

[0217] In this embodiment, the seventh internal indicator subfunction represents a company's green innovation technology capability by weighting the timeliness weight of each published patent data with the score index corresponding to the patent status. The patent adjustment coefficient allows for flexible adjustment of the impact of published patent data on the first evaluation value, adapting to differences in green transformation technology R&D capabilities among companies of different industries and sizes. The time decay subfunction accurately models the exponential decay relationship between the timeliness weight of published patent data related to a company's green transformation and time difference data, preventing outdated or weakened published patent data from affecting the accuracy of the first evaluation value. The normalization subfunction maps published patent data to a preset numerical range, achieving stable, standardized quantification of the published patent data. The resulting ninth quantification function represents the positive correlation between published patent data and the first evaluation value, accurately modeling and encouraging companies to strengthen green technology R&D.

[0218] The tenth quantification function corresponding to enterprise project data is related to the eighth internal indicator subfunction, time decay subfunction, pre-configured project adjustment coefficient, project type coefficient, and project status coefficient.

[0219] Enterprise project data: indicates the construction and implementation status of enterprises' green transformation-related projects.

[0220] Among them, the eighth internal indicator sub-function represents the expected emission reduction ratio of each project of the enterprise within the preset period. The expected emission reduction ratio represents the ratio of the expected emission reduction of the project to the carbon emissions of the enterprise. The project adjustment coefficient is used to adjust the degree of influence of the output result of the eighth internal indicator sub-function on the first assessment value. The project type coefficient is used to distinguish the degree of influence of different types of projects on the first assessment value. The project status coefficient is used to distinguish the degree of influence of different status projects on the first assessment value. The time decay sub-function indicates that the timeliness weight of the enterprise project data and the time difference data have an exponential decay relationship. The tenth quantification function indicates that the expected emission reduction ratio of the project has a positive correlation with the first assessment value.

[0221] The following formula (13) represents the tenth quantization function of one embodiment.

[0222] (13)

[0223] : The projected emission reduction of the i-th green transformation-related project of the enterprise, used to quantify the emission reduction contribution of the enterprise's green transformation-related projects, in units of tCO2e. The value is 0 when the enterprise has no green transformation-related projects.

[0224] Carbon emissions for a company over a predetermined period, such as the total greenhouse gas emissions calculated through carbon inventory within a company's accounting year, expressed in tCO2e. , It can be pre-configured to a default value of 0.05 or other non-negative numbers.

[0225] : The expected emission reduction rate of the i-th green transformation-related project of the enterprise within the preset period.

[0226] Benchmark values ​​for green transition-related projects The difference between the corresponding calculation year and the year of each green transformation-related project before that year.

[0227] : Project type coefficient for the i-th green transformation-related project of an enterprise, used to distinguish the emission reduction value and technological maturity of different projects. It can be pre-configured as a non-negative number. For example, when the project type of the i-th green transformation-related project of an enterprise is CCER, The possible value is 0.8; when the i-th green transformation-related project of an enterprise is an energy-saving technological transformation project, The possible value is 1; when the project type of the i-th green transformation-related project of an enterprise is CCU (carbon capture and utilization), The possible value is 0.8; when the project type of the i-th green transformation-related project of an enterprise is CCS (carbon capture and storage), The possible value is 1; when the project type of the i-th green transformation-related project of the enterprise is other, The possible value is 0.6.

[0228] : Project status coefficient of the i-th green transformation-related project of the enterprise This can be pre-configured as a non-negative number. For example, when the project status of the i-th green transformation-related project of an enterprise is pending initiation, The possible value is 0.2; when the project status of the i-th green transformation-related project of the enterprise is in progress, The possible value is 0.5; when the project status of the i-th green transformation-related project of the enterprise is "accepted", The possible value is 1; when the project status of the i-th green transformation-related project of an enterprise is terminated, The possible value is 0.

[0229] In one possible embodiment, the year value of the green special project is related to... Related. For example, when a project is in the "pending initiation" or "terminated" stage, the year for projects related to the Green Initiative is taken from the contract signing year of projects related to the Green Transformation. When a project is in the "in progress" stage, the year for projects related to the Green Initiative is taken from the contract signing year of projects related to the Green Transformation and the benchmark value. The average of the sums of the corresponding years. When the project status is "accepted," the year for green special projects is taken as the acceptance year for green transformation projects.

[0230] Project adjustment coefficient It can be pre-configured to the default value of 2 or other non-negative numbers.

[0231] In formula (13), This is the eighth internal index subfunction. This is the normalization subfunction.

[0232] In this embodiment, the eighth internal indicator subfunction comprehensively represents the emission reduction contribution of enterprises' green transformation-related projects by weighted summing of the expected emission reduction ratio, type coefficient, state coefficient, and timeliness weight of each enterprise's green transformation-related projects within a preset period. The green project development adjustment factor flexibly adjusts the impact of the overall level of green projects on the first assessment value; the project type coefficient distinguishes the differences in emission reduction value of different enterprises' green transformation-related projects; and the project state coefficient reflects the impact of the implementation progress of enterprises' green transformation-related projects on the first assessment value, adapting to the development differences of different industries and types of green projects. The time decay subfunction accurately models the exponential decay relationship between the timeliness weight of enterprises' green transformation-related projects and time difference data, avoiding the influence of outdated enterprise project data on the accuracy of the first assessment value. The normalization subfunction maps enterprise project data to a preset numerical range, achieving stable, standardized quantification of enterprise project data. The resulting tenth quantification function represents the positive correlation between the emission reduction contribution of enterprise project data and the first assessment value, accurately modeling and encouraging enterprises to build green projects.

[0233] The eleventh quantitative function corresponding to enterprise management data is related to the ninth internal indicator sub-function and the management type coefficient.

[0234] Enterprise management data: indicates the establishment or implementation of enterprise-related management systems and internal mechanisms for green transformation.

[0235] Among them, the ninth internal indicator sub-function represents the execution status score indicators of the enterprise's internal carbon incentive management system, dedicated sustainability or carbon management department system, and sustainability or carbon management system; the management type coefficient is used to distinguish the degree of influence of different types of management systems on the first evaluation value; the eleventh quantitative function indicates that the execution status score indicators of various management systems within the enterprise are positively correlated with the first evaluation value.

[0236] The following formula (14) represents the eleventh quantization function of one embodiment.

[0237] (14)

[0238] : Management type coefficient, for example, when the management system type is an internal carbon incentive system, it can be 0.4; 0.3 for a dedicated sustainability or carbon management department, and 0.3 for a sustainability or carbon management system; 0 if it cannot be obtained; the above values ​​can be configured to other non-negative numbers.

[0239] : Execution status score. For example, the execution status score is 1 when it is executing; 0.2 when it is pending; 0 when it is terminated; and 0 when it cannot be obtained. The above values ​​can be configured to other non-negative numbers.

[0240] i: A classification identifier for the company's internal management system. For example, 1 indicates an internal carbon incentive system, 2 indicates a dedicated sustainability or carbon management department, and 3 indicates a sustainability or carbon management system. Of course, other types are also possible.

[0241] In formula (14), This is the ninth internal index subfunction. This is the normalization function.

[0242] In this embodiment, the ninth internal indicator subfunction comprehensively characterizes the construction level and implementation effect of the enterprise's internal green management system by weighted summing of the enterprise's internal carbon incentive management system, dedicated sustainability or carbon management department system, implementation status score of sustainability or carbon management system, and management type coefficient. The management type coefficient distinguishes the differences in importance among different internal management systems, while the implementation status score reflects the impact of the actual implementation degree of the management system on the first assessment value. The eleventh quantitative function, thus determined, represents the positive correlation between the enterprise's green transformation management system construction level and the first assessment value, accurately modeling and encouraging enterprises to improve their internal green management systems and promote their implementation.

[0243] In summary, the embodiments of this application use the fourth to eleventh quantification functions to accurately quantify environmental and social governance reports, renewable energy usage data, material usage data, carbon asset data, corporate green transformation-related budget data, published patent data, corporate project data, and corporate management data based on internal corporate indicators.

[0244] Using the eleven evaluation indicator data from the example above, the corresponding eleven primary evaluation values ​​are For example, the second evaluation value can be obtained through the following formula (15):

[0245] (15)

[0246] This represents the weight of the i-th evaluation indicator data. The weight values ​​corresponding to each evaluation indicator can be 0.2, 0.2, 0.05, 0.05, 0.1, 0.1, 0.1, 0.1, 0.05, 0.05, 0.05, and 0.05, respectively. Of course, the weights of each evaluation indicator can be adjusted according to actual needs.

[0247] like Figure 2 As shown, in one possible embodiment, the enterprise green transformation data processing method further includes steps S204 to S207. Steps S204 to S207 can be performed after step S203.

[0248] S204. Call the first interface to obtain the percentage of transaction amount between each purchasing company and each supplier company within a preset period.

[0249] The purchasing company represents the demand-side enterprise in the transaction, and the supplier company represents the supply-side enterprise in the transaction. The transaction amount percentage is used to reflect the degree of purchasing dependence of a certain purchasing company on a certain supplier company within a preset period. Its value can be obtained by dividing the transaction amount of the purchasing company to the corresponding supplier company by the total purchasing amount of the purchasing company in the same period.

[0250] The following formula (16) represents the percentage of the transaction amount between the j-th purchasing company and the i-th supplier company within the preset period.

[0251] (16)

[0252] : The percentage of the total purchase amount of the purchasing company in the j-th period, when no corresponding transaction can be obtained in a certain period. 0.

[0253] : The transaction amount of the k-th transaction that occurs between the i-th supplier and the j-th buyer in the same period, expressed in ten thousand yuan.

[0254] : The total purchase amount of the j-th purchasing company in the corresponding period, in ten thousand yuan. .

[0255] S205. Determine the influence matrix based on the proportion of transaction amounts between each purchasing company and each supplier company.

[0256] based on The determined influence matrix is ​​shown in formula (17):

[0257] (17)

[0258] S206. Construct the Leontief inverse matrix based on the influence matrix to obtain the propagation matrix.

[0259] The Leontief inverse matrix represents the degree of supply chain impact experienced by each purchasing firm through each supplier firm.

[0260] The following formula (18) represents the propagation matrix:

[0261] (18)

[0262] in, for The identity matrix, and The dimensions are consistent.

[0263] The supply chain impact coefficient of the j-th purchasing enterprise through the i-th supplier enterprise includes the direct impact of the i-th supplier enterprise on the j-th purchasing enterprise, as well as the indirect impact of other enterprises in the supply chain (including the j-th purchasing enterprise itself) on the j-th purchasing enterprise through the i-th supplier enterprise. It should be noted that for large-scale sparse networks (e.g., when the number of enterprises exceeds 5000 or 10000), iterative methods (such as power series truncation) or solving linear equations can be used. Numerical methods for calculation This avoids directly finding the inverse.

[0264] S207. Based on the propagation matrix and the first evaluation matrix, determine the second evaluation matrix.

[0265] The first evaluation matrix represents the second evaluation value of each purchasing company; the second evaluation matrix is ​​used to evaluate the effectiveness of each purchasing company's green supply chain transformation.

[0266] The following formula (19) represents the second evaluation matrix:

[0267] (19)

[0268] : The third evaluation value of the j-th purchasing company within a preset period, dimensionless. The larger the value of the third evaluation value, the better the green transformation effect of the supply chain centered on the j-th purchasing company. The third evaluation value can reflect the joint green transformation effect of the j-th purchasing company and its business partners.

[0269] In this embodiment, the matrix elements of the influence matrix are constructed based on the proportion of transaction amount. This makes the row and column elements in the influence matrix correspond to the supplier and the purchaser respectively, and the matrix value represents the direct influence strength of the purchasing relationship. When constructing the Leontief inverse matrix based on this influence matrix, the difference between the identity matrix and the influence matrix can be used for inversion, or an equivalent numerical solution method can be used to obtain the propagation matrix, to avoid the increased computational time and numerical instability caused by direct inversion. The first evaluation matrix can represent the green transformation effect of the enterprise itself. After performing matrix operations on the propagation matrix and the first evaluation matrix, the second evaluation matrix is ​​obtained, which is used to output the green transformation effect of each purchaser enterprise after being affected by the supply chain transmission within a preset period.

[0270] The propagation matrix expands the direct transaction impacts in the supply chain to include indirect impacts, quantifying the comprehensive response of purchasing companies to changes in the green transformation effects of upstream suppliers. After the second assessment matrix is ​​output, electronic devices can use it as a basis for evaluating the effectiveness of green transformation in the supply chain. This allows green transformation data processing and assessment to move beyond the limitations of a single company's performance, integrating upstream and downstream transactions for comprehensive evaluation, thereby improving the relevance and interpretability of green transformation data processing and assessment.

[0271] like Figure 2 As shown, in one possible embodiment, the enterprise green transformation data processing method further includes step S208. Step S208 may be performed after step S207.

[0272] S208. Based on the first evaluation matrix, the influence matrix, and the communication matrix, determine the third evaluation matrix. Alternatively, based on the elements of the second evaluation matrix and the elements of the influence matrix, determine the fourth evaluation value. The third evaluation matrix includes the fourth evaluation value; this can be understood as the elements of the third evaluation matrix being the fourth evaluation value. The fourth evaluation value is used to assess the effectiveness of each enterprise's green transformation in response to supply chain impacts.

[0273] The following formula (20) represents the third evaluation matrix:

[0274] ,or, (20)

[0275] : The fourth evaluation value of the j-th purchasing company within a preset period, dimensionless. The higher the value of the fourth evaluation value, the more the company prefers to conduct economic activities with companies that have achieved better green transformation results. The fourth evaluation value reflects the overall green transformation effect of the j-th purchasing company's business partners.

[0276] In this embodiment, the third evaluation matrix refers to a matrix used in the supply chain to characterize the degree to which an enterprise's green transformation is influenced by upstream and downstream relationships. The influence matrix characterizes the direct influence between the purchasing and supplying enterprises, the propagation matrix characterizes the cumulative effect of this influence after multiple levels of transmission along the supply chain, and the first and second evaluation matrices correspond to the matrixed results of the enterprise's green transformation evaluation and the supply chain's green transformation evaluation, respectively. By combining these matrices, the enterprise's own green performance and the supply chain transmission relationship can be unified into the same calculation framework. Each element in the resulting third evaluation matrix represents the green transformation effect of the corresponding enterprise affected by the supply chain. The numerical change of each element in the third evaluation matrix reflects the degree of linkage between the corresponding enterprise and its partners with better green transformation results, as well as the strength of its green transformation preference within the supply chain.

[0277] Therefore, the embodiments of this application can avoid the problem of ignoring the impact of supply chain transmission based solely on the green transformation effect of enterprises, enabling the green collaborative relationship between enterprises to be quantified and improving the comparability and traceability of the degree of supply chain impact on different enterprises.

[0278] At the same time, the third assessment matrix can also provide a unified data foundation for subsequent supply chain collaborative analysis, green procurement optimization, and identification of inter-enterprise transmission impacts, thereby improving the accuracy and application value of enterprise green transformation data processing and assessment.

[0279] Figure 3 This is a flowchart of a data processing method for enterprise green transformation, which is another embodiment of this application.

[0280] like Figure 3 As shown, another embodiment of the enterprise green transformation data processing method of this application further includes steps S209 to S211. Steps S209 to S211 can be performed before step S201.

[0281] S209. Obtain external data related to the company's green transformation.

[0282] S210. Extract entities, entity attributes, and relationships between entities from external data to obtain a knowledge graph.

[0283] The entities in the knowledge graph include enterprise green transformation entities and computational result entities. Enterprise green transformation entities include multiple assessment indicator data. The relationships between entities in the knowledge graph include supply relationships and procurement relationships, which are determined by the supply and procurement relationships between supplier enterprises and purchaser enterprises.

[0284] In one possible embodiment, electronic devices can extract entities, entity attributes, and relationships between entities from external data using an ontology model to obtain a knowledge graph. An ontology model is a standardized set of rules and constraints built for enterprise green transformation, used to uniformly regulate entities, entity attributes, and relationships between entities within the knowledge graph.

[0285] S211. Update the calculation result entity based on at least one of the first evaluation value, the second evaluation value, the third evaluation value, and the fourth evaluation value.

[0286] For example, entity attributes can be dynamically expanded and can additionally record metadata such as timestamps and data versions corresponding to the first, second, third, and fourth evaluation values.

[0287] Table 1 shows examples of entities defined in the ontology model, i.e., entities in the knowledge graph. Table 1 also includes examples of entity attributes.

[0288] Table 1

[0289]

[0290] The Environmental Product Declaration (EPD) reflects a company's environmental compliance certification data throughout the entire product lifecycle, demonstrating the company's low-carbon effects. The Carbon Footprint Report reflects the carbon emissions of a company's products across the entire supply chain, from raw materials to production and final delivery, reflecting the company's carbon reduction capabilities. The entity attributes of the EPD and Carbon Footprint Report include: product type, product carbon footprint value (unit: kgCO2e), declaration unit or functional unit, reporting period, and product origin. The rules and constraints indicated by the ontology model include: when the EPD and Carbon Footprint Report are entered into the database, non-standard units (such as tCO2e) must be automatically converted to the standard unit kgCO2e, and a unit conversion log must be recorded.

[0291] The carbon inventory report reflects a company's total carbon emissions over a pre-defined period. The report's key attributes include the company's carbon emissions (unit: tCO2e), reporting period, and reporting frequency (monthly / quarterly / annual). The reporting period granularity must match the reporting frequency (e.g., if the reporting frequency is monthly, the reporting period format must be YYYY-MM). If there are duplicate reports across different frequencies (e.g., annual and monthly reports coexist), the electronic device will report an error and trigger manual verification.

[0292] The EU Carbon Border Adjustment Mechanism (CBEM) product data reflects a company's ability to meet low-carbon emission standards in its export business. The entity attributes of this data include: the product's Joint Nomenclature Commodity Code (CN code), the product's intrinsic emissions (tCO2e), the reporting period, whether it is exempt (yes / no), and the product's country of origin. The rules and constraints indicated by the ontology model include: the CN code must conform to the list of CN codes provided by the EU; otherwise, electronic devices will report errors and trigger manual verification.

[0293] The environmental dimension of the Environmental and Social Governance (ESG) report reflects a company's investment in emission reduction, energy conservation, and environmental protection; the social dimension reflects its low-carbon responsibility; and the governance dimension reflects its low-carbon management. The entity attributes of the ESG report include: reporting period and rating result (the rating result may include a specific rating or no rating). The rules and constraints indicated by the ontology model include: the reporting period for ESG reports should be in the format of an annual cycle (YYYY) or a monthly cycle (YYYY-MM). If the rating result is a rating, it must conform to a pre-defined rating list.

[0294] Renewable energy data reflects a company's energy structure and indirectly demonstrates its emission reduction effectiveness. The entity attributes of renewable energy data include: green electricity utilization rate (dimensionless), non-green electricity utilization rate (dimensionless), whether green electricity is sold externally (yes / no), and data time. The rules and constraints indicated by the ontology model include: electronic devices verifying whether the values ​​of green electricity utilization rate and non-green electricity utilization rate meet the range [0,1], and whether their sum is ≤1. If not, the electronic device reports an error and triggers manual verification.

[0295] Material usage data reflects the enterprise's resource conservation and waste reduction effects. The entity attributes of this data include: recycled material utilization rate, circular material utilization rate, solid waste output ratio, air pollutant output ratio, water pollutant output ratio, and data time. The recycled material utilization rate and other parameters are dimensionless. The rules and constraints indicated by the ontology model include: electronic equipment verifying whether the values ​​of the recycled material utilization rate and circular material utilization rate meet the range [0,1], and whether their sum is ≤1. If not, the electronic equipment should report an error and undergo manual verification.

[0296] The budget data reflects the company's budget investment in green transformation. The entity attributes of the budget data include: budget percentage (dimensionless) and data time. The rules and constraints indicated by the ontology model include: electronic devices verifying whether the budget percentage value meets the range [0,1]. If not, the electronic device reports an error and triggers manual verification.

[0297] Carbon asset data reflects a company's ability to generate revenue through emission reduction. The entity attributes of carbon asset data include: carbon allowances (tCO2e), certified voluntary emission reductions (in tCO2e), uncertified voluntary emission reductions (in tCO2e), and data time. The rules and constraints indicated by the ontology model include: electronic devices verifying whether the aforementioned carbon allowance data is ≥0; if not, the electronic device should report an error and require manual verification.

[0298] Publicly available patent data reflects a company's technological innovation capabilities in green transformation. The entity attributes of publicly available patent data include: patent name, patent status (pending / effective / expired), patent application number, and application date. The rules and constraints indicated by the ontology model include: electronic devices must verify whether the application number format and patent status match the date logic (e.g., an effective status must have an authorization date); if these conditions are not met, the electronic device should report an error and undergo manual verification.

[0299] Enterprise project data reflects the actual implementation of the enterprise's green transformation projects. The entity attributes of enterprise project data include: project name, project status (pending initiation / in progress / accepted / terminated), project type (CCER / energy-saving technological transformation / CCU / CCS / other), emission reduction (unit: tCO2e), and project initiation time. The rules and constraints indicated by the ontology model include: verifying whether the electronic equipment's initiation time is later than the current date. For example, if the initiation time is later than the current date and the status is "accepted," the electronic equipment must report an error to trigger manual verification.

[0300] Enterprise management data reflects the soft power of an enterprise's green transformation. The entity attributes of enterprise management data include: management data type (such as internal carbon incentive management data / dedicated sustainability or carbon management department / sustainability or carbon management data), execution status (in progress / pending execution / terminated), and data time.

[0301] Table 2 shows the relationships between entities defined in the ontology model, which are examples of entities in the knowledge graph.

[0302] Table 2

[0303]

[0304] In this embodiment, the electronic device can model a knowledge graph by extracting entities, entity attributes, and relationships between entities from external data. The knowledge graph represents the complex relationships of external data related to enterprise green transformation, thereby modeling multi-source heterogeneous external data related to enterprise green transformation into a structured, reasonable, and traceable knowledge graph to provide a unified data view for evaluating the effectiveness of enterprise green transformation, facilitating accurate subsequent evaluation of the effectiveness of enterprise green transformation.

[0305] By using at least one of the first evaluation value, second evaluation value, third evaluation value, and fourth evaluation value obtained in the above embodiments to update the calculated entity, the semantic integrity of the knowledge graph can be enhanced. For example, it can be used for subsequent association queries, comparative analysis, and reasoning based on entity attributes.

[0306] like Figure 3 As shown, in one possible embodiment, the enterprise green transformation data processing method further includes steps S212 and S213.

[0307] S212. If multiple duplicate entities are obtained after entity extraction, the preset discrimination key of the duplicate entities is matched based on the matching strategy to obtain the matching result.

[0308] Matching strategies include exact matching and / or fuzzy matching based on similarity.

[0309] S213. If the matching result indicates that multiple duplicate entities match the preset discrimination key, trigger the preset verification strategy.

[0310] For example, the preset verification strategy can be manual verification, or it can be to merge multiple duplicate entities or select a target entity based on the priority of the preset discrimination key.

[0311] Table 3 provides examples of duplicate entity detection. It also shows examples of preset detection keys and matching strategies.

[0312] Table 3

[0313]

[0314] In this embodiment, the electronic device collects heterogeneous external data from multiple sources. This results in diverse sources and collection methods for the external data, leading to the possibility of duplicate data being acquired from different sources and through different collection methods, and consequently, duplicate entities being extracted during entity extraction. Therefore, this embodiment performs matching processing on the extracted duplicate entities based on a matching strategy to identify duplicates. When the matching result indicates that multiple duplicate entities match a preset discrimination key, a preset verification strategy is triggered to merge or select a target entity among the duplicate entities. This improves the accuracy of entity extraction and prevents duplicate entities from affecting the accuracy of the knowledge graph.

[0315] In one possible embodiment, when an entity is updated due to external data updates, the electronic device can create a new version of the entity, mark the old version of the entity, and retain a pointer to the new version of the entity. In cases of automated verification via the electronic device or triggered manual verification, the electronic device can record the entity being verified and associate it with the entity acquired during the data collection task, thereby achieving end-to-end data traceability.

[0316] In one possible embodiment, the enterprise green transformation data processing method further includes: when the second evaluation value is lower than a preset threshold, performing reasoning processing based on a knowledge graph to identify the root cause entity that causes the second evaluation value to be lower than the preset threshold.

[0317] The second assessment value is positively correlated with the effectiveness of a company's green transformation. A second assessment value below a preset threshold indicates that the company's green transformation is not effective. The preset threshold can be determined based on the range of the second assessment value. For example, if the range of the second assessment value is [0,1], the preset threshold could be 0.3, or it could be the arithmetic mean of the second assessment values ​​of all companies.

[0318] When the second assessment value falls below a preset threshold, indicating poor green transformation results, reasoning based on a knowledge graph is used to identify the root cause entity responsible for the lower-than-preset threshold. This process eliminates reliance on human experience, thus avoiding subjective judgment bias and inefficiency, and enabling automated, accurate, and efficient tracing of the root cause entity. Based on this, a green transformation report generated from the root cause entity and / or the second assessment value can assess and diagnose the effectiveness of the green transformation, providing accurate and comprehensive evidence for compliance requirements related to green transformation.

[0319] Therefore, the enterprise green transformation data processing method of this application embodiment can realize automated processing of enterprise green transformation data and accurate evaluation of the enterprise green transformation effect.

[0320] In one possible embodiment, the electronic device can generate a corporate green transformation report based on the root cause entity and / or the second assessment value. Specifically, the electronic device calculates and records the arithmetic mean, median, standard deviation or variance, interquartile range, and range of the second assessment values ​​for all enterprises in each calculation batch, and generates a de-identified snapshot as a baseline version, assigning a version number. When pushing the corporate green transformation report, the baseline version used is clearly indicated to ensure the traceability of the corporate green transformation report. For enterprises whose second assessment values ​​are equal to or lower than a preset threshold, the report push operation is automatically triggered. After locating the root cause based on quantitative results such as knowledge graphs, the database containing the baseline value, and the second assessment results, the electronic device can query pre-stored industry best practices or potential improvement measures data to generate a customized corporate green transformation report for push. For enterprises whose second assessment values ​​are higher than the preset threshold, a self-service corporate green transformation report generation portal is provided.

[0321] For example, the electronic device includes a display interface that can display the enterprise's green transformation report to visualize the report. Additionally, the display interface can show a subscription button for the green transformation report, allowing the enterprise to set subscription conditions, such as automatically pushing the green transformation report when a certain assessment indicator falls below a corresponding threshold.

[0322] In one possible implementation, the enterprise green transformation report is pushed in HyperText Markup Language (HTML) or Portable Document Format (PDF) format with embedded links. Enterprises can click on these links to view more detailed data sources and information. Simultaneously, the electronic device can log enterprise viewing behavior. Furthermore, the electronic device's display interface can show simulation analysis buttons. When triggered, these buttons allow enterprises to adjust parameters and preview changes in assessment results, increasing engagement. All push records and report generation requests are stored in the log, facilitating the analysis of the electronic device's workload.

[0323] In one possible embodiment, the enterprise green transformation data processing method further includes: acquiring external benchmark data related to enterprise green transformation. Acquiring external data and external benchmark data related to enterprise green transformation includes: calling a second interface to invoke a corresponding data adapter to perform: connecting to the data source of the external data and external benchmark data, capturing the external data and external benchmark data, parsing the external data and external benchmark data, and verifying the external data and external benchmark data to obtain the external data and external benchmark data; wherein the interface encapsulates interfaces for multiple data adapters, which are used to process external data and external benchmark data from different data sources; the external benchmark data includes at least one of the following: carbon footprint benchmark value, carbon emissions per unit of output benchmark value, and EU carbon border adjustment mechanism product carbon emissions benchmark value.

[0324] For example, the electronic device can obtain field information related to environmental product declarations and carbon footprint reports, such as product type, product origin, and declared or functional units, from a knowledge graph, and retrieve the corresponding carbon footprint benchmark value from an external database based on the combination of this field information. The electronic device can also verify whether the carbon footprint benchmark value is greater than 0.

[0325] For example, electronic devices can obtain industry, applicable year, and other field information related to carbon inventory reports from knowledge graphs, and retrieve the benchmark value of carbon emissions per unit of output from external databases based on the combination of this field information.

[0326] For example, electronic devices can obtain information from the knowledge graph about the CN code, country of origin, and applicable year of EU carbon border adjustment mechanism products, and then search for the benchmark value of carbon emissions of EU carbon border adjustment mechanism products from an external database based on the combination of this information.

[0327] For example, each data adapter interface defines a connection method, a capture method, a parsing method, and a verification method to respectively perform the following actions: connecting to the data source of external data and external reference data, capturing external data and external reference data, parsing external data and external reference data, and verifying external data and external reference data.

[0328] Various data adapters may include: document parsing adapters, web crawler adapters, application programming interface (API) adapters, and self-reporting adapters.

[0329] In addition to performing the operations described above, the document parsing adapter integrates at least one of the following: an Optical Character Recognition (OCR) engine, a document parsing library, an entity extraction model, and a table recognition model. The OCR engine converts external data or external benchmark data in document format into readable text. The entity extraction model can be a natural language processing model such as BERT, used to extract entities from readable text. The table recognition model can include image processing sub-models such as YOLO and Faster R-CNN, and table structure sub-models such as TableNet, used to recognize tables from external data or external benchmark data in document format and output table data.

[0330] The crawler adapter incorporates anti-anti-crawling strategies such as Internet Protocol (IP) rotation, request header randomization, and CAPTCHA recognition to hide crawling characteristics and improve the success rate and stability of crawling publicly available external data or external benchmark data. The crawler adapter adheres to the Robots Exclusion Protocol File (robots.txt) and sets data crawling intervals to ensure compliant and efficient crawling of external data or external benchmark data.

[0331] The API adapter provides configurable interfaces for unified interface management and supports multiple authentication methods, including Open Authorization 2.0 (OAuth 2.0) and Application Programming Interface Keys (API Keys), to ensure the compliance and security of external data or benchmark data retrieval. The API adapter can also encapsulate request retries to improve the stability of external data or benchmark data retrieval, encapsulate rate limiting control to ensure long-term sustainable and compliant retrieval of external data or benchmark data, and encapsulate data caching logic to reduce the number of interface calls and speed up the response time for external data or benchmark data.

[0332] The self-reporting adapter provides web forms or Excel templates to supplement personalized data that cannot be obtained through automated methods. Specifically, the adapter can validate the data format and range of external data or external benchmark data to reduce data cleaning workload. The adapter can also store the submitted external data or external benchmark data in a temporary cache area for manual or automated review.

[0333] For example, external data or external benchmark data that has been manually or automatically reviewed can be stored in a data lake and its original data format can be preserved to facilitate subsequent traceability of external data and external benchmark data.

[0334] For example, in the embodiments of this application, external data and external reference data can record metadata such as data collection timestamp, data source identifier, and data batch number, which facilitates subsequent data update processing.

[0335] For example, electronic devices can record operation logs for collecting external data and external benchmark data to meet compliance requirements. In this embodiment, a first interface encapsulates the interfaces of various data adapters, which can encapsulate heterogeneous external data and external benchmark data from multiple sources, such as document parsing, web scraping, API calls, and self-reporting, into a unified processing flow. This processing flow includes connecting to the data source, scraping data, parsing data, and validating data. By calling the first interface, heterogeneous external data or external benchmark data from multiple sources can be collected efficiently. In addition, each data adapter can perform differentiated processing on its respective data source to accurately adapt to the differentiated characteristics of different data sources.

[0336] Therefore, the embodiments of this application can adapt to and efficiently collect external data or external benchmark data from multi-source heterogeneous enterprise green transformation.

[0337] In one possible embodiment, the enterprise green transformation data processing method further includes: performing data cleaning and / or data privacy protection processing on external data and external benchmark data.

[0338] Data cleaning includes at least one of the following: labeling and filling missing values, filtering outliers, and format standardization.

[0339] For example, electronic devices can identify missing values, outliers, and non-standard formats based on preset regular expressions.

[0340] For example, the electronic device can fill in missing values ​​based on a preset first rule. For instance, if a field value is missing in the current period, the average or median of field values ​​from multiple consecutive historical periods can be selected to fill the missing value. Alternatively, the electronic device can obtain the missing value from an external knowledge base. For example, if the missing value is basic enterprise information, the electronic device can obtain business registration data from an external knowledge base to fill in the missing value.

[0341] For example, the electronic device can filter outliers based on a preset second rule. For instance, the electronic device can filter outliers based on statistical rules or preset business rules. Specifically, statistical rules include 3δ rules and box plots, and business rules include, for example, that output values ​​are not negative.

[0342] For example, electronic devices can be standardized in terms of format based on a preset third rule. For instance, a unified date format of YYYY-MM-DD, a unified numerical unit, and a unified industry classification code are used as the coding system.

[0343] Data privacy protection processing includes: classifying external data and external benchmark data into privacy levels and setting corresponding encryption and / or access policies for each privacy level of external data and external benchmark data. For example, external data may include publicly available patent data related to a company's green transformation, transaction amounts, and internal management data. The privacy level for transaction amounts is Level 1, the highest level; the privacy level for internal management data is Level 2, the medium level; and the privacy level for publicly available patent data is Level 3, the lowest level.

[0344] Taking the aforementioned privacy levels, from Level 1 to Level 3, as an example, the encryption strategy corresponding to Level 1 is, for example, lightweight hash digest processing, and the access strategy is, for example, granting open access. The encryption strategy corresponding to Level 2 is, for example, AES-128 / 256 hash encryption processing and using a communication protocol that supports encryption for data transmission, and the access strategy is, for example, granting access to specific roles. The encryption strategy corresponding to Level 3 is, for example, AES-128 / 256 hash encryption processing and anonymizing external data at Level 3, while retaining its identification information for subsequent knowledge graph construction. The access strategy corresponding to Level 3 is, for example, prohibiting front-end display and requiring approval before access.

[0345] Due to the multi-source and heterogeneous nature of external data and benchmark data related to enterprise green transformation, the original external data and benchmark data often suffer from inconsistent formats and missing data. In this embodiment, the electronic device performs data cleaning through processes such as marking and filling missing values, filtering outliers, and format standardization. This ensures the integrity and compliance of the multi-source and heterogeneous external data and benchmark data, providing a valid data foundation for subsequent processing. Furthermore, the electronic device performs data privacy protection processing on the external data and benchmark data to prevent the leakage of sensitive external data, ensuring the secure and compliant processing of external data and benchmark data related to enterprise green transformation.

[0346] In one possible implementation, the cleaned external benchmark data can be output in JSON or Avro format, containing at least one of the following attributes: external benchmark data identifier, industry code, region, applicable year, type, unit, numerical value, data source, and publication time. The cleaned external data can be converted into a standardized event stream for subsequent processing modules to subscribe to, enabling real-time or batch processing. Each cleaned external data set may include a name, numerical value or text, data generation time, source type, and a pointer to the data lake.

[0347] In one possible embodiment, the enterprise green transformation data processing method further includes: updating associated data related to the target data in response to a data update event of the target data, wherein the target data includes at least one of the following: external data, external benchmark data, and quantization function.

[0348] For example, electronic devices can detect whether a data update event has occurred through methods such as web crawling, API polling, detecting manual input, setting periodic detection times, and detecting dependencies. All trigger sources for detecting whether a data update event has occurred can be uniformly encapsulated as standardized events and published through message queues such as Kafka / RocketMQ.

[0349] In this embodiment, the target data is the object data that triggers the linkage update, used to drive synchronous changes that have dependencies on it. External data includes original disclosure data related to the enterprise's green transformation, external benchmark data is the benchmark value corresponding to the external data, and the quantification function is the calculation rule that maps the original indicators to standardized evaluation values. Data update events are used to characterize the addition, correction, replacement, version switching, or source refresh of the target data. When a data update event is detected, the electronic device can initiate the recalculation and synchronization mechanism of the associated data.

[0350] For example, the electronic device can establish version identifiers and dependency indexes for external data, external benchmark data, and quantization functions, respectively, and write the associated entities into the same association table. When external data changes, the electronic device locates and updates the affected evaluation indicator data based on the association table, and updates the corresponding knowledge graph, etc. When external benchmark data changes, the electronic device synchronously corrects the benchmark values ​​and the corresponding quantization functions, and recalculates the relevant evaluation values. When the quantization function changes, the electronic device calls the new version of the quantization function to reprocess the affected evaluation indicator data, while retaining the old version results and version source for traceability.

[0351] In this embodiment, the electronic device updates related data in response to data update events of the target data, ensuring consistency in the linkage between external data, external benchmark data, and the quantization function. This guarantees the timeliness, accuracy, consistency, and traceability of the enterprise's green transformation evaluation even under changes in external data or adjustments to benchmark values.

[0352] In summary, the enterprise green transformation data processing method of this application embodiment has at least one of the following technical effects:

[0353] 1. By defining multiple types of entities, such as enterprise entities, transaction entities, and green transformation entities, and the relationships between entities, multi-source heterogeneous data is transformed into a structured knowledge graph for enterprise green transformation data processing. This achieves standardized associations between enterprise green transformation data and provides a reliable data foundation for supply chain influence dissemination analysis.

[0354] 2. Dedicated quantitative formulas have been designed for various evaluation indicator data, combined with a unified time decay sub-function, to transform qualitative descriptions and raw quantitative data into standardized, dimensionless, comparable values. The relevant parameters of the quantitative functions are configurable and support formula-level customization, enabling flexible adaptation to the changing needs of different industries and enterprise users.

[0355] 3. By constructing an influence matrix using inter-firm transaction relationships and calculating influence through the Leontief inverse matrix, the transmission effect of green transformation in the supply chain network can be accurately quantified. This allows for the identification of the indirect impact of upstream suppliers on downstream enterprises, providing an accurate basis for assessing the overall effectiveness of green transformation in the supply chain.

[0356] 4. Output three core indicators for assessing the enterprise's green transformation potential, the supply chain's green transformation potential, and the supply chain's impact potential. These indicators depict the green transformation status from three dimensions: the enterprise itself, the overall supply chain, and the supply chain's impact on individuals, providing users with a comprehensive and multi-dimensional basis for decision-making.

[0357] 5. Electronic devices can automatically generate and push enterprise green transformation reports, which may include root cause entities to achieve report-based feedback for enterprise green transformation data processing and assessment. Furthermore, the enterprise green transformation report may also include improvement recommendations, thereby realizing a closed-loop design of "assessment-intervention-reassessment," upgrading passive enterprise green transformation data processing and assessment to proactive guidance.

[0358] 6. When electronic devices adopt an event-driven architecture, they can respond in real time to changes in raw data (such as the addition of external data), automatically triggering incremental calculations such as knowledge graph construction and quantitative processing, ensuring that the enterprise's green transformation assessment is always synchronized with the latest data, thereby avoiding problems such as lag and poor timeliness in enterprise green transformation data processing and assessment.

[0359] Figure 4 This is a schematic diagram of an enterprise green transformation data processing device according to an embodiment of this application. The device is applied to an electronic device.

[0360] like Figure 4 As shown, the enterprise green transformation data processing device in this application embodiment includes: a first quantification module 301.

[0361] The first quantification module 301 is used to call the first interface to obtain multiple assessment indicator data for enterprise green transformation. The multiple assessment indicator data includes first category data and second category data. The first category data is comparable across different enterprises based on benchmark values, while the second category data does not have benchmark values ​​across different enterprises.

[0362] The first quantization module 301 is also used to call multiple pre-configured quantization functions to perform standard quantization processing on multiple evaluation index data to obtain the first evaluation value corresponding to each evaluation data. The quantization function is related to the relative benchmark sub-function and / or the internal indicator sub-function. The relative benchmark sub-function represents the degree of influence of the relative difference between the evaluation index data and the benchmark value on the first evaluation value. The internal indicator sub-function represents the degree of influence of the enterprise's internal indicators on the first evaluation value. The enterprise's internal indicators are determined based on the evaluation index data. The quantization function is also related to the normalization sub-function to transform multiple evaluation index data into standardized results that can be compared under a unified scale.

[0363] The first quantification module 301 is also used to perform weighted processing on multiple first evaluation values ​​corresponding to multiple evaluation indicator data to obtain a second evaluation value, which is used to evaluate the effectiveness of the enterprise's green transformation.

[0364] In one possible embodiment, when the first category of data and / or the second category of data have time attributes, the corresponding quantization function is also related to a time decay sub-function; wherein, the time decay sub-function is related to a pre-configured time decay coefficient and the time difference data of the evaluation index data; the time decay coefficient is used to control the decay rate of the time decay sub-function, and the time decay sub-function indicates that the timeliness weight of the evaluation index data decays exponentially with the change of the time decay coefficient and the time difference data.

[0365] In one possible embodiment, the first category of data includes at least one of the following: environmental product declarations, carbon footprint reports, carbon inventory reports, and EU carbon border adjustment mechanism product data; the first quantification function corresponding to the environmental product declarations and carbon footprint reports is related to a first sub-function, a first indicator function, a pre-configured carbon footprint averaging adjustment coefficient, and a reward coefficient.

[0366] The first sub-function is related to the first relative benchmark sub-function, the pre-configured carbon footprint adjustment coefficient, and the time decay sub-function. The first relative benchmark sub-function represents the relative difference between the carbon footprint result and the carbon footprint benchmark value. The first indicator function is used to identify and count the number of products whose carbon footprint results are lower than the carbon footprint benchmark value. The first sub-function indicates that the output result of the first relative benchmark sub-function has an exponential decay relationship with the first assessment value of the Environmental Product Declaration and the Carbon Footprint Report, and the timeliness weight of the Environmental Product Declaration and the Carbon Footprint Report has an exponential decay relationship with the time difference data. The reward coefficient is used to adjust the reward weight corresponding to the excess emission reduction products. The carbon footprint adjustment coefficient is used to adjust the degree of influence of the output result of the first relative benchmark sub-function on the output result of the first sub-function. The carbon footprint equalization adjustment coefficient is used to adjust the degree of influence of the output result of the first sub-function on the first assessment value. The first quantification function indicates that the output result of the first sub-function is positively correlated with the first assessment value, and indicates that the number of excess emission reduction products counted by the first indicator function has an exponential positive correlation with the first assessment value under the action of the reward coefficient.

[0367] The second quantification function corresponding to the carbon inventory report is related to the time decay sub-function, the pre-configured carbon inventory adjustment coefficient, and the second relative benchmark sub-function.

[0368] The second relative benchmark sub-function represents the relative difference between the enterprise's carbon emissions per unit of output and the benchmark value of carbon emissions per unit of output. The carbon inventory adjustment coefficient is used to adjust the degree of influence of the output of the second relative benchmark sub-function on the first assessment value. The second quantification function indicates that the output of the second relative benchmark sub-function has an exponential decay relationship with the first assessment value of the carbon inventory report, and the timeliness weight of the carbon inventory report has an exponential decay relationship with the time difference data.

[0369] The third quantification function corresponding to the EU carbon border adjustment mechanism product data is related to the pre-configured EU carbon border adjustment mechanism product adjustment coefficient and the third relative benchmark sub-function.

[0370] The third relative benchmark sub-function represents the relative difference between the carbon emissions of products under the EU carbon border adjustment mechanism and the benchmark value of carbon emissions of products under the EU carbon border adjustment mechanism. The adjustment coefficient of the products under the EU carbon border adjustment mechanism is used to adjust the degree of influence of the output of the third relative benchmark sub-function on the first assessment value. The third quantification function indicates that the output of the third relative benchmark sub-function has an exponential decay relationship with the first assessment value of the EU carbon border adjustment mechanism product data.

[0371] In one possible embodiment, the second category of data includes at least one of the following: environmental and social governance reports, renewable energy use data, material use data, carbon asset data, corporate green transformation-related budget data, published patent data, corporate project data, and corporate management data.

[0372] The fourth quantitative function corresponding to the environmental and social governance report is related to the pre-configured environmental and social governance report adjustment coefficient and the first internal indicator sub-function.

[0373] Among them, the first internal indicator sub-function represents the rating information indicator of the environmental and social governance report, the environmental and social governance report adjustment coefficient is used to adjust the degree of influence of the output result of the first internal indicator sub-function on the first evaluation value, and the fourth quantification function indicates that the first evaluation value corresponding to the environmental and social governance report is positively correlated with the rating information indicator.

[0374] The fifth quantification function corresponding to the renewable energy usage data is related to the second internal index sub-function, the pre-configured renewable energy adjustment coefficient, the second indicator function, the first weight, and the external green electricity sales coefficient.

[0375] Among them, the second internal indicator sub-function represents the structural information of the green electricity usage rate indicator and the non-green electricity usage rate indicator, and the second indicator function is used to identify whether the enterprise has renewable energy usage data.

[0376] The renewable energy adjustment coefficient is used to adjust the degree of influence of the output of the second internal indicator sub-function on the first evaluation value. The first weight is used to distinguish the degree of influence of the green electricity utilization rate indicator, the green electricity sales indicator, and the non-green electricity utilization rate indicator on the first evaluation value. The green electricity sales coefficient is used to control whether the weight coefficient of the green electricity sales indicator is applied. The fifth quantification function indicates that the green electricity utilization rate indicator, the green electricity sales indicator, and the non-green electricity utilization rate indicator are positively correlated with the first evaluation value.

[0377] The sixth quantification function corresponding to the material usage data is related to the third internal index subfunction, the fourth internal index subfunction, the pre-configured first material adjustment coefficient, the pre-configured second material adjustment coefficient, the third indicator function, the fourth indicator function, and the second weight.

[0378] The third internal indicator subfunction represents the structural information of the recycled material utilization rate indicator and the circular material utilization rate indicator; the fourth internal indicator subfunction represents the structural information of the solid waste output ratio indicator, the air pollutant output ratio indicator, and the water pollutant output ratio indicator; the third indicator function is used to identify whether the enterprise has data on the use of recycled or circular materials; the fourth indicator function is used to identify whether the enterprise has data on the output of waste or pollutants; the first material adjustment coefficient is used to adjust the degree of influence of the output of the third internal indicator subfunction on the first evaluation value; and the second material adjustment coefficient is used to adjust the output of the fourth internal indicator subfunction. The results affect the degree of influence of the first assessment value; the second weight is used to distinguish the degree of influence of the material utilization rate index and the waste output ratio index on the first assessment value. The material utilization rate index includes the recycled material utilization rate index and the circular material utilization rate index. The waste output ratio index includes the solid waste output ratio index, the air pollutant output ratio index and the water pollutant output ratio index; the sixth quantification function indicates that the recycled material utilization rate index and the circular material utilization rate index are positively correlated with the first assessment value, and the solid waste output ratio index, the air pollutant output ratio index, and the water pollutant output ratio index are negatively correlated with the first assessment value.

[0379] The seventh quantitative function corresponding to the funding budget data is related to the fifth internal indicator sub-function and the pre-configured funding budget adjustment coefficient.

[0380] Among them, the fifth internal indicator sub-function represents the proportion of funding budget; the funding budget adjustment coefficient is used to adjust the degree of influence of the output of the fifth internal indicator sub-function on the first evaluation value; the seventh quantitative function indicates that the proportion of funding budget is positively correlated with the first evaluation value.

[0381] The eighth quantification function corresponding to carbon asset data is related to the sixth internal indicator subfunction, the pre-configured carbon asset adjustment coefficient, the time decay subfunction, and the verification status adjustment coefficient.

[0382] Among them, the sixth internal indicator sub-function represents the carbon asset coverage ratio of the enterprise's carbon quota, certified voluntary emission reduction quota, and uncertified voluntary emission reduction quota relative to the enterprise's carbon emissions within a preset period; the carbon asset adjustment coefficient is used to adjust the degree of influence of the output of the sixth internal indicator sub-function on the first assessment value; the certification status adjustment coefficient is used to distinguish the degree of influence of certified voluntary emission reduction quota and uncertified voluntary emission reduction quota on the first assessment value; the time decay sub-function indicates that the timeliness weight of carbon asset data and the time difference data have an exponential decay relationship; and the eighth quantification function indicates that the carbon asset coverage ratio has a positive correlation with the first assessment value.

[0383] The ninth quantization function corresponding to the publicly disclosed patent data is related to the seventh internal index subfunction, the pre-configured patent adjustment coefficient, and the time decay subfunction.

[0384] Among them, the seventh internal indicator sub-function represents the score indicator corresponding to each patent status of the enterprise; the patent adjustment coefficient is used to adjust the degree of influence of the output result of the seventh internal indicator sub-function on the first evaluation value; the time decay sub-function indicates that the timeliness weight of the published patent data and the time difference data have an exponential decay relationship; the ninth quantification function indicates that the score indicator corresponding to the patent status has a positive correlation with the first evaluation value.

[0385] The tenth quantification function corresponding to enterprise project data is related to the eighth internal indicator subfunction, time decay subfunction, pre-configured project adjustment coefficient, project type coefficient, and project status coefficient.

[0386] Among them, the eighth internal indicator sub-function represents the expected emission reduction ratio of each project of the enterprise within the preset period. The expected emission reduction ratio represents the ratio of the expected emission reduction of the project to the carbon emissions of the enterprise. The project adjustment coefficient is used to adjust the degree of influence of the output result of the eighth internal indicator sub-function on the first assessment value. The project type coefficient is used to distinguish the degree of influence of different types of projects on the first assessment value. The project status coefficient is used to distinguish the degree of influence of different status projects on the first assessment value. The time decay sub-function indicates that the timeliness weight of the enterprise project data and the time difference data have an exponential decay relationship. The tenth quantification function indicates that the expected emission reduction ratio of the project has a positive correlation with the first assessment value.

[0387] The eleventh quantitative function corresponding to enterprise management data is related to the ninth internal indicator sub-function and the management type coefficient.

[0388] Among them, the ninth internal indicator sub-function represents the execution status score indicators of the enterprise's internal carbon incentive management system, dedicated sustainability or carbon management department system, and sustainability or carbon management system; the management type coefficient is used to distinguish the degree of influence of different types of management systems on the first evaluation value; the eleventh quantitative function indicates that the execution status score indicators of various management systems within the enterprise are positively correlated with the first evaluation value.

[0389] like Figure 4 As shown, in one possible embodiment, the enterprise green transformation data processing device further includes a second quantification module 302.

[0390] The second quantification module 302 is used to call the first interface to obtain the proportion of transaction amount between each purchasing enterprise and each supplier enterprise within a preset period. The second quantification module is also used to determine the influence matrix based on the proportion of transaction amount between each purchasing enterprise and each supplier enterprise. The second quantification module is also used to construct a Leontief inverse matrix based on the influence matrix to obtain a propagation matrix. The Leontief inverse matrix represents the degree of supply chain influence that each purchasing enterprise receives through each supplier enterprise. The second quantification module is also used to determine a second evaluation matrix based on the propagation matrix and the first evaluation matrix. The first evaluation matrix represents the second evaluation value of each purchasing enterprise. The second evaluation matrix represents the third evaluation value of each purchasing enterprise. The third evaluation value is used to evaluate the green transformation effect of the supply chain of each purchasing enterprise.

[0391] In one possible embodiment, the second quantification module 302 is further configured to: determine a third evaluation matrix based on the first evaluation matrix, the influence matrix, and the propagation matrix, the third evaluation matrix including a fourth evaluation value; or, determine a fourth evaluation value based on the elements of the second evaluation matrix and the elements of the influence matrix, the fourth evaluation value being used to evaluate the green transformation effect of each enterprise affected by the supply chain.

[0392] In one possible embodiment, the enterprise green transformation data processing device further includes: a data acquisition module 303, used to acquire external data related to enterprise green transformation; and a knowledge graph construction module 304, used to extract entities, entity attributes, and relationships between entities from the external data to obtain a knowledge graph; wherein, the entities in the knowledge graph include enterprise green transformation entities and calculation result entities, and the enterprise green transformation entities include multiple evaluation indicator data; the relationships between entities in the knowledge graph include supply relationships and procurement relationships, and the supplier enterprise and the purchaser enterprise are determined through the supply relationship and procurement relationship; the knowledge graph construction module 304 is also used to update the calculation result entity based on at least one of a first evaluation value, a second evaluation moment value, a third evaluation value, and a fourth evaluation value.

[0393] In one possible embodiment, the knowledge graph construction module 304 is further configured to: when multiple duplicate entities are obtained after entity extraction, match the preset discrimination keys of the duplicate entities based on a matching strategy to obtain a matching result; the matching strategy includes precise matching and / or fuzzy matching based on similarity; and when the matching result indicates that the preset discrimination keys of multiple duplicate entities match, trigger a preset verification strategy.

[0394] In one possible embodiment, the knowledge graph construction module 304 is further configured to: perform reasoning processing based on the knowledge graph when the second evaluation value is lower than a preset threshold, so as to identify the root cause entity that causes the second evaluation value to be lower than the preset threshold.

[0395] In one possible embodiment, the device is also used to acquire external benchmark data related to the enterprise's green transformation. The data acquisition module 303 is specifically used to: call the corresponding data adapter via a second interface to perform: connecting to the data source of the external data and external benchmark data, capturing the external data and external benchmark data, parsing the external data and external benchmark data, and verifying the external data and external benchmark data to obtain the external data and external benchmark data; wherein the interface encapsulates interfaces for multiple data adapters, which are used to process external data and external benchmark data from different data sources; the external benchmark data includes at least one of the following: carbon footprint benchmark value, carbon emissions per unit of output benchmark value, and EU carbon border adjustment mechanism product carbon emissions benchmark value.

[0396] In one possible embodiment, the enterprise green transformation data processing device further includes: a data update module 305, used to update the associated data related to the target data in response to a data update event of the target data, wherein the target data includes at least one of the following: external data, external benchmark data, and quantization function.

[0397] Figure 5 This is a schematic diagram of the system architecture of the enterprise green transformation data processing method, apparatus and electronic equipment according to embodiments of this application.

[0398] like Figure 5 As shown, the system architecture includes, from top to bottom, a presentation layer, a gateway layer, a processing layer, a storage layer, and a data source layer.

[0399] The data source layer includes external data and external benchmark data. Figure 5 In the examples, the sources of external data and external benchmark data include: government or enterprise association open platforms, enterprise self-reporting, third-party databases, enterprise websites or reports, literature and news, and IoT devices.

[0400] The processing layer comprises a data acquisition layer, a quantization layer, and a data update module. The acquisition layer includes a data acquisition module and a knowledge graph construction module. The quantization layer includes a first quantization module and a second quantization module. The operations performed by each module in the processing layer have been described in detail above and will not be repeated here.

[0401] In one possible implementation, the knowledge graph construction module provides two types of standardized interfaces: a batch processing interface and a real-time query interface, which are called by the first quantization module and the second quantization module. The batch processing interface uses the Parquet columnar file format, is partitioned by day and stored in the kg_snapshot / directory of the data lake, and outputs a flat view containing all entities and their relationships, facilitating offline analysis and machine learning training. The real-time query interface is based on the GraphQL protocol, supporting queries based on enterprise identifiers, data types, and time ranges. The response format is JSON, containing lightweight nested information about entity attributes and relationships, and materialized views or secondary indexes are created for high-frequency query paths to optimize performance.

[0402] The gateway layer is used for request routing, authorization verification, and traffic control to isolate the front-end presentation layer from the back-end processing layer. Figure 5 In the example, the gateway layer is the API gateway.

[0403] The storage layer is used to store the various types of data mentioned above. Figure 5 In the example, the storage layer includes a data lake, a graph database, a time-series database, and a relational database. The data lake is used to store external data, external benchmark data, etc.; the graph database is used to store knowledge graphs; the time-series database is used to store time-series data with time attributes; and the relational database is used to store relevant business rules, configuration rules, etc.

[0404] The presentation layer is used to display data such as the corporate green transformation report mentioned above through the display interface of electronic devices. Figure 5 In the example, the presentation layer is also used to display the green transformation results, supply chain, knowledge graph, and configuration entry points in a dashboard format for interface management.

[0405] In one possible embodiment, in order to efficiently cope with high-frequency changes, the updates of the target data by the above modules are mainly based on incremental calculations, that is, only the affected related data are reprocessed.

[0406] Table 4 below shows the update processing methods for each module:

[0407] Table 4

[0408]

[0409] In addition to the incremental calculations mentioned above, electronic devices can also perform full recalculation in response to target data updates. Specifically, full recalculation is performed periodically by setting a recalculation time, and includes tasks such as adjusting quantization functions, adding or removing entities, adjusting the weight column vector parameters of multiple evaluation index data, and data migration.

[0410] In one possible implementation, the electronic device reads snapshot data from the data lake at a specific point in time. Using a Spark / Flink offline batch processing job, it sequentially performs knowledge graph reconstruction, quantization, and evaluation, generating a full result which is then written to a new storage area (with version tags). During the full recalculation, incremental updates continue to be written to the old storage area, while incremental events are persisted to the incremental log. After the full computation is complete, incremental writing is paused, and the incremental log is replayed to the new storage area, ensuring that the new storage contains both full and incremental data. The storage pointer is then switched, and incremental writing resumes. If the switch fails, an automatic rollback is performed, preserving the old storage service.

[0411] In one possible embodiment, if the delay of at least one of the following indicators—the electronic device detection processing delay (in seconds), the success rate of each module, the queue backlog length, and the resource consumption indicators for incremental updates and full recalculations—exceeds a preset threshold, the success rate is lower than a preset threshold, or the queue backlog continues to grow, the electronic device triggers an alarm. Processing failures are retried a limited number of times based on the error type; if the failure persists, the event is transferred to a dead-letter queue, triggering an alarm and requiring manual intervention for repair.

[0412] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, this application embodiment provides an electronic device including a processor 401 and a memory 402. Optionally, the device further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0413] In the specific implementation process, the memory 402 stores code, and the processor 401 runs the code stored in the memory 402 to execute the method of the above method embodiment.

[0414] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0415] In the above Figure 6In the illustrated embodiments, it should be understood that the processor 401 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0416] The memory 402 may include high-speed RAM memory, and may also include non-volatile memory (NVM), such as at least one disk storage.

[0417] Bus 404 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Bus 404 can be divided into address bus, data bus, control bus, etc. For ease of illustration, the bus 404 in the accompanying drawings of this application is not limited to only one bus or one type of bus.

[0418] This application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods described in the above-described method embodiments.

[0419] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0420] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0421] This application provides a computer program product, including a computer program that, when executed by a processor, implements the methods provided in any of the embodiments described above.

[0422] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0423] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0424] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0425] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0426] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0427] If the integrated unit / module is implemented as a software program module and sold or used as an independent financial product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software financial product. This computer software financial product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0428] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0429] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0430] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A data processing method for enterprise green transformation, characterized in that, The method is applied to an electronic device, and the method includes: Call the first interface to obtain multiple assessment indicator data for enterprise green transformation. The multiple assessment indicator data includes a first category of data and a second category of data. The first category of data is comparable across different enterprises based on a benchmark value, while the second category of data does not have a benchmark value across different enterprises. Multiple pre-configured quantization functions are invoked to perform standardized quantization processing on the multiple evaluation indicator data to obtain a first evaluation value corresponding to each evaluation data. The quantization function is related to a relative benchmark sub-function and / or an internal indicator sub-function. The relative benchmark sub-function represents the degree of influence of the relative difference between the evaluation indicator data and the benchmark value on the first evaluation value. The internal indicator sub-function represents the degree of influence of the enterprise's internal indicators on the first evaluation value. The enterprise's internal indicators are determined based on the evaluation indicator data. The quantization function is also related to a normalization sub-function to transform the multiple evaluation indicator data into standardized results that can be compared under a unified scale. The multiple first evaluation values ​​corresponding to the multiple evaluation indicator data are weighted to obtain a second evaluation value, which is used to evaluate the effectiveness of the enterprise's green transformation.

2. The method according to claim 1, characterized in that, When the first category of data and / or the second category of data have time attributes, the corresponding quantization function is also related to the time decay sub-function; The time decay sub-function is related to a pre-configured time decay coefficient and the time difference data of the evaluation index data; the time decay coefficient is used to control the decay rate of the time decay sub-function, and the time decay sub-function indicates that the timeliness weight of the evaluation index data decays exponentially with the change of the time decay coefficient and the time difference data.

3. The method according to claim 2, characterized in that, The first category of data includes at least one of the following: environmental product declarations, carbon footprint reports, carbon inventory reports, and EU carbon border adjustment mechanism product data; The first quantification function and the first sub-function, the first indicator function, the pre-configured carbon footprint averaging adjustment coefficient, and the reward coefficient are related to the environmental product declaration and the carbon footprint report. The first sub-function is related to the first relative benchmark sub-function, the pre-configured carbon footprint adjustment coefficient, and the time decay sub-function. The first relative benchmark sub-function represents the relative difference between the carbon footprint result and the carbon footprint benchmark value. The first indicator function is used to identify and count the number of products whose carbon footprint results are lower than the carbon footprint benchmark value. The first sub-function indicates that the output result of the first relative benchmark sub-function has an exponential decay relationship with the first assessment value of the environmental product declaration and the carbon footprint report, and the timeliness weight of the environmental product declaration and the carbon footprint report has an exponential decay relationship with the time difference data. The reward coefficient is used to adjust the reward weight corresponding to the excess emission reduction products. The carbon footprint adjustment coefficient is used to adjust the degree of influence of the output result of the first relative benchmark sub-function on the output result of the first sub-function. The carbon footprint equalization adjustment coefficient is used to adjust the degree of influence of the output result of the first sub-function on the first assessment value. The first quantification function indicates that the output result of the first sub-function is positively correlated with the first assessment value, and indicates that the number of excess emission reduction products counted by the first indicator function has an exponential positive correlation with the first assessment value under the action of the reward coefficient. The second quantization function corresponding to the carbon inventory report is related to the time decay sub-function, the pre-configured carbon inventory adjustment coefficient, and the second relative benchmark sub-function. Wherein, the second relative benchmark sub-function represents the relative difference between the enterprise's carbon emissions per unit of output and the benchmark value of carbon emissions per unit of output, and the carbon inventory adjustment coefficient is used to adjust the degree of influence of the output result of the second relative benchmark sub-function on the first assessment value; the second quantification function indicates that the output result of the second relative benchmark sub-function has an exponential decay relationship with the first assessment value of the carbon inventory report, and the timeliness weight of the carbon inventory report has an exponential decay relationship with the time difference data; The third quantification function corresponding to the EU carbon border adjustment mechanism product data is related to the pre-configured EU carbon border adjustment mechanism product adjustment coefficient and the third relative benchmark sub-function. Wherein, the third relative benchmark sub-function represents the relative difference between the carbon emissions of EU carbon border adjustment mechanism products and the benchmark value of EU carbon border adjustment mechanism products, and the EU carbon border adjustment mechanism product adjustment coefficient is used to adjust the degree of influence of the output of the third relative benchmark sub-function on the first assessment value; the third quantification function indicates that the output of the third relative benchmark sub-function has an exponential decay relationship with the first assessment value of the EU carbon border adjustment mechanism product data.

4. The method according to claim 2, characterized in that, The second category of data includes at least one of the following: environmental and social governance reports, renewable energy use data, material use data, carbon asset data, corporate green transformation related budget data, published patent data, corporate project data, and corporate management data; The fourth quantitative function corresponding to the environmental and social governance report is related to the pre-configured environmental and social governance report adjustment coefficient and the first internal indicator sub-function. Wherein, the first internal indicator sub-function represents the rating information indicator of the environmental and social governance report, the environmental and social governance report adjustment coefficient is used to adjust the degree of influence of the output result of the first internal indicator sub-function on the first evaluation value, and the fourth quantification function indicates that the first evaluation value corresponding to the environmental and social governance report is positively correlated with the rating information indicator; The fifth quantification function corresponding to the renewable energy usage data is related to the second internal index sub-function, the pre-configured renewable energy adjustment coefficient, the second indicator function, the first weight, and the external green electricity sales coefficient. The second internal indicator sub-function represents the structural information of the green electricity utilization rate indicator and the non-green electricity utilization rate indicator, and the second indicator function is used to identify whether the enterprise has renewable energy usage data. The renewable energy adjustment coefficient is used to adjust the influence of the output of the second internal indicator sub-function on the first evaluation value. The first weight is used to distinguish the influence of the green electricity utilization rate indicator, the green electricity sales indicator, and the non-green electricity utilization rate indicator on the first evaluation value. The green electricity sales coefficient is used to control whether to apply the weight coefficient of the green electricity sales indicator. The fifth quantification function indicates that the green electricity utilization rate indicator, the green electricity sales indicator, and the non-green electricity utilization indicator are positively correlated with the first evaluation value. The sixth quantization function corresponding to the material usage data is related to the third internal index sub-function, the fourth internal index sub-function, the pre-configured first material adjustment coefficient, the pre-configured second material adjustment coefficient, the third indicator function, the fourth indicator function, and the second weight; The third internal indicator sub-function represents the structural information of the recycled material utilization rate indicator and the circular material utilization rate indicator; the fourth internal indicator sub-function represents the structural information of the solid waste output ratio indicator, the air pollutant output ratio indicator, and the water pollutant output ratio indicator; the third indicator function is used to identify whether the enterprise has data on the use of recycled or circular materials; the fourth indicator function is used to identify whether the enterprise has data on the output of waste or pollutants; the first material adjustment coefficient is used to adjust the degree of influence of the output result of the third internal indicator sub-function on the first evaluation value; and the second material adjustment coefficient is used to adjust the output of the fourth internal indicator sub-function. The result affects the degree of influence of the first evaluation value; the second weight is used to distinguish the degree of influence of the material utilization rate index and the waste output ratio index on the first evaluation value. The material utilization rate index includes the recycled material utilization rate index and the circular material utilization rate index. The waste output ratio index includes the solid waste output ratio index, the air pollutant output ratio index, and the water pollutant output ratio index; the sixth quantification function indicates that the recycled material utilization rate index and the circular material utilization rate index are positively correlated with the first evaluation value, and the solid waste output ratio index, the air pollutant output ratio index, and the water pollutant output ratio index are negatively correlated with the first evaluation value; The seventh quantitative function corresponding to the budget data is related to the fifth internal indicator sub-function and the pre-configured budget adjustment coefficient; Wherein, the fifth internal indicator sub-function represents the capital budget ratio indicator; the capital budget adjustment coefficient is used to adjust the degree of influence of the output result of the fifth internal indicator sub-function on the first evaluation value; the seventh quantification function indicates that the capital budget ratio indicator is positively correlated with the first evaluation value; The eighth quantification function corresponding to the carbon asset data is related to the sixth internal indicator sub-function, the pre-configured carbon asset adjustment coefficient, the time decay sub-function, and the verification status adjustment coefficient. Wherein, the sixth internal indicator sub-function represents the carbon asset coverage ratio of the enterprise's carbon quota, certified voluntary emission reduction quota, and uncertified voluntary emission reduction quota relative to the enterprise's carbon emissions within a preset period; the carbon asset adjustment coefficient is used to adjust the degree of influence of the output result of the sixth internal indicator sub-function on the first assessment value; the certification status adjustment coefficient is used to distinguish the degree of influence of certified voluntary emission reduction quota and uncertified voluntary emission reduction quota on the first assessment value; the time decay sub-function indicates that the timeliness weight of carbon asset data and the time difference data have an exponential decay relationship; and the eighth quantification function indicates that the carbon asset coverage ratio has a positive correlation with the first assessment value. The ninth quantization function corresponding to the publicly disclosed patent data is related to the seventh internal index subfunction, the pre-configured patent adjustment coefficient, and the time decay subfunction. Wherein, the seventh internal indicator sub-function represents the score indicator corresponding to each patent status of the enterprise; the patent adjustment coefficient is used to adjust the degree of influence of the output result of the seventh internal indicator sub-function on the first evaluation value; the time decay sub-function indicates that the timeliness weight of the published patent data and the time difference data have an exponential decay relationship; the ninth quantification function indicates that the score indicator corresponding to the patent status has a positive correlation with the first evaluation value. The tenth quantification function corresponding to the enterprise project data is related to the eighth internal indicator subfunction, the time decay subfunction, the pre-configured project adjustment coefficient, the project type coefficient, and the project status coefficient. The eighth internal indicator sub-function represents the projected emission reduction ratio of each project within a preset period, whereby the projected emission reduction ratio represents the ratio of the projected emission reduction to the company's carbon emissions. The project adjustment coefficient is used to adjust the impact of the output of the eighth internal indicator sub-function on the first evaluation value. The project type coefficient is used to distinguish the impact of different types of projects on the first evaluation value, and the project status coefficient is used to distinguish the impact of different status projects on the first evaluation value. The time decay sub-function indicates that the timeliness weight of the company's project data and the time difference data have an exponential decay relationship. The tenth quantification function indicates that the projected emission reduction ratio is positively correlated with the first evaluation value. The eleventh quantitative function corresponding to the enterprise management data is related to the ninth internal indicator sub-function and the management type coefficient; The ninth internal indicator sub-function represents the execution status score indicators of the enterprise's internal carbon incentive management system, dedicated sustainability or carbon management department system, and sustainability or carbon management system; the management type coefficient is used to distinguish the degree of influence of different types of management systems on the first evaluation value; the eleventh quantitative function indicates that the execution status score indicators of various management systems within the enterprise are positively correlated with the first evaluation value.

5. The method according to claim 3 or 4, characterized in that, The method further includes: Call the first interface to obtain the percentage of transaction amount between each purchasing company and each supplier company within a preset period; The influence matrix is ​​determined based on the proportion of transaction amounts between each purchasing company and each supplier company. Based on the influence matrix, a Leontief inverse matrix is ​​constructed to obtain the propagation matrix, wherein the Leontief inverse matrix represents the degree of supply chain influence experienced by each purchasing enterprise through each supplier enterprise. Based on the propagation matrix and the first evaluation matrix, a second evaluation matrix is ​​determined. The first evaluation matrix represents the second evaluation value of each purchasing enterprise. The second evaluation matrix represents the third evaluation value of each purchasing enterprise, which is used to evaluate the green transformation effect of each purchasing enterprise's supply chain.

6. The method according to claim 5, characterized in that, The method further includes: Based on the first evaluation matrix, the influence matrix, and the propagation matrix, a third evaluation matrix is ​​determined, wherein the third evaluation matrix includes a fourth evaluation value; or... Based on the elements of the second evaluation matrix and the elements of the influence matrix, a fourth evaluation value is determined, which is used to evaluate the green transformation effect of each enterprise affected by the supply chain.

7. The method according to claim 6, characterized in that, The method further includes: Obtain external data related to the company's green transformation; The external data is subjected to entity extraction, entity attribute extraction, and entity relationship extraction to obtain a knowledge graph; wherein, the entities of the knowledge graph include enterprise green transformation entities and calculation result entities, the enterprise green transformation entities include the multiple evaluation indicator data; the entity relationships of the knowledge graph include supply relationships and procurement relationships, the supplier enterprise and the purchaser enterprise are determined through the supply relationship and the procurement relationship; The calculation result entity is updated based on at least one of the first evaluation value, the second evaluation value, the third evaluation value, and the fourth evaluation value.

8. The method according to claim 7, characterized in that, The method further includes: If multiple duplicate entities are obtained after entity extraction, the preset discrimination key of the duplicate entities is matched based on the matching strategy to obtain the matching result; the matching strategy includes precise matching and / or fuzzy matching based on similarity. If the matching result indicates that the preset discrimination key of the multiple duplicate entities matches, a preset verification strategy is triggered.

9. The method according to claim 7, characterized in that, The method further includes: If the second evaluation value is lower than a preset threshold, reasoning is performed based on the knowledge graph to identify the root cause entity that caused the second evaluation value to be lower than the preset threshold.

10. The method according to claim 7, characterized in that, The method also includes acquiring external benchmark data related to the green transformation of enterprises; Obtaining external data and benchmark data related to enterprise green transformation includes: By calling the second interface, the corresponding data adapter is invoked to perform the following: connecting to the data source of the external data and the external benchmark data, capturing the external data and the external benchmark data, parsing the external data and the external benchmark data, and verifying the external data and the external benchmark data to obtain the external data and the external benchmark data; wherein, the interface encapsulates interfaces of multiple data adapters, which are used to process external data and external benchmark data from different data sources; the external benchmark data includes at least one of the following: the carbon footprint benchmark value, the carbon emissions per unit output benchmark value, and the EU carbon border adjustment mechanism product carbon emissions benchmark value.

11. The method according to claim 10, characterized in that, The method further includes: In response to a data update event of the target data, the associated data related to the target data is updated, wherein the target data includes at least one of the following: the external data, the external reference data, and the quantization function.

12. A data processing device for enterprise green transformation, characterized in that, The device is used in an electronic device, and the device includes: The first quantification module is used to call the first interface to obtain multiple assessment indicator data for enterprise green transformation. The multiple assessment indicator data includes a first category of data and a second category of data. The first category of data is comparable across different enterprises based on a benchmark value, while the second category of data does not have a benchmark value across different enterprises. The first quantization module is further configured to call multiple pre-configured quantization functions to perform standard quantization processing on the multiple evaluation index data to obtain a first evaluation value corresponding to each evaluation data. The quantization function is related to a relative benchmark sub-function and / or an internal indicator sub-function. The relative benchmark sub-function represents the degree of influence of the relative difference between the evaluation index data and the benchmark value on the first evaluation value. The internal indicator sub-function represents the degree of influence of the enterprise's internal indicators on the first evaluation value. The enterprise's internal indicators are determined based on the evaluation index data. The quantization function is also related to a normalization sub-function to transform the multiple evaluation index data into a standardized result that can be compared under a unified scale. The first quantification module is further used to perform weighted processing on multiple first evaluation values ​​corresponding to the multiple evaluation indicator data to obtain a second evaluation value, which is used to evaluate the green transformation effect of the enterprise.

13. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 11.

15. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 11.

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