ESG performance optimization method and system based on artificial intelligence

Abstract

The invention relates to the technical field of enterprise management, and discloses an ESG performance optimization method and system based on artificial intelligence. The ESG performance optimization method comprises the following steps: obtaining an evaluation index used for evaluating ESG performance of a target enterprise; obtaining a data demand of the evaluation index, and obtaining a data type and a data source of enterprise data used for calculating the score of the evaluation index based on the data demand; corresponding data acquisition nodes are generated based on different enterprise data, and any data acquisition node is connected with a data source storing the corresponding enterprise data; integrating a data acquisition node network by using the data acquisition nodes corresponding to different data types, and synchronously acquiring real-time enterprise data by using the data acquisition node network; and calculating a real-time score of the evaluation index by using the real-time enterprise data, and evaluating the real-time ESG performance of the target enterprise by using the real-time score. According to the invention, real-time ESG performance evaluation feedback can be provided for the enterprise, and the sustainable development capability and the social responsibility performance effect of the enterprise can be improved.

Description

Technical Field

[0001] The present invention relates to the field of enterprise management technology, and in particular to an ESG performance optimization method and system based on artificial intelligence. Background Art

[0002] As the world pays more attention to sustainable development and social responsibility, ESG performance has become an important criterion for measuring a company's long-term competitiveness and ability to fulfill its social responsibilities. However, when conducting ESG assessments, companies face major challenges due to fragmented data and the lack of a unified management mechanism.

[0003] Since each department uses different systems and data formats, traditional data management methods are unable to effectively cope with large amounts of information from multiple heterogeneous data sources, thus reducing the efficiency of data integration. At the same time, the lack of a centralized management mechanism further exacerbates the severity of the problem. Some key ESG data may not be systematically recorded in some departments, or the records are incomplete, resulting in incomplete evaluation results and affecting the company's accurate understanding of its ESG performance. In addition, each department may update data at different time points, resulting in inconsistent data timeliness, further exacerbating the problem of incompleteness. Moreover, each department manages its own data independently. This "information island" phenomenon hinders the sharing and circulation of data, making it difficult for companies to form a global perspective to evaluate their overall ESG performance. Summary of the invention

[0004] In view of the shortcomings of existing technologies and actual application needs, the present invention provides an ESG performance optimization method and system based on artificial intelligence, which aims to solve the problems of low data integration efficiency and insufficient evaluation accuracy caused by data dispersion, lack of unified management, inconsistent data timeliness and incomplete evaluation results in the process of corporate ESG evaluation.

[0005] In a first aspect, the ESG performance optimization method based on artificial intelligence provided by the present invention comprises the following steps: Obtaining evaluation indicators for evaluating the ESG performance of the target enterprise, including environmental evaluation indicators, social evaluation indicators and management evaluation indicators; Obtaining data requirements for the evaluation indicator, and based on the data requirements, obtaining data types and data sources of enterprise data used to calculate the evaluation indicator score; Based on different enterprise data, corresponding data collection nodes are generated respectively, and any data collection node is connected to the data source storing the corresponding enterprise data; Using data collection nodes corresponding to different data types, integrating a data collection node network, and using the data collection node network to synchronously acquire real-time enterprise data for calculating the evaluation index score; The real-time enterprise data is used to calculate the real-time score of the evaluation indicator, and the real-time score is used to evaluate the real-time ESG performance of the target enterprise.

[0006] Optionally, obtaining the evaluation indicators for evaluating the ESG performance of the target enterprise comprises the following steps: Obtain policy documents on ESG performance assessment of the industry in which the target enterprise is located; Evaluation indicators are extracted from the policy documents using natural language processing techniques.

[0007] Optionally, the environmental assessment indicators include but are not limited to carbon emissions, energy consumption, water resource management, waste disposal, pollutant emissions and ecological protection; The social assessment indicators include but are not limited to employee health and safety, employee diversity and inclusion, supply chain management, community investment, and labor conditions.

[0008] The management evaluation indicators include but are not limited to board structure, anti-corruption and compliance, shareholder rights protection, information transparency and enterprise risk management.

[0009] Optionally, the obtaining of the data requirement of the evaluation indicator comprises the following steps: Obtaining a score calculation model for the evaluation indicator, and obtaining definitions of various calculation parameters in the score calculation model; According to the definition of each calculation parameter in the score calculation model, the data type and data source of the enterprise data used to calculate the evaluation index score are obtained.

[0010] Optionally, the step of generating corresponding data collection nodes based on different enterprise data respectively further includes the following steps: According to the data type of the enterprise data, the data collection mode of the corresponding data collection node is matched, and the matched data collection mode is loaded into the corresponding data collection node. The data collection mode is a way of acquiring enterprise data from the corresponding data source.

[0011] Optionally, the step of integrating a data acquisition node network by using data acquisition nodes corresponding to different data types comprises the following steps: From all data collection nodes, select one data collection node as the central data collection node; The remaining data acquisition nodes are respectively connected to the central data acquisition node to obtain a data acquisition node network, and the data acquisition node network uses the central data acquisition node to control the remaining data acquisition nodes.

[0012] Optionally, the step of selecting a data collection node from all data collection nodes as a central data collection node comprises the following steps: Obtain the average data transmission bandwidth of each data acquisition node and other data acquisition nodes respectively; The data collection node corresponding to the maximum average data transmission bandwidth is set as the central data collection node.

[0013] Optionally, the average data transmission bandwidth of any data acquisition node satisfies the calculation model.

[0014] Optionally, the step of synchronously acquiring the real-time enterprise data used to calculate the evaluation index score by using the data acquisition node network comprises the following steps: Using the central data collection node, at the same time node, real-time enterprise data requests are sent to the remaining data collection nodes respectively, and any of the remaining data collection nodes sends real-time enterprise data to the central data collection node based on the corresponding real-time enterprise data request; The central data collection node is utilized to integrate real-time enterprise data from all data collection nodes.

[0015] In the second aspect, the artificial intelligence-based ESG performance optimization system provided by the present invention includes an input device, a processor, a memory and an output device, wherein the input device, the processor, the memory and the output device are interconnected, wherein the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the artificial intelligence-based ESG performance optimization method provided in the first aspect.

[0016] The benefits of the ESG performance optimization method and system based on artificial intelligence provided by the present invention are: 1. Through the automated data collection node network, the problem of scattered data and inconsistent formats within the enterprise is solved, so that ESG data from different departments can be integrated and processed efficiently and centrally.

[0017] 2. Utilize the data collection node network to synchronize the company's ESG data in real time to ensure the timeliness and consistency of the data during the evaluation, and avoid evaluation deviations caused by untimely data updates from different departments.

[0018] 3. Through real-time data collection and score calculation, the ESG performance of enterprises can be dynamically evaluated, providing immediate feedback to enterprises and helping them to quickly adjust their strategies and optimize their sustainable development and social responsibility performance.

[0019] 4. Based on the policy documents of the industry in which the enterprise is located, the evaluation indicators are automatically extracted with artificial intelligence, and the ESG score is dynamically calculated with real-time data to help enterprises understand their performance in a timely manner and make optimization adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart of an ESG performance optimization method based on artificial intelligence provided by an embodiment of the present invention; Figure 2 A schematic diagram of a data collection node provided by an embodiment of the present invention; Figure 3 A schematic diagram of a data acquisition node network provided by an embodiment of the present invention; Figure 4 Schematic diagram of an ESG performance optimization system based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0022] In one embodiment, see Figure 1 , Figure 1 This is a flow chart of the ESG performance optimization method based on artificial intelligence provided in an embodiment of the present invention.

[0023] like Figure 1 As shown, the ESG performance optimization method based on artificial intelligence provided by the embodiment of the present invention includes the following steps: S01. Obtain evaluation indicators for evaluating the ESG performance of the target enterprise, wherein the evaluation indicators include environmental evaluation indicators, social evaluation indicators and management evaluation indicators.

[0024] In this embodiment, the ESG performance of the target enterprise refers to the actual actions and results achieved by the target enterprise in the three dimensions of environment, society and governance, representing its overall performance in sustainable development and fulfillment of social responsibilities.

[0025] It is understandable that the focus and requirements of ESG performance assessment of companies in different industries are different. For example, energy companies pay more attention to environmental assessment indicators, such as carbon emissions and energy management; while technology companies pay more attention to management assessment indicators, such as data privacy and supply chain management.

[0026] Furthermore, the step S01 of obtaining the evaluation indicators for evaluating the ESG performance of the target enterprise includes the following steps: S011. Obtain the policy documents on ESG performance assessment of the industry in which the target enterprise is located.

[0027] What we know is that the ESG assessment system needs to be based on relevant policies, regulations and industry standards. These documents provide specific guidelines and requirements for ESG assessments of different types of companies.

[0028] In existing technologies, different industries and regions have special ESG policy documents or guidelines, such as the Global Reporting Initiative (GRI), the Sustainability Accounting Standards Board (SASB) standards, industry-specific policy documents, etc.

[0029] S012. Utilize natural language processing technology to extract evaluation indicators from the policy document.

[0030] Specifically, evaluation indicators for evaluating ESG performance can be extracted from one or more policy documents through existing AI models, such as ChatGPT and Wenxinyiyan.

[0031] Furthermore, the environmental assessment indicators refer to the impact of the enterprise on the natural environment and related environmental protection measures during its operations, which are used to measure the environmental sustainability of the enterprise and evaluate the enterprise's performance in resource use, pollution control and ecological protection, including but not limited to carbon emissions, energy consumption, water resource management, waste disposal, pollutant emissions and ecological protection.

[0032] Furthermore, the social assessment indicators mainly measure the performance of enterprises in social responsibility, employee welfare, community relations, etc., including but not limited to employee health and safety, employee diversity and inclusion, supply chain management, community investment and working conditions.

[0033] Furthermore, the management evaluation indicators measure the performance of enterprises in terms of corporate governance, internal control and management transparency, including but not limited to board structure, anti-corruption and compliance, shareholder rights protection, information transparency and enterprise risk management.

[0034] S02. Obtain data requirements for the evaluation indicator, and based on the data requirements, obtain data types and data sources of enterprise data used to calculate the evaluation indicator score.

[0035] It is understandable that the data requirement for obtaining the evaluation indicators in step S02 is to ensure that the necessary data can be collected to calculate the performance scores of the company in the three dimensions of environment, society and governance (ESG).

[0036] Furthermore, the step S02 of obtaining the data requirement of the evaluation index includes the following steps: S021. Obtain a score calculation model for the evaluation index, and obtain definitions of each calculation parameter in the score calculation model.

[0037] Similarly, step S021 can be implemented by referring to the natural language processing technology in step S012, that is, importing the relevant policies of ESG performance evaluation indicators into the AI ​​model, and using the AI ​​model to obtain the scoring calculation model of one or more evaluation indicators, as well as the definition of each calculation parameter in each scoring calculation model.

[0038] S022. According to the definition of each calculation parameter in the score calculation model, obtain the data type and data source of the enterprise data used to calculate the evaluation index score.

[0039] For example, according to the GRI 305 standard, the scoring calculation model for a company’s carbon emissions management is: ,in, Indicates the carbon emission management score of the enterprise, Indicates the current carbon emissions of the enterprise, in tons of carbon dioxide equivalent. Represents industry or historical benchmark emissions in tons of carbon dioxide equivalent.

[0040] Furthermore, the enterprise data used to calculate the evaluation index score include the enterprise's current carbon emissions and industry or historical benchmark emissions, both of which are numerical data. The data source of the former is the enterprise environmental management system (EMS), and the data source of the latter is the industry database or the enterprise environmental management system (EMS).

[0041] S03. Based on different enterprise data, corresponding data collection nodes are generated respectively, and any data collection node is connected to a data source storing corresponding enterprise data.

[0042] In this embodiment, in order to solve the data dispersion problem in the ESG evaluation process, corresponding data collection nodes are set for different enterprise data, and the corresponding data collection nodes are connected to the data source storing the corresponding enterprise data to ensure that the dispersed data can be collected.

[0043] See also Figure 2 , Figure 2 Schematic diagram of data collection nodes provided by an embodiment of the present invention; Figure 2As shown, in order to evaluate the ESG performance of the target enterprise, it is necessary to collect multiple enterprise data such as enterprise data 1, enterprise data 2, ..., enterprise data N-1 and enterprise data N. To ensure that each type of enterprise data can be collected, data collection node 1, data collection node 2, ..., data collection node N-1 and data collection node N are set up respectively based on enterprise data 1, enterprise data 2, ..., enterprise data N-1 and enterprise data N.

[0044] It is understandable that different enterprise data may come from different data sources, such as Figure 2 The enterprise data 1 shown in FIG. 1 comes from data source 1, and the enterprise data N comes from data source M; different enterprise data may come from the same data source, such as Figure 2 The enterprise data 1 and the enterprise data 2 shown in are both from data source 1.

[0045] Furthermore, in response to the data heterogeneity problem in the ESG assessment process, since data from different enterprises are obtained separately through corresponding data collection nodes, each data collection node only needs to be responsible for handling operations such as collection, transmission or processing of one type of data.

[0046] In this embodiment, the step S03 of generating corresponding data collection nodes based on different enterprise data respectively further includes the following steps: According to the data type of the enterprise data, the data collection mode of the corresponding data collection node is matched, and the matched data collection mode is loaded into the corresponding data collection node. The data collection mode is a way of acquiring enterprise data from the corresponding data source.

[0047] Specifically, the data types include but are not limited to conventional data types such as numerical data, text data, and document data; further, each data type corresponds to a data collection mode, such as numerical data is collected through an API interface connection, text data is collected through NLP technology, and document data is collected through a document parsing tool (such as OCR or PDF parsing tool).

[0048] like Figure 2 As shown, the corresponding data collection modes are matched based on the data types of enterprise data 1, enterprise data 2, ..., enterprise data N-1 and enterprise data N, and then the corresponding data collection modes are loaded into data collection node 1, data collection node 2, ..., data collection node N-1 and data collection node N. It can be understood that the data collection modes of different data collection nodes can be the same, such as data collection node 1 and data collection node 2 are both data collection mode 1.

[0049] S04. Using data collection nodes corresponding to different data types, integrating a data collection node network, and using the data collection node network to synchronously acquire real-time enterprise data for calculating the evaluation index score.

[0050] In this embodiment, in order to solve the data dispersion problem in the ESG evaluation process, the data collection node network is integrated to realize the automation and centralized management of data collection.

[0051] Furthermore, the data acquisition node network is integrated by using data acquisition nodes corresponding to different data types as described in step S04, including the following steps: S0411. Select one data collection node from all data collection nodes as a central data collection node.

[0052] Furthermore, the central data collection node may be selected randomly, or the data collection node with the strongest data processing capability may be selected as the central data collection node based on the data processing capability of the data collection node, or the selection may be based on other methods.

[0053] In this embodiment, the step S0411 of selecting a data collection node from all data collection nodes as a central data collection node includes the following steps: S04111. Obtain the average data transmission bandwidth of each data acquisition node and the remaining data acquisition nodes respectively.

[0054] Specifically, the average data transmission bandwidth of any data collection node satisfies the following calculation model: , where i is a positive integer from 1 to N, N is the total number of data acquisition nodes, and i represents the number of the data acquisition node. represents the average data transmission bandwidth of data acquisition node i, Represents the data transmission bandwidth between data acquisition node i and data acquisition node j.

[0055] S04112. Set the data collection node corresponding to the maximum average data transmission bandwidth as the central data collection node.

[0056] It can be understood that by selecting the node with the largest average data transmission bandwidth as the central node, the performance of the entire data collection network and the speed of data synchronization can be maximized, thereby ensuring higher efficiency in ESG data collection and processing.

[0057] S0412. Connect the remaining data acquisition nodes to the central data acquisition node respectively to obtain a data acquisition node network, wherein the data acquisition node network uses the central data acquisition node to control the remaining data acquisition nodes.

[0058] See also Figure 3 , Figure 3 A schematic diagram of a data acquisition node network provided by an embodiment of the present invention; Figure 3 As shown, data acquisition node 1 is set as central data acquisition node O, and central data acquisition node O is respectively connected to data acquisition node 1, data acquisition node 2, ..., data acquisition node N-1 and data acquisition node N, and then the central data acquisition node is used to control the remaining data acquisition nodes.

[0059] In this embodiment, in order to ensure the timeliness and uniformity of data in the ESG evaluation process, all data are collected at a unified time point, which solves the problem of inconsistent data updates and ensures that data of all dimensions are collected within the same time period, thereby improving the accuracy and timeliness of the evaluation.

[0060] Furthermore, the step S04 of using the data collection node network to synchronously obtain real-time enterprise data for calculating the evaluation index score includes the following steps: S0421. Utilize the central data collection node to send real-time enterprise data requests to the remaining data collection nodes at the same time node. Any of the remaining data collection nodes sends real-time enterprise data to the central data collection node based on the corresponding real-time enterprise data request.

[0061] In this embodiment, the central data collection node sends data requests to all other data collection nodes at the same time point, ensuring that all distributed data collection nodes can collect and return the latest enterprise data within the same period, avoiding data asynchrony caused by data collection time from different sources.

[0062] S0422. Utilize the central data collection node to integrate real-time enterprise data from all data collection nodes.

[0063] It can be understood that the central data collection node is responsible for real-time enterprise data collection, and is also responsible for aggregating and integrating data from all other nodes to ensure that data sources are diverse and comprehensive, while being able to be uniformly processed at the central node, further enhancing data consistency and availability, so that data of all dimensions can be analyzed within the same time frame.

[0064] S05. Calculate the real-time score of the evaluation indicator using the real-time enterprise data, and evaluate the real-time ESG performance of the target enterprise using the real-time score.

[0065] In this embodiment, step S05 uses real-time enterprise data to calculate the evaluation index scores of each ESG dimension, and dynamically evaluates the overall ESG performance of the enterprise based on these scores, thereby providing timely feedback to the target enterprise, helping it to discover potential problems and quickly adjust its operating strategy.

[0066] In one embodiment, see Figure 4 , Figure 4 Schematic diagram of an ESG performance optimization system based on artificial intelligence provided by an embodiment of the present invention.

[0067] like Figure 4 As shown, the artificial intelligence-based ESG performance optimization system provided by the present invention includes an input device, a processor, a memory and an output device; wherein the input device, the processor, the memory and the output device are interconnected.

[0068] Furthermore, the memory is used to store a computer program, which includes program instructions, and the processor is configured to call the program instructions to execute an ESG performance optimization method based on artificial intelligence.

[0069] Furthermore, the input device is used to input data and instructions related to the enterprise's ESG performance, allowing the user to input content such as policy documents, access rights to enterprise data sources, etc. Specifically, the input device can be a keyboard, touch screen, scanning device, etc. for manual input, or it can be integrated with an API interface for automatic data acquisition from external systems (such as enterprise management systems, government databases).

[0070] Furthermore, the output device is used to display and output the ESG performance evaluation results, providing users with real-time ESG performance scores, detailed evaluation reports, and feedback on the system operation status. Specifically, the output device can be a display, printer, data interface, etc., through which users can view the evaluation results and export the results in report form (such as PDF or spreadsheet). The output device can also directly feed back the evaluation results to the enterprise management system or regulatory agency through the API interface.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents.

Claims

1. An ESG performance optimization method based on artificial intelligence, characterized in that: The steps include: Obtaining evaluation indicators for evaluating the ESG performance of the target enterprise, including environmental evaluation indicators, social evaluation indicators and management evaluation indicators; Obtaining data requirements for the evaluation indicator, and based on the data requirements, obtaining data types and data sources of enterprise data used to calculate the evaluation indicator score; Based on different enterprise data, corresponding data collection nodes are generated respectively, and any data collection node is connected to the data source storing the corresponding enterprise data; Using data collection nodes corresponding to different data types, integrating a data collection node network, and using the data collection node network to synchronously acquire real-time enterprise data for calculating the evaluation index score; The real-time enterprise data is used to calculate the real-time score of the evaluation indicator, and the real-time score is used to evaluate the real-time ESG performance of the target enterprise.

2. The ESG performance optimization method based on artificial intelligence according to claim 1, characterized in that: The step of obtaining the evaluation indicators for evaluating the ESG performance of the target enterprise includes the following steps: Obtain policy documents on ESG performance assessment of the industry in which the target enterprise is located; Evaluation indicators are extracted from the policy documents using natural language processing techniques.

3. The ESG performance optimization method based on artificial intelligence according to claim 2, characterized in that: The environmental assessment indicators include but are not limited to carbon emissions, energy consumption, water resource management, waste disposal, pollutant emissions and ecological protection; The social evaluation indicators include, but are not limited to, employee health and safety, employee diversity and inclusion, supply chain management, community investment, and labor conditions; The management evaluation indicators include but are not limited to board structure, anti-corruption and compliance, shareholder rights protection, information transparency and enterprise risk management.

4. The ESG performance optimization method based on artificial intelligence according to claim 1, characterized in that: The step of obtaining the data requirement of the evaluation indicator comprises the following steps: Obtaining a score calculation model for the evaluation indicator, and obtaining definitions of various calculation parameters in the score calculation model; According to the definition of each calculation parameter in the score calculation model, the data type and data source of the enterprise data used to calculate the evaluation index score are obtained.

5. The ESG performance optimization method based on artificial intelligence according to claim 4, characterized in that: The step of generating corresponding data collection nodes based on different enterprise data also includes the following steps: According to the data type of the enterprise data, the data collection mode of the corresponding data collection node is matched, and the matched data collection mode is loaded into the corresponding data collection node. The data collection mode is a way of acquiring enterprise data from the corresponding data source.

6. The ESG performance optimization method based on artificial intelligence according to claim 1, characterized in that: The method of integrating a data acquisition node network by using data acquisition nodes corresponding to different data types comprises the following steps: From all data collection nodes, select one data collection node as the central data collection node; The remaining data acquisition nodes are respectively connected to the central data acquisition node to obtain a data acquisition node network, and the data acquisition node network uses the central data acquisition node to control the remaining data acquisition nodes.

7. The ESG performance optimization method based on artificial intelligence according to claim 6, characterized in that: The step of selecting a data collection node from all data collection nodes as a central data collection node comprises the following steps: Obtain the average data transmission bandwidth of each data acquisition node and other data acquisition nodes respectively; The data collection node corresponding to the maximum average data transmission bandwidth is set as the central data collection node.

8. The ESG performance optimization method based on artificial intelligence according to claim 7, characterized in that: The average data transmission bandwidth of any data acquisition node satisfies the calculation model.

9. The ESG performance optimization method based on artificial intelligence according to claim 6, characterized in that: The method of synchronously acquiring the real-time enterprise data used to calculate the evaluation index score by using the data collection node network comprises the following steps: Using the central data collection node, at the same time node, real-time enterprise data requests are sent to the remaining data collection nodes respectively, and any of the remaining data collection nodes sends real-time enterprise data to the central data collection node based on the corresponding real-time enterprise data request; The central data collection node is utilized to integrate real-time enterprise data from all data collection nodes.

10. An ESG performance optimization system based on artificial intelligence, characterized in that: It includes an input device, a processor, a memory and an output device, wherein the input device, the processor, the memory and the output device are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the artificial intelligence-based ESG performance optimization method as described in any one of claims 1 to 9.

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