Enterprise ESG index determination method and system based on data fusion

Abstract

The invention discloses an enterprise ESG index determination method based on data fusion, and relates to the technical field of enterprise ESG index determination and supervision. The enterprise ESG index determination method based on data fusion comprises the following steps of data acquisition, data conflict evaluation and data change analysis. According to the method, the conflict evaluation data is obtained through the obtained to-be-analyzed data set of the to-be-analyzed index, then the data conflict evaluation index obtained based on the conflict evaluation data is compared with the data conflict evaluation threshold value, the corresponding standby data is obtained, and then the contrast data set of the to-be-analyzed index is obtained; the change trend analysis index is obtained based on the standby data and the contrast data set, and finally the change trend analysis index is compared with the change trend judgment threshold to judge whether data checking is carried out or not, so that the accuracy of the enterprise ESG index determined based on data fusion is improved; the problem that in the prior art, data conflicts occur when enterprise ESG indexes are determined through multiple data sources is solved.

Description

Technical Field

[0001] The present invention relates to the field of enterprise ESG index determination and supervision technology, and in particular to a method and system for determining an enterprise ESG index based on data fusion. Background Art

[0002] As the concept of global sustainable development continues to deepen, the environmental (E), social (S) and governance (G) performance of enterprises has become an important indicator for measuring their long-term value and risk. Since ESG data sources are diverse and contain structured and unstructured information such as financial reports, social public opinion, policies and regulations, and environmental monitoring, data fusion technology plays a key role in determining the ESG index. Through data fusion, data from different sources, formats, and types can be integrated to achieve effective unification and analysis of information. In addition, the ESG system that integrates technologies such as artificial intelligence, natural language processing, and big data processing can dynamically capture changes in enterprises in terms of environment, social responsibility, and corporate governance. Establishing an ESG index system based on data fusion can not only improve the comprehensiveness and real-time nature of the evaluation, but also provide a scientific basis for investment decisions, risk management, and corporate strategy, and promote more transparent and responsible sustainable development of the market.

[0003] Existing methods for determining corporate ESG indices mainly rely on a single data source or limited data dimensions, such as financial data, corporate disclosures of sustainability reports, etc. These methods often have problems with incomplete data, poor timeliness, and insufficient ability to process multi-dimensional information. In addition, most existing systems use static evaluation models, which make it difficult to capture real-time changes in corporate environmental, social, and governance aspects. At the same time, natural language processing and sentiment analysis technologies still have limitations in accuracy when processing unstructured data (such as news and social media). The heterogeneity, cross-domain nature, and complexity of data further increase the difficulty of information integration, resulting in the lack of transparency, dynamism, and accuracy of existing ESG evaluation methods, making it difficult to meet the needs of investors and regulators for real-time, comprehensive, and reliable ESG evaluations.

[0004] For example, the invention patent announcement with announcement number: CN113313362B discloses a method for determining an enterprise ESG index based on data completion and related products, including: obtaining enterprise data of the enterprise to be evaluated within a preset time period; when M is less than N, completing the disclosure data related to the evaluation index A of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated within the preset time period and the enterprise data of multiple first enterprises within the preset time period, and obtaining the disclosure data related to the evaluation index A of the enterprise to be evaluated at N historical moments; obtaining the disclosure data related to each evaluation index of the enterprise to be evaluated at N historical moments based on the disclosure data related to the evaluation index A of the enterprise to be evaluated at N historical moments; performing an ESG evaluation on the enterprise to be evaluated based on the disclosure data related to each evaluation index of the enterprise to be evaluated at N historical moments, and obtaining the ESG index of the enterprise to be evaluated.

[0005] For example, the invention patent announcement with announcement number: CN113240272B discloses a method and related products for determining an ESG index for an enterprise, including: obtaining a set of evaluation indicators; obtaining historical ESG evaluation data of multiple enterprises and historical returns of multiple enterprises; using the historical ESG evaluation data of multiple enterprises as training samples and the historical returns of multiple enterprises as training labels to perform model training to obtain the actual weight of each evaluation indicator under each evaluation dimension under each evaluation dimension; determining the initial weight of each evaluation indicator under each evaluation dimension based on the historical ESG evaluation data of multiple enterprises; fusing the actual weight and initial weight of each evaluation indicator under each evaluation dimension to obtain the target weight of each evaluation indicator under each evaluation dimension under each evaluation dimension; obtaining the ESG index of the enterprise to be evaluated based on the enterprise data of the enterprise to be evaluated and the target weight of each evaluation indicator under each evaluation dimension under each evaluation dimension.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, after data collection and standardization, since the data is collected from a variety of data sources, the data disclosed by each data source for the same evaluation indicator may be different, and there is a problem of data conflict when multiple data sources are used to determine the corporate ESG index. Summary of the invention

[0008] The embodiments of the present application provide a method and system for determining an enterprise ESG index based on data fusion, thereby solving the problem of data conflict in determining an enterprise ESG index from multiple data sources in the prior art, and improving the accuracy of the enterprise ESG index determined based on data fusion.

[0009] An embodiment of the present application provides a method for determining an enterprise ESG index based on data fusion, comprising the following steps: obtaining a data set to be analyzed of an indicator to be analyzed through various data sources, and obtaining conflict assessment data based on the data set to be analyzed; obtaining a data conflict assessment index based on the conflict assessment data, comparing the data conflict assessment index with a data conflict assessment threshold obtained from a preset database and obtaining corresponding stand-by data, wherein the data conflict assessment index is used to quantify the degree of data conflict of the data set to be analyzed of the indicator to be analyzed; obtaining a control data set of the indicator to be analyzed from a preset enterprise historical database, obtaining a change trend analysis index based on the stand-by data and the control data set, comparing the change trend analysis index with a change trend judgment threshold and judging whether to perform data verification, wherein the change trend analysis index is used to quantify the degree of conformity of the stand-by data with the historical change trend.

[0010] Furthermore, the specific process of obtaining the conflict assessment data is as follows: the conflict assessment data is obtained by processing the data set to be analyzed, the data set to be analyzed represents a data set obtained from each data source for evaluating the corresponding indicator to be analyzed, and the indicator to be analyzed is used to determine the enterprise ESG index; the conflict assessment data includes variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation; the conflict assessment data is obtained, and before that, data source extraction monitoring is also performed: the specific steps of data source extraction monitoring are as follows: A1, obtaining extraction data through a network detection tool, the extraction data includes extraction time, data packet size, extraction transmission rate, data loading delay and connection retry number; A2, obtaining reference extraction data from a preset database, the reference extraction data includes Average extraction time, average data packet size, minimum network transmission rate, maximum data loading delay and maximum number of connection retries; A3, number the extracted data according to the data type. If the extracted data meets the extraction condition, the data extraction analysis index is obtained based on the extracted data and the reference extracted data of the corresponding data type, otherwise the data extraction analysis index is recorded as 1, and the extraction condition indicates that the extraction transmission rate is greater than the minimum network transmission rate and the data loading delay is less than the maximum data loading delay and the number of connection retries is less than the maximum number of connection retries; A4, compare the data extraction analysis index with the data extraction threshold: if the data extraction analysis index is greater than the data extraction threshold, extract the data of the next data source, otherwise perform secondary extraction on the data of the corresponding data source until the data of the next data source is extracted.

[0011] Furthermore, the specific process of obtaining the data conflict assessment index based on the conflict assessment data is as follows: numbering the indicators to be analyzed and obtaining the conflict assessment data; performing logarithmic processing on the variance and standard deviation of the data set to be analyzed to obtain the discrete degree assessment value of the data set to be analyzed; performing hyperbolic tangent processing on the range of the data set to be analyzed to obtain the difference degree assessment value of the data set to be analyzed; performing hyperbolic sine processing on the interquartile range, mean absolute deviation and median absolute deviation of the data set to be analyzed to obtain the aggregation degree assessment value of the data set to be analyzed; and obtaining the data conflict assessment index of the data set to be analyzed by performing square root processing and hyperbolic secant processing on the sum of the discrete degree assessment value, the difference degree assessment value and the aggregation degree assessment value.

[0012] Furthermore, the specific acquisition process of the stand-by data is as follows: a data conflict assessment threshold is obtained from a preset database, and compared with a data conflict assessment index: if the data conflict assessment index is less than the data conflict assessment threshold, the data in the data set to be analyzed is averaged to obtain the stand-by data; if the data conflict assessment index is not less than the data conflict assessment threshold, the data to be analyzed is combined with the assessment weights of each data source to obtain the stand-by data; the assessment weights include a first-level assessment weight mapping set and a second-level assessment weight mapping set, the first-level assessment weight mapping set represents a set of weights assigned to each data source according to the type and authority of each data source, and the second-level assessment weight mapping set represents a set of weights corresponding to the industry status corresponding to the same type of data source.

[0013] Furthermore, the specific method for obtaining the change trend analysis index is as follows: obtain a reference data set and number them according to the order of the data sequence; perform data processing on the obtained standby data and the reference data set to obtain the change trend analysis index; the restriction expression of the change trend analysis index is as follows:

[0014]

[0015] In the formula, d represents the number of the index to be analyzed, d = 1, 2, ..., D, D represents the total number of the index to be analyzed, q represents the sequence number of the data in the control data set, q = 1, 2, ..., Q, Q represents the total number of data in the control data set, ρ d represents the standby data of the dth indicator to be analyzed, δ q represents the qth data in the control data set, δ q+1 represents the q+1th data in the control data set, ω d It represents the change trend analysis index of the dth indicator to be analyzed, and e represents the natural constant.

[0016] Furthermore, the specific process of determining whether to perform data verification is as follows: if the change trend analysis index is greater than the change trend judgment threshold, data verification is not performed; if the change trend analysis index is not greater than the change trend judgment threshold, a preset worker is prompted to perform data verification; the change trend judgment threshold is used to determine the degree of deviation between the change trend of the stand-by data and the control data set.

[0017] Furthermore, the specific process of the data verification is as follows: step one, sending a first judgment button to a preset staff member, if the preset staff member selects yes, then determining that the stand-by data is used for the evaluation of the indicator to be analyzed, otherwise executing step two; step two, sending the data set to be analyzed and the corresponding data sources to the preset staff member and a data verification prompt instruction; step three, sending a second judgment button to the preset staff member, if the preset staff member selects correctly, then sending a data source check prompt instruction, otherwise re-extracting data from each data source and updating the data set to be analyzed; step four, re-obtaining the data conflict evaluation index according to the data set to be analyzed changed by the preset staff member until no data verification is performed.

[0018] Furthermore, the sending of the data source self-check prompt instruction also includes obtaining the feedback result of the data source self-check prompt instruction; sending the data source self-check feedback instruction to the preset staff, if the preset staff feedback that the data source data is incorrect, re-obtaining the data source and updating the data set to be analyzed; if the preset staff feedback that the data source data is correct, obtaining an updated data conflict assessment index based on the data set to be analyzed, and obtaining a conflict assessment similarity coefficient based on the absolute value of the difference between the updated data conflict assessment index and the original data conflict assessment index and the ratio of the updated data conflict assessment index to the original data conflict assessment index; comparing the conflict assessment similarity coefficient with the conflict similarity assessment threshold and judging whether there is an error in the enterprise ESG index determination method; the original data conflict assessment index represents the data conflict assessment index obtained based on the unchanged data set to be analyzed; the updated data conflict assessment index represents the data conflict assessment index obtained based on the updated data set to be analyzed.

[0019] Furthermore, the specific process of determining whether there is an error in the method for determining the enterprise ESG index is as follows: if the conflict assessment similarity coefficient is not greater than the conflict similarity assessment threshold, it indicates that the method for determining the enterprise ESG index is correct; if the conflict assessment similarity coefficient is greater than the conflict similarity assessment threshold, an error warning is issued and the programmer is requested to debug the program; the conflict similarity assessment threshold is used to determine whether there is a program error in the method for determining the enterprise ESG index.

[0020] An embodiment of the present application provides an enterprise ESG index determination system based on data fusion, characterized in that it includes a data acquisition module, a data conflict assessment module and a data change analysis module; wherein the data acquisition module is used to acquire a data set to be analyzed of an indicator to be analyzed through various data sources, and obtain conflict assessment data based on the data set to be analyzed; the data conflict assessment module is used to obtain a data conflict assessment index based on the conflict assessment data, compare the data conflict assessment index with a data conflict assessment threshold obtained from a preset database and obtain corresponding stand-by data, and the data conflict assessment index is used to quantify the degree of data conflict of the data set to be analyzed of the indicator to be analyzed; the data change analysis module is used to acquire a control data set of the indicator to be analyzed from a preset enterprise historical database, obtain a change trend analysis index based on the stand-by data and the control data set, compare the change trend analysis index with the change trend judgment threshold and determine whether to perform data verification, and the change trend analysis index is used to quantify the degree of conformity of the stand-by data with the historical change trend.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. Obtain conflict assessment data by obtaining the data set to be analyzed of the indicator to be analyzed, and then obtain the data conflict assessment index based on the conflict assessment data, compare the data conflict assessment index with the data conflict assessment threshold and obtain the corresponding stand-by data, then obtain the reference data set of the indicator to be analyzed, and obtain the change trend analysis index based on the stand-by data and the reference data set, and then compare the change trend analysis index with the change trend judgment threshold to determine whether to perform data verification, so as to more reasonably confirm the data used to evaluate the ESG index, thereby achieving an improvement in the accuracy of the enterprise ESG index determined based on data fusion, and effectively solving the problem of data conflict in determining the enterprise ESG index from multiple data sources in the prior art.

[0023] 2. The extracted data is numbered by data type. Then, if the extracted data meets the extraction conditions, the data extraction analysis index is obtained based on the obtained extracted data and the reference extracted data. Otherwise, the data extraction analysis index is recorded as 1. Finally, the data extraction analysis index is compared with the data extraction threshold to determine whether to extract data from the next data source, thereby reducing the waste of processor resources caused by data extraction errors for subsequent data processing, thereby improving the execution efficiency of the processor.

[0024] 3. By obtaining the reference data set and numbering it in order of the data sequence, the obtained stand-by data and the reference data set are processed to obtain the change trend analysis index, thereby effectively evaluating whether the stand-by data conforms to the change trend, and thus achieving a more accurate determination of the ESG index.

[0025] 4. By sending the data source self-check feedback instruction to the preset staff, if the preset staff feedback that the data source data is incorrect, the data source is re-acquired and the data set to be analyzed is updated. If the preset staff feedback that the data source data is correct, the updated data conflict assessment index is obtained according to the data set to be analyzed, and the conflict assessment similarity coefficient is obtained based on the original data conflict assessment index, which is helpful to judge whether there is an error in the method of determining the enterprise ESG index, thereby reducing the error rate of obtaining the ESG index. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flowchart of a method for determining an enterprise ESG index based on data fusion provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of the change of the conflict assessment similarity coefficient provided in the embodiment of the present application;

[0028] Figure 3 A structural diagram of a system for determining an enterprise ESG index based on data fusion provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The embodiment of the present application solves the problem of data conflict in determining the enterprise ESG index from multiple data sources in the prior art by providing a method and system for determining the enterprise ESG index based on data fusion. The method obtains the data set to be analyzed of the indicator to be analyzed through each data source, obtains conflict assessment data based on the data set to be analyzed, and then obtains the data conflict assessment index based on the conflict assessment data. The data conflict assessment index is compared with the data conflict assessment threshold and the corresponding stand-by data is obtained. Then, a reference data set of the indicator to be analyzed is obtained from a preset enterprise historical database, and the reference data set is obtained and numbered in the order of the data sequence. The obtained stand-by data and the reference data set are processed to obtain a change trend analysis index. Then, the change trend analysis index is compared with the change trend judgment threshold and it is judged whether to perform data verification, thereby improving the accuracy of the enterprise ESG index determined based on data fusion.

[0030] The technical solution in the embodiment of the present application is to solve the problem of data conflict in determining the ESG index of an enterprise from multiple data sources. The overall idea is as follows:

[0031] The conflict assessment data is obtained by acquiring the data set to be analyzed, and then the data conflict assessment index obtained based on the conflict assessment data is compared with the data conflict assessment threshold to obtain the corresponding stand-by data, and the change trend analysis index is obtained based on this and the control data set of the indicator to be analyzed. Finally, the change trend analysis index is compared with the change trend judgment threshold to determine whether data verification is performed, thereby achieving the effect of improving the accuracy of the enterprise ESG index determined based on data fusion.

[0032] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0033] like Figure 1 As shown, it is a flow chart of a method for determining an enterprise ESG index based on data fusion provided by an embodiment of the present application, the method comprising the following steps: obtaining a data set to be analyzed of the indicator to be analyzed through various data sources, and obtaining conflict assessment data based on the data set to be analyzed; obtaining a data conflict assessment index based on the conflict assessment data, comparing the data conflict assessment index with a data conflict assessment threshold obtained from a preset database and obtaining corresponding stand-by data, the data conflict assessment index being used to quantify the degree of data conflict of the data set to be analyzed of the indicator to be analyzed; obtaining a control data set of the indicator to be analyzed from a preset enterprise historical database, obtaining a change trend analysis index based on the stand-by data and the control data set, comparing the change trend analysis index with a change trend judgment threshold and judging whether to perform data verification, the change trend analysis index being used to quantify the degree of conformity of the stand-by data with the historical change trend, and data verification means re-verifying the data in the data set to be analyzed.

[0034] In this embodiment, the data set to be analyzed represents a collection of digital data used to determine the relevant indicators of the enterprise ESG index. The data set to be analyzed includes three aspects: environment, society and governance. The indicators related to the environment include energy consumption, water resource usage and total waste, etc., the indicators related to society include employee turnover rate, safety accident rate and customer complaint rate, etc., and the indicators related to governance include the proportion of women on the board of directors, shareholder voting participation rate and tax transparency, etc.; and the data sources for obtaining the relevant indicators of the enterprise ESG index usually include internal data sources of the enterprise (annual report, financial system, environmental management system and enterprise operation log, etc.), supply chain and partner data sources (supplier report, supply chain management system and third-party supplier audit, etc.), external Third-party data sources (government agencies and public databases, academic reports and market analysis reports, and third-party rating agencies, etc.), social media and public opinion data (social media, news websites, and survey reports, etc.), and industry benchmarks and peer comparison data (industry benchmark reports and peer ESG reports, etc.); through a multi-step evaluation and analysis of data conflicts and changing trends, it is possible to identify anomalies or conflicts in the data set to be analyzed, thereby screening out more reliable data to be used and ensuring data accuracy and consistency; and, by comparing with data in historical databases, it is possible to ensure that new data is consistent with historical trends and prevent data bias from affecting analysis results, thereby improving the accuracy of data management and the accuracy of the corporate ESG index determined based on data fusion.

[0035] It needs to be explained that the indicators to be analyzed can represent a variety of indicator data when determining the corporate ESG index, including total water consumption, wastewater discharge, total energy consumption, proportion of renewable energy use, carbon intensity, greenhouse gas emissions, employee turnover rate, employee gender ratio, employee racial ratio, workplace accident rate, employee training time, customer satisfaction, proportion of independent directors, gender ratio of board members and financial transparency, etc. The subsequent explanation and analysis will be based on wastewater discharge as an example.

[0036] Furthermore, the specific process of obtaining the conflict assessment data is as follows: extract corresponding data from each data source according to the indicators to be analyzed and perform statistics to obtain the data set to be analyzed; perform data processing on the data set to be analyzed to obtain the conflict assessment data, the data set to be analyzed represents the data set obtained from each data source for evaluating the corresponding indicators to be analyzed, and the indicators to be analyzed are used to determine the enterprise ESG index; the conflict assessment data includes variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation; before obtaining the conflict assessment data, it also includes data source extraction monitoring: the specific steps of data source extraction monitoring are as follows: A1, obtain extraction data through network detection tools, the extraction data includes extraction time, data packet size, extraction transmission rate, data loading delay and connection retry times; A2, obtain reference extraction data from the preset database, the reference extraction data includes mean A3, numbering the extracted data according to the data type, if the extracted data meets the extraction condition, obtaining the data extraction analysis index based on the extracted data and the reference extracted data of the corresponding data type, otherwise the data extraction analysis index is recorded as 1, and the extraction condition indicates that the extraction transmission rate is greater than the minimum network transmission rate and the data loading delay is less than the maximum data loading delay and the number of connection retries is less than the maximum number of connection retries; A4, comparing the data extraction analysis index with the data extraction threshold: if the data extraction analysis index is greater than the data extraction threshold, extracting the data of the next data source, otherwise performing secondary extraction on the data of the corresponding data source until the data extraction analysis index is greater than the data extraction threshold, and extracting the data of the next data source.

[0037] The numerical expression of the data extraction analysis index is as follows:

[0038]

[0039] Where n represents the number of the data type, n = 1, 2, ..., N, N represents the total number of data types, T n represents the extraction time of the nth type of data, D n Indicates the packet size of the nth type of data, S n represents the extraction and transmission rate of the nth type of data, L nrepresents the data loading delay of the nth type of data, C n Indicates the number of connection retries for the nth type of data. represents the average extraction time, represents the average packet size, S min Indicates the minimum network transmission rate, L max Indicates the maximum data loading delay, C max Indicates the maximum number of connection retries, DE n represents the data extraction analysis index of the nth type of data, and e represents a natural constant.

[0040] In this embodiment, data on wastewater discharge of the indicator to be analyzed is obtained from each data source to obtain a data set to be analyzed corresponding to the wastewater discharge, and variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation are obtained by performing variance calculations, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation on the data set to be analyzed, respectively. The degree of data conflict between the data sources is accurately quantified, thereby providing a basis for subsequent data screening, and ensuring the accuracy and reliability of ESG indicator data, thereby enhancing the credibility of the evaluation results of the company in terms of environment, society and governance (ESG); at the same time, by performing discreteness and central tendency analysis on the data related to the indicators to be analyzed provided by each data source, it is helpful to quickly evaluate the differences in data sources and ensure the coordination and consistency of the data after integration.

[0041] Specifically, the algorithm of this embodiment combines the extracted data and the reference extracted data for comprehensive analysis to obtain a data extraction analysis index, wherein the closer the extraction time is to the average extraction time and the closer the data packet size is to the average data packet size, the larger the data extraction analysis index is, indicating that the possibility of data extraction errors is smaller. Similarly, when the extraction transmission rate is greater than the minimum network transmission rate and the data loading delay is less than the maximum data loading delay and the number of connection retries is less than the maximum number of connection retries, the value of the data extraction analysis index is also larger, indicating that the possibility of data extraction errors is smaller. In addition, under ideal conditions where the network status is normal and the data source is stable, the extraction time should be proportional to the data packet size and inversely proportional to the extraction transmission rate, and the data loading delay and the number of retries will be reduced. By comparing the relationship between the above physical quantities, it is helpful to determine whether there are anomalies or other transmission problems in the data extraction process, and it is also beneficial to reduce the waste of processor resources caused by data extraction errors for subsequent data processing and improve the execution efficiency of the processor.

[0042] Specifically, reference extraction data is obtained from a preset database. The average extraction time is obtained by averaging the historical extraction time of the corresponding type of data, and the average data packet size is obtained by averaging the historical data packet size of the corresponding type of data; the minimum network transmission rate, the maximum data loading delay, and the maximum number of connection retries are set according to the historical data of the data type. For example, if the minimum network transmission rate for correct data extraction under the same network environment in the historical data is 60bps, the minimum network transmission rate is set to 60bps; if the maximum data loading delay for correct data extraction in the historical data is 10ms, the maximum data loading delay is set to 10ms; if the maximum number of connection retries for correct data extraction in the historical data is 3 times, the maximum number of connection retries is set to 3 times.

[0043] Specifically, the data extraction threshold is obtained from a preset database. In a specific embodiment, the extracted data corresponding to all data extraction errors in the historical data are substituted into the numerical expression of the data extraction analysis index to obtain a data set of the data extraction analysis index, and the result of the mean operation on the data set is set as the data extraction threshold.

[0044] Furthermore, the specific process of obtaining the data conflict assessment index based on the conflict assessment data is as follows: number the indicators to be analyzed and obtain the conflict assessment data; perform logarithmic processing on the variance and standard deviation of the data set to be analyzed to obtain the discrete degree assessment value of the data set to be analyzed; perform hyperbolic tangent processing on the range of the data set to be analyzed to obtain the difference degree assessment value of the data set to be analyzed; perform hyperbolic sine processing on the interquartile range, mean absolute deviation and median absolute deviation of the data set to be analyzed to obtain the aggregation degree assessment value of the data set to be analyzed; and obtain the data conflict assessment index of the data set to be analyzed by performing square root processing and hyperbolic secant processing on the sum of the discrete degree assessment value, the difference degree assessment value and the aggregation degree assessment value.

[0045] The specific restriction expression of the data conflict evaluation index is as follows:

[0046]

[0047] In the formula, d represents the number of the indicator to be analyzed, d = 1, 2, ..., D, and D represents the total number of indicators to be analyzed. represents the variance of the dth indicator to be analyzed, represents the standard deviation of the dth indicator to be analyzed, It represents the range of the dth indicator to be analyzed. represents the interquartile range of the dth indicator to be analyzed, represents the mean absolute deviation of the dth indicator to be analyzed, represents the median absolute deviation of the dth indicator to be analyzed, Represents the data conflict evaluation index of the dth indicator to be analyzed.

[0048] In this embodiment, the algorithm combines the variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation for comprehensive analysis to obtain the data conflict assessment index. Taking the wastewater discharge as an example, the variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation of the data set to be analyzed corresponding to the wastewater discharge are all negatively correlated with the data conflict assessment index. When the variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation of the data set to be analyzed corresponding to the wastewater discharge are larger, the data conflict assessment index is smaller, indicating that the data difference in the data set to be analyzed of the wastewater discharge may be larger, and thus the possibility of data conflict is also greater, and the standard deviation is the variance. The square root of , they are all calculated based on the mean and have the same direction of change. The mean absolute deviation is another form of variance, which calculates the absolute deviation of the data point from the mean. The range only considers the maximum and minimum values, so it is easily affected by extreme values, while the interquartile range focuses on the middle 50% of the data by removing the extreme values ​​in the data. Both the interquartile range and the median absolute deviation are not affected by extreme values ​​and are robust discreteness measures. The interquartile range focuses on the middle 50% of the data, while the median absolute deviation focuses on the deviation of all data points relative to the median. By combining the variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation, the data change table of the data conflict assessment index is obtained, as shown in Table 1:

[0049] Table 1 Data change table of data conflict assessment index

[0050]

[0051] As can be seen from Table 1, when the variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation of the data set to be analyzed corresponding to the wastewater discharge are all 0, the data conflict assessment index is the largest (value is 1), indicating that there is no data conflict in the wastewater discharge, and the data provided by each data source are consistent. If the variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation of the data set to be analyzed corresponding to the wastewater discharge are larger, the data conflict assessment index is smaller, indicating that the possibility of data conflict in the wastewater discharge is greater. For example, the variance of the data in the third row of the table is 24. 3. When the standard deviation is 4.93, the range is 9, the interquartile range is 9, the mean absolute deviation is 4.32 and the median absolute deviation is 10, each independent variable reaches its maximum. At this time, the data conflict assessment index of wastewater discharge is close to 0, indicating that the possibility of data conflict has reached the maximum; therefore, through a comprehensive analysis of the variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation, it is possible to effectively check the existing data conflicts on wastewater discharge, and take corresponding measures to intervene in time to improve the accuracy of the determined stand-by data.

[0052] Furthermore, the specific process of obtaining the stand-by data is as follows: obtain the data conflict assessment threshold from the preset database and compare it with the data conflict assessment index: if the data conflict assessment index is less than the data conflict assessment threshold, perform mean operation on the data in the data set to be analyzed to obtain the stand-by data; if the data conflict assessment index is not less than the data conflict assessment threshold, combine the data to be analyzed with the assessment weights of each data source to obtain the stand-by data; the assessment weights include a first-level assessment weight mapping set and a second-level assessment weight mapping set, the first-level assessment weight mapping set represents a set of weights assigned to each data source according to the type and authority of each data source, and the second-level assessment weight mapping set represents a set of weights corresponding to the industry status corresponding to the same type of data source.

[0053] In this embodiment, the first-level evaluation weight mapping set and the second-level evaluation weight mapping set are both obtained from a preset database. Specifically, the first-level evaluation weight mapping set is constructed according to the types of data sources and their corresponding weights, and one type of data source corresponds to one weight in a one-to-one correspondence. The second-level evaluation weight mapping set is constructed according to the weights corresponding to the industry status of the same type of data sources, and one data source corresponds to one weight in a one-to-one correspondence. By using the first-level evaluation weight mapping set and the second-level evaluation weight mapping set, different data sources are graded, and data sources with high authority and high industry status are given priority to ensure that the final stand-by data is more reliable, thereby improving data quality and credibility. At the same time, the corresponding calculation method (mean operation or weight operation) is selected according to the comparison result of the data conflict evaluation threshold, thereby avoiding unnecessary complex processing and improving the efficiency of data integration.

[0054] Specifically, the first-level evaluation weight mapping set is the weights corresponding to different types of data sources in the preset database, which represents the numerical value of the influence of different types of data sources on the waiting data. When used, the weights corresponding to different types of data sources can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the first-level evaluation weights corresponding to different types of data sources and the weights corresponding to the waiting data preset in the preset database form a mapping set, and the different types of data sources are input into the mapping set to obtain the corresponding weights, and the mapping relationship is one-to-one. In this example, its value range is [0, 1].

[0055] Specifically, the secondary evaluation weight mapping set is the weights corresponding to different data sources of the same type in the preset database, which represents the numerical value of the influence of different data sources of the same type on the primary evaluation weights. When used, the weights corresponding to different data sources of the same type can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the secondary evaluation weights corresponding to different data sources of the same type and the weights corresponding to the primary evaluation weights preset in the preset database form a mapping set, and the corresponding weights are obtained by inputting different data sources of the same type into the mapping set, and the mapping relationship is one-to-one. In this example, its value range is [0, 1].

[0056] Specifically, the data conflict assessment threshold is obtained from a preset database. In a specific embodiment, a data set where data conflict occurs is obtained from historical data and corresponding conflict assessment data is obtained according to the data set, these data conflict assessment data are substituted into a specific restriction expression of a data conflict assessment index to obtain a corresponding data conflict assessment index, and a mean operation is performed on the obtained data conflict assessment index to obtain a corresponding average value, which is then recorded as the data conflict assessment threshold.

[0057] Furthermore, a specific method for obtaining a change trend analysis index is as follows: obtaining a reference data set and numbering it in the order of the data sequence; performing data processing on the obtained standby data and the reference data set to obtain a change trend analysis index; the restriction expression of the change trend analysis index is as follows:

[0058]

[0059] In the formula, d represents the number of the index to be analyzed, d = 1, 2, ..., D, D represents the total number of the index to be analyzed, q represents the sequence number of the data in the control data set, q = 1, 2, ..., Q, Q represents the total number of data in the control data set, ρ d represents the standby data of the dth indicator to be analyzed, δ q represents the qth data in the control data set, δ q+1 represents the q+1th data in the control data set, ω dIt represents the change trend analysis index of the dth indicator to be analyzed, and e represents the natural constant.

[0060] In this embodiment, taking wastewater discharge as an indicator to be analyzed, the algorithm combines the standby data of wastewater discharge and the control data set of wastewater discharge to obtain a change trend analysis index, where: represents the mean of the control data set, It represents the displacement mean of the control data set. As the difference between the mean values ​​of the standby data of wastewater discharge and the control data set increases, the change trend analysis index becomes smaller, indicating that the possibility that the change trend of the standby data of wastewater discharge is inconsistent with that of the historical data is greater, wherein the control data set represents the historical data set of the indicator to be analyzed in the historical database of the enterprise; similarly, as the difference between the displacement amount and the displacement mean of the standby data of wastewater discharge and the data in the last control data set increases, the higher the degree of deviation of the displacement amount of the standby data from the data in the last control data set of the indicator wastewater discharge to be analyzed from the average data change level, the greater the possibility that the standby data of wastewater discharge is abnormal, and the mean data types of the standby data and the control data set are the same but have no influence on each other, and the displacement amount and displacement mean of the standby data do not influence each other; therefore, by analyzing and comparing the standby data of the indicator wastewater discharge to be analyzed with the control data set, it helps to improve the availability of the standby data, and also improves the reliability of the determined enterprise ESG index.

[0061] Furthermore, the specific process of determining whether to perform data verification is as follows: if the change trend analysis index is greater than the change trend judgment threshold, no data verification is performed; if the change trend analysis index is not greater than the change trend judgment threshold, a preset worker is prompted to perform data verification; the change trend judgment threshold is used to determine the degree of deviation between the change trend of the stand-by data and the control data set.

[0062] In this embodiment, the automatic decision on whether data verification is needed reduces unnecessary manual intervention, optimizes the workflow and improves efficiency. In addition, the preset change trend judgment threshold can make intelligent judgments based on data fluctuations, thereby realizing the automated management of the verification process and making the entire data processing process more intelligent. At the same time, the use of the change trend analysis index ensures that verification prompts are triggered only when data changes are abnormal, focusing on key data to ensure that stand-by data is more accurately verified.

[0063] Specifically, the change trend judgment threshold is obtained from a preset database. In a specific embodiment, a data set of indicators to be analyzed without abnormal data changes is obtained from historical data, and a corresponding change trend analysis index is obtained according to the data set, and the obtained change trend analysis index is averaged to obtain a corresponding average value, and the average value is recorded as the change trend judgment threshold.

[0064] Furthermore, the specific process of data verification is as follows: Step 1, sending a first judgment button to the preset staff. If the preset staff selects yes, it is determined that the stand-by data is used for the evaluation of the indicator to be analyzed, otherwise, step 2 is executed. The first judgment button is used to determine whether the current abnormal change trend is real; Step 2, sending the data set to be analyzed and the corresponding data sources to the preset staff and the data verification prompt instruction; Step 3, sending a second judgment button to the preset staff. If the preset staff selects correctly, a data source check prompt instruction is sent, otherwise data is re-extracted from each data source and the data set to be analyzed is updated, and the second judgment button is used to determine whether there is a data extraction error; Step 4, the data conflict evaluation index is re-obtained according to the data set to be analyzed after the preset staff changes it until no data verification is performed.

[0065] In this embodiment, data is extracted from each data source through a programming language (such as Python, Java, SQL) or a data processing tool (such as ETL tool Ta l end, Apache NiFi), and then connected to an API or database, a request is sent, data files are downloaded or web pages are crawled, and finally a stream processing tool (such as Apache Kafka, Flink) is used to process real-time data to update the data set to be analyzed; it is determined whether the current abnormal change trend is true and whether there is a corresponding development event for the enterprise to cause such an abnormal change; the reliability and accuracy of the data are guaranteed by clicking two judgment buttons and checking the data source, and the manual confirmation of the data by the preset staff can reduce the possible misjudgment caused by automatic detection, ensuring that the trend reflected by the data is the actual situation; and the automated data extraction, verification and update process reduces the frequency of manual intervention, while ensuring that the data is always kept up to date, thereby improving the efficiency of data analysis.

[0066] Furthermore, a data source check prompt instruction is sent, and then the feedback result of the data source check prompt instruction is obtained; a data source check feedback instruction is sent to a preset staff member, and if the preset staff member feedbacks that the data source data is incorrect, the data source is re-obtained and the data set to be analyzed is updated; if the preset staff member feedbacks that the data source data is correct, an updated data conflict assessment index is obtained according to the data set to be analyzed, and a conflict assessment similarity coefficient is obtained based on the absolute value of the difference between the updated data conflict assessment index and the original data conflict assessment index and the ratio of the updated data conflict assessment index to the original data conflict assessment index; the conflict assessment similarity coefficient is compared with the conflict similarity assessment threshold and it is determined whether there is an error in the enterprise ESG index determination method; the original data conflict assessment index represents the data conflict assessment index obtained based on the unchanged data set to be analyzed; the updated data conflict assessment index represents the data conflict assessment index obtained based on the updated data set to be analyzed.

[0067] The restricted expression of the conflict assessment similarity coefficient is as follows:

[0068]

[0069] In the formula, d represents the number of the indicator to be analyzed, d = 1, 2, ..., D, and D represents the total number of indicators to be analyzed. represents the original data conflict evaluation index, represents the update data conflict assessment index, α represents the conflict assessment similarity coefficient, and e represents a natural constant.

[0070] In this embodiment, taking wastewater discharge as an indicator to be analyzed as an example, the algorithm combines the original data conflict evaluation index of wastewater discharge and the updated data conflict evaluation index for comprehensive analysis to obtain the data conflict evaluation index. In the formula, when the values ​​of the original data conflict evaluation index of wastewater discharge and the updated data conflict evaluation index are closer, the value of the conflict evaluation similarity index is smaller, indicating that the possibility of errors in the enterprise ESG index determination method is smaller. If the value of the conflict evaluation similarity index is larger, the possibility of errors in the enterprise ESG index determination method is greater. Specifically, Figure 2 The diagram shows the change of the conflict evaluation similarity coefficient provided in the embodiment of the present application, from Figure 2It can be seen intuitively that when the values ​​of the original data conflict assessment index and the updated data conflict assessment index of wastewater discharge are closer, the image shows a downward trend, and the change trend is not affected by the size of the respective values ​​of the original data conflict assessment index and the updated data conflict assessment index. The change trend is first decreasing and then increasing. Moreover, regardless of whether the updated data conflict assessment index of wastewater discharge increases or decreases, the definition domain of the original data conflict assessment index of wastewater discharge is a positive real number, and the value range of the corresponding conflict assessment similarity index is always [0, 1]. At the same time, with the increase of the updated data conflict assessment index of wastewater discharge, although the change trend remains unchanged, the change speed gradually decreases, and the change curve gradually flattens, indicating that when the value of the updated data conflict assessment index is smaller, the change of the conflict assessment similarity coefficient is more rapid, and the change curve is steeper; by analyzing and comparing the original data conflict assessment index and the updated data conflict assessment index of wastewater discharge, it is helpful to analyze whether there are still deficiencies in the enterprise ESG index determination method and to promptly troubleshoot and solve problems, reduce errors in subsequent data analysis, and improve the efficiency of enterprise ESG index determination.

[0071] Furthermore, the specific process of judging whether there are errors in the method for determining the enterprise's ESG index is as follows: if the conflict assessment similarity coefficient is not greater than the conflict similarity assessment threshold, it indicates that the method for determining the enterprise's ESG index is correct; if the conflict assessment similarity coefficient is greater than the conflict similarity assessment threshold, it indicates that there are errors in the method for determining the enterprise's ESG index, and an error warning is issued and the programmer is requested to debug the program; the conflict similarity assessment threshold is used to judge whether there are program errors in the method for determining the enterprise's ESG index.

[0072] In this embodiment, after understanding the background of the problem, the programmer uses the debugging function of the IDE (such as PyCharm, VS Code), sets breakpoints, executes the code step by step, and adds debugging statements in the script, such as print() or logging, outputs intermediate results, and then conducts step-by-step troubleshooting such as logical errors, data integrity, and algorithm correctness, while checking exception handling (division by zero errors, null value processing, and data type errors). Finally, unit testing and data verification are performed to ensure that each functional module of the algorithm is correct; potential problems in the ESG index calculation process are quickly identified by analyzing the conflict assessment similarity coefficient, so as to avoid erroneous data from continuing to affect subsequent analysis and improve the accuracy of data processing; and when the conflict assessment similarity coefficient exceeds the threshold, an error warning will be automatically issued and the programmer will be requested to debug, ensuring that the problem can be handled in a timely manner and reducing the long-term impact of the error on the entire ESG analysis process. At the same time, the automation of the error detection and debugging process improves the robustness of the method, and continuous monitoring and rapid response also ensure the stability and reliability of the ESG index determination method.

[0073] Specifically, the conflict similarity assessment threshold is obtained from a preset database. In a specific embodiment, a data set corresponding to the indicator to be analyzed for which a program error exists in the method for determining the enterprise ESG index is obtained from historical data, and a corresponding conflict similarity assessment coefficient is obtained based on the data set, and the obtained conflict similarity assessment coefficient is averaged to obtain a corresponding average value, which is then recorded as the conflict similarity assessment threshold.

[0074] like Figure 3 As shown, it is a structural schematic diagram of a system for determining an enterprise ESG index based on data fusion provided in an embodiment of the present application. A system for determining an enterprise ESG index based on data fusion provided in an embodiment of the present application includes: a data acquisition module, a data conflict assessment module and a data change analysis module; wherein the data acquisition module is used to obtain a data set to be analyzed of the indicator to be analyzed through various data sources, and obtain conflict assessment data based on the data set to be analyzed; the data conflict assessment module is used to obtain a data conflict assessment index based on the conflict assessment data, compare the data conflict assessment index with the data conflict assessment threshold obtained from a preset database and obtain the corresponding standby data, and the data conflict assessment index is used to quantify the degree of data conflict of the data set to be analyzed of the indicator to be analyzed; the data change analysis module is used to obtain a control data set of the indicator to be analyzed from a preset enterprise historical database, obtain a change trend analysis index based on the standby data and the control data set, compare the change trend analysis index with the change trend judgment threshold and determine whether to perform data verification, and the change trend analysis index is used to quantify the degree of conformity of the standby data with the historical change trend, and data verification means re-verifying the data in the data set to be analyzed.

[0075] In this embodiment, by quantifying data conflicts and changing trends, the system can more comprehensively detect data quality issues and ensure data consistency between different data sources; at the same time, the system provides a full-process automated processing mechanism from data acquisition to data evaluation and verification through multi-module cooperation, which significantly improves the accuracy, consistency and timeliness of the data, thereby ensuring the scientificity and reliability of the enterprise ESG index evaluation, reducing human intervention and optimizing process efficiency.

[0076] To summarize, the embodiment of the present application obtains conflict assessment data by acquiring the data set to be analyzed of the indicator to be analyzed, and then obtains the data conflict assessment index based on the conflict assessment data, compares the data conflict assessment index with the data conflict assessment threshold and obtains the corresponding stand-by data, then obtains the control data set of the indicator to be analyzed, and obtains the change trend analysis index based on the stand-by data and the control data set, and then compares the change trend analysis index with the change trend judgment threshold to determine whether to perform data verification, thereby more reasonably confirming the data used to evaluate the ESG index, and further achieving the improvement of the accuracy of the enterprise ESG index determined based on data fusion, and effectively solves the problem of data conflict in determining the enterprise ESG index from multiple data sources in the prior art.

[0077] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0081] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0082] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for determining an enterprise ESG index based on data fusion, characterized in that: The following steps are involved: Obtaining a data set to be analyzed of the indicator to be analyzed through various data sources, and obtaining conflict assessment data based on the data set to be analyzed; Based on the conflict assessment data, a data conflict assessment index is obtained, the data conflict assessment index is compared with a data conflict assessment threshold obtained from a preset database, and corresponding stand-by data is obtained, wherein the data conflict assessment index is used to quantify the degree of data conflict of the data set to be analyzed of the indicator to be analyzed; A reference data set of the indicator to be analyzed is obtained from a preset enterprise historical database, and a change trend analysis index is obtained based on the stand-by data and the reference data set. The change trend analysis index is compared with a change trend judgment threshold to determine whether data verification is to be performed. The change trend analysis index is used to quantify the degree of conformity between the stand-by data and the historical change trend.

2. A method for determining an enterprise ESG index based on data fusion as claimed in claim 1, characterized in that: The specific process of obtaining the conflict assessment data is as follows: Processing the data set to be analyzed to obtain conflict assessment data, wherein the data set to be analyzed represents a data set obtained from various data sources for evaluating corresponding indicators to be analyzed, and the indicators to be analyzed are used to determine the ESG index of the enterprise; The conflict assessment data include variance, standard deviation, range, interquartile range, mean absolute deviation and median absolute deviation; Obtaining the conflict assessment data also includes extracting and monitoring the data source: The specific steps of data source extraction monitoring are as follows: A1, obtaining extraction data through a network detection tool, wherein the extraction data includes extraction time, data packet size, extraction transmission rate, data loading delay, and connection retry times; A2, obtaining reference extraction data from a preset database, wherein the reference extraction data includes average extraction time, average data packet size, minimum network transmission rate, maximum data loading delay, and maximum number of connection retries; A3, numbering the extracted data according to the data type, and obtaining a data extraction analysis index based on the extracted data and the reference extracted data of the corresponding data type if the extracted data meets the extraction condition, otherwise the data extraction analysis index is recorded as 1, and the extraction condition indicates that the extraction transmission rate is greater than the minimum network transmission rate and the data loading delay is less than the maximum data loading delay and the number of connection retries is less than the maximum number of connection retries; A4, compare the data extraction analysis index with the data extraction threshold: If the data extraction analysis index is greater than the data extraction threshold, the data of the next data source is extracted; otherwise, the data of the corresponding data source is extracted again until the data of the next data source is extracted.

3. A method for determining an enterprise ESG index based on data fusion as claimed in claim 2, characterized in that: The specific process of obtaining the data conflict assessment index based on the conflict assessment data is as follows: Number the indicators to be analyzed and obtain conflict assessment data; The variance and standard deviation of the data set to be analyzed are processed logarithmically to obtain the discrete degree evaluation value of the data set to be analyzed; Perform hyperbolic tangent processing on the range of the data set to be analyzed to obtain the difference degree evaluation value of the data set to be analyzed; The interquartile range, mean absolute deviation and median absolute deviation of the data set to be analyzed are processed by hyperbolic sine to obtain the aggregation degree evaluation value of the data set to be analyzed; The data conflict assessment index of the data set to be analyzed is obtained by performing square root processing and hyperbolic secant processing on the sum of the discrete degree assessment value, the difference degree assessment value and the aggregation degree assessment value.

4. The method for determining an enterprise ESG index based on data fusion as claimed in claim 3, characterized in that: The specific process of obtaining the standby data is as follows: Get the data conflict assessment threshold from the preset database and compare it with the data conflict assessment index: If the data conflict evaluation index is less than the data conflict evaluation threshold, the data in the data set to be analyzed are averaged to obtain the standby data; If the data conflict evaluation index is not less than the data conflict evaluation threshold, the data to be analyzed is combined with the evaluation weights of each data source to obtain the data to be used; The evaluation weights include a first-level evaluation weight mapping set and a second-level evaluation weight mapping set. The first-level evaluation weight mapping set represents a set of weights assigned to each data source according to its type and authority, and the second-level evaluation weight mapping set represents a set of weights corresponding to the industry status corresponding to the same type of data source.

5. The method for determining an enterprise ESG index based on data fusion as claimed in claim 4, characterized in that: The specific method for obtaining the change trend analysis index is as follows: Obtain the control data set and number it in the order of the data sequence; Processing the obtained standby data and control data set to obtain a change trend analysis index; The limiting expression of the change trend analysis index is as follows: In the formula, d represents the number of the index to be analyzed, d = 1, 2, ..., D, D represents the total number of the index to be analyzed, q represents the sequence number of the data in the control data set, q = 1, 2, ..., Q, Q represents the total number of data in the control data set, ρ d represents the standby data of the dth indicator to be analyzed, δ q represents the qth data in the control data set, δ q+1 represents the q+1th data in the control data set, ω d It represents the change trend analysis index of the dth indicator to be analyzed, and e represents the natural constant.

6. A method for determining an enterprise ESG index based on data fusion as claimed in claim 5, characterized in that: The specific process of determining whether to perform data verification is as follows: If the change trend analysis index is greater than the change trend judgment threshold, no data verification is performed; If the change trend analysis index is not greater than the change trend judgment threshold, the preset staff will be prompted to check the data; The change trend judgment threshold is used to judge the degree of deviation between the change trends of the stand-by data and the reference data set.

7. A method for determining an enterprise ESG index based on data fusion as claimed in claim 6, characterized in that: The specific process of data verification is as follows: Step 1: Send a first determination button to a preset staff member. If the preset staff member selects yes, the standby data is determined to be used for the evaluation of the indicator to be analyzed. Otherwise, step 2 is executed. Step 2: Send the data set to be analyzed and the corresponding data sources to the preset staff and the data verification prompt instruction; Step 3: Send the second decision button to the preset staff. If the preset staff selects correctly, a data source search prompt instruction is sent. Otherwise, data is extracted from each data source again and the data set to be analyzed is updated. Step 4: Re-obtain the data conflict assessment index based on the data set to be analyzed after the preset staff member changes it until no data verification is performed.

8. The method for determining an enterprise ESG index based on data fusion as claimed in claim 7, characterized in that: The sending of the data source query prompt instruction further includes obtaining a feedback result of the data source query prompt instruction; Send data source self-checking feedback instructions to the preset staff. If the preset staff feedback that the data source data is incorrect, re-acquire the data source and update the data set to be analyzed; If the preset staff member feedbacks that the data source data is correct, then the updated data conflict assessment index is obtained according to the data set to be analyzed, and the conflict assessment similarity coefficient is obtained based on the absolute value of the difference between the updated data conflict assessment index and the original data conflict assessment index and the ratio of the updated data conflict assessment index to the original data conflict assessment index; Compare the conflict assessment similarity coefficient with the conflict similarity assessment threshold and determine whether there is any error in the determination method of the enterprise ESG index; The original data conflict evaluation index represents the data conflict evaluation index obtained based on the unchanged data set to be analyzed; The updated data conflict evaluation index represents a data conflict evaluation index obtained according to the updated data set to be analyzed.

9. The method for determining an enterprise ESG index based on data fusion as claimed in claim 8, characterized in that: The specific process of judging whether there is an error in the method of determining the corporate ESG index is as follows: If the conflict assessment similarity coefficient is not greater than the conflict similarity assessment threshold, it indicates that the method for determining the corporate ESG index is correct; If the conflict evaluation similarity coefficient is greater than the conflict similarity evaluation threshold, an error warning is issued and the programmer is requested to debug the program; The conflict similarity assessment threshold is used to determine whether there are procedural errors in the method for determining the corporate ESG index.

10. A system for determining an enterprise ESG index based on data fusion, characterized by: It includes data acquisition module, data conflict assessment module and data change analysis module; The data acquisition module is used to acquire the data set to be analyzed of the indicator to be analyzed through various data sources, and obtain the conflict assessment data based on the data set to be analyzed; The data conflict assessment module is used to obtain a data conflict assessment index based on the conflict assessment data, compare the data conflict assessment index with a data conflict assessment threshold obtained from a preset database and obtain corresponding stand-by data, wherein the data conflict assessment index is used to quantify the degree of data conflict of the data set to be analyzed of the indicator to be analyzed; The data change analysis module is used to obtain a reference data set of the indicator to be analyzed from a preset enterprise historical database, obtain a change trend analysis index based on the stand-by data and the reference data set, compare the change trend analysis index with the change trend judgment threshold and determine whether to perform data verification. The change trend analysis index is used to quantify the degree of conformity of the stand-by data with the historical change trend.

Images