WO2020259716A1 - Esg indicator monitoring method, apparatus, device, and storage medium - Google Patents

Abstract

An ESG indicator monitoring method, an apparatus, a device, and a storage medium. The ESG indicator monitoring method comprises: acquiring alternate data and processing the alternate data to determine a knowledge graph (S100); extracting an ESG event from the alternate data, and in combination with the knowledge graph, performing ESG indicator scoring on the ESG event (S200); and on the basis of a score result from performing ESG indicator scoring, outputting warning information to a related party of the ESG event (S300).

Description

ESG indicator monitoring method, device, equipment and storage medium

This application claims the priority of the Chinese patent application filed on August 20, 2019, the application number is 201910776692.1, and the name is "ESG indicator monitoring method, device, terminal equipment and storage medium", the entire content of which is incorporated herein by reference Applying.

Technical field

This application relates to the field of Fintech (financial technology) technology, and in particular to an ESG indicator monitoring method, device, equipment and storage medium.

Background technique

With the rapid development of financial technology, especially Internet financial technology, more and more technologies have been applied to the financial field, among which, based on the acquired ESG (Environment, Social Responsibility, Corporate Governance: environmental, social and corporate governance, refers to the three core factors that measure the sustainability and ethical impact of a company or company’s investment) indicators to help investors better determine the future financial performance (returns and risks of the company concerned) ), is being adopted and used by more and more investors.

However, the current traditional method of obtaining market ESG indicators is through organizations like MSCI (Morgan Stanley Capital International: Morgan Stanley Capital International) by distributing questionnaires to respondents (ie companies in the relevant market). Or it can be disclosed actively by companies in the relevant market. In this way, companies usually do not disclose data that is really valuable for related parties (investors, regulators, etc.) based on prudent considerations, resulting in low accuracy and data related to ESG obsolete.

Technical solutions

The main purpose of this application is to provide an ESG indicator monitoring method, device, terminal equipment, and storage medium, which aims to solve the traditional method of obtaining ESG indicators of financial market companies, and the resulting ESG-related information has low accuracy and outdated data. problem.

In order to achieve the above objective, this application provides an ESG indicator monitoring method, which includes the following steps:

Acquiring alternative data, and processing the alternative data to determine a knowledge graph;

Extract ESG events from the alternative data, and score the ESG events with ESG indicators in combination with the knowledge graph;

Output early warning information to the related party of the ESG event according to the scoring result of ESG indicator scoring.

In an embodiment, the alternative data includes entity data, business process data, and sensor data, and the step of obtaining alternative data includes:

Extract specified entity data, business process data and sensor data from the preset data platform.

In an embodiment, the knowledge graph includes: a key information graph,

The step of processing the alternative data to determine a knowledge graph includes:

Extracting key information from the alternative data based on artificial intelligence technology, where the key information includes: key nodes and key relationships;

According to the key nodes and key relationships in the key information, a key information map is determined.

In an embodiment, the knowledge graph further includes: a structural relationship graph,

The step of processing the alternative data to determine a knowledge graph further includes:

The structured data is extracted from the alternative data to determine the structure relationship map.

In an embodiment, the step of extracting ESG events from the alternative data and combining the knowledge graph to score the ESG indicators on the ESG event includes:

Extract ESG events from the alternative data based on artificial intelligence technology based on a preset extraction method;

Perform ESG index scoring on the ESG event in combination with the knowledge graph.

In an embodiment, the step of combining the knowledge graph to score ESG indicators includes:

Analyze the impact information and conduction information of the ESG event in combination with the knowledge graph;

Score ESG indicators according to the analysis result of analyzing the impact information and conduction information of the ESG event.

In an embodiment, the step of outputting early warning information to parties related to the ESG event according to the scoring result of ESG index scoring includes:

Detecting whether the scoring result of ESG index scoring on the ESG event meets the preset conditions;

When it is detected that the scoring result meets the preset condition, output risk warning information to the parties related to the ESG event.

In addition, this application also provides an ESG indicator monitoring device, and the ESG indicator monitoring device includes:

The determining module is used to obtain alternative data and process the alternative data to determine the knowledge graph;

A scoring module, configured to extract ESG events from the alternative data, and perform ESG index scoring on the ESG events in combination with the knowledge graph;

The output module is used for outputting early warning information to parties related to the ESG event according to the scoring result of the ESG indicator scoring.

The steps of implementing the ESG indicator monitoring method as described above when each module of the ESG indicator monitoring device proposed in the present application is running, will not be repeated here.

In addition, this application also provides an ESG indicator monitoring device. The ESG indicator monitoring device includes: a memory, a processor, and an ESG indicator monitoring program stored in the memory and running on the processor, the ESG When the indicator monitoring program is executed by the processor, the steps of the ESG indicator monitoring method described above are implemented.

In addition, the present application also provides a storage medium applied to a computer, and an ESG indicator monitoring program is stored on the storage medium, and the ESG indicator monitoring program is executed by a processor to implement the steps of the ESG indicator monitoring method as described above.

This application obtains alternative data and processes the alternative data to determine the knowledge graph; extracts ESG events from the alternative data, and combines the knowledge graph to score the ESG indicators; according to the ESG index The scoring result of the scoring outputs early warning information to the parties related to the ESG event. Based on the background of the big data era, we obtain industry-related alternative data from various big data platforms, and use artificial intelligence technology to determine and represent various operating entities in the industry (such as listed companies, investors, and regulatory agencies, etc.) based on the alternative data obtained ), and further combine artificial intelligence technology to extract ESG events from the obtained alternative data, so as to score the ESG indicators of the industry individuals that trigger the current ESG event based on the constructed knowledge map, and perform ESG based on the detection The scoring results of the indicator scoring output early warning information to the parties that have an influence on the industry individual that triggered the current ESG event.

This application realizes that the acquisition of ESG indicators has been changed from passive to active, avoiding the situation that individual ESG indicators cannot effectively provide references for individual parties due to the involuntary disclosure of data by industry individuals, and improving the quantity and overall value of ESG related information , And based on the combination of artificial intelligence technology, real-time monitoring and ESG index scoring of ESG events triggered by industry individuals in alternative data, and updating and maintaining the influence relationship between industry individuals based on real-time monitoring, and based on the scoring results of ESG index scoring Timely output of early warning information to trigger individual parties has improved the accuracy of ESG-related information and the real-time and effectiveness of data.

Description of the drawings

Figure 1 is a schematic structural diagram of a hardware operating environment involved in a solution of an embodiment of the present application;

2 is a schematic flowchart of the first embodiment of the ESG indicator monitoring method of this application;

FIG. 3 is a detailed flowchart of step S100 in an embodiment of an ESG indicator monitoring method of this application;

4 is a schematic flowchart of a second embodiment of the ESG indicator monitoring method of this application;

FIG. 5 is a schematic diagram of an application scenario in an embodiment of an ESG indicator monitoring method of this application;

Fig. 6 is a schematic diagram of modules of the ESG indicator monitoring device of this application.

The realization, functional characteristics, and advantages of the purpose of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Embodiments of the invention

It should be understood that the specific embodiments described here are only used to explain the application, and are not used to limit the application.

As shown in Fig. 1, Fig. 1 is a schematic structural diagram of a hardware operating environment involved in a solution of an embodiment of the present application.

It should be noted that Fig. 1 can be a structural diagram of the hardware operating environment of the terminal device. The terminal device in the embodiment of the present application may be a terminal device such as a PC and a portable computer.

As shown in FIG. 1, the terminal device may include a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to implement connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a magnetic disk memory. Optionally, the memory 1005 may also be a storage device independent of the foregoing processor 1001.

Those skilled in the art can understand that the structure of the terminal device shown in FIG. 1 does not constitute a limitation on the terminal device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange different components.

As shown in FIG. 1, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an ESG indicator monitoring program. Among them, the operating system is a program that manages and controls the hardware and software resources of the sample terminal equipment, and supports the operation of the ESG indicator monitoring program and other software or programs.

In the terminal device shown in FIG. 1, the user interface 1003 is mainly used for data communication with various terminals; the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; and the processor 1001 can be used for calling the memory 1005 The ESG indicator monitoring program stored in and perform the following operations:

Acquiring alternative data, and processing the alternative data to determine a knowledge graph;

Extract ESG events from the alternative data, and score the ESG events with ESG indicators in combination with the knowledge graph;

Output early warning information to the related party of the ESG event according to the scoring result of ESG indicator scoring.

Further, the processor 1001 may also be used to call the ESG indicator monitoring program stored in the memory 1005 and execute the following steps:

Extract specified entity data, business process data and sensor data from the preset data platform.

Further, the processor 1001 may also be used to call the ESG indicator monitoring program stored in the memory 1005 and execute the following steps:

Extracting key information from the alternative data based on artificial intelligence technology, where the key information includes: key nodes and key relationships;

According to the key nodes and key relationships in the key information, a key information map is determined.

Further, the processor 1001 may also be used to call the ESG indicator monitoring program stored in the memory 1005 and execute the following steps:

The structured data is extracted from the alternative data to determine the structure relationship map.

Further, the processor 1001 may also be used to call the ESG indicator monitoring program stored in the memory 1005 and execute the following steps:

Extract ESG events from the alternative data based on artificial intelligence technology based on a preset extraction method;

Perform ESG index scoring on the ESG event in combination with the knowledge graph.

Further, the processor 1001 may also be used to call the ESG indicator monitoring program stored in the memory 1005 and execute the following steps:

Analyze the impact information and conduction information of the ESG event in combination with the knowledge graph;

Score ESG indicators according to the analysis result of analyzing the impact information and conduction information of the ESG event.

Further, the processor 1001 may also be used to call the ESG indicator monitoring program stored in the memory 1005 and execute the following steps:

Detecting whether the scoring result of ESG index scoring on the ESG event meets the preset conditions;

When it is detected that the scoring result meets the preset condition, output risk warning information to the parties related to the ESG event.

Based on the foregoing structure, various embodiments of the ESG indicator monitoring method of the present application are proposed.

Please refer to FIG. 2, which is a schematic flowchart of the first embodiment of the ESG indicator monitoring method of this application.

The embodiments of the present application provide an embodiment of the ESG indicator monitoring method. It should be noted that although the logical sequence is shown in the flowchart, in some cases, the sequence shown or shown may be executed in a different order than here. Describe the steps.

The ESG indicator monitoring method of the embodiment of this application is applied to the above-mentioned terminal device. The terminal device of the embodiment of this application may be a terminal device such as a PC, a portable computer, etc., which is not specifically limited here.

The ESG indicator monitoring method of this embodiment includes:

Step S100: Obtain alternative data, and process the alternative data to determine a knowledge graph.

Based on the technological background of the current big data era, extract alternative data related to industries (such as financial markets, chemical markets and other financial industries) from big environmental data to establish alternative data platforms related to industry operations, combined with AI artificial intelligence Technology to extract key information from the obtained alternative data to determine the knowledge graph that represents the influence and restriction relationships among various entities in the industry.

Further, in this embodiment, the alternative data includes: entity data, business process data, and sensor data. In step S100, the step of obtaining alternative data includes:

Step A: Extract the specified entity data, business process data and sensor data from the preset data platform.

Based on the existing data acquisition methods, the entity data, business process data and sensor data related to the designated industry are extracted from each preset data platform in the big environmental data.

In this embodiment, each preset data platform includes: major platforms that publish data information in large environmental data, such as government/structure networks, news media, post bar forums, etc., and entity data includes entities related to individuals, companies, or products. data.

Specifically, for example, by using the method of connecting to the government/structure network, extract the entity data and business processes related to the designated chemical market from the government affairs data, judicial data, and financial supervision data released on the government/structure network platform Data and sensor data, or, based on data crawler technology, extract data related to the designated chemical market or financial market from mainstream news media, post bars, forums, apps, official accounts, satellite data, e-commerce data, financial market data and other data Entity data, business process data and sensor data.

Further, please refer to FIG. 3. FIG. 3 is a detailed flowchart of step S100 of an embodiment of the ESG indicator monitoring method of this application. In this embodiment, the knowledge graph includes: a key information graph. In step S100, the alternative The steps of data processing to determine the knowledge graph include:

Step S101, extracting key information from the alternative data based on artificial intelligence technology, where the key information includes: key nodes and key relationships.

Use mature artificial intelligence technology to extract real-time data information such as key nodes and key relationships from the established data platform containing alternative data from various industries.

Specifically, for example, from the constructed alternative data platform containing a listed company in the financial industry, the data information such as the external procurement contract or bidding announcement issued by the listed company is screened out, and the NLP (natural language processing: natural language processing technology) extract the upstream and downstream nodes of the listed company and the supply relationship related to the listed company from the data information such as the external procurement contract or the bidding announcement.

Step S102: Determine a key information graph according to the key nodes and key relationships in the key information.

Based on the extracted key nodes and key relationships and other data information, it is detected whether the key information map has been established. If it is detected that the key information map has not been established, the key information used to represent the influence relationship between the operating individuals in the industry is newly constructed Atlas, if it is detected that a key information atlas has been established, the established key information atlas will be updated and maintained based on real-time key nodes and key relationships and other data information.

Specifically, for example, based on data information such as external procurement contracts or bidding announcements issued by a listed company, the upstream and downstream nodes of the listed company and the supply relationship related to the listed company are extracted when it is detected that there is no When it is used to indicate the key information map that affects the restriction relationship between the current listed company and the upstream supplier company and the downstream purchaser company, a new key information map is established, and when the key information map is detected, According to the alternative data of the listed company in the real-time monitoring, the continuous development of related public opinion information and other alternative data, the upstream supplier enterprises and the downstream purchaser enterprise nodes and influence control relationships in the key information map are continuously updated and maintained.

Further, in another embodiment, the knowledge graph further includes: a structural relationship graph. In step S100, the step of processing the alternative data to determine the knowledge graph further includes:

Step B: Extract structured data from the alternative data to determine the structure relationship map.

From the established data platform containing alternative data of various industries, extract real-time structured data information, and detect whether the structure relationship map has been established currently. If it is detected that the structure relationship map has not been established, the new construction is used to represent the industry The structural relationship map of the influence relationship between each operating entity, if it is detected that the structural relationship map has been established, the established structural relationship map will be updated and maintained based on the real-time structured data information of the monitoring.

Specifically, for example, from the constructed alternative data platform that includes the financial industry, structured data such as industry and commerce, financing, investment, etc. are extracted, and when it is detected that there is no structural relationship graph, the industry and commerce data are used to construct a new representation of equity, investment The structural relationship map that affects the relationship, the use of financial transaction registration and settlement data to establish a structural relationship map that represents the financing relationship, and when a structural relationship map is detected, the business data in the alternative data and the financial transaction registration are monitored in real time Settlement data, and continuously update and maintain the constructed structural relationship graph.

Further, in another embodiment, the professional business personnel who maintain the completed knowledge graph can also manually maintain the knowledge graph.

Professional business personnel update and maintain the constructed key information map and structural relationship map through data information such as key nodes and key relationships extracted from alternative data, or according to the extracted structured data information.

Specifically, for example, professional business personnel monitor alternative data in real time, and in data information such as external procurement contracts or bidding announcements issued by a listed company, detect the upstream and downstream nodes of the listed company and the related information of the listed company When the supply relationship of the company changes, it will immediately report the change to the professional business personnel maintaining the knowledge map, so that the professional business personnel maintaining the knowledge map will be based on the relationship between the listed company and the new upstream supplier company and the new downstream purchaser company. The relationship between key nodes and the continuous development of public opinion trends between change events and other information, in the key information map used to indicate the current impact and restriction relationship between listed companies and upstream and downstream companies, manually control upstream supplier companies and downstream purchaser companies Nodes and influence control relations are updated and maintained.

Step S200: Extract ESG events from the alternative data, and perform ESG index scoring on the ESG events in combination with the knowledge graph.

Combining artificial intelligence technology to extract real-time basic ESG events from the obtained alternative data, and based on the influence and restriction relationships between the various entities in the industry represented in the constructed knowledge graph, the ESG indicator scores for the industry individuals that trigger the current ESG event .

Specifically, for example, the same NLP natural language processing technology is used to extract ESG incidents from environmental pollution penalties for a chemical company from the environmental protection government data on an alternative data platform constructed on the basis of the acquired alternative data that includes the financial industry. , And extract the spread of the incident from the public opinion data of the entire network, and estimate the impact of the spread of the incident, and use satellite remote sensing data collection and computer vision technology to continuously monitor the company’s pollution control and production and operation status to obtain ESG indicators E (Environment) scoring in the middle; through data analysis technology, analyze the current government’s attention to related social topics in alternative data, the market value and recruitment status of social listed companies, the current social theme fund scale, and the relationship between the capital market and the social theme Attention, so as to integrate social pressure, status and response to obtain S (Social Responsibility: social) score in ESG indicators; and obtain ESG indicators based on alternative data such as monitoring a company’s punishment and litigation, operating heterogeneous types or being included in the untrustworthy list G (Corporate Governance: Corporate Governance) score in.

Step S300: Output early warning information to the parties related to the ESG event according to the scoring result of the ESG indicator scoring.

Based on the scoring result of ESG indicator scoring based on the detection, the early warning information is output to each related party that has an influence relationship with the industry individual that triggered the current ESG event.

Specifically, for example, in an application scenario as shown in Figure 5, if based on the use of NLP natural language processing technology in artificial intelligence technology, from the obtained alternative data platform containing a chemical production company in the financial industry, Extract the ESG incidents of the chemical company’s environmental pollution penalties, and combine it with a key information graph constructed based on alternative data to indicate the impact of the chemical company’s upstream and downstream nodes, and use the graph mining technology in artificial intelligence technology. Each ESG indicator is scored, and the E (environment) score, S (social) score and G (corporate governance) score in the obtained ESG indicators are handed over to the ESG early warning system for judgment, and the chemical company is determined in the ESG early warning system. When the scoring result of the ESG indicator meets the preset conditions, it will immediately output risk warning information to the upstream and downstream node companies, investment companies, or regulatory agencies that have an influential and restrictive relationship with the chemical company.

Further, step S300 includes:

Step S301, detecting whether the scoring result of scoring the ESG index on the ESG event meets a preset condition.

After obtaining the scoring result of the ESG indicator scoring of the industry individual that triggered the current ESG event, it is detected whether the scoring result meets the preset condition for outputting early warning information.

In this embodiment, the preset condition is that the current judgment is based on the scoring result of the ESG indicator scoring of the industry individual that triggered the ESG event, and the judgment standard for outputting early warning information to the parties in the industry that triggered the ESG event.

Specifically, for example, on an alternative data platform that includes the financial industry constructed from the acquired alternative data, based on extracting basic ESG events and combining the structural relationship graph or the influence restriction relationship represented by the key information graph to obtain the trigger After the E (environment) score, S (social) score, and G (corporate governance) score in the individual ESG indicators of the industry operating individual of the current basic ESG event, if each E (environment) score, S (social) score and G ( In corporate governance) scoring, any scoring value is greater than or equal to the standard threshold, that is, it is determined that the scoring result meets the conditions for outputting early warning information.

Step S302: When it is detected that the scoring result meets a preset condition, output risk warning information to the party related to the ESG event.

When it is detected that the scoring results of the ESG indicator scoring on the industry individual that triggered the current ESG event meet the preset conditions for outputting early warning information, output to the relevant parties in the knowledge graph that have an influence relationship with the industry individual that triggered the current ESG event Risk warning information.

Specifically, for example, in the E (environment) score, S (social) score, and G (corporate governance) score in the ESG indicators of the industry operating individual that is detected to trigger the current basic ESG event, any score value is greater than or equal to the standard threshold, After confirming that the scoring result meets the conditions for outputting early warning information, through messaging platforms such as SMS, WeChat, SaaS (Software-as-a-Service: Software-as-a-Service), etc., the key information graph or structural relationship graph that is constructed can be used to trigger the current The related parties that affect or restrict the relationship between industry operating entities of basic ESG events, such as regulatory agencies, investors, upstream and downstream node companies, provide risk warning, risk monitoring and other risk warning information, and in particular, can also provide investors Services such as the discovery of investment opportunities will help "green finance".

This application extracts entity data, business process data, and sensor data related to the designated industry from each preset data platform in the big environmental data based on the existing data acquisition method, and will acquire the entity data of the designated industry, The business process data and the sensor data are classified and statistically processed to form a data platform containing alternative data of various industries, using mature artificial intelligence technology to extract from the established data platform containing alternative data of various industries Real-time key nodes and key relationships and other data information, based on the extracted key nodes and key relationships and other data information, establish a key information graph that represents the influence relationship between operating individuals in the industry, and based on real-time key nodes and key Relations and other data information update and maintain the established key information map, or extract real-time structured data information from the established data platform containing alternative data for various industries, so as to establish a way to represent the influence of various operating individuals in the industry The structure relationship map of the relationship, and based on the real-time structured data information of the monitoring, the established structure relationship map is updated and maintained, and the artificial intelligence technology is used to extract real-time basic ESG events from the obtained alternative data, so as to be based on the constructed knowledge map The influence and restriction relationship between the various entities in the industry indicated in, the ESG indicator scoring of the industry individual that triggered the current ESG event, after obtaining the scoring result of the ESG indicator scoring of the industry individual that triggered the current ESG event, the scoring result is detected Whether it meets the preset conditions for outputting early warning information, and when the preset conditions are met, output risk early warning information to all parties in the knowledge graph that have an influence relationship with the industry individual that triggers the current ESG event.

Realized, combined with artificial intelligence technology to actively obtain industry-related alternative data from various big data platforms, so as to establish a knowledge graph showing the influence relationship between industry individuals. Based on the monitoring of real-time ESG events and combined with the constructed knowledge graph, the industry Middle individuals score ESG indicators, and according to the scoring results of ESG indicator scores, timely output early warning information to other parties that have an influential relationship with the current industry individual, thereby changing the acquisition of ESG indicators from passive to active, avoiding industry individuals’ inactivity Disclosure of data results in the inability of individual ESG indicators to effectively provide references for individual related parties, increasing the amount and overall value of ESG-related information, and based on the combination of artificial intelligence technology, real-time monitoring of ESG events triggered by industry individuals in alternative data Based on real-time monitoring and ESG indicator scoring, the relationship between industry individuals is updated and maintained, and based on the scoring results of ESG indicator scoring, early warning information is output to the triggering individual parties in a timely manner, which improves the accuracy of ESG-related information and data Timeliness and effectiveness.

Further, a second embodiment of the ESG indicator monitoring method of the present application is proposed.

Please refer to FIG. 4, which is a schematic flowchart of the second embodiment of the ESG indicator monitoring method of this application. Based on the first embodiment of the above ESG indicator monitoring method, in this embodiment, the above step S200 is to extract ESG from the alternative data Events, combined with the knowledge graph to score ESG indicators, including:

Step S201: Extract ESG events from the alternative data based on artificial intelligence technology based on a preset extraction method.

Use existing artificial intelligence technology to randomly extract ESG events from the acquired data platform containing alternative data in the financial industry through a preset random extraction method.

Specifically, for example, NLP natural language processing technology is used to randomly sample from the environmental protection government data on the alternative data platform of the financial industry constructed based on the obtained alternative data and from the financial market or the chemical market. An ESG event of an individual operating in any industry-a chemical manufacturer or a listed company, such as a randomly selected ESG event of a chemical company's environmental pollution penalty.

Step S202: Perform ESG index scoring on the ESG event in combination with the knowledge graph.

Continuous real-time tracking and monitoring of alternative data, the evolution process of ESG events currently extracted, and based on the influence and restriction relationships between various entities in the industry represented in the constructed knowledge graph, ESG indicators are performed on the industry individuals that trigger the current ESG event score.

Specifically, for example, based on NLP natural language processing technology, from the environmental protection government data on the alternative data platform constructed based on the obtained alternative data that contains the financial industry, the ESG incident after the environmental pollution penalty of a chemical company is extracted , Continuously track and monitor the spread of the ESG event in the public opinion data of the entire network in real time, the spread of the heat, and the current government’s attention to the relevant social topics caused by the current ESG event, etc., and the key nodes and The key information map constructed by the key relationship, or in the structural relationship map constructed based on structured data, the impact of the current ESG event is estimated based on the influence and restriction relationship between the chemical company and the upstream and downstream node companies, thereby affecting the current chemical industry Score each ESG indicator of the company.

Further, in step S202, the step of scoring ESG indicators in combination with the knowledge graph includes:

Step D: Analyze the impact information and conduction information of the ESG event in combination with the knowledge graph.

Step E: Perform ESG index scoring according to the analysis result of analyzing the impact information and conduction information of the ESG event.

In the knowledge graph constructed based on alternative data, analyze the impact information of the current extracted ESG events on the parties that have an impact and restriction relationship with the industry individuals that trigger the current ESG event, and analyze the conduction of the current ESG event The social attention aroused, combined with the analysis results to score the ESG indicators of the industry individuals that triggered the current ESG event.

Specifically, for example, in a key information graph constructed based on key nodes and key relationships in alternative data, or a structural relationship graph constructed based on structured data, upstream and downstream based on investors, regulatory agencies, and industry individuals that trigger current ESG events The relationship between the node companies and other related parties, and the industry individuals that trigger the current ESG event, analyzes the impact of the current ESG event on each related party, and monitors the public opinion data in the entire network through continuous real-time tracking. The spread of ESG events, the degree of spread, and the current government’s attention to relevant social topics caused by current ESG events, further estimate the impact of current ESG events, so as to more comprehensively and accurately score current chemical companies’ ESG indicators .

This application uses existing artificial intelligence technology to randomly extract ESG events from the obtained data platform containing alternative data in the financial industry through a preset random extraction method. In the knowledge graph constructed based on alternative data, Analyze the magnitude of the impact of the current ESG events on the parties that have an influence and restriction relationship with the industry individuals that trigger the current ESG event, and analyze the social concerns caused by the conduction of the current ESG event, and combine the analysis results Score the ESG indicators of industry individuals that trigger the current ESG event.

A more comprehensive and accurate ESG indicator scoring based on ESG events in alternative data is realized, thereby further improving the accuracy of ESG-related information and the effectiveness of providing references to investors, regulatory agencies, etc. for ESG-related information.

In addition, referring to FIG. 5, an embodiment of the present application also proposes an ESG indicator monitoring device, and the ESG indicator monitoring device includes:

The determining module is used to obtain alternative data and process the alternative data to determine the knowledge graph;

A scoring module, configured to extract ESG events from the alternative data, and perform ESG index scoring on the ESG events in combination with the knowledge graph;

The output module is used for outputting early warning information to parties related to the ESG event according to the scoring result of the ESG indicator scoring.

In an embodiment, the determining module includes:

The first extraction unit is used to extract specified entity data, business process data and sensor data from the preset data platform.

In an embodiment, the determining module further includes:

The extraction unit is configured to extract key information from the alternative data based on artificial intelligence technology, where the key information includes: key nodes and key relationships;

The first determining unit is configured to determine the key information map according to the key nodes and key relationships in the key information.

In an embodiment, the determining module further includes:

The second determining unit is used to extract structured data from the alternative data to determine the structure relationship map.

In one embodiment, the scoring module includes:

The second extraction unit is configured to extract ESG events from the alternative data based on artificial intelligence technology based on a preset extraction method;

The scoring unit is used to score the ESG index on the ESG event in combination with the knowledge graph.

In an embodiment, the scoring unit includes:

An analysis unit, configured to analyze the impact information and conduction information of the ESG event in combination with the knowledge graph;

The scoring subunit is used for scoring ESG indicators according to the analysis result of analyzing the impact information and conduction information of the ESG event.

In an embodiment, the output module includes:

The detecting unit is configured to detect whether the scoring result of ESG index scoring on the ESG event meets a preset condition;

The output unit is configured to output risk warning information to parties related to the ESG event when it is detected that the scoring result meets a preset condition.

The steps of the ESG indicator monitoring method as described above are implemented when each module of the ESG indicator monitoring device proposed in this embodiment is running, which will not be repeated here.

In addition, the embodiment of the present application also proposes a storage medium applied to a computer, that is, the storage medium is a computer-readable storage medium, and the ESG indicator monitoring program is stored on the medium, and the ESG indicator monitoring program is executed by the processor When realizing the steps of the ESG indicator monitoring method as described above.

The method implemented when the ESG indicator monitoring program running on the processor is executed can refer to the various embodiments of the ESG indicator monitoring method of this application, which will not be repeated here.

It should be noted that in this article, the terms "include", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, It also includes other elements not explicitly listed, or elements inherent to the process, method, article, or device. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, article or device that includes the element.

The serial numbers of the foregoing embodiments of the present application are for description only, and do not represent the superiority of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better.的实施方式。 Based on this understanding, the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, The optical disc) includes a number of instructions to enable a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the method described in each embodiment of the present application.

The above are only preferred embodiments of this application, and do not limit the scope of this application. Any equivalent structure or equivalent process transformation made using the content of the description and drawings of this application, or directly or indirectly used in other related technical fields , The same reason is included in the scope of patent protection of this application.

Claims (20)

1. An ESG indicator monitoring method, wherein the ESG indicator monitoring method includes:

   Acquiring alternative data, and processing the alternative data to determine a knowledge graph;

   Extract ESG events from the alternative data, and score the ESG events with ESG indicators in combination with the knowledge graph;

   Output early warning information to the related party of the ESG event according to the scoring result of ESG indicator scoring.
2. 8. The ESG indicator monitoring method of claim 1, wherein the alternative data includes entity data, business process data, and sensor data, and the step of obtaining alternative data includes:

   Extract specified entity data, business process data and sensor data from the preset data platform.
3. The ESG indicator monitoring method according to claim 1, wherein the knowledge graph comprises: a key information graph,

   The step of processing the alternative data to determine a knowledge graph includes:

   Extracting key information from the alternative data based on artificial intelligence technology, where the key information includes: key nodes and key relationships;

   According to the key nodes and key relationships in the key information, a key information map is determined.
4. The ESG indicator monitoring method according to claim 3, wherein the determining a key information graph according to the key nodes and key relationships in the key information comprises:

   According to the key nodes and key relationships in the key information, detecting whether a key information map has been established currently;

   If it is detected that a key information map has not been established, a new key information map used to represent the influence relationship between the operating entities in the industry is newly constructed;

   If it is detected that a key information map has been established, the established key information map is updated and maintained based on the key nodes and key relationships.
5. 8. The ESG indicator monitoring method of claim 1, wherein the knowledge graph further comprises: a structural relationship graph,

   The step of processing the alternative data to determine a knowledge graph further includes:

   The structured data is extracted from the alternative data to determine the structure relationship map.
6. 8. The ESG indicator monitoring method according to claim 5, wherein said extracting structured data from said alternative data to determine the structure relationship graph comprises:

   From the established data platform containing alternative data of various industries, extract real-time structured data information, and detect whether a structural relationship map has been established currently,

   If it is detected that no structural relationship map has been established, a new structural relationship map representing the influence relationship between operating entities in the industry is newly constructed;

   If it is detected that a structural relationship map has been established, the established structural relationship map is updated and maintained based on the real-time structured data information of the monitoring.
7. 4. The ESG indicator monitoring method of claim 1, wherein the step of extracting ESG events from the alternative data and combining the knowledge graph to score the ESG indicators on the ESG event comprises:

   Extract ESG events from the alternative data based on artificial intelligence technology based on a preset extraction method;

   Perform ESG index scoring on the ESG event in combination with the knowledge graph.
8. 8. The ESG indicator monitoring method of claim 7, wherein the step of combining the knowledge graph to score ESG indicators comprises:

   Analyze the impact information and conduction information of the ESG event in combination with the knowledge graph;

   Score ESG indicators according to the analysis result of analyzing the impact information and conduction information of the ESG event.
9. 8. The ESG indicator monitoring method according to claims 1 to 8, wherein the step of outputting early warning information to parties related to the ESG event according to the scoring result of the ESG indicator scoring comprises:

   Detecting whether the scoring result of ESG index scoring on the ESG event meets the preset conditions;

   When it is detected that the scoring result meets the preset condition, output risk warning information to the parties related to the ESG event.
10. An ESG indicator monitoring device, wherein the ESG indicator monitoring device includes:

    The determining module is used to obtain alternative data and process the alternative data to determine the knowledge graph;

    A scoring module, configured to extract ESG events from the alternative data, and perform ESG index scoring on the ESG events in combination with the knowledge graph;

    The output module is used for outputting early warning information to parties related to the ESG event according to the scoring result of the ESG indicator scoring.
11. An ESG indicator monitoring device, wherein the ESG indicator monitoring device includes: a memory, a processor, and an ESG indicator monitoring program stored on the memory and running on the processor, the ESG indicator monitoring program being The processor implements the following steps when executing:

    Acquiring alternative data, and processing the alternative data to determine a knowledge graph;

    Extract ESG events from the alternative data, and score the ESG events with ESG indicators in combination with the knowledge graph;

    Output early warning information to the related party of the ESG event according to the scoring result of ESG indicator scoring.
12. The ESG indicator monitoring device according to claim 11, wherein the alternative data includes entity data, business process data, and sensor data, and the step of obtaining alternative data includes:

    Extract specified entity data, business process data and sensor data from the preset data platform.
13. The ESG indicator monitoring device according to claim 11, wherein the knowledge graph comprises: a key information graph,

    The step of processing the alternative data to determine a knowledge graph includes:

    Extracting key information from the alternative data based on artificial intelligence technology, where the key information includes: key nodes and key relationships;

    According to the key nodes and key relationships in the key information, a key information map is determined.
14. The ESG indicator monitoring device according to claim 13, wherein the determining a key information graph according to the key nodes and key relationships in the key information comprises:

    According to the key nodes and key relationships in the key information, detecting whether a key information map has been established currently;

    If it is detected that a key information map has not been established, a new key information map used to represent the influence relationship between the operating entities in the industry is newly constructed;

    If it is detected that a key information map has been established, the established key information map is updated and maintained based on the key nodes and key relationships.
15. The ESG indicator monitoring device according to claim 11, wherein the knowledge graph further comprises: a structural relationship graph,

    The step of processing the alternative data to determine a knowledge graph further includes:

    The structured data is extracted from the alternative data to determine the structure relationship map.
16. The ESG indicator monitoring device according to claim 15, wherein said extracting structured data from said alternative data to determine the structure relationship graph comprises:

    From the established data platform containing alternative data of various industries, extract real-time structured data information, and detect whether a structural relationship map has been established currently,

    If it is detected that a structural relationship map has not been established, a new structural relationship map representing the influence relationship between operating entities in the industry is newly constructed;

    If it is detected that a structural relationship map has been established, the established structural relationship map is updated and maintained based on the real-time structured data information of the monitoring.
17. 11. The ESG indicator monitoring device of claim 11, wherein the step of extracting ESG events from the alternative data and combining the knowledge graph to score the ESG indicators on the ESG event comprises:

    Extract ESG events from the alternative data based on artificial intelligence technology based on a preset extraction method;

    Perform ESG index scoring on the ESG event in combination with the knowledge graph.
18. The ESG indicator monitoring device according to claim 17, wherein the step of combining the knowledge graph to score ESG indicators comprises:

    Analyze the impact information and conduction information of the ESG event in combination with the knowledge graph;

    Score ESG indicators according to the analysis result of analyzing the impact information and conduction information of the ESG event.
19. The ESG indicator monitoring device according to claims 11 to 18, wherein the step of outputting early warning information to a party related to the ESG event according to the scoring result of ESG indicator scoring comprises:

    Detecting whether the scoring result of ESG index scoring on the ESG event meets the preset conditions;

    When it is detected that the scoring result meets the preset condition, output risk warning information to the parties related to the ESG event.
20. A storage medium, wherein, applied to a computer, an ESG indicator monitoring program is stored on the storage medium, and the ESG indicator monitoring program is executed by a processor to realize the ESG indicator according to any one of claims 1 to 9 Steps of the monitoring method.