Sustainable management ESG platform

Abstract

The invention relates to the technical field of enterprise ESG digital management, in particular to a sustainable management ESG platform. The platform comprises a data access layer, a twin modeling layer, an intelligent analysis layer, a report generation layer and a collaborative interaction layer. The data access layer is used for standardized acquisition, cleaning and block chain evidence storage of multi-source ESG data; the twinborn modeling layer constructs enterprise ESG digital twinborn bodies through index mapping and scene simulation; the intelligent analysis layer realizes index dynamic monitoring, abnormity early warning and risk quantitative evaluation based on time sequence prediction and causal reasoning; the report generation layer outputs a compliance report through a multi-template automatic adaptation and natural language generation technology; and the collaborative interaction layer realizes multi-role data sharing, task tracking and strategy optimization. According to the method, multi-modal data fusion and an intelligent algorithm are fused, the efficiency of ESG data integration, risk response, report generation and collaborative management is comprehensively improved, and the problems of data isolation, response lag and manual dependence in a traditional mode are effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of enterprise ESG digital management, and more particularly to a sustainable management ESG platform. BACKGROUND

[0002] Currently, enterprises generally adopt the existing traditional ESG management mode in ESG (environment, society and governance) management, and the core operation mode and characteristics are as follows: First, the data processing level: ESG data is scattered in more than 10 independent systems such as financial systems, production monitoring systems and human resource systems, and the data formats of each system are not unified. Manual cross-system data integration is required, and the integration time accounts for 60% of the overall ESG analysis cycle. For unstructured data such as annual reports, news public opinion and social responsibility reports, manual extraction of ESG key indicators is required, and the error rate is not less than 20%, and the data reliability is difficult to guarantee.

[0003] Second, the risk monitoring level: there is no real-time monitoring mechanism, and ESG risk analysis is carried out only through quarterly or annual manual report preparation, which cannot dynamically capture real-time changes of ESG indicators and cannot cover index fluctuations in short periods.

[0004] Third, the quantitative analysis level: ESG risk assessment lacks scientific quantitative model support and mainly relies on expert subjective judgment, with only "high, medium and low" three fuzzy levels to divide risks, without forming data-based and standardized evaluation basis, and cannot provide accurate support for management decisions.

[0005] Fourth, the report generation level: when facing multiple compliance standards such as GRI Standards, ISSB IFRS and Hong Kong Stock Exchange ESG guidelines, manual adaptation of different standard report templates is required, and manual data filling and logic verification are required. The report preparation cycle is as long as 7 to 10 days, and problems such as inconsistent data and contradictory logic are prone to occur.

[0006] Fifth, the interest coordination level: there is no unified ESG data sharing channel between enterprises and stakeholders such as investors and suppliers, and there is a data transmission gap. After the implementation of ESG management measures, only manual tracking of the progress is relied on, and there is a lack of automatic closed-loop management mechanism, and the measure implementation rate is less than 50%.

[0007] Based on the above operation mode, the existing traditional ESG management mode has exposed the following core technical problems in actual application, which is difficult to meet the digitalization and compliance requirements of enterprise ESG management: Data islands are serious and of low quality: due to the dispersion of ESG data in multiple systems and the lack of uniform format, manual integration is extremely inefficient; the high error rate of manual extraction of unstructured data leads to insufficient overall data reliability, directly affecting the effectiveness of subsequent ESG analysis.

[0008] Risk monitoring is static and lagging: due to the reliance on quarterly or annual manual report analysis, it is impossible to capture ESG indicator changes in real time, about 35% of ESG violations are caused by late warning, and the average annual increase in regulatory penalties is 15%, the compliance risk of enterprises is significantly increased.

[0009] Quantitative analysis capability is weak: due to the lack of scientific quantitative models, risk assessment relies on expert subjective judgment, the risk level classification of "high / medium / low" is ambiguous, and it is difficult to accurately identify the severity of the risk, making it difficult to support enterprises to carry out targeted risk management and control.

[0010] Report generation is inefficient and compliance is poor: multiple compliance standards in parallel make manual adaptation difficult, and the report preparation cycle is long; manual filling and checking can easily cause data inconsistency and logical contradictions, and the compliance pass rate is less than 80%, which cannot meet the regulatory requirements for ESG information disclosure.

[0011] The mechanism of interest coordination is missing: there is no unified data sharing channel between enterprises and stakeholders, and ESG data is fragmented; manual tracking efficiency is low after the implementation of measures, and there are large differences in ESG performance among the various links of the supply chain, ESG management is broken throughout the chain, and a management closed loop cannot be formed.

[0012] The above technical problems are urgent and feasible to solve, with the following specific background: Policy-driven: The Securities Commission's "ESG Information Disclosure Guidelines for Listed Companies", the European Union's Corporate Sustainability Reporting Directive (CSRD), and other regulations have been introduced, which require enterprises to accurately and timely disclose ESG information, and enterprises are facing severe compliance pressure, and need to solve the problems of report compliance and data accuracy in the traditional mode.

[0013] Technically mature: technologies such as big data, artificial intelligence, and blockchain have gradually matured, providing a technical basis for the automated integration, dynamic monitoring, and intelligent analysis of ESG data, and have the conditions to solve the problems of low data processing efficiency and lagging risk warning in the traditional mode.

[0014] Enterprise pain points: large enterprises have an annual ESG management cost of more than 500,000 yuan, but due to the low efficiency of the traditional mode, the management investment and output do not match; small and medium-sized enterprises are difficult to carry out systematic ESG management due to high technical threshold and cost pressure, and the industry as a whole urgently needs digital tools to solve the above pain points. SUMMARY

[0015] In view of this, the present application provides a sustainable management ESG platform, aiming to solve the problems of serious data silos, lagging risk response, inefficient and poor compliance report generation, weak quantitative analysis capability, and insufficient benefit synergy in traditional ESG management, thereby improving the digitalization, dynamization and intelligentization level of enterprise ESG management, realizing the overall improvement of ESG data integration efficiency, dynamic risk response speed, compliance report generation efficiency and quality, risk quantitative analysis accuracy and stakeholder synergy capability, reducing the cost of enterprise ESG management, providing effective support for large enterprises to optimize the input-output ratio of ESG management, small and medium-sized enterprises to break through the technical threshold of ESG management, and releasing the value of ESG whole-cycle management.

[0016] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: A sustainable management ESG platform, comprising: a data access layer, a twin modeling layer, an intelligent analysis layer, a report generation layer and a collaborative interaction layer; The data access layer is used to realize the standardized collection and cleaning of multi-source ESG data; The twin modeling layer is connected with the data access layer and is used to build an enterprise ESG digital twin; The intelligent analysis layer is connected with the twin modeling layer and is used to complete ESG risk identification and quantitative evaluation; The report generation layer is connected with the intelligent analysis layer and is used to support the automatic output of multi-standard compliance ESG reports; The collaborative interaction layer is connected with the report generation layer and is used to realize the sharing and collaborative management of ESG data of stakeholders; Each layer is linked through a data interface to form an ESG management closed-loop system.

[0017] In a specific implementable embodiment, the data access layer comprises: A protocol adaptation module is used to support data access of multiple industrial and Internet of Things protocols; for example: supporting ModbusRTU / TCP, OPCUA, HTTP / HTTPS, MQTT, CoAP, DNP3, IEC61850, BACnet, LonWorks, Profibus-DP, EtherNet / IP, LoRaWAN10 kinds of industrial and Internet of Things protocols; A text analysis engine is used to extract ESG indicators from unstructured text by using natural language processing technology; A data cleaning tool is used to detect abnormalities and fill in missing values for access data.

[0018] In a specific implementable embodiment, the twin modeling layer comprises: An index mapping engine for dynamically mapping and managing the indexes of the environmental, social, and governance dimensions; A process visualization module for constructing a digital mirror of the ESG management process based on business process modeling standards; for example, for constructing a digital mirror of the ESG management process based on the BPMN 2.0 business process modeling standard; A scenario simulation engine for simulating the potential impact of external dynamic scenarios on the ESG performance of an enterprise.

[0019] In a specific implementable embodiment, the intelligent analysis layer includes: A real-time monitoring module for continuously dynamically monitoring ESG indexes; An anomaly identification module for anomaly detection and early warning based on time series prediction models and association rules; for example, for anomaly detection and early warning based on LSTM time series prediction models and Apriori association rules; A risk quantification engine for causal tracing and quantitative grade assessment of identified ESG risks.

[0020] In a specific implementable embodiment, the report generation layer includes: A template management module with built-in support for a variety of international ESG disclosure standard report templates; A content generation module for automatically generating analytical text and visual charts for reports; A verification engine for compliance and logicality verification of report data based on a pre-set rule library.

[0021] In a specific implementable embodiment, the collaborative interaction layer includes: A permission management module for assigning data access and operation permissions of different stakeholders based on a role-based access control model; A task tracking module for visualizing and tracking the entire life cycle of ESG improvement measures and rectification tasks; A performance optimization module for optimizing ESG management strategies based on measure feedback data.

[0022] In a specific implementable embodiment, the deployment architecture of the platform includes: Edge computing nodes for data collection and preprocessing; A cloud server cluster for deploying core platform services; A blockchain node for realizing supply chain data credible notarization and traceability.

[0023] In a specific implementable embodiment, the edge computing nodes, cloud server cluster, and blockchain node communicate through message middleware and data interfaces, wherein: The edge computing node is configured to filter, compress and protocol convert the collected raw data before uploading to the cloud server cluster. The blockchain node is configured to store and trace the hash value of the supply chain ESG data based on the alliance chain architecture.

[0024] In a specific implementation, the data access layer, the twin modeling layer, the intelligent analysis layer, the report generation layer and the collaborative interaction layer are sequentially connected to form a data flow as follows: The standardized ESG data processed by the data access layer is input to the twin modeling layer. The enterprise ESG digital twin output by the twin modeling layer is input to the intelligent analysis layer. The risk identification and quantitative evaluation results output by the intelligent analysis layer are input to the report generation layer. The multi-standard compliance ESG report generated by the report generation layer is shared and collaboratively managed in the collaborative interaction layer.

[0025] In a specific implementation, the platform is configured to perform the following core processing flow: Global ESG data fusion governance: standardized access, cleaning and fusion of multi-source ESG data, construction of high-quality ESG data set and enterprise ESG digital twin; Dynamic indicator monitoring and abnormal early warning: real-time monitoring of key indicators based on the digital twin, identification of abnormal indicators and hierarchical push of early warning information; Risk intelligent analysis and quantitative evaluation: causal chain tracing of abnormal indicators, calculation of comprehensive risk value combined with risk quantitative model and industry benchmark analysis; Compliance report automatic generation: automatic data filling, text and chart generation based on compliance templates and ESG data sets, and report verification; Stakeholder collaborative closed-loop management: ESG data sharing among stakeholders, tracking of rectification task implementation and iterative optimization of management strategy.

[0026] Compared with the prior art, the sustainable management ESG platform provided by the application is used for realizing integrated, automated and intelligent management of enterprise environmental, social and governance (ESG) performance, realizing integrated, automated and intelligent management of enterprise environmental, social and governance (ESG) performance through standardized access and fusion governance of multi-source ESG data, dynamic index mapping and real-time monitoring based on digital twinning, abnormal early warning and risk quantification driven by artificial intelligence, automatic generation and verification of multi-standard compliance reports, and closed-loop management of stakeholders, combining big data governance, artificial intelligence reasoning and blockchain traceability technology, realizing full-process closed-loop optimization of ESG data from collection, cleaning, analysis, decision-making to feedback, effectively improving data integration efficiency, risk response speed, report generation quality and management collaboration level, and having the following beneficial effects: Firstly, the problem of serious data island and low data quality in traditional ESG management is solved, and standardized automatic integration and quality control of multi-source ESG data are realized. Secondly, the problem of static lag in risk monitoring is solved, and real-time monitoring and intelligent early warning of ESG indexes are realized, which greatly shortens the risk response time. Thirdly, the problem of low efficiency and poor compliance in report generation is solved, and automatic generation and verification of multi-standard compliance reports are realized, which reduces the compliance error rate. Fourthly, the problem of weak quantitative analysis capability is solved, and scientific quantitative evaluation and industry benchmarking analysis of ESG risks are realized, which provides data support for accurate decision-making. Fifthly, the problem of insufficient interest coordination is solved, and data sharing and measure closed-loop management between enterprises and stakeholders are realized, which improves the implementation rate of ESG measures; at the same time, the cost of ESG management of enterprises is reduced, the requirements of ESG information accurate disclosure in the ESG Information Disclosure Guidelines for Listed Companies issued by the Securities Regulatory Commission, the CSRD of the European Union and other regulations are met, effective support is provided for large enterprises to optimize the input-output ratio of ESG management and small and medium-sized enterprises to break through the technical threshold of ESG management, and the value of ESG whole-cycle management is released. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0028] Figure 1 The system architecture diagram of the sustainable management ESG platform provided by the application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the scope of protection of the present application.

[0030] As shown in Figure 1 The sustainable ESG management platform comprises a hardware carrier, which is composed of the following three types of nodes. Edge computing node: deployed in enterprise production workshops, energy consumption monitoring points and other data collection scenarios, containing an Internet of Things gateway (supporting LoRa / NB-IoT protocol) and an edge server; functions for real-time collection and preprocessing of on-site data such as production energy consumption and carbon emissions, with a sampling frequency supporting adaptive adjustment of 1 Hz-1 kHz, a local data cache capacity ≥1 TB, which can avoid delay and bandwidth consumption of remote transmission of raw data; Cloud server cluster: deployed in a public cloud or private cloud environment using a distributed architecture, containing at least 3 cloud servers, supporting elastic expansion, and the overall computing capacity ≥100 TOPS; used for running core software modules such as twin modeling, intelligent analysis and report generation, which can meet the parallel computing needs of massive ESG data; Blockchain node: adopts a consortium chain architecture composed of 3-5 consensus nodes; functions for realizing on-chain storage and traceability of supply chain ESG data, with a block generation time ≤10 seconds, which can ensure data tamper-proofing and improve traceability efficiency by 80% compared with traditional methods, and reduce data tampering risk to 0.

[0031] For example, the consortium chain architecture adopts a “main chain+side chain” mode, the main chain stores core storage data (hash value, timestamp), and the side chain stores detailed business data, reducing the load of the main chain; Consensus optimization: adopts PBFT consensus algorithm (node number 3-5), with consensus round ≤2 rounds, avoiding long-period consensus; Distributed index: each node deploys a “data hash-storage address” index table, which directly locates the data node when queried, without the need for full-chain traversal.

[0032] Block generation time ≤10 seconds, traceability response time ≤10 seconds; Block generation time: refers to the time taken by the blockchain node to complete consensus verification and block packaging after collecting supply chain ESG data (such as supplier carbon emissions); Traceability response time: refers to the time from when the user initiates a data traceability request (such as querying the carbon emission records of a batch of raw materials) to when the node returns the complete traceability result (including data generation time, on-chain node and hash value).

[0033] After the experiment verification of a certain clothing enterprise supply chain (20 fabric suppliers, 100 batches of fabric ESG data on-chain traceability), the blockchain node (3 consensus nodes, single node 16-core CPU + 32GB memory, platform Hyperledger Fabric 2.5); 100 batches of fabric data are sequentially chained, and the block generation time of each batch of data is recorded; 3 times of traceability request are initiated for each batch of data, and the response time is recorded; the experimental results are as follows: Block generation time: average 4.8 seconds, maximum 7.2 seconds, meeting the requirement of “≤10 seconds”; Traceability response time: average 4.2 seconds, maximum 8.7 seconds, meeting the requirement of “≤10 seconds”; Comparative example (public chain architecture): the block generation time of a certain public chain (such as Ethereum) is ≥15 seconds, and the traceability response time is ≥20 seconds, and the efficiency of the alliance chain architecture of the present scheme is better.

[0034] On the hardware carrier level, the hardware carrier composed of edge computing nodes, cloud server clusters and blockchain nodes, and the software architecture composed of data access layer, twin modeling layer, intelligent analysis layer, report generation layer and collaborative interaction layer; each layer realizes data interaction through RESTful API interface and message queue (Kafka), and relies on distributed database (MongoDB+MySQL) to complete data persistent storage, ensuring that the peak data processing capacity is ≥1000TPS and the data query response time is ≤1 second.

[0035] The sustainable ESG platform disclosed in the present application adopts a layered design for the software architecture of the platform, including a data access layer, a twin modeling layer, an intelligent analysis layer, a report generation layer and a collaborative interaction layer, and the core components and technical features of each layer are as follows, and a closed loop is formed through data interface linkage: (1) Data access layer Function: realize standardized collection, cleaning and blockchain storage of multi-source ESG data, and provide high-quality data sources for subsequent twin modeling and intelligent analysis.

[0036] Core components: Protocol adaptation module: supports more than 10 kinds of industrial and Internet of Things protocols such as Modbus, OPCUA and HTTP, can connect different types of data sources such as ERP systems, Internet of Things devices and carbon metering systems, and ensures compatible access of multi-source data.

[0037] Text analysis engine: a natural language processing module based on BERT model, which can analyze unstructured text such as annual reports, news public opinion and social responsibility reports, extract ESG key indicators contained therein, and meet the effective utilization demand of unstructured data.

[0038] Data cleaning tool: built-in outlier detection (based on 3σ principle) and missing value filling (based on mean interpolation method) algorithm, can automatically identify and process invalid data, repair missing fields, and ensure the accuracy of the accessed data.

[0039] Example of outlier processing: 3σ principle (remove data outside the mean ± 3 standard deviation) + industry threshold check (e.g. "COD value ≤ 500 mg / L" in the chemical industry); Missing value filling: numerical data uses KNN algorithm (K=5, based on similar equipment / same period data filling), text data uses pattern matching (e.g. "training record missing" is filled with "not carried out"); Format unification: unify dates, units, etc. through regular expressions (e.g. "2024.10.01" "2024-10-01" unified as "2024-10-01", "ton" "T" unified as "t").

[0040] Technical features: supports 100,000 data per second concurrent access, data cleaning accuracy ≥99%, can quickly complete the integration and quality control of multi-source data.

[0041] The data cleaning accuracy refers to the matching rate of the data processed by the tool with the "standard data" manually labeled, covering outlier correction (e.g. sensor jump value), missing value filling (e.g. employee training record null value), format error correction (e.g. date format disorder), and the calculation method is "correctly cleaned data number / total data to be cleaned number × 100%".

[0042] After the experiment of a new energy vehicle enterprise's ESG data (100,000, including energy consumption, training, and supply chain data, 1,200 manually labeled abnormal data) in Q1-Q3 2024, the cloud server (Aliyun ECS, 32-core Intel Xeon Platinum 8369B CPU + 128GB memory), data cleaning tool version V3.0; import 100,000 data into the tool, compare with the "standard data" labeled by manual after cleaning; the experimental results are as follows: Correctly cleaned 1,194 abnormal data, misjudged 3, missed 3, accuracy 99.5% (1,194 / 1,200 × 100%), meets the requirement of "≥99%"; Comparative example (manual cleaning): 3 analysts cleaned the same data within 8 hours, accuracy 92.3%, time consumption is 32 times of this scheme (this scheme only needs 15 minutes), this scheme is better in efficiency and accuracy.

[0043] (2) Twin modeling layer Function: build and update enterprise ESG digital twin in real time, realize accurate mapping of enterprise ESG physical system and digital system, provide digital carrier for dynamic monitoring and scenario simulation.

[0044] Core components: Indicator mapping engine: Covers 1200+ ESG indicators, dynamically maps and manages core indicators in the three dimensions of environment (E), society (S), and governance (G), and is compatible with mainstream international ESG disclosure standards such as GRI 300 series, ISSB IFRS S1 / S2, etc.

[0045] Process visualization module: Based on the BPMN 2.0 business process modeling standard, it builds a digital mirror of the entire ESG management process from "data collection - indicator calculation - risk assessment - report output", supports drag-and-drop configuration of process nodes and real-time state viewing, and improves the intuitiveness of process management.

[0046] Scenario simulation engine: Built-in Monte Carlo simulation tool, which can simulate the potential impact of external dynamic scenarios such as policy changes (e.g. EU CSRD regulation updates) and market fluctuations (e.g. supply chain disruptions) on enterprise ESG performance, providing support for risk prediction.

[0047] Technical features: Synchronization delay between twin and physical system ≤1 minute, scenario simulation accuracy ≥90%, indicator coverage ≥98%, can accurately reflect the actual state and potential changes of enterprise ESG management.

[0048] The synchronization delay refers to the total time from the time the physical system indicator (such as the desulfurization efficiency of a thermal power plant or the number of employee inspections) changes T1 to the time the twin displays the update completion T2 after the data is collected, transmitted, and updated in the twin, with a delay of T2-T1, and the accuracy is accurate to milliseconds.

[0049] For example, the following strategies are used to achieve a synchronization delay of ≤1 minute: Edge preprocessing: After the edge node collects data in real time, it only transmits "change data" (such as the desulfurization efficiency increasing from 90% to 95%, only transmitting "+5%"), reducing data volume; Dynamic update strategy: Use "high-frequency indicators for real-time updates (such as energy consumption 1 time / second) and low-frequency indicators for batch updates (such as board structure 1 time / day)", to avoid resource waste; Kalman filter calibration: Use Kalman filter on the twin side to correct the deviation caused by transmission delay, improve synchronization accuracy.

[0050] After the experimental verification of a certain 1000 MW thermal power plant (synchronous 200 ESG key indicators), the edge collection end (Advantech UNO-2484G, 4-core CPU), cloud server (Huawei cloud ECS, 64-core CPU + 256GB memory), and twin modeling layer are based on Unity3D; manually adjust 5 key indicators (desulfurization efficiency, carbon emission concentration, etc.), adjust each indicator 3 times; record T1 (physical indicator change time) and T2 (twin completion time) for each adjustment, calculate the delay; the experimental results are as follows: The average delay of 15 adjustments is 230ms (≈0.38 minutes), the maximum delay is 350ms (≈0.06 minutes), which is much lower than the requirement of "≤1 minute"; Comparative example (non-twin static model): traditional static database is updated every 5 minutes, the average delay is 300 seconds (5 minutes), and the synchronous speed of this scheme is increased by 826 times.

[0051] The scene simulation accuracy refers to the deviation rate of the simulation result (such as "10% increase in EU carbon tariff on carbon emission cost") and the actual operation result, the calculation method is "1-|simulation mean-real value| / real value x 100%", and the real value needs to be based on the actual operation data of the enterprise (such as financial statements, ESG rating report).

[0052] For example, to achieve a simulation accuracy of ≥90%, the following strategies are used: Monte Carlo simulation: input the historical data of 31 industries (such as carbon tariff rate and enterprise cost correlation data), simulate 1000 times, and output the mean value as the simulation result; Industry parameter calibration: set exclusive influence factors for different industries (such as carbon cost proportion weight 0.3 in chemical industry, 0.15 in electronic industry); Dynamic correction: correct the simulation model parameters every quarter with new scene data (such as actual cost after policy change).

[0053] After a certain steel enterprise with an annual output of 5 million tons of steel, simulate "10% increase in EU CBAM tax rate" and "20% increase in raw coal price" two scene experimental verification, cloud server (Alibaba cloud ECS, 64-core CPU + 256GB memory), scene simulation engine version V1.8; simulate 1000 times of Monte Carlo results for the two scenes, take the mean value as the simulation value; collect the actual operation data of the enterprise in 2024Q1 (carbon emission cost, MSCI rating) as the real value; the experimental results are as follows: Scenario 1 (carbon tariff increase): simulate carbon emission cost 1280,000 yuan / month, real value 130,000 yuan / month, accuracy 98.46%; Scenario 2 (Original coal price rise): Simulate ESG rating BBB level (probability 92%), true value BBB level, accuracy rate 92%; Overall accuracy rate 95.23%, meet the requirement of "≥90%"; Comparative example (expert experience prediction): 5 ESG consulting experts predict the same scenario, the average accuracy rate is 78.6%, and the present scheme is more accurate based on data simulation.

[0054] (3) Intelligent analysis layer Function: Real-time monitoring of ESG indicators, abnormal warning and risk quantification evaluation, providing data support and decision basis for enterprise ESG risk control.

[0055] Core components: Real-time monitoring module: Adopt sliding window algorithm (window size is adjusted adaptively with data collection frequency), continuously dynamically monitor ESG key indicators of different dimensions; among them, the sampling frequency of environmental dimension indicators (such as carbon emission intensity, energy efficiency) is 1 time / hour, the sampling frequency of social dimension indicators (such as employee turnover rate, safety accident rate) is 1 time / day, and the sampling frequency of governance dimension indicators (such as board independence, anti-corruption training coverage) is 1 time / week.

[0056] Abnormal identification algorithm library: contains LSTM time series prediction model and Apriori association rule mining algorithm; LSTM model is used to learn historical trends of indicators and calculate dynamic warning threshold, Apriori algorithm is used to mine implicit associations between different indicators and identify associated anomalies.

[0057] Risk quantification engine: based on ISO 31000 risk matrix theory and analytic hierarchy process, it can trace the cause of abnormal indicators and quantitatively evaluate the risk from the dimensions of possibility, impact degree and controllability, and divide the risk level.

[0058] Technical features: indicator monitoring coverage rate ≥100%, abnormal identification accuracy rate ≥95%, associated anomaly coverage rate ≥90%, abnormal identification response time ≤5 seconds, risk quantification error ≤8%, which can quickly and accurately complete risk identification and evaluation.

[0059] The abnormal identification accuracy rate refers to the matching rate of abnormal data identified by the module (such as sewage COD exceeding the standard, insufficient training times) and manually labeled abnormal data, including "real-time anomaly" (single indicator exceeding the standard) and "associated anomaly" (such as "energy consumption increases by 10% and production remains unchanged"), the calculation method is "correctly identified abnormal number / total labeled abnormal number × 100%".

[0060] For example, to achieve an abnormal identification accuracy rate of ≥95%, the following strategies are adopted: Real-time anomaly detection: Use LSTM model to predict index trend (input 7-day data, predict today's value), trigger warning when actual value deviates from predicted value by >3σ; Correlation anomaly detection: Use Apriori algorithm to mine index association rules (minimum support 20%, minimum confidence 80%), identify implicit anomalies; Anomaly classification: classified into "yellow / orange / red" three levels according to severity, red anomaly priority verification (such as safety accident), improve the identification accuracy of key anomalies.

[0061] After a day of 100 tons of dairy products in a food processing plant in 2023 Q4 ESG data (500,000, 2000 manually annotated anomaly data) experimental verification, cloud server (Baidu intelligent cloud BCC, 32-core CPU + 128GB memory), anomaly identification algorithm library version V2.6; Import data module, output anomaly results and manual annotation comparison; The experimental results are as follows: Correctly identify 1925 anomalies (real-time anomaly 1455, correlation anomaly 470), miss 60, misidentify 15, accuracy 96.25%, meet "≥95%" requirements; Comparative example (static threshold method): The traditional fixed threshold (such as COD≤500mg / L) identification accuracy is 81.5%, and cannot identify correlation anomalies, this scheme has more comprehensive functions.

[0062] The risk quantification error refers to the relative error between the comprehensive risk value R calculated by the platform (formula: R=0.3P+0.5S+0.2(1-C), P=0-1, S=0-10, C=0-10) and the "true risk value Rtrue" calculated based on actual loss, the formula is "error=|R-Rtrue| / Rtrue×100%".

[0063] For example, to achieve a risk quantification error of ≤8%, the following strategies are used: Likelihood P calculation: based on historical event frequency (such as "VOCs tank leakage 3 years 2 times, P=0.67") + current working condition correction (such as device aging P increases by 20%); Impact degree S calculation: financial loss (penalty amount / annual revenue) + reputation loss (public opinion heat × brand loss coefficient) + compliance loss (penalty level corresponding score); Error correction: adjust the weights of P, S, and C every quarter with new risk event data (such as penalty cases after new regulations), to reduce the error.

[0064] Through 31 industries, 10 historical risk events (310, including actual loss data, such as "petrochemical enterprise tank VOCs leakage", "employee injury in electronic enterprise") experiment verification, cloud server (Amazon AWS EC2, 64 core CPU + 256GB memory), risk quantification engine version V3.2; input event data calculate R, compare R true based on actual loss, statistical error; The experimental results are as follows: The average error is 5.3%, the maximum error is 7.8% (1 new compliance penalty event), 98.7% of the event error is ≤7%, which meets the requirement of "≤8%"; Comparative example (manual evaluation): 3 safety engineers manually quantify the same event, the average error is 22.5%, and the quantification accuracy of this scheme is significantly better.

[0065] (4) Report generation layer Function: Automatically generate multi-standard compliance ESG reports, and ensure the compliance and accuracy of the report through verification and optimization, reduce the cost and error of manual report preparation.

[0066] Core components: Template management system: built-in report templates of more than 20 mainstream ESG disclosure standards such as GRI Standards, ISSB IFRS, and Hong Kong Stock Exchange ESG guidelines, support custom upload and update of templates, meet the report generation needs of different compliance scenarios.

[0067] Content generation module: based on large language model (LLM), can automatically generate analytical description content of the report according to ESG data and risk analysis results, and support automatic generation of trend chart, comparison chart and other visual charts, improve the readability and professionalism of the report.

[0068] Verification engine: built-in more than 500 compliance verification rules, covering data consistency, logical reasonableness, standard compliance and other verification dimensions, can automatically identify errors in the report and prompt the modification direction.

[0069] Technical features: through XML tag mapping technology, realize automatic matching of data and template fields, field matching accuracy ≥99%, report generation time ≤2 hours, compliance verification pass rate ≥98%, support online manual editing and version management, greatly improve the report generation efficiency and quality.

[0070] The time ≤2 hours refers to the total time from "import ESG data set" to "generate a PDF report that can be submitted", including template adaptation, data filling, text generation, compliance verification, PDF export whole process; The compliance verification pass rate refers to the proportion of reports that meet the target standards (such as GRI, ISSB) mandatory items, data consistency, and logical rationality after being processed by the verification engine. The calculation method is "the number of reports passing the verification / the total number of chapters × 100%".

[0071] For example, to achieve a report generation time of ≤2 hours and a compliance verification pass rate of ≥98%, the following strategies are adopted: Template adaptation: Using XML tag mapping technology, automatically match ESG data fields with template fields (e.g., "Scope 1 carbon emissions" matches GRI template "E1.1 Direct Carbon Emissions"), with a matching accuracy of ≥99%; Text generation: Based on the LLM model (Qwen-7B), generate analytical text (e.g., "Reasons for the decline in carbon intensity"), and pre-set industry rhetoric library (e.g., "Green power investment" for the power industry, "VOCs governance" for the chemical industry); Compliance verification: Built-in 500+ verification rules (e.g., "Scope 1 + Scope 2 = Total Direct Emissions", "When the number of training times > 0, the coverage rate ≤100%"), automatically mark errors and suggest correction solutions.

[0072] After the experiment of a certain electronic manufacturing listed company's 2023 GRI Standards report (50 pages, including 3 chapters and 20 charts), the cloud server (Alibaba Cloud ECS, 32-core CPU + 128GB memory), the report generation layer version V2.4; import the company's 2023 ESG data (1000+ indicators); select the GRI template and start the automatic generation process, record the total time; count the rule matching of the verification engine (pass / fail rule number); the experimental results are as follows: The total time is 104 minutes (≈1.73 hours), including data filling for 15 minutes, text generation for 59.2 minutes, verification for 25.2 minutes, and export for 4.6 minutes, meeting the "≤2 hours" requirement; Compliance verification passes 492 rules, fails 8 (all pass after manual correction), pass rate 98.4%, meeting the "≥98%" requirement; Comparative example (manual generation): 2 report specialists + 1 financial staff to prepare the same report, time-consuming 8 days (64 hours), compliance error rate 15%, the efficiency of this scheme is improved by 37 times.

[0073] (5) Collaborative interaction layer Function: Realize the sharing of ESG data, task collaboration and management strategy optimization between enterprises and stakeholders (investors, management, employees, suppliers, etc.), and build a collaborative closed loop of ESG management.

[0074] Core components: Permission management system: Based on the RBAC (Role-Based Access Control) model, it can allocate exclusive data access and operation permissions according to the role types of stakeholders (such as investors, management, employees), ensuring the security and pertinence of data sharing.

[0075] Task tracking module: Support for generating rectification tasks for high-risk points, clearly defining task responsibility departments and completion time limits; through Gantt chart visualization of task progress, automatically push reminder information for overdue tasks, and ensure the implementation of rectification measures.

[0076] Performance optimization engine: Based on the Markov Decision Process algorithm of reinforcement learning, it can collect ESG performance data after the implementation of rectification measures, optimize ESG management strategies through algorithm iteration, and improve management effectiveness.

[0077] Technical features: Permission allocation accuracy rate 100%, task tracking coverage rate 100%, performance iteration cycle ≤3 months, which can effectively realize the collaboration of stakeholders and the continuous optimization of management strategies.

[0078] At the software architecture level, with "global data fusion governance → dynamic indicator monitoring and abnormal early warning → risk intelligent analysis and quantitative evaluation → compliance report automatic generation → stakeholder collaborative closed-loop management" as the core process, it integrates big data governance, artificial intelligence reasoning, blockchain traceability and other technologies to build an "collection-analysis-decision-application" ESG management closed loop. It can be deployed on general servers and cloud computing platforms without the need for dedicated hardware support.

[0079] Based on the above product architecture, with "global data fusion governance → dynamic indicator monitoring and abnormal early warning → risk intelligent analysis and quantitative evaluation → compliance report automatic generation → stakeholder collaborative closed-loop management" as the core process, the following data processing methods can be implemented and executed. The execution subject of each step is a computer program, and the specific operation is as follows: Step one: Global ESG data fusion governance Execution subject: Data governance and twin engine (computer program); Operation target: Standardize access, clean and fuse multi-source ESG data, build high-quality ESG data set, and complete the construction of enterprise ESG digital twin.

[0080] Specific operation: 1. Standardized access of multi-source data: Structured data: Through protocol adaptation module to interface ERP system, Internet of Things device, carbon metering system, etc., use ETL tool to convert data format to unified format (support 12 formats such as JSON, XML), ensure field mapping accuracy rate ≥99%.

[0081] Unstructured data: Through the text analysis engine, annual reports, news public opinion, social responsibility reports, etc. Text processing, extraction of ESG indicators, and guarantee of entity recognition accuracy ≥ 96%.

[0082] The entity recognition accuracy refers to the matching rate of the ESG indicator entity extracted from unstructured text (such as annual reports, news public opinion) (such as "carbon intensity" in "carbon intensity " "10,000 yuan of revenue") and the artificial annotation results. The calculation method is "number of correctly identified entities / total number of annotated entities x 100%", and the entity must include the indicator name and corresponding value.

[0083] For example, to achieve an entity recognition accuracy of ≥ 96%, the following strategies are used: Model training: Based on the BERT pre-trained model (bert-base-chinese), fine-tune with 100,000 annotated ESG texts (including annual reports and social responsibility reports of 31 industries), optimize the "indicator name-value" association recognition logic; Confidence filtering: Set the extraction threshold (confidence ≥ 0.7), entities below the threshold are marked as "to be manually confirmed" to avoid misidentification; Domain dictionary supplement: Built-in 5000+ ESG professional dictionaries (such as "VOCs emissions" "Board of Directors independent director proportion"), improve the recognition ability of industry-specific indicators.

[0084] After experimental verification of the unstructured text (500 pages of annual reports, 200 pages of social responsibility reports, and 1000 news public opinions) of a certain listed pharmaceutical company from 2021-2023, the cloud server (Tencent cloud CVM, 32-core AMDEPYC7T83 CPU + 128GB memory), the text analysis engine version V2.1; 2 ESG experts independently annotated 1000 segments of text (200-500 words per segment) ESG entities, a total of 2000 annotated entities (disagreements are arbitrated by a third party); automatic extraction: import the text into the engine, output the extraction results; result comparison: statistics of correct recognition number, missed recognition number, and misrecognition number; the experimental results are as follows: Manual annotation: 1930 entities were correctly identified, 57 were missed, and 13 were misidentified, with an accuracy of 96.5% (1930 / 2000 x 100%), meeting the requirement of "≥ 96%"; Comparative example (traditional keyword matching method): Based on 1000 ESG keywords extraction, the accuracy is only 78.2%, the missed recognition rate is 18.5%, and this scheme greatly improves the recognition accuracy through semantic understanding.

[0085] ​Supply chain data: Through the connection of supplier systems by blockchain nodes, upload ESG data of the supply chain to the blockchain for notarization, ensure traceability response time ≤10 seconds.

[0086] 2. ESG digital twin construction: Index twin: Map the core indicators of the three dimensions of environment, society and governance through the index mapping engine, ensure index coverage ≥98%.

[0087] Process twin: Through the process visualization module, build a digital mirror of the entire ESG management process, realize the visualization and management of the process.

[0088] Scenario twin: Through the scenario simulation engine, build an external dynamic scenario model, simulate the impact of different scenarios on ESG performance, and ensure scenario library coverage ≥95%.

[0089] 3. Data dynamic calibration and quality control: Use multi-modal data fusion technology based on Dempster-Shafer evidence theory for data fusion, and dynamically adjust the weight of different types of data through a weighted algorithm based on information entropy. At the same time, build a data quality index to evaluate data quality. When the data quality index is lower than the preset threshold, automatically trigger the data cleaning process. The fusion formula is:

[0090] Where the conflict coefficient :

[0091] m(A) is the credibility of the fused proposition A, m i (A i ) is the credibility of the ith data source to the proposition A i , and n is the number of data sources.

[0092] Dynamically adjust the weight of different types of data through a weighted algorithm based on information entropy. The algorithm formula is:

[0093] Where information entropy :

[0094] w j is the weight of the jth type of data, p jk is the probability of the kth state in the jth type of data, and l is the total number of states.

[0095] Build data quality index Q d(0-100), evaluated by integrity (C), accuracy (A), and timeliness (T) (Q d =0.4C+0.4A+0.2T), when Q d <80 triggers data cleaning process, calibration response time ≤1 hour.

[0096] Key parameters: data access delay ≤5 minutes, twin index coverage ≥98%, data quality index compliance rate ≥95%, blockchain traceability response time ≤10 seconds.

[0097] The data access delay refers to the total time from the generation of data by the collection device / system (such as real-time energy consumption data collection by sensors, financial data generation by ERP systems) to the storage of the processed data in the distributed database (MongoDB+MySQL) through the platform data access layer, including the entire process of data collection, protocol conversion, format cleaning, and transmission to the cloud. The optimization strategies for this key parameter include: Edge computing node local preprocessing: complete data filtering (remove invalid and redundant data), compression (use LZ4 compression algorithm, compression rate ≥60%) at the data collection end (such as production workshop edge server) to reduce the amount of data transmitted to the cloud; Protocol adaptation optimization: for high concurrency scenarios, use "batch transmission + asynchronous communication" mode (based on Kafka message queue, batch size set to 100 per batch) to avoid frequent requests for single data; Network transmission guarantee: prefer industrial Ethernet (gigabit optical fiber), automatically switch to 5G private network when bandwidth is insufficient, to ensure transmission rate ≥10 Mbps.

[0098] Experimental verification: a mechanical manufacturing enterprise (annual production of 50,000 machine tools) needs to access 100 energy consumption sensors (Modbus protocol), 1 SAP ERP system (HTTP protocol), and 50 supplier blockchain data (consortium chain protocol); edge server (Dell PowerEdge R750, 8-core CPU + 16GB memory), cloud server (Alibaba Cloud ECS, 32-core CPU + 128GB memory), gigabit industrial Ethernet; monitor continuously for 72 hours, record access delay of each data source every 10 minutes, a total of 432 valid records; Results: average access delay 2.3 minutes, maximum delay 4.8 minutes (only once, due to temporary network fluctuations), 99.5% of the records have a delay ≤4 minutes, meeting the requirement of ≤5 minutes.

[0099] The twin index coverage refers to the proportion of the number of ESG indicators actually covered by the platform twin modeling layer to the total number of core indicators specified in the target industry ESG standards (such as GRI 300 series, ISSB IFRS S1 / S2). The core indicators include mandatory indicators and industry-specific indicators in the dimensions of environment (E), society (S), and governance (G). The optimization strategies for this key parameter include: The index mapping engine has a built-in "standard-enterprise" two-end mapping library: integrating GRI300 (320 core indicators), ISSB IFRS S1 / S2 (280 core indicators), and China's "Guidelines for ESG Information Disclosure of Listed Companies" (250 core indicators), forming a "standard index-enterprise business index" mapping rule (such as GRI302-1 "energy consumption" mapping enterprise "total power consumption of production workshop" "natural gas consumption of factory area"); Industry adaptation module: for 31 sub-industries (such as power, chemical industry, automobile), preset industry-specific indicators (such as "green power generation proportion" in the power industry, "VOCs emission" in the chemical industry), users can supplement and customize indicators through a visual interface; Coverage automatic verification: scan the twin index library every quarter, compare it with the latest standard index library, generate an "uncovered index list" and prompt to complete.

[0100] Experimental verification: taking the power industry (comparing GRI300 series + ISSB IFRS S1 power industry supplementary standards, a total of 310 core indicators) and the chemical industry (comparing GRI300 series + EU CSRD chemical industry standards, a total of 305 core indicators) as test objects; twin modeling layer (based on Unity3D, index mapping engine version V1.2); import enterprise business data of 2 industries respectively, and count the number of core indicators actually covered by the twin; The results are as follows: the power industry covers 306 indicators (coverage rate 98.7%), and the chemical industry covers 299 indicators (coverage rate 98.0%), both meeting the requirement of "≥98%".

[0101] The data quality index Qd (value 0-100) is calculated through "completeness (C, weight 0.4), accuracy (A, weight 0.4), and timeliness (T, weight 0.2)" three dimensions (Qd=0.4C+0.4A+0.2T), where: Completeness C: number of records without missing fields / total number of records x 100; Accuracy A: number of records without errors / total number of records x 100; Timeliness T: number of records uploaded within the specified time (such as real-time data ≤5 minutes, daily report data ≤24 hours) / total number of records x 100; The data quality index compliance rate refers to the number of data sets with Qd≥80, accounting for the proportion of the total number of ESG data sets on the platform. The optimization strategy for this key parameter includes: Data cleaning tool automatic processing: outliers are removed / modified using the 3σ principle, missing values are filled using the KNN algorithm (K=5), and format errors are checked using regular expressions; Quality index real-time calculation: after each batch of data (100 pieces / batch) is processed, Qd is automatically calculated and marked as "compliant (Qd≥80) / not compliant (Qd<80)", and non-compliant data triggers secondary cleaning (such as manual intervention to complete missing values); Quality traceability: establish a data quality log to record the Qd calculation process, cleaning measures, and responsible person for each batch of data, making it easy to trace and optimize.

[0102] Experimental verification: A food processing enterprise (daily production of 100 tons of dairy products) collects ESG data (120 batches, including energy consumption, sewage discharge, employee training, and supplier compliance data) in Q1 of 2024; data access layer (data cleaning tool version V2.1, quality evaluation model built-in); calculate Qd for each batch of data, and count the number of compliant batches and the compliance rate; The results are as follows: 115 batches of data have Qd≥80 (of which 88 batches have Qd≥90), and 5 batches of non-compliant data all meet the requirements after secondary cleaning, with a final compliance rate of 100%, meeting the requirement of "≥95%".

[0103] The blockchain traceability response time refers to the total time from the user initiating a supply chain ESG data traceability request (such as querying the carbon emission data source and flow record of a batch of raw materials) to the blockchain node completing consensus verification and returning complete traceability results (including data generation time, on-chain node, and hash value), excluding user interface loading time. The optimization strategy for this key parameter includes: Optimization of consortium chain architecture: adopt a "main chain + side chain" mode, with the main chain storing core notarization data (hash value, timestamp) and the side chain storing detailed business data (such as raw material procurement contracts, test reports), reducing the data volume on the main chain; Distributed indexing: deploy a "data hash-node address" index table on each blockchain node to directly locate the storage node during query, avoiding full-chain traversal; Simplify consensus mechanism: use PBFT (practical Byzantine fault tolerance) consensus algorithm, with 3-5 nodes and ≤2 rounds of consensus rounds to reduce consensus time consumption.

[0104] Experimental verification: A clothing enterprise (supply chain includes 20 fabric suppliers), query 100 batches of fabric "cotton planting-spinning-printing and dyeing" whole process ESG data traceability; Blockchain nodes (3 consensus nodes, single node configuration 16-core CPU + 32GB memory, consortium chain platform Hyperledger Fabric 2.5); Each batch of data initiates 3 times of traceability request, records the response time, a total of 300 effective records; The results are as follows: the average response time is 4.2 seconds, the maximum response time is 8.7 seconds (due to temporary high load of a node), 99.3% of the records have a response time ≤7 seconds, meeting the requirement of ≤10 seconds.

[0105] Step two: dynamic index monitoring and abnormal early warning Executing body: monitoring and early warning module (computer program); Operation target: based on ESG digital twin, real-time monitoring of key indicators, accurately identifying indicator abnormalities and grading early warning information according to severity, achieving timely risk discovery.

[0106] Specific operation: 1. Multi-dimensional index real-time monitoring: through the real-time monitoring module, the ESG key indicators are continuously monitored at a preset frequency, among which the environmental dimension indicators (such as carbon intensity, energy efficiency, waste recycling rate) are sampled at a frequency of 1 time / hour, the social dimension indicators (such as employee turnover rate, safety accident rate, community investment proportion) are sampled at a frequency of 1 time / day, and the governance dimension indicators (such as board independence, anti-corruption training coverage) are sampled at a frequency of 1 time / week.

[0107] 2. Self-adaptive abnormality identification: Static threshold identification: based on industry standards and enterprise historical data, set the index benchmark value, when the deviation between the actual value and the benchmark value is >10%, trigger the early warning.

[0108] Dynamic threshold identification: learn the historical change trend of the index through LSTM neural network, calculate the real-time early warning threshold, the threshold formula is

[0109] Among them, the prediction error standard deviation :

[0110] The early warning threshold at time t is , the LSTM model prediction value is , the actual value at time t is , and N is the historical data volume; When the deviation between the actual value and the predicted value is >3 times the standard deviation, trigger the early warning, ensure that the accuracy of abnormality identification is ≥95%.

[0111] Correlation anomaly identification: Through the Apriori association rule mining algorithm, the implicit correlation between different ESG indicators is analyzed, and the correlation anomaly caused by the mutual influence between indicators is identified, ensuring that the correlation anomaly coverage is ≥90%.

[0112] 3. Warning classification and push: The identified anomalies are classified into three levels of "general (yellow), important (orange), and urgent (red)" according to the severity, and the warning information is pushed to the enterprise management system (such as OA system, ERP system) through API interface, among which the emergency warning response time is ≤10 minutes, ensuring that the accuracy of the warning information is ≥98%, and ensuring that relevant personnel receive and handle the warning in a timely manner.

[0113] Key parameters: Indicator monitoring coverage ≥100%, anomaly identification accuracy ≥95%, emergency warning response time ≤10 minutes, and warning push success rate ≥99%.

[0114] The emergency warning response time refers to the total time from the identification of "urgent (red)" level anomaly (such as carbon emission concentration exceeding 30%, safety accident) by the intelligent analysis layer to the reception of the warning information pushed to the enterprise management system (such as OA system, management layer mobile APP) through API interface, including the whole process of anomaly judgment, warning classification, push transmission, and reception confirmation. The optimization strategy for this key parameter includes: Warning priority scheduling: Set the highest priority (priority 1) for "red warning" in the message queue (Kafka), higher than ordinary data transmission (priority 5), to ensure that the warning information is processed first; Multi-channel push: Push through "system pop-up window + SMS + enterprise WeChat / DingDing robot" at the same time, SMS uses the operator's special channel (delay ≤3 seconds), and system pop-up window uses WebSocket real-time communication; Reception confirmation mechanism: After the warning information is pushed, the receiving end needs to return "read" confirmation within 30 seconds, and if not confirmed, trigger secondary push (interval 1 minute).

[0115] Experimental verification: A chemical enterprise simulates "VOCs concentration in storage tank area suddenly exceeds 50%" (red warning) for 50 consecutive tests; intelligent analysis layer (anomaly identification module version V3.0), enterprise OA system (deployed on cloud server), and management layer mobile phone (supporting 5G network); record the time from anomaly identification to the receiving end returning "read"; The results are as follows: The average response time is 6.8 minutes, the maximum response time is 9.5 minutes, and all 50 tests are completed within 10 minutes, meeting the requirement of "≤10 minutes".

[0116] Step three: Risk intelligent analysis and quantitative evaluation Performing body: risk analysis module (computer program); Operation target: Conduct causal chain tracing on early warning abnormal indicators, calculate comprehensive risk value combined with risk quantification model, and analyze against industry data to clarify risk severity and enterprise ESG performance positioning in the industry.

[0117] Specific operation: 1. Risk Causal Chain Tracing: Based on the integration of 50,000+ industry risk cases, a three-level causal chain of "abnormal indicators-direct causes-root causes" is constructed (such as "carbon emissions exceeding standards → increased production load → energy management system failure"); the causal correlation of each link is calculated through Bayesian network, ensuring that the correlation confidence is ≥0.85 and the tracing accuracy is ≥92%, and the root cause of the abnormal indicator is determined.

[0118] 2. Multi-dimensional risk quantification: Risk possibility assessment: Combined with the historical frequency of abnormal indicators and the current enterprise operation conditions, the Poisson distribution model is used to calculate the risk occurrence probability P (value range 0-1), and the higher the probability value, the greater the possibility of risk occurrence.

[0119] Risk impact assessment: From the financial (such as potential fine amount, operating cost increase), reputation (such as public opinion intensity, brand image damage degree), compliance (such as regulatory penalty level, compliance rectification cost) three dimensions, the impact value S of risk on enterprise is quantified (value range 0-10), and the higher the impact value, the greater the loss caused by risk.

[0120] Comprehensive risk value calculation: The comprehensive risk value is calculated by using the weighted sum formula, and the formula is:

[0121] R is the comprehensive risk value (0-10), C is the risk controllability (0-10), ω P =0.3,ω S =0.5,ω C =0.2 is the weight coefficient; According to the comprehensive risk value, the risk is divided into 1-5 levels (1 is the lowest, 5 is the highest), ensuring that the risk quantification error is ≤8%.

[0122] 3. Industry benchmarking analysis: Connect to the ESG database covering 31 sub-industries, extract ESG performance data of enterprises in the same industry, calculate the industry percentile value of the enterprise's ESG performance, and identify the enterprise's strengths and weaknesses in the industry; benchmarking analysis takes ≤30 minutes, and single risk analysis takes ≤10 minutes, providing reference for the enterprise to develop targeted risk control measures.

[0123] Key parameters: Causal chain traceability accuracy ≥ 92%, risk quantification error ≤ 8%, industry benchmark coverage ≥ 31 industries, single risk analysis time ≤ 10 minutes.

[0124] The risk quantification error refers to: "the relative error between the comprehensive risk value R (0-10) calculated by the platform and the 'true risk value R_true' based on actual loss calculation, the formula is 'error = |R-R_true| / R_true x 100%', where R_true is calculated by weighting financial loss (fines, rectification cost), reputation loss (public opinion heat converted into economic loss), and compliance loss (regulatory penalties corresponding to loss). The optimization strategy for this key parameter includes: Risk factor calibration: based on 50,000+ historical risk cases in 31 industries, establish an "industry-risk type" factor library (such as the possibility factor of VOCs leakage in the chemical industry is 1.2 times higher than that in the mechanical industry); Loss quantification model: convert non-financial losses (such as public opinion) into calculable values (such as "every 10,000 searches increase in public opinion heat corresponds to a brand loss of 0.5 million yuan"); Error correction mechanism: quarterly correction of model parameters (such as adjustment of the weights of possibility P and impact S) using new risk event data to reduce cumulative error.

[0125] Experimental verification: select 10 historical risk events from each of the 31 industries (total of 310, including actual loss data), calculate the error between the platform risk value R and R_true; risk quantification engine (based on ISO31000 standard, version V2.5); input event data one by one, calculate error and statistics average error; Results: average error 5.3%, maximum error 7.8% (only 1, because the event involves a new type of compliance penalty, the model is initially adapted), 98.7% of the events error ≤ 7%, meet "≤ 8%" requirements.

[0126] Step four: automatic generation of compliance report Performing body: report generation module (computer program); Operation goal: based on compliance templates and ESG data sets to automatically fill in data, generate analysis text and visual charts, ensure that the report meets the corresponding standard requirements through compliance verification, and realize efficient and compliant report generation.

[0127] Specific operations: 1. Multi-standard template adaptation: Call the report template matching the target compliance standard (such as GRI Standards, ISSB IFRS, and Hong Kong Stock Exchange ESG Guidelines) from the template management system. Automatically match the ESG dataset (including index data and risk analysis results) with the fields in the template through XML tag mapping technology, ensuring a field matching accuracy rate of ≥ 99% without manual field matching.

[0128] 2. Automatic generation of report content: Data filling: Automatically fill structured data such as ESG index data and risk quantification results into the corresponding chapters of the report. When the data source data is updated, the data in the report is updated in real time, ensuring the timeliness of the data.

[0129] Text generation: Through the content generation module, automatically generate analytical description content (such as index trend analysis and risk response suggestions) based on ESG data and risk analysis logic, ensuring a text accuracy rate of ≥ 96% and a language style that meets the professional requirements of the report.

[0130] Chart generation: Automatically generate visual charts such as index trend charts, risk level distribution charts, and industry benchmark comparison charts, support interactive operations of charts (such as zooming in and viewing data details), and improve the readability and information transmission efficiency of the report.

[0131] 3. Report verification and optimization: Verify the generated report for compliance and logic through the verification engine. Verification dimensions include data consistency (such as whether the same index values in different chapters are consistent), logical reasonableness (such as whether the analysis conclusion matches the data), and standard compliance (such as whether it meets the mandatory item requirements of the corresponding disclosure standard). Automatically mark and suggest modifications for problems found during verification to ensure a report verification pass rate of ≥ 98%. Support online editing of report content by humans while preserving version records of the report, allowing traceability of modification content in different versions, and shortening the report generation cycle to 1-2 days.

[0132] Key parameters: Compliance standard coverage rate ≥ 20+, report generation accuracy rate ≥ 98%, verification pass rate ≥ 98%, and report generation efficiency improved by 80%.

[0133] Step five: Stakeholder collaborative closed-loop management Execution subject: Collaborative management module (computer program); Operation goal: Achieve ESG data sharing between enterprises and stakeholders, track the progress of rectification tasks, iteratively optimize management strategies based on performance feedback, and build a collaborative closed loop of ESG management.

[0134] Specific operations: 1. Multi-role permission management: Through the permission management system, data access and operation permissions are assigned according to the role type of stakeholders; for example, investors can only view public ESG reports and core performance indicators, management can access complete risk analysis data and rectification task progress, and employees can report social responsibility-related data (such as volunteer service length), ensuring that the accuracy of permission allocation is ≥100%, balancing data sharing and information security.

[0135] 2. Rectification task tracking: For high-risk points identified in step three, automatically generate rectification tasks, clearly define task responsibility departments, completion time limits, and assessment standards; through the Gantt chart of the task tracking module, the completion progress of the task (such as started, in progress, completed, overdue) is visualized in real time, and for overdue tasks, automatic reminder information (such as SMS, system message) is pushed to the responsible department, ensuring that task tracking coverage is ≥100%, and promoting the implementation of rectification measures.

[0136] 3. Performance iteration optimization: Collect ESG performance data after the implementation of rectification measures (such as improvement rate of indicators, risk level change), and iteratively optimize management strategies through the reinforcement learning algorithm of the performance optimization engine; the algorithm takes "ESG performance improvement rate, measure implementation cost rate, task completion rate improvement amount" as the core optimization indicators, calculates the reward value of strategy adjustment, and the formula is:

[0137] R t is the reward value at time t, ΔP t is the ESG performance improvement rate, C t is the measure implementation cost rate, ΔCmpl t is the task completion rate improvement amount, α=0.5, β=0.3, γ=0.2 are weight coefficients; Strategy iteration is completed once every quarter, ensuring that the ESG performance improvement rate is ≥5% per quarter, and achieving continuous optimization of management effectiveness.

[0138] Key parameters: permission management accuracy ≥100%, task tracking coverage ≥100%, performance iteration period ≤3 months, annual ESG rating improvement rate ≥15%.

[0139] The annual ESG performance improvement rate refers to the average improvement rate of core ESG indicators (such as carbon intensity, employee training coverage, supplier compliance rate) per quarter, calculated as "(this quarter's indicator average - last quarter's indicator average) / last quarter's indicator average x 100%", and the indicators must cover E, S, and G dimensions. The optimization strategy for this key parameter includes: Performance optimization engine: based on reinforcement learning algorithm (Markov decision process), with "performance improvement rate, measure cost rate, task completion rate" as reward function (Rt=0.5ΔPt+0.3(1-Ct)+0.2ΔCmplt), every quarter iteration optimization management strategy; Task closed-loop management: generate rectification tasks (including responsible department, time limit) for high-risk points, track progress through Gantt chart, automatically remind overdue, and ensure measures are implemented; Data feedback: collect index data after implementing measures every quarter, and correct and optimize model parameters (such as increase the weight of "high cost-effective measures").

[0140] Experimental verification: a certain automobile parts enterprise (for new energy vehicle enterprises) ESG management (select 5 core indicators: carbon emission intensity, employee training coverage, supplier compliance rate, independent director proportion of board of directors, waste recycling rate) in 2024Q1-Q4; complete platform deployment (edge node + cloud server + blockchain node), performance optimization engine version V1.5; record the average value of indicators under natural management state in 2023Q1-Q4 (baseline data); start using the platform from 2024Q1, calculate the indicator improvement rate every quarter; The experimental results are as follows:

[0141] The average improvement rate in 2024 is 6.4%, meeting the requirement of "≥5% / quarter"; Comparative example (without platform management): the natural improvement rate in 2023 is only 1.125%, and the performance is improved 5.7 times through closed-loop optimization.

[0142] This embodiment effectively solves the pain points of traditional ESG management through the synergistic implementation of the above products and methods, and all technical effects are verified through actual application scenarios, and the specific data is as follows: 1. ESG data integration efficiency is improved by leaps and bounds: in traditional ESG management, data is scattered in more than 10 systems, manual integration takes up 60% of the analysis cycle, and data accuracy is less than 85%; this platform realizes automatic data collection and verification through multi-source data standardization access and multi-modal data fusion technology, improves data access efficiency by 90%, and combined with data quality control mechanism, data accuracy is improved to ≥97%, and data quality index compliance rate ≥95%.

[0143] 2、Dynamic risk response ability is significantly enhanced: traditional ESG risk relies on quarterly manual screening, about 40% of dynamic risks cannot be discovered in time, and the risk response time is as long as 7 days; this platform realizes real-time risk warning through real-time index monitoring and adaptive anomaly identification technology, combined with three-level causal chain tracing, the risk response time is shortened to ≤10 minutes, the dynamic risk omission rate is reduced by 85%, and the anomaly identification accuracy is ≥95%.

[0144] 3、Report generation efficiency and compliance are improved: traditional manual preparation of ESG report takes 7-10 days, and the error rate of multi-standard adaptation is ≥15%; this platform shortens the report generation cycle to 1-2 days through multi-template automatic adaptation and text automatic generation technology, and the efficiency is improved by 80%, relying on the compliance verification engine, the report compliance error rate is reduced to <2%, and the verification pass rate is ≥98%.

[0145] 4、Risk quantification and decision scientificity are greatly improved: traditional ESG risk assessment is mainly qualitative description, lacking quantitative support; this platform realizes accurate risk value calculation (quantization error ≤8%) and horizontal comparison through multi-dimensional risk quantification model and industry benchmarking analysis, combined with reinforcement learning optimization strategy, the effectiveness of measures is improved by 60%, and the average ESG rating of enterprises is improved by 1-2 levels.

[0146] 5、Stakeholder collaborative closed loop is fully formed: in traditional ESG management, the data of stakeholders is fragmented, and the measure implementation rate is less than 50%; this platform realizes data sharing and rectification closed loop through multi-role permission control and task tracking mechanism, reduces the annual safety accident rate by 40%, and improves the ESG performance of the supply chain by 20%, meeting the requirements of ESG information accurate disclosure in the ESG Information Disclosure Guidelines for Listed Companies of the Securities Regulatory Commission, the EU CSRD and other regulations, while reducing the ESG management cost of enterprises, providing effective support for large enterprises to optimize the ESG management input-output ratio and small and medium-sized enterprises to break through the ESG management technical threshold.

[0147] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts of each embodiment can be referred to each other. The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications of these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A sustainable management ESG platform, characterized in that, include: Data access layer, twin modeling layer, intelligent analysis layer, report generation layer, and collaborative interaction layer; The data access layer is used to achieve standardized collection and cleaning of multi-source ESG data; The twin modeling layer is connected to the data access layer and is used to construct an enterprise ESG digital twin; The intelligent analysis layer is connected to the twin modeling layer and is used to complete ESG risk identification and quantitative assessment. The report generation layer is connected to the intelligent analysis layer to support the automatic output of multi-standard compliant ESG reports; The collaborative interaction layer is connected to the report generation layer to enable stakeholders to share and collaboratively manage ESG data. Each layer works together through data interfaces to form a closed-loop ESG management system.

2. The sustainable ESG management platform of claim 1, wherein, The data access layer includes: Protocol adaptation module, used to support data access for various industrial and IoT protocols; A text parsing engine that uses natural language processing techniques to extract ESG metrics from unstructured text; Data cleaning tools are used to detect anomalies and fill in missing values ​​in the incoming data.

3. The sustainable ESG management platform of claim 1, wherein, The twin modeling layer includes: The indicator mapping engine is used for dynamic mapping and management of indicators in the environmental, social and governance dimensions. The process visualization module is used to build a digital mirror of ESG management processes based on business process modeling standards; A scenario simulation engine is used to simulate the potential impact of external dynamic scenarios on a company's ESG performance.

4. The sustainable ESG management platform of claim 1, wherein, The intelligent analysis layer includes: The real-time monitoring module is used for continuous and dynamic monitoring of ESG indicators; Anomaly detection module, used for anomaly detection and early warning based on time series prediction models and association rules; The risk quantification engine is used to conduct causal tracing and quantitative assessment of identified ESG risks.

5. The sustainable ESG management platform of claim 1, wherein, The report generation layer includes: The template management module includes built-in report templates that support multiple international ESG disclosure standards. The content generation module is used to automatically generate analytical text and visual charts for reports; The validation engine is used to perform compliance and logical validation on report data based on a pre-built rule base.

6. The sustainable ESG management platform of claim 1, wherein, The collaborative interaction layer includes: The access control module is used to assign data access and operation permissions to different stakeholders based on a role-based access control model. The task tracking module is used to provide full lifecycle visualization tracking and management of ESG improvement measures and remediation tasks; The performance optimization module is used to optimize ESG management strategies based on feedback data from the measures.

7. The sustainable ESG management platform of claim 1, wherein, The deployment architecture of the platform includes: Edge computing nodes used for data acquisition and preprocessing; A cloud server cluster used to deploy core platform services; Blockchain nodes used to achieve trusted storage and traceability of supply chain data.

8. A sustainable management ESG platform according to claim 7, characterized in that, The edge computing nodes, cloud server clusters, and blockchain nodes communicate with the data interface through a message middleware, wherein: The edge computing node is configured to filter, compress, and convert the collected raw data before uploading it to the cloud server cluster. The blockchain node is configured based on a consortium blockchain architecture to store and trace the hash values ​​of supply chain ESG data.

9. A sustainable management ESG platform according to claim 1, characterized in that, The data access layer, twin modeling layer, intelligent analysis layer, report generation layer, and collaborative interaction layer are connected sequentially to form the following data flow: The standardized ESG data processed by the data access layer is then input into the twin modeling layer. The enterprise ESG digital twin output by the twin modeling layer is input into the intelligent analysis layer; The risk identification and quantitative assessment results output by the intelligent analysis layer are input into the report generation layer. The multi-standard compliant ESG reports generated by the report generation layer are shared and collaboratively managed by the collaborative interaction layer.

10. A sustainable management ESG platform according to claim 1, characterized in that, The platform is configured to execute the following core processing flow: Comprehensive ESG data fusion and governance: Standardized access, cleaning and fusion of multi-source ESG data to build a high-quality ESG dataset and an enterprise ESG digital twin; Dynamic indicator monitoring and anomaly early warning: Based on the digital twin, key indicators are monitored in real time, anomalies are identified, and early warning information is pushed out in a tiered manner; Risk intelligent analysis and quantitative assessment: Conduct causal chain tracing of abnormal early warning indicators, calculate comprehensive risk value by combining risk quantification model and conduct industry benchmarking analysis; Automated generation of compliance reports: Automatically populates data, generates text and charts based on compliance templates and ESG datasets, and completes report verification; Stakeholder collaborative closed-loop management: Enable ESG data sharing among stakeholders, track the implementation of rectification tasks, and iteratively optimize management strategies.

Images