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CN-122022470-A - Multi-source data fusion-based supply chain ESG risk dynamic assessment method and system

CN122022470ACN 122022470 ACN122022470 ACN 122022470ACN-122022470-A

Abstract

The invention discloses a supply chain ESG risk dynamic assessment method and system based on multi-source data fusion, comprising the following steps of firstly, collecting multi-source heterogeneous data at each node of a supply chain, processing the data and generating a node data set containing ESG risk characteristics; the method comprises the steps of generating a node data set in the first step, generating a dynamic evaluation reference in a self-adaptive mode based on the node data set generated in the first step and combining attribute parameters of a supply chain node and external dynamic information, constructing a risk conduction model based on the node data set generated in the first step and the dynamic evaluation reference generated in the second step, performing real-time risk evaluation, and outputting an evaluation result containing a risk conduction path.

Inventors

  • ZHOU HONGYAN

Assignees

  • 周虹言

Dates

Publication Date
20260512
Application Date
20260128

Claims (10)

  1. 1. The method for dynamically evaluating the risk of the ESG of the supply chain based on the multi-source data fusion is characterized by comprising the following steps of: Collecting multi-source heterogeneous data at each node of a supply chain, and processing the data to generate a node data set containing ESG risk characteristics; step two, based on the node data set generated in the step one, combining the attribute parameters of the supply chain nodes and external dynamic information, and adaptively generating a dynamic evaluation benchmark; and thirdly, constructing a risk conduction model for real-time risk assessment based on the node data set generated in the first step and the dynamic assessment reference generated in the second step, and outputting an assessment result containing a risk conduction path.
  2. 2. The method of claim 1, wherein in step one, the processing the data comprises trusted processing the data to generate a trusted data base, and the trusted processing comprises verifying at least one of a data acquisition, transmission or processing link.
  3. 3. The method for dynamically evaluating risk of a supply chain ESG based on multi-source data fusion according to claim 1, wherein the trusted processing is realized by a blockchain technology, and specifically comprises uploading key information of data in a processing full link to a supply chain alliance chain for verification, generating a corresponding hash value, verifying data integrity based on the hash value, and distributing data fusion weights to the data according to a data source type.
  4. 4. The method of claim 1, wherein in the second step, the adaptively generating the dynamic evaluation reference is implemented by a machine learning model, and the machine learning model iteratively optimizes risk evaluation reference parameters of each dimension of the ESG by taking the matching degree of the historical risk evaluation result and the actual risk event as an optimization target.
  5. 5. The method for dynamically evaluating risk of a supply chain ESG based on multi-source data fusion as recited in claim 1, wherein the machine learning model is a reinforcement learning model, and the external dynamic information comprises compliance requirement information extracted from industry standard texts and policy and regulation texts through text semantic parsing technology.
  6. 6. The method for dynamically evaluating risk of a supply chain ESG based on multi-source data fusion of claim 1 wherein in step three, constructing a risk conduction model for real-time risk evaluation comprises: processing node data in real time by adopting a stream computing architecture, and generating a risk pulse signal when the data trigger the dynamic evaluation reference; abstract the supply chain link points and the association relation thereof into a complex network, update the risk conduction weight among the nodes based on real-time data, and quantify the conduction intensity and time delay of the risk.
  7. 7. The method for dynamically evaluating risk of a supply chain ESG based on multi-source data fusion as recited in claim 1, wherein the method further comprises processing historical risk trend data using a batch computing architecture and weighting and fusing the batch analysis results with real-time results of stream computation to optimize accuracy of risk evaluation.
  8. 8. The multi-source data fusion-based supply chain ESG risk dynamic assessment system of any one of claims 1-7, comprising: The edge computing devices are respectively deployed at each node of the supply chain and are used for collecting and preprocessing multi-source heterogeneous data of the node; And the central server is in communication connection with the edge computing device.
  9. 9. The system for dynamically evaluating risk of a supply chain ESG based on multi-source data fusion of claim 8 further comprising a blockchain network for certifying data processing link information uploaded by said edge computing device or a central server, wherein a benchmark calibration engine running in said central server is a model based on reinforcement learning training.
  10. 10. The system for dynamically evaluating risk of a supply chain ESG based on multi-source data fusion as recited in claim 8, wherein the risk conduction analysis engine in the central server comprises a flow calculation module and a batch calculation module, and the system further comprises an early warning output module for outputting an evaluation result containing a risk conduction path in a form of a risk thermodynamic diagram and interfacing with a supply chain management service system at an instruction level.

Description

Multi-source data fusion-based supply chain ESG risk dynamic assessment method and system Technical Field The invention belongs to the technical field of intelligent supply chain management and risk management, and particularly relates to a supply chain ESG risk dynamic assessment method and system based on multi-source data fusion. Background With the deepening of global sustainable development concepts and the enhancement of social responsibility consciousness of enterprises, environmental, social and governance (ESG) performances have become key indexes for measuring the long-term value and the toughness of supply chains of enterprises. In this context, ESG risk assessment and management of the supply chain becomes critical. Currently, the common assessment method in the industry mainly depends on annual social responsibility reports autonomously disclosed by enterprises, questionnaires of third-party institutions and periodic on-site audits. In the technical application level, the existing scheme mostly adopts a centralized data management system, and a core enterprise distributes standardized investigation forms to suppliers thereof to collect static data of the core enterprise in the aspects of energy consumption, labor equity, commercial moral and the like. And then, calculating by a scoring card model based on fixed weights and industry average values, and finally generating a periodical ESG performance rating report of the supplier. Some advanced practices have also begun to attempt to integrate limited sources of public data, such as environmental monitoring data or public opinion information, to enrich the assessment dimension. However, prior art solutions face a number of systematic limitations in practice. Firstly, the data foundation is seriously dependent on active reporting and manual arrangement of a main body, so that the problems of subjective data source, lag in updating and difficulty in cross verification exist, and the objectivity and the creditability of an evaluation result are restricted. Secondly, the standard and the threshold value on which the evaluation is based are usually static and unified, cannot flexibly adapt to the specificity of different supply chain link points in industry, region and operation scale, and are more difficult to respond to the dynamic changes of external legal policies and market environments in real time, so that the accuracy of risk evaluation is insufficient. More importantly, the existing methods generally treat each node as an independent individual for evaluation, and lack effective analysis tools for conducting, overlaying and amplifying effects of risks in a complex network structure of a supply chain, so that evaluation conclusion is difficult to reveal the overall appearance of systematic risks. Finally, the period from evaluation to output application is too long, and the results are often presented in a static report form, so that immediate, visual and operable support cannot be provided for real-time monitoring and agile decision of a supply chain, and the actual effectiveness and the preventive value of risk management measures are limited. Disclosure of Invention In order to overcome the defects in the prior art, the invention provides a supply chain ESG risk dynamic assessment method and system based on multi-source data fusion, which solve the problems of subjective hysteresis of data, static solidification of a benchmark, lack of risk conduction analysis and deviation of assessment results from real-time decisions in the prior art. In order to achieve the above purpose, the present invention provides the following technical solutions: A method and a system for dynamically evaluating risk of a supply chain ESG based on multi-source data fusion comprise the following steps: Collecting multi-source heterogeneous data at each node of a supply chain, and processing the data to generate a node data set containing ESG risk characteristics; step two, based on the node data set generated in the step one, combining the attribute parameters of the supply chain nodes and external dynamic information, and adaptively generating a dynamic evaluation benchmark; and thirdly, constructing a risk conduction model for real-time risk assessment based on the node data set generated in the first step and the dynamic assessment reference generated in the second step, and outputting an assessment result containing a risk conduction path. Preferably, in the first step, the processing of the data includes performing trusted processing on the data to generate a trusted data base, where the trusted processing includes performing certification or integrity verification on at least one of the data acquisition, transmission or processing links. The trusted processing is realized by a blockchain technology, and concretely comprises the steps of uploading key information of data in a processing full link to a supply chain alliance chain for verification, generating a