CN-121981537-A - Digital economic risk assessment method and system based on artificial intelligence

CN121981537ACN 121981537 ACN121981537 ACN 121981537ACN-121981537-A

Abstract

The invention provides a digital economic risk assessment method and system based on artificial intelligence, wherein the method comprises the steps of collecting risk data related to digital economy from a plurality of preset data sources, fusing the risk data to form a risk analysis data set of the digital economy, extracting risk characteristics of the risk data set in a plurality of preset dimensions to obtain the risk characteristics of the digital economy in each preset dimension, obtaining an operation log of the digital economy, training a preset unsupervised learning model by using the operation log, obtaining an abnormal score of the digital economy according to the unsupervised learning model, obtaining a preset intelligent risk assessment model, and fusing the risk characteristics and the abnormal score by using the preset intelligent risk assessment model to obtain a risk score of the digital economy. The technical scheme of the invention can improve the accuracy and reliability of the risk assessment result.

Inventors

CHEN YIXUAN
LI ZHUOYANG
GUO MINGQI
ZHANG XUE
LI MINGYU
ZHANG JUNYUE

Assignees

陈逸轩

Dates

Publication Date: 20260505
Application Date: 20260115

Claims (8)

1. A digital economic risk assessment method based on artificial intelligence, comprising: collecting risk data related to the digital economy from a plurality of preset data sources and fusing the risk data to form a risk analysis data set of the digital economy; extracting risk characteristics of the risk data set in a plurality of preset dimensions to obtain risk characteristics of the digital economy in each preset dimension, wherein the plurality of preset dimensions comprise data security, data compliance, data value and quality and data circulation and ecology; Acquiring a risk scoring sub-model of each preset dimension, calculating a risk sub-score of the digital economy in each preset dimension according to corresponding risk characteristics by adopting each risk scoring sub-model, and fusing a plurality of risk sub-scores to obtain a total risk score of the digital economy; acquiring an operation log of the digital economy, training a preset unsupervised learning model by adopting the operation log, acquiring safe abnormal points of the digital economy according to the unsupervised learning model, and determining abnormal scores of the digital economy according to the number of the safe abnormal points; and fusing the total risk score and the abnormal score to obtain a risk score of the digital economy.
2. The digital economic risk assessment method according to claim 1, wherein, After the step of calculating the risk sub-score of the digital economy in each preset dimension according to each risk feature, the method further comprises: And generating a risk degree radar chart of the digital economy according to each risk sub-score so as to display the distribution of each risk sub-score.
3. The digital economic risk assessment method according to claim 1, wherein, After the step of calculating the risk sub-score of the digital economy in each preset dimension according to each risk feature, the method further comprises: and acquiring a historical risk score of the digital economy in each preset dimension, and respectively generating a risk degree line graph of the digital economy in each preset dimension according to each historical risk degree and the corresponding risk sub-score.
4. The digital economic risk assessment method according to claim 1, wherein, The step of collecting risk data related to digital economics from a plurality of preset data sources, comprising: Data asset metadata, system log and network traffic data, external threat intelligence data, law and policy text data, and data market transaction data are collected from a plurality of said preset data sources.
5. The digital economic risk assessment method according to claim 1, wherein, Before the step of fusing the risk data to form a digitally economical risk analysis dataset, a cleaning process and a normalization process are also included on the risk data.
6. The digital economic risk assessment method according to claim 1, wherein, After the step of fusing the total risk score and the anomaly score to obtain a risk score for the digital economy, further comprising: and acquiring the risk type of the digital economy according to the risk sub-score, and determining a security optimization strategy of the digital economy according to the risk type.
7. The digital economic risk assessment method according to claim 6, wherein, The step of obtaining the risk type of the digital economy according to the risk sub-score comprises the following steps: and obtaining a plurality of clustering clusters corresponding to the preset risk types, calculating the clustering clusters corresponding to the economic data according to the risk sub-scores, and taking the preset risk types corresponding to the clustering clusters as the risk types of the data types.
8. An artificial intelligence based digital economic risk analysis evaluation system comprising a processor and a memory, wherein the memory stores a computer program, the processor being configured to execute the computer program to implement the steps of the digital economic risk evaluation method of any one of claims 1-7.

Description

Digital economic risk assessment method and system based on artificial intelligence Technical Field The invention relates to the technical field of digital economic risk assessment, in particular to a digital economic risk assessment method and system based on artificial intelligence. Background Digital economy refers to a series of economic activities that take digitized knowledge and information as key production elements, modern information networks as important carriers, and efficient use of information communication technology as an important driving force for efficiency improvement and economic structural optimization, i.e., the sum of all the economic activities of the digital technology in the past. Digital economics, while creating tremendous value, are accompanied by complex and hidden risks, including not only traditional data security risks (e.g., leakage, tampering, loss), but also data compliance risks (e.g., violations of GDPR, personal information protection laws, etc.), data value risks (e.g., low data quality, valuation bias), and data circulation risks (e.g., data abuse, rights and interests), etc. At present, the analysis of the digital economic risk is mostly dependent on manual evaluation and a static compliance checklist, and the analysis mode is single, so that the accuracy and the reliability of the risk evaluation cannot be ensured. Disclosure of Invention The invention provides a digital economic risk assessment method and a digital economic risk assessment system based on artificial intelligence, which are used for assessing the risk level of digital economy and improving the accuracy and reliability of a risk assessment result. Specifically, in a first aspect, the present invention provides an artificial intelligence based digital economic risk assessment method, including: collecting risk data related to the digital economy from a plurality of preset data sources and fusing the risk data to form a risk analysis data set of the digital economy; extracting risk characteristics of the risk data set in a plurality of preset dimensions to obtain risk characteristics of the digital economy in each preset dimension, wherein the plurality of preset dimensions comprise data security, data compliance, data value and quality and data circulation and ecology; Acquiring a risk scoring sub-model of each preset dimension, calculating a risk sub-score of the digital economy in each preset dimension according to corresponding risk characteristics by adopting each risk scoring sub-model, and fusing a plurality of risk sub-scores to obtain a total risk score of the digital economy; acquiring an operation log of the digital economy, training a preset unsupervised learning model by adopting the operation log, acquiring safe abnormal points of the digital economy according to the unsupervised learning model, and determining abnormal scores of the digital economy according to the number of the safe abnormal points; and fusing the total risk score and the abnormal score to obtain a risk score of the digital economy. Further, after the step of calculating the risk sub-score of the digital economy in each preset dimension according to each risk feature, the method further comprises: And generating a risk degree radar chart of the digital economy according to each risk sub-score so as to display the distribution of each risk sub-score. Further, after the step of calculating the risk sub-score of the digital economy in each preset dimension according to each risk feature, the method further comprises: and acquiring a historical risk score of the digital economy in each preset dimension, and respectively generating a risk degree line graph of the digital economy in each preset dimension according to each historical risk degree and the corresponding risk sub-score. Further, the step of collecting risk data related to digital economics from a plurality of preset data sources includes collecting data asset metadata, system log and network traffic data, external threat intelligence data, legal and policy text data, and data market transaction data from a plurality of preset data sources. Further, before the step of fusing the risk data to form a digitally economical risk analysis dataset, a cleaning process and a normalization process are performed on the risk data. Further, after the step of fusing the total risk score and the anomaly score to obtain a risk score for the digital economy, further comprising: and acquiring the risk type of the digital economy according to the risk sub-score, and determining a security optimization strategy of the digital economy according to the risk type. Further, the step of obtaining the risk type of the digital economy according to the risk sub-score includes: and obtaining a plurality of clustering clusters corresponding to the preset risk types, calculating the clustering clusters corresponding to the economic data according to the risk sub-scores, and taking the preset r