UDTR Global Rights of Nature Radar

Digital twin-based intelligent risk assessment and decision support system for university student entrepreneurial projects

Identification

Public canonical key

radar:r-e5bcc51d39bf7a3658ae4f25294b7057

Authors

Rongting Qin · Xiaojie Zi · Xiaoxi Ge

Reported year

2026

Reported language

en

Source type

article

First detected

2026-11-14T09:38:34.395707+00:00

Last metadata observation

2026-11-14T09:38:34.395707+00:00

Last HTTP verification

Not checked

Institutions reported by the source

Chongqing University of Arts and Sciences

Reported affiliation countries

CN

Documentary abstract — original language

University student entrepreneurial ventures face significantly higher failure rates compared to traditional businesses, primarily due to inadequate risk assessment and decision-making challenges. This research develops an innovative digital twin-based intelligent risk assessment and decision support system specifically designed for student entrepreneurial projects. The system integrates digital twin technology with machine learning algorithms to create comprehensive virtual representations of entrepreneurial ventures, enabling real-time risk monitoring, predictive analytics, and intelligent decision recommendations. The proposed framework employs a multi-layered architecture encompassing data acquisition, digital twin modeling, risk assessment engines, and intelligent decision support modules. Experimental validation using 2,847 entrepreneurial projects demonstrates superior performance with 94.2% prediction accuracy, compared to 78.5% for traditional statistical methods and 85.7% for standard machine learning approaches. The system provides early warning capabilities with average lead times of 22.1 days and achieves 23.7% improvement in project success rates. Results indicate significant enhancements in decision-making effectiveness, risk mitigation capabilities, and overall entrepreneurial project outcomes, with user satisfaction scores averaging 4.47 out of 5.0. This research contributes to the theoretical understanding of digital twin applications in entrepreneurial contexts while providing practical solutions for improving student venture success rates through intelligent risk management and decision support.

Identifiers

OPENALEX

https://openalex.org/W7124830906

DOI

10.1038/s41598-026-36111-2

PMID

https://pubmed.ncbi.nlm.nih.gov/41554832

PMCID

https://www.ncbi.nlm.nih.gov/pmc/articles/12894666

Documentary context

Documentary family

article

Source-reported types

article

Publishers

Nature Portfolio

Entity mentions are not evidence of legal recognition. Jurisdiction studied is separate from author affiliation country.

Sources

OpenAlex · Record 1

• Document · https://doi.org/10.1038/s41598-026-36111-2

Documentary status

Relevance pending

Library

This identity is not yet included in the Legal Library.