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

Identification

Clé publique

radar:r-e5bcc51d39bf7a3658ae4f25294b7057

Auteurs

Rongting Qin · Xiaojie Zi · Xiaoxi Ge

Année déclarée

2026

Langue déclarée

en

Type source

article

Première détection

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

Dernière observation des métadonnées

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

Dernière vérification HTTP

Non effectuée

Institutions déclarées par la source

Chongqing University of Arts and Sciences

Pays d’affiliation déclarés

CN

Résumé documentaire — langue originale

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.

Identifiants

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

Repères documentaires

Famille documentaire

article

Types déclarés par les sources

article

Éditeurs

Nature Portfolio

Les mentions d’entités ne constituent pas une preuve de reconnaissance juridique. La juridiction étudiée est distincte du pays d’affiliation des auteurs.

Sources

OpenAlex · Document 1

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

Statut documentaire

Pertinence à examiner

Bibliothèque

Cette identité n’est pas encore intégrée à la Bibliothèque juridique.