Research on Online Anomaly Detection of Business Processes Based on Digital Twin-Intelligent Body Collaborative Architecture | Zendy

Juan Li | Zendy; WenPeng Lv | Zendy; Yan Wan | Zendy

Open Access

Research on Online Anomaly Detection of Business Processes Based on Digital Twin-Intelligent Body Collaborative Architecture

Author(s) -

Juan Li,

WenPeng Lv,

Yan Wan

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3598321

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

The increasing complexity of business processes makes traditional rigid structure business process management methods difficult to apply in complex scenarios. This study establishes a collaborative integration framework of twin-intelligent bodies, combining digital twin and intelligent body technologies, based on dynamic declarative constraints. The digital twin enhances agents’ perception and decision-making, allowing real-time constraint adjustments. An association rule algorithm mines multi-perspective business process data to detect anomalies. Experiments on the DCGAN platform show an F1 score of 0.8–1 and a false alarm rate under 13%. The method outperforms LSTM and Transformer in comparative tests. The case study validated the effectiveness of the proposed model in real-world scenarios.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research