Work

DeepSeek-Assisted Dynamic Impact Monitoring System for Industrial Chain

Year: 2026

Type: article

Abstract: In recent years, frequent emergencies such as the COVID-19 pandemic and regional conflicts have significantly heightened uncertainty risks in global industrial chains. Safeguarding industrial chains is crucial for sustaining national economic stability, and while major countries prioritize industrial chain security, establishing an effective industrial chain monitoring system remains a substantial challenge. This paper establishes a dynamic impact monitoring system (DIMS), which can dynamically monitor the risk level of the industrial chain. The DIMS consists of an industrial chain static risk assessment model (IC-SRAM) and an industrial chain dynamic event monitoring model (IC-DEMM). The IC-SRAM is used to evaluate the risks of the industrial chain itself, and the IC-DEMM is used to monitor external risk events in real time. DeepSeek is used to associate events with industrial chain nodes. The dynamic industrial chain risk value is formed based on the results of static risk assessment and dynamic risk event monitoring. Finally, an empirical analysis of the new energy vehicle industrial chain is carried out. The results show the effectiveness of the proposed DIMS and the accuracy rate of associating industrial chain nodes using DeepSeek reaches 91%.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc10090302

Authors: Huimin Tang, Yuan Yuan, Tianxiao Gong

Institution: China Academy of Information and Communications Technology

Language: English

Topic: Supply Chain Resilience and Risk Management

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,172