Industrial Chain Disruption Events Monitoring with Deep Learning Methods: A Practical Application in China

Globalization has made the industrial chain longer and more complex, resulting in greater vulnerability to emergencies, such as the COVID-19 pandemic, earthquakes, and wars. Emergencies will lead to plant shutdowns, supply shortages, market blockades, and other risk events, leading to large-scale supply chain disruption, also known as industrial chain disruption. The safety and stability of the industrial chain are the foundation of national economic stability. All countries attach great importance to risk monitoring leading to the interruption of the industrial chain. However, at present, the risk monitoring method of the industrial chain is mainly to screen out the risk events that may cause the industrial chain disruption from news by manually monitoring the news media. It is of great significance to establish an efficient automatic monitoring system for industrial chain disruption events (ICDE). In this paper, an ICDE monitoring model is proposed to identify ICDE automatically using deep learning technology. The ICDE monitoring model consists of an ICDE identification model and an ICDE correlation model. The former identifies risk events from online news through the Ernie model, while the latter matches risk events with industrial chain nodes through similar nodes and virtual nodes. Similar nodes refer to synonyms in industrial chain nodes. Virtual nodes refer to the words that appear in a large number in the news and do not exist in the industrial chain, but they form an inclusive relationship with the nodes of the industrial chain. Finally, the model is applied to the new energy vehicle industry chain as an example. The application results show that the model can monitor ICDE on each node of the industry chain in real time, and the identification accuracy of ICDE is 92%. Through the ICDE monitoring model, the national or local government can formulate measures to reduce industrial losses in time and track the risk status of the industrial chain in real time.