Research on the Resistance and Early Warning of the Grain Stock MarketUnder Emergencies
LIU Jiangang, CHEN Luxia
a. School of Science; b. Hunan Key Laboratory of Hunan Province for Statistical Learning and Intelligent Computation Hunan University of Technology and Business, Changsha 410205, China
Abstract：In order to study the impact of different types of emergencies including COVID-19, July 20 Heavy rainstorm in Zhengzhou and variant strains of the new coronavirus attacked Shanghai in 2022 on the performance of food network security, the Grainger causal detection method was used to construct four different stages of food stock correlation network. Through the analysis of the network topology characteristics, node importance and anti-destruction performance in the three periods, it is found that in the process of facing the invasion of overall or local emergencies, the internal structure of the food-related network will be effectively adjusted with the degree of impact, and the nodes that occupy the leading role form a fast and effective response mode according to geographical factors, and the maintenance and protection of these important nodes of food supply is often the focus. The attack simulation results show that deliberate attacks are more persecutive than random attacks, and the food related networks in the second stage are disintegrated most rapidly, and the anti-destruction performance of the third and fourth stages is more unstable. Finally, the early warning effect of CUSUM control chart used in the grain stock market is also well reflected.
刘建刚, 陈芦霞. 突发事件下粮食股市的抗毁性及预警研究[J]. 复杂系统与复杂性科学, 2024, 21(2): 52-59.
LIU Jiangang, CHEN Luxia. Research on the Resistance and Early Warning of the Grain Stock MarketUnder Emergencies[J]. Complex Systems and Complexity Science, 2024, 21(2): 52-59.
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