Research on the security of China's oil resources supply based on the objective weight method

With the rapid development of China's economy,external dependence on petroleum resources continues to increase,and their security has become an important part of national security.To evaluate the security of China's petroleum resource supply in a scientific and objective manner,this study establishes a corresponding petroleum life-cycle evaluation index system,based on the theory and method of the whole life-cycle security evaluation of mineral resources,and conducts further independence and grey correlation analysis on the indexes for the purpose of evaluating the petroleum risk situation in China,based on relevant public data from the past 10 years.The results show that the overall trend of China's oil risk has a“U”-shaped characteristic of first decreasing and then increasing.Furthermore,the analysis finds that China's mineral resources have been greatly influenced by the domestic production situation and international trade.These results suggest that the security of petroleum supply can be improved by safeguarding international trade in petroleum resources,strengthening the strategic reserves of domestic petroleum resources,and developing new alternative clean energy sources to improve the resilience of petroleum supply security.This study's research methodology is more logical and systematic than traditional methods,and the analysis of the factors is comprehensive and of high application value,providing implications for the establishment of a big data analysis and evaluation index system for oil resource security.

Reasoning Disaster Chains with Bayesian Network Estimated Under Expert Prior Knowledge

With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers.The Bayesian network(BN)was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies.To capture the interacting relationships among different factors,a scenario representation model of disaster chains was developed,followed by the determination of the BN structure.In deriving the conditional probability tables of the BN model,we found that,due to the lack of data and the significant uncertainty of disaster chains,parameter learning methodologies based on data or expert knowledge alone are insufficient.By integrating both sample data and expert knowledge with the maximum entropy principle,we proposed a parameter estimation algorithm under expert prior knowledge(PEUK).Taking the rainstorm disaster chain as an example,we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior(MAP)algorithm and the direct expert opinion elicitation method.The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.