To scientifically and quantitatively evaluate the current city safety resilience and improve the city safety resilience level, this project puts forward the concept and degree of city safety resilience based on the systematic analysis of the city safety resilience curve and establishes the framework of a city safety resilience evaluation index system, including predisaster prevention, disaster-bearing carrier, emergencies and emergency management. The Delphi method and cloud model are used to construct the city safety resilience evaluation model, and the weight and application of the model are analyzed and discussed. The research results show that the theory and method of Chinese city safety resilience evaluation based on the Delphi method and cloud model have important guiding significance for improving the city safety resilience level.

Improving the resilience of enterprise safety production is one of the important ways to deal with the frequency of safety accidents. Based on the definition of enterprise safety production resilience, we fully consider the impacts of recovery resilience, self-organizing resilience, and learning resilience as the three dimensions of enterprise safety production resilience. We build a back propagation (BP) neural network model that analyzes the main factors of enterprise safety production resilience using the results of gray relational analysis as an input that can assess the resilience of enterprise safety production and provide a valuable reference for the improvement of an enterprise’s safety production level. The results show that the resilience of production safety obviously increased after the Chinese enterprises with low resilience (as predicted by the model) adopted the corresponding early warning methods. The gray relational degree analysis method can incorporate well the variables for the establishment of the BP neural network prediction model.