A Dynamic Prediction Model of Complex System Accident Risk Based on Tensor Field
The accident risk faced by complex safety-related systems during operation is influenced by different components in the system.In the process of system operation,in order to make an accurate analysis and prediction of the probability and severity of accidents at a certain time,it is necessary to build a series of event-based models and describe extremely complex causal relationships to achieve this.In order to overcome this difficulty,based on the view that accident risk is a tensor field function,this paper constructs a system accident risk model based on the tensor field.The model realizes the monitoring of accident risk by transforming the observation coordinate system of accident risk from the parameter coordinate system composed of accident probability and severity to the parameter coordinate system composed of other observable safety-related physical quantities.The rationality of the model is verified by the results of actual data analysis.
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