In view of the shortcomings of the existing accident causation models, such as simple listing and superposition of causative factors, not considering the coupling effect between factors, unable to make quantitative analysis and so on,a new nonlinear multi⁃factor coupling accident causation model was proposed and applied to the prevention of oil depot accidents. In the new model, the calculation formulas of the main influencing factors, the influence coefficient and the multi factor coupling rheological nonlinear damage were proposed. The most prominent feature of the model is that it can distinguish the dynamic influence of the rheology of the main influencing factors on the damage of the complex system under the coupling action of multiple factors. MATLAB software was used to simulate the changing state of the oil depot system after single factor rheology, main influencing factor rheology and non⁃main influencing factor rheology. The results show that the main factor affecting the safety of oil depot is the lack of safety consciousness,and the lack of safety consciousness of oil depot personnel has the greatest influence on other factors. For oil depot accident prevention, according to the nonlinear multi⁃factor coupling accident cause model proposed in this paper, it is necessary to take effective measures to control the main influencing factors and seize the main time nodes.

Because there is a large error defect of predicting the severe fluctuating fire data by traditional grey forecasting model, the article expends the original data sequence of grey prediction model using Taylor formula and expends Lagrange type remainder, which modified the traditional grey forecasting model and improved the traditional grey prediction algorithm. The article used Matlab software to program, and used the traditional gray prediction algorithm and improved gray prediction algorithm,respectively.Then the article selected three sets of data to predict aiming at the mini zone bathtub shape fire accident. As the results show that: modified grey forecasting model prediction curve gives satisfactory accuracy comparing to the characteristic of bathtub curve, which has a high consistency with the original data.And the poor quality, large error and low accuracy is improved. Compared with the traditional grey prediction model, the error of the improved grey prediction model has reduced 86.59%, 55.32%, and 55.70%, respectively. The prediction results of the improved grey prediction model can meet the requirements in three aspects: accuracy, relative error and correlation.