In order to reinforce the efficiency and initiative of accident prevention for reducing accident loss, the surest way is to strengthen the prediction. In view of difficulties in aviation accident prediction, a prediction method based on Auto-Regressive Integrating Moving Average (ARIMA) is proposed. In this method, a trial model is first identified for an accident time series, and then the diagnosis is executed with the necessary adjustments. The calculation process including identification, estimation and diagnosis is repeated until obtain a satisfactory model. The example shows that, ARIMA has a good prediction for aviation accident and provides an important technique support for decision-making of aviation safety.

The paper illustrates mechanism construction and analysis of aviation service safety culture on safety management based on system synamics (SD),analysis and set up the parameter.The model gets better results through a simulation.And based on the simulation,construction the influence mechanism of aviation service safety culture on aviation service safety management.

The necessity of PDF417 barcode technology in application of aviation spare parts management is described. And through the careful requirement analysis and discussion the feasibility of automatic identification system for aviation spare parts management information barcode is also discussed. In addition, the development and design ideas of the production system and application system on PDF417 barcode are introduced. It can be included from the research that, the technology can effectively overcome the drawbacks such as error easiness, low efficiency that the manual input bring on in supply and management for aviation spare parts, and can realize the automatic identification, input and monitoring for aviation spare parts information.

Flight safety is the basis of the Air Force combat effectiveness. We propose a fault tree approach to qualitatively identify risk factors that affect flight safety. Risk factor identification index system of flight safety is established, and the risk coefficient method is developed to identify the risk factors quantitatively. We finally study an example of a Military Region, the risk factors of which are from different sources, and establish the classification system. The results can provide an objective basis for headquarters offices and flight safety management department policy formulation, which can lay the foundation for further development of flight safety risk assessment.

In order to accurately depict the longitudinal aerodynamic characteristics of flight vehicle, a new aerodynamic modeling method based on Support Vector Machine (SVM) is proposed. SVM is a machine learning method that solves the problem by mean of optimization method on the basis of statistics learning theory, and introduces the structural risk minimization to coordinate the relationship between fitting degree and generalization ability, and the training samples are mapped into high dimensional feature space for linear regression, which can solve the practical problems such as nonlinearity, high dimension, over-learning, local minima, curse of dimensionality and so on. The experimental result shows that the method has a good modeling accuracy and speed training, and also is easy to implement. It is fully competent for longitudinal aerodynamic modeling for flight vehicle.