In order to improve the credibility of the damage degree evaluation to the aviation network due to cascading failures caused by emergency， considering the redundancy ability of airport nodes for the load， which means if the overload occurs in a certain spatial range， the node will not fail immediately but has a certain overload handling ability， an aviation network cascading failure model was proposed based on overload condition and failure probability. Firstly， the overload coefficient， weight coefficient， distribution coefficient， and capacity coefficient were introduced into the traditional "load-capacity" Motter-Lai cascading failure model. Then， the redundant capacity characteristics of network nodes were described by overload condition and failure probability， and different load redistribution strategies were applied to the failed and overloaded nodes to make the model more consistent with the aviation network reality. Theoretical analysis and simulation results show that increasing the overload coefficient within a certain range helps to reduce the impact of cascading failures， but the improvement effect is not obvious after increasing to a certain degree； with the optimal intervals for parameters of the model. the aviation network can maintain better robustness while spending smaller construction cost， and the optimized allocation of aviation network resources can improve the network’s resistance to cascading failures.

With the increasing scale and complexity of computer softwares， code defect in software has become a serious threat to public safety. Aiming at the problems of poor expansibility of static analysis tools， as well as coarse detection granularity and unsatisfactory detection effect of existing methods， a static code defect detection method based on program slicing and semantic feature fusion was proposed. Firstly， key points in source code were analyzed through data flow and control flow， and the program slicing method based on Interprocedural Finite Distributive Subset （IFDS） was adopted to obtain the code snippet composed of multiple lines of statements related to code defects. Then， semantically related vector representation of code snippet was obtained by word embedding， so that the appropriate length of code snippet was selected with the accuracy guaranteed. Finally， Text Convolutional Neural Network （TextCNN） and Bi-directional Gate Recurrent Unit （BiGRU） were used to extract local key features and context sequence features of the code snippet respectively， and the proposed method was used to detect slice-level code defects. Experimental results show that the proposed method can detect different types of code defects effectively， and is significantly better than static analysis tool Flawfinder. Under the premise of fine granularity， IFDS slicing method can further improve F₁ score and accuracy，reach 89.64% and 92.08% respectively. Compared with the existing methods based on program slicing， when key points are the Application Programming Interface （API） or the variables， the proposed method has the F₁ score reached 89.69% and 89.74% respectively， and the accuracy reached 92.15% and 91.98% respectively， and all of them are higher. It can be seen that without significantly increasing time complexity， the proposed method has a better comprehensive detection performance.