In order to increase the creability of the damage degree evaluation of cascading failure caused by emergency to carbon emission correlation system, considering the redundancy ability of individual members to load, an overload failure probability was proposed based on "load-capacity" cascade failure model of traditional complex network, and a cascading failure model was constructed considering load overload. Then, based on the characteristics of nodes, six load allocation strategies for overloaded nodes were raised. The simulation results show that, in the load allocation strategies of the overloaded nodes, the integrated allocation strategy is superior in general, which can effectively control the scale of cascading failure and increase the robustness of network; increasing the overloaded parameters in a certain range can help to reduce the impact of cascading failure, while the improvement effect is not significant when the parameters are too large; under different load allocation strategies, the residual coefficient has an optimal value and capacity adjustable parameters have optimal ranges which can keep the carbon emission correlation network in good robustness with low construction cost while the tight allocation strategy means high costruction cost.

There are several problems that the System Dynamics (SD) model cannot express both random delay and conditional transitions between different states, and the Stochastic Petri-Net (SPN) itself still has the defect of insufficient computing ability. In order to solve the problems, firstly, the SPN was expanded and the Stochastic Function Petri-Net (SFPN) model was proposed. Secondly, combining SFPN with SD, the SFPN-SD model was put forward. Because the transits in SFPN could be used to accurately describe random delay, therefore, the first problems in SD model was solved. Because the conditions arcs in SFPN could be used to express the conditional transferring among places, as a result, the second problem in SD was solved. Finally, some state variables and state transition equations were appended in places and transitions of SPN, while these state variables and state transition equations were the different interpretations of level, auxiliary and rate variables as well as level and rate equations in SD model. The state transition equations could realize complicated computations, and thus the problem of insufficient computing ability in SPN was solved. The SFPN-SD model inherited all the features of the SD model, at the same time, all the features of SPN were incorporated into the SFPN-SD model. Compared with the SD model, the proposed SFPN-SD model has such advantages that system states and the meaning of their types as well as the process of state evolution become more clear. And the system's dynamic changes are driven by events in SFPN-SD, which it can describe autonomous dynamic stochastic evolution of complex system more realistically. The case studies show that, compared with the SD model, the proposed SFPN-SD model has stronger, more comprehensive abilities such as relevancy analysis description and simulation of complex system.

Network vulnerability assessment which intends to safety situation analysis and establishment of defensive measures before attack is a kind of active defense technology, but the traditional quantitative analysis models cannot show the dynamic interactive relationship between entities, and most of them cannot get global results for risk diffusion. With reference to the influence of social network in the process of communication, a new network vulnerability diffusion analysis method based on cumulative effect was proposed. The defined vulnerability diffusion analysis model described subject relation structure in a more detailed level, and the algorithm proposed by using the accumulation characteristics in attack effects described vulnerability diffusion rule more accurately to ensure better influence range. At last, the model and algorithm were verified by a typical example, the horizontal comparison analysis on some aspects such as simplicity of the model description, accuracy of the analysis results, rationality of the safety recommendations were given. The results show that the method has an advantage in visual assessment results and the formulation of the cost minimum security measures.

At present, data obfuscation is usually used for single concrete data structure. In order to apply same obfuscation method to different data structures, the obfuscation was considered as data refinement, and treated as an abstract data type. General equations were established so as to prove the correctness of obfuscation. The meaning of matrix was concealed by splitting the matrix and altering the pattern of elements. Based on the operations of this data type, the obfuscation framework of the standard operations of the matrix was constructed using functional language. That how to use matrix to confuse the scalar and its arithmetic operations was also described. The correctness of the obfuscation operations was proved by mathematical method. The results show that the complexity of obfuscation operations is the same as the original operations, indicating that this kind of obfuscation method increases the difficulty of operations, and is an effective method of data obfuscation.

In order to solve the problem that the domain partition of Zaike variable precision rough set can not overlap, an expansion was made on the domain of Zaike variable precision rough set based on multi-set, a multiple variable precision rough set model was put forward, and its corresponding definitions, theorems and properties were fully described, which included definitions of multiple domain and multiple variable precision approximate sets, proofs of their properties, and relations between multiple Zaike variable precision rough set and multiple variable precision rough set. These definitions, theorems and properties have not only differences but also relations between multiple variable precision rough set and Zaike variable precision rough set. Multiple variable precision rough set can fully describe overlap among knowledge particles, difference of significance among objects and polymorphism of objects, and can conveniently find associated knowledge from data saved in a relation database, having one-to-many and many-to-many dependency, and thought to have no relations.