To solve the problems of external disturbances and the uncertainty of system model parameters for the underactuated quadrotor Unmanned Aerial Vehicle (UAV) existing in actual flight, a flight control scheme based on Extended State Observer (ESO) and integral backstepping sliding mode was designed. Firstly, according to the semi-coupling characteristics and the strict feedback architecture of system, a backstepping control was adopted to design the attitude inner loop and the position outer loop controllers. Then, a sliding mode algorithm with strong anti-jamming ability and integral control were incorporated to enhance system robustness and reduce static error respectively. Finally, ESO was used to eliminate the total internal and external disturbances and to compensate the interference in the control law online. The closed-loop control system was proven to be globally asymptotically stable by the Lyapunov stability analysis. In addition, the effectiveness and robustness of the proposed flight control scheme were verified through simulation analysis.

Aiming at the problem that the existing crowd-sourcing system can not effectively control the active enthusiasm of the workers and the quality of task completion in the process of crowd-sourcing interaction, a worker reputation model based on active degree was proposed to realize the quality control of the crowd-sourcing platforms. The model improved the average reputation model, and the concepts of active factor and historical factor were put forward from the point of view of workers' active degree and historical reputation value. First, the active factor of the worker was calculated according to his participating days in the crowd in the last 30 days, and then the historical reputation value of the crowd-sourcing worker was calculated according to the historical factor. Finally, the reputation value of the crowd-sourcing worker based on active degree was calculated based on the calculated active factor and historical reputation value, which was used to measure the ability of the crowdsourcing worker. The theoretical analysis and test results showed that compared with the average reputation model, the task completion quality of crowdsourcing workers selected by the worker reputation model based on active degree was increased by 4.95% and the completion time was decreased by 25.33%; compared with the trust model based on evidence theory, the task completion quality was increased by 6.63% and the completion time was decreased by 25.11%. The experimental results show that the worker reputation model based on active degree can effectively improve the quality of crowdsourcing tasks and reduce the completion time.

To deal with the problems of low accuracy and local convergence in conventional Particle Swarm Optimization (PSO) algorithm, the chaos algorithm and cloud model algorithm were introduced into the evolutionary process of PSO algorithm and the chaos cloud model particle swarm optimization (CCMPSO) algorithm was proposed. The particles were divided into excellent particles and normal particles when CCMPSO was in convergent status. To search the global optimum location, the cloud model algorithm as well as excellent particles was applied to local refinement in convergent area, meanwhile chaos algorithm and normal particles were used to global optimization in the outside space of convergent area. The convergence of CCMPSO was analyzed by eigenvalue method. The simulation results prove the CCMPSO has better optimization performance than other main PSO algorithms.

To solve the problem of inference on mass ontology, a new algorithm was proposed based on cloud computing platform. Ontology schema was transformed into graph, inference strategy was designed accordingly. Inference algorithm was designed based on the computing model of Map/Reduce. After one time iteration, mass ontology could be inferred in the course of Map. Later, duplicated triples were eliminated in the course of Reduce. The experimental results show that inference of one hundred million triples costs less than four minutes. The algorithm is effective.

A new shuffled frog leaping algorithm based on immune evolutionary particle swarm optimization was proposed in order to avoid premature convergence and to improve the precision of solution by using basic Shuffled Frog Leaping Algorithm (SFLA). The proposed algorithm integrated the global searching idea in the Particle Swarm Optimization (PSO) into SFLA, to pursue the information of two optimal solutions in the sub-swarm and the whole-swarm simultaneously, so as to search thoroughly near by the space gap of the worst solution, and also integrated the immune evolutionary algorithm into SFLA making immune evolutionary iterative computation to the optimal solution in the whole-swarm, so as to use the information of optimal solution fully. This algorithm can not only get free from trapping into local optimum and be close to the global optimal solution with higher precision, but also speeds up the convergence. Calculation results show that the Immune Evolutionary Particle Swarm Optimization-Shuffled Frog Leaping Algorithm (IEPSO-SFLA) has better optimal searching ability and stability as well as faster convergence than those of basic SFLA.

Role-based Access Control (RBAC) has been widely used as an international norm, but it can only give users authority to operate a particular operating environment issues, and it cannot be used to solve the access control problems of different subsets of operating objects under same conditions. Especially in the workflow system, access control of different set of objects, and different nodes is particularly important. To address this problem, role-based access control technique and workflow technique were studied. An access control model of workflow permissions was proposed based on group/role, which achieved two-dimensional operation set access control permissions. It is a better solution to the cross-regional case, the multi-sector work-based workflow system object access control and access control problems. Currently, the model has been used in the construction of oilfield integration system, and the application shows that the model is scientific, reasonable and feasible.