The use of Unmanned Aerial Vehicle （UAV） to continuously monitor designated areas can play a role in deterring invasion and damage as well as discovering abnormalities in time， but the fixed monitoring rules are easy to be discovered by the invaders. Therefore， it is necessary to design a random algorithm for UAV flight path. In view of the above problem， a UAV persistent monitoring path planning algorithm based on Value Function Iteration （VFI） was proposed. Firstly， the state of the monitoring target point was selected reasonably， and the remaining time of each monitoring node was analyzed. Secondly， the value function of the corresponding state of this monitoring target point was constructed by combining the reward/penalty benefit and the path security constraint. In the process of the VFI algorithm， the next node was selected randomly based on ε principle and roulette selection. Finally， with the goal that the growth of the value function of all states tends to be saturated， the UAV persistent monitoring path was solved. Simulation results show that the proposed algorithm has the obtained information entropy of 0.905 0， and the VFI running time of 0.363 7 s. Compared with the traditional Ant Colony Optimization （ACO）， the proposed algorithm has the information entropy increased by 216%， and the running time decreased by 59%，both randomness and rapidity have been improved. It is verified that random UAV flight path is of great significance to improve the efficiency of persistent monitoring.

Virtual digital currency provides a breeding ground for terrorist financing， money laundering， drug trafficking and other criminal activities. As a representative emerging digital currency， Monero has a universally acknowledged high anonymity. Aiming at the problem of using Monroe anonymity to commit crimes， Monero anonymity technology and tracking technology were explored as well as the research progresses were reviewed in recent years， so as to provide technical supports for effectively tackling the crimes based on blockchain technology. In specific， the evolution of Monero anonymity technology was summarized， and the tracking strategies of Monero anonymity technology in academic circles were sorted out. Firstly， in the anonymity technologies， ring signature， guaranteed unlinkability （one-off public key）， guaranteed untraceability， and the important version upgrading for improving anonymity were introduced. Then， in tracking technologies， the attacks such as zero mixin attack， output merging attack， guess-newest attack， closed set attack， transaction flooding attack， tracing attacks from remote nodes and Monero ring attack were introduced. Finally， based on the analysis of anonymity technologies and tracking strategies， four conclusions were obtained： the development of anonymity technology and the development of tracking technology of Monero promote each other； the application of Ring Confidential Transactions （RingCT） is a two-edged sword， which makes the passive attack methods based on currency value ineffective， and also makes the active attack methods easier to succeed； output merging attack and zero mixin attack complement each other； Monero’s system security chain still needs to be sorted out.

For the localization and static semantic mapping problems in dynamic scenes， a Simultaneous Localization And Mapping （SLAM） algorithm in dynamic scenes based on semantic and optical flow constraints was proposed to reduce the impact of moving objects on localization and mapping. Firstly， for each frame of the input， the masks of the objects in the frame were obtained by semantic segmentation， then the feature points that do not meet the epipolar constraint were filtered out by the geometric method. Secondly， the dynamic probability of each object was calculated by combining the object masks with the optical flow， the feature points were filtered by the dynamic probabilities to obtain the static feature points， and the static feature points were used for the subsequent camera pose estimation. Then， the static point cloud was created based on RGB-D images and object dynamic probabilities， and the semantic octree map was built by combining the semantic segmentation. Finally， the sparse semantic map was created based on the static point cloud and the semantic segmentation. Test results on the public TUM dataset show that， in highly dynamic scenes， the proposed algorithm improves the performance on both the absolute trajectory error and relative pose error by more than 95% compared with ORB-SLAM2， and reduces the absolute trajectory error by 41% and 11% compared with DS-SLAM and DynaSLAM respectively， which verifies that the proposed algorithm has better localization accuracy and robustness in highly dynamic scenes. The experimental results of mapping show that the proposed algorithm creates a static semantic map， and the storage space requirement of the sparse semantic map is reduced by 99% compared to that of the point cloud map.

A new Web service composition method based on service communities and service chains was proposed in this paper to improve the time efficiency of service composition. In the method, a service network was constructed for the Web service collection, the service community discovery algorithm based on information center was applied to find service clubs in the service network, and then the community service chain discovery algorithm and Web service composition algorithm based on service chain were built. With these algorithms, all of service interface associations in a service club were changed into service chains, and the Web service composition process based on community service chains and Quality of Service (QoS) pruning was implemented. The experimental results indicate that, compared with the traditional service composition method based on graph depth traversal, the response time on five test sets in the service composition method with community service chains is on average improved by 42%, and up to 67%. Community service chains can effectively reduce the service search space for the current service request and improve the time efficiency of service composition.

Using economic model to research grid resource management is a new hot topic in the field of grid computing. Based on current grid resources management methods, an online reverse auction technologybased approach to allocating computational grid resources was proposed concerning oversupply environment. The corresponding QoS function is defined to analyze the application range of this approach as well as its advantages. Finally, the results of simulation experiments indicate that this method is effective for computational grid resource allocation.