In order to solve the problem of insufficient mining of potential association between remote nodes in human action recognition tasks， and the problem of high training cost caused by using multi-modal data， a multi-scale feature fusion human action recognition method under the condition of single mode was proposed. Firstly， the global feature correlation of the original skeleton diagram of human body was carried out， and the coarse-scale global features were used to capture the connections between the remote nodes. Secondly， the global feature correlation graph was divided locally to obtain the Complementary Subgraphs with Global Features （CSGFs）， the fine-scale features were used to establish the strong correlation， and the multi-scale feature complementarity was formed. Finally， the CSGFs were input into the spatial-temporal Graph Convolutional module for feature extraction， and the extracted results were aggregated to output the final classification results. Experimental results show that the accuracy of the proposed method on the authoritative action recognition dataset NTU RGB+D60 is 89.0% （X-sub） and 94.2% （X-view） respectively. On the challenging large-scale dataset NTU RGB+D120， the accuracy of the proposed method is 83.3% （X-sub） and 85.0% （X-setup） respectively， which is 1.4 and 0.9 percentage points higher than that of the ST-TR （Spatial-Temporal TRansformer） under single modal respectively， and 4.1 and 3.5 percentage points higher than that of the lightweight SGN （Semantics-Guided Network）. It can be seen that the proposed method can fully exploit the synergistic complementarity of multi-scale features， and effectively improve the recognition accuracy and training efficiency of the model under the condition of single modal.

To deal with the phenomenon of "information and value islands" caused by the lack of interoperation among the increasingly emerging blockchain systems， a federated?autonomy?based cross?chain scheme was proposed. The elemental idea of this scheme is to form a relay alliance chain maintained by participated blockchain systems using blockchain philosophy， which is supposed to solve the data sharing， value circulation and business collaboration problems among different blockchain systems. Firstly， a relay mode based cross?chain structure was proposed to provide interoperation services for heterogeneous blockchain systems. Secondly， the detailed design of the relay alliance chain was presented as well as the rules for the participated blockchain systems and their users. Then， the basic types of cross?chain interactions were summarized， and a process for implementing cross?chain interoperability based on smart contracts were designed. Finally， through multiple experiments， the feasibility of the cross?chain scheme was validated， the performance of the cross?chain system was evaluated， and the security of the whole cross?chain network was analyzed. Simulation results and security analysis prove that the proposed channel allocation strategy and block?out right allocation scheme of the proposed scheme are practically feasible， the throughput of the proposed shceme can reach up to 758 TPS （Transactions Per Second） when asset transactions are involved， and up to 960 TPS when asset transactions are not involved； the proposed scheme has high?level security and coarse? and fine?grained privacy protection mechanism. The proposed federated?autonomy?based cross?chain scheme for blockchain can provide secure and efficient cross?chain services， which is suitable for most of the current cross?chain scenarios.

Aiming at the problem of frequent migration of containers triggered by dynamic changes of cluster workload, a container deployment method based on resource reservation was proposed. Firstly, a dynamic change description mechanism of single-container resource demand based on Markov chain model was designed to describe the resource demand situation of single container. Secondly, the dynamic change of multi-container resource was analyzed based on the single-container Markov chain model to describe the container resource demand state. Thirdly, a container deployment and resource reservation algorithm for dynamic workload was proposed based on the multi-container Markov chain. Finally, the performance of the proposed algorithm was optimized based on the analysis of container resource demand characteristics. The simulation experimental environment was constructed based on the domestic software and hardware environment, and the simulation results show that in terms of resource conflict rate, the performance of the proposed method has the performance close to the optimal peak allocation strategy named Resource with Peak (RP), but its number of required hosts and container dynamic migration number are significantly less; in terms of resource utilization rate, the proposed method has the number of hosts used slightly more than the optimal valley allocation strategy named Resource with Valley (RV), but has less dynamic migration number and lower resource conflict rate; compared with the peak and valley allocation strategy named Resource with Valley and Peak (RVP), the proposed method has better comprehensive performance.

A monocular vision obstacle avoidance method for quadrotor based on deep learning was proposed to help quadrotors to avoid obstacles. Firstly, the position of object in the image was obtained by object detection, and by calculating the height of the object box in the image, the distance between quadcopter and obstacle was estimated. Then, whether performing obstacle avoidance was determined by synergetic computer. Finally, experiments were conducted on a flight test platform based on Pixhawk flight control board. The results show that the proposed method can be applied to quadcoptor obstacle avoidance with low speed. Compared with traditional active sensor methods, the proposed method greatly reduces the occupied volume with only one monocular camera as sensor. This method is robust and can identify people with different postures as obstacles.

Concerning the problems of low accuracy, limitations of stagnation and slow convergence speed in the later evolution process of Artificial Fish Swarm Algorithm (AFSA), a Parallel Dynamic weigh Niches Artificial Fish Swarm (PDN-AFS) algorithm based on multi-core cluster was proposed. Firstly, the advantages and disadvantages of AFSA were analyzed, and dynamic weighting factor strategy and niche mechanism were adopted, hence a new Dynamic weigh Niches Artificial Fish Swarm (DN-AFS) algorithm was put forward. Then parallel design and analysis of DN-AFS algorithm based on parallel programming model (MPI+OpenMP) were introduced. Finally, the simulation experiments on multi-core cluster environment were given. The experimental results show that PDN-AFS can effectively improve the convergence speed and optimization performance of the complex multimodal function optimization problem, and achieve high speed ratio.