As a type of data with both temporal and spatial dimensions， spatio?temporal data is widely used in supply chain management， e?commerce and other fields， which integrity and security are of great importance in practical applications. Aiming at the problems of lack of transparency and easily being tampered of data in the current centralized storage of spatial?temporal datasets， a blockchain construction and query method for spatio?temporal data was proposed by combining the decentralized， tamper?proof and traceable characteristics of blockchain technology with spatio?temporal data management. Firstly， an improved Directed Asycline Graph Blockchain （Block?DAG） based blockchain architecture for spatio?temporal data， namely ST_Block?DAG （Spatio?Temporal Block?DAG）， was proposed. Secondly， to improve the efficiency of spatio?temporal data storage and query， a storage structure based on quadtree and single linked list was adopted to store spatio?temporal data in the ST_Block?DAG blockchain. Finally， a variety of spatio?temporal data query algorithms were implemented on the basis of the storage structure of ST_Block?DAG， such as single?value query and range query. Experimental results show that compared with STBitcoin （Spatio?Temporal Bitcoin）， Block?DAG and STEth （Spatio?Temporal Ethereum）， ST_Block?DAG has the spatio?temporal data processing efficiency improved by more than 70% and the comprehensive query performance of spatio?temporal data improved by more than 60%. The proposed method can realize fast storage and query of spatio?temporal data， and can effectively support the management of spatio?temporal data.

Concerning the problem that traditional trajectory pattern mining methods have the problems of slow mining and large maximum amount of memory, a method of mining trajectory interval patterns based on spatial proximity searching was proposed. The implementation of the proposed method consists of five phases:1) Space-time discretization is performed on the trajectories, and space-time cell sequences corresponding to trajectories are achieved. 2) All the space-time cell sequences are scanned to get all no-duplication spatial cells, and all frequent spatial cells are obtained by the inclusion operation of the spatial cells and the cell sequences. 3) Frequent spatial cells are transformed into frequent interval patterns of length one. 4) Candidate interval patterns with the frequent spatial cells as units are generated by spatial proximity searching, and the support value of the candidate patterns are calculated by matching the patterns and the space-time cell sequences. 5) Based on the set support threshold, all frequent interval patterns are obtained. The experimental results show that the proposed method has the advantages of faster mining and less maximum amount of memory than traditional methods. Furthermore, in terms of running time, the proposed method has better stability and scalability performance than traditional methods. This method is helpful to the trajectory pattern mining methods to increase the mining speed and reduce the maximum amount of memory.

In the field of dense crowd scene, in order to improve the defects of present anomaly detection methods in real-time performance and applicability, a real-time method was proposed based on the optical flow feature and Kalman filtering. Firstly, the global optical flow value was extracted as the movement feature. Then the Kalman filtering was used to process the global optical flow value. The residual was analyzed based on the assumption that the residual obeyed a Gauss distribution in normal condition which was validated by the hypothesis testing. Then the parameter of the residual probability distribution was calculated through the Maximum Likelihood (ML) estimation. Finally, under a certain confidence coefficient level, the confidence interval of normal condition and the judgment formula of abnormal condition were obtained, which could be used to detect the abnormal events. The experimental result shows that, for the videos with the size of 320×240, the average detection time of the proposed method can be as low as 0.023 s/frame and the accuracy can reach above 95%. As a result, the proposed method has high detection efficiency and good real-time performance.

Aiming at the problem of the large amount of data and the complicated algorithm in 3D scene feature recognition process, an feature extraction and reconstruction of environmental plane algorithm based on Kinect was proposed. Firstly, a method of RANdom SAmple Consensus (RANSAC) environment segmentation with the combination of geometrical and color information was proposed, which overcame the over segmentation and lack-segmentation based on geometric characteristic and improved the accuracy. Secondly, according to the principle of perspective projection, the three-dimensional transformation matrix was derived, which guided the 3D environment mapped into a plane. The extraction of contour points in two-dimensional space was realized by searching object boundary information which used convex hull concept. Finally, the 3D information of the contour points was recovered by the rotating inverse transform and the reconstruction of environment features was completed. Three groups of scene data were used to verify the algorithm and the experimental results show the proposed algorithm gains more precise segmentation, reduces over segmentation phenomena, and also has better reconstruction effect for objects with different shape features.

To solve the problem that the factors, which affect the coalition efficacy in Multi-Agent Systems (MAS), have strong ambiguity and uncertainty, fuzzy soft set theory was adopted to make a comprehensive evaluation on Agent coalition. First, the coalition to be evaluated gave its own property, each expert gave evaluation set of indexes and corresponding evaluation matrix according to his knowledge and experience. Then, fuzzy soft set theory was used to fuse the evaluation matrix and obtain the results of final evaluation. Finally, a practical example was given to prove that the method can deal with ambiguity and uncertainty of information effectively and reasonably, and the process of evaluation accords with human thinking and judgment.

In order to overcome the weakness of direction and the poorness of purpose in multidimensional search and the premature convergence, this paper presented an improved particle swarm optimization algorithm. For both the best and the worst information of the cognitive part and the best and the worst information of the social part, the improved algorithm respectively assigned different learning factors, and the algorithm has a greater ability to learn. Each particle associatively memorized the best information and the worst information in its history, and then found the optimal position in accordance with the principle of chasing the best and avoiding the worst. Associative memory overcomes the weakness of direction and the poorness of purpose in multidimensional search. The principle of chasing the best and avoiding the worst keeps the diversity of population, helps to improve the convergence speed, and overcomes the premature convergence. Simulation test of the benchmark function has verified the validity of the algorithm.

Network management becomes more difficult for the increase of data transmission speed and network complexity, so the paper presented an intelligent multi-Agent model for virtual resource, described the process of the multi-Agent to virtual resource, and discussed the processing mechanism of different Agent. The proposed model was able to analyze social media resources in real-time by user context and system state. It automatically allocated resources suitable for users according to the virtual resource usage type and the information demand analysis of user context. The model was evaluated by dynamic scheduling method of virtual resources in cloud computing and the MovieLens system. The results show that the proposed model has better performance, can achieve the dynamic scheduling and load balancing of virtual resource, so that users can utilize efficiently virtual resource in the cloud computing.

The traditional Orthogonal Frequency Division Multiplexing (OFDM) scheduling does not consider the proportional fairness among users' rates while allocating resource. To solve this problem, a new proportional fair scheduling scheme was proposed in multi-user OFDM system for heterogeneous classes of traffic in this paper; its users' queues carry heterogeneous classes of traffic. The scheme maximizes the sum of system's weight capacity under the constraint of the proportion among users' rates; provides different types of packets in users' queues different weight factor, and calculates the user's weight by the packet weight factor; not only defines the channel priority factor, but also allocates users sub-carriers by the factor under the constraint of the proportion among users' rates while allocating sub-carriers; finally derives a linear power allocation pattern. The simulation results and analysis demonstrate that the proposed scheme better satisfies users' requirements of throughput and strictly guarantees the fairness among users' capacity on the basis of improving system's throughput.

Large clusters have been developed to multicore era, and multicore architecture makes new demands on parallel computation. Message Passing Interface (MPI） is the most commonly used parallel programming model, and collective communications is an important part of the MPI standard. Efficient collective communications algorithm plays a vital role in improving the performance of parallel computation. This paper first analyzed the architecture features of KD60 and communication hierarchy characteristics under multicore architecture, and then introduced the implementation of collective communications algorithm in MPICH2 and pointed out its deficiencies. At last, this article took broadcasting as an example, using an improved algorithm based on CMP architecture，which changes the communication mode of the original algorithm. At the same time, this paper optimized the algorithm according to the architecture characteristics of KD60. The experimental results show that the improved algorithm improves the performance of broadcast in MPI.

In an open real-time system, the coexistence of different kinds of real-time and non real-time applications makes the system scheduling mechanism face new requirements and challenges. One scheduling mechanism of the open systems was introduced, and that the nonpreemptable sections may influence system schedulability in non real-time applications scheduling was pointed out. And then, a scheme which can remedy the limitations about the previous scheduling scheme was presented and the two-level scheduling scheme of open real-time system was improved. Ultimately, the schedulability of hard real-time applications, soft real-time applications and non real-time applications was guaranteed.

Variety bias is a prevalent problem existing in decision tree algorithms.For solving this problem,a new decision tree algorithm,AF algorithm,was proposed.The mechanism how it avoided this default was analysed.Experiments compared to ID3 algorithm show that AF algorithm can avoid the variety bias of ID3 algorithm, and has no worse performance in classifying instances then ID3 algorithm.