With the advancement of technologies such as sensor networks and global positioning systems， the volume of meteorological data with both temporal and spatial characteristics has exploded， and the research on deep learning models for Spatiotemporal Sequence Forecasting （STSF） has developed rapidly. However， the traditional machine learning methods applied to weather forecasting for a long time have unsatisfactory effects in extracting the temporal correlations and spatial dependences of data， while the deep learning methods can extract features automatically through artificial neural networks to improve the accuracy of weather forecasting effectively， and have a very good effect in encoding long-term spatial information modeling. At the same time， the deep learning models driven by observational data and Numerical Weather Prediction （NWP） models based on physical theories are combined to build hybrid models with higher prediction accuracy and longer prediction time. Based on these， the application analysis and research progress of deep learning in the field of weather forecasting were reviewed. Firstly， the deep learning problems in the field of weather forecasting and the classical deep learning problems were compared and studied from three aspects： data format， problem model and evaluation metrics. Then， the development history and application status of deep learning in the field of weather forecasting were looked back， and the latest progress in combining deep learning technologies with NWP was summarized and analyzed. Finally， the future development directions and research focuses were prospected to provide a certain reference for future deep learning research in the field of weather forecasting.

Compared with ordinary networks， hypernetworks have complex tuple relationships， namely hyperedges. However， most existing network representation learning methods cannot capture the tuple relationships. To solve the above problem， a Heterogeneous hypernetwork Representation learning method with Hyperedge Constraint （HRHC） was proposed. Firstly， a method combining clique extension and star extension was introduced to transform the heterogeneous hypernetwork into the heterogeneous network. Then， the meta-path walk method that was aware of semantic relevance among the nodes was introduced to capture the semantic relationships among the heterogeneous nodes. Finally， the tuple relationships among the nodes were captured by means of the hyperedge constraint to obtain high-quality node representation vectors. Experimental results on three real-world datasets show that， for the link prediction task， the proposed method obtaines good results on drug， GPS and MovieLens datasets. For the hypernetwork reconstruction task， when the hyperedge reconstruction ratio is more than 0.6， the ACCuracy （ACC） of the proposed method is better than the suboptimal method Hyper2vec（biased 2nd order random walks in Hyper-networks）， and the average ACC of the proposed method outperforms the suboptimal method， that is heterogeneous hypernetwork representation learning method with hyperedge constraint based on incidence graph （HRHC-incidence graph） by 15.6 percentage points on GPS dataset.

The task of spatio-temporal sequence prediction has a wide range of applications in the fields such as transportation， meteorology and smart city. It is necessary to learn the spatio-temporal characteristics of different data with the combination of external factors such as precipitation and temperature when making station wind speed predictions， which is one of the main tasks in meteorological forecasting. The irregular distribution of meteorological stations and the inherent intermittency of the wind itself bring the challenge of achieving wind speed prediction with high accuracy. In order to consider the influence of multi-site spatial distribution on wind speed to obtain accurate and reliable prediction results， a Graph-based Dynamic Switch-Attention Network （Graph-DSAN） wind speed prediction model was proposed. Firstly， the distances between different sites were used to reconstruct the connection of them. Secondly， the process of local sampling was used to model adjacency matrices of different sampling sizes to achieve the aggregation and transmission of the information between neighbor nodes during the graph convolution process. Thirdly， the results of the graph convolution processed by Spatio-Temporal Position Encoding （STPE） were fed into the Dynamic Attention Encoder （DAE） and Switch-Attention Decoder （SAD） for dynamic attention computation to extract the spatio-temporal correlations. Finally， a multi-step prediction was formed by using autoregression. In experiments on wind speed prediction on 15 sites data in New York State， the designed model was compared with ConvLSTM， Graph Multi-Attention Network （GMAN）， Spatio-Temporal Graph Convolutional Network （STGCN）， Dynamic Switch-Attention Network （DSAN） and Spatial-Temporal Dynamic Network （STDN）. The results show that the Root Mean Square Error （RMSE） of 12 h prediction of Graph-DSAN model is reduced by 28.2%， 6.9%， 27.7%， 14.4% and 8.9% respectively， verifying the accuracy of Graph-DSAN in wind speed prediction.

To improve the accuracy of watershed runoff volume prediction， and considering the lack of model transparency and physical interpretability of data-driven hydrological model， a new runoff forecast model named Graph Attention neTwork and Dual-stage Attention mechanism-based Long Short-Term Memory network （GAT-DALSTM） was proposed. Firstly， based on the hydrological data of watershed stations， graph neural network was introduced to extract the topology of watershed stations and generate the feature vectors. Secondly， according to the characteristics of hydrological time series data， a runoff forecast model based on dual-stage attention mechanism was established to predict the watershed runoff volume， and the reliability and transparency of the proposed model were verified by the model evaluation method based on attention coefficient heat map. On the Tunxi watershed dataset， the proposed model was compared with Graph Convolution Neural network （GCN） and Long Short-Term Memory network （LSTM） under each prediction step. Experimental results show that， the Nash-Sutcliffe efficiency coefficient of the proposed model is increased by 3.7% and 4.9% on average respectively， which verifies the accuracy of GAT-DALSTM runoff forecast model. By analyzing the heat map of attention coefficient from the perspectives of hydrology and application， the reliability and practicability of the proposed model were verified. The proposed model can provide technical support for improving the prediction accuracy and model transparency of watershed runoff volume.

In the era of big data， the application of spatial-temporal trajectory data is increasing and these data contain a large amount of information， and the similarity measurement of the trajectory plays a pivotal role as a key step in the trajectory mining work. However， the traditional trajectory similarity measurement methods have the disadvantages of high time complexity and inaccuracy caused by the determination based on the trajectory points. In order to solve these problems， a Triangle Division （TD） trajectory similarity measurement method with the trajectory area metric as theory was proposed for trajectories without road network structure. By setting up “pointer” to connect the trajectory points between two trajectories to construct the non-overlapping triangle areas， the areas were accumulated and the trajectory similarity was calculated to confirm the similarity between the trajectories based on the thresholds set in different application scenarios. Experimental results show that compared with the traditional trajectory point-based spatial trajectory similarity measurement methods such as Longest Common Subsequence （LCSS） and Fréchet distance metric， the proposed method improves the recognition accuracy， reduces the time complexity by nearly 90%， and can better adapt to the trajectory similarity measurement work with uneven distribution of trajectory points.

The system adopted artificial immune theory. Through analyzing the detection results of the traditional real-time intrusion detection system Snort, and according to the characteristic that antibody concentration dynamically changes with the network intrusion intensity, the current risk value of network was calculated to reflect all kinds of attacks and overall risk profile. Snort relies on the rule matching to detect data packets. The detection process does not take into account the current network risk, resulting in the problem of high false positives rate. This system set pass threshold and dropped threshold based on different degree of attack danger to reduce the false alarm rate of Snort, and took “pass, alarm, discard packet, etc.” as response measures according to the risk value. The experimental results show that the system can calculate the real-time risk faced by the host and network accurately, reduce the false positive rate and take response measures according to risk value effectively.

For resolving the problem of the existing noise sample and large number of dimensions, the k-nearest neighbors classifier over manifolds was presented in this paper. Firstly the classic k-nearest neighbors was extended by Bayes theorem and local joint probability density was estimated by kernel density estimation in classifier. In addition, after building the noise sample model, an objective function was defined via improved marginal intrinsic graph and its weight matrix for searching the optimal dimension reduction mapping matrix. At last, details about k-nearest neighbors algorithm over manifolds were provided. The experimental results demonstrate that the presented method has lower classification error rate than six kinds of classic methods in most cases on twelve data sets.

A trust model capturing some set-up assumptions is necessary to guarantee the existence of Universally Composable (UC) security computation. Little domestic research on the trust model of UC security computation has been made. In this paper, the essential requirement of UC security and its limitation on UC security computation were researched to find out why these set-up assumptions were used. And then overseas representative trust models were analyzed and their merits and demerits were compared respectively. Finally, the research trend in future was pointed out.

In allusion to the insufficiency of current P2P Geographic Information System (GIS) in utilizing network resources of clients, based on analyzing and summarizing the existing hybrid P2P spatial indexing network, a new idea of group strategy was proposed in view of practice. In this idea, peers with the same spatial data semantics were joined in the same group in which the burden of query was shared by group members together. Furthermore, a replacement algorithm of current index nodes and backup strategy were proposed to improve the query performance and stability of the overall network. The experimental results indicate that the indexing network with group strategy can effectively make use of clients' network resources and improve the query performance when a large number of queries request concurrently.