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.

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.

Functional query is an important operation in big data application， and the problem of query answering has always been the core problem in database theory. In order to analyze the complexity of the functional query answering problem on big data， firstly， the functional query language was reduced to a known decidable language by using mapping reduction method， which proves the computability of the functional query answering problem. Secondly， first-order language was used to describe the functional query， and the plexity of the first-order language was analyzed. On this basis， the NC-factor reduction method was used to reduce the functional query class to the known \mathrm{\Pi } {\mathrm{{\rm T}}}_Q -complete class. It is proved that functional query answering problem can be solved in NC time after PTIME （Polynomial TIME） preprocessing. It can be conducted that the functional query answering problem can be handled on big data.

Chaos synchronization is the essential theoretical basis of chaotic secure communication. Since time delay of function projective synchronization had rarely been considered, the adaptive controllers and parameter update laws were designed based on Lyapunov stability theory and adaptive control method, and generalized function projective lag synchronization of a class of hyperchaotic system was achieved. Then, taking hyperchaotic Lorenz-Stenflo (LS) system and hyperchaotic Lü system with fully uncertain parameters as an example the correctness and effectiveness of the method was varified, and the influence of external disturbance and time delay on the effect of the synchronization control were studied. The numerical simulations show the effectiveness, feasibility and robustness of the proposed method.

The multi-designated verifiers signature is a special digital signature that the signature could only be checked by several designated verifiers. A multi-designated verified signature scheme and an identity based multi-designated verified signature scheme were proposed based on the technique of multilinear forms. The security analysis of the schemes shows that they have the property of unforgeability, source hiding and privacy of signer's identity.