Accurate traffic forecasting not only improves the efficiency and safety of the traffic system， but also promotes the sustainable social and economic development. Although a large number of studies have been devoted to modeling spatiotemporal correlation， existing methods still have significant limitations： most models tend to collectively predict the traffic flow of all regions in all time periods， ignoring spatio-temporal heterogeneity， especially the impact of the traffic status of the current region on the future traffic status of related regions. To address this problem， a Time-Interdependency-aware Dynamic Bayesian Network for traffic prediction （TIDBN） method was proposed. Using pre-trained modules， TIDBN employed a time-varying dynamic Bayesian network to capture the complex temporal relationships in time-series data arising from simultaneous and lagged effects. To further improve its ability to capture spatio-temporal correlation， a spatio-temporal attention mechanism was introduced for in-depth analysis. Subsequently， a Graph Convolutional Network （GCN） was utilized to model the spatio-temporal topological structure， generating more accurate traffic predictions. The experimental results show that TIDBN performs excellently on two real traffic prediction tasks， especially for 1-hour prediction. On the PeMS-BAY dataset， the Mean Absolute Error （MAE） of TIDBN is 4% lower than that of the second-best baseline method.

In the spatiotemporal hybrid models that integrate meteorological， spatial， and temporal information， the modeling of temporal changes is usually done in one-dimensional space. To solve the problems that one-dimensional sequences are limited in sliding windows and is lack of the flexibility of multi-scale feature extraction， a Multi-domain SpatioTemporal Hierarchical Graph Neural Network （MST-HGNN） model was proposed. Firstly， two levels of hierarchical graphs were constructed， namely， city-wide global scale one and station-level local scale one， so as to perform spatial relationship learning. Secondly， the one-dimensional air quality sequences were transformed into a set of two-dimensional tensors based on multiple periods， and multi-scale convolution in two-dimensional space was used to capture frequency domain features by periodic decoupling. At the same time， Long Short-Term Memory （LSTM） network in one-dimensional space was employed to fit temporal features. Finally， to avoid redundant information aggregation， a gating mechanism fusion module was designed for multi-domain feature fusion of frequency domain and temporal domain features. Experimental results on Urban-Air dataset and the Yangtze River Delta city cluster dataset show that compared with Multi-View Multi-Task Spatiotemporal Graph Convolutional Network model （M2）， the proposed model has lower Mean Absolute Error （MAE） and Root Mean Squared Error （RMSE） than the comparison model in predicting air quality at the 1 h， 3 h， 6 h， and 12 h. It can be seen that MST-HGNN can decouple complex time patterns in the frequency domain， compensate for the limitations of temporal feature modeling using frequency domain information， and predict air quality changes more comprehensively by combining time domain information.

Useful knowledge can be extracted from equipment fault diagnosis data for construction of a knowledge graph， which can effectively manage complex equipment fault diagnosis information in the form of triples （entity， relationship， entity）. This enables the rapid diagnosis of equipment faults. Firstly， the related concepts of knowledge graph for equipment fault diagnosis were introduced， and the framework of knowledge graph for equipment fault diagnosis domain was analyzed. Secondly， the research status at home and abroad about several key technologies， such as knowledge extraction， knowledge fusion and knowledge reasoning for equipment fault diagnosis knowledge graph， was summarized. Finally， the applications of knowledge graph in equipment fault diagnosis were summarized， some shortcomings and challenges in the construction of knowledge graph in this field were proposed， and some new ideas were provided for the field of equipment fault diagnosis in the future.

Image inpainting techniques for non-regular missing regions are versatile but challenging. To address the problem that existing inpainting methods may produce artifacts， distorted structures， and blurred textures for high-resolution images， an improved image inpainting network， named Gconv_CS（Gated convolution based CSFF and SAM） incorporating Supervised Attention Module （SAM） and Cross-Stage Feature Fusion （CSFF） was proposed. In Gconv_CS， the SAM and CSFF were introduced to Cconv， a two-stage network model with gated convolution. SAM ensured the effectiveness of the incoming feature information to the next stage by providing a real image to supervise the output features of the previous stage. CSFF fused the features from the encoder-decoder of the previous stage and fed them to the encoder of the next stage to compensate for the loss of feature information in the previous stage. The experimental results show that， at a percentage of missing regions of 1% to 10%， compared with the baseline model Gconv， on CelebA-HQ dataset， Gconv_CS improved the Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity index （SSIM） by 1.5% and 0.5% respectively， reduced Fréchet Inception Distance （FID） and L1 loss by 21.8% and 14.8% respectively； on Place2 dataset， the first two indicators increased by 26.7% and 0.8% respectively， and the latter two indicators decreased by 7.9% and 37.9% respectively. A good restoration effect was achieved when Gconv_CS was used to remove masks from a giant panda’s face.

In order to remove the effect of weather in degraded image, a fast haze removal algorithm for single image based on human visual characteristics was proposed. According to the luminance distribution of the hazy image and the human visual characteristics, the proposed method first applied luminance component to estimate coarse transmission map, then used a linear spatial filter to refine the transmission map and obtained the dehazed image by the atmospheric scattering model. Finally a new image enhancement fitting function was applied to enhance the luminance component of the dehazed image to make it more natural and clear. The experimental results show that the proposed algorithm effectively removes haze and is better than the existing algorithms in terms of contrast, information entropy and computing time.

To acquire an accurate patient positioning in image-guided radiotherapy, an improved Demons deformable registration method was developed. The FDK algorithm was adopted to reconstruct Cone Beam CT (CBCT) and the reconstruction result was visualized by a volume rendering method with Visualization ToolKit (VTK). Based on the Insight segmentation and registration ToolKit (ITK), the Demons algorithm was completed incorporating the gradient information of fixed image and floating image by the concept of symmetric gradient, and a new formula of Demons force was demonstrated. Registrion experiments were carried out using medical images both from single modality and multi-modality. The results show that the improved Demons algorithm achieves a faster convergence speed and a higher precision compared with the original demons algorithm, which indicates that the Demons algorithm based on symmetric gradient is more suitable for the registration of CBCT reconstruction image and CT plan image in image-guided radiotherapy.

As to the reasonable matching between the resources needed by multi-task and M-dimensional resources offered by multiprocessing nodes, the author offered multiple dimension set partitioning optimization models, defined the function of resource balancing degree and proposed a Multiple Dimension Set Partitioning Load Balancing Resource Optimization Allocation Algorithm(MDSPLBROAA), which can optimize to solve the NP problems. The experimental results presented show that the algorithm has better practicability and feasibility. Furthermore, it has higher efficiency than the traditional heuristic algorithm.

The multicast routing problem with multiple QoS constraints is NP complete problem. A network model suitable for investigating the routing problem was described based on delay, delay jitter, bandwidth and packet loss metrics. A multicast routing algorithm with multiple QoS constraints based on GA and TS hybrid strategy was presented. This algorithm took advantage of GA and TS (Tabu Search), and overcame the shortcomings  of GA in solving the multicast routing problem with multiple QoS constraints-poor climbing ability and immature convergence. Simulation results show that the algorithm is an effective approach to multicast routing decision with multiple QoS constraints.