For the current water conservancy dams mainly rely on manual on-site inspections， which have high operating costs and low efficiency， an improved detection algorithm based on YOLOv5 was proposed. Firstly， a modified multi-scale visual Transformer structure was used to improve the backbone， and the multi-scale global information associated with the multi-scale Transformer structure and the local information extracted by Convolutional Neural Network （CNN） were used to construct the aggregated features， thereby making full use of the multi-scale semantic information and location information to improve the feature extraction capability of the network. Then， coordinate attention mechanism was added in front of each feature detection layer of the network to encode features in the height and width directions of the image， and long-distance associations of pixels on the feature map were constructed by the encoded features to enhance the target localization ability of the network in complex environments. The sampling algorithm of the positive and negative training samples of the network was improved to help the candidate positive samples to respond to the prior frames of similar shape to themselves by constructing the average fit and difference between the prior frames and the ground-truth frames， so as to make the network converge faster and better， thus improving the overall performance of the network and the network generalization. Finally， the network structure was lightened for application requirements and was optimized by pruning the network structure and structural re-parameterization. Experimental results show that on the current adopted dam disease data， compared with the original YOLOv5s algorithm， the improved network has the mAP （mean Average Precision）@0.5 improved by 10.5 percentage points， the mAP@0.5：0.95 improved by 17.3 percentage points； compared to the network before lightening， the lightweight network has the number of parameters and the FLOPs（FLoating point Operations Per second） reduced by 24% and 13% respectively， and the detection speed improved by 42%， verifying that the network meets the requirements for precision and speed of disease detection in current application scenarios.

Knowledge Graph (KG) strongly support the research of knowledge-driven artificial intelligence. Aiming at this fact, the existing technologies of knowledge graph and knowledge hypergraph were analyzed and summarized. At first, from the definition and development history of knowledge graph, the classification and architecture of knowledge graph were introduced. Second, the existing knowledge representation and storage methods were explained. Then, based on the construction process of knowledge graph, several knowledge graph construction techniques were analyzed. Specifically, aiming at the knowledge reasoning, an important part of knowledge graph, three typical knowledge reasoning approaches were analyzed, which are logic rule-based, embedding representation-based, and neural network-based. Furthermore, the research progress of knowledge hypergraph was introduced along with heterogeneous hypergraph. To effectively present and extract hyper-relational characteristics and realize the modeling of hyper-relation data as well as the fast knowledge reasoning, a three-layer architecture of knowledge hypergraph was proposed. Finally, the typical application scenarios of knowledge graph and knowledge hypergraph were summed up, and the future researches were prospected.

Aiming at the danger and unsatisfactory effect of dense crowd evacuation in public places in emergency, a motion planning algorithm of robots for crowd evacuation based on Deep Q-Network (DQN) was proposed. Firstly, a human-robot social force model was constructed by adding human-robot interaction to the original social force model, so that the motion state of crowd was able to be influenced by the robot force on pedestrians. Then, a motion planning algorithm of robot was designed based on DQN. The images of the original pedestrian motion state were input into the network and the robot motion behavior was output. In this process, the designed reward function was fed back to the network to enable the robot to autonomously learn from the closed-loop process of "environment-behavior-reward". Finally, the robot was able to learn the optimal motion strategies at different initial positions to maximize the total number of people evacuated after many iterations. The proposed algorithm was trained and evaluated in the simulated environment. Experimental results show that the proposed algorithm based on DQN increases the evacuation efficiency by 16.41%, 10.69% and 21.76% respectively at three different initial positions compared with the crowd evacuation algorithm without robot, which proves that the algorithm can significantly increase the number of people evacuated per unit time with flexibility and effectiveness.

IPv6 Neighbor Cache (NC) was very vulnerable to be attacked, therefore, an improved method named Reversed Detection Plus (RD+) was proposed. Timestamp and sequence were firstly introduced to limit strict time of response and response matching respectively; RD+ queue was defined to store timestamp and sequence, and Random Early Detection Based on Timestamp (RED-T) algorithm was designed to prevent Denial of Service (DoS) attacks. The experimental results show that RD+ can effectively protect IPv6 NC to resist spoofing and DoS attacks, and compared with Heuristic and Explicit (HE) and Secure Neighbor Discovery (SEND), RD+ has a low consumption of resources.

For single sensor data mining prediction problem of the existence of one-sidedness, proposed the multi-sensor data mining prediction model of combining of information fusion technology and phase-space reconstruction technology. A variety of underground sensors, including gas concentration, wind speed, temperature sensors, are fusion forecasted. To many types of sensor time series data for the study, the first using the method of information fusion, respectively, followed by all kinds of data sensor data level fusion, feature level fusion; Then using the method of correlation integral the integration of two sensor data, respectively, to determine the time delay τ and embedding dimension m two parameters for the reconstruction phase; Finally, combined with the techniques of multivariate phase space reconstruction, fusion phase space the various types of sensor data, using the predictive models based on the weight one-rank local-region of K-Means clustering of multi-sensor data. The data is from the coal mines in Shanxi Province and the New King Wu Yi mine, collection of nearly 20G data to the gas concentration, wind speed, temperature experiment three sensor data, the results show that: For the feature level fusion, the data every 15 minutes period of time after fusion to be effective as a measure of the characteristics of this period, after the prediction model calculations, compared with the time period ,5 minutes, 10 minutes, 20 minutes, the error is minimum ESS=0.003, compared with the current minimum error value of 0.05, the error is greatly decreased, therefore, the integration forecasts’ better, it can more accurately predict the future after 15 minutes of sensor data, people have sufficient time to further provide for the safety assessment of underground basis for making decision.

In the current research and design of spectrogram based on Field Programmable Gate Array (FPGA), the direct indication of the spectrogram is not able to reflect the detail variation of spectrum. To solve this problem, a method for the display and enhancement of spectrogram based on FPGA was proposed in this paper. With nonlinear transformation, the high-resolution gray image was compressed to low gray-resolution image, so the detail variation of spectrum would be better reflected. Meanwhile，human vision is less sensitive to the difference between gray-scale pixels than that of colors, so with pseudo-color processing of the gray images, the results are displayed through Video Graphics Array (VGA). The experimental results show that more detail variation of spectrum can be obtained by the method.

To solve the problem of biologic database integration, combining the metabolic pathway data feature and automated schema matching methods, based on the characteristics that the same molecular and enzyme reaction have the same representation, a similarity derivation based metabolic pathway data integration algorithm was proposed. The method was verified by being effectively applied to LIGAND, EcoCyc and MetaCyc metabolic pathway databases. In addition, considering a friend interface for end user, a metabolic pathway visualized integration tool was designed and implemented using dynamic layout graphic user interface.