Aiming at the problem that existing machine reading comprehension-based event detection models are difficult to mine the long-distance dependencies between keywords when facing long text context with complex syntactic relations， a Machine Reading Comprehension event detection model based on Relation-Enhanced Graph Convolutional Network （MRC-REGCN） was proposed. Firstly， a pre-trained language model was utilized to jointly encode the question and the text to obtain word vector representations incorporating the priory information. Secondly， the dynamic relationship was introduced to enhance the label information， and an REGCN was utilized to deeply learn syntactic dependencies between words and enhance the model’s ability to perceive the syntactic structure of long text. Finally， the probability distributions of the textual words under all event types were obtained by the multi-classifier. Experimental results on the ACE2005 English corpus show that the F1 score of the proposed model on trigger classification is improved by 2.49% and 1.23% compared to the comparable machine reading comprehension-based model named EEQA （Event Extraction by Answering （almost） natural Questions） and the best baseline model named DEGREE （Data-Efficient GeneRation-based Event Extraction） respectively， which verify that the MRC-REGCN has a better performance in performing event detection.

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 the accuracy and real-time effects of virtual-real registration in Augmented Reality (AR) based on vision are greatly affected by the changes of illumination, occlusion and perspective, which is easy to lead to failure of registration, a virtual-real registration method of natural features based on Binary Robust Invariant Scalable Keypoints-Speeded Up Robust Features (BRISK-SURF) algorithm was proposed. Firstly, Speeded Up Robust Features (SURF) feature extractor was used to detect the feature points. Then, Binary Robust Invariant Scalable Keypoints (BRISK) descriptor was used to describe the feature points in binary, and the feature points were matched accurately and efficiently by combining Hamming distance. Finally, the virtual-real registration was realized according to the homography relationship between images. Experiments were performed from the aspects of image feature matching and virtual-real registration. Results show that the average precision of BRISK-SURF algorithm is basically the same with that of SURF algorithm, is about 25% higher than that of BRISK algorithm, and the average recall of BRISK-SURF is increased by about 10% compared to that of BRISK algorithm; the result of the virtual-real registration method based on BRISK-SURF is close to the reference standard data with high precision and good real-time performance. The Experimental results illustrate that the proposed method has high recognition accuracy, registration precision and real-time effects for images with different illuminations, occlusions and perspectives. Besides, the interactive tourist resource presentation and experience system based on AR is realized by using the proposed method.