Aiming at the problem of incomplete semantic information of word vectors and the problem of word polysemy faced by text feature extraction， a BERT （Bidirectional Encoder Representation from Transformer） word vector-based Twice Attention mechanism weighting algorithm for Relation Extraction （TARE） was proposed. Firstly， in the word embedding stage， the self-attention dynamic encoding algorithm was used to capture the semantic information before and after the text for the current word vector by constructing Q， K and V matrices. Then， after the model output the sentence-level feature vector， the locator was used to extract the corresponding parameters of the fully connected layer to construct the relation attention matrix. Finally， the sentence level attention mechanism algorithm was used to add different attention scores to sentence-level feature vectors to improve the noise immunity of sentence-level features. The experimental results show that compared with Contrastive Instance Learning （CIL） algorithm for relation extraction， the F1 value is increased by 4.0 percentage points and the average value of Precision@100， Precision@200， and Precision@300 （P@M） is increased by 11.3 percentage points on the NYT-10m dataset. Compared with the Piecewise Convolutional Neural Network algorithm based on ATTention mechanism （PCNN-ATT）， the AUC （Area Under precision-recall Curve） value is increased by 4.8 percentage points and the P@M value is increased by 2.1 percentage points on the NYT-10d dataset. In various mainstream Distantly Supervised for Relation Extraction （DSRE） tasks， TARE effectively improves the model’s ability to learn data features.

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.

To enhance the computing speed of the molecular dynamics simulations on the shared memory servers, the performance of parallel molecular dynamics program based on Open Multi-Processing (OpenMP) approach with the critical section method was analyzed and improved. After testing performance on a multi-core server, as well as the calculations of speedup and parallel efficiency, an optimized triangle method was developed. In this method, stationary atom sets were assigned to threads respectively, and the number of atoms increased stepwise, which made the threads arrive at critical sections at different time. The triangle method can efficiently halve the idle time in critical sections and therefore can significantly enhance the parallel performance.

A novel method for reconstructing 3D motion of human avatar from real-time orthogonal images by using artificial neural network techniques was proposed. The input vector to the network was constructed by using the extracted coordinates of the feature points, while the output one indicated the 3D coordinates of the representative points and joints of the real human. The fitting process of the neural network was based on some proper neural network learning techniques with a set of sample data pairs that were obtained by using a motion capture system ReActor. The proposed method was implemented on a personal computer and ran in real-time applications. And experimental results confirm both the feasibility and the effectiveness of the proposed method for estimating 3D human motion (reconstruction error in MSE is less than 5%).