Aiming at the problems of high cost, poor reusability and low reconstruction quality in traditional digital core reconstruction technology, a 3D shale digital core reconstruction method based on Deep Convolutional Generation Adversarial Network with Gradient Penalty (DCGAN-GP) was proposed. Firstly, the neural network parameters were used to describe the distribution probability of the shale training image, and the feature extraction of the training image was completed. Secondly, the trained network parameters were saved. Finally, the 3D shale digital core was constructed by using the generator. The experimental results show that, compared to the classic digital core reconstruction technologies, the proposed DCGAN-GP obtains the image closer to the training image in porosity, variogram, as well as pore size and distribution characteristics. Moreover, DCGAN-GP has the CPU usage less than half of the classic algorithms, the memory peak usage only 7.1 GB, and the reconstruction time reached 42 s per time, reflecting the characteristics of high quality and high efficiency of model reconstruction.

A lightweight framework of Internet of Things (IoT) information sharing security based on blockchain technology was proposed to solve the problems of IoT's information sharing, such as source data susceptible to tampering, lack of credit guarantee mechanism and islands of information. The framework used double-chain pattern including data blockchain and transaction blockchain. Distributed storage and tamper-proof were realized on the data blockchain, and the registration efficiency was improved through a modified Practical Byzantine Fault Tolerance (PBFT). Resource and data transactions were realized on the transaction blockchain, the transaction efficiency was improved and privacy protection was realized through the improved algorithm based on partial blind signature algorithm. The simulation experiments were carried out to analyse, test and verify anti-attack capability, double-chain processing capacity and time delay. Simulation results show that the proposed framework has security, effectiveness and feasibility, which can be applied to most situations of the real IoT.

Influence maximization problem in micro-blog cannot be solved by simple user rank algorithm. To solve this problem, a greedy algorithm based on Extended Linear Threshold Model (ELTM) was proposed to solve Top-K problem in microblog. A concept of influence rate and a WIR (Weibo Influence Rank) algorithm were established to determine the user's influence by summarizing the key factors. Then, based on WIR values, an influence propagation model was proposed. After using greedy algorithm, the Top-K nodes were excavated. A simulation test based on Sina micro-blog was performed to validate the effectiveness of the proposed method. The result shows that the method outperforms the traditional algorithm.

A kind of Boolean operational method based on a three-dimensional grid model was proposed. Firstly, through collision detection algorithm based on hierarchical bounding box tree of Oriented Bounding Box (ORB), the intersecting triangles could be got. Through the intersection test of the triangles, the intersecting lines could be obtained and the intersecting lines topology relations with the triangles could be established. Secondly, a regional division for the intersecting triangles was made through processing the three types of intersecting lines, so as to get a series of polygons, and carry out Delaunay triangulations for polygon to get the result area. Lastly, relation adjacency list was constructed based on solid containing relations, the polygons internal relation and external relation with other entities were judged, and the triangles were located according to the mesh model topology relations. Simultaneously, according to such Boolean operations as the intersection, union, and differences, according to the grid model topology relations were judged, the position of the triangles were judged and then the final results could be obtained. Experimental results show that this algorithm can achieve better results. Experimental results show that the lithology of intersecting parts is consistent with the entities and can verify the correctness and feasibility of the algorithm.