In the field of indefinite spatial data reconstruction, Multiple-Point Statistics (MPS) has been widely used, but its applicability is affected due to the high computational cost. A spatial data reconstruction method based on a multi-resolution Generative Adversarial Network (GAN) model was proposed by using a pyramid structured fully convolutional GAN model to learn the data training images with different resolutions. In the method, the detailed features were captured from high-resolution training images and large-scale features were captured from low-resolution training images. Therefore, the image reconstructed by this method contained the global and local structural information of the training image while maintaining a certain degree of randomness. By comparing the proposed algorithm with the representative algorithms in MPS and the GAN method applied in spatial data reconstruction, it can be seen that the total time of 10 reconstructions of the proposed algorithm is reduced by about 1 h, the difference between the average porosity of the algorithm and the training image porosity is reduced to 0.000 2, and the variogram curve and the Multi-Point Connectivity (MPC) curve of the algorithm are closer to those of the training image, showing that the proposed algorithm has better reconstruction quality.

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.

Uncertain spatial information is widely used in many scientific fields. However, the current methods for uncertain spatial information reconstruction need to scan the Training Image (TI) for many times, and then obtain the simulation results through complex probability calculation, which leads to the low efficiency and complex simulation process. To address this issue, a method of Fisher information and Variational Auto-Encoder (VAE) jointly applying to the reconstruction of uncertain spatial information was proposed. Firstly, the structural features of the spatial information were learned through the encoder neural network, and the mean and variance of the spatial information were obtained by training. Then, the random sampling was carried out to reconstruct the intermediate results according to the mean and variance of the sampling results and the spatial information, and the encoder neural network was optimized by combining the optimization function of the network with the Fisher information. Finally, the intermediate results were input into the decoder neural network to decode and reconstruct the spatial information, and the optimization function of the decoder was combined with the Fisher information to optimize the reconstruction results. By comparing the reconstruction results of different methods and the training data on multiple-point connectivity curve, variogram, pore distribution and porosity, it is shown that the reconstruction quality of the proposed method is better than those of other methods. In specific, the average porosity of the reconstruction results of the proposed method is 0.171 5, which is closer to the 0.170 5 porosity of the training data compared to those of other methods. Compared with the traditional method, this method has the average CPU utilization reduced from 90% to 25%, and the average memory consumption reduced by 50%, which indicates that the reconstruction efficiency of this method is higher. Through the comparison of reconstruction quality and reconstruction efficiency, the effectiveness of this method is illustrated.

Big data processing with complex process mostly relies on pipeline systems. However, the pipeline systems of big data processing have some shortcomings in usability, function reusability, expansibility and processing performance. In order to solve the problems and improve the construction and development efficiency of big data processing environment and optimize the processing flow, a model driven big data pipeline framework called PiFlow was proposed. Firstly, the big data processing process was abstracted as a directed acyclic graph. Then, a series of components were developed to construct the data processing pipeline, and the pipeline task execution mechanism was designed. At the same time, in order to standardize and simplify the pipeline framework description, a model driven big data pipeline description language called PiFlowDL was designed, which described the big data processing tasks in a modular and hierarchical way. PiFlow configures the pipeline in a What You See Is What You Get (WYSIWYG) way, and integrates the functions such as status monitoring, template configuration, and component integration. Compared with Apache NiFi, it has the performance improvement of 2-7 times.

Aiming at the low efficiency and the complex simulation process of the traditional reconstruction methods for porous media such as Multi-Point Statistics(MPS)which require scanning the training image many times and to obtain simulation results by complex probability calculations,a method to reconstruct porous media using adaptive deep transfer learning was presented. Firstly,deep neural network was used to extract the complex features from the training image of porous media. Secondly,the adaptive layer was added in deep transfer learning to reduce the difference in data distribution between training data and prediction data. Finally,through copying features by transfer learning,the simulation result consistent with the real training data was obtained. The performance of the proposed method was evaluated by comparing with the classical porous media reconstruction method MPS in multiple-point connectivity curve,variogram curve and porosity. The results indicate that the proposed method has high reconstruction quality. Meanwhile,the method has the average running time reduced from 840 s to 166 s,the average CPU usage dropped from 98% to 20%,and the average memory utilization decreased by 69%. The proposed method significantly improves the efficiency of porous media reconstruction under the premise of ensuring better quality of reconstruction results.

The mining and analysis of smart traffic card data can provide strong support for urban traffic construction and urban management. However, most of the existing research data only include data about bus or subway, and mainly focus on macro-travel patterns. In view of this problem, taking a city traffic card data as the example, which contains the multi-source daily travel data of urban residents including bus, subway and taxi, the concept of tour chain was put forward to model the behavior of residents. On this basis, the periodic travel characteristics of different dimensions were given. Then a spatial periodic feature extraction method based on the longest common subsequence was proposed, and the travel rules of urban residents were analyzed by clustering analysis. Finally, the effectiveness of this method was verified by five evaluation indexes defined by the rules, and the clustering result was improved by 6.8% by applying the spatial periodic feature extraction method, which is helpful to discover the behavior pattern of residents.

In the classical Petri-net, the workflow has no strict restrictions and definition, the transition token (including type, quantity, flow direction) binding and arriving of the subsequent places in different ways, and the description and analysis of multi-performance cannot be handled effectively. A workflow weighting colored Petri-net modeling method was proposed by defining the workflow structure and color set of Petri net and adding the multi-performance analysis. The conception, weight vector and the structure of the method were introduced, and the process of dangerous chemicals logistics was teken as an example to put forward a method of modeling and performance measurement of the process of dangerous chemicals logistics from two dimensions including time and safety. Then the method was used for modeling, measuring and analyzing performance of the process of dangerous chemicals logistics, the total value of performance was 3.8094. At last, by screening the weakness of local performance, bottleneck of process of dangerous chemicals logistics was found out, thus proves that the method is a scientific method for multi-performance analysis of workflow.

To resolve the problem of "free riding" and "tragedy of the commons" in peer-to-peer live streaming systems, a cooperation model was proposed in a game theoretic framework. The proportional fairness optimal strategy was proved under Nash equilibrium and Pareto optimality. And then the corresponding node behavior strategy was analyzed considering their cheating behaviors. Finally, the analytical results show that the model can effectively stimulate node cooperation and prevent cheating.