To address the problems of poor targeted performance， unclear classification and lack of datasets faced by agricultural news， an agricultural news classification model based on Enhanced Representation through kNowledge IntEgration （ERNIE）， Deep Pyramidal Convolutional Neural Network （DPCNN） and Bidirectional Gated Recurrent Unit （BiGRU）， called EGC， was proposed. The dataset was first encoded by using ERNIE， then the features of the news text were extracted simultaneously by using the improved DPCNN and BiGRU， and the features extracted were combined and the final results were obtained by Softmax. To make EGC model more suitable for applications in the field of agricultural news classification， the DPCNN was improved by reducing its convolution layers to preserve more features. Experimental results show that compared with ERNIE， the precision， recall and F1 score of the proposed EGC model are improved by 1.47， 1.29 and 1.42 percentage points， respectively， verifying that EGC is better than traditional classification models.

To address the problems of limited information expression， imbalance， and dynamic spatio-temporal characteristics of accident data， an accident prediction model fusing heterogeneous traffic situations was proposed. In which， the semantic enhancement was completed by the spatio-temporal state aggregation module through traffic events and weather features representing dynamic traffic situations， and the historical multi-period spatio-temporal states of four types of regions （single region， adjacent region， similar region， and global region） were aggregated； the dynamic local and global spatio-temporal characteristics of accident data were captured by the spatio-temporal relation capture module from both micro- and macro-perspectives； and the multi-region and multi-angle spatio-temporal states were further fused by the spatio-temporal data fusion module， and the accident prediction task in the next period was realized. Experimental results on five city datasets of US-Accident demonstrate that the average F1-scores of the proposed model for accident， non-accident， and weighted average samples are 85.6%， 86.4%， and 86.6% respectively， which are improved by 14.4%， 5.6%， and 9.3% in the three metrics compared to the traditional Feedforward Neural Network （FNN）， indicating that the proposed model can effectively suppresses the influence of accident data imbalance on experimental results. Constructing an efficient accident prediction model helps to analyze the safety situation of road traffic， reduce the occurrence of traffic accidents and improve the traffic safety.

In order to solve the problem that it is difficult to obtain a composite service with high overall performance in a large-scale Web service environment， a large-scale Web service composition method was proposed. Firstly， Document Object Model （DOM） was used to parse the user demand document in XML format to generate an abstract Web service composition sequence. Secondly， the service topic model was used for service filtering， and Top-k specific Web services were selected for each abstract Web service to reduce the composition space. Thirdly， in order to improve the quality and efficiency of service composition， an Optimized Grey Wolf Optimizer based on Logistic chaotic map and Nonlinear convergence factor （OGWO/LN） was proposed to select the optimal service composition plan. In this algorithm， chaotic map was used to generate the initial population for increasing the diversity of service composition plans and avoiding multiple local optimizations. At the same time， a nonlinear convergence factor was proposed to improve the optimization performance of the algorithm by adjusting the algorithm search ability. Finally， OGWO/LN was realized in a parallel way by MapReduce framework. Experimental results on real datasets show that compared with algorithms such as IFOA4WSC （Improved Fruit Fly Optimization Algorithm for Web Service Composition）， MR-IDPSO （MapReduce based on Improved Discrete Particle Swarm Optimization） and MR-GA （MapReduce based on Genetic Algorithm）， the proposed algorithm has the average fitness value increased by 8.69%， 7.94% and 12.25% respectively， and has better optimization performance and stability in solving the problem of large-scale Web service composition.

In the two components of hybrid image segmentation algorithm, the initial segmentation cannot form the over-segmentation region sets with low wrong segmentation rate, while region merging lacks the label selection mechanism for region merging and the method of determining region merging stopping moment in this component commonly does not meet the scenario requirements. To solve the above problems, a Multi-level Region Information fusion based Hybrid image Segmentation algorithm (MRIHS) was proposed. Firstly, the improved Markov model was used to smooth the superpixel blocks, so as to form initial segmentation regions. Then, the designed region label selection mechanism was used to select the labels of the merged regions after measuring the similarity of the initial segmentation regions and selecting the region pairs to be merged. Finally, an optimal merging state was defined to determine region merging stopping moment. To verify MRIHS performance, comparison experiments between this algorithm with Multi-dimensional Feature fusion based Hybrid image Segmentation algorithm (MFHS), Improved FCM image segmentation algorithm based on Region Merging (IFRM), Inter-segment and Boundary Homogeneities based Hybrid image Segmentation algorithm (IBHHS), Multi-dimensional Color transform and Consensus based Hybrid image Segmentation algorithm (MCCHS) were carried out on Visual Object Classes (VOC), Cambridge-driving labeled Video database (CamVid) and the self-built river and lake inspection (rli) datasets. The results show that on VOC and rli datasets, the Boundary Recall (BR), Achievable Segmentation Accuracy (ASA), recall and dice of MRIHS are at least increased by 0.43 percentage points, 0.35 percentage points, 0.41 percentage points, 0.84 percentage points respectively and the Under-segmentation Error (UE) of MRIHS is at least decreased by 0.65 percentage points compared with those of other algorithms; on CamVid dataset, the recall and dice of MRIHS are at least improved by 1.11 percentage points, 2.48 percentage points respectively compared with those of other algorithms.

The Automated Essay Scoring (AES) technology can automatically analyze and score the essay, and has become one of the hot research problems in the application of natural language processing technology in the education field. Aiming at the current AES methods that separate deep and shallow semantic features, and ignore the impact of multi-level semantic fusion on essay scoring, a neural network model based on Multi-Level Semantic Features (MLSF) was proposed for AES. Firstly, Convolutional Neural Network (CNN) was used to capture local semantic features, and the hybrid neural network was used to capture global semantic features, so that the essay semantic features were obtained from a deep level. Secondly, the feature of the topic layer was obtained by using the essay topic vector of text level. At the same time, aiming at the grammatical errors and language richness features that are difficult to mine by deep learning model, a small number of artificial features were constructed to obtain the linguistic features of the essay from the shallow level. Finally, the essay was automatically scored through the feature fusion. Experimental results show that the proposed model improves the performance significantly on all subsets of the public dataset of the Kaggle ASAP (Automated Student Assessment Prize) champion, with the average Quadratic Weighted Kappa (QWK) of 79.17%, validating the effectiveness of the model in the AES tasks.

In the container rail-water terminal, the railway operation area is the essential node linking rail transportation and water transportation and its efficiency can influence the efficiency of container rail-water transportation. Firstly, the features of the "ships, trains" operation mode and the "ships, yard, trains" operation mode were analyzed and compared, and a hybrid operation mode was proposed by combining the actual operation of container rail-water intermodal terminal. Next, with the goal of minimizing the completion time of rail-mounted gantry crane, a mixed integer programming model was developed. The model considered the allowable operating time window constraints of trains and ships, and the realistic constraints such as the interference and safety margin between the rail-mounted gantry cranes as well as the continuous operation and waiting time of rail-mounted gantry crane and container truck. Aiming at the insufficient local search ability of genetic algorithm, a Hybrid Genetic Algorithm (HGA) was proposed by combining the heuristic rules with genetic algorithm to solve the collaborative scheduling problem of rail-mounted gantry crane and container truck, and experiments were conducted. Experimental results verified the effectiveness of the proposed model and the hybrid algorithm. Finally, some experiments were designed to analyze the impact of the number of containers, the proportion of quayside containers, the number of rail-mounted gantry cranes and the number of container trucks on the completion time of rail-mounted gantry crane and container truck. It is found that under the same number of containers, the number of rail-mounted gantry crane should be increased with the increase of proportion of quayside containers to reduce the completion time.

Aiming at the problems of blurred target boundaries and low image segmentation efficiency in the image segmentation process of ancient murals, a multi-class image segmentation model fused with a lightweight convolutional neural network named MC-DM (Multi-Class DeepLabV3+MobileNetV2 (Mobile Networks Vision 2)) was proposed. In the model, DeepLabV3+ architecture and MobileNetV2 network were combined together, and the unique spatial pyramid structure of DeepLabV3+ was utilized to perform multi-scale fusion of the convolutional features of the mural to reduce the loss of image details during the mural segmentation. First of all, the features of the input image were extracted by MobileNetV2 to ensure the accurate extraction of image information and reduce the time consumption at the same time. Secondly, the image features were processed through the dilated convolution, so that the receptive field was expanded, and more semantic information was obtained without changing the number of parameters. Finally, the bilinear interpolation method was utilized to up-sample the output feature image to obtain a pixel-level prediction segmentation map, so that the accuracy of image segmentation was ensured to the greatest extent. In the JetBrains PyCharm Community Edition 2019 environment, a dataset made of 1 000 mural scanning pictures was used for testing. Experimental results showed that the MC-DM model had a 1% improvement in training accuracy compared with the traditional SegNet (Segment Network)-based image segmentation model, and had a 2% improvement in accuracy compared with the image segmentation model based on PSPNet (Pyramid Scene Parsing Network), and the Peak Signal-to-Noise Ratio (PSNR) of the MC-DM model was 3 to 8 dB higher than those of the experimental comparison models on average, which verified the effectiveness of the model in the field of mural segmentation. The proposed model provides a new idea for the segmentation of ancient mural images.

Brain tumor Magnetic Resonance Imaging(MRI) has problems such as multi-modality, lacking of training data, class imbalance, and large differences between private databases, which lead to difficulties in segmentation. In order to solve these problems, the few-shot segmentation method was introduced, and a Prototype network based on U-net(PU-net) was proposed to segment brain tumor Magnetic Resonance(MR) images. First, the U-net structure was modified to extract the features of various tumors, which was used to calculate the prototypes. Then, on the basis of the prototype network, the prototypes were used to classify the spatial locations pixel by pixel, so as to obtain the probability maps and segmentation results of various tumor regions. Aiming at the problem of class imbalance, the adaptive weighted cross-entropy loss function was used to reduce the influence of the background class on loss calculation. Finally, the prototype verification mechanism was added, which means the probability maps obtained by segmentation were fused with the query image to verify the prototypes. The proposed method was tested on the public dataset BraTS2018, and the obtained results were as following:the average Dice coefficient of 0.654, the positive prediction rate of 0.662, the sensitivity of 0.687, the Hausdorff distance of 3.858, and the mean Intersection Over Union(mIOU) reached 61.4%. Compared with Prototype Alignment Network(PANet) and Attention-based Multi-Context Guiding Network(A-MCG), all indicators of the proposed method were improved. The results show that the introduction of the few-shot segmentation method has a good effect on brain tumor MR image segmentation, and the adaptive weighted cross-entropy loss function is also helpful, which can play an effective auxiliary role in the diagnosis and treatment of brain tumors.

Cleaning tasks of the cleaning service company often have the characteristics such as different levels, different durations and different cycles, and lack a general cleaning scheduling problem model. At present, the solving of cleaning scheduling problem is mainly relies on manual scheduling scheme, causing the problems such as time-consuming, labor-consuming and unstable scheduling quality. Therefore, a mathematical model of cleaning scheduling problem with constraints, which is a NP-hard problem, was proposed, then Simulated Annealing algorithm (SA), Bee Colony Optimization algorithm (BCO), Ant Colony Optimization algorithm (ACO), and Particle Swarm Optimization algorithm (PSO) were utilized to solve the proposed constrained cleaning scheduling problem. Finally, an empirical analysis was carried out by using the real scheduling state of a cleaning service company. Experimental results show that compared with the manual scheduling scheme, the heuristic intelligent optimization algorithms have obvious advantages in solving the constrained cleaning scheduling problem, and the manpower demand of the obtained cleaning schedule reduced significantly. Specifically, these algorithms can make the cleaning manpower in one year scheduling cycle be saved by 218.62 hours to 513.30 hours compared to manual scheduling scheme. It can be seen that the mathematical models based on heuristic intelligent optimization algorithms are feasible and efficient in solving cleaning scheduling problem with constraints, and provide making-decision supports for the scientific management of the cleaning service company.

In order to improve the efficiency of detecting defects on the metal surface of refrigerators and to deal with complex production situations, the Metal-YOLOv3 model was proposed. Using random parameter transformation, the defect data was expanded hundreds of times; the loss function of the original YOLOv3 (You Only Look Once version 3) model was changed, and the Complete Intersection-over-Union (CIoU) loss function based on CIoU was introduced; the threshold of non-maximum suppression algorithm was reduced by using the distribution characteristics of defects; and the anchor value that is more suitable for the data characteristics was calculated based on K-means clustering algorithm, so as to improve the detection accuracy. After a series of experiments, it is found that the Metal-YOLOv3 model is far better than the mainstream Regional Convolutional Neural Network (R-CNN) model in term of detection speed with the Frames Per Second (FPS) reached 7.59, which is 14 times faster than that of Faster R-CNN, and has the Average Precision (AP) reached 88.96%, which is 11.33 percentage points higher than Faster R-CNN, showing the good robustness and generalization performance of the proposed model. It can be seen that this method is effective and can be practically applied to the production of metal products.

For the fact that institutional trading in the financial market is highly misleading to retail investors in the financial market, a trend prediction method based on the impact of institutional trading behaviors was proposed. First, using the time series Matrix Profile (MP) algorithm and taking the stock turnover rate as the cut-in point, a knowledge base of turnover rate fluctuations based on the influence of institutional trading behaviors under motifs with different lengths was constructed. Second, the motif's length, which leads to the high accuracy of the prediction result of the stock to be predicted was determined. Finally, the fluctuation trend of single stock under the influence of institutional trading behaviors was predicted through the knowledge base of this motif's length. In order to verify the feasibility and accuracy of the new method of trend prediction, the method was compared with Auto-Regressive Moving Average (ARMA) model and Long Short Term Memory (LSTM) network, and the Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) evaluation indicators were used to compare the 70 stocks' prediction results of three methods. The analysis of experimental results show that, compared with the ARMA model and the LSTM network, in the prediction of 70 stock price trends, the proposed method has more than 80% of the stock prediction results more accurate.

For the problem of exponential explosion of joint action space with the increase of the number of agents in multi-agent systems, the "central training-decentralized execution" framework was adopted to solve the curse of dimensionality of joint action space and reduce the optimization cost of the algorithm. A new global credit assignment mechanism, Reward HighWay Network (RHWNet), was proposed to solve the problem that only the global reward corresponding to the joint behavior of all agents was given by the environment in multiple multi-agent reinforcement learning scenarios. By introducing the reward highway connection in the global reward assignment mechanism of the original algorithm, the value function of each agent was directly connected with the global reward, so that each agent was able to consider both the global reward signal and its actual reward value when making strategy selection. Firstly, in the training process, each agent was coordinated through a centralized value function structure. At the same time, this centralized structure was also able to play a role in global reward assignment. Then, the reward highway connection was introduced in the central value function structure to assist the global reward assignment, thus establishing the reward highway network. Then, in the execution phase, each agent's strategy depended only on its own value function. Experimental results on the StarCraft Multi-Agent Challenge (SMAC) microoperation scenarios show that the proposed reward highway network achieves a performance improvement of more than 20% in testing winning rate on four complex maps compared to the advanced Counterfactual multi-agent policy gradient (Coma) and QMIX algorithms. More importantly, in 3s5z and 3s6z scenarios with a large number and different types of agents, the proposed network can achieve better results when the required number of samples is only 30% of algorithms such as Coma and QMIX.

At present, the deployment of nodes in wireless sensor network mainly adopts the algorithm based on Voronoi diagram. In the process of deployment using Voronoi algorithm, due to the large number of nodes involved in the deployment and the high complexity of the algorithm, the iteration time of the algorithm is long. In order to solve the problem of long iteration time in node deployment, a Deployment Algorithm based on Basic Architecture (DABA) was proposed. Firstly the nodes were combined into basic architectures, then center position coordinates of the basic architecture were calculated, finally the node deployment was performed by using Voronoi diagram. The algorithm was still able to realize the deployment effectively under the condition that there were obstacles in the deployment area. The experimental results show that DABA can reduce the deployment time by two thirds compared with the Voronoi algorithm. The proposed algorithm can significantly reduce the iteration time and the complexity of the algorithm.

Concerning the problem that the local feature and rotation invariant feature cannot be jointly paid attention to in traditional attention-based neural networks, a multi-branch neural network model based weakly supervised fine-grained image classification method was proposed. Firstly, the lightweight Class Activation Map (CAM) network was utilized to localize the local region with potential semantic information, and the residual network ResNet-50 with deformable convolution and Oriented Response Network (ORN) with rotation invariant coding were designed. Secondly, the pre-trained model was employed to initialize the feature networks respectively, and the original image and the above regions were input to fine-tune the model. Finally, the three intra-branch losses and between-branch losses were combined to optimize the entire network, and the classification and prediction were performed on the test set. The proposed method achieves the classification accuracies of 87.7% and 90.8% on CUB-200-2011 dataset and FGVC_Aircraft dataset respectively, which are increased by 1.2 percentage points, and 0.9 percentage points respectively compared with those of the Multi-Attention Convolutional Neural Network (MA-CNN) method. On Aircraft_2 dataset, the proposed method reaches 91.8% classification accuracy, which is 4.1 percentage points higher than that of ResNet-50. The experimental results show that the proposed method improves the accuracy of weakly supervised fine-grained image classification effectively.

The Convolutional Neural Network (CNN) treats the potential object information and background information equally in the input image. However, there are many small objects and complex background in remote sensing scene images. To solve the problem above, a scale-attention network was proposed based on attention mechanism and multi-scale feature transformation. Firstly, a fast and effective attention module was developed, and the attention map was generated based on optimal feature selection. Then, with the attention map embedded, the multi-scale feature fusion layer added and the fully connected layer redesigned on the basis of ResNet50 network, a scale attention network was proposed. Secondly, the pre-training model was used to initialize the scale-attention network, and the training set was employed for the fine-tuning of the network. Finally, the fine-tuned scale-attention network was used to realize the classification prediction of test set. The classification accuracy of the proposed method on the AID scene dataset is 95.72%, which is 2.62 percentage points higher than that of ArcNet. On the NWPU-RESISC scene dataset, this method achieves classification accuracy of 92.25%, 0.95 percentage points higher than that of IORN （Improved Oriented Response Network）. The experimental results demonstrate that the proposed method is able to improve the classification accuracy of remote sensing image scenes.

Considering the adoption of information and energy simultaneous transmission in wireless networks to improve the performance of wireless relay systems， a bidirectional transmission full-duplex relay system with self-energy recycling based on wireless radio frequency network was proposed by using Simultaneous Wireless Information and Power Transmission （SWIPT） technology. It is a new attempt to apply SWIPT in bidirectional full-duplex relay system. The energy-constrained destination node used the energy harvested from the relay and the loop channel to send feedback information， and the logical structure of the full-duplex relay system and the physical structure of the energy-constrained destination node were given. Then， the system performance was described by using the minimization of the system transmit power sum as the optimization target， the power allocation scheme was used for information decoding and energy harvest， the semi-definite programming， rank relaxation and Lagrange methods were used to transform the original non-convex optimization equation into a solvable convex optimization problem， and the solution of the problem was found. The relay transmission power， transmit beamforming vector and power allocation ratio were jointly optimized. Finally， the proposed system was compared with the traditional bidirectional transmission relay system by experimental simulator. The results verify that the self-energy recycling can not only eliminate self-interference， but also significantly optimize the system transmission power sum， and reveal that the proposed system has higher performance gain than the traditional bidirectional transmission system due to the combination of SWIPT technology and full-duplex relay system.

Aiming at the problems that there is no unified registration of files, the whereabouts of files are not tracked, and the process of circulation is not standardized in the circulation of electronic files under the Internet ecology, a blockchain-based electronic file circulation scheme was proposed. Firstly, the design goals and design architecture of the electronic file circulation system based on blockchain were proposed using the multi-centralized system of the consortium blockchain in the blockchain. Secondly, blockchain-based electronic file circulation system was implemented by using a cloud storage platform to upload files for electronic file storage and adding time-stamps of the ownership transfer data of files to make the circulation process continuous, relevant, traceable, honest and credible. The data synchronization and tracing of the blockchain-based electronic file circulation system was achieved through using database calls to realize the data access. Finally, a smart contract for electronic file ownership transfer and query to verify and protect the contents of the files by reading the file identification. The security analysis and performance tests show that compared to the original one, the proposed scheme is more secure and enhances the credibility of the circulation information, at the same time, the shorter execution time of the smart contract makes the system have better reliability and traceability.

In the Device-to-Device (D2D) cache network, the cache space in the mobile terminal is relatively small with many multimedia contents. In order to realize the efficient use of cache space in mobile terminals, a D2D cache deployment algorithm based on user preference and replica threshold was proposed. Firstly, based on the user preference, a cache revenue function to determine the cache value of caching each file was designed. Then, with the goal of maximizing the cache hit ratio of system, the cache replica threshold was designed based on convex programming theory to deploy replica number of the files in the system. Finally, combining the cache revenue function with the replica threshold, a heuristic algorithm was proposed to implement file cache deployment. Compared with the existing cache deployment algorithm, the proposed algorithm can significantly improve the cache hit rate and the offload gain with the reduction of service delay.

Since existing dynamic multi-keyword ranked search schemes over encrypted data in cloud storage can not support semantic extension and do not have forward and backward security, a multi-keyword ranked search scheme over encrypted cloud data was proposed, which supported semantic search and achieved forward and backward security. The semantic extension of query keywords was achieved by constructing semantic relationship graph, the retrieval and dynamic update of data were achieved by use of tree-based index structure, the multi-keyword ranked search was achieved based on vector space model, and the extended index and query vectors were encrypted by using secure K-nearest neighbor algorithm. Security analysis indicates that the proposed scheme is secure under the known ciphertext model and achieves forward and backward security during dynamic update. Efficiency analysis and simulation experiments show that this scheme is superior to the same type schemes with the same security or function in server retrieval efficiency.

In order to reduce the matching error rate of ORB (Oriented FAST and Rotated BRIEF) method caused by the scale invariance of the feature points in the algorithm and enhance the robustness of the descriptors of Binary Robust Independent Elementary Features (BRIEF) algorithm to noise, an improved feature point matching method was proposed. Speeded-Up Robust Features (SURF) algorithm was used to extract feature points, and BRIEF algorithm with direction information was used to describe the feature points. Random pixel pairs in the neighborhood of the feature point were selected, the comparison results of the grayscales and the similarity of pixel pairs were encoded respectively, and Hamming distance was used to calculate the differences between the two codes. The similarity between the feature points were measured by the adaptive weighted fusion method. Experimental results show that the improved method has better adaptability to the scale variance, illumination variance and blurred variance of images, can obtain a higher feature point correct matching rate compared with the conventional ORB method, and can be used to improve the performance of image stitching.

Corresponding reduction algorithms for relative indiscernibility and discernibility relation were proposed. Firstly, considering the reduction of the relative indiscernibility relation in equivalence relation, the corresponding discernibility matrix was proposed and a reduction algorithm was proposed based on the matrix. Then, a reduction algorithm for relative discernibility relation was proposed according to the complementary relationship of the relation. Secondly, the concepts such as relative indiscernibility relation were expanded to the general relation. The corresponding discernibility matrix was proposed for the relative indiscernibility relation reduction in the relation decision system, and the corresponding discernibility matrix for the relative discernibility relation reduction was obtained by using the complementary relationship of the relation, so the reduction algorithms for both relations were obtained. Finally, the proposed algorithms were verified on the selected UCI datasets. In the equivalence relation, the algorithm of the relative EQuivalence INDiscernibility relation reduction based on absolute reduction (EQIND) and the algorithm of the relative BInary INDiscernibility relation reduction (BⅡND) have the same results. The algorithm of the relative EQuivalence DIScernibility relation reduction based on absolute reduction (EQDIS) and the algorithm of the relative BInary DIScernibility relation reduction (BIDIS) have the same results. Meanwhile, BⅡND and BIDIS are suitable for the incomplete decision table. The feasibility of the proposed algorithms were verified by the experimental results.

Focusing on the shortages of easily falling into local optimal, low convergence accuracy and slow convergence speed in Particle Swarm Optimization (PSO) algorithm, an improved Particle Swarm Optimization based on HierarChical autonomous learning (HCPSO) algorithm was proposed. Firstly, according to the particle fitness value and the number of iterations, the population was dynamically divided into three different classes. Then, according to characteristics of different classes of particles, local learning model, standard learning model and global learning model were respectively adopted to increase particle diversity and reflect the effect of individual difference cognition on performance of algorithm and improve the convergence speed and convergence precision of algorithm. Finally, HCPSO algorithm was compared with PSO algorithm, Self-adaptive Multi-Swarm PSO algorithm (PSO-SMS) and Dynamic Multi-Swarm PSO (DMS-PSO) algorithm on 6 typical test functions respectively. The simulation results show that the convergence speed and convergence accuracy of HCPSO algorithm are obviously higher than these of the given algorithms, and the execution time difference of the proposed algorithm and basic PSO algorithm is within 0.001 orders of magnitude. The performance of the proposed algorithm is improved without increasing complexity.

Considering the time delay and the energy consumption of terminal equipment caused by high-speed data transmission and calculation, a transmission scheme with equal power allocation in uplink was proposed. Firstly, based on collaborative properties of Augment Reality (AR) services, a system model for AR characteristics was established. Secondly, system frame structure was analyzed in detail, and the constraints to minimize total energy consumption of system were established. Finally, with the time delay and energy consumption constraints satisfied, a mathematical model of Mobile Edge Computing (MEC) resource optimization based on convex optimization was established to obtain an optimal communication and computing resource allocation scheme. Compared with user independent transmission scheme, the total energy consumption of the proposed scheme with a maximum time delay of 0.1 s and 0.15 s was both 14.6%. The simulation results show that under the same conditions, compared with the optimization scheme based on user independent transmission, the equal power MEC optimization scheme considering cooperative transmission between users can significantly reduce the total energy consumption of system.

Concerning the NP (Nondeterministic Polynomial)-hard problem of localizing link failures from end-to-end measurement in communication network, a new tomography method which relaxes the Boolean constraints was proposed. Firstly, the relationship between path state and link state in a network was modeled as Boolean algebraic equations, and the essence of localizing failures was treated as an optimization problem under the constraints of these equations. Then the NP property was derived from the binary states (normal/failed) of link state in the optimization problem. Therefore, by relaxing the Boolean constraints, the proposed method simply transformed the problem into a Linear Programming (LP) problem, which can be easily solved by any LP solver to get the set of failed links. Simulation experiments were conducted to identity link failures in real network topologies. The experimental results show that the false negative rate of the proposed method is 5%-30% lower than that of the classical heuristic algorithm TOMO (TOMOgraphy).

To solve the problem that infected nodes move out of the system during the process of rumor propagation, a new SIR (Susceptible-Infective-Removal) rumor propagation model on dynamic homogeneity network was proposed by improving the normalization conditions of classical SIR rumor propagation model. Firstly, according to the propagation rules and mean field theory, the rumor propagation dynamics equation was established on the homogenous network. Then the steady state and infection peak of the rumor propagation process were theoretically analyzed. Finally, the influence of factors on rumor propagation was studied through numerical simulation, which including infection rate, immune rate, real immune coefficient and average degree of network. Research indicates that, as infected nodes move out of the system, steady state value decreases and infection peak slightly increases, compared with the classical SIR rumor propagation model. The study also shows that the peak value of rumor infection increases as the infection probability increases and the immune probability decreases. As the real immune coefficient increases, the steady state value of immune nodes increases. The network average degree has no influence on the steady state of rumor propagation. The larger the average degree is, the earlier the arrival time of the infection peak. This research expands the application scope of SIR propagation model from a closed system to a non-closed system, providing guidance theory and numerical support for making rumor prevention measures.

Aiming at the problem of field segmentation in the reverse analysis of binary protocol message format, a novel algorithm with format keywords as the reverse analysis target was proposed, which can optimally determine the boundaries of binary protocol format keywords by improved n-gram algorithm and optimal path search algorithm. Firstly, by introducing the position factor into n-gram algorithm, a boundary extraction algorithm of format keywords was proposed based on the iterative n-gram-position algorithm, which effectively solved the problems that the n value was difficult to determine and the candidate boundary extraction of format keywords with fixed offset position in the n-gram algorithm. Then, the branch metric was defined based on the hit ratio of frequent item boundaries and the left and right branch information entropies, and the constraint conditions were constructed based on the difference of n-gram-position value change rate between keywords and non-keywords. The boundary selection algorithm of format keywords based on the optimal path search was proposed to determine the joint optimal bound for format keywords. The experimental results of testing on five different types of protocol message datasets such as AIS1, AIS18, ICMP00, ICMP03 and NetBios show that, the proposed algorithm can accurately determine the boundaries of different protocol format keywords, its F values are all above 83%. Compared with the classical algorithms of Variance of the Distribution of Variances (VDV) and AutoReEngine, the F value of the proposed algorithm is increased averagely by about 8 percentage points.

In the process of rumor spreading, nodes with different degrees of recognition have different recognition abilities. A evolution model of dynamic complex network was proposed based on game theory, in which a new game gain was defined according to node degree. In this model, considering the fact that rumor propagation was often related to node interests, the non-uniform propagation rates of different nodes and propagation dynamics of rumors were described by introducing the recognition ability, and two rumor suppression strategies were proposed. The simulation were conducted on two typical network models and verified in the Facebook real network data. The research demonstrates that the fuzzy degree of rumor has little effect on the rumor propagation rate and the time required to reach steady state in BA scale-free network and Facebook network. As rumors are increasingly fuzzy, the scope of rumor in the network is expanding. Compared with Watts-Strogtz (WS) small-world network, rumors spread more easily in BA scale-free network and Facebook network. The study also finds out that immune nodes in the WS small-world network grow more rapidly than immune nodes in BA scale-free network and Facebook network with the same added value of immune benefits. In addition, there is a better rumor suppression effect by suppressing the degree of node hazard than by suppressing the game gain.

The traditional algorithms are difficult to implement the Top- K subgraph query on large-scale dynamic labeled graph due to high time or space complexity. For this reason, a dynamic Top- K interesting subgraph query method named DISQtop- K was proposed. In this algorithm, a Graph Topology Structure Feature (GTSF) index that include Node Topology Feature (NTF) index and Edge Feature (EF) index was established, which can effectively prune and filter the invalid nodes and edges. Then a multi-factor candidate set filtering strategy was put forward based on GTSF index, which can be used to further prune the query graph candidate sets. Considering that the dynamic changes in the graph may have an impact on the matching results, to ensure the real-time and accuracy of the query results, a new matching-verification method for Top- K interesting subgraph was also given, which has two stages of initial matching and dynamic correction. Experimental results show that compared with RAM and RWM, DISQtop- K method costs shorter time for index creation and occupies less space, which can effectively deal with dynamic Top- K interesting subgraph query on large-scale labeled graph.

Concerning the low recommendation accuracy of personalized commodity recommendation methods, a Commodity Recommendation Method Integrating User Trust and Brand Recognition (TBCRMI) was proposed. By analyzing the user's purchase behavior and evaluation behavior, the user's recognition of brands and the activities of users themselves were calculated. Then Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm was used to cluster the users, based on which the user trust relationship was fused, and the nearest neighbors were obtained by Top- K method. Finally, the target user commodity recommendation list was generated based on the nearest neighbors. In order to verify the effectiveness of the algorithm, two datasets (Amazon Food and Unlocked Mobile Phone) were used, User based Collaborative Filtering (UserCF) algorithm, Collaborative Filtering recommendation algorithm with User trust (SPTUserCF) and Merging Trust in Collaborative Filtering (MTUserCF) algorithm were chosen, and the accuracy, recall and F1 value were compared and analyzed. The experimental results show that TBCRMI is superior to the commonly used personalized commodity recommendation methods in either multi-brand comprehensive recommendation or single brand recommendation.

In underwater acoustic communication, multipath effect channel can cause severe Inter-Symbol Interference (ISI). In view of the problems of the existing equalization algorithms when dealing with ISI, including slow convergence speed and huge steady-state error, as well as the complicated algorithm and being difficult to carry out hardware migration, a new variable step Least Mean Square (LMS) algorithm was proposed with anticosine step function and three adjustment parameters within the Feed-Forward Equalizer and Decision Feed-back Equalizer (FFE-DFE) structure. Firstly, simulations of three adjustment parameters including α, β, r were given to optimize the algorithm and compare it with traditional LMS algorithm, Modified Arctangent based Variable Step LMS (MA-VSLMS) and Hyperbolic Secant function based Variable Step size LMS algorithm (HS-VSLMS) in convergence and steady-state error. The simulation results showed that compared with the traditional LMS algorithm, the convergence speed of the proposed algorithm was 57.9% higher, and the steady-state error was reduced by 2 dB; compared with HS-VSLMS and MA-VSLMS, the convergence speed of the proposed algorithm was 26.3% and 15.8% higher, respectively, and the steady-state error was reduced by 1-2 dB. Finally, the proposed algorithm was transplanted to signal processing module and tested in an underwater experiment. Experimental results indicate that the signal is recovered very well after the equalizer, and the ISI problem caused by multipath effect is solved in the actual scene.