Damage to sensors in Industrial Internet of Things （IIoT） system due to continuous use and normal wear leads to hidden anomalies in the collected and recorded sensing data. To solve this problem， an anomaly detection algorithm based on Local Sensitive Bloom Filter （LSBF） model was proposed， namely LSBFAD. Firstly， the Spatial Partition based Fast Johnson-Lindenstrauss Transform （SP-FJLT） was used to perform hash mapping to the data， then the Mutual Competition （MC） strategy was used to reduce noise， and finally the Bloom filter was constructed by 0-1 coding. In simulation experiments conducted on three benchmark datasets including SIFT， MNIST and FMA， the false detection rate of LSBFAD algorithm is less than 10%. Experimental results show that compared with the current mainstream anomaly detection algorithms， the proposed anomaly detection algorithm based on LSBF has higher Detection Rate （DR） and lower False Alarm Rate （FAR） and can be effectively applied to anomaly detection of IIoT data.

In view of the problems of low efficiency, error propagation, and entity redundancy in traditional entities and relations annotation methods, and for the fact that there is the characteristic of "the overlapping relationship between one entity (main-entity) and multiple entities at the same time" in corpuses of some domains, a new annotation method for joint extraction of domain entities and relations was proposed. First, the main entity was marked as a fixed label, each other entity in the text that has relation with the main-entity was marked as the type of relation between the corresponding two entities. This way that entities and relations were simultaneously labeled was able to save at least half of the cost of annotation. Then, the triples were modeled directly instead of modeling entities and relations separately, and, the triple data were able to be obtained through label matching and mapping, which alleviated the problems of overlapping relation extraction, entity redundancy, and error propagation. Finally, the field of crop diseases and pests was taken as the example to conduct experiments, and the Bidirectional Encoder Representations from Transformers (BERT)-Bidirectional Long Short-Term Memory (BiLSTM)+Conditional Random Field (CRF) end-to-end model was tested the performance on the dataset of 1 619 crop diseases and pests articles. Experimental results show that this model has the F1 value 47.83 percentage points higher than the pipeline method based on the traditional annotation method+BERT model; compared with the joint learning method based on the new annotation method+BiLSTM+CRF model, Convolutional Neural Network (CNN)+BiLSTM+CRF or other classic models, the F1 value of the model increased by 9.55 percentage points and 10.22 percentage points respectively, which verify the effectiveness of the proposed annotation method and model.

The performance of swarm intelligent algorithms in solving large-scale permutation and combinatorial optimization problems is influenced by the large search space, so a Solution Space Dynamic Compression (SSDC) strategy was proposed to cut down the search space of the algorithms dynamically. In the proposed strategy, two times of initial solutions of the the permutation and combination optimization problem were obtained by the intelligent algorithm firstly. Then the repetitive segments of the two solutions were recognized and merged together. And the new points after merging were taken into the original solution space to perform the compression and update of the solution space. In the next intelligent algorithm solving process, the search was carried out in the compressed feasible space, so as to improve the searching ability of the individuals in the limited space and reduce the searching time cost. Based on five high-dimensional benchmark Travel Salesmen Problems (TSP) and two Vehicle Routing Problems (VRP), the performances of several swarm intelligent algorithms combined with the solution space dynamic compression strategy were tested. The results show that the swarm intelligent algorithms combined with the proposed strategy are superior to the corresponding original algorithms in the search accuracy and stability. It is proved that the solution space dynamic compression strategy can effectively improve the performance of swarm algorithms.

Particle Swarm Optimization (PSO) algorithm is easy to be premature and drop into the local optimum and cannot jump out when solving complex multimodal functions. Related researches show that changing the topological structure among particles and adjusting the updating mechanism are helpful to improve the diversity of the population and the optimization ability of the algorithm. Therefore, a Hybrid PSO with Topological time-varying and Search disturbance (HPSO-TS) was proposed. In the algorithm, a K-medoids clustering algorithm was adapted to cluster the particle swarm dynamically for forming several heterogeneous subgroups, so as to facilitate the information flow among the particles in the subgroups. In the speed updating, by adding the guide of the optimal particle of the swarm and introducing the disturbance of nonlinear changing extreme, the particles were able to search more areas. Then, the transformation probability of the Flower Pollination Algorithm (FPA) was introduced into the position updating process, so the particles were able to transform their states between the global search and the local search. In the global search, a lioness foraging mechanism in the lion swarm optimization algorithm was introduced to update the positions of the particles; while in the local search, a sinusoidal disturbance factor was applied to help particles jump out of the local optimum. The experimental results show that the proposed algorithm is superior to FPA, PSO, Improved PSO (IPSO) algorithm and PSO algorithm with Topology (PSO-T) in the accuracy and robustness. With the increase of testing dimension and times, these advantages are more and more obvious. The topological time-varying strategy and search disturbance mechanism introduced by this algorithm can effectively improve the diversity of population and the activity of particles, so as to improve the optimization ability.

The image quality assessment is to give a reasonable assessment for the quality of image processing algorithm, and No-Reference (NR) quality evaluation method is applied in a lot of situations of being unable to get the original reference image. The result of structure analysis of the infrared image shows that the uncertainty of the image is fuzzy, but not random. Therefore, the concept of fuzzy entropy was introduced into the quality assessment of infrared image. A method of no-reference quality assessment for blurred infrared image was proposed, comparisons and analysis on performance of the algorithm were given from the following aspects: efficiency, consistency and accuracy. The simulation results show that this method has the characteristics of low computation complexity, fast operation speed and consistence of subjective and objective evaluations. And the general performance is better than the assessment based on Mean Squared Error (MSE) and Peak Singal-to-Noise Ratio (PSNR).

To improve the survivability of networks, a strategy selection model based on stochastic game was proposed. According to the damage impact on system made by the attacker behavior, the game process was divided into the resistance phase, the recognition phase, and the recovery phase. The relationship between the transition of system state and strategy selection was depicted from a specific view, based on it, a strategy selection analytical method was presented. A representative network example was provided to verify the feasibility and validity of the proposed method in the prediction of attacker behavior and decision of optimal strategy.

Efficient Graph-Based algorithm is a novel image segmentation method based on graph theory and it can segment an image at an extraordinary speed. However, it is easily influenced by the threshold value and the segmentation result is imprecise when dealing with the border and texture. Here, an improved algorithm is proposed, which has three main contributions: 1) RGB color space is replaced by Lab color space; 2) Laplacian operator is used to divide the edges of weighted graph into border edges and non-border edges, and those non-border edges are given priority; 3) the optimum threshold is evaluated based on uniformity measurement. Experimental results show that the improved algorithm is more accurate and adaptive than traditional Graph-based algorithms, and segmentation results are closer to human vision property.

The security of E-commerce determines its development; however, traditional means of security certification cannot meet the demand on reliability. To ensure the security in E-commerce communication, a new method of security certification was proposed based on rolled fingerprint reconstruction and digital signature. Firstly, the image sequence of rolling fingerprints was reconstructed into a high-quality complete fingerprint image, and then the features were extracted as the key of digital signature. Thirdly, the message embedded with digital signature was transferred. At the same time, the correctness and completeness of the message were checked, and the validity of the sender was also identified. The experimental results show that the proposed algorithm can be used for rolling fingerprint reconstruction in any direction to get a high-quality fingerprint image. And it can run in real-time because of low complexity. The rolling fingerprint, as the security certification medium, ensures the security of E-commerce.