To monitor changes of indoor carbon dioxide concentrations in real time, a carbon dioxide alarm based on a gas sensor with a STC89C52 microcontroller as the core is designed. When the CO2 concentration in the air exceeds the preset value, the sound and light alarm function can be activated, and the indoor CO2 concentration value can be displayed in real-time. The hardware system includes a carbon dioxide sensor, signal conditioning circuit, analog-to-digital conversion circuit, STC89C52 microcontroller, and acousto-optic alarm unit. The software system includes data acquisition, data processing, alarm logic, and other functional units. The alarm can be activated in time when indoor CO2 concentration exceeds 1. 5% . 

In order to improve the search speed and shorten the search time of robot path planning，the characteristics of various algorithms is summarized． First，the history of mobile robot development and outline the key technologies of path planning are reviewed． Secondly，the mobile robot path planning is classified and summarized． From the perspective of the mobile robot's grasp of the environment，the mobile robot path planning is divided into two categories: global planning and local planning． Then the related algorithms of global planning and local planning are reviewed，and the development status，advantages and disadvantages of related algorithms are summarized． Finally，the future development trend of robot path planning technology in further research，hybrid algorithm， multi-robot collaboration， complex environment and multi-dimensional environment is pointed out．

In order to solve the human intervention problem of existing human target recognition and tracking
system，an automatic human detection and tracking system is designed，which is composed of embedded system，
wireless communication technology and upper computer． The automatic detection and tracking system is divided
into two parts: the upper computer and the lower computer． Using STM32F103ＲCT6 as the control unit，the
lower computer detects the position of the human body through the SHＲAP-GP2Y0A21YK0F infrared ranging
sensor，and then controls the steering gear． The steering gear is equipped with a camera to collect the video
signal which is transmitted to the upper computer using WIFI( Wireless Fidelity) wireless technology． The upper
computer is developed by Eclipse-Android system development platform，which can display the video signal in
real time in the monitoring center． It has been proved that the system is easy to install and operate，can
accurately realize the automatic recognition and tracking process of human body，and can be widely used in the
industry of indoor non-interference infrared work．

Aiming at the problem that the traditional named entity recognition method can not fully learn the complex sentence structure information of ancient Chinese and it is easy to cause information loss in the process of long sequence feature extraction, an ancient Chinese fusion of SikuBERT ( Siku Bidirectional Encoder Representation from Transformers) model and MHA (Multi-Head Attention) is proposed. First, the SikuBERT model is used to pre-train the ancient Chinese corpus, the information vector obtained from the training into the BiLSTM (Bidirectional Long Short-Term Memory) network is input to extract features, and then the output features of the BiLSTM layer are assigned different weights through MHA to reduce the information loss problem of long sequences. And finally the predicted sequence labels are obtained through CRF (Conditional Random Field) decoding. Experiments show that compared with commonly used BiLSTM-CRF, BERT-BiLSTM-CRF and other models, the F1 value of this method has been significantly improved, which verifies that this method can effectively improve the effect of ancient Chinese named entity recognition.

The process of behavior recognition can be regarded as the combination of feature extraction and
classifier to a large extent． Compared to static image object recognition，video feature extraction of human
behavior recognition is more susceptible to such factors as dynamic background，acquisition device motion，
perspective and illumination，so it poses great challenges to researchers． Based on the systematic classification of
behavior recognition feature extraction，according to the different types of behavior recognition feature extraction
methods and common behavior recognition database，the behavior recognition feature extraction is systematically
classified to expound the latest research progress． And the current research difficulties and possible future
research directions are discussed．

To address the problem of significantly degraded image quality in hazy weather, a new multi-task learning method is proposed based on the classical atmospheric scattering model. This method aims to jointly learn the transmission map and dehazed image in an end-to-end manner. The network framework is built upon a new biconditional generative adversarial network, which consists of two improved CGANs( Conditional Generative Adversarial Network). The hazy image is inputted into the first stage CGAN to estimate the transmission map. Then, the predicted transmission map and the hazy image are passed into the second stage CGAN, which generates the corresponding dehazed image. To improve the color distortion and edge blurring in the output image, a joint loss function is designed to enhance the quality of image transformation. By conducting qualitative and quantitative experiments on synthetic and real datasets, and comparing with various dehazing methods, the results demonstrate that the dehazed images produced by this method exhibit better visual effects. The structural similarity index is measured at 0. 985, and the peak signal-to-noise ratio value is 32. 880 dB.

The connection between mobile internet devices based on the Android platform and people’s lives is becoming increasingly close, and the security issues of mobile devices have become a major research hotspot. Currently, many visual Android malware detection methods based on convolutional neural networks have been proposed and have shown good performance. In order to better utilize deep learning frameworks to prevent malicious software attacks on the Android platform, a new application visualization method is proposed, which to some extent compensates for the information loss problem caused by traditional sampling methods. In order to obtain more accurate software representation vectors, this study uses the Swin Transformer architecture instead of the traditional CNN(Convolutional Neural Network) architecture as the backbone network for feature extraction. The samples used in the research experiment are from the Drebin and CICCalDroid 2020 datasets. The research experimental results show that the proposed visualization method is superior to traditional visualization methods, and the detection system can achieve an accuracy of 97. 39% , with a high ability to identify malicious software.

 Existing performance prediction research focuses on how to build prediction model, ignoring the importance of prediction time. In order to solve this problem, a prediction model based on BP ( Back Propagation) neural network is proposed to find out the potential relationship between freshmen's grades and graduation grades and realize the principle of early guidance and early effect. Through a random prediction experiment on the grades of 2016 students majoring in information and computing science in a university, it is proved that there is a potential relationship between freshman scores and graduation scores. The proposed prediction model has excellent prediction accuracy and good practicability and popularization, which can become an important part of improving teaching quality and play a greater role in realizing the goal of talent training.

To ensure network security and timely control the security situation, a security threat quantification assessment algorithm is proposed for wireless networks based on susceptible, infected, and susceptible infected recovered models. Asset value, system vulnerability, and threat are selected as quantitative evaluation indicators. Value and vulnerability quantification values are obtained based on the security attributes of information assets and the agent detection values of host weaknesses, respectively. Based on the propagation characteristics of the virus, the SIR ( Susceptible Infected Recovered) model is improved, the propagation characteristics of the virus are analyzed. A quantitative evaluation algorithm for wireless network security threat situation is established based on the quantification of three indicators, and the obtained situation values is used to evaluate the network security situation. The test results show that the security threat situation values of the host and the entire wireless network evaluated by this method are highly fitted with the expected values, and the evaluation time is shorter. It can be seen that the proposed algorithm has good evaluation accuracy and real-time performance, which can provide effective data basis for network security analysis and provide reliable decision- making support to administrators in a timely manner.

fMRI ( functional Magnetic Resonance imaging) is an efficient research method for brain imaging technique. In order to reduce the redundancy of the fMRI data and transform the fMRI data to the constructed features with more classification potential, a feature construction method based on the siamese network named as SANet(Siamese Network) is proposed. It engineered the brain regions features under multiple scanning points of an fMRI image. The improved AlexNet is used for feature engineering, and the incremental feature selection strategy is used to find the best feature subset for the encephalopathy prediction task. The effects of three different network structures and four classifiers on the SANet model are evaluated for their prediction efficiencies, and the ablation experiment is conducted to verify the classification effect of the incremental feature selection algorithm on the SANet features. The experimental data shows that the SANet model can construct features from the fMRI data effectively, and improve the classification performance of original features.

In order to solve the problem of inefficiency of traditional bird-driving methods at the Airport，this paper introduces the technology of multi-UAVs cooperation into this field． We study a bird-driving technology by using multi-UAVs cooperative． Based on the self-created“bird-ambushed”strategy，a mission planning model is built and a task assignment method is designed based on genetic algorithm． In order to evaluate the parameters of bird strike threat，the UAV interception efficiency which are the preconditions of the mission planning． The reasoning tree analysis and fuzzy logic analysis were introduced to optimize the parameters，respectively． In order to ensure the actual effect of mission planning，multiple constraints are designed，and the mission time of UAV is estimated based on proportional guidance law． The numerical simulation results show that，under the condition of multiple constraints and multiple bird targets simultaneously，the developed method can effectively accomplish the work of multi-UAVs cooperative bird-driving．

In order to solve the problems of misjudgment, oscillation and tracking velocity of traditional perturbation observation method, a control strategy based on power prediction variable step size perturbation observation method is proposed. When the sampling frequency is fixed and the light intensity remains constant and the temperature changes, the output power of the photovoltaic cell can be approximately linear in unit time. The rapidness of the traditional perturbation observation method with large step size is used to track the maximum power point, and then the linear power prediction is carried out near the maximum power point, so as to accurately track the maximum power point. A simulation model is built in the simulation platform, and the results show that the new control strategy improves the tracking speed and accuracy of the system, and optimizes the output performance of the system.

Efficient and accurate water injection flow prediction can help oilfield departments formulate reasonable production plans, reduce the waste of resources, and improve the injection-production rate of the oilfield. RNN( Recurrent Neural Networks) in deep learning is often used for time series prediction, but it is difficult to extract features from historical series and can not highlight the impact of key information. Early information is also easy to lose when the time series is too long. A method of oilfield water injection flow prediction based on dual attention mechanism CNN ( Convolutional Neural Networks)-BiLSTM ( Bi-directional Long Short-Term Memory) is proposed. Taking the historical water injection data of the oilfield as the input, the CNN layer extracts the characteristics of the historical water injection data, and then enters the feature attention mechanism layer. The corresponding weights are given to the features by calculating the weight value. The key features are easier to get large weights, and then have an impact on the prediction results. The BiLSTM layer models the time series of data and introduces the time step attention mechanism. By selecting the key time step and highlighting the hidden state expression of the time step, the early hidden state will not disappear with time,

which can improve the prediction effect of the model for long time series, and finally complete the flow prediction. Taking public datasets and oilfield water injection data from a certain region in southern China as examples, and comparing them with MLP ( Multilayer Perceptron), GRU ( Gate Recurrent Unit), LSTM ( Long Short Term Memory), BiLSTM, CNN, it is proven that this method has higher accuracy in oilfield water injection flow prediction, can help oilfield formulate production plans, reduce resource waste, and improve injection recovery rate, and has certain practical engineering application value.

Under the current situation of normalized management of epidemic situation, in order to ensure the normal development of computer experiment courses in colleges and universities, a virtual laboratory for computer basic experiments is established based on Docker technology. Students can access the server through a browser to obtain an independent experimental environment. The Docker-Compose tool is used to create, open, stop, delete and other multi-dimensional management of students' experimental environments, and to ensure their performance, which is equivalent to moving the offline laboratories online. This scheme can meet the needs of online computer basic experiments and provide high-quality experimental services for corresponding theoretical teaching.

Existing industrial time series data anomaly detection algorithms do not fully consider the temporal data on time dependence. An improved HTM(Hierarchical Temporal Memory)-Attention algorithm is proposed to address this problem. The algorithm combines the HTM algorithm with the attention mechanism to learn the temporal dependencies between data. It is validated on both univariate and multivariate time series data. By introducing the attention mechanism, the algorithm can focus on the important parts of the input data, further improving the efficiency and accuracy of anomaly detection. Experimental results show that the proposed algorithm can effectively detect various types of time series anomalies and has higher accuracy and lower running time than other commonly used unsupervised anomaly detection algorithms. This algorithm has great potential in the application of industrial time series data anomaly detection.

Desert seismic recordings contain lots of complex noise which reduces signal-to-noise ratio. To solve this problem, an adaptive rank convergence denoising algorithm combining VMD ( Variational Mode Decomposition) with MoG-RPCA (Mixture of Gauss-Robust Principal Component Analysis) is proposed. The desert seismic data is firstly decomposed by VMD. All the decomposed modalities are rearranged into a new signal matrix, and then the matrix is subjected to low-rank decomposition by MoG-RPCA. When the error of decomposition satisfies the pre-determined requirement, the efficient low-rank component is extracted. Finally superimpose all the modalities of each channel signal in the low-rank matrix and substract from the original seismic data to achieve denoising. This method avoids choosing the modes of VMD and performs an adaptive rank convergence to the traditional low-rank decomposition. Simulation experiment and actual data processing show that the algorithm can effectively suppress low-frequency noise while maintaining more than 85% amplitude of the effective signal.

In order to promote the development of path planning technology，the planning ideas and forms of path planning are analyzed． The path planning algorithms are divided into the traditional classical algorithms and modern intelligent algorithms in two categories，and some commonly used algorithms are analyzed and summarized．And the current research hotspots and future development trends are pointed out from the three aspects of improving the application of modern intelligent algorithms in path planning，amalgamation of multiple algorithms and the research of four-dimensional path planning algorithms for multiple UAVs．

In recent years, microblog has become an important platform for Internet public opinion disseminationand public opinion mining. In order to analyze the impact of epidemic events on Netizens' emotions, we should do a good job in prevention and control publicity and public opinion guidance scientifically and efficiently.Therefore, we integrate different deep learning methods to conduct emotional analysis of microblog comments on the COVID-19 outbreak at the end of 2020. A hybrid model based on RNN(Recursive Neural Network) and LSTM (Long Short-Term Memory) and using the FastText word vector representation in the embedding layer is proposed to reduce the noise data in the word vectors and thus obtain high-quality word vectors with semantically
rich and less noise. Training on Weibo corpora and compared with Bayesian and Support Vector Machine, RNN,LSTM multiple methods, the results show that the accuracy of the emotion analysis model proposed in this paper reaches 98. 71% , which proves that the model can effectively improve the accuracy of emotion analysis.

Aiming at the problems of difficult modeling and low calculation efficiency of equivalent harness method, the effect of electromagnetic pulse radiation on the vehicle harness is studied using CST ( Computer Simulation Technology). The influence of the number of vehicle cables on the electromagnetic coupling effect of the harness is analyzed. By controlling the variables, we changed the number of cables in the harness and observed the maximum value of the coupling voltage in the harness. We also studied the maximum coupling voltage and current in the harness by varying the cable size and load resistance. The simulation results show that the peak value of the coupling voltage decreases linearly with an increase in the number of cables and increases linearly with an increase in cable size. The peak value of the coupling current decreases with an increase in load resistance, which follows a power series relationship. Finally, we combined the simulation results and fitted the maximum coupling voltage and current under different parameters, drawing a conclusion about the relationship between them, which provides a reference for the electromagnetic protection of vehicle wiring harnesses. 

The sharp increase of data center network traffic leads to frequent abnormal traffic attacks, which seriously threatens the user data security. Therefore, a data center network abnormal traffic detection algorithm based on SDN( Software Defined Networking) technology is proposed. The data flow transmission process is constructed according to the SDN technology network framework and the time and frequency set method, and then the data flow characteristics are extracted using fuzzy C-means clustering, quadruple, BP(Back Propagation) neural network and other algorithms. The traffic feature subspace is established using the principal component analysis algorithm, and the matrix method is used to project to the subspace. Finally, the set threshold and projection period data vector are used to judge whether there is abnormal traffic in the data center network. The experimental results show that the proposed algorithm is an simple and ensures the accuracy of abnormal traffic detection results, and effectively maintains the stability and security of the data center network.

Taget Detection of Photovoltatic Remote Sensing Based on Improved Yolov5 Model TONG Xifeng, DU Xin, WANG Zhibao (School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China) Abstract: Aiming at high-sensing photovoltaic image resolution, high environmental noise, and complex background, an improved Yolov5 model is proposed to achieve positioning of photovoltaic power plants. First of all, the CA(Coordinate Attention) mechanism is added to the compassionate layer of the main feature extraction network to improve the learning ability of the network characteristics; second, the Ghostconv network structure is added to Backbone, useing the Ghostconv network module to replace the Conv network module, designing a new GhostC3 network network instead of the original C3 network module to improve the learning efficiency of the model; finally, the GIoU_Loss function is changed to the SIoU_Loss function. Compared with the original Yolov5 method, the average accuracy of the improved algorithm mAP, accuracy, and recall rate reached 97. 5% , 98. 9% , and 94. 9% , respectively, which have increased by 1. 8% , 1. 7% , and 5. 8% , respectively. The algorithm has a good effect on photovoltaic detection.

Unlike single noise, mixed noise has inconsistent characteristics and is difficult to suppress. In order to improve the noise suppression effect and image clarity, a digital image mixed noise suppression algorithm based on lifting wavelet is proposed. By using probabilistic neural networks, digital image noise is divided into pulse noise and Gaussian noise. The median filtering method is used to remove pulse noise from the digital image, and the lifting wavelet method is used to remove Gaussian noise from the digital image, achieving mixed noise suppression. The experimental results show that the proposed algorithm achieves higher image clarity and signal-to-noise ratio, and significantly improves the ENOB( Effective Number Of Bits) value of the digital image after denoising, indicating that the hybrid noise suppression effect of the algorithm is better.

In order to solve the parameter immobilization of the traditional pulse-coupled neural network，the
memory properties of the memristive components are applied in the image processing，and two memristive
components in the anti-parallel are used in the connection strength analog between the neurons of pulse neural
network． A novel memristive pulse neural network is constructed to realize the dynamic change of the connection
strength between neurons，and the new network is used for image denoising． The Matlab simulation experiment is
carried out to verify the good performance of the improved new network in image denoising． The peak signal-tonoise
ratio and image similarity index prove that the method has good effect on image denoising．

A football competition risk intelligent warning method based on an improved Copula model is proposed to address the issues of large errors between warning values and actual values, and multiple false alarms in football matches. Based on the fuzzy comprehensive evaluation matrix, the evaluation system for football competition risk indicators is determined. The indicator level status is classified, the Copula function is selected, and an improved Copula football competition risk intelligent warning method is constructed to accurately judge football competition risks and reduce risk losses. The experimental results show that the interference suppression of this method is high, maintained above 20 dB, and have high anti-interference ability. It can effectively suppress interference. This method also reduces the error between the warning value and the actual value, reduces the number of false alarms in the warning, and verifies the practicality and feasibility of this method.

A novel approach is proposed to address the issues of high accuracy but slow speed or low accuracy but appropriate training speed in traditional image recognition methods for fault diagnosis of oil pumps. The proposed method is based on an enhanced residual network model, with several improvement strategies. Firstly, the first-layer convolution kernel of the model is replaced with a smaller one. Secondly, the order of residual modules is changed. Thirdly, the fully connected layer of ResNet50( a Residual Network model) is replaced with an RBF( Radial Basis Function) network as an additional classifier. Finally, data augmentation techniques are used to expand the dataset, and transfer learning is utilized to obtain pre-trained weight parameters on ImageNet for the improved ResNet50-RBF model. Experimental results demonstrate that the proposed model achieves 98. 86% accuracy in pump curve recognition, exhibiting stronger robustness and improved speed compared to other networks. This provides some reference for fault diagnosis of oil pumps. The proposed method can significantly enhance the efficiency and accuracy of image recognition in fault diagnosis for oil pumps, which is of great significance for practical applications in the industry.

Aiming at the problems that dynamic objects (such as pedestrins, vehicles, animals) appear in visual SLAM(Simultaneous Localization and Mapping) in real scenes, affect the accuracy of algorithm positioning and mapping, the YOLOv3-ORB-SLAM3(Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping 3) algorithm is proposed based on ORB-SLAM3. The algorithm adds a semantic thread on the basis of ORB- SLAM3, and the thread uses YOLOv3 to perform semantic recognition target detection on dynamic objects in the scene. The outliers are removed from the extracted feature points on the tracking thread, and the static environment area extracted by the ORB feature, thereby the positioning accuracy of the visual SLAM algorithm is improved. The TUM(Technical University of Munich) data set is used to verify the positioning accuracy of the algorithm in monocular and RGB-D(Red, Green and Blue-Depth) modes. The verification results show that the dynamic sequence of the YOLOv3-ORB-SLAM3 algorithm in monocular mode is about 30% lower than that of the ORB-SLAM3 algorithm in RGB-D mode, the dynamic sequence decreases by 10% , and the static sequence does not decrease significantly.

To address the problem that RGB ( Red Green Blue ) modal and thermal modal information representations are inconsistent in form and feature information can not be effectively mined and fused, a new joint attention reinforcement network-FCNet ( Feature Sharpening and Cross-modal Feature Fusion Net ) is proposed. Firstly, the image feature mapping capability is enhanced by a two-dimensional attention mechanism. Then, a cross-modal feature fusion mechanism is used to capture the target region. Finally, a layer-by-layer decoding structure is used to eliminate background interference and optimize the detection target. The experimental results demonstrate that the improved algorithm has fewer parameters and shorter operation times, and the overall detection performance of the model is better than that of existing multimodal detection models.

In order to deepen students' understanding of semiconductor device performance, a comprehensive experimental project and performance tester of static parameters of MOS ( Matal-Oxide-Semiconductor ) is designed and developed according to the characteristics of undergraduate experimental teaching. This tester can measure a number of parameters either by item or by combination. It is helpful to deepen students' understanding of device performance, and is helpful to cultivate students' practical ability. Years of practice have shown that this comprehensive experimental project is helpful to improve students' learning interest and thinking of the basic course “Semiconductor Device Physics". In teaching practice, students change from passive learning to active experimental designers and participants. It has realized the interaction between teachers and students and the cooperation between students, and achieved good teaching results.

 Microseismic inversion is an important way to complete main task of microseismic monitoring by inverting and inferring information such as the location of the epicenter, the time of occurrence of the earthquake, the true magnitude, the initial amplitude of the hypocenter, the focal mechanism and the medium parameters based on the microseismic monitoring data. Through the study of microseismic inversion technology, more accurate microseismic information can be obtained, thereby improving the reliability of reservoir fracture evaluation, reducing development costs and improving oil and gas recovery. We review microseismic inversion from three aspects: microseismic focal location inversion, microseismic focal mechanism inversion, and microseismic multi-parameter joint inversion. The research progress of microseismic inversion technology in recent years is reviewed, and the principle, advantages and disadvantages of various microseismic inversion methods are summarized, the improvement and application of various microseismic inversion techniques are summarized, and the future research ideas and development directions are prospected. It provide reference for further development of microseismic inversion in the future.

In view of the fact that the fault tree analysis method can not analyze the pipeline risk polymorphism, and can not realize two-way reasoning, a calculation method of pipeline failure probability based on Bayesian network is proposed. Firstly, the fault tree model of oil field pipeline failure risk is established, and the Bayesian network structure is determined by the transformation of fault tree and Bayesian network to complete the construction of the Bayesian network model structure of pipeline failure risk. Secondly, considering the large estimation error of network parameters determined by expert knowledge experience and expectation maximization algorithm, genetic algorithm is introduced to complete the Bayesian network structure. Finally, this method is applied to the actual risk problem of oil field pipeline, and the failure probability of oil field pipeline is calculated by using the genie Bayesian network simulation software. And each risk factor is analyzed and the cause chain affecting the pipeline failure is obtained. The experimental results show that the method proposed has significantly improved the evaluation accuracy.

When multiple UAVs (Unmanned Aaerial Vehicle) fight in coordination, cooperative path planning is needed to improve mission success rate. After transforming constraints of cooperative path planning into multiple targets, the fusion design of NSGA(Non-Dominated Sorting Genetic Algorithm)-Ⅲ algorithm and potential field ant colony algorithm are carried out. Firstly, the potential field of the map is constructed to make the nodes close to the obstacles difficult to be selected, and to guide the search direction. Then, the path cost, spatial cooperative constraint and temporal cooperative constraint are modeled and converted into numerical indicators, and are set as multiple targets of NSGA-Ⅲ algorithm. For NSGA-Ⅲ algorithm, critical layer selection method and evolutionary algorithm are designed. Finally, in two-dimensional and three-dimensional grid map, the improved NSGA-Ⅲ algorithm uses each population to search the desired path for each UAV. Simulation results show that the UAV paths obtained by planning are safe and cost less.

In order to solve the effective refractive index measurement problem of the ridged waveguide phase modulation, a three-port MZI(Mach-Zehnder Interferometer) structure is proposed. It can quantitatively measure and analyze the relative changes of the real and imaginary parts of the effective refractive index with the voltage varies of the PN ridged silicon optical waveguide and the polynomial fitting equation is derived. The experimental results are in good agreement with the fitting results, and then finally the characteristics of the ridged waveguide in the phase modulation process are obtained. This measurement method is simple and feasible, and can be used in silicon based optoelectronic integrated chips as a quantitative device for the detection of carrier modulation characteristics.

During the retrieval process of imbalanced stream data, the performance of data retrieval decreases due to the presence of imbalance in the data stream and the susceptibility to differential and edge data. In order to reduce the impact of the above factors, an imbalanced stream data retrieval method based on multi similarity fuzzy C-means clustering is proposed. This method calculates the multiple similarities between imbalanced flow data, and uses fuzzy C-means algorithm to cluster data with different similarities. By constructing a octree retrieval model, the data after clustering is stored, encoded and judged to complete the retrieval of unbalanced stream data. The experimental results show that the retrieval time of the proposed method is less than 20 seconds, and the recall and precision rates remain above 80% , with high NDCG( Normalized Discounted Cumulative Gain) values.

In order to give full play to the overall combat superiorities of the aircraft cluster to obtain optimal target allocation plan, we use an improved wolf pack algorithm to solve the battlefield situation model. In order to improve the global optimization ability of the algorithm and ensure the optimization efficiency of the algorithm,the concept of the second wolf is introduced to improve the calling and siege behavior of the wolf pack, and the update mechanism of the wolf pack algorithm is optimized. The simulation results show that the proposed method can quickly and accurately find the optimal objective function value, and to a certain extent improves the situation that the traditional wolf pack algorithm is easy to fall into the local optimum.

Multimedia Information Encryption Algorithm Based on Big Data Analysis MARDAN Zunon (Department of Information Engineering, Xinjiang Institute of Engineering, Urumqi 830023, China) Abstract: The traditional method of digital multimedia information encryption is based on the knowledge of a single mathematical field, which has some problems such as easy leakage, easy cracking and low security level. As a result, a digital multimedia information encryption method based on big data analysis is proposed. The encryption method of the cloud and encryption hardware is combined to write the digital multimedia information encryption algorithm into the cloud database. Users use cryptographic hardware for identity binding, creating isolated spaces for information interaction. Complete the encryption of digital multimedia information. The experimental results show that the digital multimedia information encryption method based on big data analysis reduces the crack rate of theoretical encrypted files and has good information encryption effect, high safety factor, and strong practicability. 

In order to solve the program code similarity detection measurement which ignores the program semantics and the invalid measurement, we present
 an AST(Abstract Syntax Tree) based on the program code similarity measure method. Through the pretreatment redundancy removal in AST generation and the lexical grammar analysis, get the corresponding AST; and then according to the adaptive threshold method,using the AST traversal which include the sequence and process attributes to take the similarity calculation;finally,determine whether plagiarism and generate the test report.The experimental results show that this method can effectively detect a variety of plagiarism java code.

The improvement of consensus mechanism is a key research content in the development process of blockchain technology. In the traditional consensus mechanism, all endorsement nodes participate in endorsement, which consumes a lot of time, and has the possibility of forging and manipulating the consensus process with low security. Based on the verifiable random function, the endorsement nodes in the candidate set of endorsement nodes for trading and any endorsement node for endorsement operation are randomly selected which can effectively improve the processing efficiency and reduce the processing time of the consensus mechanism. Based on theoretical analysis and experimental verification of Hyperledger fabric model, the results show that the optimized consensus mechanism has faster transaction processing speed, lower delay time and higher security.

A network intrusion detection algorithm that combines systematic data pre-processing and hybrid sampling is proposed for the problem of class imbalance in intrusion detection datasets. Based on the feature distribution of the intrusion detection dataset, the feature values are systematically processed as follows: for the three categorical features, “Proto’’,“Service’’ and “State’’, minor categories within each feature are combined to reduce the total dimension of one-hot encoding; the 18 extremely distributed numerical features are processed with logarithm and then standardized according to the numerical distribution. The class imbalance processing technology, which combines Nearmiss-1 under-sampling and SMOTE ( Synthetic Minority Over-sampling Technique) is designed. Each class of samples in the training dataset is divided into sub-classes based on the “Proto’’,“ Service’’ and “ State’’ categorical features, and each sub-class is under-sampled or oversampled in equal proportion. The intrusion detection model PSSNS-RF ( Nearmiss and SMOTE based on Proto, Service, State-Random Forest) is built, which achieves a 97. 02% multiclass detection rate in the UNSW-NB15 dataset, resolving the data imbalance problem and significantly improving the detection rate of minority classes.

Because the sEMG( Surface Electromyography) is very sensitive to muscle fatigue，different patients
and electrode displacement，it is an arduous task to design a reliable robust and intelligent hand rehabilitation
device． To address these difficulties，a neural decoding method of rehabilitation gestures based on deep learning
is presented by using sEMG on the forearm of patients and CNN ( Convolutional Neural Network) to recognize the
movement intention． A combined feature extraction method is proposed to extract the combined features of each
channel of 8-channel sEMG． The combined feature includes 32 features which are wavelet packet decomposition
energy features，time-domain features and frequency-domain features． The eight channel features are formed into
an 8 × 32 numerical matrix and grayscale processed into a feature map，to train the convolutional neural network．
For five different gestures recognition，the classifier’s accuracy reached 98． 1%． Finally，according to the
classification results，STM32 I /O port outputs the corresponding PWM ( Pulse Width Modulation) signal，which
shows the feasibility of this method and laying a foundation for further control of rehabilitation glove movement．