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．

 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 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．

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．

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.

 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.

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.

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．

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. 

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.

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.

Federated learning has become increasingly important for modern machine learning, especially for data privacy sensitive scenarios. It is difficult to carry out secure machine learning between heterogeneous data islands. A federated learning communication mode between heterogeneous data islands is proposed, which realizes the hybrid federated learning communication between horizontal and vertical, and breaks the communication barrier of the disunity of model structure between horizontal and vertical participants in traditional federated learning. Based on the special privacy requirements of the government, banks and other institutions, the third party aggregator is further removed on the basis of the hybrid federated learning model, and the calculation is carried out only among the participants, which greatly improves the privacy security of local data. In view of the computational speed bottleneck caused by vertical homomorphic encryption in the communication process in the above model, by increasing the local iteration round q, the encryption time of vertical federation learning is shortened by more than 10 times, and the computational bottleneck between horizontal and vertical participants is reduced, and the accuracy loss is less than 5% .

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.

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.

In order to solve the problem of using voice signals to accurately identify animals so as to protect and research wild animals. We propose a FCNN (Fully Convolutional Neural Network) combining a fully connected algorithm and a sparse connection algorithm for automatic speech recognition. The fully connected algorithm is used to extract more combined features, and the sparse connection algorithm to select important features to speed up the convergence. The specific model structure and algorithm flow are given, and speech recognition experiments are carried out. The experimental results show that the fully convolutional neural network deep learning algorithm is an effective method for automatic speech recognition. It can solve the problem of frog sound recognition and provide a reference for animal speech recognition.

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.

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.

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.

The existing tool fault diagnosis system has the problems of huge system, high cost, low precision and so on. It is urgent to develop a high precision and low cost tool fault diagnosis system. A tool fault diagnosis system which based on bionic strain sensors for CNC ( Computerized Numerical Control ) machine tools is proposed. Firstly, the bionic flexible crack array vibration sensitive component which has high-precision and inexpensive encapsulated into a rigid bionic strain sensor, making it suitable for collecting machine tool vibration signals. Then time-frequency features are extracted from the tool vibration signals and tool fault diagnosis model is built by support vector machine algorithm. Through the diagnosis of offline faults and online faults in the real-time machine-processing environment, the results show that the proposed tool fault diagnosis system has an accuracy of greater than 88% in machine-processing fault diagnosis. And the cost of system is reduced. This is a brand-new attempt to apply high-sensitivity and low-cost bionic flexible sensitive component to industrial fault diagnosis.

There are many defects in the mobility and convenience of the handwritten digit recognition system running on the computer. In order to make improvement for these defects, a handwritten digit recognition system based on the visual library OpenCV is designed, which transplants the digit recognition algorithm into the flexible and small high-performance embedded equipment. By adjusting the shooting Angle of the steering gear and using the technology of picture splicing and digital segmentation, the handwritten digit recognition of short distance and large area is realized. The recognition speed, recognition accuracy and model volume of the models trained by KNN(K-Nearest Neighbor), support vector machine and artificial neural network are compared. After testing, the identification time of Raspberry Pi by using the artificial neural network algorithm can be as low as 0. 115 s, and the recognition accuracy can reach 72% , which has a certain application value.

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.

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.

 In order to improve the accuracy and precision of binocular stereo vision device，a calibration
technology based on binocular stereo vision is proposed． According to the characteristics of two cameras in
binocular stereo vision，using the calibration plate of circular symbol array as the image source，through the
preprocessing of the image，under the constraint of radial error，the method of extracting feature points by using
geometric relation of circle and least square data processing is used to obtain coordinate of identification point and
homography matrix of image． The ideal external and internal parameters are inferred． The accurate parameters of
binocular stereo vision device are obtained． This test provides accurate data basis and reliable correction
conditions for the device to acquire measured data．

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．

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.

There is a deficiency of information available on waste classification, and many municipalities and educational institutions struggle with this issue. We address this challenge by utilizing the efficiency and accuracy of the neural networks to classify items and implement waste image classification with a deep residual shrinkage network built on the ResNet network and SENet network. By filtering the Garbage dataset to obtain the data set necessary for the experiment, and by enhancing ResNet, SENet and soft threshold processes are incorporated into the ResNet structure. And by training the network and optimizing its hyperparameters, a greater recognition rate and recognition effect are achieved for the classification of campus waste. The experimental findings indicate that the proposed approach is feasible to a certain extent.

 In order to facilitate the testing of students’ English language application ability, many schools hope to apply computers to language testing, but there are doubts about the effect of computer-assisted language testing. In view of this situation, a computer-aided language testing validity evaluation algorithm based on entropy weight method is studied. The validity evaluation index is selected by gray correlation analysis, and the evaluation index system is constructed. The entropy weight method is used to calculate the weight of each index. Through fuzzy comprehensive evaluation, the index weight and the index membership degree are fuzzy synthesized to obtain the validity evaluation score. The validity grade is obtained by referring to the principle of maximum membership degree. The results show that the validity of computer-aided language testing system in junior and senior high schools has reached a very high level, while in universities the validity has decreased, but it still reaches a high level, which shows that computers perform well in computer-aided language testing and have strong practicability.

 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.

Recently, with the rapid improvement of Chinese economy and people's living standards, the demand for the exploitation and transportation of energy is continuously increasing. As one of the important transportation modes, the length of petroleum and gas pipelines in China has increased greatly. Pipeline leakage detection technology is one of the basis for efficient and safe pipeline transportation. This article firstly introduces the development history and existing research achievements of pipeline leakage detection and location technology, and analyzes various common methods. Secondly, the fundamentals and operation manners of numerous routine pipeline leakage detection approaches are described. Then, based on the actual industrial situation, the characteristics and application scenarios of each detection method are described. At last this article analyzes the challenges of the mentioned detection technologies, and looks forward to the development direction of pipeline leak detection technology.

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.

In order to solve the problems of management, coordination and association of cross regional scientific and technological resource sharing and collaborative innovation, practical application and theoretical research of scientific and technological resource platform are carried out to build an ecological digital display system of scientific and technological innovation. First, the definition, composition and index system of scientific and technological innovation elements are constructed, and innovation map, innovation ability, innovation subject, innovation carrier are proposed. The digital display system with innovation resources and innovation environment is the core. Taking Jilin Province as an example, the index method is used to monitor the level of regional scientific and technological innovation, the text mining method is used to analyze the hot spots of scientific and technological policies, and the system architecture and function design are carried out based on the Vue + Django + MySQL technology architecture. Finally, the ecological digital display of scientific and technological data innovation in the region is realized, so that the system has data sharing, data association and intelligent service functions such as decision support. The application practice shows that the system enriches the visualization of scientific and technological data resources, improves the visualization of scientific and technological innovation ecology, and improves the expansibility, efficiency and performance of the system.

To ensure stable network access and reduce resource waste, an open-source big data load balancing optimization algorithm based on real-time node load is proposed. An open-source big data node computing capability model is established, timely feedback and adjustments based on the size of node load are provided, the next action based on the number of requests received by servers in the region is predicted, exponential smoothing method is used to calculate the predicted number of server requests per second, the lag deviation problem of first- order exponential smoothing method is improved, and the comprehensive server load is calculated. Add a load agent and load monitor on the node to balance the number of blocks and the load of sharded nodes, and place undeleted shards and blocks into the minimum unit candidate list to achieve load balancing optimization. Through experiments, it has been proven that the proposed algorithm can improve network resource utilization and load balancing, ensuring a more stable and secure network during access.

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.

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.

To make AGV ( Automated Guided Vehicle ) work efficiently in various environments, it is necessary to select a suitable path planning algorithm according to the actual terrain. We use the A* and DWA ( Dynamic Window Approach ) hybrid path planning algorithm and build four typical terrains,U-shaped, S-shaped, L-shaped and narrow passage in the simulation environment to conduct pathfinding experiments. Furthermore, we improve the weight recursive formula of Gmapping, remove the dependence on the previous moment data and improve the efficiency of the algorithm. The results show that the hybrid path planning algorithm has faster pathfinding speed and better obstacle avoidance ability than the single algorithm. It has the fastest pathfinding speed in L-shaped terrain and was relatively slow in U-shaped terrain and S-shaped terrain.

In order to solve the problem that the traditional access control system has low security and poor user experience and can not meet the social demand for smart home，an artificial intelligence access control system based on remote terminal controllable is developed． The system is based on the Intel UP2 embedded platform．The Ubuntu operating system is the software foundation． It integrates technologies such as face recognition，fingerprint recognition，voice recognition，and ＲF card recognition． Write a graphical interface in PyQt language and builds a cloud server to implement remote terminals control． The system integrates a variety of identification and unlocking methods，which can realize the intelligent and convenient unlocking，and better adapt to different life situations．

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 problem of automatic identification and detection of violent speech on Weibo network, after analyzing the domestic and foreign research on violent text recognition, based on microblog corpus, a data set is established, and data cleaning work is carried out. An improved TFIDF text vectorization method is proposed. The vector of traditional method and the vector constructed by this method are used for the input of the logistic regression model, and the logistic regression violent text classification models of the traditional method and the improved method are created respectively. The above models are evaluated and compared. The experimental results show that the AUC and accuracy of the improved method are 0. 969 and 0. 970, respectively, which are 14. 4% and 15. 5% higher than those of the traditional method.

In traditional statistical methods, the conjoint analysis method is can not estimate variables for a large number of parameters at the same time, therefore, a Bayesian 茁 regression model is proposed. In the newly established model, the Dirichlet distribution is used as the prior distribution of the model parameters, and the relevant MCMC(Markov Chain Monte Carlo) algorithm is designed to fit the model. By analyzing the results of applying the model to the evaluation of discrete index variables, it is shown that the model has a good fitting effect on the data and the algorithm has a fast convergence speed. It shows that the Bayesian hierarchical model makes up for the defects of the traditional conjoint analysis method, and optimizes and improves the conjoint analysis method.