The current frequent occurrence of cyberspace security incidents has resulted in huge losses to national security and the real economy, demonstrating that the information security threats confronting nations have transcended the traditional concept of invasion warfare. Therefore, network security vulnerability scanner is an important means to prevent network attacks. Vulnerability scanners currently on the market are usually designed using brute?force scanning, which has problems such as limited detection dimension, slow speed and low accuracy. This paper proposes a distributed multi?dimensional assessment and detection model using Docker technology for multi?node deployment and simultaneous information collection. It divides information into multiple dimensions and quantifies them. The model introduces a fuzzy hierarchical evaluation method to assess the vulnerability values of target systems, and enhances the attention to corresponding systems based on their vulnerability levels. It combines fingerprinting technology with vulnerability detection methods. Tests conducted using a scenario?based Combat Network Shooting Range (CFS) show a significant improvement in detection efficiency compared to commonly used enterprise?level network scanners, outperforming traditional one?dimensional vulnerability detection methods in terms of hit rate and efficiency.

Synchronous method is used to establish an integrated model for batch process production scheduling and control. In the scheduling section, a production scheduling model is established based on the State Equipment Network (SEN) and the unit?specific event?based continuous time modeling method; the integrated model of scheduling and control belongs to a mixed integer dynamic optimization problem, and solving it requires a large amount of complex computation, in order to alleviate the burden of online computing, Explicit Model Predictive Control (EMPC) is utilized for offline solving; the MPT toolbox is used to solve the dynamic problem of EMPC; introducing binary variables, converting the obtained explicit control solution into explicit linear constraints, and adding them to the common constraint objective in the scheduling model; through case analysis, the optimization results were compared and analyzed with the pure scheduling model, and the economic feasibility of the integrated model is verified.

An improved ant colony algorithm is proposed to address issues such as susceptibility to local optima and slow convergence speed. Firstly, the relationship between the current target node and the next target node and the normal distribution function are introduced into the heuristic function, enhancing the algorithm's search capability in the early stages. In addition, by introducing an inflection point factor, the diversity of directional selection is enhanced. Secondly, an adaptive dynamic pheromone volatility coefficient is proposed to adjust the pheromone evaporation rate adaptively, modifying the pheromone update rules. Finally, simulation experiments were conducted using Matlab to compare the traditional ant colony algorithm and the improved ant colony algorithm on three different grid maps. The experimental results demonstrate that, compared with the traditional ant colony algorithm, the improved algorithm exhibits advantages such as faster convergence speed, shorter paths, and fewer inflection points.

As a high?risk area, the fire safety has always attracted much attention. Although smoke and flame alarm have been widely used, there are still problems such as single ?point detection and easy environmental impact. In response to such problems, a multi?way flame smart video monitoring system based on the B/S architecture is designed and implemented, and it is presented in the form of a web system. In the system, an improved YOLOV5 flame detection algorithm is integrated. The Ghost convolution is used to replace in conventional convolution to achieve the lightweight of the network, and the improved attention mechanism modules and small target detection anchor frame is added to enhance small target detection ability. Finally, the flame movement information extracted from the Optical flow network and the original flame data is sent into the improved YOLOV5 flame detection algorithm to further improve the detection accuracy of the flame. A large number of on?site test proves that the system can identify and locate the flames in the plant in real time. The detecting frame rate can reach 15 ms/frame, and the detection rate reaches 100%, which has high stability. An efficient and reliable fire monitoring solution is provided for the chemical industry.

Aiming at the problems of low positioning accuracy and poor stability in multi?effect and non?line?of?sight conditions, a new indoor positioning system Chan?Taylor?Unscented Kalman Filter (C?T?UKF) combined positioning algorithm is designed based on the time of flight positioning algorithm, combined with the Chan?Taylor (C?T) cooperative positioning algorithm, and fused with the Unscented Kalman Filter (UKF) algorithm. The system mainly consists of positioning base stations, positioning tags, wireless communication systems and upper computers, etc. The Chan algorithm is adopted to calculate the distance measured by the time of flight method, and the calculated coordinates are used as the initial value of the Taylor algorithm for iterative calculation. The iterative results are smoothed by the Unscented Kalman algorithm. The results show that the positioning system based on this algorithm has the characteristics of high accuracy, strong stability and low cost. The average positioning errors in line?of?sight and non?line?of?sight conditions are less than 0.17 m and 0.20 m respectively, and it can be applied to high?precision positioning scenarios.

To address the issues of scarce small defect samples and poor detection accuracy in magnetic flux leakage (MFL) testing of oil and gas pipelines, this paper proposes a small defect detection network for oil and gas pipelines on the basis of shallow feature suppression. First, an adversarial generative network is utilized, which incorporates prior knowledge to generate high?quality small defect samples. Subsequently, a defect feature suppression module is introduced during the feature extraction process, which suppresses the semantics of large defects in shallow pyramid features, thereby enhancing the features of small defects. Finally, a multi?scale Transformer is employed to fully leverage the local details and global information of the feature images, improving the accuracy of pipeline defect detection. The experimental results demonstrate that the accuracy of this model is 95.1%, which is 7.8% higher than the average value of existing faster R?CNN and other methods methods.

A perception and detection system based on cameras achieves target detection with lower cost and higher resolution. Target detection is performed using bird's?eye view (BEV) features generated by six monocular cameras. These BEV features include the position and scale of objects, making them suitable for various autonomous driving tasks. BEV detectors are typically combined with the deep pre?trained image backbones, but directly connecting the two does not effectively highlight the correspondence between 2D and 3D features. To address this issue, Channel Attention is applied to weight and adjust the proposed feature channels in the output feature map, and combined with a depth estimation module to emphasize the relationship between 2D and 3D features. Furthermore, a temporal aggregation fusion method is employed to solve the problem of gradual information loss in traditional fusion methods, ensuring that the model can fully leverage historical information. Extensive experiments on the NuScenes dataset show that the model achieves a Normalized Discounted Cumulative Score (NDS) of 0.604, a 0.035 improvement over the BEVFormer model, validating the effectiveness of the proposed approach.

Traditional mechanism models of greenhouses are difficult to reflect the real greenhouse environment due to nonlinear, multivariate, and strongly coupled characteristics. In this paper, extreme learning machine (ELM), back propagation (BP) neural network, and support vector machine (SVM) are used to predict and analyze the temperature, humidity, and light intensity of the greenhouse. The results show that the predicted values of ELM model are the most similar to the real?time parameters of greenhouse environment. In order to further improve the prediction accuracy of environmental parameters in the greenhouse, the improved sparrow search algorithm (ISSA) is used to optimize ELM model in this paper. The predicted environmental parameters are in good agreement with the measured data of a greenhouse in Tianjin, which confirms the feasibility of the proposed prediction model for the control of greenhouse environment.

In view of the huge scale of urban underground pipe network, the traditional manual detection method can no longer meet the needs of the current projects. In this paper, the MobileNetv3?YOLOv7 network model is proposed as the algorithm for target detection of underground pipeline defects to improve the accuracy and speed of detection. First, the pipeline image dataset is preprocessed, and the input image is grayscale and resampled to balance the number of samples. Secondly, the lightweight network MobileNetv3 and YOLOv7 network frameworks are combined to increase the BiFPN feature pyramid structure to improve accuracy. Then, in terms of data processing, Mosaic data augmentation is used to improve the robustness of the model. Finally, a comparative experiment with the YOLOv7 network model is designed to verify the feasibility of the model. In this paper, the MobileNetv3?YOLOv7 network model is verified under the framework of Pytorch experiment, and the experimental results show that the model greatly reduces the amount of parameter calculation and improves the average accuracy.

The parameters of PID controller determine the stability and speed of tension control system, so it is important to study the parameter tuning optimization of classical PID controller in winding tension control. The PID tension controller based on the modified whale algorithm is designed by combining PID and modified whale optimization algorithm with winding tension control as an entry point. The improved whale algorithm (L?WOA) is combined with PID in order to improve its convergence speed and convergence accuracy when rectifying the PID parameters. A mathematical model and a dynamic torque balance equation are developed to analyze the effect of wire speed and web diameter on web tension. The parameters are optimized using the modified whale algorithm and various other algorithms, respectively, and the results show that the PID controller optimized by the improved whale algorithm proposed in this paper has the advantages of rapid response, more steady output, sturdy anti?interference ability and better robustness when the PID controller is controlled.

The combination of remote sensing image information extraction and artificial intelligence algorithms is an important technical tool for land use status survey, monitoring and management in land resources and environmental departments.Aiming at the problems of insufficient spatial information localization and inaccurate multi-scale target feature segmentation generated by U-net in remote sensing image extraction, a CA-Res2-Unet model incorporating an attention module into the head of Res2Net to replace the coding part of U-net is proposed, which aims to enhance the spatial localization and multi-scale feature information segmentation capability of U-net.Experiments were carried out on mainstream networks and improved models through the WHDLD public data set and the self-made data set of Shenfu New District. The results show that compared with the basic model, OA, M_IoU and F1 indexes of the experiment on the WHDLD public data set and the self-made data set of Shenfu New District increased by 0.92%, 2.00%, 1.58% and 1.18%, 2.87%, 1.91%, respectively. The visual effect and quantitative indexes of the proposed method are superior to other mainstream semantic segmentation networks, which can provide scientific basis for the investigation of the status quo of regional land use and the decision-making of relevant departments.

The defect detection of non-woven fabrics can help enterprises improve production efficiency and save costs. Due to the local characteristics of the convolution kernel, the object detection algorithms based on CNN lack the global modeling of the image, and the detection effect is not ideal for defect detection with a large range of scale changes. Therefore, a non-woven fabric defect detection method is proposed based on the combination of Swin Transformer and YOLOv5, which encodes and decodes features through its powerful self-attention. The network can obtain a larger receptive field and fully relate to the context. The layered construction based on the feature pyramid of Swin coincides with the design of the neck of YOLOv5. It can help the network predict the target on the multi-scale feature map. On this basis, CBAM attention mechanism is introduced to help the network focus on important information. Through Mosaic and MixUp data augmentation, the data distribution is enriched and the robustness is increased. Finally, the anchor size of the prediction target frame is fine-tuned to make the regression prediction more accurate. The effectiveness of the proposed method is verified on the self-made data set, and the detection performance of non-woven fabrics is improved.

The data of chemical processes often contains dynamic timing characteristics, and traditional fault detection has low usage of dynamic information, which limits the fault diagnosis performance. To address this problem,a new method of chemical process fault diagnosis based on an attention?enhanced encoder?decoder network model (AEN) was proposed. The coding part uses the LSTM to extract the feature information of the process data and combine it with the attention mechanism to utilize the dynamic information among the process data more effectively； the decoding part uses the LSTM and combines the context vector provided by the attention mechanism to provide more accurate state information for the softmax regression, and finally, the softmax regression is used to obtain the probability value of the fault category for each sample data. The introduction of the attention mechanism improves the efficiency of the model in using process dynamic information in the time domain. The proposed method is experimented with using Tennessee Eastman process data and compared with the results of standard PCA?SVM, DBN and ResNet, and the results show that the proposed method is more effective in diagnosing faults.

Magnetic tomography method has been widely used for nondestructive external inspection of buried and submarine pipelines, which is based on the principle of metal magnetic memory to discern the danger level and location of the stress concentration zone by measuring the anomalies in the spatial magnetic field distribution outside the pipeline. The distribution characteristics and spatial propagation law of pipeline inspection signal detected by magnetic tomography method, the energy distribution and change law of spatial magnetic memory signal in the stress concentration zone of magnetized pipelines are studied in this paper. The magnetic dipole field is used to establish the magnetic field model in the stress concentration zone of the inner wall of the pipeline, and the magnetic energy and energy density of spatial magnetic memory signals under different lift?off values outside the pipeline are finite element calculated based on the magnetic energy theory to derive the distribution law of spatial magnetic field and the correlation of magnetic energy density of magnetic signals under different lift?off value is analyzed. The results show that the spatial magnetic field energy outside the pipe decays with the increase of lift?off value, and the decay is the fastest within the distance of 50 mm from the outer wall of the pipe to the physical force; the correlation of magnetic energy density of different lift?off values shows that the magnetic signal detected by magnetic tomography method outside the pipe is homologous with the signal in the stress concentration zone of the inner wall of the pipe. Theoretically, it explains the effectiveness of magnetic tomography method and also provides evaluation indexes for extracting effective signals from the detection data.

In view of the irregularity of the bottom floor of working face and the diversity of the shape of the flying gangue in steeply dipping coal seam, based on the geographic information system data such as contour line of bottom floor of working face, the 3d grid model of bottom floor is established, combined with the energy tracking method(ETM) C + + programs, four typical shapes of flying gangue with the same mass and different shapes are simulated to obtain the motion trajectories of the migration of flying gangue in the actual working face, as well as the velocity, angular velocity and energy change curves at any time. The influence of the shapes on the motion of flying gangue is analyzed. In order to verify the accuracy and feasibility of the method in this paper, the trajectory simulated by Rockyfor3D software is compared. The results show that the transport capacity of ellipsoidal flying gangue is much higher than that of polyhedral flying gangue. Compared with common polyhedral flying gangue, the regular polyhedral flying gangue has farther migration distance and less energy loss due to collision. The number of edges of flying gangue of regular polyhedron is inversely proportional to the energy loss of flying gangue in collision, which indicates that flying gangue of regular polyhedron with multiple edges is most likely to cause danger．

Recognition of weather phenomena based on images is essential for the analysis of weather conditions. To address the problems that traditional machine learning methods are difficult to accurately extract various weather features and poor in classifying weather phenomena and the accuracy of deep learning for weather phenomena recognition is not high, a weather recognition model based on image block and multi?headed attention mechanism is proposed. The model introduces Swin Transformer into the field of weather recognition for the first time, and adopts a multi?headed attention mechanism combining window multi?head self?attention layer and shifted?window multi?head self?attention layer, whose regionally relevant features extraction capability makes up for the shortcomings of traditional methods and can extract complex weather features from images. The model is trained using transfer learning, and the fully connected parameters of the fine?tuned model are input to the Softmax classifier to achieve recognition of multi?category weather images with 99.20% recognition accuracy, which is better than several mainstream methods in comparison, and it can be applied to ground weather recognition systems as a weather recognition module.

The robustness of the particle swarm system is great, which is very helpful for solving ill?conditioned problems such as image reconstruction. However, the large number of pixels in the reconstructed image leads to a large dimension of particle and it is difficult for the particle to achieve the optimal solution in the optimization process. In order to solve this problem, a constraint is added to the particle position, imaging by Tikhonov regularization algorithm is used as the reference of particle position. The search for particles is constrained to the range of Tikhonov regularization algorithm reconstructs the image. Using the penalty function to solve the constraint problem to improve the particle search speed. Linearly decreasing weights as inertial weights for particle swarms optimization to realize the adaptive dynamic adjustment of the inertia weight and improve the flexibility of the algorithm; the chaotic operator is added to the position search process of the particle swarm optimization, when the particle falls into the local optimum, the chaotic variable will fluctuate within a certain range, reducing the missed rate of the optimal solution. The simulation results show that The improved particle swarm algorithm is more accurate and efficient than the traditional LBP algorithm and Tikhonov regularization algorithm.

The paper aims to study the problem of obstacle avoidance in air?ground cooperative tracking control for the unmanned aerial helicopter (UAH),in which a new approach of designing the path obstacle avoidance plan and controller design is proposed.Initially, as for the uncertain linear UAH,by processing and judging two?dimensional environmental information within the warning range for the UAH,an obstacle avoidance strategy is proposed with the help of wall?following algorithm,and the flight angle of obstacle avoidance path and the tracking speed that can make up for bypass distance are calculated.Secondly,the proposed obstacle avoidance method is extended to the three?dimensional case,and the flight angle of the UAH is determined based on the obstacle information in the horizontal and vertical directions,which can reduce the bypass distance caused by the obstacle avoidance link as possible.Thirdly,based on two derived obstacle avoidance algorithms above,the artificial neural network (ANN) is introduced to estimate model uncertainty,and then the tracking control design schemes are established by using feedforward compensation and optimal control technologies.some simulations demonstrate the effectiveness of the proposed obstacle avoidance strategy and control algorithm.

The COVID?19 epidemic is facing the influence of a variety of complex practical factors, which makes the development of the epidemic uncertain. In order to overcome the problem of large error in epidemic forecasting results due to the limitations of many ideal assumptions based on the infectious disease compartment model, a time series forecasting model based on deep learning is adopted to predict the epidemic development, and an informer model based on transformer model is established. Attention mechanism and distillation mechanism are applied to the time series forecasting of epidemic data. The threshold autoregressive (TAR) model and a variety of mainstream recurrent neural time series prediction models are used as comparison models. Through simulation experiments, the current number of remaining infections in the epidemic data of China, America and Britain is predicted in the short term, and RMSE and MAE are used as evaluation indicators, and then the best model is selected for medium ? and long?term prediction. The experimental results show that the indicator value of the informer model is optimal in both RMSE and MAE, further indicating that the prediction accuracy of the informer model is higher than that of other comparative models in China, America and Britain. Finally, the Informer model is used for the development of the epidemic in China，America and Britain medium and long?term prediction.

With the improvement of environmental uncertainty, the demand for supply chain stability of petrochemical enterprises in China is rising day by day. The evaluation of supply chain resilience of petrochemical enterprises has become an important means to judge the risk coping ability of petrochemical enterprises. This paper constructs a supply chain resilience evaluation index system in the petrochemical enterprises. And fuzzy analytic hierarchy process and BP neural network are used to evaluate the toughness strength of petrochemical enterprise supply chain, so as to determine the toughness level of petrochemical enterprise supply chain.It is found that the strength of supply chain toughness of petrochemical enterprises is uneven, and the overall level of supply chain resilience is low. On the basis of the research results, some practical suggestions are put forward for the forging of resilient supply chain in petrochemical enterprises.

In order to solve the problems of short life cycle and low data throughput caused by too fast energy consumption in cluster head node selection of LEACH protocol in wireless sensor networks, a leach optimization algorithm based on cluster head node energy balanced selection is proposed. The algorithm selects ordinary nodes with high residual energy in WSNs as cluster head nodes, and considers the distance between ordinary nodes and cluster head nodes and between cluster head nodes and base station, as well as the residual energy and average energy of all nodes to select communication mode and transmission path. The proposed algorithm is simulated by MATLAB. The simulation results show that in the 100 m×100 m small and monitoring area and 200 m×200 m large and monitoring area, compared with LEACH, DEEC and IMP?LEACH algorithms, the algorithm reduces the energy consumption of nodes in WSNs, prolongs the life cycle and improves the data throughput.

In the field of point cloud processing, deep learning is a mainstream method, but the existing methods do not fully utilize the local structure information of 3D point clouds, and have less local shape perception. We proposes a 3D point cloud processing model based on improved PoinetNet. Network model introduces position adaptive convolution into PointNet. The position?adaptive convolution constructs the kernel function by combining the weight matrices in the weight bank in a dynamic way, in which the coefficients of the weight matrix are adaptively learned from the relative positions of the points through the position?relative coefficient network. The kernel function constructed in this way can better solve the problem of irregularity and disorder of point cloud data. The classification accuracy of the position?adaptive network in the 3D object classification experiment is 3.60% higher than that of PointNet, and the average intersection ratio in the 3D object part segmentation experiment is 2.20% higher than that of PointNet. In the 3D scene semantics In the segmentation experiment, the average intersection and union ratio is improved by 9.14% compared with PointNet.

Aiming at the problems of traditional neural network in mining transformer time series relationship, poor generalization of classification and low accuracy of classification for heterogeneous data, this paper proposes an improved transformer fault diagnosis model based on bidirectional recurrent neural network. The model extracts features through bidirectional recurrent neural network, fuses the features at the front and back time, classifies the feature data using multi?kernel learning support vector machine method, and fuses the features in multi?kernel learning support vector machine, so as to improve the accuracy of feature data classification. The accuracy and validity of the bidirectional recurrent neural network based on multi?kernel learning support vector machine model are tested through numerical simulation analysis of the temporal channel length for sequential network diagnostic performance, the influence of and multi?kernel learning on the generalization ability of support vector machines, and the influence on heterogeneous data processing capabilities. The experimental results show that the diagnosis performance of the bidirectional recurrent neural network based on multi?kernel learning support vector machine is better, and the prediction accuracy of the model is higher than that of several commonly used neural networks.

The fast braking of resistant load and the falling speed control of the potential energy load are of great significance to improve the operation efficiency and the safety of motor drive equipments. In this paper, a method of constant current braking and dynamic adjustment of falling speed of lifiting objects for DC motor is proposed, in which the conventional energy consumption braking resistor is replaced by a power MOSFET and the equivalent resistance between its leakage and source can be adjusted by voltage control. Under the condition of no need to change the hardware, the double closed?loop control technology of current and speed is adopted to realize the requirements of the whole process constant current fast parking brake, the fast dropping of suspended objects and the falling speed continous adjustment control in the motor drive system. Theoretical analysis and experimental results show that the time for the parking brake and the time for the falling speed of the liftting object to reach stability are reduced respectively to 66.7% and 33.3% of the conventional braking method without chaning changing the hardware system, which effectively improves the dynamic working efficiency of the motor drive system and the flexibility of the control system.

Due to the huge amount of the customers'data the rise of various financial products and the short?term impact of the epidemic, banks are facing with increasing pressure resulting in the business volume declined sharply. The traditional classification tree model can not make more accurate prediction of long?term deposits and carry out accurate marketing to customers according to customer information. Therefore, this paper proposes a three?layer neural network model. Through the experiment, the customer data of grape Island banking institutions are predicted, and compared with the prediction results of a traditional decision tree, random forest model, AdaBoost model and XGBoost model. The experiment shows that compared with the other four models, the neural network model has a better effect of prediction, the model evaluation AUC reaches 0.977 7 and the accuracy reaches 99.06%.

In the research on the thermal efficiency of atmospheric tube heating furnaces, it is necessary to conduct effective online measurement of thermal efficiency and select relatively reliable advanced control methods. On the basis of the research on the combustion mechanism of the heating furnace, the online measurement method based on the principle and data processing is used to process the thermal efficiency, and the "dynamic matrix control" is introduced in the process of optimizing the control of the heating furnace efficiency, which is compared with the traditional control method. The introduction of dynamic matrix control makes the system have a better control effect. At the same time, the "particle swarm algorithm" was selected to optimize the parameters of dynamic matrix control. In the optimization process of dynamic matrix parameters, the particle swarm algorithm relatively shortens the optimization time and improves the control quality, so as to achieve a more satisfactory control effect. Finally, compare with internal model control, it shows that dynamic matrix control can achieve relatively better control effect.

Aiming at the problem of insufficient representation of interactive semantic information in the double human interaction behavior recognition method based on graph convolutional neural networks，a new double human interactive spatial?temporal graph convolution network (DHI?STGCN) was proposed for behavior recognition. The network contains spatial sub?network modules and temporal sub?network modules. Based on the 3D skeleton data obtained from the interactive action video, a spatial action graph of double human interactive action was generated for the representation of spatial information. In the graph, the connecting edges between double human were given different weights according to the joint point position information. The connection of context time information was added in the constructed adjacency matrix, and the joint points in the graph were connected with their nodes within a certain time range in time information processing. The generated spatial?temporal graph data was sent to the spatial graph convolution network module, and the temporal graph convolution network module was combined to enhance the continuity of inter frame motion features for modeling in time. The model fully considers the close relationship of double human interaction. The comparative experimental results on NTU?RGB+D dataset show that the algorithm has strong robustness and obtains better interaction recognition effect than the existing models.

The vibration signal during the operation of high voltage circuit breaker can reflect the mechanical state of circuit breaker. Aiming at the shortcomings of feature extraction and fault diagnosis accuracy of shallow vibration signal analysis model, a fault diagnosis method of high voltage circuit breaker based on convolutional neural network optimized by genetic algorithm was proposed. Using the global optimization ability of genetic algorithm, the optimal initial network structure parameters and the number of neurons in the whole connection layer were obtained through the selection, crossover and mutation of genetic algorithm to optimize the convolutional neural network, and the optimized convolutional neural network is applied to the fault diagnosis of high voltage circuit breaker. The results show that the diagnosis performance of the proposed network model is better than that of convolution neural network, dynamic support vector machine and multilayer perceptron.

For a long time, researchers mostly analyze the transmission process of infected nodes in complex networks to get the target of forecasting and arresting the extend of the infectious diseases. In this article, the SEIR propagation dynamics model was extended to the undirected and powerless large small world network, and the weights between nodes were given as infection ability. Two initial node selection methods were selected to carry out multiple simulation experiments. Based on the traditional method of judging the impact of transmission by the number of infected people and infection threshold, the specific values of infection probability, peak value and inflection point time were added to analyze the impact of initial node selection on transmission process more comprehensively. The compared experimental results show that the initial node which the degree is larger and the betweenness is larger, the larger the propagation scale, the faster the propagation speed and the shorter the equilibrium time. This study provides some reference value for guard against and control of the extend of infectious diseases.

Aero?engine usually contains a large number of pipes, the arrangement sequence of these pipes has a certain impact on the overall layout effect of the system. In order to reduce the degree of cross layout of multiple pipes, the evaluation method of pipe disassembly complexity was designed based on product assembly and disassembly, a Discrete Chicken Swarm Optimization (DCSO) algorithm was used to solve the pipe layout sequence planning. First, a calculation method of pipe disassembly complexity was proposed to evaluate the complexity of pipe system layout scheme. Next, the pipe was pre?planned by A* algorithm. Then, an obstacle avoidance algorithm was designed based on engineering rules to adjust the pipe. Finally, taking the pipe length and disassembly complexity as the optimization objectives, the pipe layout sequence was optimized based on DCSO, and the feasibility of the proposed method was verified by a layout example.

Aiming at the low efficiency of Apriori algorithm in scanning database and low dimensional frequent itemset, an efficient implementation method of Apriori algorithm was proposed, which is called EI_Apriori algorithm. This method utilizes the vector?based storage structure and pre?pruning to reduce the number of scanning databases and low?dimensional frequent itemsets and thus improves the efficiency of the Apriori algorithm. According to the actual situation of student achievement analysis, the constraints on the sequence relationship between courses are added in the association rule mining, and the constraints on the score level range are added in the association rules. The adjusted EI_Apriori algorithm was applied in score association analysis. The results show that the EI_Apriori algorithm can accurately find the association rules that meet the real needs, which proves the superiority of EI_Apriori algorithm.

The dry point of gasoline is difficult to be measured in real time. A large number of data samples need to be extracted to test the quality of each section of the oil. In order to solve this problem, predictive control was carried out by establishing a soft sensor model. The least squares support vector machine model is too sensitive to outliers, which is easy to affect the prediction accuracy. By establishing the weighted least squares support vector machine model (WLSSVM), the fitting error is weighted, which weakens the influence of outliers on the model and improves the anti?interference ability of the model. The improved WLSSVM was applied to the prediction of gasoline dry point. The results show that the maximum absolute error of the improved WLSSVM is 11.65% lower than that of the least squares support vector machine model, and its prediction performance and robustness have obvious advantages.

Aiming at the problem of temperature tracking and balance control of each branch pipe of the heating furnace, an improved genetic algorithm was proposed to optimize the multi?deviation control of the temperature tracking and balance of the branch pipe temperature of the heating furnace. The scheme used the temperature deviation of the raw materials of each branch pipe after mixing and the temperature deviation of each branch pipe. By adjusting the feed flow rate and fuel flow rate, it not only ensured that the flow rate of the main pipe is constant during the regulation process, but also realized the dual goal of temperature tracking and balance of each pass. Multi?passes are analyzed as a whole, so the temperature comparison of adjacent branch pipes was avoided repeatedly. At the same time, the improved genetic algorithm was used to optimize the controller parameters of the differences control technique, which overcomes the difficulty of controller parameter tuning. The simulation results show the feasibility and effectiveness of the improved genetic algorithm to optimize the differences control scheme.

For the MIMO nonlinear systems, a multivariable ORVFL neural network adaptive predictive control algorithm based on Improved Sparrow Search Algorithm was proposed in this paper. The algorithm uses the ORVFL network to approximate the nonlinear system model, and applies to the multi?step prediction of the system process. In order to improve the performance of the Sparrow Search Algorithm, the algorithm is used to optimize the system performance index online and solve the optimal control law of each sampling period. The results show that the algorithm has good control performance and good anti?model mismatch ability.

Gaussian mixture model (GMM) is easily affected by noise, and Markov random field (MRF) model can well describe the spatial characteristics. The combination of the two is suitable for image segmentation with noise, but MRF model is prone to over segmentation. To solve this problem, an improved image segmentation algorithm based on adaptive weight coefficient was proposed, which can segment cerebrospinal fluid, gray matter and white matter from magnetic resonance imaging (MRI). Firstly, the K?means algorithm was used to obtain the initial segmentation results, and the Expectation?Maximization (EM) algorithm was used to estimate the parameters of GMM, and then the joint probability energy function of the pixel gray level of the image was obtained. Then, the adaptive weight coefficient was obtained by using the gray value, posterior probability and Euclidean distance of the center pixel and the neighboring pixels of the MRF neighborhood system, and the prior probability energy function was obtained by MRF. Finally, the final image segmentation results were obtained by Bayesian criterion. Experimental results show that the algorithm has strong adaptability, can better overcome the impact of noise on image segmentation. Compared with similar algorithms, the proposed algorithm has higher segmentation accuracy for brain MR images with noise, and obtains better segmentation results.