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.

The ATMADDPG ( Attention Mechanism based Multi-Agent Deep Deterministic Policy Gradient) algorithm is proposed to improve the navigation ability of a multi-unmanned ship formation system. In the training phase, the algorithm trains the best strategy through a large number of experiments, and directly uses the trained best strategy to obtain the best formation path in the experimental phase. The simulation experiment uses four ' Baichuan' unmanned ships as experimental objects. The experimental results show that the formation maintenance strategy based on the ATMADDPG algorithm can achieve stable navigation of multiple unmanned ship formations and meet the requirements of formation maintenance to some extent. Compared to the MADDPG (Multi-Agent Depth Deterministic Policy Gradient ) algorithm, the developed ATMADDPG algorithm shows superior performance in terms of convergence speed, formation maintenance ability, and adaptability to environmental changes. The comprehensive navigation efficiency can be improved by about 80% , which has great application potential.

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.

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.

A control method based on the combination of the CSA ( Cuckoo Search Algorithm) and the conductivity incremental method INC( Incremental Conductivity method) is proposed to improve the speed and accuracy of the maximum power point tracking as well as to reduce the loss and harmonic content of the output power of the PV power generation system when the PV array is locally shaded. To prevent the algorithm from settling on the local optimal solution in the early stages, the cuckoo algorithm is used for global search. Later, a thorough search within a limited range is carried out using the incremental conductivity method in order to lock the maximum power point. And to see if it satisfies the criteria for grid connected harmonic content, this algorithm applied to grid connected control. Then a different strategy suggested. The results of a simulation model created in Matlab / Simulink demonstrated that the composite algorithm based on CSA paired with the conductivity increment approach has a faster tracking speed, less error, and satisfies the grid connected harmonic content requirements.

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.

To extend the lifetime of oil and gas IoT( Internet of Things), a dual cluster head based oil and gas IoT routing algorithm is proposed. The algorithm fully considers the current residual energy of sensor nodes, historical average energy, distance between nodes and base stations, density of neighboring nodes and distance between nodes and energy harvesting sources in the cluster head election process, and elects dual cluster heads in the same cluster, while proposes a novel routing method to balance the energy consumption of cluster heads in the data transmission phase. A novel node working mode switching strategy is adopted along with the introduction of energy harvesting techniques. Simulation experiments show that the algorithm can balance the network energy consumption more effectively and extend the network lifetime compared with the traditional algorithm.

With the gradual development of oil and gas exploration and development into deep and complex formations, the risks and rewards faced in the drilling process are increasing. The market increasingly needs software that can integrate monitoring and control to ensure safe and efficient drilling. Using managed pressure drilling technology can effectively control the pressure in the gas production process and make it more convenient to deal with equipment failure. It can realize real-time monitoring of fluid pressure and density changes in the production process, effectively reducing potential safety hazards, preventing the occurrence of downhole accidents, and providing a guarantee for the safe and stable production of oil and gas wells. The Managed Pressure Drilling Simulation and Control Software aims to obtain drilling-related information from logging, PWD ( Pressure While Drilling ), MWD ( Measure While Drilling ), pressure control, and other equipment. It establishes hydraulic models to calculate wellbore pressure, flow, and other parameters. This software adopts client / server architecture, which allows multiple clients to connect to a server simultaneously and synchronize data. The client data synchronization effect has been verified on-site, meeting the needs of single machine use and facilitating network connection. The results indicate that this software can accurately simulate and calculate various drilling parameters, ensuring safe and efficient drilling. The centralized analysis, processing, and remote control have created a good foundation for pressure control drilling, transitioning from on-site engineer processing mode to a data platform-based approach. This lays the foundation for interconnectivity between multiple on-site pressure control drilling equipment on one platform, effectively promoting the development of intelligent pressure control drilling.

A modified receiving coil derived from the BP( Bipolar Pad) coil structure is proposed to address the problem of mutual inductance dips and increased mutual inductance fluctuations in the process of wireless charging of dynamic electric vehicles using magnetically coupled resonant radio energy transmission technology during straight-line driving and turning. The modified coil solves the problem of reduced effective area of positive pair coupling caused by decoupling of conventional BP coils by means of double coils fitted to the inner and outer diameter of the track respectively, and Ansys / Maxwell software is used to carry out simulation to find out the reasonable design position and relative size of the compensation coil. The experimental data shows that the new receiver coil can suppress the mutual inductance fluctuation and enhance the mutual inductance value to a certain extent, in the process of turning and driving in a straight line. The peak mutual inductance fluctuation rate is 5. 1% , and the maximum mutual inductance reception is 28. 7% higher than that of the traditional BP coil.

To improve the efficiency of path planning and avoid ant colony algorithm outputting non optimal paths, a multi-agent path planning model is proposed. The grid method is used to establish the environment awareness model of agents, improving the local and global pheromone update rules in the ant colony algorithm, and constraining the ants to travel by adjusting the number of turns and pheromone concentration. The algorithm can intelligently enlarge or reduce the pheromone concentration in the path. When the number of iterations reaches the set maximum, the output value is the optimal path planning result. Experimental results have shown that the improved algorithm achieves shorter planning paths and faster iterative convergence speed.

The obstacle avoidance control problem of UAVs( Unmanned Aerial Vehicles) and UGVs ( Unmanned Ground Vehicles) named heterogeneous unmanned swarm system is studied using a game-theoretic approach combined with the artificial potential field method. Unlike most multi-agent formation control schemes that only consider the group formation objective, we allow each agent to have individual objectives, such as individual tracking and obstacle avoidance. Specifically, when the heterogeneous unmanned swarm system completes the cooperative formation, each agent needs to track the target point and avoid obstacles in real-time according to its own interests. The heterogeneous unmanned swarm system formation problem is transformed into a non- cooperative game problem between agents because there may be conflicts between the individual and group objectives of the agents. Real-time obstacle avoidance is realized by adding an obstacle avoidance term based on the artificial potential field function into the cost function. And the controller is designed based on the Nash equilibrium seeking strategy to achieve a balanced formation mode of individual and group objectives. Finally, the correctness of the theoretical results is verified through simulation experiments. The proposed method can enable heterogeneous unmanned swarm system to achieve formation motion and real-time obstacle avoidance.

In order to reduce the access risk of medical information shared by multiple end users, the compulsory access control system for medical information sharing is optimized and designed from two aspects of database and software functions. The database tables of medical shared information and users are built, connecting the database tables according to the logical relationship, and completing the design of the system database. The process of medical information sharing is simulated and the sensitivity level of medical information sharing is determined. Multi-terminal user roles and permissions are allocated, abnormal access behaviors of access users are detected in real time, and authorization and behavior detection results are combined to realize the mandatory access control function of medical information sharing of the system. The test results show that the access control error rate of the design system is reduced by about 24. 4% , and the access risk of medical shared information is significantly reduced under the control of the design system.

To improve the recognition accuracy of occluded faces, an intelligent recognition method for occluded faces based on the improved Gabor algorithm is proposed. Firstly, the dynamic range of facial images is compressed and the anti sharpening mask filtering algorithm is selected for image enhancement processing. Secondly, Gabor filters are used to extract features from half faces with relatively complete information preservation and high brightness. Finally, the extracted Gabor features are input into an extreme learning machine to achieve intelligent recognition of occluded faces. The experimental results show that the proposed method has good processing performance for occluded facial images, and the processed facial image recognition has high accuracy and short recognition time.

Fundus images often contain a large number of small blood vessels with significant noise interference and blurred boundaries, making segmentation challenging. To address these characteristics, a fundus image segmentation method called MDF _Net&CD ( Multi-Directional Features neural Network and Connectivity Detection) is proposed, based on multidirectional features and connectivity detection. A deep neural network model, MDF_Net( Multi-Directional Features neural Network), is designed to take different directional feature vectors of pixels as input. MDF_Net is used for the initial segmentation of the fundus images. A connectivity detection algorithm is proposed to revise the preliminary segmentation results of MDF _ Net, according to the geometric characteristics of blood vessels. In the public fundus image dataset, MDF_Net&CD is compared with recent representative segmentation methods. The experimental results show that MDF_Net&CD can effectively capture the detailed characteristics of pixels, and has a good segmentation effect on irregular, severely noisy, and blurred boundaries of small blood vessels. The evaluation indices are balanced, and the sensitivity, F1 score, and accuracy are better than other methods participating in the comparison.

In order to improve the quality and accuracy of residual oil edge detection, a method of residual oil edge detection with improved multi-direction Sobel operator is proposed. The improved bilateral filtering method is used to remove the image noise of the microscopic residual oil distribution, so as to achieve the purpose of edge preserving and denoising. Combined with Otsu algorithm, the optimal threshold of the residual oil image can be obtained adaptively. The amplitude and direction of the gradient of the remaining oil image are calculated by using the Sobel operator with the amplitude of 3×3 in the four improved directions. Non-maximum suppression algorithm is used to filter out the pseudo-edge pixels to obtain the final remaining oil edge detection image. The experimental results show that the proposed method can accurately detect the information of residual oil edge in the microscopic residual oil distribution image while removing the noise of the image.

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.

In order to clearly understand the working principle and mechanism of events, to analyze the role, response principle, and response process of events in real-time operating systems, based on the KL36 microcontroller, a PC-like printf output method is adopted to analyze the event response mechanism of mbedOS from the aspects of scheduling process timing, response time performance, etc. The experimental results show that the printf function can intuitively output information such as thread address, queue address, queue content, thread in and out queue status, and event bits during the event response process. This provides convenience for readers to understand the event response principle and process of mbedOS from the bottom layer, and also provides a method reference for analyzing the context structure of other synchronization and communication methods of mbedOS.

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.

Due to the large scale and variety of information data in the process of internet information security warning, the warning accuracy is low and the time is long. To improve the efficiency of early warning, a security warning for unknown access sources in mobile network privacy information databases is proposed. Principal component analysis method is used to reduce the dimensionality of information base data to reduce the difficulty of detection. The IMAP( Iterative Multivariate AutoRegressive Modelling and Prediction) algorithm is used to carry out data clustering processing, to extract discrete isolated data points, and complete the screening of unknown access source data in the information base. Unknown access source data is inputted into a support vector machine, a time window is used to transform the construction problem of the information base security warning model into a convex optimization problem of support vector machine learning. Security warning results are outputted, and globally optimize the construction parameters of the warning model are optimized to improve the warning output ability of the security warning model. The experimental results show that the proposed method has high security detection efficiency for information databases, and can achieve stable and accurate warning output in the face of multiple types of information database intrusion attacks.

To ensure the security of cross domain data flow in the Internet of Things, a security detection algorithm for cross domain data flow sharing the same frequency in the Internet of Things is proposed. This method calculates the information entropy of the data set based on the data outlier characteristics, takes the data points with larger information entropy calculation results as the cluster center, and analyzes the data distribution characteristics through the calculation of the cluster center distance. The data distribution features are inputted into the BP( Back Propagation) neural network and combined with genetic learning algorithms to achieve deep mining of shared cross domain data on the same frequency. Wavelet analysis is used to segment effective signals and noise signals in the same frequency shared data, and introduce the wrcoef function achieving the reconstruction output of noise free signals. Based on the Markov chain state transition probability matrix, a detection model of Markov cross domain data flow security is established. By calculating the relative entropy difference value between the test sample and the standard sample, the security detection of cross domain data flow for the same frequency sharing of the Internet of Things is completed. The simulation results show that this method can effectively improve the efficiency of data flow security detection and achieve accurate perception of data flow trends across domains.

To address the issue of source code plagiarism detection and the limitations of existing methods that require a large amount of training data and are restricted to specific languages, we propose a source code plagiarism detection method based on pre-trained Transformer language models, in combination with word embedding, similarity and classification models. The proposed method supports multiple programming languages and does not require any training samples labeled as plagiarism to achieve good detection performance. Experimental results show that the proposed method achieves state-of-the-art detection performance on multiple public datasets. In addition, for scenarios where only a few labeled plagiarism training samples can be obtained, this paper also proposes a method that combines supervised learning classification models to further improve detection performance. The method can be widely used in source code plagiarism detection scenarios where training data is scarce, computational resources are limited, and the programming languages are diverse.

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.

Traditional manual repair of fishing nets is inefficient and costly. To solve the problem of traditional manual repair of fishing nets, a robotic arm that can automatically sew fishing nets is designed. The robotic arm is mainly controlled by STM32F103C8T6, and its functions are achieved through infrared sensor module, motor actuator module, and communication module. The infrared sensor module is responsible for detecting the position of damaged fishing nets. The motor actuator module drives the motors in six axis to flexibly send the sewing device to the preset node, and the communication module is responsible for transmitting the detected information of damaged fishing nets to the controller for processing. After practical testing, the designed robotic arm can achieve high-precision, fast, and effective automatic sewing of fishing nets. Compared with traditional manual repair methods, this automatic repair technology improves the efficiency and quality of fishing net repair, reduces labor costs, and has obvious advantages.

In order to solve the problem of droplet transmission risk of breath alcohol detector, a portable and pollution-free blood alcohol concentration detection scheme based on near-infrared spectroscopy is proposed. we successively completed the signal acquisition, amplification, filtering and re-amplification with hardware, and obtained the standard pulse wave signal with software algorithms such as Kalman filtering and three-sample interpolation method to remove the base. Later, the mathematical model is established in the upper computer by the measurement results of the expiratory type and the amplitude of the pulse wave signal, and the mathematical model is written into the microcontroller to realize the offline measurement. The accuracy of the mathematical model is tested through validation experiments. The results show that it meets the accuracy requirement, the relative error is less than 10% and meets the practical requirements.