In order to improve the recognition performance and accuracy of traditional object detection and to accelerate the computation speed, a CNN( Convolutional Neural Network) model with more powerful feature learning and representation capabilities and with related deep learning training algorithms is adopted and applied to large-scale recognition tasks in the field of computer vision. The characteristics of traditional object detection algorithms, such as the V-J(Viola-Jones) detector, HOG(Histogram of Oriented Gradients) features combined with SVM( Support Vector Machine) classifier, and DPM ( Deformable Parts Model) detector are analyzed. Subsequently, the deep learning algorithms that emerged after 2013, such as the RCNN ( Region-based Convolutional Neural Networks) algorithm and YOLO(You Only Look Once) algorithm are introduced, and their application status in object detection tasks is analyzed. To detect fallen individuals, the YOLOv5(You Only Look Once version 5) model is used to train the behavior of individuals with different heights and body types. By using evaluation metrics such as IoU(Intersection over Union), Precision, Recall, and PR curves, the YOLOv5 model is analyzed and evaluated for its performance in detecting both standing and fallen activities. In addition, by pre- training and data augmentation, the number of training samples is increased, and the recognition accuracy of the network is improved. The experimental results show that the recognition rate of fallen individuals reaches 86% . The achievements of this study will be applied to the design of disaster detection and rescue robots, assisting in the identification and classification of injured individuals who have fallen, and improving the efficiency of disaster area rescue.

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.

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.

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.

To address the efficiency and quality issues in current molecular diagnosis talent cultivation, the application status and future development trends of AI(Artificial Intelligence) technology in molecular diagnosis talent cultivation is explored. The research content covers the current application of AI technology in molecular diagnostics, its advantages and challenges, and focuses on analyzing how AI can enhance the efficiency and quality of talent cultivation through automated experimental processes, precise data analysis, and
interdisciplinary knowledge integration. The study summarizes practical experiences from domestic and international universities in integrating AI with molecular diagnostic talent cultivation and outlines future development trends, including the integration of VR ( Virtual Reality ) and AR ( Augmented Reality )technologies, the precision of intelligent diagnostic systems, and the intelligence of personalized learning platforms. The conclusion of the study indicates that AI technology holds great potential in the cultivation of
molecular diagnostic talents, significantly enhancing their comprehensive competitiveness and promoting the further development of molecular diagnostic technologies to provide robust talent support for precision medicine. However, the application of AI technology still faces multiple challenges, including the integration of interdisciplinary knowledge, data quality, and ethical and privacy issues, which need to be addressed through the joint efforts of educational institutions, industries, and governments.

 In response to the chattering phenomenon that traditional SMO(Sliding Mode Observer) faces during switching functions, novel sliding mode observer using saturation function instead of switching function is proposed to weaken chattering. In the process of extracting position information, a phase-locked loop is selected to replace the traditional arctangent method, there by improving the observation accuracy of PMSM(Permanent Magnet Synchronous Motor) rotor position. In the Matlab environment, by comparing traditional SMO and new SMO, it can be observed that the speed error of the rotor has increased by about 14 r/ min, and the position error of the rotor has increased by about 0. 03 rad.

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.

Computation offloading strategy in mobile edge computing can help users decide how to execute tasks, which is related to user experience, and has become a research hotspot in mobile edge computing. At present, most computation offloading strategies are carried out under the condition of overall task offloading, and only consider a single indicator of delay or energy consumption, and do not combine the two for optimization. To solve this problem, this paper takes the weighted sum of task processing delay and energy consumption as the optimization goal, and proposes a partial offloading algorithm based on reinforcement learning. We divide the processing of a single task into local computing and partial offloading computing, and introduce a variable to determine the offloading weight in partial offloading. Finally, we use reinforcement learning Q-learning to complete the computation offloading and resource allocation of all tasks. Experimental results show that the proposed algorithm can effectively reduce the delay and energy consumption of task processing.

 The current learner model lacks exploration of this dimension of cognitive load, which, as a load generated by the cognitive system during the learning process, has a significant impact on the learning state of learners. Based on the CELTS-11(China E-Learning Technology Standardization-11) proposed by the China E-Learning Technology Standardization Committee, cognitive load is integrated into the learner model as a dimension, and an LMICL ( Learner Model Incorporating Cognitive Load) combining static and dynamic information is constructed. Afterwards, relying on an adaptive learning system, the data of the unmixed cognitive load learner model and the LMICL data were used as the basis for recommending learning resources, resulting in two different learning resource recommendation results. Two classes of learners were randomly selected to learn system, and then their academic performance. The results of cognitive load and satisfaction were used to validate the effectiveness of LMICL , and it was found that the recommendation learning effect based on LMICL was better than that of the learner model without integrating cognitive load.

To solve the problem that Q function overestimation may cause SAC ( Soft Actor Critic) algorithm trapped in local optimal solution, SCSAC ( Stable Constrained Soft Actor Critic) algorithm is proposed for perfectly resolving the above weakness hidden in maximum entropy objective function improving the stability of Stable Constrained Soft Actor Critic algorithm in trailing process. The result of evaluating Stable Constrained Soft Actor Critic algorithm on the suite of OpenAI Gym Mujoco environments shows less Q value overestimation appearance and more stable results in trailing process comparing with SAC algorithm.

 Due to the dense traffic flow and stochastic uncertainty of vehicle behaviors, the scenario of unsignalized intersection poses significant challenges for autonomous driving. An innovative approach for autonomous driving decision-making at unsignalized intersections is proposed based on the MAPPO(Multi-Agent Proximal Policy Optimization) algorithm. Applying the MetaDrive simulation platform to construct a multi-agent simulation environment, we design a reward function that comprehensively considers traffic regulations, safety including arriving safely and occurring collisions, and traffic efficiency considering the maximum and minimum speeds of vehicles at intersections, aiming to achieve safe and efficient autonomous driving decisions. Simulation experiments demonstrate that the proposed decision-making approach exhibits superior stability and convergence during training compared to other algorithms, showcasing higher success rates and safety levels across varying traffic densities. These findings underscore the significant potential of the autonomous driving decision-making solution for addressing challenges in unsignalized intersection environments, thereby advancing research in autonomous driving decision-making under complex road conditions.自动驾驶;智能决策;无信号灯交叉口;MAPPO算法 

Aiming at the problem of information loss, such as details of remote sensing images reconstructed by a super-resolution algorithm, in order to ensure that remote sensor reconstruction images contain more texture and high-frequency information, a remote-sensitive image super resolution network is proposed based on a pyramid- based attention mechanism and the generation of confrontational networks. Firstly, a new pyramidal dual attention module is designed, including channel attention network and spatial attention network. Pyramid pooling is used instead of average pooling and maximum pooling in the channel attention network structure to enhance the feature representation capability from the perspective of global and local information. The spatial attention network structure adopts large scale convolution to expand the integration capability of local information, which can effectively extract texture, high frequency and other information. Secondly, the dense multi-scale feature module is designed to extract feature information at different scales using asymmetric convolution, and the extraction accuracy of texture, high frequency and other information is enhanced by fusing multi-level scale features through dense connection. Experimental validation is performed on the publicly available NWPU- RESISC45 dataset, and the experimental analysis shows that the algorithm outperforms the comparison methods in both subjective visual effect and objective evaluation metrics, and the reconstruction performance is relatively good. 

AI(Artificial Intelligence) plays an important role in medical imaging education, driving innovation in teaching methods and medical education. With the continuous development of AI technology, especially breakthroughs in deep learning, image recognition, and natural language processing, AI is gradually demonstrating its unique advantages in the field of medical imaging education. AI helps students and healthcare professionals quickly identify disease features, and provides automated image analysis results, allowing learners to intuitively understand the imaging manifestations of different diseases. It enhances the interactivity and practicality of learning. AI can offer personalized learning paths recommending relevant educational content or exercises based on the student’s progress and understanding, ensuring that learners receive tailored educational services. The efficiency and accuracy of AI assist students in better comprehending complex medical imaging content improving learning outcomes. However, AI in medical imaging education also faces certain challenges.As technology continues to advance, AI will play a more significant role in medical imaging education. Future educational systems are likely to become more intelligent, integrating technologies such as virtual reality and augmented reality to provide students with a more immersive learning experience.

To provide more efficient solutions for handling high-dimensional matrices and applying SVD(Singular Value Decomposition) in the context of big data, with the aim of accelerating data analysis and processing, how to quickly calculate the eigenvalues and eigenvectors ( singular value singular vectors) of high-dimensional matrices is studied. By studying random projection and Krylov subspace projection theory, six efficient calculation methods are summarized, making comparative analysis and related application research. Then, the six algorithms are applied, and the algorithms in related fields are improved. In the application of spectral clustering, the algorithm reduces the complexity of the core step SVD( Singular Value Decomposition), so that the optimized algorithm has similar accuracy to the original spectral clustering algorithm, but significantly shortens the running time. The calculation speed is more than 10 times faster than the original algorithm. When this work is applied in the field of image compression, it effectively improves the operation efficiency of the original algorithm. Under the condition of constant accuracy, the operation efficiency is improved by 1 ~ 5 times.

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.

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 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.

In the field of indoor applications of UWB(Ultra Wide Band) positioning technology, it is important to establish an efficient and accurate 3D coordinate positioning system to overcome signal interference. Machine learning methods are used to investigate the problem of accurate positioning of indoor UWB signals under interference. Firstly, various statistical analysis models are used to clean up invalid or error measurements, then the a priori knowledge of TOF ( Time Of Flight) algorithm is combined with neural network and XGBoost algorithm to build a neural XGB(Exterme Gradient Boosting) 3D oriented system. The system can accurately predict the coordinate value of the target point by “ normal data冶 and “ abnormal data冶 ( disturbed), the coordinates of four anchor points, and the final error is as low as 5. 08 cm in two鄄dimensional plane and 8. 03 cm in three鄄dimensional space. A neural network classification system is established to determine whether the data is disturbed or not, with an accuracy of 0. 88. Finally, by combining the above systems, continuous and regular motion trajectories are obtained, which proves the effectiveness and robustness of the systems.

In order to have a clear understanding of the exclusive access principle and mechanism of mutex on shared resources, based on the brief analysis of the meaning, application occasions, scheduling mechanism and key elements of mutex in real-time operating systems, the mbedOS mutex scheduling mechanism are theoretically analyzed. Takes the KL36 chip as an example the mbedOS mutex is realized and the scheduling process information of thread response mutex is output spontaneously based on the sequence diagram and printf method. And the real-time performance of mutex scheduling mechanism is analyzed. The analysis of mutex scheduling mechanism is helpful to further analyze other synchronization and communication methods of mbedOS, and can also provide reference for in-depth understanding of other real-time operating system synchronization and communication methods. 

 The design of the loop filter in noise-shaping SAR ADC(Successive Approximation Register Analogto Digital Converter) is the key to the effect of noise shaping and is also an important module to achieve high accuracy performance. Compared with the active lossless integral loop filter, the traditional passive lossy integral loop filter has the characteristics of low power consumption and simple circuit design, but its NTF(Noise Transfer Function) is smooth and the noise shaping effect is weak. To solve this problem, a passive lossless second-order integral loop filter is proposed, which retains the advantages of the passive lossy integral loop filter and has a good noise shaping effect. A hybrid architecture noise-shaping SAR ADC with a resolution of 16 bits and a sampling rate of 2 Ms/ s is also designed. The simulation results show that high SNDR( Signal to Noise and Distortion Ratio) (91. 1 dB), high accuracy ( 14. 84 bits), and low power consumption ( 285 uW) are achieved when the bandwidth is 125 kHz and the oversampling ratio is 8.

Incomplete big data, due to its irregular structure, has a large amount of computation and low interpolation accuracy when interpolation misses values. Therefore, a missing value interpolation algorithm for incomplete big data based on spatial autoregressive model is proposed. Using a migration learning algorithm to filter out redundant data from the original data under dynamic weights, to distinguish abnormal data from normal data, and to extract incomplete data. Using least square regression to repair the incomplete data. The missing value interpolation is divided into three types, namely, first order spatial autoregressive model interpolation, spatial autoregressive model interpolation, and multiple interpolation. The repaired data is interpolated to the appropriate location according to the actual situation, implementing incomplete big data missing value interpolation. Experimental results show that the proposed method has good interpolation ability for missing values. 

The dung beetle optimization algorithm suffers from the problems of easily falling into local optimum, imbalance between global exploration and local exploitation ability. In order to improve the searching ability of the dung beetle optimization algorithm, a mixed-strategy dung beetle optimization algorithm is proposed. The Sobol sequence is used to initialize the population in order to make the dung beetle population better traverse the whole solution space. The golden sine algorithm is added to the ball-rolling dung beetle position updating stage to improve the convergence speed and searching accuracy. And the hybrid variation operator is introduced for perturbation to improve the algorithm’s ability to jump out of the local optimum. The improved algorithms are tested on eight benchmark functions and compared with the gray wolf optimization algorithm, the whale optimization algorithm and the dung beetle optimization algorithm to verify the effectiveness of the three improved strategies. The results show that the dung beetle optimization algorithm with mixed strategies has significant enhancement in convergence speed, robustness and optimization search accuracy.

To effectively reuse URLLC(Ultra-Reliable Low-Delay Communications) and eMBB(enhanced Mobile Broad Band) on the same carrier band and improve the performance of the hybrid traffic system, a dynamic bandwidth allocation strategy based on scalable transmission time interval is proposed. The system bandwidth is dynamically divided according to the traffic type. URLLC scheduling priority is promoted in time domain. And in the frequency domain, different lengths of TTI ( Transmission Time Interval) are adopted to carry out user- centered wireless resource allocation. The dynamic system-level simulation shows that, compared to the traditional wireless resource allocation algorithm, the proposed scheme can effectively meet the delay requirements of URLLC users and optimize the throughput consumption of eMBB users under different load levels. The maximum delay gain of URLLC users is 83. 8% . The quality of service for different types of traffic in the 5G hybrid traffic system is satisfied.

Microseismic monitoring technology has been widely used in unconventional oil and gas development. The microseismic signal has weak energy and strong noise, which makes the follow-up work difficult and requires high-precision and accurate data. To solve the problem of extracting weak microseismic signals, an EM-KF (Expectation Maximization Kalman Filter)-based method is proposed for denoising microseismic signals. By establishing a state space model that conforms to the laws of microseismic signals and using the EM(Expectation Maximization) algorithm to obtain the optimal solution of the parameters for the Kalman filter, the signal-to-noise ratio of microseismic signals can be effectively improved while retaining the effective signals. The experimental results of synthetic data and real data show that this method has higher efficiency and better accuracy than traditional wavelet filtering and Kalman filtering.

In the context of rapid advancements in information technology, the application of digital technology in higher education has become a crucial means of driving educational reform. Addressing issues such as unequal resource distribution and low teaching efficiency in traditional teaching models, the paper proposes the “6i” digital education application and support service system, encompassing teaching (iLearn), learning (iSocial), evaluation (iSense), management (iEdu), services (iHelp), and environment (iMeta). Through specific measures such as the construction of three types of classrooms, the development of digital teaching resources, teaching model reform, digital teaching quality management, and the exploration of new digital technology applications in teaching, Jilin University has achieved a digital transformation of teaching resources and innovation in teaching models. The results indicate that the application of digital technology significantly enhances teaching quality and learning outcomes, providing students with more opportunities for autonomous and personalized learning. The findings of this study offer valuable insights for other universities in promoting digital transformation in education, helping to build a more open, efficient, and intelligent educational system, and advancing high-quality educational development.

This study aims to improve the intelligence and accuracy of fault diagnosis in oilfield wastewater systems. A composite neural network is constructed using convolutional neural networks and long short-term memory networks, and the structure is optimized using Adam and random gradient descent method to improve the convergence speed and fault diagnosis accuracy of the model. The study is validated through relevant experiments, and the experimental results show that the optimization algorithm used in the study improves the accuracy of the model to around 0. 87 and reduces the diagnostic loss rate of the model to around 0. 032. The average detection accuracy of the composite neural network structure reaches 0. 888, with an accuracy value of 0. 883 and a recall rate of 0. 789. The composite neural networks is applied to fault diagnosis of oilfield wastewater systems, can achieve intelligent fault detection, reduce economic costs, and build smart oilfield.

With the development of Internet technology, the application of AI(Artificial Intelligence) technology in education has become a key trend of educational innovation. The research focuses on the role of AI in key areas such as classroom teaching optimization, student evaluation, exam assessment, and teacher training. By analyzing existing AI education products, summarizing the current application status of AI in the field of education, and looking forward to future development trends. The results indicate that AI technology can significantly improve teaching efficiency, achieve intelligent analysis of students’ learning situations, and provide personalized teaching support. In addition, AI has shown great potential in the allocation of educational resources and the analysis of learner characteristics, which can help achieve personalized and precise education. Although there are challenges in terms of application scope, research and development efforts, and application modes, the in-depth application of AI technology is expected to promote the development and progress of the education industry.

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.

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

The core of the cadre information management system lies in enhancing the leadership level of the Party, strengthening the governance capacity of the Party, and reinforcing the Party’s self-construction, thereby promoting the sustained and healthy development of the cause of socialism with Chinese characteristics. By introducing information technology, the system improves the scientificity and standardization of cadre selection and appointment through modeling, visualization, and intelligence. It comprehensively supervises the daily ideology, work, and conduct of cadres, achieving comprehensive assessment and standardized management. The enhanced systemimproves the accuracy and efficiency of cadre selection and appointment, it enhances daily cadre management and supervision,it achieves the scientific and standardized performance assessment of cadres. In conclusion, through the cadre information management system, the Party’s level of development will be further elevated, and organizational work will progress towards scientification, standardization, and refinement, thereby achieving a new leap forward in the construction of Party member and cadre teams. 

The optical crossbar chip is the core device used to realize optical routing in the field of optical communication. A control and driver system is designed based on a multi-channel DAC ( Digital to Analog Converter) array to achieve optical routing through the optical crossbar chip. The system consists of a control system module, a multi-channel drive circuit module, and a host computer control module. This system has several advantages, including simple adjustment, bipolar output, more output channels, and higher power accuracy. It solves the problems of the current driving circuit, such as complex operation, single power polarity, fewer output channels, and poor accuracy. The host computer control module can control the driving circuit to apply the control voltage and receive the optical power signal collected from the data acquisition device as the feedback signal of the control driving system. By analyzing the relationship between the control voltage and the received optical power, the best control driving voltage of the optical crossbar chip can be obtained. The system test results show that the system can provide high-precision bipolar driving voltage to effectively drive the optical crossbar chip and can calibrate the control voltage of the optical switch in a short time, fully meeting the requirements of the driving voltage in the active optical crossbar chip control. We believe that this system could be useful for optical crossbar chip control.

To ensure timely distribution of emergency power supplies, enable them to quickly restore power supply, and reduce economic losses, a planning method for the distribution path of emergency power supplies is proposed based on geographic information systems. Firstly, based on the Map X component in the GIS (Geographic Information System) geographic information system, a preprocessing model for geospatial data is constructed. Then, based on the processed data, a mathematical model and constraint conditions for distribution path planning are established. Finally, genetic algorithm, mountain climbing algorithm, and ant colony algorithm are integrated, and the mathematical model is iteratively operated to obtain the optimal distribution path. The experiment is based on a power equipment emergency. When the material demand is met, the delivery time of the planned path under normal and abnormal road conditions is reduced by 14 minutes and 30 minutes respectively, and the cost is reduced by 10. 9 yuan and 5. 09 yuan respectively. This proves that the designed planning method has significant superiority. 

A fusion model based on material balance equation and Informer is proposed to solve the prediction problem of oilfield development indicators. Firstly, the mechanism model before and after the decline of oil field development production is established through the knowledge of the material balance equation field. Secondly, the established mechanism model is fused with the loss function of the Informer model as a constraint to establish an indicator prediction model that conforms to the physical laws of oil field development. Finally, the actual production data of the oil field is used for experimental analysis. The results indicate that compared to several purely data -driven cyclic structure prediction models, this fusion model has better prediction performance under the same data conditions. The mechanism constraints of this model can guide the training process of the model, so that its rate of convergence is faster, and the prediction at the peak and trough is more accurate. This fusion model has better predictive and generalization abilities, and has a certain degree of physical interpretability. 

The training actions of skiing athletes can directly reflect their level, but traditional methods for identifying and evaluating actions have shortcomings such as subjectivity and low accuracy. To achieve accurate analysis of skiing posture, a motion analysis algorithm based on improved OpenPose and YOLOv5(You Only Look Once version 5) is proposed to analyze athletes爷 movements. There are two main improvements. First, CSP-Darknet53(Cross Stage Paritial-Network 53) is used as the external network for OpenPose to reduce the dimension of the input image and extract the feature map. Then, the YOLOv5 algorithm is fused to optimize it. The key points of the human skeleton are extracted to form the human skeleton and compared with the standard action. According to the angle information, the loss function is added to the model to quantify the error between the actual detected action and the standard action. This model achieves accurate and real-time monitoring of athlete action evaluation in training scenarios and can complete preliminary action evaluation. The experimental results show that the detection and recognition accuracy reaches 95%, which can meet the needs of daily skiing training. 

In order to solve the problem of heterogeneous data from multiple sources encountered by electric power construction companies in the process of transitioning to whole-process consulting business, as well as the challenge of design managers needing to work together remotely due to unforeseen circumstances, it is proposed to construct a knowledge graph by using the enterprise’s private cloud as the basic environment and combining the technology of fusion of heterogeneous data from multiple sources. The system optimises the data management process and ensures data consistency and availability by integrating diverse data sources from various provinces and cities across the country. It ultimately realises distributed collaborative management of the whole process of consulting business and significantly improves the core competitiveness of the enterprise. At the same time, it effectively solves the problems of data variety, wide range of sources and diverse and inconsistent protocols, improves data quality and accuracy, and unifies the storage architecture to enhance the overall data management efficiency and decision-making support capabilities.

Although traditional recommendation systems use graph structure information, most of them only consider the basic attributes of users and items, ignoring the important factor of contextual interaction information between users and items. Even if contextual interaction information is taken into account, there is a lack of attention in the layer combination stage. force mechanism to assign weight. To solve this problem, a CIAGCN (Context Information Attention Graph Convolutional Networks) recommendation model that integrates contextual interactive information and attention mechanism is proposed. This model utilizes the contextual interaction

information of users and items while applying the high-order connectivity theory of graphs to obtain deeper collaborative signals. An attention mechanism is introduced in the layer combination stage to improve the interpretability of this stage. The model was experimentally compared on the Yelp-OH, Yelp-NC and Amazon- Book data sets. The results showed that the model had a certain effect compared with other algorithms, indicating that the recommendation effect was better than some traditional recommendation models.

To address the issue of frequent faults in direct current electric vehicle charging piles and the difficulty of precise diagnosis, a fault diagnosis method based on an improved BP(Back Propagation) neural network is proposed. Firstly, the operation data set of the charging pile is preprocessed, such as normalization and filling in missing values, and the processed data set is input into the BP model for training. Secondly, an optimization method based on the BOA-SSA ( Butterfly Optimization Algorithm improved Sparrow Search Algorithm) is introduced to optimize the weights and thresholds of the BP model to obtain the optimal model. Finally, the fault status of the charging pile is diagnosed based on the optimized BP model. The simulation results show that the proposed BP method has good computational advantages in terms of MAE(Mean Absolute Error), MAPE(Mean Absolute Percentage Error), and RMSE(Root Mean Square Error). Compared to the traditional BP algorithm, the diagnostic accuracy of the improved BP method has increased by 14. 85% , which can diagnose the state of the charging pile accurately, providing a strong guarantee for the fault diagnosis of electric vehicles.

With advancements in modern science and technology across structural design, materials, and sensor manufacturing processes, pipeline leak detection sensors are becoming increasingly miniaturized and intelligent, and technologies for measuring physical changes caused by pipeline leaks continue to mature. For the convenience of technology selection and optimzation in pipeline leak detection, a systematic review of widely used piezoelectric, optical fiber, and laser sensors in pipeline leak detection is provided, with a focus on analyzing their characteristics and differences in materials, structures, and working principles. It further explores their performance in practical applications and the current state of research both domestically and internationally, offering theoretical support and technical references for the selection, optimization, and future development of pipeline leak detection technologies.

Due to the complexity of digital information, massive and multi-angle unstructured big data, and external interference, data structure damage and other factors cause its information loss, a missing value interpolation algorithm for unstructured big data based on transfer learning is proposed. Through the migration learning algorithm, the missing parts of unstructured big data are predicted, and the naive Bayesian algorithm is used to classify data features, to measure the weight value between attributes, to clarify the feature difference vector of data categories, and to identify the degree of feature difference. The kernel regression model is used to implement nonlinear mapping for the missing part of the data, and the polynomial change coding is used to describe the cross-space complementary condition of the data, completing the interpolation of the missing value of unstructured big data. The experimental results show that the proposed algorithm can effectively complete the interpolation of missing values of unstructured large data, has good interpolation effect and can improve the interpolation accuracy.

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.