Intelligent Communication Technologies and Applications

The convergence of Artificial Intelligence (AI) and Blockchain technologies is gradually reshaping the educational landscape, offering significant advancements in teaching methods and learning experiences. This paper examines the theoretical frameworks, modelling algorithms, and empirical data to evaluate the impact of an intelligent education system (IES) on academic performance at SY Secondary School. Before the implementation of AI, students’ mathematics scores were typically distributed around the median in a traditional educational setting. Following the introduction of AI through a smart classroom VR environment, there was a notable improvement in student performance, suggesting that AI’s adaptive learning algorithms, which personalize learning content and pace, can significantly enhance academic outcomes. The observed increase in student attainment after AI intervention highlights the role of AI in optimizing learning, although the decline in some students’ grades underscores the complexity of its impact. Blockchain technology, while not directly influencing academic scores, supports these advancements by ensuring secure, transparent educational records, thereby increasing trust in the educational process and facilitating resource sharing. In conclusion, the integration of AI and blockchain holds great potential to improve student learning outcomes and operational efficiency. However, the variability in achievement growth calls for inclusive and tailored education strategies to maximize the benefits of these technologies for all students.

With the expansion of enterprise scale and business growth, the volume of customer service calls and the number of business ticket order templates have expanded rapidly. The high labor costs brought by the traditional manual order making method have overwhelmed enterprises. In order to improve the efficiency of enterprise customer service order making, this paper proposes a multi-level business ticket generation method. This method achieves efficient identification of unbalanced ticket types by cascading multiple CNN classifiers, thereby completing business ticket order generation. Experiments show that the method proposed in this article is effective.

With the increasing adoption of low-voltage measurement devices in distribution networks to accommodate customers’ diversified electricity consumption characteristics, the surge in low-voltage equipment has rendered traditional topology management methods inefficient in meeting real-time query and analysis demands. To address this issue, this paper proposes a hybrid structure combining graph and relational models to reconstruct conventional power information models. The graph model characterizes the topological relationships among low-voltage devices, while the relational model describes their electrical measurement parameters. By leveraging subgraph aggregation features of device topology, the energized state of switches is treated as connectivity credentials between graph nodes, and their closing duration serves as a coefficient measuring connection tightness. The edge attributes in the graph model store time-series data of the energized states at both ends, enabling online topology reconstruction during backtracking by referencing these sequences. Furthermore, the topology graph is partitioned using minimum spanning tree (MST) and spectral clustering algorithms to decompose large-scale low-voltage device nodes into multiple subgraphs, thereby reducing the time complexity of topology queries. Non-switching devices’ measurement parameters are described via the relational model, where node identifiers from topology traversal results are joined with the relational model to retrieve voltage and other data. The fusion of these models facilitates low-voltage topology backtracking and analytical applications. Compared to standalone relational or graph models, the proposed method demonstrates superior performance and storage efficiency, providing methodological support for complex computations in large-scale low-voltage topology management.

With the increasing operational complexity of power grid business and the rising demand for intelligence and automation, digital transformation has become a crucial direction for power grid development. To enhance power grid management efficiency and reduce operational costs, it is particularly important to conduct research on power grid optimization scheduling using modern digital information technologies. This study proposes a hybrid optimization scheduling method that combines genetic algorithms (GA) and particle swarm optimization (PSO), aiming to minimize power grid operational costs while ensuring system stability. Experimental results demonstrate that the GA-PSO algorithm consistently exhibits lower scheduling costs throughout the optimization process, especially after 1000 iterations, where the scheduling cost drops to 3200, significantly lower than other algorithms. GA-PSO not only optimizes power grid scheduling costs but also performs exceptionally well in constraint satisfaction, with a constraint violation of 50 kW, far below other methods. Overall, the research findings confirm the superiority of the GA-PSO algorithm in power grid scheduling, particularly in complex environments with load fluctuations, energy instability, and grid faults, demonstrating higher adaptability and robustness. This provides strong technical support for the digital transformation of power grid business.

The forecasting of global natural gas trade flows is of great significance to the stability and development of international energy markets. However, the complex trade environment and real-time data requirements pose great challenges for traditional prediction methods. In order to solve these problems, this study proposes a natural gas trade flow prediction framework that combines 5G communication technology and XGBoost model. 5G communication technology realizes real-time collection and efficient transmission of global natural gas trade data by virtue of its low-latency and high-bandwidth characteristics, which provides high-quality data input sources for the model. At the same time, the XGBoost model shows excellent performance in the processing and prediction of multidimensional complex data by virtue of its powerful nonlinear modeling capability and built-in feature selection mechanism. Experimental results show that compared with traditional regression models and random forest models, the framework achieves significant improvements in indicators such as mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2). In addition, the feature importance analysis reveals that natural gas price and export volume are the key factors affecting trade flows. This study not only verifies the feasibility of the combination of 5G communication technology and XGBoost model, but also provides an innovative technical framework and practical guidance for energy trade forecasting, which has important academic value and application potential.

The unified reservation service system for medical institutions based on cloud platform and artificial intelligence (AI) technology is a research direction of applying cloud computing and AI to the medical field. The system aims to provide a platform for centralized management and scheduling of medical resources, so that patients can make medical appointments conveniently. This paper introduces a unified reservation service system for medical institutions based on cloud platform, which combines artificial intelligence technology with medical management in today’s society and provides a two-way choice platform for users and medical institutions. In this paper, the current situation of medical services in China is analyzed firstly, and the relevant information about cloud computing platform is introduced. Combined with the demand of unified appointment service for medical institutions in China, a unified appointment service system for medical institutions based on cloud platform and AI technology is proposed. Aiming at the service system, this paper introduces the improved scheduling algorithm, and finally investigates the system by simulation research method. The results show that the improved scheduling rule can be applied in most environments when the number of patients is low. When the number of patients is relatively high, its performance effect is the best.

With the wide application of unmanned aerial vehicles (UAVs), the reliability of communication links has put forward higher requirements. Traditional radio communication is susceptible to interference in complex electromagnetic environments, which makes it difficult to meet the practical requirements. With high bandwidth and strong anti-interference, wireless optical communication (FSO) has become an important direction for UAV communication. The optoelectronic platform, as the key hardware to achieve high-precision optical pointing and stabilisation of the communication link, is crucial to the system performance. However, the traditional optoelectronic platform has problems such as large volume, high weight, complex structure and optical axis calibration error, which is difficult to meet the UAV’s demand for lightweight and high precision. For this reason, this paper proposes a lightweight two-axis stable optoelectronic platform design scheme, systematically introduces the function and composition of the optoelectronic platform for unmanned aerial vehicles (UAVs), elaborates on the mechanical structure, interface and optical path design, and demonstrates its high-precision pointing and stable control capability based on the analysis of the platform’s stability accuracy, which provides the theoretical basis and technical reference for the engineering design and application of UAVs’ optoelectronic platform.

With the expansion of the scale and complexity of railway communication systems, relay protection devices are prone to malfunction and missed faults in high-interference and multi-state overlapping environments. This study proposes a deep diagnosis method based on a multi-scale attention network, combined with convolution extraction, frequency domain enhancement and label correction mechanism, to improve the ability to identify key abnormal features in time series signals. Comparative experiments show that MSANet has an accuracy of 94.48%, an F1 score of 93.22% and a misdiagnosis rate of 3.02%, which is better than CNN-GRU, VAE-CNN and other methods. The anti-interference performance is stable, and the false alarm recovery time is shortened to 2.3 s. It still maintains an accuracy level of 88.6% under strong noise disturbance. This method has outstanding diagnostic adaptability and engineering deployment potential in complex signal environments.

With the development of information technology, traditional network transmission and equipment fault monitoring suffer from high false alarm and missed alarm rates. In complex network environments, it is difficult to identify faults timely and effectively. To address this issue, this study proposes a fault monitoring system that combines information visualization and human-computer interaction technologies through the integration of compressed sensing (CS) and the sliding window (SW) method. Experimental results demonstrate that the system based on the CS + SW method achieves a fault image information conversion accuracy of 85.29%, significantly higher than the 66.37% of traditional methods and 78.22% of the Support Vector Machine (SVM) method. The misjudgment rate of CS + SW is only 9.07%, much lower than other comparative algorithms, indicating its high efficiency and stability in practical applications. The visual communication effect of CS + SW is significantly enhanced, improving from 45.3% to 91.28%. The CS + SW method not only improves diagnostic accuracy but also enhances the clarity and accuracy of image communication. In summary, CS + SW can effectively improve the accuracy and real-time performance of network transmission fault monitoring in complex and highly dynamic network environments, exhibiting high practical value. This research provides new ideas and methods for future fault diagnosis systems and demonstrates good potential in information visualization and human-computer interaction technologies for fault monitoring.

The fifth generation monitoring system is the main part of the technical prevention and control in the assets management of our army's aerial ammunition equipment, however, there are still deficiencies in the two links of control and Prevention in advance, at the same time, the security problems such as the weak protection of the depot and the outer airspace of the camp, the low level of intelligence of the ground monitoring, the barbaric operation of the personnel can not be effectively prevented, etc. Aiming at the security gap of the artificial intelligence integrated security management system which can not be effectively protected in low altitude and ultrna-low altitude, the paper adds intelligent robots and UAV to improve the ability of assistant blind compensation and in-case processing, the application of artificial intelligence analysis technology to improve the utilization ratio of the information collected by the front-end equipment, and the combination of safety management and daily management to improve the intelligence level of the safety management of aerial ammunition, to promote the development of the safety management of aerial ammunition from traditional management to intelligent management.

With the rapid development of society today, people’s demands for living environments are continuously rising. Considering users’ needs for indoor environments and safety concerns, we have developed an indoor environment monitoring system based on STM32. This system uses temperature and humidity sensors, smoke sensors, light sensors, air quality sensors, and human body infrared sensors to monitor various indoor environmental parameters. The data is displayed in real-time on a mobile APP. Additionally, by using the WIFI module ESP8266-01S, the monitoring results are uploaded to a cloud platform, allowing users to remotely view relevant parameters through the mobile APP. The system sets safety thresholds for the smoke sensor and air quality sensor; if these thresholds are exceeded, the system will trigger a light warning. If the light level drops below the threshold or if the human body infrared sensor detects a person, the system will also trigger a light warning. This system aims to overcome the limitations of on-site monitoring in terms of time and space, optimize living environments, enhance the quality of living environments, reduce power consumption, and increase user safety.

This study aims to explore the application and effect of affective computing technology in consumer experience evaluation and satisfaction prediction. Through in-depth analysis of consumer reviews on online platforms, this study uses natural language processing technology to extract emotional features and builds an affective computing model to evaluate consumers’ emotional tendencies. The study discovered a strong positive correlation between emotional propensity scores and consumer satisfaction. A satisfaction prediction model using these emotional characteristics showed good predictive performance. Despite the positive results of the study, several limitations were identified, including the adaptability of the model and the challenges of processing complex text expressions. Future research will explore ways to improve affective computing models, introduce more dimensions of data, and develop cross-cultural and cross-language models, aiming to improve the accuracy and generalization capabilities of the models and provide enterprises with deeper consumer insights and strategies to optimize customer experience.

With the wide application of cloud computing and 5G technology, modern call platforms have evolved from traditional telephone switching systems to intelligent service platforms based on IP networks. However, the open network architecture also makes these platforms become the main target of network attacks, especially Distributed Denial-of-service (DDoS) attacks presenting new features such as diversified attack methods, continuously expanding scale, and more precise targets. In this paper, an intelligent dynamic defense system based on a generative adversarial network (GAN) is proposed to train a detection model with strong generalization ability by simulating diverse attack scenarios. The system adopts a closed-loop architecture of “detection-defense-optimization”, which contains three core modules: Conditional GAN (CGAN) -based attack traffic generation module, improved MobileNetV3 edge detection module, and dynamic resource elastic scheduling module. Experimental results show that the system maintains 98.5% detection accuracy in normal business traffic scenarios, and the false alarm rate is controlled within 0.5%. Compared with the traditional threshold detection system, the method in this paper improves the detection rate of new attacks by about 20%, and at the same time, the response delay of the system is controlled within 10ms, which fully meets the real-time requirements of the call platform. This study provides a new solution for the security protection of call platform, and shows excellent performance in three dimensions: attack detection, resource scheduling and log analysis.

Traditional warehousing cannot meet the growing logistics demand and scale, and digital twin technology has become a key enabling technology for digital warehousing, which is of great significance to improve efficiency, reduce costs and ensure safety. This paper aims to study the intelligent storage workstation system for digital twin technology. Firstly, discuss the application architecture of digital twin technology, use Process Simulate (PS) software for ontology modeling, kinematic analysis and simulation prediction of intelligent storage workstation, and use TIA Portal V16 to realize PLC (Programmable Logic Controller) programming control. The twin model is constructed by OPC UA (OLE for Process Control Unified Architecture) to communicate with the actual work station, then the design of intelligent storage workstation is completed. This design provides a new solution for the intelligence, automation and high efficiency of logistics storage equipment.

To improve the effect of intelligent terminal route planning, this paper proposes a route planning model combined with intelligent visual recognition algorithm. It is necessary to model the inspection environment route. In this paper, the matrix method is used to construct the map of inspection route for route planning, and the superiority of the improved algorithm in obstacle environment is verified by experiments. Through the simulation results, it can be seen that this algorithm is superior to other algorithms in both experimental environments, whether it is the worst solution, the optimal solution or the average solution. The experimental results show that the improved method has obvious advantages and can play a greater role in the future intelligent terminal work.

This paper investigates and designs a 5 GHz wireless router antenna based on phased array technology. With the advancement of wireless communication technology, the 5 GHz band has become essential for high-speed data transmission and low-latency communication. However, signals in this frequency band are susceptible to attenuation and obstruction by walls, limiting communication quality and coverage. Phased array technology optimizes signal transmission by dynamically adjusting the beam direction, overcoming the limitations of traditional antenna designs. This paper first introduces the principles and advantages of phased array technology, then details the design process of the 5 GHz wireless router antenna, including determining the fundamental parameters of the array antenna, testing and adjusting microstrip antenna elements, and conducting array generation and simulation analysis. The results show that the designed phased array antenna performs well in terms of bandwidth characteristics, sidelobe levels, and directivity, meeting the performance requirements of modern wireless routers. Finally, this paper summarizes the advantages and disadvantages of phased array antennas and discusses future improvements, including enhancing element performance, improving beamforming techniques, and reducing system cost and power consumption.

In view of the problem of safe driving caused by visual blind areas, installing the vehicle’s panoramic camera system has become a good solution, but the image stitching technology has some complexity, and the quality of the image stitching plays a key role in improving the safety of assisted driving. After analyzing and studying the common image quality evaluation indicators and image stitching quality damage factors, this paper proposes the evaluation indicators and calculation methods for the image stitching quality of the vehicle’s panoramic camera, covering the aspects of illumination uniformity, clarity, symmetry, stitching gap, splicing loss, splicing dislocation and splicing ghost, which provides a strong support for the standardization of the panoramic camera test.

With the increasing number of aerospace product models and the growing demand for personalized customization, the small-batch production mode has become increasingly common in the aerospace manufacturing industry. The low level of process maturity severely restricts both the efficiency of product development and the stability of quality. Therefore, this study aims to establish a scientific and systematic process maturity evaluation system in the context of small-batch aerospace manufacturing. This system can provide decision-making support for improving process capabilities and optimizing management in aerospace enterprises. This study used the Analytic Hierarchy Process (abbreviated as AHP) to an evaluation index system for the aviation manufacturing processes maturity, including target layer, First-level indicators, and Second-level indicators. The system includes four primary dimensions: process equipment, process review, production site, and project management, which are further refined into 26 s-level indicators. Expert scoring and pairwise comparison matrices are used to calculate the relative weights. This article evaluates and analyzes the process data of a certain aerospace product through practical application cases. The results show that “process review” is the most critical factor affecting process maturity (weight: 0.4674), with “process finalization” (weight: 0.1272) and “process verification” (weight: 0.1181) playing central roles. “Process equipment” ranks second (weight: 0.2983), with particular focus needed on improving “tooling design” and “equipment support.” The main innovation of this study lies in the use of AHP method to process maturity evaluation system for the small-scale aerospace production, and to prove the weight of the maturity evaluation system through specific practical operation methods.

The development of the industrial robot industry in the Pearl River Delta (PRD) region in the era of digital intelligence is a key driver of socioeconomic growth in this area and an important foundation for the development of advanced manufacturing. By developing industrial robots for various specialized environments, it can effectively enhance production efficiency and accuracy in specific industrial sectors and reduce the safety risks for personnel working in special scenarios. Therefore, by analyzing the current status of the industrial robot industry in the PRD region during the era of digital intelligence, we can understand the industry characteristics and grasp the specific implementation details directly related to industrial development, such as policies and enterprises, thereby summarizing the economic development of the industrial robot industry in the PRD. Furthermore, by designing a technology roadmap for the industrial robot industry in the PRD region, we can effectively promote the comprehensive development of this industry and provide guidance and reference for the innovation and construction of the industrial robot industry in the PRD region in the era of digital intelligence.

With the continuous improvement of agricultural automation, addressing the issue of target detection and classification for diversified agricultural products, this study proposes an automated sorting and classification system that combines a Genetic Algorithm (GA) and Random Forest (RF). The GA is utilized to optimize feature selection for agricultural products, while the RF is employed for classification, ultimately achieving precise and efficient sorting operations. The research results demonstrate that the GA-RF algorithm exhibits excellent performance in target detection, with an accuracy of 91%, a recall rate of 90%, and an F1-score of 90%. Compared to traditional algorithms, the GA-RF algorithm significantly reduces sorting times in various agricultural product sorting tasks, achieving times of 14.1 s, 12.8 s, 13.4 s, etc., with misclassification rates of 3.4%, 3.1%, and 3.5%, respectively. Furthermore, the system receives high evaluations across multidimensional ratings from different practitioners, particularly in terms of ease of operation, accuracy, and stability, gaining recognition from most operators and technicians. The GA-RF algorithm not only improves the accuracy of agricultural product sorting and classification but also effectively enhances sorting efficiency, making it particularly suitable for agricultural image sorting tasks under complex backgrounds.

Numerical simulation, as a research method based on mathematical models and computer technology, using numerical calculation methods for simulation analysis, is commonly used in physics, engineering, and natural phenomena. Its key point is the application of discrete mathematical equations to predict system behavior, optimize design, or reveal the inherent laws of complex phenomena. In the construction of weak stratum highway tunnels, the control of surrounding rock deformation is the core challenge to ensure engineering safety and efficiency. Its intelligent control technology integrates numerical simulation, real-time monitoring, artificial intelligence algorithms, and dynamic adjustment strategies to form a “prediction warning intervention” closed-loop system, effectively responding to risks such as large deformation and collapse of weak surrounding rock. Therefore, this article starts from a theoretical perspective and deeply explores the deformation of surrounding rock and the initial support intrusion control technology for weak stratum highway tunnels, providing certain theoretical guidance and technical references for weak stratum highway tunnel engineering.

The application of multi-robot collaborative localization and tracking in complex environments is becoming increasingly widespread. To enhance the localization accuracy and real-time performance of multi-robot systems in dynamic environments, this study proposes an Extended Kalman Filter-Evidential Particle Filter (EKF-EPF) algorithm based on Ultra-wideband (UWB) technology. This algorithm addresses nonlinear issues inherent in traditional localization methods and improves the system's accuracy and stability. Experimental results indicate that the EKF-EPF algorithm achieves a Root Mean Square Error (RMSE) of 0.60 m in localization accuracy, significantly lower than those of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). In terms of real-time performance, the EKF-EPF algorithm has a computational time of 14.2 s, markedly reduced compared to the Particle Filter (PF) algorithm, and a latency of 95 ms, demonstrating superior real-time responsiveness. Regarding system stability, the EKF-EPF algorithm attains a stability score of 9.5, significantly higher than other algorithms. The EKF-EPF algorithm exhibits outstanding accuracy and real-time performance in multi-robot collaborative localization and tracking tasks, making it suitable for dynamically changing complex environments. It effectively enhances localization accuracy while reducing computational complexity and system latency.

The safety of fresh agricultural products is related to public health and social stability, so it is very important to build an efficient safety supervision mechanism and traceability system. Due to the lack of transparency of information, easy tampering of data, broken traceability chain and other issues, the traditional supervision model has been unable to meet the needs of the current complex market environment, and the blockchain technology provides an innovative solution for the safety supervision of fresh agricultural products with its unique advantages of decentralization, non tampering and traceability. Based on the above conditions, this paper deeply studies and designs a set of comprehensive safety supervision mechanism and traceability system of fresh agricultural products based on blockchain Technology. The data link enables transparent sharing and accurate tracing of information throughout the process, Through system analysis and verification, the design scheme can effectively improve the safety monitoring level of fresh agricultural products, enhance consumer trust and provide strong technical support for the quality and safety of fresh agricultural products. At the same time, the system has important practical significance and theoretical value for promoting the healthy and sustainable development of fresh agricultural products industry.

In view of the fact that the quality of post-grouting of pile ends in sand layers in Yangon is affected by complex geological conditions (high permeability and heterogeneity) and the low efficiency, high cost and difficulty in dynamic evaluation of detection methods such as artificial coring and static load tests, this study proposes an intelligent detection and evaluation system for the quality of post-grouting of pile ends based on multi-source data fusion and deep learning. First, this paper introduces the grouting methods and grouting methods in sand layer areas. Secondly, a three-dimensional dynamic diffusion model of sand layer grouting is constructed to quantify the coupling relationship between grouting pressure-diffusion radius-soil strength. Then, a lightweight convolutional neural network (CNN) and long short-term memory network (LSTM) fusion algorithm is designed to realize intelligent classification and abnormal location of grouting uniformity and density. Finally, a quantitative evaluation report is generated based on the data collected by the multi-sensor collaborative acquisition module to the visualization evaluation platform. The experimental results show that the accuracy of the system in grouting quality detection is 92%, which is significantly higher than the traditional static load test (85%) and manual coring (80%). In terms of detection efficiency, the intelligent detection system only takes about 1 h to complete, while the traditional methods take about 3 days and 5 days, respectively. In addition, the intelligent detection system has significant advantages in cost control and has a low overall cost. The experiment verifies the effectiveness and superiority of the system in complex geological environments, and provides strong technical support for the dynamic evaluation of the quality of post-grouting at the pile end.

With the rapid development of the Internet, the amount of data generated by users is exploding. Accurately mining user interests from massive data and realizing personalized recommendation has become an important research topic in the field of information services. Personalized recommendation systems can enhance user experience and bring significant economic benefits to businesses. The aim of this study is to construct a personalized Artificial Intelligence (abbreviated to AI) recommendation system based on big data science, which integrates multi-source heterogeneous data to achieve deep analysis of user behavior and intelligent recommendation, which can improve the accuracy and real-time performance of recommendations. This article studies the use of MapReduce distributed computing framework for preprocessing massive data, combined with collaborative filtering, content recommendation, and real-time recommendation algorithms to design and implement an integrated recommendation platform. By introducing user interest models and dynamic update mechanisms, the system enhances its responsiveness to changes in user behavior. Through extensive experimental verification, real-time recommendation algorithms are superior to traditional recommendation algorithms in key indicators such as accuracy, recall, and coverage. The experimental results show that real-time recommendation algorithms can improve the platform’s commercial conversion rate. The innovation of this study lies in proposing a personalized recommendation architecture that combines big data distributed processing with multi algorithm fusion, effectively improving the scalability and real-time response capability of the system.

The research on computational intelligence-based smart city architectural planning and landscape design highlights a significant advancement in urban development strategies. By deploying sophisticated algorithms like Improved Ant Colony Optimization (IACO), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and traditional Ant Colony Optimization (ACO), the study delves into optimizing urban layouts and enhancing the aesthetic and functional aspects of city landscapes. Empirical evaluations of these algorithms demonstrated that IACO, in particular, exhibits superior performance in handling complex, large-scale urban challenges, evident from its high convergence rates and overall efficiency. A key aspect of this research was the integration of resident preferences and behaviors into the urban planning process, ensuring that the design of urban spaces aligns with the needs and desires of city dwellers. Despite the success in various metrics such as safety, convenience, and functionality, the study also identified areas for improvement, particularly in increasing participation and user engagement. This research paves the way for future urban planning endeavors, emphasizing the need to further refine these algorithms to create more inclusive, adaptable, and responsive urban environments, thereby fostering the development of sustainable and intelligent urban landscapes.

With the widespread application of machine learning techniques in various fields, this research explores the application of Support Vector Machine (SVM) in teacher performance evaluation. Teacher performance evaluation is an essential aspect of educational management, and traditional evaluation methods often rely on student feedback and subjective judgments from administrators, potentially lacking objectivity and consistency. To overcome these limitations, this study utilizes the SVM algorithm, a powerful supervised learning model, to analyze and predict teacher performance. We first collected data, including factors such as teaching quality, student feedback, peer evaluations, and other relevant metrics. Then, we used this data to train SVM models to identify key factors influencing teacher performance and make effective predictions. Experimental results demonstrate that SVM exhibits higher accuracy and reliability in teacher performance evaluation compared to traditional methods. This research not only demonstrates the potential application of SVM in the field of education but also provides a new and more scientific approach to teacher performance evaluation.

Based on a distributed microservices architecture, this paper develops an intelligent recommendation system for fashion retail. In order to achieve millisecond recommendation response, this system combines deep learning models with a real-time computing framework. The recommendation accuracy and user shopping conversion rate are significantly improved by optimising feature engineering and constructing a product knowledge graph. Multi-tier fault-tolerance mechanisms help maintain stable performance and high availability as well. For the fashion retail industry, this provides a reliable technical solution for personalised recommendations. In practice, the system is an effective improvement to the user’s shopping experience and the platform’s operating efficiency.

In the process of human-computer emotional interaction, the accuracy of emotion recognition and real-time feedback directly affect the quality of interaction. The emotion recognition and intelligent feedback system designed based on multimodal deep learning integrates speech, expression and text features, and adopts the improved Transformer architecture to realise emotion state recognition, achieving an accuracy rate of 92.8%. Combined with the reinforcement learning algorithm to construct an intelligent feedback mechanism, experiments show that the system can effectively improve the user's emotion improvement rate, with good practicality and promotion value.

In order to explore the application value of virtual reality technology in urban micro-renewal, the immersive experience design of pocket parks is taken as an example to study the impact of multimodal interactive environment on spatial perception and public participation. An immersive design system based on high-precision 3D modeling, physical engine simulation and biological data feedback is constructed to optimize the landscape layout, walking flow and environmental parameters, and to enhance the accuracy and scientificity of spatial experience. We analyze the role of multimodal data fusion in user behavior perception, scene adaptability assessment and decision optimization. The results show that virtual reality technology can effectively improve the accuracy of spatial cognition, the fluency of interactive experience and scenario understanding, and shorten the design iteration cycle. The scenario optimization mechanism based on physiological signal feedback enhances the applicability of the scenario, and provides a quantifiable assessment system and technical path for the renewal of small-scale spaces in high-density urban environments.

Aiming at the problem of collaborative optimization of code security and development efficiency in new complex scheduling systems, a code detection automation security protection system based on compilation is proposed. By integrating Zentao project management platform, GitLab platform and Jenkins automation tool, a full-process static scanning tool chain is constructed. This method realizes three-stage collaboration in the development of intelligent scheduling systems: iterative development management of complex systems, access control during code merging, and in-depth secondary scanning embedded in the Continuous Integration and Continuous Delivery/Deployment (CI/CD) process. Applications show that this solution provides an extensible engineering practice for the next-generation scheduling systems with high security requirements, achieving joint optimization of security and development efficiency.

With the rapid development of artificial intelligence (AI) technology, its application in urban park landscape design has gradually attracted attention. This paper systematically discusses the application of AI in the whole life cycle of urban park landscape design, including demand analysis and data-driven decision-making in the early stage of design, intelligent generation and optimization in the middle stage of design and intelligent operation and maintenance and ecological monitoring in the late stage of design. Through case study, the application path of AI technology in a smart park is analyzed, and the advantages of AI in improving the scientific design, optimizing resource allocation and enhancing ecological benefits are demonstrated. At the same time, in view of the technical, social, ecological and economic challenges, the corresponding countermeasures are put forward, which provide theoretical support and practical guidance for the deep integration of AI and landscape design and promote the improvement of the quality of urban public space.

In BIM (Building Information Modeling) application system, the real information of building components is described by a series of digital object attributes and relationships. These object attributes can be independent of each other or interrelated according to some customized rules, so they are parameterized. This scheme integrates the information structure, excavates the data value, has the advantages of strong standardization and high compatibility, and brings efficient and stable energy-saving benefits for building operation and maintenance. The application architecture and functional modules of lightweight BIM architectural design system based on building big data are designed. The overall architecture is divided into data acquisition layer, data processing layer, data analysis layer and application layer. The hierarchical architecture is used to separate the storage and utilization of model information, the information is integrated and managed by database to realize the sharing of model information, and the graphic platform is used to display the data graphically. Experiments show that the algorithm proposed in this paper has a good lightweight visual optimization effect for large-scale BIM 3D scenes. The reliability and practicability of the system are verified.

In the field of education, the application of Big Data (BD) has become more and more extensive. These data can not only help educators better understand students’ learning characteristics and problems, but also provide important support for personalized teaching and precise management. The purpose of this paper is to study a 3D reconstruction algorithm of environmental design image based on Convolutional Neural Network (CNN), and compare its performance with that of Support Vector Machine (SVM) in modeling accuracy and user experience. By constructing a multi-level CNN structure and automatically learning and extracting features from the original image, this algorithm can effectively suppress background information and enhance feature information related to environmental design. The experimental results show that the modeling accuracy of this algorithm is improved by 24.82%, which shows that this algorithm can better restore the feature information of the environment image and suppress the background information. Moreover, the instructional platform constructed by the algorithm in this paper has a high score among learners, which shows that the instructional platform has high practical value and popularization value, and can provide strong support for education and training in the field of environmental design.

Live teaching can provide students with a rich display of learning resources and the pleasure of learning that they can easily feel in the online learning environment of offline traditional classrooms, thereby enhancing their motivation to learn. Therefore, perceived pleasure has a positive impact on college students’ willingness to use live teaching. This paper uses structural equation modeling to quantitatively describe the causal relationship between variables and the impact of variables on live teaching usage behavior. Taking the use of smart education platform as an example, its purpose is to combine the technology acceptance model and the unified model of technology acceptance and use, and use structural equation modeling to better understand and predict the impact of desire factors on the use behavior of higher vocational college students in the system. Moreover, students generally recognize the online teaching method and hope to combine online teaching with offline teaching.

This paper proposes a recommendation model based on deep reinforcement learning, aiming to realize the dynamic adjustment of cultural communication content recommendation system. By constructing a recommendation framework based on DQN and combining user behavior and cultural content characteristics, the system can adapt to changes in user interests in real time and optimize the recommendation effect and cultural communication efficiency. The experimental results show that the model performs well in precision, recall, cultural communication influence score and F1 score. The precision rate is 0.87, the cultural communication influence score is as high as 0.92, and the recommendation accuracy rate after dynamic adjustment is increased from 0.62 to 0.91, which is significantly better than random recommendation and popularity-based benchmark models. The study verifies the applicability of deep reinforcement learning in the field of cultural communication and provides new ideas for the development of intelligent recommendation systems.

The purpose of this study is to build a case-based reasoning (CBR) theoretical Intelligent Question Answering System Based on large language model. By integrating artificial intelligence and natural language processing technology, the effectiveness and advantages of the system in solving complex problems and enhancing user interaction experience are explored. The study mainly uses large language model to build data sets for model training and carefully designs the system structure to support efficient question answering processing and case-based reasoning mechanism. After receiving user questions, the system accurately extracts relevant cases from the database with the help of semantic matching and case retrieval technology, and generates answers through intelligent reasoning, so as to provide users with accurate and insightful analysis and suggestions. At the same time, the impact of different language models on the system performance is further analyzed, and targeted optimization strategies are proposed to continuously improve the reasoning ability of the system for various complex problems, provide a new perspective and practical support for the development of intelligent question answering system, and lay a solid foundation for its wide application in the fields of education, consulting and technical support in the future.