Volume 46, Issue 3

The study explores the innovation of economic transaction model in the framework of blockchain technology and optimizes the key algorithms in the transaction. The study integrates the advantages of traditional sharing contracts and smart contracts to construct a blockchain model for sharing economic transaction innovation mode. Based on the feature selection method of chi-square test and feature correlation, the gated recurrent unit (GRU) is combined with the support vector machine (SVM) algorithm to form a detection model for abnormal blockchain transactions. For the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism in the economic model, the study proposes a node pre-prepared layered consensus protocol to optimize it, and introduces a reputation model to rank nodes in terms of their reputation value, enhance the node’s ability to defend itself against Sybil’s witch attack, and improve node’s motivation. The research results show that the anomaly detection model based on feature selection can effectively realize the accurate detection of transaction anomalies, and the detection performance of the GRU-SVM model improves the F1 score by 1.11% in the feature subset than in the full feature dataset. The improved PBFT consensus algorithm has significant improvement over the original PBFT algorithm in terms of algorithmic complexity, communication complexity, and throughput, which effectively guarantees the security of sharing economy transactions.

With the continuous development and popularization of information technology, the field of product packaging design is inseparable from the application of computer graphics design. In order to realize the visualization of complex forms of packaging design, this paper proposes a packaging design image processing algorithm based on the visual characteristics of the human eye to enhance the packaging design pattern. Combining the Transformer and Generative Adversarial Network (GAN) algorithms, the DPformer-GAN model is constructed to realize the layout innovation of the product packaging, and the loss function is designed. The packaging creative design method based on DPformer-GAN model proposed in this paper is applied to design the packaging of clothing products. The average value of the designed clothing product packaging is higher than 4 in each dimension of the evaluation of the effect of color elements, and in the evaluation of the effect of the logo elements of the packaging, the prominence of the packaging logo is the most prominent, and the average value of the psychological evaluation of the purchase intention and the degree of brand impression reaches a high rating of 4.18 and 4.09. The overall visual layout of the packaging elements is also more effective, which strengthens the unique characteristics and visual recognition of the packaging design. The overall visual layout of the package is also more effective, strengthening the unique characteristics and visual recognition of the package design.

With the rapid development of emerging technologies, legal text datatization provides the possibility of intelligent legal judgment assisted decision making. In this paper, a multi-task judgment prediction model based on data mining algorithm and Lawformer is designed to introduce Lawformer pre-training model, incorporate legal theories, and utilize HAN encoder to encode the case text at the word level and sentence level in order to better capture the semantic information. Then the support vector machine algorithm is integrated into the model to explore the crime composition, and a multi-task learning framework is constructed for the intelligent legal judgment assisted decision-making model. The experimental results show that the model in this paper performs well on the CAIL2018-Small public dataset, which significantly improves the model’s legal judgment prediction accuracy and interpretability, with micro-averaged F1 values of 87.3 and 85.4 on the two tasks of crime prediction and law recommendation, and the overall legal case prediction correctness is as high as 99.48%. The research in this paper provides a new working path for the field of intelligent justice, which contributes to the scientific and intelligent development of legal judgment assisted decision-making.

In order to protect and inherit the excellent traditional culture and enhance the economic value of cultural industry, the article proposes a method of digital extraction and modern design application of traditional patterns based on digital means to adapt to the development of today’s society and the transformation of consumer demand. The article first combines the traditional pattern pattern with the characteristics of texture directionality, and proposes an adaptive weighted feature fusion traditional pattern feature extraction algorithm that introduces wavelet energy analysis. Subsequently, a traditional pattern style classification algorithm based on capsule network is proposed to improve the traditional pattern classification accuracy. Then a traditional pattern style migration method based on VGG19 network is designed, which is applied to modern design, and the performance test and subjective and objective evaluation of this paper’s method are conducted. The experimental results show that the method of this paper can realize the modern design of traditional patterns with good effect, and compared with the classical slow style migration and fast style migration methods, it has better image conversion quality and higher conversion efficiency, which can provide a certain design and development of reference value for modern pattern design.

With the in-depth development of education informatization, the optimal construction of co-curricular heterogeneous course resources and the recommendation of personalized learning paths have become an important research field in the education sector. In this paper, we propose an algorithm for the optimal construction of heterogeneous course resources and learning path recommendation based on subject knowledge mapping. Firstly, based on the linear chain conditional random field model and the relational multi-classification model, the recognition of named entities and the extraction of entity relations are accomplished respectively. Then the relationships between the obtained subject knowledge points are inputted into Neo4j graphical database to complete the visualization of subject knowledge graph. The smart learning E-GPPE-C model and the constructed subject knowledge graph are combined to construct the learner portrait and complete the design of the learning path recommendation algorithm from four cognitive levels. The entity recognition and relationship extraction models in this paper both obtain more excellent experimental results, and the learning path conforms to the law of gradual progression in education, obtaining much higher than the comparative algorithm P@20 value, which reaches 75.45. The method in this paper can effectively explore more scientific learning paths, provide a method for the optimization of the resources of co-curricular heterogeneous courses, and provide support for personalized education.

Tactical communication datalink, as an important part of networked national defense informatization construction, is more difficult to quantitatively assess its effectiveness and transmission load balancing. In this paper, the natural connectivity of tactical communication datalink network is introduced, and an interference effectiveness assessment method based on weighted natural connectivity is proposed to realize the time sequence analysis of tactical communication datalink network. The AGCH algorithm is used to group and cluster the nodes of the tactical communication datalink network, and the adaptive genetic algorithm is used to solve the established model to improve the load balancing effect of network transmission. Relying on the tactical communication datalink network simulation system architecture, the transmission load balancing test of tactical communication datalink network is carried out. The transmission load balancing algorithm proposed in this paper has a convergence speed of 340s, and when the number of tasks increases from 50 to 300, its corresponding task completion time grows from 68s to 275s, and the load balancing differential value decreases from 0.4 to 0.34, which is always the lowest when comparing with other algorithms, such as ACO, ICA, PSO, PSO-ACO, and so on.

As a large agricultural country in China, the development of rural industry is a key part of realizing rural revitalization. The article takes 31 provinces (autonomous regions and municipalities directly under the central government) in China as the object of empirical research, and constructs the evaluation system of the level of development of digital countryside industry from four dimensions: digital countryside capital investment, information base, service base, and industrial development. Then, the model of this paper is used to empirically analyze the influencing factors of digital village industry development. Then, the kernel density estimation and other methods are used to assess the level of industrial development of digital villages, and the spatial and temporal characteristics of digital village development are analyzed in depth from the whole, the region, and the dimensions. Finally, based on the research conclusions and the actual situation, the enhancement path of rural industrial revitalization is proposed. From 2012 to 2024, the comprehensive level of China’s rural industrial development as a whole shows the evolutionary characteristics of rising, then declining, then fluctuating level. The development level of rural industrial revitalization has three major characteristics: significant regional differences, uneven development between coastal and inland, and positive correlation with the level of economic development.

Highway and bridge pavement engineering materials are facing great aging, depletion and disease pressure under the cross-impact of traffic load, climate and geological environment, etc. Therefore, real-time monitoring of pavement health is particularly important. In this paper, based on the fuzzy mathematical algorithm and multi-factor analysis, the road and bridge health monitoring data assessment method is explored. First, the hierarchical structure of road and bridge health comprehensive monitoring indexes was constructed by combing the literature and utilizing the hierarchical analysis method. Then, the indicators were weighted by combining the G1 method and DEMATEL method. Finally, the cloud model was used to conduct a fuzzy comprehensive evaluation of road and bridge health. The results of the example application show that the method of combining the G1 method and DEMATEL method for indicator assignment in this paper takes into account the causal relationship between each indicator, and the results of the comprehensive weighting are more scientific and effective, which is of practical significance for assessing the health status of road and bridge. In addition, the fuzzy comprehensive evaluation model based on the cloud model in this paper can make up for the shortcomings of the traditional evaluation of the road and bridge health status reflecting insufficiently, and the mean value of the fuzzy evaluation agreement ratio of each data set is greater than 0.87, and the fuzzy evaluation agreement ratio increases year by year with the passage of time. In addition, this paper explores the shortcomings of the methods used and provides directions for further improvement of the model.

In this paper, sensor data are collected by group wise perception for preprocessing and fusion, SSA method is introduced to divide the output of LSTM into temperature loading effect with periodic trend, and residual data as vehicle loading effect and noise, combined with BDLM method to reduce the noise, increase the accuracy and stability of the model, and effectively monitor the bridge structure in real time. Then the Stacking integrated learning algorithm is used to mix different kinds of base learners to reduce the variance, which effectively improves the generalization ability of the model and realizes the fault warning of the bridge structure. The results show that the proposed method can effectively reduce noise, increase the accuracy and stability of the model, and alleviate the risk of overfitting. The LSTM-SSA-BDLM model can obtain vehicle-induced strain data under lossy and nondestructive conditions, and the four damage assessment indexes of “k, R², b and Ta” are stably distributed in the range of 0.45~1.55, which can effectively identify the hypothetical bridge damage. The baseline value of the warning threshold is pre-tested and estimated using the Pareto distribution model, and the value of the mid-span disturbance for a suspension bridge with 95% guarantee is obtained as -0.7076m, which ensures that the baseline value of the threshold can meet the standard of material strength.

In this paper, the entropy weight method and fuzzy comprehensive judgment model are combined with the evaluation index system of cultural development of film and media and digital cultivation of talents to realize the measurement of the level of cultural development of film and media and digital cultivation of talents. The coupling coordination degree model is used to calculate the coupling coordination degree between cultural development and digital cultivation of talents, and Dagum Gini coefficient, kernel density estimation and convergence test are used to explore the reasons for the differences in the coupling coordination degree between cultural development and digital cultivation of talents, the distribution characteristics, and the convergence. The results of the study show that the mean value of the coupling coordination degree between cultural development and digital cultivation of talents in Guangdong, Hong Kong and Macao Greater Bay Area ranges from 0.466 to 0.501 from 2013 to 2023, and shows a fluctuating and slowly increasing trend. The average contribution of intra-group gap, inter-group gap and hypervariable density in the three metropolitan areas of Guangzhou-Foshan-Zhaoxing, Shenzhen-DongguanHuizhou and Zhuhai-Zhongjiang are 28.676%, 40.102% and 31.221%, respectively, and the inter-group gap is the main factor contributing to the difference in the coupling coordination degree of the three metropolitan areas. Meanwhile, the convergence of the coupling coordination degree of the Pearl River Delta and the three metropolitan areas is good, and improving the coordination between cultural development and digital cultivation of talents in cities with low levels of coupling coordination degree can effectively narrow the gap between them and cities with high levels of coupling coordination degree.

The implementation of traditional energized line operation relies on manual experience, which has the problems of outdated control technology and insufficient reliability, and seriously affects the operation safety. In this paper, based on the hierarchical algorithm and visual sensor, after completing the preprocessing of the visual sensor data, the point cloud data is combined with the power line operation to obtain the point cloud data characteristics under the power line operation scene. At the same time, a point cloud alignment method based on octree and KD tree multilayer index structure is proposed to improve the traditional ICP point cloud alignment algorithm for threedimensional modeling of the safety distance of power line. Then the safety distance of the power line operation is calculated to realize the safety distance localization of the power line by visual sensors. Compared with the traditional BIM and GIM modeling methods, the 3D model elevation point location curve under the modeling method of the safety distance of the power line in this paper has a higher degree of fit with the original curve and higher modeling accuracy. The safety distance localization method of energized lines in this paper also meets the safety distance warning requirements of 10kV and 35kV voltage levels at the same time, and the validity has been verified.

This paper denoises the collected stock market return data during the audit period based on wavelet transform and constructs a GARCH-MIDAS model to capture the volatility characteristics of the return data. The autoregressive conditional heteroskedasticity (ARCH) test, Q-test and ADF test are used to demonstrate the reasonableness of the GARCH-MIDAS model construction in this paper. The parameter estimation of the GARCHMIDAS model is carried out using the great likelihood estimation method to further illustrate the validity of the model in this paper. The results show that the denoised yield data using wavelet transform is smoother than the original data, and can retain the main volatility characteristics in the original data, providing good data support for the subsequent capture of volatility characteristics. The macroeconomic and economic policy uncertainty variables are basically significant in the parameter estimation of one-factor and two-factor GARCH-MIDAS models, which can effectively reflect the overall and long-term volatility characteristics of yield data.

Weir flow measurement is an important content for safety monitoring of hydraulic buildings, and it is of great significance to strengthen the seepage monitoring of hydraulic buildings to ensure the safety of hydraulic buildings and feedback design. Based on the basic principles of optimal design of engineering structures and the theory of structural reliability, this paper uses nonlinear planning to optimize the structural design and flow calculation method of water-measuring weir in water conservancy canal system, and analyzes the hydraulic characteristics of the optimized water-measuring weir through numerical simulation, which verifies the effectiveness of the proposed method. The simulation results show that the influence of the new trapezoidal water-measuring weir with different orifice heights on the outflow over the weir gradually decreases with the increase of the total head in front of the weir, and the fitted flow equations are concise, easy to use and highly generalized, with an average relative error of 2.55%. At the same time, the average relative error of the flow and water depth of the U-shaped channel triangular profile weir is 0.22% and 5.76%, respectively, and the flow measurement accuracy of the long-throat channel optimized by the method of this paper is also controlled within 6%, which is in line with the flow measurement requirements of the water measuring channel, which indicates that the simulation results have a certain degree of reliability, and can provide a reference basis for the optimization of the design of the engineering structure of the water measuring facilities.

This paper constructs a digital maturity model of asset management for the elderly with 4 target layers and 13 factor layers. On the basis of the evaluation index system established by the hierarchical analysis method, it combines the fuzzy comprehensive evaluation method to establish the risk evaluation model of elderly asset management. Then the fuzzy set qualitative comparative analysis method is used to explore the digital transformation of assets to provide a scientific and feasible way to make the process of digital transformation of elderly asset management more standardized and procedural. The weighting results of the target layers show that the weights of the four target layers, namely, “organization construction, technology application, operation guarantee and innovation drive”, are small, ranging from 0.2303 to 0.2654, while the weights of the factor layers, namely, “innovation of data technology, business model and technological achievement”, and “innovation of technology results”, are the only factors to be considered. In the factor layer, only “data technology business model and technological innovation” has a weight of more than 0.1, which shows that the digital transformation path of assets is closely related to the development of science and technology. In addition, indicators such as talent and maintenance management are also extremely important to the digital transformation of asset management for the elderly. This paper also emphasizes the importance of the three indicators of “capital investment, internal control of marginal conditions, and talent reserve” to realize the digital transformation of asset management for the elderly.

Online electronic transactions in the big data era form the basis of the application environment for realtime dynamic pricing, and automated dynamic pricing for e-commerce platforms has become a trend. In this paper, the dynamic pricing strategy optimization problem for e-commerce platforms is deeply explored based on deep reinforcement learning methods. The study defines the dynamic pricing problem and transforms this problem into a Markov decision process model, based on which the A3C algorithm is applied to decouple the model to realize the dynamic pricing strategy optimization of e-commerce platform. Experiments show that. The dynamic pricing algorithm for e-commerce platforms based on the A3C method can show better gain results, with an average gain as high as 4033, and is more stable compared to other benchmark algorithms, which can adapt to the complex demand level as well as a large state space. In addition, the effects of gain threshold and loss threshold on the ordering decision of e-commerce platform mainly occur in the low initial inventory region, and the effects on the pricing decision are more significant in the high initial inventory region. This paper has strong application value for pricing strategy optimization and inventory control decision of e-commerce platforms.

With the arrival of the digital era, how enterprises can promote their financial performance through the implementation of digital transformation has become a hot issue. This paper establishes an enterprise financial performance evaluation system based on fuzzy theory and BP neural network. First, a reasonable neural network structure including the number of network layers, the number of nodes in the hidden layer, the training method and other related parameters are set, and then the adaptive step maximum gradient descent method is applied to train and test the BP neural network, and the constructed fuzzy system, which is combined with the BP neural network, is applied to the evaluation of corporate financial performance. Finally, taking Company Q in the traditional Chinese medicine industry as the research object, the construction of the financial performance evaluation index system is completed, and the four common factors representing 85.692% of the information of the original variables are extracted by using the factor analysis method to complete the scientific description of the enterprise’s financial performance management level. The evaluation model based on fuzzy logic and BP neural network achieves an average prediction accuracy of more than 95% for the test samples, realizes excellent financial performance optimization and innovation, and puts forward comprehensive measures such as setting up internal enterprise financial performance management values, exerting the performance appraisal mechanism and guiding the systematization of performance evaluation, which to a certain extent improves the enterprise performance management system.

The development of vocational education cannot be separated from skill competitions, and skill competitions in higher vocational colleges and universities are an important platform for testing the improvement of students’ skills. In this paper, we take the students of S higher vocational colleges in Guangdong Province, China, who are in the Internet international trade competition for higher vocational colleges as the research sample, and choose descriptive statistical analysis and correlation analysis as the data statistical methods of this research. From the perspective of learner portrait, we constructed a student ability portrait model based on the objective function of clustering algorithm FCM, calculated fuzzy division coefficients through the number of classifications and attribution probability matrix, determined the appropriate number of student clusters, and obtained the classification results of the portrait. In the analysis of student ability portrait, the optimal number of clusters is determined to be 4, and four groups of learners, namely, active catch-up, potential constructor, active collaborator and passive receiver, are classified, and at the same time, based on the group characteristics presented by different learning ability portraits, the precise cultivation paths are proposed for different groups of learners in preparation for the process of Internet International Trade Competition.

With the rapid development of the intelligent construction industry, the demand of enterprises for highquality talents specialized in intelligent construction is becoming more and more urgent. This paper discusses the evaluation model of talent cultivation effect of intelligent construction professionals under the innovation of BIM education system based on support vector machine (SVM) and principal component analysis (PCA). Firstly, the evaluation index system of intelligent construction professional talent training effect is constructed, due to the excessive evaluation indexes of intelligent construction professional talent training effect, the multi-dimensional data of talent training effect is downgraded by principal component analysis to avoid the estimation distortion of the model. Then the principal components with a determined number are transformed into the principal component matrix and input into the SVM, and the parameters of the SVM are selected based on the grid search algorithm, and the SVM is trained. The study shows that the assessment model of this paper can effectively evaluate the training effect of intelligent construction professionals in different institutions, and obtains the average absolute error \(E_{MA}\) and the mean square error \(E_{MS}\) with the optimal performances, and the values of the two indexes are 0.03 and 0.005, respectively. The innovative education effect assessment method of this paper provides a scientific basis for the optimization of the teaching content and the improvement of teaching methods. It provides a scientific basis and lays a foundation for improving the quality and knowledge level of intelligent construction professionals.

Benford’s law is a commonly used method to test the quality of financial data, and introducing Benford’s law into the logistic regression model for financial risk early warning can increase the number of effective variables representing the quality of financial data and improve the prediction accuracy of the early warning model. This paper applies Benford’s law to test the quality of financial data, constructs modified Benford’s factor, and combines it with financial variables to establish a Benford-Logistic model for financial risk warning. Taking Chinese A-share listed companies from 2006 to 2023 as samples, the Lasso method is used to screen the explanatory variables and determine the optimal model so as to realize the risk assessment of the financial data, and the validity of the model is verified by taking Company A as the target. The results of the study show that the introduction of the modified Benford quality factor into the logistic regression model can improve the accuracy of the early warning model for the risk of corporate financial data, and the model constructed is effective when applied to Company A. It is in line with the actual situation of Company A. The early warning model is of great significance in the prevention of the financial risk of the enterprise.

Landscape greening system plays an important role in promoting urban construction, improving ecological environment and improving residents’ quality of life. This paper introduces the complex network theory, constructs a network model of landscape green space system, and takes the correlation, connectivity, robustness and basic static statistical characteristics of the network as the indexes of landscape green space system to provide a method for the topological analysis of the spatial structure of landscape green space system. Taking Ordos City, Inner Mongolia, China, as the study area, the topological analysis of the spatial structure of landscape green space system is carried out, and then in response to the optimization demand, a multi-objective optimization model for the layout of urban parks is constructed based on the NSGA-II algorithm, which is used as the basis for the screening of candidate points of landscape green space to be constructed under planning. Using the model of this paper to optimize the spatial structure of Ordos City landscape garden green space system, most of its node degree, important nodes and connectivity are improved significantly, and the number of network attack nodes reaches 300 after optimization, and the connectivity robustness is still around 0.2.

With the continuous change of the global economy and the increasingly fierce competition in the market, the financial risks faced by enterprises are increasingly complex and diverse. This paper constructs a LSTM neural network financial risk early warning model for enterprises based on recursive neural network. 130 listed companies were selected as the research object, and 28 financial indicators and 2 non-financial indicators were obtained as the research samples. Then the 30 financial early warning indicators were downscaled using factor analysis to extract the principal component factors. The principal component factor scores are input to the LSTM network, the relevant parameters of the network are set, and the network is trained to complete the construction of the enterprise financial risk early warning model. The training results of this paper’s model show that the model tends to be balanced after three hundred iterations, and the fit of the model is better, and the loss value is only 0.12. The empirical results of the model’s financial risk prediction show that this paper’s model has a better performance than the traditional prediction models, such as Random Forest, in terms of the prediction of corporate financial risk. The application of LSTM neural network to the financial warning of enterprises has obvious advantages.

The emergence of virtual reality technology provides great convenience for English teaching work and greatly promotes the reform and innovation of English education. In order to effectively integrate virtual reality technology with English contextual teaching, this paper constructs an English teaching context based on virtual reality panoramic video. Paired-sample t-test and social network analysis are chosen as the data processing methods to carry out a college English contextual teaching experiment in two parallel classes in the second year of non-English majors in D colleges and universities in Xi’an City, Shaanxi Province, China, to explore the effect of the application of the English contextual teaching model based on VR panoramic video proposed in this paper on the students’ English learning. The posttest score of the experimental class is 68.69, which is 6.8 points higher than that of the control class, and presents a significant difference with a significance probability of 0.012<0.05 in the ttest result. The clustering coefficient of the community network of English translation activities in the experimental class is as high as 0.85, the social network connection is dense, and the students are all able to actively participate in English learning activities.

The digital twin virtual simulation platform has strong visualization and interactivity, which can transform abstract theoretical knowledge into vivid and imaginative virtual scenes, helping to improve students’ learning interest and enthusiasm. This paper introduces the digital twin education application framework, discusses the specific application methods of digital twin technology in Russian distance teaching, and constructs the corresponding Russian language teaching mode. For the examination management module, an improved genetic algorithm is used to realize the intelligent grouping of papers for remote Russian language teaching. The comparison results of the genetic algorithm before and after the improvement show that the fastest time of the genetic algorithm before the improvement is not less than 7500ms, and the difficulty of the generated test paper is not in the moderate [0.4~0.6) range. While the improved genetic algorithm is in the moderate range in terms of grouping difficulty, and the time can be reduced by at least 2 s. The article also proposes to introduce the improved fuzzy clustering algorithm incorporating AFSA into learning behavior analysis to portray learners. The final experiment confirms that the AFSA-FCM based model can accurately cluster learners, thus portraying learner profiles in a deeper and more comprehensive way. It helps teachers to carve out the characteristics of different learner groups and provides a key basis for decision-making in teaching and learning support services.

The safety of children’s products is closely related to children’s health and life safety, and is an important part of the design process that cannot be ignored. This paper takes children’s products as the research object, and explores the method of children’s product safety assessment from the perspective of image feature recognition and classification. The images of children’s products are preprocessed by geometric transformation, grayscaling and image enhancement to extract their color, texture and shape features. Relying on the image features of children’s products, a VGG-based image feature recognition model of children’s products is constructed, and the model performance is improved by the increase of residual module and the equivalent conversion of multi-branching model into a single-path model, so as to realize the safety assessment of children’s products. In the safety assessment experiments of children’s products, the model in this paper achieves optimal results in four aspects, namely, accuracy, precision, recall and F1 value, which reach 98.34%, 98.34%, 98.33% and 98.34%, respectively. Compared with the original ResNet18 model, the model also has better recognition accuracy in the face of different categories of images of children’s products, and can play an effective role in the work of children’s product safety assessment.

In this paper, based on the characteristics of English vocabulary and students’ behavioral data, a cognitive level test learning model based on IRT theory is constructed, and for the defects existing in K-means clustering algorithms, a user clustering recommendation algorithm based on the minimum variance is obtained by using the minimum variance optimization initial cluster heart method. On this basis, a personalized recommendation platform for English vocabulary learning based on students’ vocabulary level with dichotomous K-means clustering is designed and implemented, and the effectiveness of the platform is verified. The experimental results show that the method proposed in this paper can very directly observe that the topic parameters and students’ learning ability values match the information reflected in the actual data. In addition, the accuracy of this paper’s model in successfully recommending learning resources can be improved by up to 58.23% compared with the traditional model, and relevant extended knowledge of non-vocabulary subjects such as oral expressions is given in the recommendation results, which alleviates the problem of increasingly narrow vision of students caused by the cocoon effect. Teaching experiments show that this strategy can significantly improve students’ learning of English vocabulary and increase the mean value of their English scores by 7.4628 points. Obviously, the model in this paper solves the defects of the existing English vocabulary learning software that does not meet students’ individual needs.

In order to fully explore the fault information embedded in electromechanical faults and realize the accurate judgment of electromechanical faults, this paper improves the FOA algorithm with an electromechanical fault prediction model based on IFOA-SVM. The population division process is added, the better subpopulation and the worse subpopulation are updated according to different steps, the balance of the algorithm’s search ability in different periods is realized, and the optimal parameters of the support vector machine are obtained by using the improved fruit fly algorithm. It not only realizes the diagnosis of electromechanical faults with higher accuracy, but also solves the defects that the fruit fly algorithm is easy to fall into the local optimum and has low convergence accuracy in the later stage. In the experimental validation, the gun control box component in the fire control system of a certain tank is selected as the research object, and the IFOA algorithm is compared with BP neural network, GA-SVM, GWO-SVM and other algorithms, and the average prediction accuracy of the three experiments reaches 100.00%, and optimal fitness is obtained when the number of iterations reaches 10 times. The research in this paper provides a new effective method for electromechanical fault prediction, which has important theoretical and practical application value.

Industrial robotic arms have flexible maneuverability and have been widely popularized in various industries, therefore, the study of mobile robotic arm motion planning and control system has very important theoretical and practical value. In this paper, an optimal time-impact trajectory planning method for the robotic arm is proposed, which adopts five times non-uniform B-spline function to construct interpolation curves in the joint space, replaces the motion constraints of the robotic arm with the constraints of the control vertices of the B-spline curves of each order, and uses multi-strategy improved viscous bacterial algorithm to optimize the objective function. Simulation and experimental results show that the proposed improved mucilage algorithm can effectively improve the performance of the SMA algorithm, and the optimal trajectory planner is able to obtain a safe and smooth timeimpact optimal trajectory under the premise of satisfying the joint constraints with the running time less than 15s and the impacts in the range of [10-7 rad, 10-3 rad]. This study ensures that smaller shocks are generated during the motion process, which makes the motion control performance of the robotic arm improved.

Under the rapid development of digital education, the introduction of artificial intelligence technology into the reform of language education can make up for the shortcomings of traditional methods and improve the quality of language education. In this paper, we first design the joint extraction algorithm of entity relationship based on graph convolutional neural network, which improves the accuracy of recognition by fusing the designed ontology attributes. Then it proposes the method of extracting the features of language education entities in the knowledge graph using R-GCN model for classifying and predicting the potential relationships between entities, so as to realize the construction of the knowledge graph of language subjects. Finally, a reform path for language education is proposed, with a view to providing a useful reference for promoting language education to a higher level. In the dissimilarity matrix analysis of high-frequency keywords in language education research. It is found that the relationships with language education are, in descending order, language teaching, comprehensive learning, language education, curriculum standards, language teachers and so on. Thus, it can be concluded that “language education” and “language teaching” are most closely related in the research process of language education.

Effective allocation of multiple types of resources in large-scale construction projects is a key issue facing project management theory and engineering management practice at this stage. This paper constructs a system dynamics model of resource allocation for construction projects, and builds a resource allocation optimization model for construction site of construction projects with BIM and genetic algorithm, uses NSGA-III genetic algorithm to solve the model to obtain the Pareto frontier solution set, and adopts the method of distance between superior and inferior solutions (TOPSIS) to obtain the optimal solution, and verifies its feasibility and validity in the actual cases. The simulation results show that the insufficiency of funds and materials will directly produce a large schedule deviation, while the sufficient amount of funds and materials, although it can guarantee the construction progress to a certain extent, if it cannot be reflected in the number of labor and machinery, it will cause mismatches in the number of labor, materials and machinery, which will affect the efficiency of resource allocation. The NSGA-III genetic algorithm used in this paper can effectively solve the problem of optimal allocation of multi-project resources, and there is a significant reduction in the variance of resource consumption after configuration optimization. In addition, the degree of harmony of multi-project resource management using the method of this paper reaches the excellent level, which verifies the high efficiency of the model of this paper in the resource allocation in the construction site of a building project.

Speech recognition systems face the challenge of speech recognition from people with different accents from different countries and regions, and the research on multi-accent speech recognition methods has received extensive attention. In this paper, an end-to-end English accent recognition method based on Bidirectional Long Short-Term Memory Network Linked Temporal Classification and Attention Mechanism (BiLSTM-CTC-AM) is proposed and combined with the speech separation model of Parameter-Free Fourier Transformer Network (FNet), the design of an automatic recognition system for English accents is realized. Then the English text obtained from the recognition is used as the input corpus, and a grammar error correction model based on augmented multi-head attention is constructed. The experimental results show that the performance of speech recognition enhanced by accent information is further improved under the multi-task learning framework, and the word error rate is absolutely reduced by 0.4% and 1.1% on the Common Voice and AESRC2020 datasets, respectively. Comparing the RNNCTC and LSTM-CTC models, the word error rate of the BiLSTM-CTC-AM model in this paper is reduced by 11.23% and 3.68% to only 9.70%, which verifies the superiority of the model. In addition, for the correction of all types of errors, this paper’s English grammatical error automation is superior to the UIUC method, indicating that this paper’s method is effective. This paper provides a practical tool for automatic recognition of spoken language and automatic correction of grammatical errors in the teaching of spoken English in colleges and universities.

With the accelerating process of globalization, the overseas dissemination of Chinese culture has ushered in new opportunities and challenges. This paper takes TCM acupuncture in Chinese culture as an example to evaluate and analyze the effect and acceptance of overseas communication of Chinese culture. The entropy weightTOPSIS method is used to obtain the weights of the indicators of Chinese culture dissemination effect, and quantitative evaluation analysis is carried out. With the help of Qualitative Comparative Analysis (QCA) method, the consistency and coverage monitoring analysis of Chinese culture’s overseas communication effect continues. The results of the ranking of the evaluation indicators of the effect of Chinese culture dissemination overseas based on the weights are: cognitive indicators (54.03%) > behavioral indicators (31.1%) > attitudinal indicators (14.87%). And in the similarity analysis, the communication effect score of the nine content modules of Chinese medicine and acupuncture averaged 0.11, the overall overseas communication effect is not satisfactory, and it is still necessary to further improve the overseas communication effect of Chinese culture.

The economic and social environment is constantly changing, the government’s economic management functions are facing transformation, and the exploration and innovation of the construction of local government economic management performance system is necessary. This paper takes fairness and efficiency as the basic value orientation, selects key indicators from multiple dimensions, and constructs a set of scientific and feasible local government economic management performance evaluation indicators. Twenty provinces in China are selected as research objects, and the entropy method is adopted to determine the weights of each index, combined with the TOPSIS method to make a horizontal comparison of different provinces. And the kernel density estimation method and convergence model are used to analyze the dynamic evolution characteristics and convergence characteristics of the economic management performance level of the Chinese government. The experimental results show that although the average value of the Chinese government’s economic management performance composite score has increased by 0.118 during the period of 2016-2021, there are still some differences in the government’s economic management performance level in different provinces. Except for the Northeast region, all other regions in China have \(sigma\) convergence and conditional \(beta\) convergence. The research in this paper provides a scientific basis for the government to formulate targeted regional economic management strategies and helps to promote the coordinated development of regional economy.

Images play an important role in information communication and information preservation, which can meet the needs of the construction of visual learning resources in the education industry. This paper constructs a deep convolutional generative adversarial network model for text-generated images based on conditional enhancement and attention mechanism, which adopts a bidirectional long and short-term memory network to extract textual features, and enriches the feature information of the text through the conditional enhancement module. Subsequently, textual and visual features are fused and output in the generative adversarial network, and the detailed adjustment of the output features is accomplished based on the attention mechanism to generate educational visual image resources containing important features of textual descriptions. The text-to-generateimage method proposed in this paper obtained excellent IS scores on both CIFAR10 and CelebA datasets, with their mean values higher than 7. The visual learning resources, designed in conjunction with the content of the mathematics curriculum, help to enhance the multifaceted mathematical literacy and learning effectiveness of secondary school students, and have gained the approval of the majority of students, with an average satisfaction score of around 4 on the student questionnaire. The text-image generation method in this paper provides new ideas for the construction of visual learning resources for mathematics, which helps learners to better understand mathematics learning methods and concepts

With the continuous maturity of deep learning technology, its role in stage character design is becoming more and more important. The article proposes a deep neural network-based multi-view human tracking method for stage environment and designs the stage lighting control system. And on this basis, it designs an automatic stage lighting tracking system, including control and management module, actor identification and localization module, data processing module, and speed interpolation module, which facilitates the control of lighting on the stage. Finally, a series of experimental tests are conducted to verify the effectiveness of the method of this paper. The experimental results on simulated and real labeled datasets show that the binary cyclic code of this paper’s method can still achieve more than 91% recognition accuracy under 60% occlusion rate, which has a very good anti-occlusion performance. Without affecting the stage performance and viewing experience, this paper’s method solves the tracking instability problem caused by stage darkness and actor’s apparent similarity, which is highly feasible and has a wide range of application prospects in the stage performance industry.

With the rapid development of cloud computing technology, unified management of multi-cloud heterogeneous resources has become a key technology to improve resource utilization and system elasticity. Aiming at the complex problem of unified management of resource operation in multi-cloud heterogeneous environment, a multi-cloud heterogeneous resource unified management platform is built to realize the global view and unified scheduling of resources. Subsequently, a combined prediction model based on ARIMA-LSTM is proposed, which effectively solves the problem of load prediction accuracy that a single model cannot more accurately perform unified management of multi-cloud heterogeneous resources. At the resource scheduling level, a container scheduling strategy based on Kubernetes is designed, combined with an improved load scheduling algorithm (LSA) to dynamically optimize the container deployment location, and a horizontal elasticity scaling strategy based on deep reinforcement learning is implemented for autonomous decision making in unified management of multi-cloud heterogeneous resources. The results show that this paper’s method reduces up to 28.32% resource wastage and 50.75% normalized cost compared to existing resource scheduling algorithms, resulting in an average CPU utilization of 92.8389%, while there is no significant increase in the model’s response time. The results prove the effectiveness of the model in this paper in the unified management of multi-cloud heterogeneous resources, providing theoretical support and practical reference for the intelligent use of resources in complex environments.

Heterogeneous cloud infrastructures in cloud computing resource provisioning can improve the fault tolerance of cloud computing environments, but they also bring about difficulties that bring about cloud computing resource allocation. In order to enhance the efficiency of heterogeneous resource scheduling and allocation, this paper proposes a heterogeneous cloud resource scheduling algorithm based on the Improved Competitive Particle Swarm Algorithm (ICSO), which utilizes the chaos optimization strategy to initialize the particle swarm, and introduces Gaussian variants to update the victory particle positions to improve the diversity of the populations and to enhance the global searching ability. Simulation experiments of heterogeneous cloud resource scheduling are set up to explore the optimization effect of heterogeneous cloud resource scheduling of ICSO algorithm. Comparing the CSO algorithm and the ACO algorithm, the ICSO algorithm in this paper has a lower GD value and obtains a larger HV value in all 15 experimental data, and is able to search for the optimal solution for heterogeneous cloud resource scheduling in about 100 generations.

In the context of the digital era, the use of intelligent algorithms to improve production efficiency and decision support is receiving attention from many production enterprises. In this paper, an AttLR-LSTM-based time series model of production data is proposed based on time series data features, combined with LSTM network and attention mechanism to realize accurate prediction of key indexes of production devices, and an intelligent recommender system based on collaborative filtering algorithm is designed to improve the decision support capability and efficiency in the production process. In the comparison experiments with different machine learning models, the prediction effect of this paper’s method is improved by 88.91% and 60.92% compared with the LSTM with long-term time series data as input and the LSTM with short-term time series data as input, which fully proves the validity and stability of this paper’s method in the prediction of the operating state of production devices. Meanwhile, the real-time business indicator recommendation system designed in this paper not only has high satisfaction, but also receives unanimous praise for its accuracy and confidence.

With the continuous development of artificial intelligence technology, deep neural networks show great potential in the field of intelligent music creation. In this paper, we first extract the CQT and Meier spectral features of music, deform and fill the biphasic information by WaveNet decoder, and realize the overall style migration of emotional music. Then, we design a music emotion representation model that integrates the Plutchik and Thayer bi-emotion models and devise a fusion method for the bimodal emotion results, based on which rhythmic control and tonal conditions are introduced to generate music that contains multiple emotions. The model in this paper can effectively merge audio tracks, and the average style transformation intensity of music of the same style reaches 0.80 and above, and can accurately express negative and positive emotions and transform them into emotional music representations, obtaining a music quality score of 4.1. It adds a scientific supporting theory to the field of intelligent composition research.

The G218 Narathi-Balentai highway is exposed to the impact risk of avalanche loading. This paper explores the constructional characteristics, column materials and structural design of the control fence of this section of highway, simulates avalanche based on RAMMS-AVALANCHE model, analyzes the force situation of the fence under the impact of avalanche with different parameters, and provides relevant countermeasures for the structural arrangement of the control fence. The avalanche dynamic process is simulated by the tanα parameter graphing method and the probabilistic relationship graph between avalanche throw and fall. The finite element analysis model of different types of fences is established to simulate the force state of fences when the height of avalanche impact is 1m, 2m and 3m. Under the consideration of different working conditions, the displacements and stresses of different types of fences were calculated, and the force characteristics of the fences were analyzed. By analyzing and demonstrating the force under avalanche load of three types of fences, the force characteristics of different forms of fences under avalanche load are obtained, and finally the diversion tip wedge structure is adopted to provide reference for similar disaster prevention projects.

In the process of constructing a new type of power system, energy storage configuration plays an important role in supporting the stable operation of a new type of power system mainly based on new energy. This paper constructs an optimization model with the constraints and optimization objectives of the optimal configuration of the new power system. The improved MOPSO algorithm is proposed to be written in Matlab language for model solving. The standard test functions ZDT1-ZDT4 are selected as test examples to verify the effectiveness and performance advantages of the proposed algorithm. The improved MOPSO algorithm and the typical multi-objective algorithms NSGA-II and MOSPO are evaluated by the multi-objective algorithm evaluation index. The test results show that the improved MOPSO algorithm has the best overall performance, and the Pareto front solution is more uniformly distributed and more diversified. Taking the IEEE-34 node system with wind/light/diesel/storage islanded grid topology selected as an example, the improved MOPSO algorithm is used to design the energy storage network structure and improve the system stability. The optimal access locations of energy storage are found to be nodes 834, 860, and 836, and the multi-storage configuration scheme designed in this paper improves the voltage stability by 77.96%. The research results have important theoretical and engineering value for exploring the optimal configuration scheme of energy storage in distribution networks.

“Youth night school” is a mode of education and training that provides vocational and academic training during free time at night, and is an important way for young people in society to improve themselves. This paper discusses in detail the participating members and corresponding responsibilities in the “Youth Night School” in higher vocational colleges and universities, as well as its organizational framework model. The hypergraph neural network algorithm is used to capture complex relationships in educational data based on the existing “Youth Night School”. The mapping function is constructed by multilayer perceptron (MLP) to eliminate the information bias between the domains, so as to realize the information conversion between the domains. We also design a rating prediction and construct a course recommendation system for “Youth Night School”. Comprehensive above, the formation of higher vocational colleges and universities “youth night school” education model, the design of comparative experiments, application experiments to verify its effectiveness. The recommendation algorithm designed in this paper has a hit rate of up to 0.874 and a coverage rate of more than 95%, which can accurately and comprehensively recommend the courses of “Youth Night School” and improve the teaching effect.

Aiming at the main point of the traditional teaching mode in which the language ability cultivation and the Civic and Political education are separated, this study proposes a CD-CAT system for college English based on the blended teaching mode. It constructs a course Civic and Political objective system containing a three-dimensional dynamic objective matrix, and develops a dual-coordinate question bank system integrating linguistic knowledge points and Civic and Political elements. Intelligent algorithms such as Shannon entropy and KL information quantity are introduced to optimize the topic selection strategy, and a dynamic learning path planning model is established by combining Q learning. Under the condition of item parameter U(0.05,0.4), the cognitive model including basic knowledge, basic skills and cognitive process dimensions with a total of 22 attributes is constructed. Student A is selected for application case study. Overall, Student A has a better mastery of basic knowledge and basic skills, with 7 attributes fully mastered, but a poorer mastery of cognitive process attributes, with no attributes fully mastered and more than 50% of attributes in a state of no mastery.

Listening ability is the core part of English language application in practice, so the evaluation of its teaching effect and the optimization of teaching design are the focus of improving the quality of English teaching in colleges and universities. In this paper, the sophomore English majors in college I are selected as the research object, and the English listening teaching evaluation system is established through the form of questionnaire survey. Then a logistic regression analysis model is established, which is used as an analysis method of the effect of English listening teaching in colleges and universities. With the support of the logistic regression model, the evaluation model of English listening teaching effect in colleges and universities is proposed. Through the logistic regression analysis of multiple influencing factors of teaching effect, the column-line diagram model is constructed for the evaluation of teaching quality. In the correlation analysis between different influencing factors and teaching effect, the correlation coefficient between teaching effect and teaching content is the highest among the four influencing factors at 0.936, which is more significant. The study points out that English listening teaching in colleges and universities should pay attention to the design and arrangement of classroom teaching content, and promote the improvement of English listening teaching effect by setting reasonable teaching content.

The rapid development of new media technology has increased the complexity of predicting the communication effects of language and culture in Chinese international education. This paper combines the Necessary Condition Analysis (NCA) and Qualitative Comparative Analysis (QCA) methods to identify the necessary conditions and combinations of conditional factors that affect the dissemination effect of short videos on language and culture in Chinese international education. A temporal convolutional network (TCN) is constructed to realize the prediction of communication effects of short videos in the new media framework. A multilayer deep time convolutional extended residual network network structure (MDTCNet) is proposed to optimize the prediction accuracy with respect to the prediction lag of TCN. The results show that the condition of “content theme” simultaneously satisfies the efficiency measure d > 0.2, with a p-value of <0.05, and the consistency index is 0.848, close to 0.85, which is a necessary condition for the high-quality dissemination of short videos about language and culture in Chinese international education. The existence of three combinations of conditional factors has strong explanatory strength for the dissemination effect. The improved MDTCNet model propagates heat prediction error of no more than 0.1 with an R² score of 0.88 for its prediction. The value is closer to the real value. Using the MDTCNet model to process the short video related condition data can effectively improve the prediction accuracy of the dissemination effect of language and culture short videos in Chinese international education.

As an important data of annual operation and production overview and financial situation of listed companies, the analysis of their text sentiment has an important application value in financial risk identification. In this paper, the financial reports of listed companies are taken as the research object, and TF-IDF is used to extract the structured, data-oriented and visualized information in the text. Then, using N-Gram model, the text information is processed by word vector. Subsequently, the improved sentiment co-occurrence algorithm is used to extract and expand the general sentiment lexicon to construct the financial report sentiment lexicon. Meanwhile, the SEN-TF-IDF algorithm is introduced to build the annual report sentiment dataset. The construction and improvement of the financial report sentiment dictionary is completed through the extraction of financial report text information and the learning of word vectorized representation. Comparing with the general sentiment dictionary, the financial report sentiment dictionary has the highest F1 value of 0.872 under the research threshold of 0.6, which demonstrates its superiority in analyzing and mining the sentiment tendency in the field of financial reporting.

This paper constructs an efficient computational framework for style migration and melody generation. A decoding architecture from MIDI audio to CQT spectrogram is proposed based on the diffusion model, and a onedimensional U-Net structure is introduced to optimize the noise prediction process and improve the inference efficiency of the traditional diffusion model. Utilize VAE to map high-dimensional audio data to low-dimensional space to save computational cost. Design the conditional diffusion model based on cross-attention mechanism to realize high-fidelity migration of music styles. Propose a melody generation method based on LabVIEW random numbers to balance creative inspiration and melodic structural integrity. The model of this paper is applied to the practice of music style migration and melody generation to verify the practical value of the model. The results show that the quality of CQT spectrograms generated by the model in this paper is more than 80%, and the style migration rate is more than 90%. Using the piano roll window to visualize the music melody generated after style migration can enhance the melody intuition and the flexibility of segmentation adjustment. In the subjective evaluation, the generated melodies of this paper’s model get the best results in two dimensions: coherence and emotional expression, which can effectively realize the music style migration and high-quality melody generation.

This study takes Fujian Putian mountain settlement as the object, integrates the graph theory algorithm and spatial syntax analysis, constructs a multi-scale quantitative index system, and systematically researches the topology and functional attributes of the spatial form of the settlement by combining with GIS technology. By integrating multi-source data and extracting 782 samples of settlements, five types of quantitative indexes, namely, aspect ratio λ, modified shape index S, compactness W, average width of edge space L and dispersion K, are proposed, which are combined with the parameters of spatial syntax, such as integration and comprehensibility, to analyze the law of spatial differentiation of settlements. The empirical analysis shows that: the foothills and riverbanks type of clusters have the mean value of shape index up to 2.89 (S≥2), and the aspect ratio λ=2.39, which shows a significant banding feature, and the river valley and flat dam type of clusters have a larger area, such as 188,899.85m² in Village V, but the shape index is medium, S=1.54. The mountain type of clusters have the lowest integration degree, with the mean value of 0.618, and the degree of spatial discretization is high. The study was visualized by GIS with linear fitting, and the goodness of fit R² ≥ 0.98 revealed the strong correlation between geographic constraints and colony shape, providing data-driven theoretical support for the protection and planning of mountainous colonies.

As the main transmission equipment of electric energy, cable is one of the key electric facilities to build stable and efficient power network. In recent years, ± 800kV high-voltage DC cable buffer layer erosion accidents, this paper launches the real-time monitoring research on the status of the cable. By analyzing the structure of the cable buffer layer and fault characteristics, the research foundation is laid. Then, the linear tensile test of the strain relationship between the high-voltage DC cable and the sensing fiber, as well as theoretical analysis, to establish the strain relationship between the high-voltage DC cable and the sensing fiber. Then the strain measured by the distributed fiber optic sensing technology is used to calculate the strain of the HVDC cable body and realize the monitoring of the HVDC cable. At the same time, the finite element method is used to establish the cable monitoring model and complete the strain calculation. In the K-fold cross validation, the average accuracy of the cable monitoring model based on distributed fiber optic sensing is 92.4% and relatively stable, with strong generalization ability as well as stability.

This paper constructs a pyramid structure hierarchical model through fast non-dominated sorting and adaptation grading, realizes intra-layer competition and collaboration, and finds the optimal frontier. Combining entropy weight method and fuzzy comprehensive evaluation, the influence weights of external environment and individual ability are quantified, and a multi-objective optimization model of higher education talent cultivation path is established. The improved ant colony algorithm is introduced to enhance the global search capability of multi-objective optimization by using the state transfer rule and pheromone updating mechanism. Through the practice of higher education talent cultivation path optimization, as well as multi-algorithm comparison experiments, the optimization effectiveness of this paper’s method is verified. The results show that: using the method of this paper to determine the optimization of higher education talent cultivation path is divided into 3 stages, and the probability of achieving ability cultivation in each stage is 0.4, 0.3, 0.3, respectively. The method of this paper takes between 150-200ms to run in 5 test functions, and only needs a small number of iterations to get a stable fitness value. Using the method of this paper to continuously optimize the training path, the students’ employment rate, the rate of obtaining professional-related certificates, and the enterprise satisfaction rate are increased by 14%, 36%, and 11%, respectively.

With the rapid development of the Internet, traditional teaching methods have been unable to meet the learning needs of college teachers and students. First, we use network crawler technology to collect research data, complete data preprocessing work through a series of operations such as word splitting, deactivation of words, feature extraction, etc., and input the processed data into polynomial plain Bayesian classifier for training to realize the classification and analysis of music emotion features. On this premise, with the help of similarity algorithm and K nearest neighbor algorithm, the music course content recommendation algorithm is constructed. With the support of this paper’s algorithm and related development software, the design of music course content recommendation system in colleges and universities is completed, and the system is empirically analyzed. Compared with other systems, the real-time update delay and real-time recommendation delay of this paper’s system are shorter, the update delay is less than 1000ms, and the corresponding recommendation delay is less than 500, which verifies that this paper’s system has excellent operational performance, can bring students and teachers a comfortable experience, and promote the development of intelligent music teaching in colleges and universities.

This paper takes Marxist philosophy as the theoretical framework and combines the method of multiple regression analysis to explore its multidimensional role mechanism in ideological and political education. Taking the effect of ideological and political education as the dependent variable, and the three dimensions of theory arming, practice nurturing and culture leading as the independent variables, a multiple regression model is constructed to verify the six relevant hypotheses proposed in this paper. The empirical analysis shows that Marxist philosophy positively influences ideological and political education through the triple path of theoretical armament, practical nurturing and cultural leadership. The coefficients of influence of core elements such as participation in practical activities and campus culture construction on education effect are 0.236 and 0.176 respectively, constituting the main framework of the action mechanism. The study suggests to improve the effectiveness of the role from three aspects: attaching importance to the theme of education, combining practical education, and optimizing the evaluation system.

Smart city is developing at a rapid speed, which will make a huge change in the production mode and industry, and “smart” design and construction in environmental art design will be the future development trend. This paper takes the construction of park green space as a research case, establishes a multi-objective optimization model based on genetic algorithm by combining the characteristics of the research area, and plans 33 candidate park green spaces in the area with low level of park green space service by combining the information of urban land use planning. In view of the shortcomings of traditional genetic algorithm, which is easy to fall into local optimization, simulated annealing algorithm is introduced to improve the standard genetic algorithm by composing hybrid geneticsimulated annealing (GA-SA) algorithm. The results show that the convergence speed of the hybrid geneticsimulated annealing algorithm is significantly improved, which is stronger than the simple genetic algorithm. The optimization model can ensure the goal of planning the minimum green area of the park, and the spatial distribution separation is kept in the range of 0.577 to 0.596, which has a better design. The research method of this paper is of reference and guidance to the environmental design and planning of the smart city.

Red culture brings a new “formula” for the reform and innovation of ideological education in colleges and universities, which can promote the optimization and upgrading of the presentation form and teaching method of ideological education in colleges and universities. The article establishes a red culture resource database for the Civic and Political Education in colleges and universities with B/S structure, and categorizes the red culture content through red culture data collection. Then, the LDA model and Word2Vec model are combined to design the TLAD- 2Vec model to analyze the trend of theme evolution of red culture inheritance in ideological and political education in colleges and universities. Finally, the degree of understanding and the dissemination mode of college students under the red cultural heritage were statistically analyzed. The results show that under different time windows, Revolutionary Retrospective (Topic1), Red Events (Topic2) and Red Activities (Topic7) are stable and popular themes, which are highly concerned by the Civic and Political Education in colleges and universities. There are some differences in the degree of understanding of red cultural heritage among students of different genders, grades and majors, and there is a lack of intelligent communication paths for red cultural heritage. Therefore, in the era of artificial intelligence, the ideological education of colleges and universities and the inheritance of red culture need to pay attention to the construction of digital platforms, relying on a perfect regulatory mechanism to ensure that the red culture can be inherited and continued.

In order to permanently improve the level of national economic and social development and international competitiveness, talent cultivation is one of the four basic functions of colleges and universities, and the cultivation of innovative and entrepreneurial talents adapted to the requirements of the times will become a major mission for Chinese colleges and universities to promote the realization of the innovation-driven strategy. This paper uses the information gain method, optimizes and improves the decision tree model, uses the model to analyze the characteristics of the collected student data, and calculates the factors affecting the innovation and entrepreneurship effect of differentiated teaching. For the influence factor of whether it is a high-tech field or not, the first influence is the national scholarship, with a Gini coefficient of 0.589; the second influence is the participation in scientific research activities and academic performance, with Gini indexes of 0.545 and 0.546, respectively; and the third influence is the academic qualification and entrepreneurial field, with Gini indexes of 0.469 and 0.436, respectively. assessing the innovation and entrepreneurship and scientific research ability of the college students level, a total of 408 people are interested in scientific research, accounting for 69.74%, the number of people who have been authorized by the patent is 128, accounting for 21.88%, which is closely related to better academic performance, and a few of the subject students have scientific research ability.

In this paper, Japanese syntactic analysis and text embellishment techniques are designed to improve students’ Japanese writing skills. Since Japanese dependency parsing is an important part of Japanese syntactic analysis. In this regard, this paper adopts the SVM model to generate a classifier using the labeled corpus as a way to determine whether there is a dependency relationship between two text sections. In order to improve the parsing accuracy of the SVM model, this paper proposes a Japanese dependency parsing method based on NN-LSVM pruning of a large-scale training corpus for dependency parsing on the basis of SVM. After that, a text touch-up technique based on syntactic structure is designed, which introduces a contrastive representation learning method and pushes the model to deeply understand the modeling relationship between semantic and syntactic structural information by adjusting the loss function to further mine more appropriate syntactic structures and expressions in order to improve the effect of the text touch-up technique. After verifying that the two techniques are feasible, this paper designs a practical task for teaching Japanese writing. During the practice, the Japanese writing scores of the experimental class using the NN-LSVM model and the language generation model designed in this paper for writing tutoring improved significantly (P<0.05), and there was no significant change in the control class. It shows that the technique in this paper can have the effect of promoting students' Japanese writing ability.

Farming culture is an important support for current rural revitalization. The inheritance of farming culture can drive the new development and cultural prosperity of the countryside. In this paper, we establish a model of farming culture resource-task allocation problem, and use the combination of column enumeration method and pairwise test method to solve the linear planning model of farming culture resource allocation. In order to reduce the amount of computation, an improved genetic algorithm incorporating a learning mechanism is designed. The algorithm uses greedy operator to encode the chromosomes, and considers a single chromosome as an orderly arrangement of several villages, and finally determines the overall allocation plan by sequentially allocating farming cultural resources to villages. The improved genetic algorithm incorporating the learning mechanism is used to solve the linear planning model of farming culture resource allocation to find the allocation efficiency of farming culture resources before and after resource allocation in 2024. The combination of each key resource input after the optimization process according to the linear planning model of optimal resource allocation is (17.5263, 23.6659, 23.2143, 24.3662, 23.0045, 23.8596, 24.6365, 24.9631). The resource allocation capacity of farming culture is 85.0027. The enhancement of resource allocation efficiency of farming culture is more conducive to the development of rural revitalization.

The data volume of high-definition (HD) photographic images is proliferating, which puts forward higher requirements for efficient compression and high-quality recovery techniques, for which an innovative technique for all-media converged HD image compression and recovery based on improved multiple-external optimize algorithm is proposed. By improving the traditional genetic algorithm to acquire more advanced computational precision and faster convergence speed, the improved arithmetic is applied to optimizing the weightings and value range of the BP neuro mesh to enhance the generalization perform of the neuro mesh. And the combined pattern is applied to the HD camera image compression and recovery under the all-media convergence, which calculates and retains the image compression and recovery data, and reconstructs the HD image from the data blocks. The average GD and IGD of this paper’s method are 0.0312 and 0.0703 for image compression and recovery strategy optimization, which are more advanced than the comparison ways. With the improvement of the degree of graphic compression, the image compression evaluation indexes of the method are improved to different degrees. For image 1, the PSNR of image compression of this method is 30.45db and MS-SSIM is 0.91, which is better than the pass comparison method. In five types of image recovery tasks, the PSNR of this paper’s model is between 31.69 and 34.76db, and the SSIM is between 0.846 and 0.924, and the image recovery tasks can be accomplished excellently.

In recent years, with the rapid development of the Internet, the e-commerce platform has become an indispensable part of people’s daily life, which can generate personalized recommendations for users to meet their daily life purchase needs. The research adopts the MCTS method combining deep learning and Monte Carlo tree search algorithm, fuses the strategy network, value network and risk network with the strategy value risk network, and combines the improved MCTS method to construct a self-learning strategy value risk network applied to the ecommerce platform recommendation system. The improved MCTS method is verified through experiments, and the improved MCTS method has high comprehensive performance, compares the recommendation effect of the selflearning strategy value risk network algorithm with the UCT algorithm, and utilizes the hybrid reward function to achieve the balance between the accuracy and diversity of recommended products. The improved MCTS method is tested in terms of coverage, diversity and user satisfaction, and the performance is good, with the highest coverage of 0.91 and the highest diversity of 0.9941. In this paper, the system realizes the diversified recommendation of products to meet the user’s need for recommender systems.

Fluoride contamination in water bodies is one of the serious environmental problems, and groundwater fluoride contamination is a constraint affecting the use of groundwater and even a threat to human health. Therefore, it is necessary to find widely applied and cost-effective methods for fluoride removal from water bodies. In this paper, we express the adsorption kinetics by constructing the reaction rate equation of the adsorption process and investigate the adsorption mechanism in the adsorption process by quasi-primary and quasi-secondary kinetic models. Langmuir isothermal and Freundlich isothermal models were set up sequentially to design the intermittent adsorption test. X-ray diffraction analysis and Fourier transform-infrared spectroscopy were used to calculate the kinetic and microscopic mechanisms of fluoride ions on the surface of metal composites. The fit R² of the proposed secondary kinetics at reagent concentrations of 10 mg/L, 30 mg/L and 100 mg/L were all 0.9999, and the fit of the proposed secondary kinetics model was much higher than that of the proposed primary kinetics model. A new peak was observed at 702.4553 eV for activated alumina (GAA) C, indicating the successful loading of fluoride species onto the GAA adsorbent.

In this paper, we mined the financial data of several enterprises in CSMAR database and constructed an asset pricing model based on level, slope, and curvature (LSC). By combing the characteristics of data assets and their value drivers, the key factors affecting asset pricing are extracted with the help of principal component analysis. Then the relationship between corporate credit risk and asset excess return is analyzed by using Fama-Macbeth one-factor regression method. And combined with GRS and other tests, the explanatory power of asset pricing on returns under different models is comparatively assessed. The credit risk coefficients of the models in the FamaMacbeth test range from -0.473 to -0.115, and there is a significant negative correlation between them and the portfolio excess returns. There are monotonically increasing or decreasing trends in indicators such as expected excess return and average excess return. And the predicted return of the asset pricing method in this paper is closer to the real return, with the same upward trend. The first three components obtained by principal component analysis explain 90.3%, 3.7%, and 1.4% of the portfolio, respectively. The GRS statistics of the LSC pricing model in this paper are lower than the baseline model by 0.286 to 0.930, which has stronger pricing explanatory power. This study expands the theoretical framework of data asset pricing, which is of value in the marketized allocation of enterprise data assets in the era of digital economy.

A music teaching system based on virtual reality technology can virtualize teacher resources and teaching environment resources. Through the unique immersion, interactivity and conceptualization of virtual reality technology to create the environment needed for music learning, thus making the use of resources more convenient. This paper constructs a music teaching system that includes teaching content and teaching form, music teaching system, and teaching evaluation system. Among them, the music teaching system allows online instruction, virtual environment learning, and human-computer interaction. In order to create a good simulation environment, lightweight deep reinforcement learning method is proposed for the problem of high requirements of deep reinforcement learning training resources in virtual resources, and a deep separable convolutional deep reinforcement learning model is designed. Compared with the deep reinforcement learning model, this model can obtain a higher reward value when the learning rates are all 0.001, and is more suitable for the construction of the simulation environment of the music teaching system. After the teaching practice for freshmen majoring in music in S school, the comprehensive evaluation score of teaching effect is 84.29, which indicates that the teaching system designed in this paper can ensure the quality of music teaching.

In this paper, in the planning stage of BIPVT building, the influence of light changes on the temperature of PV panels is considered, and the maximum light radiation of PV panels with different tilt angles is set to determine the optimal installation tilt angle. The photovoltaic part, light part, and photovoltaic photothermal integrated water circulation system are designed in stages. Introduce IoT technology into the BIPVT building, connect the BIPVT building to the Home Assistant system, and design the water circulation control system, intelligent control and remote control, NFC automation control, voice control and other control methods. Considering that the long-term operation of the distribution network of the building system leads to the heating of wires and losses, the Kruskal algorithm is used to solve the mathematical model with the minimization of line losses as the objective function and the improvement of voltage quality as the goal. In the empirical analysis, after the reconfiguration of the distribution network by Kruskal algorithm, the node voltage is increased by 1.25% and the loss is reduced by 15.30%, which proves that the Kruskal algorithm can effectively reduce the loss of the distribution network of the building system and improve the node voltage.In the characterization and testing of the BIPV/T building intelligent system, it is found that the BIPV/T building intelligent system based on the Internet of Things (IoT) is able to effectively improve the indoor thermal environment.

With the deepening of the new curriculum reform, physical education is receiving more and more attention from the state and schools. Aiming at the learning data of physical education students, a physical education learning behavior recognition model based on Adaboost-BP neural network is proposed. The BP neural network is used as a weak predictor in MapReduce environment, and a strong predictor is constructed by the Adaboost algorithm combining the results of the weak predictor to recognize the physical education learning behavior. On this basis, the physical education teaching strategy is adjusted, and physical education stratified teaching based on the Adaboost-BP model is proposed, and the teaching experiment is implemented to evaluate its application effect.The Adaboost-BP model has a good effect of recognizing physical education learning behaviors, and the average error of the recognition error has an absolute value of 0.029 and a relative value of 3.79%, which is smaller than the comparative method.The model will The model identifies the physical education learning behaviors of 100 students into three categories: “excellent”, “moderate” and “poor”. After the adjustment of teaching strategies, the three categories of students were improved in all indicators of physical fitness, and in terms of course enjoyment, motivation, skill mastery, course content rationality, learning experience, etc., the Adaboost-BP model can realize the stratification of students’ behaviors, and then guide the adjustment of learning behaviors and physical education courses, and promote the improvement of physical education teaching quality.

The rural social pension replacement rate reflects the level of social pension protection for individuals by the rural social pension insurance pillar, and is a relative indicator capable of measuring the level of pension protection by inter-period comparison under different levels of economic development. Aiming at enhancing the sustainable development strategy of rural cultural pension system, this paper establishes a sustainable development index system, uses Random Forest, Logistic Regression and Bayes as the base classifiers, proposes an integrated learning model based on the Stacking integration strategy, and Support Vector Machines as the metaclassifiers for parameter optimization, to assess the sustainable development of the pension system. Secondly, SHAP is introduced to explain the influencing factors of pension system sustainability found by the model. According to the empirical analysis and visualization analysis, the Stacking integration method is better than other single learner models in terms of accuracy, checking rate, AUC value, etc., except for the true instance rate which is lower than random forest, with values of 0.953, 0.877, 0.972, 0.949, respectively, indicating that Stacking integration can be better applied to the pension system sustainability in the assessment. Integrating the influencing factors, it is found that the development speed of the system, the economic affordability at all levels, the reasonableness of the institutional setup, and the efficiency of the management service are the main influencing factors for the sustainable development of the pension system. Finally, recommendations are made to optimize the demographic structure, improve the quality of the population, and active aging in order to cope with the risk of population aging and unsustainability of rural pensions.

In the era of “Artificial Intelligence”, the introduction of multimodal information fusion into vocabulary teaching is an important breakthrough in the reform of university English teaching in colleges and universities. In this paper, multimodal data are extracted from text, pictures and other domains, and the information of different modal data is fused through heterogeneous data fusion. Add positional coding and word vector embedding coding fusion operations in the information initialization stage, extract image features and text features, and send the information to the lexical model for fusion coding, use Transformer learning to decode the source utterance into the target utterance through the decoder, and use the Glove word vector model to realize the knowledge point vectorization operation in the knowledge point embedding layer. Design empirical analysis experiments to study the application effect of multimodal information fusion in English vocabulary teaching. The significance levels of the two classes of subjects in contextual discrimination and word selection by looking at pictures are 0.028 and 0.035 respectively, with the significance level less than 0.05, which indicates that the vocabulary learning method using multimodal information fusion algorithm is more effective in memorizing the words than the traditional mode. The network security mechanism is established, and the multimodal heterogeneous data operation security is evaluated through simulation experiments. The method in this paper can guarantee the data processing volume of 2~2.1Mb/s, and has high storage efficiency.

In traditional teaching, it is time-consuming and labor-intensive to ensure the teaching quality only through teachers’ observation of students’ behavioral status. Therefore, deep learning algorithms and image processing algorithms are used to construct an intelligent assisted teaching system, so as to realize classroom target detection, interactive behavior recognition and classification. By analyzing students’ interactive behaviors and monitoring the classroom status in real time, the quality of education and teaching is further improved. Taking 14 examples of Civics and Political Science classes in a university as research samples for empirical analysis, it can be seen that the accuracy rate of the algorithm proposed in this paper for detecting standing behavior reaches 84%, and the accuracy rate of the overall behavior detection system is 81.4%, which is a good effect. Teacher-student interaction behavior is most often characterized by “instruction-passive response”. Student-teacher interaction behavior “Passive Response-Lecture” appeared most frequently. The student-student interaction behavior is most often “debriefing-debriefing”.

Based on the social network analysis method SNA and expanding gravity model, this study explores the network structure characteristics and influencing factors of agricultural trade between China and SCO member countries in the Shanghai Cooperation Organization (SCO) from the perspective of international trade networks. Through the construction of the global agricultural trade network, it is found that from 2008 to 2024, the density of SCO regional trade network increased from 0.4834 to 0.6294, indicating that the trade links between member countries were significantly enhanced, and the intermediate central potential index decreased from 35.23% to 27.42%, reflecting the evolution of the trade network from “single-core” to “multi-core” structure. The regression results of the extended gravity model show that China’s economic scale β=0.384 and the economic level of SCO countries β=0.378 have a significant positive effect on agricultural exports, while geographic distance β=-0.843 shows an inhibitory effect, and institutional factors such as common language also play a potential role. The endogeneity test and spatial correlation analysis further verify the robustness of the model. The article’s study of agricultural trade in the SCO is beneficial to China’s food production storage in order to dynamically regulate the national strategy of balancing food supply and demand, and to ensure the quality and safety of agricultural products and security of supply.

This paper proposes a music composition model based on neural network optimization algorithm, which integrates genetic algorithm and improved BP neural network to realize the intelligence and efficiency of music composition. The problem of insufficient diversity of traditional methods is solved by a multidimensional coding strategy (12-bit octal, quadratic and octal coding for scales, registers and beats, respectively), combined with genetic operators to dynamically optimize melodic segments. For polyphonic music counterpoint vocal part generation, the elastic gradient descent method is introduced to improve the BP network, which effectively overcomes the defects of the traditional algorithm that is slow to converge and prone to trap local extremes. The experiments use LakhMIDI and MUT datasets, and compare with RNN, LSTM, Seq2Seq and other models, and the results show that the similarity between the generated music and the database waveforms reaches 86.74%, and the chord rule matching is significantly consistent. In the manual evaluation, the model is fully ahead in fluency of 4.23, consistency of 4.49, and rhythm of 4.57, with an average score of 4.34. The evaluation of the music theory features shows that the note repetition degree is 18.77% and the style matching degree is 91.48%, which are better than the benchmark model. The study shows that the model significantly improves the automation level and artistry of music generation by synergistically optimizing the coding strategy and network structure.

This study proposes a cross-cultural semantic association computational model for German literary translation, aiming to address the shortcomings of traditional machine translation in terms of cultural differences and semantic complexity. By fusing RNN and Self-Attention Network SAN encoder, the semantic associations of German words, sentences and paragraphs are quantified layer by layer, and a cross-language semantic ontology structure model is constructed to realize accurate semantic mapping between German and Chinese. The hybrid translation model is further designed to combine four pre-trained encoders with Marian decoder to optimize the translation generation in literary context. The experiments are based on the German-Chinese parallel corpus of WMT2018 and WMT2022.In the German-Chinese translation task, the correlation misalignment rate of this paper’s model is only 4.82%, which is 49.05% lower than that of the baseline model, QE-BERT.The BLEU value is up to 26.21, which is significantly better than that of the comparison model, e.g., 20.05 for Gen-Det.In addition, the model performs stably in the multi-language task. The highest BLEU value of 31.86 in German-English language translation and content word ratio experiments show its robustness to complex semantic elements. The study shows that cross-cultural semantic association computation and hybrid model design can effectively improve the accuracy and cultural appropriateness of literary translation

This study explores the history of multi-ethnic interaction and the path of cultural integration from the perspective of national unity education, combining association rule mining and questionnaire survey. Based on Apriori algorithm for association term analysis, text classification is carried out using plain Bayesian network. Based on the improved Apriori text mining model, combined with the field questionnaire survey data and the historical ethnic interaction event database, the strong correlation rules such as “traditional festival participation → crossethnic dinner” (confidence 83.1%) and “keeping promises → employment mutual assistance” (78.3%) were excavated, and it was found that the average confidence of food culture rules (0.79) was significantly higher than that of religious ritual rules (0.64), revealing the important role of material and cultural exchange in ethnic integration. Based on the comparative experimental results, the detection accuracy of the improved Apriori algorithm is verified. The data of 790 questionnaires show that the awareness of “religious belief” and “customs” of various ethnic groups constitutes the most significant difference dimension, and the average awareness of the difference in “customs and habits” reaches 60.3%. Ethnic interaction attitudes show a high degree of consistency, with more than 95% of the respondents taking honesty and reliability as the main preferred criteria for choosing friends. Accordingly, this paper proposes a third-order ethnic interaction history and cultural integration path, which provides a methodological support with both spatio-temporal precision and cultural depth for multi-ethnic interaction in the new era.

Cloud computing is a kind of service system mainly controlled by cloud data centers, and the increasing scale of information transmission puts forward higher requirements on its scheduling ability. This paper takes rationalization of resource scheduling as the research objective and launches the research on cloud computing network load imbalance problem. By analyzing the network resource load based on processing time, a cloud computing network load balancing model is constructed. It also proposes a dynamic load balancing strategy for cloud computing network. The strategy utilizes the distributed computing and storage capabilities of the cloud computing platform to reasonably migrate virtual machines online. In this way, it ensures that the load of each server tends to be balanced, so as to realize the dynamic balance and control of cloud computing network load. Under a variety of experimental environments, the model proposed in this paper not only optimizes the average system response time by more than 30%, but also tends to be smoother. It shows that the model can improve the effectiveness of the data analysis problem in the communication process, and then optimize the cloud computing network load balancing.

Finding a teaching path that can correctly and effectively improve students’ ideological quality is the top priority of the current model of large ideological education. Based on the current content of the objectives of students’ moral education and the characteristics of students’ ideological quality performance within the school, this paper puts forward the evaluation system of students’ ideological quality consisting of four first-level indexes: ideological and political quality, moral quality, legal and disciplinary quality, and physical and mental quality. The weight modeling method combining hierarchical analysis and Delphi method is used as the indicator assignment method of the evaluation system, and the structural equation modeling method is used as the evaluation and analysis method at the same time. In order to verify the structural validity of the proposed evaluation indicators of students’ ideological quality, eight research hypotheses are proposed and a standardized path model is constructed by using structural equation modeling method based on the contents of each first-level and second-level indicator. The standardized path coefficients (F) of the influencing factors of students’ ideological quality are basically above 0.5, which are supported by the structural equation model, indicating that ideology and politics, moral behavior, awareness of law and discipline as well as physical and mental health are the effective paths to assist in improving the ideological quality of students.

“Dual-teacher” teachers have become the focus of teacher training in the integration of industry and education because of their ability to teach both theory and practice. Based on the research perspective of students’ experience, this paper preliminarily constructs an assessment system for the quality of “dual-teacher” teacher training, which contains 5 primary indicators and 33 secondary indicators. In order to further rationalize the structure and content of the assessment system, an exploratory factor analysis method is used to reconstruct the assessment system. Finally, the assessment system of teacher training quality is obtained, which contains 6 first-level indicators and 28 second-level indicators. At the same time, the hierarchical analysis method was chosen as the assignment method for the indicators of the assessment system, and the fuzzy comprehensive evaluation method was used to obtain the objective scoring performance. The comprehensive cloud method is used to calculate the cloud characteristic parameters of each index, and the evaluation cloud diagram is drawn to obtain the evaluation grade, so as to construct the cloud model of teacher training quality evaluation. The judgment matrices of the constructed teacher training quality evaluation system for each level of indicators satisfy CR<0.1, which meets the requirements of objective and fair assessment of teacher training quality.

This study proposes an efficient collaborative vocational education model based on AI algorithms, focusing on the deep integration of curriculum design and employment adaptability. Methodologically, the intelligent organization and structured association of course content is achieved through knowledge graph technology, and a joint LSTM-BERT-Attention extraction framework is constructed to solve the difficulties of text named entity recognition and relationship extraction. The model validation shows that the learning rate after hyperparameter optimization is 2×10-⁵, batch_size=16, and the F1 value of named entity recognition of LSTM-BERT-Attention on DCNE reaches 85.08%, which is significantly improved compared with the 78.73% of BiLSTM-CRF and the Flat model’s 83.67% is significantly improved. The F1 value of this paper’s model reaches 83.58% in course concept extraction for the dataset with 14 labels. In the knowledge graph construction, Neo4j visualization verifies the hierarchy and completeness of the knowledge network. The employment suitability experiment shows that the degree of modularization of 100%, the proportion of practical teaching of 20% and the intensity of technology tool integration of 1 correspond to the students’ employment suitability ability of 17.31±1.07, 17.07±1.27 and 17.57±1.13, respectively, and the AI-driven curriculum design significantly optimizes the matching between the teaching structure and job skills.

In this paper, we propose a topology optimization method based on Generative Adversarial Networks (GANs), which considers a two-dimensional topology as an image generation problem. The quality of the generated image is improved by a weighted loss function of the adversarial loss and the mean square error. Analyze the parametric constraint solving method, describe the geometric elements and their constraint relationships through geometric constraint graph (GCG), define the concepts of degrees of freedom and constraints, classify the constraint problems, and clarify the evaluation criteria of the algorithms. Analyze the generative graphic design process and emphasize the central role of algorithms in graphic generation. Verify the visual quality and creative advantages of the generated graphics of this paper’s method by means of digital graphic design practice and comparative evaluation of effects. The results show that the overall visual effect of the generated graphics of this paper’s method has an aesthetic score between 2 and 5. The scores are higher than those of the comparison method in the seven indicators of the detailed layout performance of the generated graphics. When the embedded coded information reaches 205000 bits, the average values of PSNR, SSIM, and LPIPS are 26.20334, 0.98112, and 0.00424, respectively, with good quality of visual perception. And the generated graphs are more non-orderly and have excellent creative effect.

This paper defines the knowledge tracking task based on a computational framework, and portrays the evolution law of students’ Civics knowledge mastery state by introducing the knowledge point relationship graph and forgetting factor analysis. Aiming at the antecedent-successor relationship of Civics education knowledge points, a knowledge graph embedding method based on RotatE model is proposed, incorporating a type-aware mechanism to enhance semantic smoothness. The fine-grained matrix embedding technique is introduced to explore the implicit correlation features between exercises and knowledge points, which further improves the prediction effect of the Civic and Political Education knowledge tracking model. The application effect of the constructed model is examined through multi-group experiments. The results show that the predictive performance of this paper’s model for question-answering situation reaches 87.3% and 86.9% in the two indicators of ACC and AUC. Embedded composite features of this paper’s model predicts the answering situation AUC index is 0.85. This paper’s model can well classify the cognitive hierarchy of students’ knowledge mastery and accurately analyze the average knowledge mastery of the students in the three sections of high school and low school, so as to improve the accuracy rate of the recommendation of knowledge mapping resources.

The study was conducted to explore the effects of atmospheric pollutants on allergic skin diseases, combining principal component analysis and multiple linear regression modeling to statistically analyze the data of atmospheric pollutants (PM2.5, PM10, etc.) and outpatient cases of allergic skin diseases in hospitals in Nanchong City from 2020 to 2024. The main components of air pollutants were extracted by dimensionality reduction using principal component analysis to solve the problem of multicollinearity among variables. Regression models were also constructed to analyze the association between principal components and the incidence of allergic skin diseases. The results showed that the principal component analysis extracted three principal components in the atmospheric pollutants with a cumulative variance contribution of 91.97%. The annual average concentrations of the atmospheric pollutants PM2.5, PM10, NO₂, SO₂, CO, and O₃ were 31.267 µg/m³, 51.109 µg/m³, and 24.252 µg/m³, respectively, from 2020 to 2024, 7.765 µg/m³, 0.951 mg/m³, and 131.452 µg/m³. The regression model showed that a 1-unit increase in atmospheric CO concentration was associated with a significant increase in the risk of allergic skin visits by 6.22. The regression model provided a good fit with errors between the predicted and true values of the number of visits for urticaria, eczema, and contact dermatitis in 2 general hospitals ranging between 0 and 1. The study confirmed that atmospheric oxidizing pollutants are the main environmental risk factors for allergic skin diseases in Nanchong City.

In this paper, a multimodal joint analysis method based on frequency domain feature extraction and deep learning is proposed. Firstly, frequency domain decomposition and threshold denoising of video frames are performed using wavelet transform to improve the image characterization ability by retaining low-frequency key information and high-frequency detailed features. Second, the RNN model is improved by combining the multiplepate notice machine-made to realize the emotion-semanteme fusion across visual-text modalities. Finally, the probabilistic clustering of communication sequences based on the GMM pattern is engaged in analysis the spatiotemporal evolution pattern of opinion diffusion. The test consequences indicate that the proposed method realize an image extraction precision of 92.59% on the VOT-2024 dataset and an F1 value of 84.29% for RNN-AM in the CMU-MOSI sentiment analysis task, which outperforms existing mainstream models. When applied to the COVIDHATE dataset, it successfully discovers the sentiment relevance of video content and captures the 48-hour intervention window and 7-day decay cycle of video dissemination, indicating that online public opinion intervention behaviors need to be carried out within 48 hours in order to achieve better results.

This paper proposes an efficient attribute approximation algorithm based on partition method. Through the predefined attribute order relationship, the positive region of decision table is decomposed into multiple equivalence classes, combined with the fast sorting method to reduce the computational complexity and realize the efficient attribute approximation. The knowledge structure tree model is constructed, and the core keyword similarity calculation and subtree clustering methods are integrated to obtain the knowledge point aggregation relevance and dynamically aggregate the cross-curricular knowledge points of Civic and Political Education. A collaborative filtering recommendation model based on knowledge association is introduced, combining the TF-IDF algorithm and user similarity measure to optimize the prediction accuracy of resource scores and realize personalized recommendation of resources. The method of this paper is applied to the knowledge association mining of Civics and Political Science courses in colleges and universities, and the optimization of resource recommendation realizes the planning and guidance of students’ learning path. The study shows that the method of this paper can effectively mine the knowledge point association and calculate the aggregated correlation degree, and the aggregated correlation degree of the three main Civic and Political Science courses is more than 0.75, 0.85, and 0.60, respectively. The maximum RMSE value of the recommendation model is 2.03563, the maximum MAE result is 1.50122, and the accuracy of the score prediction is better than the comparison method.

This study proposes a method for constructing and analyzing the knowledge graph of English learners in colleges and universities based on the local linear embedding LLE algorithm, which optimizes personalized learning support through dynamic characterization and cross-domain knowledge migration. The dynamic knowledge graph covering 9673 nodes is constructed with the core literacy of English discipline as the orientation, and the adjacency matrix (dimension 9673×9673) and 512-dimensional feature vector are generated using the feedback data of exercises. The joint knowledge migration method HLLEJKT is proposed to achieve cross-linguistic representation space alignment through kernelization extension and label optimization, and achieves 75.13% and 85.75% accuracy in XNLI and STS 2020 benchmark tasks, respectively, which is an improvement of 55.69 and 124 percentage points compared with the traditional method mBERT. In practical applications, the graph-based intelligent retrieval system achieves an average accuracy rate of 96.55% in the retrieval of teaching resources in English, Chinese, Spanish, and Arabic, with an accuracy rate of 98.47% in English and 94.56% in Arabic, and the length of the retrieval path is shortened to 21.60, which is 17.40% lower than that of the traditional method of fuzzy retrieval path for teaching resources. The method effectively integrates semantic association mining and knowledge migration mechanism, providing a theoretical breakthrough and practical paradigm for multilingual education technology.

This paper organically integrates the acoustic model topology, BIC and PSO, and improves the ASR system by optimizing the HMM acoustic model topology. The improved ASR system is used as an auxiliary teaching tool to optimize English listening teaching in colleges and universities by adopting TTS technology. Design the caMST model based on Rasch model to realize the accurate assessment of students’ English listening ability. Four English major classes of a key university in city A with a total of 120 students were selected for experiments to build an ASR system with optimal acoustic topology. The unidimensionality calibration of the Rasch model is incorporated to ensure the quality of the ca-MST question bank. Putting the proposed system into use, the results of the teaching experiment showed that the mean value of the listening score of the experimental class increased from 18.085 to 22.189 after the experiment, and the t-value of the change in the score was 2.874, whose significance (two-tailed) was 0.003, which was lower than the significance level of 0.05. The control group’s achievement improvement is small, and the listening ability does not show significant improvement, which in turn proves that using the system in this paper to assist teaching helps students’ listening ability, and provides more choices for the optimization of English listening teaching in colleges and universities.

This study focuses on the interaction mechanism between regional economic growth and eco-agricultural benefits under water resource constraints, and takes County Yunyang as a typical case for empirical analysis. Based on the SEM method of structural equation modeling, the complex relationship between latent variables is quantified by integrating multi-source data, and the path of water resource constraints on the synergistic development of the system is revealed. The results show that the direct effect of agricultural resource endowment on the coupled state is significant, with a path coefficient = 0.731, P = 0.011, and indirectly drives the standardized impact coefficient of economic benefits η through the coupled state of 3.722. The negative effect of industrial posture on economic benefits, with a path coefficient = -4.683, P = 0.020, suggests that an over-reliance on the traditional agricultural model may inhibit the ecological benefits. The model fitness test showed that the chi-square degrees of freedom ratio x²/df=1.384, goodness-of-fit index GFI=0.947, and root mean square of approximation error RMSEA=0.057 met the statistical standard, verifying the reliability of the theoretical framework. The degree of coupling and coordination of ecological-agricultural-economic systems in Yunyang County continued to increase from 0.578 in 2015 to 0.694 in 2024, with an annual average growth rate of 1.2%, reflecting the synergistic effect of policy optimization and resource integration. This study provides a quantitative basis for the synergistic development of regional economy and eco-agriculture under water resource constraints, and realizes the coupled optimization of eco-agriculture with resources and economy.

Personalized learning is a strategy that recommends the best learning strategies (including learning resources, test questions, etc.) based on the individual learner’s situation, so that the learner can obtain the optimal development. This paper takes the automatic prediction and recommendation of test question difficulty as the research object, and proposes a convolutional neural network model based on semantic attention mechanism for college English test questions. The model is based on the practice characterization method of semantic comprehension to extract semantic features and quantify the semantic dependence degree of English reading test questions, so as to assess the quality of reading test questions. At the same time, the automatic prediction model of test question difficulty is constructed by taking into account the content, difficulty and objectives of multiple test question types in English learning. The model is based on convolutional neural network and deep attention network algorithms to realize automatic prediction of English test difficulty. And through reinforcement learning settings, personalized rewards are set to guide the recommendation, and the test question recommendation model is constructed by combining the difficulty prediction of test questions. The model always shows optimal results in predicting the difficulty of test questions on different datasets, and the average absolute error is lower than 0.33 and the root mean square error is lower than 0.38, which demonstrates the high reliability of personalized recommendation of test questions.

Composition is an important expression of the artistic power of fine art landscape oil painting, however, its teaching has long faced the dilemma of subjective judgment and vague guidance. This paper takes the evaluation of fine art landscape oil painting composition as an entry point, describes the feature selection method, evaluation algorithm process, and the realization of each feature evaluation in image evaluation. At the same time, combined with the idea of multiple linear regression, it puts forward the evaluation method of landscape oil painting image composition based on multiple linear regression. After the objective composition performance results are obtained through the calculation of the multiple linear regression method, the content-based image scaling algorithm is used. Under the premise of ensuring that the image content is not changed, image scaling is performed to construct a composition optimization model for landscape oil painting images. The model, guided by the results of the regression algorithm, increases the optimization score of the actual oil painting image composition from the original 0.26 to 0.849, showing high feasibility and application value.

Onboard Carbon Capture System (OCCS) realizes carbon emission reduction of ships by capturing carbon dioxide (CO₂) exhaust gas generated during ship navigation and liquefying and storing it until it is offloaded ashore for professional treatment. Since the offloading of liquid carbon dioxide (LCO₂) has a certain risk, and the operation is complicated and requires high professional technology, this paper launches a research on the risk assessment method of liquid carbon dioxide offloading. Based on the storage conditions of LCO₂ in the shipboard carbon capture system and the unloading method and process, we analyze the content of the unloading risk assessment and the acceptable level. A fuzzy system is introduced to accurately reflect the degree and level of liquid carbon dioxide offloading risk and form a liquid carbon dioxide offloading risk assessment method. Collect the risk data in the actual unloading process of liquid carbon dioxide, and use the fuzzy system method to calculate the unloading risk data set. Based on this dataset, an offloading risk assessment model is proposed by combining the BP neural network algorithm. After training and iteration, the model has an accuracy rate of 95%, and the loss value is maintained at a very low level of 0.099, which shows an excellent risk assessment capability.

The study systematically explores the synergistic mechanism between complex structural performance enhancement and formal aesthetic expression of topology optimization techniques in art design through numerical computation methods. Based on the homogenization theory and the grid point density method, a macro-micro coupled microstructure topology optimization framework is constructed, combining the quadratic filter and the Heaviside function to realize the structural boundary smoothing, ensuring the unity of mechanical properties and visual fluency. For the complexity of multi-material composite structures, an improved MBESO algorithm is proposed, which balances the performance of tensile and compressive regions by dynamically adjusting the material distribution and stress criterion, and realizes the synergistic optimization of lightweight and aesthetics. Experimental validation shows that the MBESO algorithm reduces the number of iterations to 40 times compared with the traditional BESO, the volume fraction is reduced to 3.3×10-9, and the error of multi-material optimization is less than 7.59%. Through eye tracking and physiological response experiments, it is found that the average gaze time of high-density information-based design is 238.99 ms, which is significantly higher than that of minimalist white spacebased design of 205.66 ms, and the emotion-driven design significantly improves the heart rate by 0.387±0.806 bpm, and the change of respiratory rate by 0.906±0.499 resp, which verifies the strong correlation between formal aesthetics and user perception.

In this paper, a hybrid approximation \( F_{h\alpha} = a_1 + a_2 (fh\alpha) + a_3 (fh\alpha)^2 \) and prediction \(\min I (a_1, a_2, a_3) = \sum_{i=1}^n [a_1 + a_2 x_i + a_3 x_i^2 – y_i]^2\) model based on the generalized extension method is constructed for the optimization problem of nonlinear systems, and a Lyapunov stability analysis is carried out. The model is applied to the nonlinear gear micro-parameter optimization system and the trajectory tracking optimization system for optimal parameter solving and physical trajectory prediction reduction. During the gear microscopic parameter optimization process, most of the optimal parameter solving errors of the generalized extension method are between 0.00 and 0.02, which have high accuracy. The convex eigenfunction initial value construction and the spectral method discretization are utilized to solve the problem of efficiently solving the nonlinear algebraic equation system in the case of multiple eigenvalues, and the flight physical trajectory is effectively restored. The Lyapunov stability analysis shows that the generalized prolongation method satisfies the spectral stability condition under subharmonic perturbation.

Under the rapid development of information technology, the problem of computer software copyright infringement is becoming more and more prominent, and the traditional infringement determination methods are facing the challenges of low efficiency and lack of accuracy when dealing with massive data and complex code logic. Considering the different characteristics of computer software copyright infringement determination and traditional works, this paper discusses the important rules of copyright infringement determination (substantial similarity plus contact rule) from the perspective of academic theory. Under the guidance of the rule theory, the copyrighted software and the allegedly infringing software source code data are transformed into TF-IDF vectors using the optimized K-Means mean clustering algorithm. A high-dimensional mixed-attribute data similarity algorithm is used to calculate the similarity of the software source code data vectors and obtain the final mining results. Thereby, a computer software copyright infringement determination method based on the data mining algorithm is proposed. In the infringement determination experiment, the accuracy rate of this method is as high as 85% and above, and the highest recall rate is only 87.60%, which can provide powerful technical support for the determination of computer software copyright infringement.

The synergistic development of compulsory education and technology makes the education of children with special needs gradually receive extensive attention from the society, and how to help children with special needs realize effective mental health education has become a research hotspot. Starting from the factors affecting the mental health of children with hearing impairment, this paper constructs an intelligent art therapy platform for the mental health intervention of children with hearing impairment by combining art therapy and intelligent interaction design strategies. In this platform, the fuzzy logic algorithm is used to establish an intelligent retrieval model of painting information, which is combined with the vector space model to realize the keyword weighting, so as to improve the retrieval efficiency of children with hearing impairments on the information of art paintings. Then, a clinical experiment was designed based on the intelligent art therapy platform to reflect the feasibility of the application of the intelligent art therapy platform by comparing the mental health intervention effects of hearingimpaired children in the experimental group and the control group. The experiments showed that the average retrieval time of 500 documents by the fuzzy logic-based information retrieval method for paintings and artworks was 5.67 ms, and the scores of the experimental group were higher than those of the control group in the areas of auditory assessment, speech intelligibility, thematic dialogues, pictorial speech, auditory demonstration, and imitation of sentence length (P<0.05), and the scores of the experimental group in the areas of self-consciousness and social adaptability were significantly different than those of the control group before the intervention (P<0.05). The combination of intelligent technology and art therapy can provide new research findings for mental health intervention for children with hearing impairment.

The rapid development of the era of melting media has made the dissemination and creation of popular music more free, thus promoting the combination of life and art, and further narrowing the distance between audiences and music creators. The article combs through the changes of popular music communication characteristics and interaction modes in the era of melting media, and explores the specific manifestations of audience behavior in the process of music communication. The audience participating in music short video communication in DY short video platform is taken as the research object, and its dynamic behavioral characteristics are captured to produce data for audience behavior prediction. Combining 3D-CNN network with GRU in recurrent neural network, Conv3D-GRU model was constructed for predicting audience dynamic behavior in music communication. The results show that compared with GMSDR, the RMSE and MAE of this paper’s model are significantly improved by 13.08% and 14.77%, respectively, and the PCC value of the model can reach up to 97.79%.The Conv3D-GRU model possesses a better two-category error and accuracy, and the overall dynamic analysis efficiency reaches 87.44%. Combining neural network technology with music communication audience behavior prediction helps to expand the scope of music communication in the melting media environment.

Green development is an important support for realizing high-quality urban economic growth. The study utilizes polynomial regression algorithms and response surface analysis to investigate the impact of urban green development paths on economic growth, and conducts regression analysis and robustness tests on sample data from multiple cities. It is found that the slopes of the consistency curve and the inconsistency curve are both significantly positive at the 5% level, the curvature of the former is positive, and the curvature of the latter is negative, both of which are not significant, and the results of the robustness analysis are consistent. The results indicate that urban green development paths consistent with green actions do not necessarily promote economic growth, and cities with high levels of both green development paths and green actions have better economic growth compared to cities with low levels of both green development paths and green actions. At the same time, a high green development path-low green action combination promotes more synergistic economic growth than a low green development path-high green action combination. This paper studies the relationship between urban green development paths and synergistic economic growth from a consistency perspective, which is of great significance in guiding the government on how to better guide green strategies and economic development.

With the improvement of economic development and consumption level, the portfolio problem has become a concern of more and more people. Under the financial market risk management framework, this topic is based on the classical portfolio MV model, and the generalized MV model with multi-conditional constraints is established by introducing a variety of trading constraints existing in the real trading, which is solved by using the Lagrange multiplier method. Collecting trading data from the Chinese securities market for empirical study of the model, it is found that the portfolio returns of securities in industries with small correlation are higher than those of securities in industries with large correlation, and the risk-to-investment ratios of securities in different industries and the same industry are 0.834-1.057 and 0.823-1.038 in the multi-group test, and diversification of investment in different industries can reduce investment risk. The model in this paper performs better in all indicators of portfolio performance, its cumulative return is higher than the comparison method by 0.118~0.213, and the yield curve is stable at -0.024~0.025.The results show that the proposed multi-constraint portfolio model is not only reasonable and effective, but also can better guide investors to choose the optimal and robust investment program.

CNC machine tool is the basis of equipment manufacturing industry, with the continuous improvement of product precision requirements, the demand for high reliability and high precision machine tools is also more and more urgent, and the reliability of machining accuracy is an important indicator for evaluating the ability of machine tools to maintain machining accuracy. The study introduces the fuzzy logic algorithm, designs the adaptive fuzzy PID control controller for CNC machine tools, and realizes the error compensation according to the sensor fusion technology. The method of this paper is added to the speed loop control to improve the optimization parameters of the system, and then the method of this paper is compared with other control methods to calculate the estimated value of trajectory error in the machining process of CNC machine tools. According to the results of the study, the method of this paper has higher control accuracy and better convergence compared with other control methods, realizes the control of CNC machine tool machining accuracy in adaptive optimization model, effectively reduces the machining error of CNC machine tool, and has high practical application value.

With the rapid development of modern science and technology, various radio systems require more and more performance of antennas in complex environments, and traditional antennas are more and more difficult to meet the needs of modern radio systems. In this paper, the basic principle of finite element-boundary element hybrid algorithm to analyze the cavity is deduced by taking the three-dimensional open cavity on the infinite conductive surface as an example. And the SSPP circularly polarized dipole array antenna structure is designed, and the finite element-boundary element hybrid algorithm is used to analyze the radiation characteristics of the SSPP circularly polarized antenna, and the case of the SSPP circularly polarized antenna radiating beam is divided into the symmetric and asymmetric cases, and the radiation characteristics of the antenna are analyzed by simulation experiments. By changing the parameters in the impedance modulation method, the antenna is able to achieve dual beam radiation with dual circular polarization in any direction with circular polarization purity of more than 97.5%, and secondly, the antenna can achieve an impedance bandwidth of 66% (1.3 GHz-2.4 GHz) as well as an axial ratio bandwidth of 34% (1.22 GHz-1.85 GHz).

As the use of 3D modeling becomes more widespread, animation 3D techniques are facing more challenges. Neural radiation field provides a new idea to solve this problem by virtue of its ability to reconstruct a realistic 3D scene from sparse 2D images. In this paper, we use the depth camera (i.e., RGBD camera) to obtain the image depth information and generate the 3D point cloud data, combined with the greedy projection triangulation algorithm to reduce the 2D triangular mesh mapping to 3D space to form a 3D space triangular topology network structure, and obtain the 3D surface model of the object. Improve the neural radiation field for animation scene viewpoint synthesis, and design the neural radiation field framework based on space distortion. Comparison tests are carried out to analyze the reconstruction performance and rendering effect of the two algorithms. The greedy projection triangulation reconstruction algorithm takes only 12.043s to process the instance horse, and the maximum deviation distance, average deviation distance, standard deviation and root mean square error do not exceed 0.1 mm. The viewpoint synthesis method based on the improved neuroradiometric field shows certain algorithmic advantages in the viewpoint synthesis of animated scenes.

Aiming at the fundamentals and importance of sports action technology, combined with the existing development of sports action image recognition and analysis technology, this paper introduces the corner mechanism of quantum revolving door into the quantum genetic algorithm, and proposes the gradient-based adaptive quantum genetic algorithm. Using bilinear interpolation method to geometrically transform the sports action images, combining the normalized interrelationship measure with the improved quantum genetic algorithm to realize the sports action image matching process. To statistically compare the performance of the improved quantum genetic algorithm with other algorithms for sports action image segmentation. Analyze the total mean scores of teaching ability of sports trainee teachers when using quantum genetic algorithm to adjust sports movement techniques. In the function test, the improved quantum genetic algorithm adopts the quantum gate adaptive rotation angle strategy, which can avoid algorithm divergence or premature convergence, improve the accuracy and speed of algorithm optimization, and make the algorithm more robust. The total mean scores of teaching ability of physical education trainee teachers in the initial stage and the end stage of the internship were 3.49 and 4.45 respectively. The difference of the total mean scores of teaching ability proved the feasibility of the image matching technique of quantum genetic algorithm for the improvement of movement skills and the enhancement of teaching professionalism of physical education teachers.

The adjustment of agricultural economic structure under environmental change has become a key path to realize sustainable agricultural development. The purpose of this paper is to construct an analytical model to provide theoretical basis and practical guidance for agricultural economic restructuring. The model reflects the dynamic process of agricultural economic restructuring under environmental change by introducing indicators such as environmental technology efficiency and green total factor productivity. The model adopts the SBM directional distance function (SBM-DDF) and combines it with the Malmquist-Luenberger (ML) productivity indicators to measure the intertemporal dynamic changes of environmental technical efficiency and green total factor productivity in Chinese agriculture. The empirical analysis shows that the effect of environmental regulation on agricultural productivity in China has a distinct geographical nature. Currently, the environmental regulation construction in Chinese agriculture is relatively weak in general, and there is a great potential to improve the technical efficiency of agriculture, and the western part of the country should be a key area of concern for the future construction of environmental regulation in agriculture.

How to reasonably plan the transportation path of hazardous chemical vehicles and reduce the risk and cost of hazardous chemical transportation is an urgent and very realistic problem. In this paper, a multi-objective planning model of multi-vehicle path considering the cost and risk of dangerous goods vehicle transportation is constructed. In the model solution, the multi-objective problem is firstly transformed into a single-objective problem using the   constraint method. Then it is solved using the Adaptive Inertia Weights and Cauchy Variation Method Improved Particle Swarm Algorithm (ACMPSO). Finally, the simulation test is carried out according to the solution process of ACMPSO. The simulation results verify that the ACMPSO algorithm can effectively ensure transportation safety while taking into account the benefits of transportation enterprises. And the ACMPSO algorithm takes less time and has higher performance than the PSO algorithm in solving the optimal path. The results show that the ACMPSO algorithm can achieve the optimization of road transport paths for hazardous chemicals by taking the risk and cost as the main optimization objectives and ensuring that the risk is under control. In order to ensure the safety of road transportation of hazardous chemicals, improvements can be made in several aspects, including transportation companies, regulatory mechanisms, vehicle and personnel management, emergency management and accident rescue.

Data protection and privacy encryption technology face heavy challenges in the billing big data environment. In this paper, after combing the current problems of privacy protection and data encryption, RSA homomorphic encryption algorithm is combined with CP-ABE attribute encryption protection algorithm to construct RSA+CP-ABE hybrid encryption mechanism. In order to explore the optimization effect of RSA+CP-ABE hybrid encryption algorithm, the security and performance of hybrid encryption algorithm in this paper are analyzed. The hybrid encryption algorithm of RSA+CP-ABE in this paper improves the security while maintaining the diffusivity and obfuscation.The encryption time of RSA+CP-ABE algorithm is comparable to that of the original RSA algorithm, while the decryption time is significantly reduced.The RSA+CP-ABE algorithm improves the key security and decryption speed.The encryption efficiency of the RSA+CP-ABE scheme is comparable to that of the RSA scheme. The encryption efficiency of RSA+CP-ABE scheme is reduced compared with that of RSA scheme, but the degree of efficiency improvement is reasonable and can meet the practical applications.

The article constructs TSM Tree storage model for big data stream, optimizes the fast query method by using APSO algorithm thinking, and constructs fast query model for big data stream by using discrete wavelet transform. The storage and retrieval performance of the big data stream storage model and fast query model are verified respectively.The TSM Tree storage model has high storage efficiency. The storage rate of the TSM Tree storage model increases rapidly when the file size is larger than 20MB, and the TSM Tree storage model is suitable for storing big data files over 20MB. In this paper, the fast indexing method has the highest Put execution efficiency and becomes the secondary indexing method for single condition query. The fast indexing method in this paper is optimized on multi-conditional query with the fastest response time of 0.954, 0.898, and 0.907s.

Natural language processing and computer vision technologies have greatly contributed to the development of large language models. In this paper, we focus on the introduction of Adaptive Module method in the pre-trained model to realize the efficient migration of the model and improve the performance of the model. In the applied research in the field of natural language processing, the Adapters module is introduced into the ALBERTBiLSTM-CRF model to tune the overall model. The adapter mechanism is utilized to improve the representation ability in the visual Transformer model. The results show that, through the comparative analysis of a large number of transfer learning methods, it can be seen that Adapters achieved a high average performance, with a tuning parameter number of only 0.23%. Therefore, Adapters is selected for the case study.The average number of parameters in the ALBERT-BiLSTM-CRF model with the addition of Adapters module is only 30M with an F1 value of 94.41%.The Adapters adapter component mechanism is capable of adapting to a wide range of downstream tasks and obtaining a better image representation.

The rapid development of the Internet of Things (IoT) has brought unprecedented opportunities and challenges to decision-making in engineering projects. This paper constructs intelligent decision-making method by utilizing the Internet of Things, artificial intelligence and other technologies. The Internet of Things is utilized to collect real-time engineering project status information and monitor the abnormal status. Then artificial intelligence is combined with operation research optimization algorithm to propose an intelligent decision-making method based on IoT data-driven. By verifying the overall performance of the system and analyzing the application examples, it can be found that the data delay of the IoT device is randomly distributed between 50ms-150ms, which is relatively smooth and has no obvious changing trend. The IoT device responds to the project 1000 data requests more accurately. The intelligent decision-making method based on artificial intelligence was adjusted at the 100th artifact. This ministry effectively reduces production performance loss, avoids the problem of inventory surge, and always maintains the inventory at a reasonable level.

Due to the complex structure of high-rise buildings, the diversity of use functions and other characteristics, resulting in once a high-rise building fire occurs, the number of deaths, injuries, and direct economic losses caused by the number of people is huge. This paper takes the fire incident of the telecommunication building in S city of N province as an example, establishes the fire spread model of high-rise building facade insulation material through Pyrosim software, and constructs its fire spread kinetic model. The linear regression and multi-physics field model are combined to realize the reconstruction of the fire accident scene, so as to realize the accurate simulation and analysis of various parameters in the fire spreading process. It is found that the heat release rate curve of the fire spread of the facade insulation material of the high-rise building is in the form of a double peak, and the first and second peaks appear at about 210s and 840s, respectively, and the heat release rate of the second peak reaches 2.15*106 kW. In the composition of the fire spread, the flame change of the flammable material B3 is the most drastic, and the temperature change is the most rapid. The difference between the simulation results and the measured results is relatively small, and the overall temperature change trend is more consistent with the actual results. By simulating the fire spread process of high-rise building facade insulation materials, it is necessary to strengthen the first mobilization of forces, actively develop flame retardant or non-combustible exterior insulation materials, and multi-point monitoring to enhance the fire rescue initiative when carrying out fire spread control.

The article constructs the covariance matrix as well as the mean vector of the stochastic differential equation and tests its hypotheses using the EM algorithm estimation. The model is applied to the PV carrying capacity assessment of county distribution networks, and the method is utilized to solve for the maximum PV capacity under the constraints such as the discard rate. Meanwhile, the distribution of PV carrying capacity prediction error uncertainty is analyzed by cloud modeling, and the kernel density estimation is used to quantify the prediction error confidence interval. Combined with the collected PV historical operation and real-time observation data, the carrying capacity assessment and uncertainty analysis are carried out. When the PV abandonment rate is 3.5%, the maximum overload and maximum network loss are 6.495 MW and 0.712 MW, respectively, and the PV acceptable capacity reaches a steady state. The results of PV carrying capacity assessment of this paper’s method and Monte Carlo method in power supply station area are close to 6.73MW and 6.71MW, respectively, but the calculation time of this paper’s method is faster. The application of stochastic differential equation modeling yields high accuracy of PV carrying capacity prediction results, which fall completely inside the 85% to 95% confidence interval. The PV carrying capacity prediction result of this paper’s method for 816 households in a county is 2652 kW. This paper’s method can effectively predict the PV carrying capacity of county distribution networks and realize accurate assessment. The confidence interval of the carrying capacity is quantified by combining the nonparametric kernel density estimation method.

Sports big data-driven combined with time series analytics greatly improves the prediction of competitive status fluctuations in elite athletes. In this study, an athletic state prediction framework incorporating Informer-based time series analysis is constructed based on multi-source sports big data. The model is utilized to learn the physiological data characteristics of athletes in different competitive states. The prediction performance of the model is specifically analyzed by combining the root mean square error and other evaluation indexes. Athletic state case prediction of 30 elite athletes is realized by deep learning feature data. The physiological data collected in the study show that athletes in the best competitive state generally have body temperatures between 36.5°C and 38°C, and other indicators are maintained in a relatively normal range. The model in this paper achieves good prediction results in different athletic states, with F1 values above 0.94 and prediction errors between 0 and 0.2. The model has a small error value in performing physiological data prediction, such as the absolute error of body temperature is between 0~0.5℃, which realizes the accurate prediction of elite athletes’ competitive state.

With the continuous development and power of big data and artificial intelligence technology, the application of artificial intelligence technology in English learning is becoming more and more popular. In this paper, based on the knowledge of multi-task learning theory, the optimal learning path recommendation method based on K nearest neighbor algorithm is designed in order to enrich the current English teaching path. It is found that the English vocabulary learning path still has the problem of low degree of interaction, accordingly, a sentiment prediction model based on principal component analysis and K nearest neighbor algorithm is constructed, and the model is verified and analyzed. The prediction accuracy of the traditional K-nearest neighbor algorithm is 77.73%, while the prediction accuracy value of the model in this paper is 96.03%, which indicates that the introduction of principal component analysis algorithm on the basis of the traditional K-nearest neighbor algorithm can improve the prediction accuracy of students’ emotions, and enhance the interactive effect of teaching by accurately capturing students’ emotional state.

Teachers’ teaching ability directly determines the quality of high-end talent cultivation, and objectively assessing the status of university teachers’ teaching ability is of great significance for improving their teaching ability and boosting the quality of Chinese higher education. This paper defines the abstract attributes and spatial interaction process of heterogeneous network multidimensional data, improves the equilibrium of heterogeneous network data, introduces the decision logic theory, establishes the granularity computation model, and mines to more associated data and hidden data. Using OLAP technology, we construct a multidimensional data warehouse and apply simplified logic mathematical formulas to calculate and solve the multidimensional data model. Using the multidimensional data computational model to assess teachers’ teaching competence, the highest value of teaching competence was found in teacher 6 among the subject teachers, and the competence values of the six assessment factors were 56.82, 68.81, 79.21, 89.62, 85.55, and 80.12, respectively. Curriculum development competence was found to be very important, and its importance and recurrence were 3.8 and 3.72, respectively, and emphasis should be placed on the assessment of this teacher’s competency should be evaluated.

In order to take the advantage in the market competition under the integration of cultural and tourism resources, the only way to open the cultural and tourism market and increase the popularity is to study the regional characteristics in depth and shape a distinctive and unique art tourism image and brand. In this paper, the VGG19 network model is used as a benchmark, so as to extract the multi-scale features of regional characteristic art image, and then introduce linear discriminant analysis to downsize the data of tourists’ comment images, and then construct a regional characteristic art image recognition model by combining the loss function. The validity of the model is analyzed by self-constructed dataset. Based on the identification results of the regional characteristic art image, the regional characteristic brand construction system is constructed by combining the regional brand culture, and the strategies related to the regional characteristic brand construction are proposed. Relying on policy leadership, planning guidance, technical empowerment and publicity optimization, it can provide new opportunities for regional characteristic art brand construction under the integration of cultural and tourism resources, thus helping the highquality development of regional characteristic cultural and tourism industry.

Aiming at the problem of poor accuracy in tracking the knowledge status of students’ civic education, this study proposes a deep knowledge tracking model incorporating domain features with the support of educational knowledge graph, the DKT-KG model. The model filters important assessment behavioral features through a decision tree and incorporates the knowledge dependencies characterized by the educational knowledge graph to solve the problem of poor prediction accuracy of the original deep knowledge tracking model due to the lack of domain features. The experimental results on the ASSISTments09, Junyi, and KDD datasets show that the DKTKG model can more accurately track knowledge points of the mastery level, and the AUC index and F1 scores are higher than those of other comparison models. Empirical analysis was conducted using historical data of question answering in four Civics courses taken by learners on the online education platform. The average knowledge mastery probability and its corresponding prediction accuracy derived when learners do different amounts of questions show that the deep knowledge tracking model constructed in this paper can accurately predict students’ knowledge mastery.