Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
In the context of mobile Internet, the art teaching platform pays more attention to anytime, anywhere, and users can learn anytime, anywhere, regardless of the location of the environment. Therefore, it is necessary to design a reasonable interactive teaching system for analysis and testing, provide users with matching and learning of online art courses, and provide corresponding response measures. To properly solve the problem of course matching and selection, we have developed a new system that combines matching trees and embedded technology. Firstly, the matching tree algorithm is used for similarity diagnosis between art courses and user preferences and has achieved good results in similarity diagnosis and matching. Secondly, an interactive teaching system architecture for online art courses was built using embedded technology, which improved the efficiency of the interactive teaching system. Finally, the applicability of the established similarity matching model was verified through simulation testing of the learning system.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
This study explores the main influencing factors of college teachers’ ability to teach English reading comprehension through quantitative analysis. In this paper, we designed the scale of “Questionnaire on Teaching Ability of College Teachers’ English Reading Comprehension” and selected the group of M college teachers as the target of the survey. And on the basis of the collected data, using SPSS software, T-test, correlation analysis and multiple linear regression were carried out. The results showed that there was a significant difference (P<0.05) between the teaching effectiveness of teachers in English reading comprehension skills when their education level was below 30 years old or college and below, and that of teachers aged 31 to 40 years old or other highly educated teachers. There is a statistically level difference (P<0.05) between different categories of teachers in both logical reasoning and information processing skills. Teachers' teaching ability passed the significance level test (P < 0.05) with all four independent variables. Their effects on teaching ability are, in descending order: language comprehension ability, information processing ability, logical reasoning ability and cultural comprehension ability, with corresponding regression coefficients of 0.3076, 0.2867, 0.2484 and 0.1225, respectively. It is possible to enhance the college English reading comprehension teaching.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
Sentiment analysis belongs to a classification technique with strong practical value, which can identify the hidden imagery in the text. In this paper, we study the sentiment tendency of words in Chinese language and literature texts, design a fuzzy ontology-based method for calculating textual sentiment tendency, build a textual sentiment tendency analysis model (SDNN) based on deep neural network and sentiment attention mechanism, and use the model in Chinese language and literature texts for empirical analysis. At 202 iterations, the Loss value of the SDNN model reaches the lowest value of 0.4626, and the corresponding accuracy, recall, and F1 values are 90.44%, 79.68%, and 84.72%, respectively, and tend to be stable. It indicates that the model has better classification performance and can be used in the task of analyzing the sentiment of Chinese language and literature texts. In addition, this paper explores the rhyme structure of character emotion vocabulary in Chinese language literary discourse, and takes Liu Zongyuan’s related works as an example for empirical analysis of the model. The results show that Liu Zongyuan’s emotion during his ten years in Yongzhou is generally in low, and the proportion of his negative emotion has been above 75%, which is reflected in the text vocabulary emotion is more sad than happy. This paper is of some significance for the study of the emotional tendency of vocabulary in Chinese language literary texts.
Traditional mechanical manufacturing experimental teaching is limited to one teacher demonstrating operations to several students at the same time, which is difficult to take into account and evaluate the differences in knowledge mastery of different students. In order to improve the above teaching defects, firstly, the teaching evaluation of students’ experimental level is carried out based on their experimental operation behaviors through K-means clustering. On this basis, a deep learning-based knowledge tracking SAFFKT model is designed to empower and update students’ knowledge status. A personalized teaching recommendation method for virtual simulation is proposed based on students’ knowledge state, and the hidden semantic matrix decomposition recommendation algorithm for teaching recommendation is improved and implemented. The AUC and ACC of SAFFKT model are significantly higher than that of the comparison model (p<0.01), and it is robust. The F1 value of the recommended experiments was 0.775, indicating a better recommendation effect. The teaching evaluation model achieves accurate classification of students' experimental behavior and yields different learning characteristics of three types of students. Therefore, the innovative work of virtual simulation teaching strategy in this paper is of practical significance.
In order to solve the enterprise data asset pricing problem in the digital economy environment, this paper utilizes machine learning algorithms such as multiple regression model, BP neural network, and random forest regression, respectively, to price enterprise data assets. Subsequently, the data obtained from each model is fused using the integrated Stacking algorithm to construct an enterprise data asset pricing model with integrated machine learning algorithms. Predictive estimation of the pricing of enterprise data assets is carried out after a detailed justification of the parameter selection of the model. The results show that data capacity, size, quality and freshness are the main influences on data asset pricing. The results of the parameter investigation show that the overall performance of the model is best when the number of node features is 7, at which time the explanatory degree and goodness of fit of the model are 94.33% and 97.27%, respectively. The accuracy, precision, recall and F1 value of the Stacking-based fusion model for enterprise data asset pricing prediction model increased by about 10% compared to the other three models, respectively, to achieve accurate pricing of enterprise data assets.
Teachers’ information literacy is related to the quality and efficiency of education and teaching in higher vocational colleges and universities. In this paper, a dynamic planning-based scheduling method is constructed to improve teachers’ time allocation efficiency and information literacy. First of all, according to the factors and constraints involved in the scheduling problem to determine the goal of solving the scheduling problem, mathematical model, and then the constraints involved in the scheduling of classes, converted into a dynamic planning of the mutually independent and related stages, with 1, 0 indicates whether to meet the constraints. By solving each stage and analyzing the solution of each stage, the optimal value function is summarized, and ACAA is used to traverse all the optimal solutions for each set of constraints. Examples are selected for scheduling test to verify the effectiveness of the algorithm, and the teacher information literacy assessment scale is designed. Applying the class scheduling algorithm to a higher vocational college, the mean value of the overall information literacy scores of the surveyed teachers is 0.15 points higher than the standard reference value, and the effectiveness of the class scheduling algorithm in this paper is verified. Practical experience (58.27%), teaching philosophy (50.19%), and subject requirements (33.36%) are the top three factors affecting teachers’ information literacy.
In the context of building an international consumption center city, it is of great significance to further study the competitiveness of the fashion industry and effectively grasp the direction and focus of the development of the fashion industry in order to promote the construction of an international consumption center city. The study adopts the entropy weight-TOPSIS method to measure the competitiveness of Tianjin’s fashion industry from 2020 to 2023, and compares it with typical provinces in order to have a comprehensive understanding of its fashion industry competitiveness level. Then, the spatial structure characteristics of the distribution of fashion industry facilities in Tianjin were further explored through the kernel density analysis method and the radius of gyration analysis method. Finally, Ripley’s K function is used to calculate the level of agglomeration and the range of the most significant agglomeration scale of each type of fashion industry, which summarizes the distribution characteristics of strategic fashion industries at the overall level. Horizontally, the competitiveness level of Tianjin’s fashion industry shows an upward trend from 2020 to 2023, and vertically, the competitiveness level of Tianjin’s fashion industry is ranked in the middle range of the country, with a certain gap between it and the strong provinces such as Jiangsu, Shandong and Guangdong. The most significant agglomeration scale of the new generation electronic information technology industry is 22,000 meters at maximum, and its DiffK value also reaches 13,317.938.
Piano timbre recognition and intelligent synthesis are of great significance in realizing the intelligent teaching of piano timbre. This paper takes the piano timbre teaching based on artificial intelligence interaction as the research object, constructs the timbre expression spectrum based on harmonic structure through the exploration of timbre synthesis, timbre features and other related theories, proposes the timbre feature extraction method based on the time-frequency cepstrum domain of the piano music signal, and then constructs the piano timbre recognition and intelligent synthesis system, realizes the simulation of the piano music, and then provides an intelligent interactive tool for the piano timbre teaching. The method is used to construct a piano tone recognition and intelligent synthesis system. When using the method in this paper, the amplitude of the piano tends to be stable when the frequency is 1600Hz~2400Hz, and there is no noise interference, and when the frequency is 2500Hz and 2800Hz, the amplitude is the lowest, and the recognition performance of the piano timbre is better. Meanwhile, the correct rate of timbre recognition of this method reaches 87.83%, which is better than 58.54% of the comparison method. In addition, the musical tone signals simulated by the method in this paper are very close to the theoretical values of each note of the real piano instrument captured, with an accuracy rate of up to 99%, which proves the accuracy of the simulated piano sounding. And the method can effectively promote the combination of artificial intelligence technology and piano teaching concept, the confidence level of quantitative regression analysis is high, and the evaluation results of teaching quality are good, which provides a reliable theoretical and practical basis for realizing the high-quality teaching of piano timbre.
The value assessment of ancient literary texts and the mining of linguistic features are indispensable parts of academic research and ancient cultural inheritance. This paper uses the multiple regression model as a quantitative analysis tool for value assessment to evaluate the value of ancient literary texts. At the same time, for the linguistic features of ancient literary texts, we put forward the quantitative descriptive definitions of words, phrases, sentences and other multi-layer and multi-latitude, and establish the corresponding calculation formulas. After the assessment of the value of ancient literary texts, it can be learned that, except for the artistic law and the breadth of dissemination, the ancient literary texts are positively correlated with other influencing factors such as the writing method and the rhythm and rhyme, and the gap between the predicted value of the value assessment and the real value is small, with an error of 40% or less in 90% of the cases. In the mining analysis of linguistic features using The Peony Pavilion and The West Wing as research objects, the average word length of the former is slightly higher than that of the latter, while the difference in the distribution of long and short sentences of the latter is relatively large. Meanwhile, the average dependency distance of The Peony Pavilion is 2.42, which is higher than that of The Story of the Western Wing by 0.1, making syntactic analysis more difficult.
Focusing on the learning behavior patterns of students with network behavior, this study mainly adopts sequence cluster analysis and lag sequence analysis to convert learning behaviors into sequences, and constructs a learning behavior pattern recognition model based on network behavior sequences. Aiming at different types of classroom learning behaviors in civic education under the network behavior sequence, a targeted teaching intervention mechanism is designed to help students convert their learning behavior patterns and thus improve their learning effects. In this paper, the online behaviors are clustered into four categories of “integrated, autonomous, compliant, and deviant” according to six level 1 codes, and the correlation coefficients of the online behaviors in the four learning categories range from 0.8539 to 0.9944, which is a very strong correlation. Finally, a survey of the results of the intervention in the classroom of Civic Education found that 75.22% of the students believed that the intervention had improved the learning effect of Civic Education. 67.7% and 77.54% of the students believed that the intervention had improved the enthusiasm and motivation of Civic Education learning. 79.04% of the students were willing to continue to learn independently according to the learning behavior pattern after the intervention.
Physical education teaching resources are an important part of teaching resources, and it is necessary to adopt a sustainable development approach to ensure the rational utilization of resources. In this paper, firstly, the factors affecting the allocation of physical education teaching resources in colleges and universities are analyzed by using principal component analysis and systematic cluster analysis, and the validity of the method is verified. Secondly, it constructs the influential element model of regional physical education teaching resources allocation efficiency level based on Tobit regression, and explores the locational factors affecting the distribution of physical education teaching resources. Finally, relevant countermeasure suggestions were put forward based on the analysis results. Using principal component analysis to downscale the 17 indicators of the influencing elements of physical education teaching resource allocation in the statistical data, four principal components were obtained, whose cumulative contribution rate was as high as 90.22%, which was greater than 85%, i.e., it had a 90.22% degree of explanation for the original data. Then, the dimensionality-decreased data were clustered and realized to evaluate and rank the allocation of physical education teaching resources in 23 sample universities. In addition, the results of Tobit multiple regression analysis showed that factors such as regional geographic location, regional population density, regional economic development and the scale of investment in physical education teaching resources all have different degrees of influence on the allocation efficiency of regional physical education teaching resources.
Existing translation teaching content has certain deficiencies, this paper discusses the computational methods to optimize the translation teaching content by combining the semantic association network model. A domain translation model with joint semantic information is proposed, which constructs a bilingual mapping relation of domain-specific word vectors to obtain the semantic k-nearest neighbors of words in a specific domain,so as to estimate the domain intertranslation degree of words and improve the adaptive ability of the domain translation model. Then a semantic similarity computation model (SRoberta-SelfAtt) incorporating Robert’s pre-training model is proposed. The model incorporates a self-attention mechanism to extract the association of different words within the text, and acquires richer sentence vector information. The proposed domain translation model is able to obtain more accurate translation results while spending less time. Compared with the stability of the iterative process of the basic model, the SRoberta-SelfAtt model has higher iterative stability. The Roberta-based semantic similarity computation model can effectively improve the performance of the word vector model. The experimental results show that the domain translation model with joint semantic information and the SRoberta-SelfAtt model are more practical for the task of optimizing translation teaching content.
Promoting the output and transformation of scientific and technological achievements of higher vocational colleges and universities is not only the topic of promoting the high-quality development of education in higher vocational colleges and universities, but also the way to deeply implement the innovation-driven development strategy. Taking higher vocational colleges and universities in four municipalities directly under the central government as research samples, this study first utilizes the DEA model to measure the transformation efficiency of scientific and technological achievements of higher vocational colleges and universities in four municipalities directly under the central government in the period of 2014-2023, and combines with the literature analysis method to dig out the key influencing factors of their transformation energy efficiency. Then, the fuzzy set qualitative comparative analysis method (fsQCA) is used to carry out empirical research on the transformation efficiency due to inputs and outputs of scientific and technological achievements of the studied higher education institutions and the interactions between their influencing factors, so as to analyze the grouping path of the improvement of the energy efficiency of the transformation of scientific and technological achievements of the higher vocational colleges and universities. In the analysis of the results of measuring the efficiency of the transformation stage of scientific and technological achievements, the efficiency of the transformation stage of scientific and technological achievements of local higher vocational colleges and universities in D city is generally at a high level, with an average value of 0.427. Meanwhile, regional development factors (consistency 0.9081>0.9) and policy factors (consistency 0.9322>0.9) are the necessary conditions for the efficient transformation of scientific and technological achievements of higher vocational colleges and universities, and they are the key influences to improve the energy efficiency of scientific and technological achievements transformation.
Shaanxi folk women’s red has beautiful graphic patterns, which is a treasure of Chinese folk culture. In order to better realize the inheritance and innovation of folk women’s red, this paper refers to the idea of multi-objective optimization, and innovatively designs the composition of ornaments through genetic algorithm and bipartite continuous pattern design method. In order to find out the deep meaning and cultural value of Shaanxi needlework decoration and the unique aesthetic, emotional and life experience of women hidden behind the decoration. In addition, further research on Shaanxi needlework decoration art through multi-objective optimization will not only help to deeply understand the common characteristics of national art, but also help to deeply understand the characteristics of folk art itself. The research shows that the composition scheme designed in this paper has been positively evaluated by experts and consumers, and can promote the inheritance and innovation of Shaanxi folk needlework.
In the long-term teaching practice, various disciplines have accumulated a large number of teaching resources but cannot function fully and efficiently. For this reason, this study constructs a knowledge mapping of college disciplines based on deep learning. First of all, the overall construction of the atlas is planned, the core concepts of the discipline are identified, the relationships between the knowledge points are defined, and the resources corresponding to the knowledge entities and attributes are expanded. Then deep learning is utilized for the entity construction of the subject knowledge graph, the neural network models BiLSTM+CRF and BiLSTM+Attention are used for the subject entity identification and relationship extraction, and finally the subject knowledge fusion and storage is carried out, and the effectiveness of the designed algorithms is verified on the dataset. The data show that the knowledge representation of knowledge graph is conducive to demonstrating the logical meaning between learning materials, facilitating learners to correlate what they have learned previously with what they are learning now, fusing old and new knowledge, and facilitating learners to meaningfully construct knowledge.
In order to realize the intelligent operation and maintenance of electrochemical energy storage power station and make the working process of the power station battery more efficient, stable and safe, this paper establishes a safety monitoring system of electrochemical energy storage power station through multimodal fusion sensing technology. The multi-sensor fusion technology and multi-sensor calibration process are proposed, and the Kalman joint filter fusion algorithm is obtained based on the traditional Kalman filter extension, which fuses the collected multi-modal sensing data to realize the real-time detection of the state information of each battery of the energy storage power station. Simulation experiments are carried out to verify the reliability of the Kalman joint filter fusion algorithm, and the deviation value of this algorithm in the filter fusion processing is only 0.1426, which is lower than that of the comparative sliding average filtering algorithm. The RMSE values of X-axis and Y-axis in the motion target tracking experiments are less than those of the comparative mean drift algorithm 0.189 and 0.1412, and in the speed, they are less than those of 0.0062 and 0.0073, which are better in terms of accuracy performance. And in the application practice of battery safety monitoring system for electrochemical energy storage power station, the error between SOC estimation and actual value is less than 5% in either DST condition or UDDS condition, and the internal resistance 0R change curve is similar to the actual value of the internal resistance, and the estimation error is less than 4%.
The article constructs binocular vision 3D image structure by feature extraction and data acquisition of animated images, setting the base modeling points multi-level, establishing texture mapping modeling relationship, then designing key frame interpolation algorithms such as segmented cubic spline interpolation and quaternionic spherical linear interpolation, and applying geometric algebra to 3D animation modeling, and using a conformal geometric algebra approach to describe the 3D model as well as the dynamic model. Calculation results. The 3D animation modeling using the method of this paper reduces the error of 36.8mm compared with the same type of method, so the effect of using the method of this paper is better than 1other algorithms in 3D human body modeling. In the subjective evaluation of the visual effect of 3D animation video, 19 people think that the video has a strong sense of spatial three-dimensionality, and on the whole, the majority of people think that the animation video developed using the method of this paper is clear, realistic, has a sense of spatial three-dimensionality, smooth movement of the object, and the use of the lens is comfortable, which has a better visual communication effect.
Human-computer interaction scenarios have a broad prospect in the field of English learning. In this paper, a human-computer dialogue interaction system for English learning scenarios is designed based on deep reinforcement learning and artificial intelligence interaction technology. Firstly, a speech enhancement method based on collaborative recurrent network is proposed to optimize the speech analysis module. On this basis, we design the framework of human-computer interaction system, and construct a human-computer dialogue interaction system for English learning scenarios that contains three modules: natural language understanding (NLU), knowledge retrieval enhancement, and natural language generation (NLG), in which knowledge retrieval enhancement utilizes ChatGPT for document reordering design. In the speech enhancement simulation experiments, the mean value of network congestion for the speech enhancement method designed in this paper is 0.073, which achieves at least 50% performance improvement, reduces speech distortion and optimizes the signal-to-noise ratio at the same time. The system is experimentally analyzed for two tasks, conversation state tracking and conversation reply generation, and outperforms the baseline model on both tasks. Finally, a subjective evaluation is conducted, and the system in this paper scores 3.766, which is obviously a smoother human-computer interaction experience, and the English learning interaction experience has a greater advantage compared with the other methods. This paper provides innovative ideas and feasible methods for combining cutting-edge information technology with interactive English teaching.
Based on the relevant theoretical basis and research experience, this paper constructs a three-in-one, subjective and objective evaluation index system of inclusive preschool public service quality of “flexible”, “green” and “soft” quality. Subsequently, the GA-BP neural network quality assessment model based on machine learning algorithm was constructed by utilizing BP neural network analysis and hierarchical analysis to assign weights to the indicators. It was applied in a scientific operation process to synthesize subjective and objective data to understand the quality of public services of inclusive preschool education, and to propose an improvement path in combination with the IPA analysis model. The results show that the weights of the three first-level indicators are 0.428, 0.4231 and 0.1489, respectively, and the weights of the four second-level indicators, including special child care and migrant child care, are more than 0.1, while the weights of the other second-level indicators are all less than 0.1. Among the third-level indicators, the weights of the reasonable sharing of the cost of pre-school education among the government, families, kindergartens and the society, and the synergy of education and rehabilitation are more than 0.05, while the weights of the other third-level indicators are all less than 0.5, and the weights of the other third-level indicators are more than 0.1. In addition, the difference between the actual evaluation results and the simulation evaluation results of the teaching quality of universal preschool education public services is relatively small. And the error between the real values of GA-BP model is extremely small, and its average error is only 0.483.
Smart grid technology is developing rapidly around the world and is gradually applied to the operation and maintenance management of power systems, and its main advantage lies in its integration capability, which can effectively realize the high efficiency, security and reliability of power system operation and maintenance. This paper explores the integration of grid operation and maintenance by integrating computing and information theory using multidimensional data mining and analysis methods. The operation data of smart grid is first preprocessed, including resampling and PCA dimensionality reduction of multidimensional data signals. Then, a CNN-based power operation state prediction model and an R-CNN-based grid fault diagnosis model are constructed to ensure the stable operation and timely maintenance of the smart grid, and the predicted and actual values of the smart grid operation state of the CNN model are basically consistent with each other, with the MAE, MSE, and RMSE of 0.00104, 0.00014, and 0.012, respectively, and the prediction results are good. The effect is good. Compared with CNN and SVM, the performance of R-GNN model is better, and after PCA dimensionality reduction, the fault identification rate of R-GNN model is as high as 98.91%. And the delay of the R-GNN method for fault diagnosis is only 0.04s, while it can realize the comprehensive and accurate localization of the fault area. This paper provides methodological reference for the utilization of multidimensional data mining and analysis technology to realize the operation and maintenance integration of smart grid.
Service quality is the key for takeaway platforms to maintain their advantages in the fierce market competition. In this study, we construct a mathematical model to solve the takeaway delivery problem by ant colony algorithm, so as to realize the takeaway delivery path planning based on ant colony algorithm. The grey neural network model is used to predict the order demand in the takeaway platform, and the fruit fly algorithm is used to fine-tune and optimize the parameters in the grey neural network model to avoid the model from falling into the local optimum and to improve the accuracy of the model in predicting the takeaway demand. Through simulation experiments, it is found that the planning algorithm in this paper can successfully realize the reasonable planning of takeaway delivery paths when the initial positions of merchants, users and delivery workers are known. The gray neural network optimized using the fruit fly algorithm is also able to accurately predict the takeout demand of platform users based on the order data provided by the takeout platform. Using the method of this paper for the improvement of the service quality of the takeaway platform can significantly improve the delivery efficiency of takeaway orders and develop personalized service strategies according to user demand, thus enhancing user satisfaction with the takeaway platform.
With the rapid development of China’s economic level and the significant improvement of people’s living standard, the quality issue of peaches has become more and more strict. In this paper, based on deep learning algorithm, we propose the recognition method of peach fruit color, size and fruit shape features, combined with near-infrared spectroscopy detection technology, to quantify the peach fruit components and discriminate its maturity. Differential algorithm, standard normal transform, and multiple scattering correction are applied to pre-process peach fruit data. Based on M-YOLOv5s target detection framework, spectral analysis and image characterization techniques were used to jointly detect the degree of peach fruit disease. The distribution of peach fruit quality parameters was investigated, and the test results showed that 39.19% of the samples with measured values of fruit size were concentrated at 1.60-6.40 cm, and 61.79% of the samples with predicted values were concentrated at 2.50-7.50 cm, which was located at around the mean value of 4.763 cm.The classification accuracies of the information modeling set and validation set for the combination of the spectral analysis and image eigenvalue detection techniques were 91.439% and 88.487%, respectively, and the combined use of the two techniques had a high accuracy for the differentiation of diseased peach fruits. Based on the experimental results, the application of spectral detection technology in food freshness detection as well as pesticide residues and illegal additives is explored.
This paper designs the system structure to meet the impact test of aircraft landing, and utilizes finite element calculation to derive the maximum impact stress of the impact platform and the maximum bearing stress. Analyze the attitude combination measurement system, based on the coordinate transformation theory to build a digital level, attitude probe and inclination sensor combination of attitude measurement model, the horizontal attitude angle of the object to solve the calculation. And the robustness overall least squares method is applied for plane fitting. The overall flow of the attitude measurement experiment is designed to analyze the stability and accuracy of the spatial attitude measurement system based on the combination of multi-sensors, and analyze the measurement error of the measured target in different states (translation or deflection). Different attitude solving algorithms are used to measure the attitude angle of the dynamic simulation experiment, and the measurement errors of the roll angle, pitch angle, heading angle and the root-mean-square error are compared. The RMS errors of the roll angle, pitch angle and heading angle measured by the attitude solution model in this paper are 0.2982, 0.2214 and 1.0333, respectively.Comparing with the data in the charts and graphs, it can be seen that the measurement errors and RMS errors of the attitude solution algorithm used in this paper are smaller, which are more in line with the requirements of the target spatial attitude measurement.
The development of communication technology and the rapid growth of the number of mobile network service users have made the competitive situation in the market of communication service increasingly fierce, and maintaining the stock of users is of great significance to the sustainable development of telecommunication enterprises. In this paper, we collect relevant data features of telecommunication users, and after pre-processing the features with RFM model, we use XGBoost model to analyze the importance of each user’s feature value. Then we use the secondary classification Stacking integration model that combines the base learner and the meta-learner to predict the telecom subscriber churn. Comparative validation reveals that the prediction model in this paper shows excellent prediction performance in all four datasets. Practical application results show that the effectiveness of churn maintenance efforts by telecom companies is improved after applying the model, and the average maintenance response rate reaches 50.63% in the first quarter of 2024. The prediction model proposed in this paper based on the binary classification method can assist telecommunication companies to manage the stock of subscribers, optimize the maintenance work plan, and reduce the subscriber churn rate in the telecommunication work period.
The employment and entrepreneurship career choice planning of college students is an important constituent module of the talent training system of colleges and universities in the new era. Aiming at the traditional ant colony algorithm with poor realm adaptability and a large number of inflection points, this paper proposes an ant colony algorithm based on Sigmoid statistical iteration. The Sigmoid activation function distribution strategy is adopted to reduce the blindness of the algorithm’s presearch, and the heuristic function is dynamically adjusted by the introduction of the adaptive factor to reduce the convergence time of the algorithm, and finally the pheromone update function is dynamically adjusted according to the number of iterations to construct the career choice path planning model and apply the model to the career choice planning path recommendation system. When the number of users is 1000, the average response time of the proposed system is only 322ms, the throughput is 394, and the pass rate is 100%, and the CPU occupancy and memory usage are lower than those of the traditional system (35.32% and 39.83%).
With the rapid development of distribution networks in China and the increasing penetration of renewable and traditional energy sources, it is necessary to study the optimal allocation of capacity and optimal operation for the two stages of pre-planning and practical application of distribution networks. In this paper, the probability density function is used to model the uncertainty of “source” and “load” respectively, and the optimal allocation model of distributed power supply capacity of distribution network system is constructed by the equipment models of “wind generator”, “photovoltaic generator”, “diesel generator” and “battery”. Comprehensive cost and power supply security are taken as the objective function and constraints, respectively, to improve the distributed power supply capacity optimization, and adaptive sparrow search algorithm is applied to solve the model. In the comparative analysis of source-load synergy, source-load synergy and energy storage system joint optimization configuration scheme, the joint planning of DPV and ESS enhances the installed capacity of DPV by about 13.45%, and the average power generation of the joint planning scheme is 88.35 kW/h. The joint planning obviously enhances the installed capacity of DPV under the condition of slightly increasing the DPV curtailment. Examples are examined to verify the practical application of the proposed adaptive sparrow search algorithm in configuring the power supply capacity of the hybrid generation system, and the cost of using the cyclic charging operation scheme is 81,067 yuan lower than that of using the load-tracking scheme, and the economic effect has been significantly improved.
In order to solve the adverse effects of uncontrolled charging of electric vehicles on the distribution network, the study constructs a Monte Carlo-based uncontrolled charging load model to calculate the effects of uncontrolled charging on the electric vehicle side on the distribution network load and voltage. Based on this, the electric vehicle trip chain is modeled by Bayesian network so as to manage the charging options of electric vehicles. The charging loads of EVs managed by the Bayesian network at different sizes and different charging locations are predicted to explore the impact of the Bayesian network on EV charging and distribution grid loads. The peak weekday grid base load occurs at 11:00 AM (3695 kW) and 20:00 PM (3656 kW). On weekdays, the grid base load occurs at 12:00 pm (3495 kW) and 20:00 pm (3725 kW), and the peak load increases significantly with the increase of penetration rate and the time is gradually advanced. The end node 18 has the lowest voltage and the lowest value of voltage at node 18 is 0.9135 and 0.9140 on weekdays and bi-weekdays respectively when only the base load is present. At 100% penetration, the minimum voltage is 0.9015 and 0.9008 on weekdays and bi-weekdays, respectively. When the penetration rate of electric vehicles is 20% and 30%, the average value of peak load of electric vehicle charging power increases to 150.05kW and 220.85kW. When the charging scheme of residential charging + office charging is used, the peak load of EV charging in residential areas is reduced by 60.3%.
Higher-order cognitive computational modeling focuses on the large amount of data generated by learners during their educational activities in order to make predictions and inferences and obtain their cognitive characteristics. In this paper, the original ant colony system algorithm is improved. Considering learners as ants, through state transfer probability calculation, pheromone updating, and continuous iteration of multiple ants with the same cognitive characteristics, the optimal teaching path suitable for the learner can be derived. After analyzing, it can be seen that comparing with the data of other GA and ACO algorithms, the improved ACO algorithm in this paper achieves the optimal training effect. By setting up the experimental group and the control group, it can be found that the teaching paths of the five students who did not use the method of this paper were all longer. Therefore, a concise and precise teaching path can be designed from the complicated learning resources and activities. Compared to the control group, the students in the experimental group presented more significant grammar scores and grammar learning attitudes (p<0.001).
Smart grid technology is developing rapidly around the world and is gradually applied to the operation and maintenance management of power systems, and its main advantage lies in its integration capability, which can effectively realize the high efficiency, security and reliability of power system operation and maintenance. This paper explores the integration of grid operation and maintenance by integrating computing and information theory using multidimensional data mining and analysis methods. The operation data of smart grid is first preprocessed, including resampling and PCA dimensionality reduction of multidimensional data signals. Then, a CNN-based power operation state prediction model and an R-CNN-based grid fault diagnosis model are constructed to ensure the stable operation and timely maintenance of the smart grid, and the predicted and actual values of the smart grid operation state of the CNN model are basically consistent with each other, with the MAE, MSE, and RMSE of 0.00104, 0.00014, and 0.012, respectively, and the prediction results are good. The effect is good. Compared with CNN and SVM, the performance of R-GNN model is better, and after PCA dimensionality reduction, the fault identification rate of R-GNN model is as high as 98.91%. And the delay of the R-GNN method for fault diagnosis is only 0.04s, while it can realize the comprehensive and accurate localization of the fault area. This paper provides methodological reference for the utilization of multidimensional data mining and analysis technology to realize the operation and maintenance integration of smart grid.
In this paper, the Gamma process is used to describe the change of cutting force coefficient and analyze the time-varying stability of chattering, and then the time-varying reliability model of chattering of turning machining system is established. The optimal Coupla function model is selected by the AIC criterion, and the reliability analysis of the turning machining system is carried out by using the Monte Carlo method and the VC-MCS method which introduce the Coupla function, and at the same time, the fuzzy factors of the turning machining process are taken into consideration, and the fuzzy optimization mathematical model of turning machining is set up with the goal of the lowest machining cost, and then the model is solved by using the multi-objective particle swarm optimization algorithm, which realizes the fuzzy optimization in the aerospace manufacturing. Then the model is solved using a multi objective particle swarm optimization algorithm to realize the reliability optimization of turning machining process in aerospace manufacturing, and the fuzzy optimization mathematical model of turning machining is experimentally verified by taking common plane milling and cylindrical turning as an example. The experimental results show that the analysis results of the VC-MCS method and the Monte Carlo method with the introduction of Coupla function are almost the same, which verifies that ignoring the correlation between the parameters affects the turning reliability results, and secondly, the turning machining system operates well at full rotational speeds when the turning width b=0.63mm. Finally, according to the case results, the effectiveness and feasibility of the proposed optimization method is proved, which can provide certain optimization objectives for improving the efficiency of turning machining.
This study focuses on the “blockchain + education” perspective, focusing on the integration of edge computing in the higher education resource sharing system. Through the benign interaction between blockchain and edge computing in the system data management system, the security and efficiency of data storage and transmission of shared resources in the system can be improved. In order to improve the performance of the system’s educational resource sharing, this paper utilizes the node identification model on the basis of the traditional PBFT consensus algorithm for the selection of master nodes and the monitoring of malicious nodes. Meanwhile, in order to ensure the balanced allocation of educational resources within the sharing system as much as possible, this paper utilizes the differential evolution (DE) algorithm for the balanced allocation of system resources and the educational resources within the system. The results of experiments and system tests show that the improved PBFT consensus algorithm (NR-PBFT) in this paper shows obvious superiority in tests such as throughput and latency. Although the educational resource allocation model performs poorly in the allocation of resources with larger technology such as digital books, the results for the allocation of teacher resources can effectively prove the effectiveness of the resource allocation model in this paper. In addition, the system test results also show that the system in this paper has good performance, and the introduction of edge computing can significantly reduce the packet loss rate of resource sharing, which has considerable application value.
Mental health issues have become a global concern. Aiming at the complexity of individual facial emotion expression in the task of analyzing mental health status, this study proposes a face emotion recognition method oriented to psychological intervention. The method integrates image recognition and sentiment analysis techniques, adopts Adaboost algorithm for face detection, generates an emotion region suggestion network based on face image recognition, and constructs an image sentiment classification network through feature map mapping and shared convolution. The method is then applied to the mental health recognition system. The model in this paper avoids the effects of individual and illumination differences. It has good face emotion recognition on several datasets, and the prediction accuracies are above 90%, especially for Happy emotion. In the comparison with other recognition methods, the recognition accuracy of this paper’s model is improved by 12.92% to 22.95%. The experiments show that the proposed face emotion recognition method can effectively predict the emotion of facial expression data in the mental health recognition system, and promote the assessment of individual mental health status and emotion management.
In order to optimize the design effect of cultural and creative products with non-heritage patterns, this paper uses image reconstruction algorithm and image recognition algorithm to process non-heritage problem patterns. By combining the processed non-heritage cultural patterns with consumer demand for cultural and creative products, non-heritage cultural pattern cultural and creative products are designed to meet market demand. On the basis of recursive network, we add multi-scale feature extraction module and attention feature fusion module, choose L1 loss function to optimize the details of image reconstruction, and construct image super-resolution reconstruction algorithm based on multi-scale recursive attention feature fusion network. And the image feature extraction network containing MSA module is designed, which is the fine-grained image recognition network based on multi-scale attention. The non-heritage cultural pattern dataset is established, and in order to optimize the recognition rate of non-heritage patterns, the image reconstruction based on multi-scale recursive attention feature fusion network is carried out on the non-heritage cultural pattern data. In view of the creative design strategy of non-heritage culture, the evaluation indexes of non-heritage cultural and creative product design are obtained from the consumer research, and the implementation suggestions of non-heritage pattern cultural and creative product design are derived based on the ranking of the importance of the evaluation indexes. The multi-scale recursive attention feature fusion network proposed in this paper achieves 34.89dB and 90.52% indicator scores on the Set5 dataset. For the design of cultural and creative products with non-heritage patterns, consumers make more suggestions in terms of functional differentiation, having a response rate of 21.58%.
The article calculates the average image entropy of the image domain, quantitatively analyzes the information has richness asymmetry in the task of digitally generating ink paintings, and constructs an asymmetric cyclic coherent ink painting digital generation model based on graphical algorithms. The model integrates a generative adversarial network, and the generator is centered on the Dense Block and replaces the residual block with a dense block to improve the characterization ability. The position fusion attention network is utilized to capture the main body region of the ink painting and combined with the edge extraction technique to extract the significant main body edges of the image and simulate the salient features of the ink painting strokes. The model is integrated into the teaching of “Children’s Ink Painting” course in a high school teacher, and students are instructed to use the algorithm to generate digital ink paintings to further explore the effectiveness of the teaching method. In this paper, the model is iterated for 30 times, and the total objective function converges to the minimum value of 0.85, and the measured values on PSNR, UIQM and UCIQE are improved by 4.44, 0.3 and 0.68 respectively compared with the optimal values of the comparison model, and the model can obtain the highest evaluation score (8) of the generated image at the fastest convergence speed (50 epochs), and the degree of overlap with the real image on the LPIPS distance is higher. After the experiment, the dimensions of digital pedagogical literacy level of the experimental class increased by 3.37 to 7.63 points compared with the control class and showed significant differences. As for the satisfaction of learning experience, students’ satisfaction with digital teaching resources is the highest, which is 4.70 points. The experimental results show that the model constructed in this paper has good performance of ink painting image generation and can be used as a digital teaching method for children’s ink painting course in high school teachers.
In this paper, the strain law of natural gas pressure vessel steel fatigue is firstly analyzed through the stress-strain curve and steel fatigue life curve, and the finite element model of natural gas pressure vessel is constructed by combining ABAQUS simulation software, and the fatigue performance of natural gas pressure vessel steel is analyzed from the cyclic softening behavior of the material and SN curve. Then the stress intensity factor theory combined with Paris formula is introduced to calculate the crack expansion rate, and ABAQUS and FTANC3D are jointly simulated to study the crack expansion law. Finally, the fatigue yield strength of the natural gas pressure vessel was analyzed based on the elastic-viscoplastic constitutive model combined with the finite element model. It is found that the stress intensity factor along the path under hydrostatic loading is larger than that under stresscontaining loading, but the difference in stress intensity factor is only about 1.42%. When the cracks of the natural gas pressure vessel extended to the vicinity of 20 mm, its crack extension rate showed a sharp downward trend. When the temperature comes to 900°C, the yield strength value of the steel of natural gas pressure vessel is only 280.42 MPa.Exploring the steel fatigue performance and crack extension rate of natural gas pressure vessel can help to better ensure the stable and safe operation of natural gas pressure vessel.
The article explores the method of diversified modeling of college sports track and field data, aiming to provide a basis for scientific training of college sports track and field. In this article, the diversified modeling of college sports track and field data is carried out by using multiple linear regression model, testing method and mathematical statistics method in order to analyze the sports characteristics and training needs of college track and field athletes. Using multiple linear regression model to analyze the influencing factors of track and field special movement patterns, then, on the basis of clarifying the training needs of track and field special movement patterns, combining the theoretical study of functional movement screening with the actual practice of track and field sports, carrying out the FMS test of the research object, and proposing the optimization plan of college sports track and field training after analyzing the results of functional movement screening of different track and field events. By using the multi-dimensional modeling method of college sports track and field data proposed in this paper to analyze the influencing factors of athletes’ track and field special action patterns, it is found that there is a significant medium correlation between the “torso forward swing and hip and knee rotation speed” in the buffer action link and the “torso extension speed” in the kick and stretch action link and the in-situ jump height. At the same time, there was a significant correlation between the common factor “trunk forward swing and hip and knee rotation speed” and the “trunk extension speed” in the push and extension link.
Using digital back camera to complete the traditional national costume image acquisition work, and then with the help of VOLO model to segment and colorize the image, the traditional national costume elements were successfully extracted. By fusing them with smart wearable devices, a detailed fusion implementation scheme is developed, which contains constraints and objective functions. In the context of numerical computation optimization, the fruit ϐly algorithm (FAO) is used to explore the fusion design scheme of the two in depth. The values of the four objective factors of the fusion design are 0.233, 0.232, 0.348, 0.144, and the ϐinal value of the objective function is 0.957, which indicates that the results of this paper not only can improve the comfort of the device and the user’s experience, but also can provide a new idea and method for the fusion of the apparel industry and the wearable device industry.
With the development of big data and education informatization, education reform and talent cultivation mode are facing digital reform. In this paper, the important feature selection algorithm based on random forest is used to select the relevant features that affect the application effect of teachers’ practice teaching cultivation and innovation mechanism, which lays the foundation for constructing the practice teaching data mining model based on Light GBM. Then the data processed by feature selection is preprocessed and standardized, and then the processed data is partitioned and the model is trained in turn to get the prediction results. The Light GBM-based practical teaching data mining model was compared with other classification models in different datasets, and the experimental results showed that the model in this paper has an advantage over other classification models in a number of evaluation indexes, with the highest accuracy rate of 13.07%, and the model data mining results accurately locate the open innovation experimental indexes that have a lower score of importance to students’ development, and provide a good basis for the optimization of teaching paths and students’ development. , which provides ideas for the optimization of teaching paths and the improvement of the impact of students’ future development.
Sparse decomposition has been generally emphasized in signal processing theory. In this paper, a nonelectrical signal feature dataset of key components of high-voltage DC converter valve is established by using principal component analysis to streamline the data volume. The compression-aware feature extraction algorithm based on polynomial matrix sparse coding is used to extract and collect the nonelectrical signal parametric data. Through the performance over the experimental signal analysis, it can be known that the eigenvalues of a total of 10 parameters, including the infrared temperature measurement results, the appearance, the presence of corrosion or dirt, and the presence of abnormal vibration and sound, are all greater than 1. Therefore, these 10 parameters are identified as the key parameters. When the number of measurement points is between 64 and 200, the algorithm in this paper can satisfy the need of feature extraction when the signal length is insufficient, compared with the traditional approach. In the empirical analysis of the vibration signal as an example, the method of this paper can effectively extract the frequency and time domain of the vibration signal.
As the core equipment of high-voltage direct current transmission system, the operation status of the converter valve directly affects the safety of the power grid. In this paper, we first construct a multisource data fusion system to realize the error-free fusion of fault information parameters. Then, combined with the random forest algorithm, the time-varying law of the electrical characteristics of the converter valve based on harmonic theory is extracted. Finally, the collected time-varying laws of electrical characteristics are input into the constructed Random Forest particle swarm optimization model, and the trained model is used to monitor the status of the converter valve. In the simulation experiment, the ±800kV UHV DC transmission system is built by PSCAD/EMTDC software, from which the current waveforms are collected when the converter valve fails, the time domain features of the current are extracted, and the obtained converter feature indicators are selected using the Random Forest algorithm, and 10 important features will be finally identified to construct the converter valve feature indicator set, and input into the Random Forest Particle Swarm Optimization model and the other comparative models for training and testing. The accuracy of this model is 97.5%, which is better than other comparative models. The study provides a high-precision solution for converter valve condition monitoring and effectively extends the application of multi-source data fusion in power equipment.
This paper proposes a real-time computational method for multidimensional dynamic data fusion (VIO-SLAM) for intelligent monitoring of seat belts in the grid construction environment. In this paper, the optical flow method is first used to process and track point features, and the geometrically constrained line matching algorithm is utilized to improve the accuracy of feature matching. Combined with IMU modeling and pre-integration techniques, it effectively reduces the computation of high frequency IMU data and improves the system efficiency. At the same time, a real-time lightweight semantic segmentation system is constructed to achieve fast semantic understanding of the construction scene. The real-time and accuracy of data processing is further improved by sliding window method with BA optimization. On this basis, a VIO-SLAM algorithm based on EKF fusion of multidimensional dynamic data is proposed to realize real-time monitoring and localization of seat belt status. The results show that when a dangerous collision occurs in a complex power grid construction environment, the protection performance of shoulder belt, neck bending moment force and head acceleration of the construction personnel under the method of this paper is much higher than that of the traditional seat belt. In the process of emergency collision avoidance, the VIO-SLAM algorithm is able to tighten the seat belt in advance for the construction personnel, which has better protection performance and can achieve the purpose of “collision avoidance and damage reduction”. The pre-tensioning force for eliminating the gap in the webbing of seat belts and the pre-tensioning force for somatosensory warning reminders are also determined to improve the protection performance of construction workers.
At present, the physical training of public security police has not formed a unified training system in the country, and various places ignore the cultivation of other aspects of the ability to take skill training as the leading role, and solve the problem of how to train through the construction of the system, so as to ensure that the physical training of public security police is carried out effectively. This paper explores the impact of physical training on college students’ professionalism in public security colleges, constructs the K nearest neighbor classification algorithm, and introduces the relevant activation function to deal with more complex students’ physical training exercise trajectories. ATT-DAN multitarget tracking model is constructed to extract the feature information of college students’ physical fitness training, obtain the target movement trajectory, and parameterize the representation of students’ physical fitness training programs. The correlation ranges of frequency, average score, highest grade score of physical fitness training and occupational ability were between 0.415~0.632, 0.452~0.769, 0.412~0.715, respectively, and the credibility and stability of the occupational ability characteristics were good. Meanwhile, the linear regression of the two showed that the correlation P value of age, 30-second deep squat, pull-up, 3200 meters, and 15-second repetitive straddle with occupational ability was less than 0.05, and there was a positive correlation between the two.
Power system simulation training is one of the important means to improve the quality of operators and ensure the safe and effective operation of power systems. Research based on digital twin technology, combined with configuration algorithms to give the substation integration diagram model generation method, developed a smart substation virtual training system. The intelligent monitoring is studied, the digital twin-based substation output voltage anomaly detection method is designed using the tracking differentiator method, and finally the simulation test of the intelligent substation virtual training system is carried out. The analysis shows that the voltage anomaly detection method in this paper is highly accurate and can extract the voltage anomaly waveform, and the offset rate of its collected signal is significantly lower than that of the comparison method (11.58%~14.84%), which is only 0.54%. The training test of fast distance protection, differential protection and zero sequence protection verifies the feasibility and effectiveness of the virtual training system in practical application. The platform can effectively promote the reform of applied electric power practice courses and provide a backbone for the training of new power system talents.
The rapid development of information technology has put forward higher requirements for teachers, and the traditional training model is difficult to meet the demand. The article constructs a teacher digital competency framework based on the ASTD model, realizes the division of teachers’ professional competence, and explains the professional core connotation of teacher digital competency in detail. A personalized resource recommendation model for teachers is constructed using artificial intelligence technology, which provides accurate recommendations for teachers through candidate resource extraction and learning resource screening. At the same time, with the help of Google Cloud Services digital tools, the design of teachers’ digital teaching and research activities was accomplished, and communication and cooperation with users in the virtual community was promoted. The combination of the two is integrated into the development of teachers’ professional skills to enhance their teaching competence. The mean values of accuracy, applicability, timeliness, personalization, and diversity of learning resource recommendations under artificial intelligence technology ranged from 4.123 to 4.544, with good recommendation performance. The Google Cloud Services platform can promote teaching and research exchange activities among teachers. The use of artificial intelligence and digital tools makes teachers improve their professional skills in knowledge base, instructional design, teaching and research between 24.04% and 91.00%, and with their intervention, teacher competency shows significant improvement.
Under the environment of plateau alpine region, the new model of substitute construction separating government construction and management functions has gained great development in barracks construction, which significantly improves the risk management level of barracks facilities to some extent. From the significance of barracks facilities construction guarantee in highland alpine area, the article proposes a risk identification framework for the substitute construction unit of Someplace facilities in highland alpine area based on the whole life cycle of engineering projects. Combined with the risk identification framework, the risk evaluation index system of the agency construction unit is constructed, and then the AHP hierarchical analysis method is introduced to solve the weight of the indexes, and combined with the fuzzy comprehensive evaluation method, the AHP-FCM evaluation model is constructed. A barracks facilities project in a camp area is selected as a case study, and Company T is used as the research object to carry out data analysis of its risk degree using the AHPFCM model. In the construction of barracks facilities in highland and alpine areas, the biggest risk faced by the construction unit is the project implementation stage, the weight of which reaches 29.93%, and the fuzzy comprehensive evaluation of Company T’s risk score is 3.182, which is between medium and large risks. Therefore, the agency needs to examine and check its own risk factors in time, in order to lay a solid foundation for ensuring the smooth implementation of the agency project of barracks facilities in highland alpine areas.
The study is based on the important role of graph theory in the teaching of physical dance and aesthetic education, integrating the concept of graph theory into it and designing the training path of physical dance and aesthetic education based on graph theory. Taking two classes in a university as the research object, the teaching experiment is conducted to compare their physical quality and course performance after the experiment, and the aesthetic education evaluation index system is constructed, and the index weights are measured using the combination assignment method to carry out the comprehensive scoring. After the experiment, the students improved in physical quality, course grades and aesthetic effect, and as far as the students of traditional teaching class are concerned, the experimental students improved in course grades and aesthetic effect by 18.17% and 7.52% respectively. The teaching practice of integrating the concept of graph theory and the curriculum of physical education dance and aesthetic education not only embodies the concept of cross-disciplinary teaching, but more importantly improves the physical quality, physical education dance level and aesthetic effect of students in colleges and universities, and provides a reference for the teaching reform of physical education dance and aesthetic education in colleges and universities.
Transportation demand is gradually increasing and road traffic congestion is becoming more and more serious. Traffic state prediction is one of the important bases for accurate traffic management and control. This paper investigates a traffic state prediction method based on a deep learning algorithm fusing spatio-temporal graphical convolutional networks, and explores the law of path selection decision-making of pedestrians under different traffic flow prediction and guidance strategies, and analyzes the effect of the implementation of the information guidance policy by traffic managers in realistic scenarios using evolutionary game theory. The simulation results combined with the traffic simulation model show that the traffic state prediction method proposed in this paper is more effective compared with other models. The evolution results are more reasonable when the value of the path adjustment rate in the replicated dynamic model is the inverse of the number of iterations. In the perceptual error analysis, when the value of perceptual error 1 is taken to be too large, i.e., when the perceptual error of the first type of travelers is small and small, it tends to be a deterministic choice. Finally, a traffic simulation model is implemented to validate the performance of the proposed model and propose congestion mitigation strategies.
The construction of information resource management system is a promotion for upgrading industrial structure and enhancing independent innovation capability. Based on the city-level data of a region from 2010 to 2022, the evaluation index system of information resource management system and regional economic development mode is established, and the variables are measured according to the collected data using gray correlation analysis method. Then double machine learning method is applied to explore the influence effect of information resource management system on regional economic development model. The baseline regression analysis reveals that the information resource management system can promote the regional economic development model, with a regression coefficient of 0.029, and the conclusion still holds after the stability test. The heterogeneity results show that regions with better economic foundation (0.067) and peripheral cities (0.036) are more significantly affected by the positive spillover effect of the information resource management system. This paper combines machine learning algorithms with traditional causal inference to explore the role path of information resource management system to promote regional economic development model, which provides empirical evidence and decision-making reference for promoting regional economic development.
With the rapid development of the global cruise transportation industry and the worldwide increase of cruise ship transportation year by year, fire accidents on passenger and roll-on/roll-off ships (P/ROCs) pose a serious threat to economic properties. The article establishes a fire model of a passenger-roller ship carrying electric vehicles using the basic equation of dynamics, a large eddy simulation model, and a mixed fraction combustion model. The mesh division is used to improve the solving accuracy of the kinetic equations. The fire simulation conditions of the electric vehicle carried by a passenger-roller ship are designed to analyze the fire combustion characteristics of the passenger-roller ship transported in terms of wind speed, fire intensity, and ignition power in multiple dimensions using the FDS simulation software as a carrier. Based on the YOLOv5s network and combined with the improved non-great suppression algorithm, a statistical model for target detection of electric vehicles carried by a passenger-roller ship is designed, and the corresponding loss function is designed. When the external ambient wind speed was increased from 0.5 m/s to 6.5 m/s, the maximum temperature at the fire center of the electric vehicle carried by the passenger-roller ship was reduced from 883.93°C to 748.57°C. The improved YOLOv5s model has the highest mAP of 96.67% on the target detection of EVs after fire damage and an accuracy of 92.96% for counting the number of EVs after fire. The state of electric vehicles after fire damage can be obtained under fire dynamics simulation, and the target detection and quantity counting of electric vehicles can be effectively realized by combining deep learning technology.
Background: Ultraviolet radiation (UVR) causes premature skin aging. Litchi seed (LS) is considered a natural plant extract with potential antioxidant, anti-aging and anti-inflammatory properties. However, the mechanisms of LS’s protective effects on skin photoaging remain unclear. Objective: This study aims to perform a rapid and efficient virtual screening of the main targets and possible mechanisms of the protective effect of LS on skin photoaging through network pharmacology, bioinformatics and molecular docking. Methods: The primary active compounds and their corresponding targets of LS were obtained from the TCMSP, STP, and UniProt databases. Concurrently, photoaging-related targets were mined from the GEO, GeneCards, and OMIM databases. “LS-photoaging” targets were identified using Venn diagrams created with R software. Protein-protein interaction (PPI) networks and “compound-target-disease” networks were constructed and analyzed using Cytoscape. GO and KEGG pathway enrichment analyses were then performed to predict the protective mechanisms of LS against skin photoaging. Finally, key targets and active compounds were validated through molecular docking using AutoDock Vina. Results: The screening identified 368 targets of LS active compounds and 872 photoaging-related targets. Network topology analysis revealed 87 common targets, with AKT1, IL6, TP53, and CASP3 as core targets. Enrichment analysis reveals that LS can modulate the ROS/MAPK/AP-1 pathway, thereby inhibiting inflammatory responses and reducing oxidative stress, which leads to a decrease in pro-inflammatory factors. Additionally, it promotes collagen restoration by suppressing the expression of MMPs. Molecular docking validation demonstrated a strong binding affinity between the core targets and the key compounds. Conclusion: LS shows potential for treating photoaging by counteracting inflammation and oxidative stress, regulating collagen and lipid metabolism, and inhibiting apoptosis.
The Tradable Green Certificate (TGC) system scientifically guides renewable energy investment by internalising the positive externalities of renewable electricity. With the promotion of energy transition, the demand for TGC has increased significantly, and the scale of market players has gradually expanded. Market players will imitate other players’ trading strategies for reasons such as herd mentality, which is manifested as herd behaviour. If TGC market players ignore high-quality information and blindly imitate the behaviour of other players, it will limit the diffusion of effective information in the market and reduce the pricing efficiency of the market. Therefore, this paper explores the emergence law of herd behaviour in the TGC market based on a hybrid system dynamic model, with a view to providing theoretical and methodological support for the immediate identification of market risk. This paper portrays the emergence process of herd behaviour of TGC trading subjects, and analyses the emergence law through multi-scenario computational experiments. The results show that (1) herd behavior will emerge from all kinds of strategy subjects and there is a positive feedback relationship between the emergence speed and the return difference between subjects. (2) The emergence of herd behaviour of fundamental strategy subjects has scale and structural effects, and only when the initial imitation scale of such subjects reaches 40% or the market share is less than 50%, will the emergence of herd behaviour, and the depth of its emergence shows an ‘S’ type growth. (3) The herd mentality and the weakening of cognitive bias of TGC trading subjects will reduce the emergence speed of herd behaviour, but have almost no effect on the depth of emergence.
This paper studies integrated process planning and scheduling (IPPS), a typical workshop scheduling problem, and mainly investigates the uncertain problems in the actual industrial production process. Then, we introduce the theoretical knowledge of interval numbers and adopt the interval number comparison method. Specifically, interval numbers are used to replace the determined processing time, and uncertain IPPS problems are modeled based on the interval number theory. Based on this, a hybrid particle swarm algorithm is proposed to solve the uncertain IPPS. Meanwhile, the genetic operator is introduced to improve its ability to deal with combined optimization problems. The above theoretical results are applied to the process planning and scheduling of a mechanical workshop, thus verifying the effectiveness of the proposed method.
Rural digitalization and rural tourism are important tasks to achieve the goal of rural revitalization strategy, and researching whether there is a connection between them and the degree of association is helpful to accelerate the transformation of rural digitalization and promote the quality and upgrading of rural tourism. This paper constructs an evaluation system of rural digitalization and rural tourism, adopting 253 counties in China as samples to measure the development differences between regions of the two systems. A coupled coordination model is applied to explore the relationship between the two systems and reveals the distribution characteristics of the level of coupling and coordination in China. The findings show that the difference in the overall score of rural digitalization between counties is greater than that of rural tourism industry. There is a high degree of coupling between rural digitalization and rural tourism systems, and the two systems are currently at a barely coordinated stage in China. In addition, the degree of coordination varies significantly between counties, presenting a phenomenon of higher coupling coordination in the eastern coastal region, intermediate in the central and western inland regions, and lower in the northwest. This paper supports and validates some results of rural development projects in the research area to provide theoretical and decision support for coordinating rural digitalization and rural tourism services.
A fault diagnosis method for wind turbine gearbox based on adaptive probability random forest is proposed to address the issue of noise pollution in SCADA data of wind turbine gearbox. Firstly, SMOTE oversampling is used to balance sample categories, and then CART is trained and classified by constructing multiple balanced subsets. The sample error rate represents the weight of sample ambiguity, and the label uncertainty is determined. Monte Carlo simulation is used to calculate the mean distribution of features, which is fused with each sample instance to obtain the uncertainty of sample features. Utilizing adaptive labels and sample uncertainties as inputs to probabilistic random forest can enhance the ability to manage feature noise and label noise, thereby improving the robustness of fault diagnosis. Conduct an experimental evaluation using the SCADA dataset of wind turbine gearbox. The results show that this model outperforms other methods in terms of false alarm rate, false alarm rate, and F1 rating metrics when dealing with missing values, Gaussian noise, and label noise in the dataset, as compared to other methods. This method is of great significance for improving the accuracy and robustness of wind turbine gearbox fault diagnosis.
This paper focuses on the coupling and coordinated development of provincial sports industry and tourism industry. In view of the integration trend of the two as the pillar of the tertiary industry and driven by relevant policies, in view of the insufficient quantitative and regional comparison of existing studies, data from 31 provinces from 2014 to 2021 were selected for analysis. The connotation mechanism of coupling coordination is explained from the economic, social, ecological and cultural levels, and the system including industrial scale and structural indicators is constructed, and the coupling coordination degree model is used to calculate. The results show that the coupling coordination degree of the country is rising in a step, with the eastern starting point being high, the central part making great progress and the western part growing fast. The types of industrial development vary between regions and over time. The global Moreland index shows that there are significant autocorrelation and clustering in the space, the local “high-high” cluster in the east and part of the middle, and the “low-low” cluster in the west. Further, suggestions were put forward to strengthen policy guidance, optimize industrial structure, promote the development of talents and technology, and strengthen the protection and utilization of ecological culture, so as to provide decision-making reference for industrial upgrading and sustainable development of regional economy.
Traditional construction project cost estimation methods rely on expert experience and statistical models, which are difficult to handle complex data and multimodal features effectively and have low prediction precision. This paper constructs an intelligent building engineering cost estimation model that combines subtractive clustering, a self-learning mechanism, and convolutional neural networks (CNN) to address this problem. In the data preprocessing stage, subtractive clustering is applied to optimize multimodal data, screen key features, and eliminate redundant information. Subsequently, the model parameters are dynamically adjusted according to the error feedback through a self-learning mechanism to improve its adaptability to diverse construction projects. In the feature extraction and estimation stage, the CNN module is combined to extract deep features from images, texts, and numerical data to achieve high-precision estimation. The experimental results show that the model in this paper outperforms traditional methods in terms of MSE (mean-square error), MAE (mean absolute error), R² (coefficient of determination), MAPE (mean absolute percentage error), with the mean values being 73.18, 8.33, 0.9477, and 5.33%, respectively. In summary, the model in this paper demonstrates superior precision, adaptability, and robustness in construction project cost estimation.
Foreign direct investment plays a more important role in China’s economic development. This paper examines the impact of FDI on China’s GDP and analyzes regional variability through OLS and quantile regression models. Then the spatial correlation-Moran, I scatter plot is used to visualize the clustering pattern of regional units. The analysis shows that FDI has a significant positive effect on China’s high economic growth at the 25% quantile. However, the higher the economic growth rate, the margin of positive effect of FDI on economic growth gradually decreases. China’s regional economic development is characterized by a dualistic structure. The elasticity coefficient of FDI in the eastern region is 0.099, and that in the western region is 0.05. Therefore, FDI has a greater impact on the eastern region than on the western region. With the development of China, foreign investment began to discrete, gradually spreading from coastal areas to inland areas.
In the current context of China’s economic transition, focusing on the issue of corporate innovation performance can lay a solid foundation for the acceleration of the digital transformation process as well as the improvement of corporate innovation performance. This paper selects the relevant data of a listed enterprise from 2018 to 2023 as a research sample for empirical analysis. Combined with the DIT model to test the role of digital transformation on innovation performance, and on the two perspectives of financing constraints and intellectual property protection, it specifically studies the mediating effect and adjustment mechanism between digital transformation and enterprise innovation performance. Finally, from the perspective of enterprise heterogeneity (whether stateowned or not, enterprise size, geographical policy), the actual impact of digital transformation on performance under different enterprises is specifically analyzed. The results show that digital transformation has a positive effect on enterprise innovation performance, and digital transformation can reduce financing constraints to a certain extent, ensure sufficient financial support for enterprise operations, and contribute to the improvement of enterprise innovation performance. Research on the moderating mechanism shows that intellectual property rights have a positive impact on digital transformation to promote the enhancement of enterprise innovation performance. Further heterogeneity analysis shows that digital transformation has a more prominent effect on innovation performance in large-scale enterprises.
Along with the fast developing of IT, it is more and more popular to apply the modem interaction technique to the educational domain, particularly in the college musical educational potentiality. Based on the perspectives of psychology and interactive technology, the author analyzes the latest progress of interactive technology in human-computer interaction, emotional computing, and design psychology, as well as its impact on music education in universities. It is found that the educational effectiveness of MCAI has been maintained at 92 percent and that of the others has been rising. However, there are some differences between them and the new system. Interactive technology can not only optimize the learning experience and enhance teacher-student interaction, but also provide personalized and intelligent learning support for students through emotional computing and ubiquitous computing technology, thereby enhancing learning effectiveness and artistic creativity. By building a student-centered teaching ecosystem, the deep integration of technology and art education will help promote innovation and improvement in music education in universities in the information age.
The aggravation of population aging makes the demand for elderly care expanding. In this paper, we propose an integrated care model based on deep learning to build an intelligent service robot system for elder care organizations by integrating sentiment analysis and knowledge reasoning techniques. The model is driven by the dynamic needs in long-term care scenarios, and two modules are innovatively designed. In the sentiment analysis module, multimodal sensors (facial expression, audio state, textual content) and graph attention networks are integrated, and global contextual information is modeled on these features to identify long-distance emotional dependencies of the elderly. In the knowledge inference module, graph representation learning is combined with knowledge graph temporal inference to construct an inference model to speculate the care needs of the elderly. The experiment shows that after the system performs long-term service, the depression condition of the elderly is significantly improved, and the nursing care safety risk perception shows a significant difference from that before the system is used (P<0.001). The integrated care model studied in this paper provides a practical technical solution to the problem of aging care resource shortage.
In order to optimize the performance of generative adversarial networks on automatic advertisement image generation, this paper combines the variational self-encoder with generative adversarial networks, which consists of four parts: encoder network, decoder network, target-to-be-attacked network, and discriminator network to form a new adversarial sample generation method based on GANs, i.e., AdvAE-GAN model. To make the generated samples more clear and natural, the adversarial learning mechanism and similarity metric (PCE) are added to the AdvAE-GAN model. To obtain the performance of the model in diverse image coloring, multiple methods are elicited for subjective and objective qualitative evaluation and model complexity analysis, respectively. Combining the four standard datasets of AWA, CUB, SUN and FLO, zero-sample image recognition, generalized zero-sample learning experiments are carried out sequentially to derive the loss value curve of the model. The visual effects of animated advertisements generated by AdvAE-GAN model are rated using questionnaire research. For the product effect of animated advertisements generated by AdvAE-GAN model, the category diversity, design diversity, animation contour completeness, and image clarity indexes with scores above 7 account for 70.47%, 85.82%, 76.73%, and 84.02%, respectively. The animated advertisement generation model based on improved generative adversarial network is recognized by the market as well as the society and can be deepened.
Financial fraud, as a global problem in the financial industry, brings huge economic losses to financial institutions and customers. In this paper, a multi-task financial fraud detection model is constructed based on heterogeneous graph neural network with deep reinforcement learning, combined with variational self-encoder. In this model, the variational self-encoder is combined with graph convolutional network to construct the node input representation coding module, as a way to enhance the multi-task financial fraud data and better mine the structured features of different nodes. The attention mechanism is then introduced to build the relation-aware attention, which deeply mines the input node features, further acquires the neighbor-generated features of different nodes in the network, and combines the mutual information to measure the nonlinear correlation between different random nodes. Then the financial fraud node representation is mapped into the highdimensional space by the multilayer perceptron, and then the financial fraud prediction confidence of the model is obtained, and different types of loss functions are set to ensure the detection efficiency of the model. The results show that the F1-macro and AUC values of the financial fraud detection model on the self-constructed FFD dataset are 0.749 and 0.925, respectively. Relying on the heterogeneous graphical neural network and the variational autocoder, a multi-task financial fraud detection model can be constructed, which provides a new idea for solving the suspected fraud and money laundering cases that may exist in the field of finance and economy.
Under the current development trend of global economic integration, countries around the world are interconnected and influenced by each other in international trade, and the connection of world trade forms a complex network. This paper constructs a global trade network based on global trade theory and social network analysis theory, and selects indicators such as the number of network nodes and network diameter to characterize the topological structure of the global trade network. The Transformer model is designed based on the gating mechanism unit and dynamic attention mechanism to analyze the multimodal, high-dimensional and heterogeneous global trade time series data. The empirical analysis finds that the characteristics of the global trade network structure change over time, the trade network between countries and regions becomes more and more close, and there is an impulse effect of the country’s GDP and other influencing variables on the structure of the global trade network. This paper reveals the multi-path influence effect of global trade network through empirical analysis, and improves the related research on the structural change and positive evolution of global trade network, with a view to providing useful reference and guidance for the formulation of national trade countermeasures.
The article proposes a novel cross-modal adversarial learning framework for analyzing the emotional dynamics of non-English learners during classroom engagement and predicting their individualized behaviors. The framework combines multilevel feature extraction and Transformer CNN-LSTM integrated model to handle multimodal data more efficiently and capture the complex relationship between emotions and behaviors. Low-level and high-level multilevel features are then extracted from the raw multimodal data. Meanwhile, Transformer is utilized to mine long-distance dependencies between multimodal data, CNN extracts local features, and LSTM is used to model dynamic changes in time series. In addition, the framework introduces adversarial training to learn shared features across modalities. Before 50 rounds of training, the CL-Transformer model loss function, emotion recognition accuracy, and behavior prediction accuracy converge, showing the fastest training speed and training results. The algorithm in this paper has more than 90% precision, recall, and F1 scores for emotion recognition and behavior prediction, and the recognition accuracy for different emotions is up to 0.96. In the fifth stage of the case study, the classroom emotion conversion rate and arousal is up to 0.66, and the model predicts that the probability of cell phone playing behavior is the highest for learners who are in angry moods, which is 64.7%. The learners’ classroom emotional acceptance as well as behavioral integration have an impact on their classroom engagement.
The study combines hierarchical Bayesian model and adversarial neural network according to the model architecture of neural machine translation, and introduces the domain generalization method based on cross-domain gating to solve the domain generalization problem, and constructs the neural machine translation system based on hierarchical Bayesian model. Translation performance experiments are conducted on this translation system to test the cross-domain generalization performance of the neural machine translation system based on hierarchical Bayesian model in this paper. The translation method of this paper significantly outperforms the baseline system of statistical machine translation in the direction of translation for all the inter translated languages and medial languages of the European Parliament corpus. The statistical machine translation model and the standard neural machine translation model have maintained a stable performance during the growth of the interpolation coefficients, while the performance of this paper’s hierarchical Bayesian-based neural machine translation system grows rapidly to the maximum when the interpolation coefficients grow to 0.3 or 0.4, and its overall average BLEU value always outperforms that of the statistical machine translation model and the standard neural machine translation model. The BLEU values of the hierarchical Bayesian-based neural machine translation system are 35.26% and 34.28% for bidirectional Chinese-English translation, and 26.42% and 25.96% for bi-directional Chinese-Western translation, which are better than those of the neural machine translation based on the attentional mechanism and variational scoring. And the hierarchical Bayesian-based neural machine translation system has strong stability on the translation of low-resource languages.
As a conventional technique in lacquer painting, the abrasion painting technique is widely used in the creation of modern lacquer painting. In order to promote the digital innovation of the abrasion painting technique in the creation of lacquer paintings, a fusion scheme of the abrasion painting technique and color distribution in the creation of lacquer paintings is formulated. According to the relationship between color and gray scale, the color mapping of image coloring algorithm is proposed under the framework of energy optimization algorithm to realize algorithm-driven lacquer painting color generation. In addition, with the technical support of the renderer, the color distribution of lacquer paintings is integrated with the milling technique according to the principle of texture mapping. With the help of evaluation indexes and experimental platforms, we simulate and analyze the techniques and colors in lacquer painting. In the color generation of lacquer paintings, the indicators of this paper’s method are 34.09, 0.964, 0.025 and 4.28 in order, which verifies the application effect of this paper’s method in the color generation of lacquer paintings. In addition, the speed of this paper’s rendering method (42-86FPS), fully meets the requirements of real-time drawing, this method better promotes the fusion of grinding and painting techniques and color distribution in the creation of lacquer paintings, which is of great significance to the digital dissemination of traditional culture of non-heritage.
As an emerging form of cultural communication, microshort dramas have emerged in the audiovisual industry. In order to explore the optimization method of international communication of short microdramas, this paper takes the selected short micro-dramas of an international video platform as samples, selects the influencing factors of the international communication effect of short microdramas, constructs the optimization model of international communication of short micro-dramas by using Bayesian network, and adopts the Great Likelihood Estimation Algorithm as its parameter learning method. The performance of the Bayesian network model is explored through model comparison, node sensitivity analysis and scenario simulation. The results show that the Bayesian network model has good prediction performance, and its AUC value is greater than 0.8 in both training and testing results. The entropy reduction percentages of publisher’s fan number, video duration and localized creation are all greater than 0.07%, which have the most obvious influence on the effect of international dissemination of microshort dramas. Scenario simulation verifies the influence of each variable on the optimization of the international dissemination effect of micro-short dramas, and the probability value of the obtained optimal solution with a strong dissemination effect is 83.5%. It is recommended to actively guide the creation of high-quality products, carry out in-depth localized creation, accelerate the integration of art and technology, and strengthen the comprehensive governance of the industry, so as to promote the global dissemination of China’s online micro short dramas.
In order to solve the problem of vagueness and uncertainty, which is difficult to deal with in traditional education assessment, this paper introduces the theory of fuzzy matrix logic, and constructs a multilevel assessment model of education quality by means of the affiliation function and multilevel weight allocation. Through fuzzy reasoning and cognitive estimation techniques, combined with knowledge graph visualization, the cognitive level of learners is accurately estimated to achieve personalized learning resource recommendation. The quality assessment of physical education teaching in colleges and universities is taken as an example to verify the application value of the model. The constructed PE teaching quality evaluation index system contains 3 level 1 indicators, 11 level 2 indicators, and 38 level 3 indicators.The initial index scoring result of the PE classroom by 5 raters is an average score of 100.8, which is 89.2 points different from the full average score.The weights of the indicators within the 3 levels do not differ much. Students’ levels of knowledge of the 6 initial physical education concepts ranged from 0.53 to 0.86 points. The maximum inter-conceptual influence strength was 0.86 and the minimum was 0.18. After the interference of the resource recommendation, the cognitive level increased to between 0.67-0.98 points. The maximum inter-conceptual influence intensity reaches 1. The Sig value is greater than 0.05, and the results of the model calculations have reliability and can be used for education quality assessment and dynamic learning planning method improvement.
Due to the complexity of the ship product structure and process, long production cycle and other factors, ship enterprises are plagued by the problem of profitability. Strengthening cost prediction and budget control is a very important means for ship enterprises to improve their profit margins. By analyzing the cost structure of shipbuilding, this paper proposes a rolling forecast model of shipbuilding cost based on long and short-term memory neural network (LSTM) as the estimation method of shipbuilding cost. Meanwhile, the traditional earned value method and target cost method are combined to sort out the shipbuilding cost control process and prepare the cost control plan as the control strategy of shipbuilding cost. Then we take the manufacturing data of a shipyard as the experimental object, use this paper’s model for data mining, compare the data performance of this paper’s model with similar algorithms, and verify the feasibility of this paper’s model. Finally, the model of this paper is applied to real cases. In the comparison of the estimation results between this paper’s model and the commonly used algorithms, the average error of cost estimation of this paper’s model is ±4.95%, which is better than the average error of the commonly used algorithms. The superior accuracy of this paper’s model in shipbuilding cost estimation is verified.
In this paper, K-prototype algorithm is chosen to cluster and analyze the data of students’ behavior in the educational field. Further, a model of students’ employment interest is constructed based on the job rating data of different classes of students. The timeliness is introduced in the model to improve the recommendation accuracy. Synthesize the algorithm and model to build an employment support system. Apply the system to the clustering study of college students’ behavioral data to verify its career recommendation value. Set up comparison experiments to find the optimal similarity fitting parameters and number of neighbors to improve the system recommendation accuracy and judge the system recommendation effect. Preliminarily divide students into 3 categories by analyzing students’ online behavior and book borrowing behavior. Preliminarily categorize students into 4 categories based on their grades. Combined with the performance labels and grade categories of professional courses, the employment direction of students was finally clustered into four categories, namely “postgraduate entrance examination”, “civil servant application”, “company work” and “others”. The highest accuracy of the system job recommendation is achieved when the similarity fitting parameter λ = 0.5 and the number of neighbors N = 50.The RMSE value of the K-prototype algorithm ranges from 0.6011 to 0.731, and the recommendation effect is better than the comparison algorithm.
As a key component of urban environmental resources, the design of landscape paths and facility layouts of urban public environments is not only related to the overall aesthetics of the city, but also to the quality of life of urban residents. In this paper, from the perspective of landscape layout, the ecological landscape spatial network is constructed by calculating the ecological landscape environmental adaptation degree and the ecological landscape pattern index. On this basis, the traditional ant colony algorithm is introduced and its heuristic function and path selection are improved, and the adaptive adjustment factor and angle guiding factor are added to improve the diversity and efficiency of path searching, so that the landscape layout optimization model based on the ant colony algorithm is obtained. Using this model to design a landscape layout optimization scheme for a scenic spot, the average fulfillment time of the optimized landscape path is 20.73 minutes, which is 19.52 minutes shorter than the average fulfillment time of the original planning scheme, indicating that the model in this paper is able to carry out the landscape layout optimization design effectively.
ECG and PCG reflect the activity characteristics of the heart, and the combination of the two can record the electromechanical activity information of the heart more comprehensively. In this paper, we design a heart failure prediction model based on Transformer, and utilize Transformer Encoder to complete the feature fusion of ECG and PCG. Feature classification is performed using ResNet-18 to achieve the prediction of nine typical arrhythmias. Evaluate the classification results on the dataset to explore the performance level of the proposed model. Obtain ECG and PCG data in real situations, and select entropy analysis and heart rate variability metrics to quantify the physiological signal time series complexity. The model classification accuracy, specificity and sensitivity are compared to analyze the effect and superiority of the proposed model in practical applications. The results show that the average accuracy of the model on the four datasets reaches 92.28%, and the highest average F1 score is 0.930. In practical applications, the classification accuracy, specificity and sensitivity of the proposed model in this paper are 96.79%, 97.47% and 96.77%, respectively. Through the fusion analysis of ECG signal and heart sound signal characteristics, the model fully reflects the HRV change characteristics of heart failure patients and can effectively predict heart failure.
National security education in the new era puts forward new and higher expectations on the scope, degree, speed, and object of knowledge dissemination, while presenting new dissemination characteristics such as all-media and group emergence.Based on graph theory algorithm, this study proposes a dissemination model with credibility constraints about national security education knowledge.Text mining is used to analyze discussions of social network users on national security education knowledge from Sina Weibo and Baidu Search. The dissemination mechanism of national security knowledge is explored through text analysis. Based on this, different expectations of information dissemination are set to conduct numerical simulation. The simulation results show the model is highly sensitive to parameter changes. In the case of R < 1, with the increase of β, the time for S to reach the steady state decreases, and the time for I to reach the maximum value decreases, while the maximum value increases.When β = 0.03, Max I = 39.86; and when μ = 0.3, Max I = 37.23. The model plays an important role in controlling and managing knowledge dissemination.The proposed graph theory-based knowledge diffusion model achieves an average knowledge stock of 0.924 under regular networks and 0.726 under scale-free networks. In terms of knowledge diffusion rate, this model outperforms both the traditional knowledge diffusion model and the random diffusion model.
In this paper, DAG is utilized to represent the dependencies between musical features, and a topological sorting algorithm based on layer order relationships is used as the sampling algorithm for AI music generation models. The feature de-entanglement mechanism of VAE is utilized to learn multiple feature representations, and Transformer-XL is used as the encoder and decoder of the model to design the Control-VAE model that manipulates the latent variable representations to change the music structure. Statistical autocorrelation coefficients, spectral analysis, and diversity auto assessment metrics data were used to evaluate the model performance in terms of three dimensions: melody, timbre, and diversity. The feasibility of Control-VAE model AI music generation and melody optimization is examined through the evaluation of practical application effects. The results show that the autocorrelation coefficients and frequency amplitudes of the music generated by Control-VAE model are basically consistent with the original music, and reach human-like PPL values, seIf-BLEU values and Zipf coefficients near p=0.95.The music pieces generated by Control-VAE model have a certain degree of musicality, and the melody-optimized music is clear, accurate and novel and interesting.
Based on the background of information technology, this paper proposes a multimodal blended learning model of English listening based on “WeChat+Classroom+TED-Ed”. It focuses on the experimental teaching of multimodal learning and English listening comprehension, and describes the object of the study, the design of the study and the process of the study. Based on the research idea, the experimental variables were designed, and the empirical analysis was carried out by using multiple linear regression model. The teaching effect of multimodal teaching is examined by comparing the differences in the total English listening scores of the two groups of students before and after the experiment. With the help of Pearson correlation analysis, the correlation between the experimental variables is explored. The value of R² was determined through the multiple regression model to determine the magnitude of the explanatory power of multimodal learning on English listening comprehension ability. The results showed that the scores of the control class improved by 1.19 points and the experimental class improved by 4.19 points in the experimental posttest, with a significance (two-tailed) p-value = 0.008<0.05. The explanatory power of the combined three modalities of learning on English listening performance was 15.4%, and classroom learning had the highest level of significance in terms of its explanatory power on listening comprehension, and the test of regression coefficients reached the level of significance (t=3.862, p= 0.002<0.05).
As artiϐicial intelligence technology becomes more and more mature, it is both a challenge and an opportunity for English speaking teaching. Aiming at the poor generation of virtual English teaching resources due to the training problems of traditional generative adversarial network, dual generative adversarial network is used to optimize the above problems and select the virtual English teaching resources that meet the requirements with the help of Pielou. At this level, the HTC VIVE suite, high performance computer system, Unity 3D development engine, and joystick control are integrated to jointly complete the work of English speaking teaching scene design. Combining the research data and evaluation indexes, the practical application efϐicacy of the scenario is analyzed. From the overall performance of different methods in the four datasets, this paper’s method is superior to the other four methods, that is, this paper’s method is able to generate high-quality virtual spoken English teaching resources. And the practical application efϐicacy in terms of test scores, learning effects, satisfaction, and English speaking teaching background is better than traditional multimedia, which is more conducive to promoting the development of English speaking teaching.
In order to more comprehensively study the influencing role mechanism of consumer behavioral decision-making process in the digital economy platform and explore the influencing factors of consumer behavioral decision-making, this paper constructs a model of consumer behavioral decision-making process based on Bayesian network. With the help of Netica software to construct the Bayesian network topology, using EM algorithm to learn the parameters of the Bayesian network model, and proposed to use the Bayesian network to carry out sensitivity analysis and probabilistic inference, and formulate the corresponding Bayesian network model framework. Subsequently, the influencing factors of channel search willingness and purchase willingness and their relationships in the consumer behavioral decision-making process in the digital economy platform environment are analyzed. The structural equation model is introduced, the measurement equation and structural sub equation calculation methods are determined, and the sample data are collected by means of questionnaires to carry out the test and analysis of the model of consumer behavioral decision-making process. The CR value of each variable in the model of this paper is higher than 0.7, and the AVE values are all greater than 0.5, and the model performs well in terms of intrinsic quality. The exogenous latent variables such as perceived benefits, channel trust, and transfer costs have a significant positive effect relationship on the endogenous latent variables such as search behavior and purchase intention (P<0.05).
Dress metaphor is a very important way of expression in the novel text of Ming Dynasty, and the recognition and interpretation of the metaphor play a very important role in really understanding the novel text. This paper proposes a dress metaphor recognition model based on Transformer and graph convolutional neural network, and a dress metaphor interpretation method based on Seq2seq framework. The apparel metaphor recognition model performs feature extraction of global and local information of apparel metaphor sentences by Transformer. Graph Convolutional Neural Network is utilized to obtain syntactic structure information and sentence dependencies, in order to complete multi-word dress metaphor recognition. Then the obtained deep metaphor features and syntactic structure information of the sentence are input to the classification layer. The metaphor decoding method carries out costume metaphor understanding through the encoder-decoder, which chooses the LSTM network structure for both encoder and decoder to better obtain the semantic features of the novel text. The dress metaphor recognition model improved the recognition correctness on the dataset by 17.97% and 7.28%. The dress metaphor interpretation method based on the Seq2seq framework elaborates the interpretation content and can more accurately interpret the dress metaphors in Ming Dynasty novels. It verifies the practicality of the metaphor recognition and interpretation model in this paper in the task of interpreting dress metaphors in Ming Dynasty novel texts.
The higher the corporate financial transparency, the more it can reduce the information asymmetry, which can enhance the market trust and improve the corporate performance. In order to improve corporate financial transparency, the study constructs a financial fraud identification model by improving the machine learning model based on XG Boost algorithm from the financial fraud factors. Based on the XG Boost algorithm, the model integrates the decision rules through the weighted fusion method to generate a new decision tree to determine the financial fraud. In order to improve the ability of enterprise performance assessment, the baryon support vector machine method is used to classify the performance of enterprise employees, and the nonlinear baryon support vector machine is used to establish the enterprise performance assessment model. In the process of verifying the effect of the two models, text indicators are extracted using big data technology to provide a rich feature set for the financial fraud identification model. The data from ERP, CRM and other systems are integrated to provide a comprehensive and high-quality data set for the enterprise performance assessment model. After empirical analysis, the combination of big data and machine learning can improve the effect of financial fraud identification, and then effectively improve the transparency of corporate finance. The enterprise performance evaluation model provides a scientific and efficient quantitative evaluation tool for enterprise managers, and effectively improves the enterprise performance evaluation capability.
The integration and development of Sichuan’s rural music and cultural tourism industry is of great signiϐicance in the context of rural revitalization strategy. The purpose of this paper is to construct a multilevel regression model to deeply explore the inϐluencing factors and role mechanisms of the integration of the two. Through theoretical analysis and empirical research, the research variables are clariϐied, and the null model, random effect model and complete model are constructed and data validation and analysis are carried out. The results show that the richness of rural music resources, the level of cultural and tourism industry, policy guidance and support, market demand and human resources have a signiϐicant positive impact on the integration of rural music and cultural and tourism industry in Sichuan. The results of the full multilevel regression model show that the same level of rural music resource abundance has different impacts on the integration of rural music and cultural and tourism industries due to regional differences. The results of the study provide theoretical support for the development of cultural tourism industry in Sichuan Province, and deeply help the implementation of rural revitalization strategy in Sichuan Province.
In order to improve the accuracy and efficiency of medical image segmentation, this paper designs and proposes a medical image visualization method containing Sobel edge detection operator and 3D UNet network based on deep learning and edge detection. The 3D U-Net network is used to capture the morphological and edge features of medical images on the public dataset, and the image binarization is performed on the result of its operation. The binarized image processed by corrosion and expansion algorithms is multiplied by the corresponding elements of the matrix with the medical image to obtain the visualization of the medical image. Different comparison algorithms and data sets are selected to verify the effectiveness of the optimized 3D U-Net network module and feature fusion module. Parameter settings are carried out, and the LIDC-IDRI dataset is used as the algorithm training base data to analyze the segmentation accuracy of the image processing method that fuses the edge detection operator with the 3D U-Net network. The algorithm ablation experiments are carried out according to different pruning degrees and training methods. The algorithm in this paper can achieve more than 80% segmentation accuracy on LIDC-IDRI dataset, in which the segmentation accuracy of liver reaches 97.1%.
In order to improve the teaching effect of dynamic structural behavior simulation in structural engineering teaching, this study develops a dynamic structural behavior simulation teaching model combined with the finite element method to explore the effect of its application in teaching. This paper first introduces the process of applying the finite element method to simulation teaching and the steps of structural engineering system development. After that, it introduces the common structural engineering analysis functions under ANSYS software and its application in various aspects of structural engineering teaching. Then the construction process of the dynamic structural behavior simulation teaching model is briefly described, and the finite element principle is combined with the actual engineering problems through the integration of case teaching to realize the deep integration of theory and practice. Finally, the teaching model of dynamic structural behavior simulation is constructed and the teaching evaluation system after applying the model. The results of teaching practice show that more than 95% of the students maintain a positive attitude towards the use of the model in this paper. Under the teaching mode of the simulation model visualizing dynamic behavioral characteristics, the average grade of students in the experimental group was significantly higher than that of the control group by 14.96 points, and the difference between the grades of students in the two classes was significant (P=0.000). It can be seen that the use of the model can improve the students’ understanding of dynamic structural mechanical behavior and the application of finite element analysis tools, which provides an efficient platform for combining theory and practice for structural engineering teaching.
Following the footsteps of the times, an excellent and complete movie cannot be separated from the application of digital modeling. In this paper, we mainly use 3D modeling, motion capture, rendering and other related technologies to edit and produce the character’s physique, proportion, contour, etc., design the character’s expression, color and action, and build the film and television scenes in 3D space. Thus, it realizes the characterization and emotional expression in film and television. Will be through the traditional 2D film and television and three-dimensional film and television control experiments, from the experimental data can be seen, in the frame rate, three-dimensional modeling technology film and television than the traditional 2D film and television on average 14% to 20% higher. There is also a leading edge in the number of textures. The data color emotion analysis indicated that the color shift and strong contrast connects the plot and the audience’s feelings. The quantitative survey of emotional experience through questionnaires shows that the audience in the 3D film and television group is higher than the traditional 2D film and television in terms of immersion experience, interaction experience and learning and enjoyment experience. Therefore, 3D modeling technology plays an important role in the creation of film and television art.
The study proposes a dynamic resource allocation model suitable for English language teaching, which combines learner characteristics, learning progress and resource availability to achieve real-time optimal allocation of resources through mathematical optimization algorithms. A multi-objective optimization model is constructed based on the key factors in resource allocation for English teaching. Facing the optimization objectives of maximizing learning efficiency and minimizing resource idleness, NSGA-II algorithm is used to construct a non-dominated solution to achieve global sorting, and combined with congestion calculation to complete global quality population screening. At the same time, the branch delimitation algorithm is utilized for local search of optimal solutions, and merged with the population of NSGA-II to generate the new generation of optimal populations. The optimization probability of the combined algorithm in this paper is 0.85, and the average convergence error is only 0.01081, which has excellent optimization performance. The resource allocation delay of this algorithm is around 0.1ms, and the allocation efficiency is more than 95%, and the comprehensive effectiveness is better than the comparison algorithm. The dynamic allocation model of resources in this paper improves the balance of resource allocation of English teaching and auxiliary room area, the number of teaching materials, the number of full-time teachers and teaching equipment. At the same time, it prompted the average English score of the experimental class to exceed 80, which was significantly higher than that of the control class.
How to give full play to the clarinet in the symphony orchestra in the sound advantages and characteristics of the role, undoubtedly is an important topic of the current music research. Combined with years of working practice and learning experience in the symphony orchestra, the author explains the tonal advantages and characteristics of the clarinet in the symphony orchestra. For the study of the relationship between its tonal advantages and characteristics and the symphonic concerto, the author combines the finite element method in the music education environment, through the method of computational simulation, to explore the symphonic performance conditions, as well as the main discussion on the analysis of the boundary conditions with the vibration velocity and sound-absorbing materials, in order to achieve the purpose of improving the clarinet’s musical and artistic level in the symphony orchestra. Through the study, we found that the numerical simulation of the relationship between the clarinet technology and the symphony orchestra concerto is analyzed by the local fundamental solution method with high computational accuracy, which lays the foundation for the successful application of this method to the numerical simulation of the sound field of the complex music education environment.
Cheerleading events are flourishing in China, the level of competition is rising, the number of competition groups and programs is increasing, the competition is becoming more and more intense, and the innovative research on formation design is an inevitable demand for the development trend of cheerleading. The study designed a multi-objective path planning model based on the intensity of willingness and consultation strategy, so that college cheerleading can avoid conflicts and reach the goal point of cheerleaders in the complex environment. Then an improved multi objective particle swarm algorithm (MOPSO-CA) based on meta cellular automata is proposed and applied to college cheerleading formations to realize the design of college cheerleading formations. The simulation results show that the MOPSO-CA algorithm can re-select the optimal movement direction angle according to the real-time positions of the moving obstacles and moving targets, which illustrates the effectiveness of the algorithm. Secondly the feasibility of the formation design conditions are suggested as: keeping the originality of the movement, the use of the moving route of the formation and the space of the venue, and the type of formation change.
The article solves problems such as personalized investment, and then achieves the expected effect of investment decision-making. The article firstly designs an investment decision support model based on collaborative filtering, elaborates the implementation path to realize investment decision support from the perspective of machine learning, and then combines the user image technology to design the user image labeling system and model construction. Finally, the effectiveness and rationality of the proposed method in this paper are verified through experiments. Experiments on a corporate investment decision support task on a company’s dataset reveal that the method proposed in this paper has good performance on all metrics, with the highest value of 0.6985 on AUC.This gives an indication of the effectiveness of the financial data analysis and investment decision support model proposed in this paper.
The Three Gorges Reservoir Area is a hotspot for landslide disasters, with many landslide development patterns and influencing factors remaining unclear. The slip zone soil, a weak interlayer between the sliding mass and the bedrock, has inherently low strength, which is a critical factor in landslide occurrence. Water is one of the most active elements reducing the shear strength during the formation of the slip zone. Given the particularity of reservoir bank water-related landslides, the mineral composition and geochemical characteristics of the slip zone and its surrounding rocks and soils exhibit significant variations across different geological periods and environments. These changes reveal the mechanisms and extent of water-rock interactions, further clarifying the fundamental reasons for the reduction in shear strength of the slip zone. The results show that in the Liujiaobao landslide in Quchi Township, Wushan County, Chongqing, within the Three Gorges Reservoir area, the composition of minerals and the content of major chemical elements in the slip zone soil and its surrounding rocks and soils indicate that the slip zone and surrounding rocks and soils form the material basis for the slip zone. During its formation, the groundwater in the slip zone is closely connected with external hydraulic forces, continuously influenced by groundwater, leading to changes in the physical properties of the rock and soil mass. This is accompanied by the hydrolytic mudification of marl debris, dissolution of calcite, and interconversion among clay minerals, which are the main reasons for the attenuation of the shear strength of the slip zone soil.
In this paper, on the basis of relevant theories, based on the adversarial training of BERT-PGD-BiLSTMCRF entity recognition model and relationship extraction technique to complete the entity extraction and relationship extraction, and then use the entity linking method that fuses attribute and semantic features (BERT+CBOW+CLS) to complete the construction of the knowledge graph and the supplementation of the knowledge graph, and the data is imported into the Neo4j Storage and Display. The symbols contained in the above knowledge graph for the city cultural image in translanguaging practice are divided into three hierarchical symbols, and the symbols are analyzed in terms of flow. In terms of single language usage, English has the highest proportion (22.57%), and Chinese has the best proportion (63.19%) in the process of urban cultural image construction, highlighting the dominant position of Chinese in urban cultural image construction. During the twenty-year period from 2004 to 2023, the trend of social behavioral symbols growth is significantly higher than that of material and spiritual symbol layers, which fits well with the current social development trend.
Artificial Intelligence (AI) is increasingly used in medical research, especially in the analysis and interpretation of medical data. In this study, based on the traditional CARS model, we built a framework for thesis abstract language step research by categorizing fuzzy steps into optional steps and adding appropriate key steps to the language steps. With the help of artificial intelligence technology, an extraction model of key elements of abstracts incorporating the attention mechanism is constructed, aiming at screening the elemental utterances in abstracts. Finally, by collecting data from medical related papers in CNKI, Web of Science and other databases, the CARS modeling strategy based on artificial intelligence is implemented in the comparative analysis of medical paper abstracts in English and Chinese. Through the comparative analysis, it is found that the number of sentences in English abstracts is concentrated in 6-7 sentences, while the number of sentences in Chinese abstracts is scattered in 2-8 sentences. The percentage of the use of Chinese sentences on English abstract writing is the highest, with an average percentage of 45.24%. The frequency of the first 20 words of fuzzy restrictive phrases in English abstracts was significantly higher than that in Chinese abstracts. The organization of Chinese and English abstracts was mostly in the structure of “introduction method-results-discussion”, which accounted for 54% and 71%, respectively. In addition, the frequency of steps indicating gaps in the research area is higher in English than Chinese abstracts.
In the current process of social development, reimbursement has become a generally accepted phenomenon. With the improvement of economic level and the improvement of people’s living standards, all walks of life have developed rapidly, which also provides new ideas for the financial reimbursement system and financial management. At present, most of the financial reimbursement processing is conducted manually, which can not meet people’s requirements for work efficiency. Moreover, there are many limitations, which are very unfavorable for enterprises. Therefore, it is necessary to take reasonable and effective measures to strengthen the improvement and optimization of the financial reimbursement system, so as to ensure the safe and efficient operation of funds. Image recognition technology is an indispensable and important means of modern information management. It can automatically extract data information and analyze statistics, which brings great convenience to financial reimbursement. This paper mainly studied the problems related to financial reimbursement based on the process of image recognition and denoising, and put forward some suggestions for the design of financial reimbursement image recognition system. It is hoped that it can promote its better application in practical work, so as to achieve the purpose of improving economic efficiency and ensuring the security of funds, and at the same time help further promote the healthy and orderly development of enterprise construction. This paper compared the traditional manual reimbursement method with the financial reimbursement automatic entry system based on image recognition. The results showed that the error of automatic input system was smaller than that of manual mode, and the degree of automation was higher; in addition, the accuracy rate of reimbursement voucher identification and review had also increased by about 6.34%. Therefore, this method has good advantages and practicability, and this method is conducive to reducing the workload of staff and facilitating the follow-up work. To sum up, electronic imaging technology can analyze and process data with the help of image processing means, thus obtaining corresponding results. It is convenient to adjust the accounting process as needed and timely in the process of financial management, so as to make the overall financial reimbursement work more standardized and unified.
The development of society has led to the continuous development and progress of artificial intelligence technology, and has also led to an increasing demand for graphic design. In order to better solve the problems of color deviation, poor design effect, and high design cost in traditional graphic design, this article applied artificial intelligence image identification system to graphic design to overcome the problems of traditional graphic design. The elements extracted from the graphic database were denoised and enhanced by means of mean filtering and histogram equalization; after image preprocessing, Deep Learning (DL) algorithms were used to construct an image identification system, and the modules and visualization interfaces of the system were introduced. Through experiments, it could be found that the average expert rating of the graphic design scheme designed by the DL based image identification system was 8.818 points, and the satisfaction rate of the 20 users selected for the DL based image identification system was above 93.4%. In summary, using DL to construct an image identification system and applying it to graphic design could effectively improve the overall effect of graphic design and increase user satisfaction with the designed graphic scheme.
Adapt to the new competitive environment, the supply chain concept and management model of horizontal integration and cooperation between enterprises have begun to rise, and continuously demonstrate enormous competitive strength and superiority. However, the existing enterprise supply chain management (SCM) system has problems of low security, low efficiency, and high management costs. In view of the above problems, this paper studied the enterprise supply chain management and its information assurance mechanism based on the error back propagation algorithm. By analyzing the problems in enterprise supply chain management and introducing error back propagation algorithm as an optimization method, the efficiency and accuracy of the supply chain have been improved. At the same time, corresponding guarantee mechanisms were proposed to address the importance of information security in the enterprise supply chain. The research results indicated that the information leakage rate of the supply chain information protection mechanism based on the error back propagation algorithm was below 3.21%, and the average leakage rate of 20 experiments was 2.654%. For supplier management in enterprise supply chain management systems, the selected users scored the system based on error back propagation algorithm at least 8.84 points, and the average score of 10 users was 8.995 points. Enterprise supply chain management and information assurance mechanism based on error back propagation algorithm can effectively improve the effect of supply chain management and enhance the security of information.
Karst water plays a vital role in meeting daily population needs. Determining groundwater sources, understanding chemical changes, and accurately evaluating flow paths and evolution stages are essential for the protection and sustainable use of groundwater resources in mining areas.This study collected 10 sets of karst groundwater and surface water samples from the Anle Village mining area. Using multivariate statistical analysis, Piper trilinear diagrams, Gibbs diagrams, and isotopic techniques, we analyzed the hydrogeochemical characteristics of both contaminated and uncontaminated water samples.The results show that uncontaminated groundwater and surface water are slightly alkaline and dominated by Ca2+ and Mg2+ cations, along with HCO3− and SO42− anions. Hydrochemical facies include HCO3−-SO42−-Ca2+-Mg2+ and HCO3−-Ca2+-Mg2+.Uncontaminated samples contain high levels of impurities, with dominance of Ca2+, Mg2+, and SO42−. These waters are mainly recharged by atmospheric precipitation and influenced by evaporation. Their chemical composition is primarily driven by the weathering and dissolution of carbonate, sulfate, and silicate rocks.Nitrate (NO3−) concentrations in surface water suggest influence from agricultural fertilizers, while contaminated groundwater is closely linked to mineral resource development.These findings are significant for understanding the circulation and evolution of karst water in Anle Village and for informing the protection and utilization of local water resources.
As an indicator of climate change, the change of vegetation cover directly reflects the ecosystem dynamics of the region. In this paper, the spatial and temporal characteristics of vegetation cover in the headwaters of the Fen River and the effects of temperature, precipitation, GDP and population on the changes of vegetation cover were statistically analyzed by using the Theil-Sen median slope and the Mann-Kendall test and Pearson’s correlation coefficient from 2000 to 2020. The results showed that: (1) from 2000 to 2020, the vegetation cover of the Fen River headwaters showed an overall upward trend, and the mean value of NDVI was 0.55. The fluctuation increased from 2000 to 2011; the significant increase was observed from 2011 to 2013; and the fluctuation of the value of NDVI from 2013 to 2020 was relatively small p 0.01 . (2) Climate change affects changes in vegetation cover. On the time scale, the 2000-2020 mean NDVI values are positively correlated with temperature and precipitation, but the correlation is not significant p p 0.053 0.05, 0.185 0.05 . On the spatial scale, vegetation cover was weakly negatively correlated with air temperature as a whole, while positively correlated with precipitation as a whole. (3) The influence of human activities on vegetation cover was dominant, NDVI and GDP were positively correlated, with only 5.13% negatively correlated in the central and northeastern part of the region, and NDVI and population were strongly positively and negatively correlated, with alternating distribution in the study area. (4) The vegetation cover of the Fen River headwaters area shows an increasing trend, but there are still ecological and environmental problems, and it is necessary to continue to improve the implementation of the relevant ecological protection policies in order to achieve the goal of sustainable development. The results of the study can provide scientific references for the restoration of vegetation cover and protection of fragile ecosystems in the transition zone of semi-arid and semi-humid climate.
In enterprise cost accounting and control research, traditional activity-based costing (ABC) relies on detailed activity tracking data and complex cost allocation models, which makes data acquisition difficult, has low-cost allocation accuracy, ignores dynamic changes, and has the problem of insufficient flexibility. This paper constructs an improved ABC application framework, builds an activity-driven cost accounting model, analyzes the daily activity data of the enterprise, determines the key factors related to cost, and establishes a mapping relationship between activity and cost. This paper introduces a dynamic adjustment mechanism to adjust the weights and parameters in the cost accounting model in real time according to changes in the external environment and internal operations, thereby improving the flexibility and accuracy of cost accounting. It can integrate the ERP (Enterprise Resource Planning) system with the cost accounting model, integrate the company’s financial data, production data and sales data, use information tools to automatically update activity costs, and provide timely feedback to the cost control system; it can closely combine cost accounting and control, monitor and adjust costs in real time during the accounting process, and take timely control measures when abnormalities occur. Experiments show that in terms of cost allocation accuracy, the average SE (Standard Error) of the improved ABC in enterprises with different employee sizes is 2.1, and the average MSE (Mean Squared Error) is about 5.5. It is more stable when processing enterprise data and can better reflect the actual cost allocation. The response time of the improved ABC is 5.7 seconds when the raw material price increases by 25%. It can make adjustments faster, with better flexibility and dynamic adaptability; the experiment proves the effectiveness of this paper in the research of enterprise cost accounting and control.
In response to the shortcomings of traditional enterprise financial management information platforms in data processing and analysis efficiency and decision support capabilities, this study introduces intelligent decision support systems to fundamentally improve these issues. In this study, we automated data collection through API (Application Programming Interface) technology, used ETL (Extract, Transform, Load) tool for data format conversion, and strictly performed data cleaning and standardization to ensure data quality. The article uses association rules and support vector machine machine learning algorithms for in-depth analysis and prediction of financial data, and optimizes decision-making scenarios based on multi-criteria decision analysis, Monte Carlo simulation and linear programming techniques. Evaluation results show that the system significantly improves the speed and accuracy of data processing, with an increase in processing efficiency of more than 70% and a decision-making accuracy rate of up to 95%. The intelligent decision support system effectively improves the informatization level of enterprise financial management and provides more scientific and reliable decision support for the enterprise leadership.
Focused crawlers are targeted to search the internet for web pages on specific topics. Its main task is to collect preprocessed and topic related web pages and ignore irrelevant web pages. Traditional focused crawlers have limited success in achieving multi-text categorization of web pages. Due to the large amount of unstructured data present in web pages, the correct classification of web pages based on a given topic is the main practical challenge for focused crawlers.The main objective of this work is to design an improved focused crawling approach using web page classification. In this paper, a text classification model based on the combination of GloVe word vector model and TF-IDF weighting technique is proposed to improve the accuracy of web page classification. The GloVe-based text classification model is further utilized to guide focused crawlers to classify web pages.The proposed GloVe and TF-IDF text categorization models are validated on 10 different datasets and the results are compared with traditional machine learning algorithms as well as different methods based on Naive Bayes, Bag-of-Words and Word2Vec. According to the experimental results, the proposed text classification model is 7-12% better than traditional machine learning algorithms.
In order to solve the problems of traditional traffic accident scene investigation, such as taking a long time, evidence easily lost and difficult to save in case of bad weather, low survey accuracy, and field measurement data, DJI Mavic 3E UAV is used to convert the collected data into digital two-dimensional ortho image and three-dimensional model by using DJI Intelligent map software, such as mid-way point flight, map construction aerial photography and oblique shooting. One-stop help traffic accident investigation comprehensively improve the efficiency of scene investigation, standard forensics, improve the accuracy of accident scene investigation, in order to quickly restore traffic order, ease the demand for police, and improve the identifiability, safety and timeliness of traffic accident scene investigation.
By improving the standard U-Net architecture, this paper proposes a novel semantic segmentation model, which incorporates multiple attention mechanisms to enhance the model’s capacity to capture multi-scale features. Specifically, we introduce the Efficient Multi-Scale Attention Module with CrossSpatial Learning (EMA), Spatial and Channel Squeeze and Excitation (SCSE), and Squeeze-andExcitation (SE) mechanisms into the standard U-Net network. These modules assist the network in learning significant information from feature maps at multiple scales while suppressing interference from irrelevant background. Experimental results demonstrate that incorporating attention mechanisms effectively enhances the prediction accuracy of the standard U-Net network for lane line semantic segmentation. The new model outperforms the standard U-Net model on our custom dataset, with particularly significant improvements in lane detection accuracy in scenarios with certain interference.
This research proposes a new optimization technique for reinforcement concrete filled structural tubular columns using genetic algorithms and unified strength theory. A complete theoretical model to determine the axial bearing capacity of reinforced CFST columns incorporating modified confinement coefficients and enhanced steel section properties was developed. The optimization procedure deals with performance of the structure, materials usage and construction convenience as the optimization goals. Experimental validation for ultimate bearing capacity of five full scale specimens was carried out and the deviation was found out to be 5.2% which was found to be predicted by the theoretical model accurately. Internal stiffeners are likely to increase axial capacity by about 15.7%-23.4% over traditional CFST columns. The relationship between stiffener parameters and performance of the structure was found to be critical with optimal height to thickness of the stiffener to be in the range of 30 to 45 and space to diameter ratio no greater than 0.5. The problem sets out such mathematics as is nowadays simply necessary for the modern construction world to have at their disposal, as well as reasons for designing reinforced CFST columns.
This paper presents the design and implementation of the non-electric contacting power supply system for the electronic scale, which mainly focuses on improving power transfer and measurement accuracy. The whole system architecture includes electromagnetic coupling, an advanced algorithm of control, and safety. Simulation results have shown that, under standard conditions, it is possible to reach a high power transfer efficiency higher than 90% while keeping voltage regulations within ±1% and limiting current ripple to below ±1.8%. Therefore, this provides a measurement resolution of ±0.1g for the system while granting stable performance for a variety of conditions in both coupling and load. Protection mechanisms set within the system ensure reliable operation; fault detection time is less than 10μs. The proposed method represents a relatively good guide for non-contact power supply towards precision measurement, thus solving the challenge of WPT in an electronic scale system.
Under the background of globalization and knowledge economy, the importance of innovation and entrepreneurship education for college students is becoming more and more prominent. This paper combines fuzzy logic and decision tree algorithm to construct a cultural confidence recognition model of innovation and entrepreneurship education. Feature selection and classification are carried out on the salient features of the collected data information on innovation and entrepreneurship education. First, eight types of statistical features, such as the degree of integration of excellent traditional culture, the degree of value leadership and moral cultivation, the innovative power of grounded cultural knowledge, and the effect of social responsibility cultivation, are extracted as inputs to the C4.5 algorithm, and a decision tree is constructed for feature selection. Then, according to the constructed decision tree, the affiliation function and IF-THEN rule of the fuzzy inference model are designed. Finally, the designed fuzzy inference model is used to classify the degree of cultural confidence. The method achieves 100% accuracy in recognizing the lack of cultural self-confidence in innovation and entrepreneurship education, and more than 90% in recognizing the overall effect of general cultural self-confidence and rich cultural self-confidence. The experimental results show that the combination of decision tree and fuzzy inference modeling is feasible for the detection and classification of college students’ innovation and entrepreneurship education, and has strong practical application value.
The development of blockchain technology in modern business and finance is of great importance. The study delves into the blockchain-based shareholder voting system and the role of blockchain on corporate governance. On this basis, relevant research hypotheses are formulated. After completing the definition of research variables, the research model is constructed to empirically investigate the impact of blockchain-based shareholder voting system on corporate governance. The research hypotheses are tested through regression analysis and the robustness test is utilized to ensure the reliability of the research findings. The minimum value of blockchain-based shareholder voting and corporate governance level are both 0, the maximum value is 4.954, 0.624, and the average value is 0.821, 0.089, respectively. There is variability in shareholder voting and corporate governance level across companies. Before and after the control variables, the coefficients of blockchain-based shareholder voting system are 0.225 and 0.247 respectively, and both are significantly positive at 1% level. Blockchain-based shareholder voting system can improve corporate governance.
This paper constructs the key business index system of electric power system consisting of electric power supply, electric power transmission, electric power distribution, electric power equipment and electric power system management. By evaluating the validity optimization, reliability optimization, and redundant indicator removal based on the neural network analysis method of the indicator system, a new power system key business indicator system is formed, and the weights of the optimized indicators are calculated. The power system key business indicator control program is designed based on the weight parameters, and a new power system key business indicator control platform is developed. Extract power data using the weighted FCM clustering algorithm, and classify user power data on the cloud platform. Resource utilization and performance response analysis are performed on the power system key business index control platform. The power system key business index control platform designed by index weights developed in this paper is able to meet the transaction demand under different concurrent user numbers, and always maintains a memory utilization rate within 10, with good operating conditions.
In the context of the digital economy driven by the Internet of Everything, the dissemination of cultural heritage is facing the challenge of transitioning from traditional to digital media. The study develops an introduction to the visual SLAM system, models the binocular camera configuration and the indoor and outdoor dense 3D reconstruction process, and designs a complete set of algorithms based on the calibration of the actual binocular camera, image correction, binocular stereo matching algorithm (SELAS), and real-time dense point cloud 3D reconstruction. Based on the real laboratory scene, the original ELAS algorithm is compared with the improved method for experiments, and the results show that the mean value of the deviation of the optimized S-ELAS algorithm is -0.046m, and the algorithm accuracy is remarkable. Then a virtual cultural relics museum based on the combination of visual SELAM system and VR technology is designed to realize close interaction with S-ELAS stereo matching algorithm. In order to test the performance of the designed cultural relics museum system, the users are firstly acclimatized, and then the screened users are tested to experience the virtual museum system, and the MOS scores are made after the test. The MOS scores show that the virtual cultural relics museum system has better interactivity and experience.
In order to alleviate the problems of short supply of parking spaces and traffic congestion, intelligent driving solutions have emerged. Automatic parking has now become the first application scenario for driverless driving due to the more fixed scenario and lower traveling speed. In this study, the traditional A* algorithm is improved using the cost function, and the hybrid algorithm of parking space path search and planning is designed by combining the improved A* algorithm with the Reeds-Shepp curve, and then combined with the collision constraints to improve the algorithm’s path planning performance. The results of simulation experiments and in-loop test experiments show that the maximum lateral error and heading error are low in parallel and perpendicular parking scenarios, and it is found that the average lateral error during the whole parking process is only 0.177m in the in loop test, which is a good tracking effect for vehicles. The path search and planning algorithm designed in this paper can better realize the autonomous parking function and has high tracking accuracy and stability in the simulation scenario.
The process of innovative education is not only a purely intellectual activity process, it needs innovative emotion as a driving force, such as strong interest, strong passion, the motivational function of evaluation, harmonious teacher-student relationship and other non-intellectual factors cultivation, in order to obtain a comprehensive effect. This study is oriented to the intelligent distribution platform of journalism and communication content to study its teaching value and innovation emotion. The Information Adoption Model (IAM) was adopted as the theoretical basis for the study of content intelligent distribution platforms, the characteristics of the platforms were summarized, and the impact of the platforms on teaching value was studied using regression analysis. The result table of the study found that the content intelligent distribution platform’s exhaustiveness, readability, and objectivity had a significant positive correlation on the usefulness of educational value, and that the influence of interactivity on perception and participation did exist and had a certain impact on educational usefulness. Finally, this paper also takes S colleges and universities as an example to assess and calculate the innovative emotion and innovative ability of the platform’s teaching value, further analyzes the teaching value of the intelligent distribution platform, and provides suggestions for the cultivation of the innovative emotion in combination with practical research.
As one of the important stakeholders in ecotourism, community residents play a crucial role in ecotourism development. This study takes interactive emotional cognition, social exchange theory and the concept of psychological carrying capacity as the guiding theories, and designs the community residents’ questionnaire from the aspects of emotional cognition and psychological carrying capacity, respectively. Correlation analysis and regression modeling were used to test the influence of interactive emotional cognition on the psychological carrying capacity of ecotourism community residents. The calculation results show that the psychological carrying capacity of ecotourism community residents is positively correlated with positive interactive emotional cognition (r>0) and negatively correlated with negative interactive emotional cognition (r<0). It was also found that community residents' proud emotional perception of tourism development had the highest degree of influence on the psychological adjustment capacity variable (R²=0.299). This study verifies the mechanism of community residents' interactive emotional cognition on their psychological carrying capacity and enriches the theoretical research on promoting ecotourism development.
This paper improves the prediction accuracy of financial crisis of listed companies by optimizing the traditional Z-score model and taking the financial warning indicators as the input features of the neural network. The study selected the financial data of listed companies in a certain place from 2017 to 2023 as a sample, compared and analyzed the early warning performance of multiple traditional machine learning algorithms with this paper’s method, and assessed the reliability of this paper’s model in the early warning of financial quality by combining with cases. The neural network-based Zscore model has an AUC value of 0.914 on financial quality early warning, which is close to 1, and the prediction results are reliable. The model’s overall financial quality early warning accuracy in year t-1 is elevated by 16.61% to 19.35% compared with the comparison algorithm, and has a faster error has convergence speed. The Z-value calculation predicts that three companies will appear to have financial quality risk in 2017, which is consistent with the actual results. The algorithm of this paper predicts that company 9 has a Z-value of 3.79 in 2031, which may have financial quality risk. The results of this paper are reliable and show the early warning method of financial quality of listed companies in a new perspective, which is an important reference value for investors and managers.
Aiming at the difficulties faced by traditional industries, this paper formulates a smart blockchain solution for sustainable industrial digitalization. Through the theoretical analysis of blockchain technology integration into industry, it provides theoretical support for the application of intelligent blockchain technology in industrial digital transformation. Combining the above three algorithms and the actual situation of industrial digitalization development, an industrial digital transformation scheme integrating intelligent blockchain technology is designed, and a case study of the scheme is conducted. The delay mean value of this paper’s scheme is within the allowable range at the maximum throughput, indicating that the scheme can promote the sustainable development of industrial digitalization. In the actual application scenario, the CD-PBFT consensus algorithm performs more prominently, and in addition, it can be seen that the industrial blockchain solution, which can enhance the product recycling rate, well practices the concept of sustainable development.
The emergence of artificial intelligence has changed the traditional visual communication design mode to a great extent. This study aims to conduct an in-depth theoretical discussion and empirical analysis of the intersection of artificial intelligence and visual communication design, for the generative design application of AI technology in visual communication design, based on the AttnGAN algorithm, designing the adaptive word attention module and feature alignment module, constructing the ACMA-GAN text image generation model, and evaluating its visual communication design by combining quantitative and qualitative experiments to assess its The effect of ACMA-GAN on visual communication design is evaluated by combining quantitative and qualitative experiments. Combined with OLS algorithm, the empirical analysis of the effect of AI technology on visual communication design is carried out, and the ACMA-GAN model achieves excellent performance in the evaluation of assisted visual communication design, with the BLEU-3 and CIDEr scores higher than the next highest scores by 7.48% and 7.35%, and the average scores of each qualitative index are over 4.5, which indicates the feasibility and good utility of AI technology in assisting visual communication design. AI technology can positively act on visual communication design through image recognition and analysis, image generation and creation assistance, personalized design and workflow optimization.
This paper adopts research methods such as literature method and questionnaire survey method to take the cultural inheritance and development of Lanzhou Taiping Drum as the research object, and conducts in-depth discussion on the characteristics, social background and development of Lanzhou Taiping Drum. The research and analysis of the influence of the inheritance of Lanzhou Taiping Drum was also carried out by using principal component analysis and stepwise regression method in combination with the actual situation. It is found that many factors of Lanzhou Taiping Drum itself and government factors have significant influence on its inheritance. On the basis of the results of this study, we explore the ways and contents of the protection and inheritance of Lanzhou Tai Ping Drum, and put forward the digital inheritance of Lanzhou Tai Ping Drum and the path of cultural ecological reconstruction in terms of the influencing factors.
In today’s digital era, user interface (UI) design is crucial for enhancing user experience and strengthening user engagement. The study uses heatmap analysis, K-means clustering algorithm and random forest regression algorithm to comprehensively analyze the characteristics of user behavior in UI pages. The predicted results of user behavior in UI pages are visualized and analyzed through heatmaps. Cluster classes are divided according to user behavioral characteristics to generate user profiles with the same behavior. Combine Random Forest and Logistic regression algorithm to get the key indexes of UI optimization design and predict their impact on user behavior experience. The research results show that the MAE and SMAPE values of Random Forest regression algorithm on user behavior prediction are 133.55 and 8.18%, respectively, with an R² of 0.96, and the accuracy rate of behavior prediction is more than 80%, which shows a good performance of user behavior prediction. The clustering algorithm divides the user behavioral characteristics into 6 clusters based on their behavioral characteristics, including cluster class 1 (browsing and exploring class), which accounts for 11.5% of the number of investigators. The weight of the top 8 of the importance of UI optimization design obtained by the random forest regression analysis algorithm is 70.26%. And the user behavior experience can be improved by 5.377~9.925 times when each element is improved by one unit.
This topic obtains the data of featured vocabulary under the technical architecture of big data platform and saves it in the form of dataset. Standing on the perspective of the principle of translation of featured words in foreign propaganda, the improved K-means algorithm and attention mechanism are utilized to design the translation model of featured words. The model of this paper is validated and analyzed from two aspects, namely, performance indexes and application effect, respectively. In the six performance indexes, this paper’s model performs better compared to the other two control models. After the experience, the control group and the experimental group show a significant difference, i.e., the introduction of data mining algorithm is more effective in translating the featured vocabulary on the traditional model.
The development of artificial intelligence has brought new development opportunities for modern enterprises, but employees present a certain degree of resistance to the introduction of AI technology. The author tries to dissipate employees’ resistance and improve their acceptance of AI through organizational training. After researching organizational training and employees’ perceived awareness of AI, organizational training and employees’ acceptance of AI are taken as antecedent and consequent factors to construct a structural equation research model of the two. The research hypotheses are proposed based on the theoretical study of the two. Regression analysis of the effect of organizational training on employees’ AI acceptance is conducted through structural equations. The regression results show that training investment, employee motivation and knowledge training in organizational training all have a significant positive effect on both employees’ AI perceived ease of use and AI perceived usefulness. Employee AI perceived ease of use and AI perceived usefulness have a positive effect on employee behavioral intention to use AI for knowledge creation and automation. Employees’ behavioral intention to use AI for knowledge creation will have a positive effect on AI for knowledge creation, and behavioral intention to use AI for automation will have a positive effect on AI for automation.
New energy vehicles have a broad market, and the pricing and after-sales service of new energy vehicle enterprises have become the effective competitiveness of new energy vehicle enterprises. Therefore, this paper studies the pricing and after-sales service decision-making of new energy vehicles on the basis of game theory, and the study first gives a brief overview of game theory. Then, in the context of new energy vehicle subsidies, the optimal pricing under different sales modes is studied using game theory models. It also studies the utility of service stores of the same level of new energy vehicles with the support of game theory, and finally puts forward service suggestions from four aspects: optimizing offline service stores, expanding online services, developing service projects, and developing personalized services. This study can also provide valuable references for the pricing and service marketing of new energy vehicle enterprises, improve the competitiveness of after-sales service at the same time, and also put forward feasible suggestions for the future after-sales marketing methods of new energy vehicle manufacturers.
Achieving high-quality development has become the core essence of tourism industrialization, and is also a necessary step for the construction of ecological civilization to make new achievements. The article establishes the index system of China’s tourism high-quality development, and uses the entropy weight-TOPSIS model to measure the tourism high-quality development of China’s tourism in each region from 2013 to 2021. On this basis, it comprehensively applies density estimation, Dagum Gini coefficient and convergence modeling methods to analyze the regional differences and convergence of China’s tourism development. The study shows that the level of high-quality development of China’s tourism industry is gradually rising, and the regional differences in high-quality development of tourism are generally narrowing, with insignificant changes in intra-regional differences and narrowing of inter-regional differences, though. The overall trend of wave height in the central region is increasing, the wave height in the western region is decreasing and the width is increasing, and the wave height in the northeast region is increasing and the width range is decreasing. At the same time, convergence coefficient shows that the gap between the level of high-quality development of tourism economy in the eastern, central and northeastern regions shows a trend of convergence, while the western region increases from 0.373 in 2012 to 0.388 in 2021, that there is no trend of convergence.
This paper synthesizes relevant theoretical knowledge and construction principles, selects 20 evaluation indicators to constitute the evaluation system, and divides the evaluation system into two subsystems in order to more intuitively demonstrate the relationship between international trade network optimization and regional economic synergy. Setting the source of research data, due to the initial data outline is not uniform, the research data for the dimensionless processing. Then the weight values of each index are calculated with the help of entropy weight method, and their values are substituted into the coupled synergy model of the fusion evolutionary algorithm. It is calculated that the synergy level of international trade network optimization and regional economy is medium in the period of 2014~2016, the coordination level of the two has been significantly improved in the period of 2017~2021, and the coordination level is good, and the coordination level of international trade network optimization and regional economy rises to excellent in the period of 2022~2023.
As an environmentally friendly and efficient public transport, the optimization of the operating frequency of electric buses is of great significance for improving passenger satisfaction and reducing operating costs. This paper proposes an optimal electric bus frequency setting method that combines LSTM prediction and two-layer planning. First, LSTM neural network is utilized to predict the passenger flow of electric buses. Second, a two-layer planning model is constructed, with the upper model aiming at frequency optimization and the lower model aiming at electric bus frequency setting. Finally, this two-layer planning model is solved by genetic algorithm to obtain the optimal electric bus frequency setting. The inbound and outbound passenger flow data of the 5th station of 363 electric bus in Q city are used for practical verification. The prediction results of the LSTM model on inbound and outbound passenger flow on weekdays and natural days are basically consistent with the actual values. The optimal frequency of 62 trips was solved using genetic algorithm. The maximum deviation of the actual capacity supply from the actual capacity demand curve is only 0.09% when the frequency setting is verified under the scenario of thousands of passenger flows. From the above analysis, it is shown that it is practical to design the optimal electric bus frequency using LSTM prediction and two layer planning model.
At present, drilling fluid leakage in oil and gas drilling engineering in complex formations is a worldwide technical problem. The study explains the mechanism of dense pressure-bearing plugging at the bottom of the fracture, explores the influencing factors of the pressure-bearing capacity of the leakage prevention and plugging working fluid, and establishes a mathematical model by using multivariate nonlinear regression analysis. Based on the machine learning technology, the support vector machine algorithm is selected as the prediction method of the particle size of the working fluid for leakage prevention and plugging, and the system model of the ultra-high-temperature dense pressurized leakage prevention and plugging working fluid is constructed. It is found that the established multivariate nonlinear regression analysis has good fit and accuracy, and the average relative error is only 2.9%, and the seam width (-0.694) and formation pressure (0.502) have the greatest influence on the pressure-bearing capacity of the working fluid for leakage prevention and plugging. The prediction accuracy of the support vector machine model for the working fluid particle size was 95.36%, and the prediction F1 values on multiple datasets were all greater than 0.9, showing excellent prediction results. The constructed mathematical model can be used to guide the field operation, which is conducive to the long-term stable plugging and scientific leakage prevention of fissure-based leakage.
Chinese oil painting art is an important carrier of contemporary Chinese cultural identity features, the identification and quantitative study of the color and texture of the picture can help to understand the characteristics of the oil painting works more deeply. Therefore, this paper proposes a feature recognition method for oil painting art based on deep learning method. The Otsu threshold method and DeeplabV3+ network model based on DeeplabV3+ are selected for image graying and segmentation processing. The global color histogram and ring LBP are used to extract the color and texture features of the picture respectively, and the oil painting feature recognition is completed based on the regularized limit learning machine. In several sets of quantitative results, the methods in this paper all have better oil painting color and texture feature recognition, among which the RELM algorithm has the highest detection accuracy at low correlation features. It shows that the deep learning based Chinese oil painting art and cultural identity feature recognition method can effectively extract oil painting features and realize the quantitative research on oil painting.
The article firstly establishes a mathematical model of the FMS shop floor planning process problem, and combines the rescheduling strategy and rolling scheduling strategy for solving the FJSP problem. Subsequently, the simulated annealing genetic algorithm is improved by relying on genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm, and the application of hybrid optimization algorithm in problem solving is proposed. The simulated annealing algorithm is incorporated into the crossover and mutation operations of the genetic algorithm to strengthen the local search capability, and then the global annealing operation is incorporated into the new individuals obtained. The overall design of the mixed reality-based FMS virtual simulation system was tested with a view to optimizing the external tool library tool limitation problem in the FMS shop floor planning process. The results of the simulation experiments show that although the algorithm of this paper, SaDE and CoDE algorithms can reach the optimal solution, the convergence speed of the algorithm proposed in this paper is significantly better than the other two algorithms. Based on the experimental results, the article finally constructs a mixed reality-based FMS virtual simulation system to solve the external tool library tool limitation problem in the FMS shop floor planning process.
Agent technology is widely used in intelligent manufacturing and digital workshop as a new method to solve complex, dynamic and distributed artiϐicial intelligence application problems. This paper ϐirstly summarizes the application steps of Agent technology in 3 aspects of modeling, simulation and monitoring of intelligent manufacturing system on the basis of a brief description of multi-agent system. Then, based on reinforcement learning theory, a multi-agent collaborative algorithm SRL_M3DDPG based on state representation learning is proposed.Finally, the algorithm model is tested and applied to the smart shop scheduling problem. The learning curve of the SRL_M3DDPG algorithm in the example remains relatively stable after the 3400th round, and the maximum completion time of the scheduling is 29. Comparing with other composite scheduling rules, the delay rate of this paper’s algorithmic model is the lowest, which is only 15.47%, which indicates that the algorithm is able to signiϐicantly reduce the delay rate of the workpiece. In addition, this paper’s algorithm achieves better results in adaptive intelligent manufacturing workshop scheduling, ϐinding the shortest machining completion time of 221 unit time, which can adapt to the dynamic intelligent manufacturing workshop environment.
This paper constructs a comprehensive evaluation system based on the CIPP model, covering multiple dimensions such as input evaluation and outcome evaluation, in order to comprehensively measure the effect of college students’ mental health education in the new media environment. In terms of weight determination, the subjective weights are obtained by hierarchical analysis method, then the objective weights of each index are calculated by entropy value method based on the actual data, and then the combination assignment method is used to organically combine the subjective and objective weights to obtain the ϐinal indexes. The relationship matrix was constructed on the basis of a large amount of collected data, and the fuzzy comprehensive evaluation method was used to comprehensively assess the implementation effect of college students’ mental health education. The results of the study show that the overall level of the effect of college students’ mental health education is good, with the ratings of 79.54 and 78.28 for their mental health knowledge and ideological awareness evaluation, respectively, and that the mastery of mental health methodology and the awareness of proactively seeking psychological help are the main factors affecting the mental health of college students. In addition, the mastery level of college students’ mental health practice ability is average (69.52), and there is an obvious deϐiciency in their theory to practice, which also adds difϐiculty to the construction of college students’ mental health. Therefore, the fuzzy comprehensive evaluation method can be used to optimize the evaluation system of college students’ mental health education in the new media environment.
With the rapid development of artificial intelligence technology, the research on personalized learning in the field of ideological and political intelligence education is increasingly active. In this paper, an improved locust optimization algorithm is proposed, which is applied to the intelligent grouping strategy of ideological and political education. Then a knowledge state-oriented hypergraph self attention knowledge tracking model is proposed, which consists of a hypergraph module and a self attention module, and is capable of predicting students’ future interaction sequences through their past interaction sequences. In order to realize students’ personalized test question matching needs, a Civics test question recommendation algorithm based on the neural graph model is proposed, based on which a personalized Civics test question recommendation exam system is designed and implemented. The intelligent grouping strategy based on the optimized locust algorithm achieves a total score accuracy of 100% in the Civics grouping task. The knowledge tracking model accurately predicts students’ knowledge status, and the attention weights of students’ learning paths based on this paper’s recommendation algorithm are all higher than 0.5. It shows the effectiveness of this paper’s strategy of automatic generation of Civics education content based on the locust optimization algorithm and the personalized test question matching model on the students’ in-depth understanding of the Civics knowledge and improvement of learning efficiency.
In the face of the traditional agricultural marketing model is difficult to continue the status quo, agricultural marketing competition – cooperation relationship for agricultural enterprises of commodity marketing and long-term development is also increasingly important. In this paper, game theory is introduced into the study of competition and cooperation strategy of agricultural products marketing, the strategic behavior of two agricultural products enterprises in the agricultural products industry cluster is constructed into the corresponding matrix, and evolutionary dynamic stability analysis is carried out to establish the replication dynamic equations and Jacobi matrix to solve the evolutionary stability strategy (ESS), so as to provide reference for the formulation of the competition and cooperation strategy of the enterprise’s agricultural products marketing. Using simulation to explore the influencing factors of the evolutionary direction of the marketing competition and cooperation strategy of agricultural products enterprises. When the probability of winning the joint bidding is greater than 0.8, it will evolve into a cooperative strategy, and when it is less than 0.7, it will evolve into a competitive strategy, and with the increase of the allocation coefficient of the investment amount of the project construction, the agricultural products enterprise 1 and the agricultural products enterprise 2 will gradually shift from a competitive strategy to a cooperative strategy. The lower the cost allocation coefficient is, the higher the probability that enterprises will evolve to cooperative strategy. The increase of cooperative transaction cost then accelerates the evolution of enterprise 1 and enterprise 2 to competitive state.
The traditional Chinese culture contacted in history education has many common points with the Civic and Political Education, which has become a new method of value penetration of Civic and Political Education. This paper reveals the value penetration of traditional Chinese culture in Civic and political education from the perspective of innovative cultural topology, and puts forward three strategies to innovate the concept of Civic and political education, such as enhancing the effect of aesthetic connotation of Civic and political education. On this basis, variables are designed, structural equation model is constructed, and the role of teaching concept and other variables on the value penetration of traditional Chinese culture Civic and political education is analyzed through the reliability test and factor analysis. Combined with system dynamics, the system causality diagram is drawn according to the causal feedback relationship between internal and external factors to explore the causal relationship affecting the value penetration of Civic and Political Education, and then explain the mechanism of the role of traditional Chinese culture Civic and Political Education. It was found that all five paths among latent variables passed the significance level test of 0.001, and the teacher’s mission and ideal belief in teaching philosophy had the most significant effect on the value penetration of traditional Chinese culture Civic and political education, with path coefficients of 0.98. In the process of Chinese traditional culture civic education, it is necessary to reflect the unity of humanistic spirit and modern spirit, the unity of professional ethics and values, and to form the style of course civic education and course civic education characteristics with Chinese traditional culture.
At present, machine translation performs better in the general domain translation effect of large-scale bilingual corpus, but the translation effect in specific domains still needs to be improved. In order to optimize the accuracy of machine translation in the domain of English translation of professional terms, this paper proposes a translation model that incorporates syntactic knowledge and terminology. Aiming at the problem of more limited translation domain knowledge in the RNMT and Transformer models based on the self-attention mechanism, an optimization method is proposed. According to the domain characteristics of English translation of professional terms, English syntactic keywords are incorporated into the model training process, the special information contained inside the text of professional terms is learned, and the lexical properties of each word in the dataset are recognized before they are input into the model. Then attempts are made to incorporate the specialized terminology into the model to enrich the parallel corpus required by the model. The experiments confirm the excellent performance of the optimized translation model in this paper on the De→En terminology translation task, which improves 22.67 BLEU values compared to the base model. And the fluctuation of its BLEU value with the change of sentence length is small, which further indicates that the method optimizes the accuracy of the machine translation model in the English translation of professional terms.
Aiming at the needs of reconstructing the structure of calligraphic seal cutting strokes and virtual display, this study designs a GAN technique that integrates three models, namely, “WGAN, DCGAN and CGAN”. The Cycle GAN model is used to obtain the mapping relationship between learning and style migration by utilizing its cyclic consistency loss. Adaptive pre-morphing technique is introduced to process the input image to capture the outline information and morphological features of calligraphic seal carvings, and a Generative Adversarial Network-based Generative Model for Structural Reconstruction of Calligraphic Fonts (CRA-GAN) is proposed. Meanwhile, an online virtual display system is designed to provide users with a good sense of experience in the virtual display of calligraphy. The results show that the CRA-GAN model can better capture the details and global information of the fonts, and its recognition rate of the eight calligraphic fonts ranges from 90.42% to 97.38%, and the MOS rating value of the text image is > 8.5 points, and its recognition results are in line with the observation characteristics of the human eye for calligraphic images. The FID calculation result of the CRA-GAN method ( 204.361) of the CRA-GAN method is much lower than that of other methods, which obviously improves the diversity and visual quality of the generated calligraphic fonts. This paper evaluates the user’s experience of the system from five aspects: narrative experience, emotional experience, sensory experience, cognitive experience and interactive experience, and calculates that the final score of the system is in the range of 80-100, which indicates that the user’s satisfaction is very high after actually experiencing the virtual display system.
Financial performance optimization is an important embodiment of enterprises to improve operational efficiency and optimize management level. The article proposes a method of financial performance optimization and evaluation using group intelligence algorithm in order to optimize the financial performance of enterprises. EVA is introduced to establish the evaluation index of enterprise financial performance. The financial performance prediction model is constructed according to the propagation process of BP neural network, and the IPSO-BP algorithm is utilized to avoid BP from falling into local optimum and improve the prediction accuracy. In the learning ability test, the relative errors of the EVA value, EVA payoff and EVA rate of the IPSO-BP algorithm are controlled within 6%, 8% and 10% respectively, and the average relative error of the model application results is 3.87%. The model in this paper can achieve more accurate financial performance assessment and prediction, which is conducive to the optimization of financial performance management of enterprises.
The problem of English education quality is worth exploring in depth, and quantifying the indicators of English education can help to understand the problems in teaching and improve the quality of teaching. The study firstly establishes the English education quality evaluation index system, including five first-level indexes of teaching resources, teaching content, teacher quality, teaching effect and teaching quality feedback and 15 second-level indexes, such as network resources, book resources and comprehensive teaching content. On this basis, the combination weights are determined by fusing the G2 method and the projection tracing method through the combination assignment method to eliminate the one-sidedness problem of adopting a single assignment method, and then the cloud model theory is introduced to establish the English education evaluation model based on the cloud model. Problems and shortcomings of multi-objective linear programming weight allocation in English education evaluation system are found through the evaluation results, which lead to low multi objective linear programming weight allocation in English education evaluation system.
In order to enable ships to operate stably for a long time under complex sea conditions, all kinds of ships have an urgent need for gyroscopic rocking reduction devices. This paper takes the double gyro rocking reduction device with better rocking reduction effect as the research object, establishes its corresponding nonlinear dynamic equations, adopts the energy method to establish the differential equations of motion, and deduces the dynamic model of the rocking reduction double gyro. A parameter optimization model is established with the main objective of improving the shaking reduction effect, and the key components of the shaking reduction double gyro are optimized. The bacterial foraging optimization algorithm is selected to solve the model, and the multi-objective parameter optimization model is established. For one to five wave classes, the middle value of the wave height of the meaningful wave is selected for the dynamic simulation experiment of the double gyro. When the wave level is less than three time level, the rocking reduction performance of the rocking reduction double gyro reaches 87.5%, 78.1% and 77.78%, respectively, and the transverse rocking reduction performance is good. Under the simulation environment of sea state I (wave height 2.5m, average period 7s) and sea state II (righteous wave height 2.5m, average period 12s), the rocking reduction efficiencies of the ship after parameter optimization are improved by 6.44% and 10.09%, respectively.
With the rapid development of computer vision technology, image enhancement technology involves an increasingly wide range of research content. At the current stage, picture hierarchy enhancement technology is a research hotspot in the field of image enhancement. This paper proposes an oil painting image enhancement network based on positive probability distribution guidance. The multidimensional spatial information of the samples is obtained through the multibranch information extraction architecture in the network structure, and the probability distribution estimation module estimates the probability distribution through the obtained multidimensional spatial information. In addition, a new image enhancement method based on the RGB color balance method is proposed, which combines the multi-scale Retinex enhancement algorithm with color recovery and the RGB, Lab color space histogram adaptive stretching algorithm, to further improve the effect of oil painting image display. The experimental results show that the method has a better image color bias correction effect compared with the existing techniques. In terms of subjective evaluation, the average subjective score of this paper’s method in three different aesthetic levels reaches 9.15, obtaining a high evaluation. The samples enhanced based on this paper’s algorithm all obtained high aesthetic index scores, indicating that the oil paintings under this paper’s algorithm are in line with the public aesthetics, which is of great significance to the work of oil painting artists.
AI technology can accurately capture and feedback user emotions in digital media interaction to realize precise interaction. In this paper, we design an AI emotion interactivity enhancement model based on multimodal fusion, and apply the neural network model of Bi-GRU and dual attention mechanism to fuse the long and short-term emotion classification results of the tested samples at the decision level to obtain the final emotion classification results. Then the weight coefficient vector of each sentiment category is calculated based on the sentiment classification confusion matrix of the classifier, which is used as the a priori knowledge for multimodal sentiment analysis for decision fusion. The performance is examined on the MOSI dataset and the AI-based interaction design strategy in digital media is proposed. Analyzing the interaction design effect, the interaction design applying the model of this paper has better user experience sense, emotional arousal, pleasure level, and emotional feedback effect in subjectivity evaluation than the control group, and 75% of the experimental subjects think that the feedback-adjusted digital media has a better pleasure level.
In the era of artificial intelligence, human-computer collaborative teaching has become a new picture of future development in the field of education. Based on the theory of human-computer collaboration and the theory of production-oriented approach (POA), this paper constructs a university English POA teaching model based on human-computer collaboration. It also combines the speech recognition algorithm, S-T behavioural analysis method and social network analysis method to conduct a case study on the current situation of college English classroom teaching under this instructional design model. Meanwhile, a teaching experiment is designed to verify the effectiveness of the constructed POA teaching model. The results of the case study show that most of the university English courses favour the lecture mode, with less interaction between students, and the classroom is dominated by teacher lectures and teacher-student interactions, but at the same time, many teachers begin to experiment with the discussion mode, which increases teacher-student interactions and student-student interactions in the classroom. In addition, the experimental group adopts the POA teaching mode and the control group adopts the traditional lecture mode, and its independent samples t-test results show that the experimental group is significantly better than the homogeneous control group in the dimensions of interest, ability, attitude, and test scores in English literacy after the experiment (P<0.05), which suggests that the combination of AI technology and the production-oriented method can effectively improve the effectiveness of the design of university English literacy teaching and achieve better teaching effectiveness and has potential application value.
Market economy is characterized by the uncertainty of supply and demand, so enterprises can realize the optimization of inventory cost control only by reasonably forecasting the demand of supply chain. This paper studies a supply chain demand forecasting method based on machine learning. The factors affecting supply chain demand are collected and analyzed, and the ARMA model, which combines autoregressive model and moving average model, is used to forecast supply chain demand. Then, through the introduction of procurement cost, storage cost and time cost, a multi-level inventory model is established, and the immune genetic algorithm is used to solve the model to find the optimal inventory cost. The experimental results show that the prediction model has good forecasting performance. After using the optimized scheme, the total inventory cost of the enterprise supply chain is reduced by 17.35% and 13.69% respectively. It can be seen that, on the whole, the method in this paper has a good effect of supply chain demand forecasting and cost control.
Service Oriented Architecture (SOA), as a distributed computing architecture, is widely used to build efficient, maintainable and scalable information systems. This paper focuses on SOA design optimization based on reinforcement learning and cloud computing to achieve resource scheduling optimization with a view to improving the service quality of SOA applications. The asynchronous dominant action evaluation algorithm (A3C) based on policy gradient is used as the decision core of the cloud resource scheduler, and the residual recurrent neural network (R2N2) is introduced to construct the cloud resource scheduler based on the A3C-R2N2 algorithm to promote resource scheduling optimization. In the resource scheduling deployment strategy performance experiments, the median average latency of the stochastic dynamic scheduling strategy based on policy gradient learning proposed in this paper is reduced to 9.99% and 56.25% of the direct deployment, respectively, and the CPU utilization rate is also improved by 20.72% compared to the direct deployment. The loss function and reward function of the A3C-R2N2 algorithm in this paper begin to converge after the number of practice reaches 10,000 times and the number of training episodes reaches 300, respectively. Compared with random deployment and nearby deployment strategies, the deployment strategy based on A3C-R2N2 algorithm in this paper has an average service response time of 9.3622s, which is optimal.
Large-span steel structures are prone to wind vibration under wind loads, which affects the safety and performance of the structure, and wind vibration control is the key to its design. This paper takes the large-span steel structure as the research object, firstly introduces the theory and method related to wind vibration control analysis, constructs the topology-optimized inertial capacitance damper controlled wind vibration response dynamic equation of super high-rise building to analyze the influence law of wind speed and wind direction on the dynamic characteristics of the structure, and then further strengthens the vibration control ability of the structure through reasonable arrangement and parameter adjustment. The deformation of ETABS model in y-direction is larger than that in xdirection under 50-year wind load, and the maximum displacements in y- and x-directions are 18.72 mm and 11.65 mm, respectively. The y-direction interstory displacement angle meets the code requirement limit (2.65×10-4). The amplitude of the acceleration time-range curve of its top floor structure is between ±0.08, which meets the requirements for comfort. The optimization of the reinforcement layer using continuum topology optimization is better than the optimization of the optimal location arrangement according to finite element software. The results of node displacements and inter-story displacement angles of each story of the modified structural model under wind load meet the limits of top story displacement and inter-story displacement angle, and the performances are similar to those of the extended-arm truss structural model.
The introduction of performance evaluation in the educational management of colleges and universities is conducive to the formation of result-oriented concepts and management methods of student educational management. In this paper, we select the indicators of educational management conditions, processes and results to design the performance evaluation index system of educational management. Using the hierarchical analysis method, the eigenvectors and maximum eigenvalues are calculated to determine the weights of each index element of the index system. Then apply the gray correlation method to evaluate the educational management performance of the five universities by calculating, one by one, the absolute difference between each indicator sequence (comparative sequence) and the corresponding element of the reference sequence of the object to be evaluated after the data are dimensionless. The analysis found that, according to the formula for calculating the degree of correlation between the actual level of educational management performance and the ideal educational management performance situation, the comprehensive correlation degree of each sample of colleges and universities in the five stages is Z = (0.3333, 0.3951, 0.4600, 0.5031, 0.5946, 1.0000), and the rankings of colleges and universities in terms of the performance of educational management from the highest to the lowest are Academy 4, Academy 2, Academy 5, Academy 3, Academy 1. HEI 3 and HEI 1 should reflect on the shortcomings, enhance the digital construction of teaching informationization, deepen the collaboration between schools and enterprises, and improve the performance of educational management of colleges and universities.
The application of artificial intelligence on the field of art can be used to assist the creation of musicians and provide new creative ideas for musicians. In this paper, firstly, an ARIMA model is established for the prediction problem of opera style, which is used to predict the trend of the development of opera style sequence, and the best model is selected according to the minimum information criterion and Bayesian criterion. Then an automatic music melody generation method based on the generative adversarial network framework is proposed, which applies the trained natural language generation model to music generation to textualize the music melody and reduce the model running time. In addition to this a barization music melody generation method is also used, which divides a large music melody into melodic segments and generates them segment by segment, reducing the difficulty of the model in generating the music melody. Finally, the Fourier transform method is used to extract the features of the music melody and complete the visualization of the music melody. The model ARIMA(2,1,1)(2,1,0)12 that best fits with the time-series prediction of the development of opera styles was identified through empirical analysis. The PB value of Leak-GAN_2 model in this paper is improved by 41.38% compared with MusicGAN. It shows that both the opera style prediction model and the music melody multimodal generation model constructed in this paper have better effect and certain advancement.
In order to improve the attendance rate of students and optimize the quality of teaching, this paper proposes a method of predicting the attendance rate of students in colleges and universities based on multivariate regression analysis. Firstly, we obtain the factors affecting students’ attendance rate through sample survey and conduct correlation analysis, and then summarize and refine the three dimensions of students, teachers and schools. The above dimensions are used as independent variables to construct regression equations, and the regression equations are used to predict the attendance rate of students, so that teaching managers can optimize the management. The analysis found that the factors such as the college to which the truant students belonged, the reason for truancy, and the grade level showed diversity and complexity. Overall male students have more truancy rates than female students, and lecturers with higher titles have lower truancy rates. Regression modeling and prediction of truancy rate found that the prediction results are closer to the real results. Therefore, the method of this paper can be combined to optimize and adjust the attendance rate from the aspects of regulations, work allocation, teaching management and ideological education.
Personalized learning, in which learners set their own pace and select their own resources according to their own learning needs and characteristics, is the trend of Chinese education and teaching. In this paper, we design a personalized teaching path recommendation model for Chinese education based on reinforcement learning. The knowledge tracking prediction model LTKT is designed to integrate multiple knowledge points as information dimensions for model learning in the data preprocessing stage. The sparse self-attention mechanism is introduced into the encoder and decoder structure and embedded with location coding containing absolute and relative distances to enhance the model’s perception of location information. Finally, the RL4ALPR algorithm is designed to model the changing knowledge level, the candidate learning item filtering algorithm is used to narrow down the scope of the recommended learning items, the reinforcement learning algorithm assumes the role of a recommender, and the degree of change in the knowledge level of the learner is regarded as a reward for the improvement of the reinforcement learning recommendation strategy. Simulation experiments are conducted on datasets such as ASSISTments and compared with baseline models such as KNN, GRU4Rec, Random, etc. The model in this paper has an F1 value and an AUC of 0.635 and 0.956 respectively in the evaluation of learning effect, which are the highest among the models. The study makes a useful exploration for the informatization of Chinese education and teaching.
The mechanism study of steel pipe welding in Dianzhong water diversion project is very complicated, and there are many process parameters affecting the temperature distribution of high-frequency heating of welded steel pipe, and the degree of influence and the influence law are not the same. In this paper, Abaqus software is used to carry out the finite element analysis of the steel pipe welding process, and the displacement variational method (i.e., Ritz method) is introduced to derive the radial displacement of the steel pipe when it is subjected to the action of the centralized force, so as to realize the finite element simulation of the welding process of the steel pipe. At the same time, the optimization of the welding process parameters of the steel pipe is realized by combining the radial basis function neural network (RBF) and particle swarm algorithm (PSO). The simulation results show that the Von mise equivalent residual stress at the weld seam reaches the nominal yield strength of the material on both the internal and external surfaces of the steel pipe, while the axial residual stress has a very different distribution law on the internal and external walls of the steel pipe, which belongs to the tensile stress and weld residual compressive stress at the weld seams on the internal and external walls of the steel pipe, which are about 0.4 times the yield strength of the material and 0.7 times the yield strength of the material, respectively. The ring residual stress distribution law of the steel pipe is similar to the axial residual stress, but both reach the nominal yield strength of the material. Through parameter optimization, this paper determines that when the opening angle is 5°, the current frequency is 217.35 kHz, and the distance from the coil to the V-point is 252 mm, the corresponding optimization target values are all smaller, and the welding quality of the corresponding weld seam is better. The research in this paper provides a theoretical basis for further improving the welding quality of steel pipe in Dianzhong water diversion project.
The field of education is paying more and more attention to the fundamental task of education by establishing morality, and ideological and political education has become a major project in which all the teaching and learning links cooperate with each other and are accomplished in a concerted manner. This study explores the method of organic integration of ideological and political education and teaching and data visualization technology to enhance the effect of ideological and political teaching. Firstly, the method of portrait construction is introduced, combined with the student behavior dataset, and the student behavior data is preprocessed. Using the user portrait construction method as a hub, a gradient boosting decision tree model was used to predict the students’ Civics learning performance. The improved K-prototypes clustering algorithm was used to categorize student groups, which facilitated teachers to develop targeted learning strategies. Finally, group portraits and feature labels are extracted from the students to further help teachers accurately determine the types of student groups and carry out personalized teaching. The classroom teaching model in this paper classifies students into four categories with obvious behavioral characteristics, which increases teachers’ understanding of students, and the model not only improves students’ academic performance in Civics, but also significantly improves students’ level of course Civics and increases students’ classroom active response rate by 19.625%. The Civics education data visualization technology proposed in this paper reveals the rules of Civics education and improves teachers’ work efficiency.
This study aims to investigate the influence of university language education on students’ expressive ability, and uses a questionnaire to collect the relevant factors affecting the relationship between students’ expressive ability and university language education. The key principal factors were extracted from many variables by principal component analysis to simplify the data structure and retain the main information. Subsequently, a multiple linear regression model was constructed and the least squares method was applied to estimate the model parameters in order to quantitatively analyze the linear relationship between each principal component and students’ expressive ability. In this paper, four principal factors, namely, “language organization ability, communication ability, language use ability and intonation ability”, were identified under the principal component analysis technique, and their total variance explained reached 56.326%. It is found that the average score of students’ expression ability is in the middle normal level, but the extreme difference of score between different students is as high as 27, which shows that there is a big gap between students’ expression ability. The correlation coefficient between students’ expressive ability and university language education is 0.8947, and the correlation coefficients of the four sub-dimensions of the two sig values are less than 0.01, indicating that the stronger the university language education, the higher the level of students’ expressive ability. And the regression equation of students’ expression ability and university language education is obtained as Y=0.893X-15.874.
In this paper, time series analysis is used to monitor and predict the performance of athletes in sports training. A smooth time series model ARMA p q , model is established, a fixed-order method based on autocorrelation function and partial correlation function is proposed, and the parameters of the model are estimated, and least squares prediction is used for model prediction. The monitoring test data of hemoglobin (HGB) in sports performance of Z athletes of a club were used as the research object, and the smooth time series test was conducted to determine the ARMA (1,1) model as the optimal time series fitting model, and the fitting effect was tested. In the application of blood oxygen saturation (BOS) index, ARMA (1,1) model can predict the trend of BOS of athlete Z with good application effect. Based on the prediction of athletes’ performance by ARMA (1,1) model, this paper further proposes the integrated neuromuscular training method (INT), and integrates it with physical training will to develop the INT physical education training strategy. In the application experiment of INT physical education training strategy, the test results of the experimental group of athletes applying the INT physical education training strategy in the six events of T-test sensitive running, agility ladder, vestibular step, blindfolded one-legged standing, 30-meter sprint running, and 60-meter sprint running presented P<0.05, and the athletes' performance was significantly better than that of the control group.
Teachers and students will form a variety of dependent behaviors and interactions centered on teaching activities in the teaching process, thus, the teaching process can be regarded as a typical game process. This paper invokes game theory, takes teacher-student behavioral interaction as the research object, constructs a game model of teacher-student behavior in the process of English teaching, and proposes a teaching optimization strategy for English flipped classroom. At the same time, numerical simulation of the teacher-student game model is carried out to explore the dynamic game equilibrium under the cooperative behavior of teachers and students. The simulation results show that in the teacher-student game network, the strategy choices of teachers and students change over time, and different benefit-loss parameter μ, additional gain parameter β₀, and cost-saving parameter ψ have a greater impact on the replication of the strategy choice behaviors of the game parties. In addition, the increase of the parameters of the gain PT obtained by the instructor’s conscientious instruction, the gain PS obtained by the student’s conscientious learning, and the loss KS of the punishment that the student receives for not learning conscientiously are conducive to the promotion of the instructor and the student’s strategy evolution towards cooperation (conscientious instruction, conscientious learning), while the increase of the instructional cost CT of the instructor’s conscientious instruction and the learning cost CT paid by the student’s conscientious learning are not conducive to the promotion of the two parties’ cooperation. And when the proportion of instructors and students initially choosing cooperation is larger, the likelihood of both parties evolving toward cooperation is greater. This paper provides theoretical support for the optimization of English teaching process.
College students’ physical fitness is an important part of national health, and analyzing physical fitness data in college physical education teaching helps to dig out the factors affecting students’ physical fitness and adjust the teaching plan in time. The article reviews some basic regression tools and selects variables such as BMI dietary habits for logistic regression analysis to analyze the factors affecting students’ physical fitness. The similarity, uncertainty and dissimilarity between students and their friends are calculated by Top-N recommendation set algorithm, and the physical education teaching program is dynamically adjusted with the new SFD recommendation algorithm. Finally, values were assigned to different movement banks and risk factors, and the experts’ agreement with the new adjusted program was examined. The intensity of physical activity had the greatest relationship with passing or failing physical fitness among all factors (regression coefficient = 0.927, p70%), reflecting the rationality and feasibility of this study.
In order to optimize the pattern design method in lacquerware decoration design, this paper first analyzes the discrete and continuous situation of the pattern in time and frequency by Fourier transform method, and explains the mapping principle of Fourier variation. After that, the original image is processed such as sharpening and smoothing under the Fourier transform algorithm, and the lacquer decorative pattern after automatic deformation is obtained through interaction on the basis of 2D affine transformation technology. Finally, the geometric deformation of the lacquer decoration design from 2D to 3D is simulated and verified. The results show that in this paper, the threshold value, brightness and contrast of the lacquer decorative design patterns can be obtained by the geodesic distance deformation algorithm under the Fourier transform in MATLAB software to get the geometric patterns of the lacquer decorative design with the main color of the appropriate filler blocks. The corresponding blue values of the four patterns are 418, 38, 104 and 256; the optimal values of green are 256, 100, 87 and 405; and the optimal values of red are 256, 57, 63 and 117. 3-D imaging simulation experiments show that the average absolute error, root mean square error and maximum absolute error of the depth of the geometric patterns of the 3-D imaging method and the geometric patterns proposed in this paper are all significantly reduced, and the depth of the geometric patterns in the 20- mm depth range are reduced significantly. and the advantages of this paper’s method are more obvious in the depth variation range of 20mm. It can be seen that the algorithm of this paper can improve the deformation effect of geometric patterns in lacquer decorative design.
In this paper, the problem of piano practice time allocation is categorized as an integer planning problem, and focuses on 0-1 integer planning in integer planning. Based on the advantageous information in the 0-1 integer programming problem, the value of feasible solutions and the index set corresponding to the feasible solutions are proposed to realize the piano practice time allocation based on integer programming. For the evaluation of piano playing effect, a piano playing effect evaluation method based on the extraction of musical melody features is proposed, which adopts the base note cycle extraction algorithm based on the short-time autocorrelation method to extract the base note of the musical melody, and improves the linear scaling algorithm to solve the problem of uneven playing speeds and so on. In the piano practice practice allocation experiment, the average allocation time of player A applying the time allocation method of this paper is 2516s, which is higher than that of player B with the traditional allocation time, and the average concentration time accounts for 98.53% of the average time, which is better than that of player B’s 95.43% share. Compared with the traditional manual evaluation method, the evaluation results of this paper’s piano playing effect evaluation method in different test times sum up to 1, and the evaluation effect is better.
This paper constructs the evaluation index system of city image IP brand communication efficacy, and utilizes hierarchical analysis and fuzzy comprehensive evaluation to construct a comparison matrix to assign and quantify them. Then, it constructs a regression model to analyze the influencing factors of city brand image communication efficacy with city brand image communication management power, communication power and relationship power as independent variables and city brand image perception as dependent variable. With empirical factor analysis, the chi-square degrees of freedom ratio CMIN/DF is 1.034, and the root mean square of approximation error RMSEA is 0.017, the assessment model has a good fit, which verifies the scientificity of the communication effectiveness assessment framework system. The communication effect of a city’s brand image is assessed and found to have a comprehensive score of 86.16. The city brand image communication management power, communication power and relationship power all have a positive influence on the city brand image communication effectiveness.
Bridge construction is an important link in the construction of transportation infrastructure, which plays a key role in ensuring the smoothness and safety of road traffic. This paper systematically organizes the process of laser point cloud technology in bridge quality monitoring, and proposes an improved adaptive hyperparametric RANSAC point cloud segmentation algorithm to realize the bridge quality monitoring. Firstly, the basic process of RANSAC algorithm is sorted out, and the mean downsampling operation is adopted to replace the center of gravity downsampling method, which improves the point average degree of downsampling. Next, the FPS algorithm is combined with the method of selecting seed points to expand the range of selected values of seed points under the premise of meeting the relevant requirements. After splitting multiple fitting surfaces, the split fitting surfaces are combined to optimize the unfitted points and improve the fitting rate of the algorithm. The detection accuracy of the bearing flatness of bridge number 3 under the method of this paper is improved by 78.26%, and the maximum deviation of the detected bridge constitutive point offset is only 0.623m, which is within the acceptable range of bridge error monitoring. The feasibility of laser point cloud technology for bridge quality monitoring is verified.
Wireless sensor networks, which integrate a variety of technologies such as sensors, microelectromechanical systems, wireless communications, and distributed information processing, have become a cutting-edge field for studying the behavior of intelligent autonomous self-governing systems in groups. This paper explores distributed sensor networks in intelligent buildings, uses QoS routing algorithm based on ant colony optimization to implement the strategy of energy efficiency regulation of distributed sensor networks, and conducts experimental analysis on the performance of the algorithm as well as distributed sensor networks. Compared with the PCCAA algorithm, the node degree variance and channel percentage variance of this paper’s algorithm are smaller, the network link distribution and channel allocation are more balanced, and the topology is better. Meanwhile, the average power of this paper’s algorithm is slightly larger than that of the PCCAA algorithm, which is able to increase the robustness of the network while reducing the energy consumption and BER to ensure the network performance. In addition, the variance of the node energy consumption of this paper’s algorithm in different networks is smaller than that of the PCCAA algorithm, which indicates that this paper’s algorithm can make the node energy consumption of the whole network more balanced, and then improve the energy efficiency of the whole network. Simulation experiments prove that the algorithm in this paper effectively allocates node bandwidth through the quantization mechanism, thus reducing the amount of inter-node communication, while the corresponding sampling interval extension strategy can save the overall energy consumption of the network. The algorithm proposed in this paper has important practical value for energy efficiency regulation of sensor networks in intelligent buildings.
With the arrival of the big data era, a huge amount of text data of college language is generated, and how to manage these text data efficiently and mine useful information has become the focus of many scholars. The study first preprocesses and represents the university language text data, proposes a feature screening method based on Shannon entropy and JS-scatter, and then combines the principal component analysis algorithm with the dimensionality reduction of the extracted features on this basis. Subsequently, a pre-trained high-dimensional word vector spatial mapping model is introduced to generate richer semantic representations, and a pre-trained high-dimensional word vector spatial mapping model based on the pre-trained high-dimensional word vector spatial mapping model is designed. Finally, the method proposed in this paper is tested experimentally. Under different feature dimensions, the macro-averages of this paper’s method are 72%, 44.2%, 67.1%, and 3.3% higher than those of IG, PMI, ANOVA, and JS methods. At the feature dimension k=350, the macro-mean of this paper’s method is 0.853, when the classification effect reaches the optimization. In the spatial mapping relationship of word vectors, the accuracy of the mapping of this paper’s method also reaches 11.2% for the words with word frequency sorted from the first 5000 to the first 6000. This proves the effectiveness and feasibility of this paper’s method.
Knowledge mapping technology can effectively integrate and manage knowledge, and fully show the relationship between knowledge. Based on this, knowledge mapping is applied to the construction of the resource base of the ideology and politics course to explore its association with the teaching content. After sorting out the relevant concepts and construction methods of knowledge mapping, this paper proposes the design method of course ideology based on knowledge mapping. The web crawler tool is utilized to crawl the text data of the Civics material and preprocess the data. The seven-step method and Protégé, an important tool for ontology modeling, were used to complete the construction of the ontology model of the curriculum Civics and Politics domain. Finally, BERT, GGAT, CRF, and graph pooling techniques are combined to construct the general architecture of the Civics knowledge extraction model to realize the extraction of Civics knowledge. The method of Civics knowledge relation extraction in this paper performs well in the comparison experiment, and the AUC value of the method reaches 41.59%. More than 90% of the students express their liking and agreement with the teaching model based on knowledge graph, which verifies that the teaching model based on knowledge graph proposed in this paper has a positive and active effect on the learning aspect of students’ Civics knowledge.
Frequent lightning activity has the potential to cause damage to man-made facilities, cause forest fires and other hazards, and the prediction of lightning activity can help to avoid the occurrence of these disasters. In this paper, based on the lightning activity data of a region, the distribution pattern of lightning activity is identified at different elevations and latitudes and longitudes. Then geodetic distance and contributing nearest-neighbor similarity are introduced, and a GS-DBSCAN clustering algorithm is proposed to realize the spatial prediction of lightning activity by using the method of leastsquares fitting of prediction equations. The lightning activity directions after data clustering show topographic correlation, and the overlap between lightning activity directions and topography is about 35%. Combined with the prediction images, it is found that the lightning activity prediction results of this paper’s method are closer to the real value than other algorithms, with an average offset error of less than 1.1km, an accuracy rate of >85%, and a false alarm rate of <35%, which reflects a good prediction performance.
Increasing the degree of mixed use of urban land and building diverse and multifunctional urban spaces are important ways to shape urban vitality and promote healthy development of neighborhoods and social inclusion. Taking the urban area of City A as the research object, the article screens and classifies the collected POI data, and realizes the division and identification of functional areas in the core urban area of City A by calculating the degree of chaotic urban land use in parcels based on entropy under the fine-grained grid scale of the road network. Subsequently, the calculation methods of spatial weights and bandwidths of the model based on ordinary least squares and the Moran’s index eliciting the GWR model are introduced. Finally, eight factors that have an impact on neighborhood and social inclusion were selected as explanatory variables, and an empirical study of the spatial distribution of neighborhood and social inclusion and the influencing factors was carried out using the geographically weighted regression model. The study found that the functional mixing degree in the main urban area of City A generally shows the spatial distribution of high-mixing degree plots of land with “center clustering and multi-point scattering”, and locally shows the characteristics of piecewise clustering in the central area, linear clustering along the main roads, and pointwise clustering around the subway stations. The four influencing factors of common habits, psychosocial distance, social contact behavior and external behavioral interference are positively correlated with the changes of neighborhood relationship and social inclusion.
In this paper, a pressure distribution model of seepage field based on complex reservoir conditions is established based on a finite element mathematical model. Due to the non-homogeneity and multiple flow characteristics of the reservoir, the mathematical model of fractured horizontal wells based on reservoir and fracture is established by solving the finite element equations of oil-phase pressure and water-phase saturation under the two-dimensional oil-water two-phase finite element model. Through numerical simulation of the coupling between the permeability change of the fractured fracture and the bedrock in the oil seepage field, the influence of different fracture parameters on the pressure distribution is analyzed, and each parameter is optimized. Investigations of stress-strain, porosity and permeability in time and space in low-permeability reservoirs found that in the region near the bottom of the well, each parameter varies more, while the farther away from the bottom of the well region the less affected it is. The relative position of the fracture to the well has a large effect on the production of fractured horizontal wells, but this parameter can be artificially regulated. Repeated fracturing cumulative oil incremental analysis found that “fracture network bandwidth, main fracture half-length and main fracture inflow capacity” have the greatest influence on the high permeability strip, the factors of angular wells and low permeability zones, and the repeated fracturing cumulative oil incremental simulation of each fracture parameter has the greatest effect on the fracture network bandwidth, main fracture half-length and main fracture inflow capacity under the coupled model of Well 3 (23.25%), and the optimal values of the parameters are 100m, 100m, 100m, 100m, 100m, 100m and 100m respectively. optimal values of the parameters are 100 m, 150×10-3μm2·m, 20 m and 45×10-3μm2·m, respectively.
There is a close relationship between adolescent mental health and physical health, so it is of great practical signiϐicance to explore the speciϐic inϐluencing factors and early warning model of students’ mental health. In this paper, the early warning model of students’ mental health risk is constructed. Firstly, the association rules and Apriori algorithm are used to explore the relationship between the important inϐluencing factors of students’ mental health and common psychological problems, and then the CMA-ES-XGBoost prediction model is proposed to address the defects of the XGBoost prediction model that has high complexity and low prediction accuracy. It adopts the hyperparameters of CMA-ES optimization algorithm to ϐind the optimal hyperparameter solution, and solves the fuzzy phenomenon existing in the early warning of mental health risks by fuzzy logic method, which reduces the error of prediction results. It is experimentally veriϐied that the mental health prediction method based on CMA-ES-XGBoost performs well on the task of students with mental health risk, and the prediction accuracy is 89.66%, which is better than the comparison model. It can accurately detect the mood ϐluctuations of students with different types of personality when they are exposed to multiple extroverted stimuli, and accurately predict the emotional risk. It shows that the model in this paper realizes the function of predicting students’ mental health status and achieves the expected goal of model design.
How to form a personalized shortest learning path for vocal skills based on learners’ individual characteristics is the key to improve the efficiency of vocal music teaching. In this paper, on the basis of dynamic key-value memory network, a gating mechanism is used to update students’ knowledge mastery status, and a knowledge tracking model based on dynamic key-value gated recurrent network is proposed to realize the accurate assessment of students’ vocal music level. On this basis, after searching the suboptimal path using the particle swarm algorithm, the shortest path is searched using the ant colony algorithm, which solves the shortcoming of the blindness of the initial search direction of the single ant colony algorithm, and constructs a recommendation model for optimization of the learning path of vocal skills. The results of simulation experiments show that the model AUC and ACC on the ASSIST2015 dataset are 0.7468 and 0.7654, respectively, which are much higher than the highest 0.7281 and 0.7528 in the baseline model. Path optimization was achieved for both ordinary and excellent vocal students, and the average optimization was 4.297 and 3.242 on ASSIST2009, and 3.819 and 3.044 on ASSIST2015.This paper makes an innovative exploration to improve the quality of vocal music teaching.
In the era of artificial intelligence, the technology of speech conversion has developed rapidly and has gradually become a hot topic of research in the field of speech processing. This paper explores the problem of speech signal extraction and generation based on Wave RNN model, and constructs a speech conversion generation model driven by artificial intelligence. First, the short-time Fourier transform is utilized to convert and preprocess the speech signal in the time-frequency domain. Second, a stepwise speech enhancement model is proposed to enhance the perceived strength of the speech signal. Then, a speech generation model based on improved self-attention mechanism and RNN is designed to realize the generation of speech signals. Finally, the model effect is evaluated for application. The time-frequency domain feature that mixes time-domain features and frequencydomain features is able to capture the characteristics of speech signals more comprehensively than a single time-domain feature and frequency-domain feature, which corresponds to a higher recognition accuracy and a lower training loss value. Meanwhile, after speech enhancement, the average accuracy of model A~D speech recognition is improved by 19%~25%, which indicates that the stepped speech enhancement model used in this paper can substantially enhance the perceptual strength of speech signals. In addition, the language conversion model in this paper outperforms other speech conversion models in both MCD and RMSE, and its advantage in rhyme mapping is obvious, and the pitch of the output speech is more accurate and natural. The model in this paper has high practical value in speech signal generation and conversion.
As the father of musical instruments, the piano is commonly used in solo, repertoire, accompaniment and other performance processes. In the process of piano playing, the quality of its sound is closely related to the playing skills. The article analyzes the structural composition of the piano as well as the physical mechanism of sound generation, and summarizes the characteristics of the four elements of piano music, namely pitch, intensity, timbre and duration, on the mathematical basis of the twelve equal temperament laws and the vibration equations of the strings. Subsequently, we analyze the time and frequency domain characteristics of the piano’s musical technique evolution, and calculate the main physical parameters that can affect the piano timbre. Finally, based on the theoretical study and characterization, the corresponding result evaluation experiments were conducted. It is concluded that by analyzing the root-mean-square and mean values of the vibration time-domain signals of piano soundboards excited at different points, it can be seen that, for different structures of piano soundboards, there are excitation points that can maximize their vibration signals. At the same time, the time-domain characteristic index crag factor is analyzed, and it is found that there is no obvious pattern in the crag factor value of the vibration signal of the soundboard with different point excitations.
The aim of this study is to develop a near-infrared photothermally controlled nano-retarded release system loaded with the anticancer drug Adriamycin optimized based on numerical simulation calculations. Firstly, the instruments, agents and experimental methods for the preparation of selfassembled albumin-loaded nanoparticles were introduced.The cumulative absorption wavelengths of the albumin nanoparticles were investigated by UV and IR spectroscopy, and it was found that the maximal absorption wavelengths of DOX and BDC were distributed at 487 nm and 435 nm, and that the UV maximal absorption wavelength of CUR was 435 nm.In the in vitro slow-release performance, it was found that the cumulative release rate of DOX reached 97.36% when pH 5.0 was used, and that when CUR was used, the cumulative release rate of DOX reached 97.36%. The cumulative release rate of DOX reached 97.36% at pH 5.0, while it was only 59.15% and 30.81% at pH 6.0 and 7.0. The cumulative release rates of CUR at the three pH values were 58.69%, 29.98% and 16.81%, respectively, which were basically the same trend of the retardation curves of the two drugs. The nanoparticles degraded morphology showed the widest and narrowest particle size distribution in PBS buffer solution at pH=5.0 and 7.0, respectively. The loading capacity of the optimized model showed good consistency of effect on measured (11.03%) and predicted (10.87%) values.The photothermal conversion experiments of DOX nanoliposomes were found to have concentration and time dependent photothermal conversion effects. In this paper, from the optical characterization of albumin drugcarrying nanoparticles, it was found that UV light was able to excite PFNSNO for photodynamic therapy as well as NO release through the fluorescence resonance energy transfer process.
Giving full play to the vitality and autonomy of inter-governmental departments can improve the national governance system and enhance the modernized governance capacity. This paper conducts a relevant research on the coordination mechanism between multiple government departments. According to the network analysis method, the relationships of departments in different service items and fields are studied, and the causes of the problems of power distribution and coordination mechanism in the network of departmental relationships are explained, and the analysis of the influence mechanism of departmental coordination is completed by using the random forest algorithm. The analysis results show that the power of government departments in the fields of housing security, social insurance, labor, employment and entrepreneurship, public education, and health care is more concentrated, and the Ministry of Civil Affairs and the Ministry of Human Resources and Social Security have an active position in the coordination process. Cultural cognitive bias, imbalance of power and responsibility, lack of coordination system guarantee and insufficient support of coordination environment are the causes of problems in the coordination mechanism. In addition, ambiguous coordination responsibilities, imperfect institutionalized coordination and lack of supervision system are important factors affecting the multisectoral coordination mechanism.
Rural tourism, as an important part of the tourism service industry, the study of the spatio-temporal evolution and influence mechanism of rural tourism flow has also become a hot topic at present. This paper takes Jiangsu Province of China as the research area, proposes the heat measurement and identification method of rural tourism based on network data, constructs a heat measurement model, takes standard deviation ellipse analysis, average nearest neighbor index method, kernel density analysis as the core method of spatial analysis, and proposes the hotspot identification method on the demand of spatial relevance analysis, so as to provide the method and means for the analysis of the spatio-temporal evolution of the rural tourism flow. In the analysis of the influence mechanism of rural tourism flow, the QAP model is used as a research tool to explore the influencing factors of rural tourism flow.The value of rural tourism hotness was low during 2014-2017, and it has rapidly increased and maintained a high growth trend since 2018. The Gini coefficient of rural tourism hotness increased from 0.51 in 2014 to 0.72 in 2018, and then fell back to 0.65 in 2023, and the degree of spatial difference of rural tourism hotness showed a weakening trend, and the hotspot areas of rural tourism were increasing. The structure of tourism flow is affected by a variety of factors such as spatial proximity, tourism income, and the impacts produced by the factors change somewhat in different time periods.
Due to the complex structure of multi-dimensional anthropomorphic wind turbine and the harsh operating environment, in order to reduce its maintenance cost, it has become a popular research hotspot to get fast and effective condition diagnosis and fault early warning through big data mining and analysis of wind turbine condition monitoring. The article clarifies the basic mechanism and typical faults of multi-dimensional anthropomorphic wind turbine, and after analyzing the characteristic frequency of faults on the transmission chain of multi-dimensional anthropomorphic wind turbine, it proposes the anomaly detection method of wind turbine condition monitoring data based on the auxiliary eigenvectors improved density clustering (DBSCAN), which realizes the accurate identification of different types of normal data, valid anomalous data containing fault information, and invalid anomalous data in the monitoring data. It realizes the accurate identification of different types of normal data, valid abnormal data with fault information, and invalid abnormal data in monitoring data. Subsequently, the actual historical data of the wind farm is used as the experimental data set to realize the identification of the operating status of the wind turbine. Finally, the DBN-Dropout wind turbine fault identification method is proposed by combining Deep Confidence Network and Dropout technique. The experimental results indicate that the recognition rate of this paper’s model for nine faults is as high as 99.88%, and the superiority and accuracy of this paper’s model in feature extraction and fault diagnosis are verified by comparing its performance with other fault detection models.
In this paper, a personalized scheme recommendation method for dance movements based on ontological similarity is proposed. An ontology model of trainers is established, and in order to explore the interactions between trainers’ attribute features and their influence on core parameters, SWRL rules are established using Jena inference engine for the inference of core parameters of training programs. The similarity degree is calculated according to the different types of user variables respectively, and the artificial neural network model is used to determine the degree of similarity between different trainers, in order to complete the recommendation of personalized training programs for dance movements. And then the requirements of the system are summarized to achieve the framework construction of the personalized dance movement training program recommendation system to achieve the health management in the training process. The recommendation effects presented by the similarity calculation method of this paper have reached the design goal of this paper, and the personalized recommendation system of this paper has also significantly improved the physical fitness level and the performance effect of dance skills of the experimental group of dance trainees, and the success rate of the kicking back leg movement has reached 91.67%. However, the system’s function of improving health knowledge and health awareness needs to be further upgraded.
In this paper, the autoregressive moving average model (ARMA) and LSTM deep neural network are first introduced, and the time series are decomposed into high volatility components and low volatility components by MA filtering method. Then the time series forecasting model ARMA and deep neural network LSTM are combined on the basis of MA filtering method to form ARMA-LSTM combination model based on MA filtering method, and the application effect of this model in financial market volatility forecasting and risk response is verified through empirical evidence. The results show that the ARMA-LSTM_t model will achieve relatively good results in predicting the GDP_IG of the current year using the data of the 12 months of the current year and the last month of the previous year, and the training and prediction sets of the ARMA-LSTM combination model proposed in this paper have the best results. In addition, there is a positive relationship between investment-related indicators and GDP_IG, and the addition of investment network search data improves the estimation accuracy of the model, obtains smaller prediction errors, and improves the prediction accuracy of the ARMA-LSTM model in the short and medium term.
In the context of urban elderly human resource development, differential evolutionary algorithms can be used to optimize the development strategy and improve the efficiency of resource utilization. The study constructs a multi-objective scheduling optimization model for human resources based on an improved differential evolutionary algorithm, which searches for the optimal development strategy by simulating the mutation, crossover, and selection operations in the process of biological evolution. In addition, the model combines a multi-objective feature selection algorithm to capture the data information of urban elderly resource development more accurately and ensure the scientific and practicality of the strategy. The pareto front of this paper’s algorithm on the optimal solution test function is more in line with the real frontier, and the GD value is between 0.00171 and 0.0325, which has better convergence. The execution time of this algorithm for elderly manpower resource scheduling is shortened compared to the comparison algorithm, and the convergence of different task sizes is accomplished when iterating to 110~150 rounds. The ADE-MOFS algorithm has the lowest running cost and the shortest completion period on elderly manpower resource scheduling. The research in this paper shows new ideas and methods for the rational development and utilization of urban elderly manpower resources, which has important theoretical and temporal significance.
In recent years, the green economy has been developing rapidly, and the environmentalization of industries has been widely popularized in various industries. This paper carries out an in-depth study on the relationship between agricultural carbon finance and carbon emission reduction, and after understanding the theory related to carbon finance and carbon emission, it adopts the method of system GMM estimation to construct a dynamic panel model for the study of agricultural carbon finance and carbon emission, and selects research variables. The development of agricultural carbon financial innovation and carbon emission in 30 provincial-level administrative regions in China from 2014 to 2024 is studied, and regression analysis is carried out using system GMM so as to obtain the relationship between the impact of agricultural carbon financial innovation on carbon emission reduction, and the robustness test is carried out. The maximum values of agricultural carbon financial scale, carbon financial efficiency, carbon financial structure, and per capita carbon emission are 16.942, 7.052, 1.926, and 128.945 respectively, while the minimum values are 0.965, 0.048, 0.079, and 0.145 respectively, and the maximum values are 17.56, 146.92, 100.33, and 889.28 times of the minimum values. There are large differences in the development of agricultural carbon financial innovation and carbon emission reduction effects among different provinces. Per capita carbon emissions are reduced by 22.5%, 20.5% and 24.5% for each unit increase in carbon financial scale, carbon financial efficiency and carbon financial structure, respectively. The parameter estimates of carbon financial scale, carbon financial efficiency, and carbon financial structure are significant at the levels of 10%, 1%, and 5%, respectively. It indicates that the innovative development of agricultural carbon finance can effectively promote carbon emission reduction.
Due to global warming and drought, land desertification has become more serious all over the world, and desertification control has become the focus of global attention. Salix sand barriers as a desert wind and sand area engineering sand control mechanical sand barriers have been more widely used. For this reason, this paper analyzes the demand for sand willow sand barriers, starting from the causes of desertification research, and for the sand willow sand barriers laying method for in-depth analysis. Taking the research on the governance of salix sand barriers as the theoretical basis, the self-propelled salix sand barriers horizontal laying machine insertion cutter head is designed, in order to improve the efficiency of salix sand barriers laying and the survival rate of salix spike insertion. Through the test data, the driving force, resistance, and tractability parameters of the laying machine when traveling were theoretically calculated. And according to the different slip rate when the drive seeks to come up with the slip rate when the whole machine is actually working, and finally determine whether the tractor meets the requirements of the passability. Through simulation calculations, it can be seen that the cutterhead laying machine designed in this paper can overcome the resistance generated by acceleration and climbing in the application of sand willow sand barrier laying, and has good versatility in the desert environment.
Bridges as the basic transportation facilities in the national daily life, with the development of road transportation, the number of urban viaducts and cross-sea bridges grows year by year, and the role of bridges in the transportation network becomes more and more significant. The article selects the Qingshuihe Bridge demolition project as the research object, and designs the Qingshuihe Bridge demolition implementation program based on the engineering characteristics. For the mechanical property changes and structural residual bearing capacity calculation during the demolition process of Qingshuihe Bridge, this paper constructs a finite element model of Qingshuihe Bridge based on ANSYS software, and analyzes the structural reliability of Qingshuihe Bridge after the demolition project by combining with structural reliability indexes. Under the same mid-span displacement condition, the cracking load error between the finite element simulation values and the experimental values is relatively small, and its fluctuation range is between 2.12%% and 6.58%. In the first 5 years after the demolition of the bridge section, the residual load capacity of the bridge structure increased from 5.23*103kN to 5.51*103kN. The reliability index value of the bridge over the Qingshui River changed relatively slowly in the early stage, and began to decline gradually in the later stage, and the greater the strength of the steel girders the higher time-varying reliability index of the bridge over the Qingshui River. In this paper, the design of the Qingshui River bridge demolition implementation program has a strong feasibility, in order to ensure that the Qingshui River bridge structural residual bearing capacity on the basis of the completion of the demolition of part of the bridge abutment, and can guarantee the safety of the bridge operation.
In recent years, it has become the frontier and hotspot of research in the field of intelligent robotics. In this study, a robot vision-guided unloading system is designed, and a robot grasping control method based on fuzzy mathematical method is proposed for the robot unloading problem under the uncertain information environment, and the particle swarm optimized fuzzy PID control algorithm is introduced into the grasping force control field. Comparison experiments of robot joint trajectory tracking, position and control inputs are carried out in the simulation environment, and the method in this paper can realize accurate tracking of motion trajectory and weaken the vibration phenomenon. The robot unloading experiments show that the success rate of single target and multi-target grasping and placing are both above 91% and 85% respectively, which verifies the effectiveness of the particle swarm fuzzy PID control algorithm of this paper in robotic grasping, and it has a certain value of engineering application for robotic unloading control.
With the in-depth promotion of ecological civilization education, the grassland natural ecology study course has become an important way to realize the comprehensive development of students. The article takes the design idea of grassland study course as the entry point, analyzes the design links of grassland ecology study course development, and proposes the design process of grassland ecology study course development based on this. Taking the second grade students of the first middle school in X city as the research object, the decision tree algorithm is used to optimize the relevant variables in the development of the grassland ecological study course, and the optimization model of the study path of the grassland ecological study course is established starting from the shortest distance and the least time. Based on the variables optimized by the decision tree algorithm, a DT-SVM model is built by combining the support vector mechanism to solve the learning path of the grassland ecological study course. Through the simulation and example data, it can be seen that the convergence accuracy of the DT-SVM algorithm shows the optimal accuracy under all experiments, and its mean value can be up to 3.652, and the average time consumed for obtaining the optimal learning path of the grassland ecology research course is only about 3.17min. And more than 65% of the students agreed with the teaching effect of the grassland ecology study course. The grassland ecology study course can significantly improve students’ core literacy in geography, enhance their independent living skills, and better realize the school’s teaching goal of promoting moral education.
The aim of this study is to construct a deep learning-based biomechanical model of musical instrument playing action that integrates skeletal pose estimation and action recognition techniques. PHRNet-based human pose estimation can extract the skeletal key points of a player from video data, and these key points provide basic data for instrumental performance action recognition and analysis. The human skeletal action recognition method based on diversity rewarded reinforcement learning framework (DDRL-GCN) classifies the extracted key point sequences into specific playing actions, and the musical instrument playing actions are successfully modeled. The biomechanical model of musical instrument playing action designed in this paper is applied to recognize the playing action of five different musical instruments, and the recognition accuracy can reach more than 90%. This paper is designed to distinguish between different musical instruments, the recognition effect is more satisfactory.
The planning of green logistics networks has gradually become the focus of attention in both academic and business circles, as it has been increasingly emphasized on environmental protection. This study aims to explore how to combine machine learning and carbon emission constraints to construct a more efficient and environmentally friendly green logistics network planning strategy. A machine learning-based logistics demand forecasting model is constructed by Support Vector Regression (SVR) machine, and the model parameters are optimized using genetic algorithm to improve the model accuracy. Analyze the sources of carbon emissions in the logistics network and establish a carbon emission calculation model. Construct a green logistics network planning model considering carbon emission constraints, and analyze the feasibility of the model through practical examples. The method of this paper can effectively measure the carbon emissions in the transportation and storage phases of the logistics network. Under the condition of considering carbon emission constraints, positioning the upper limit of carbon emission below 270,000 can realize a stable balance of economic and environmental benefits.
This paper carries out a research on patients’ lower limb posture capture strategy based on the lower limb rehabilitation of patients with sports function injury. The study is based on the posture filtering algorithm and designed a lower limb joint localization model based on the quaternion Kalman filter. The model utilizes five IMUs to capture the patient’s lower limb movements to determine the posture of the patient’s critical limbs in three-dimensional space and establish the joint coordinate system. Based on the filtered pose quaternions, the joint coordinate system of the lower limb is solved to obtain the optimal estimation of the lower limb pose. The results of simulation experiments show that the algorithm of this paper can make the motion data smoother and satisfy the motion requirements. The valuation of this paper’s algorithm on the Z-axis in the single-axis rotation experiment is stable from – 90° to 90°, while the valuation on the X-axis and Y-axis is near 0°. And the error in the ankle motion trajectory is small, with a mean value of 1.36°. The example results illustrate that the rehabilitation system equipped with the algorithm of this paper is basically consistent with the thigh elevation curve of the optical method in the patient’s lower limb motion monitoring during walking, and the error is within 6°. The research in this paper provides a new technical means for lower limb rehabilitation training, which helps to improve the personalization and precision of rehabilitation training.
This paper is based on the definition of novel distribution system panoramic perception technology under the perspective of generative artificial intelligence. The preprocessed data are put into forward GRU neurons and reverse GRU neurons as model input variables for multi-task assisted training, and the model outputs distribution system perception results to complete the task of constructing a new distribution system panoramic perception model based on BiGRU. When the distribution system current and voltage data is zero, it will lead to a reduction in the current and voltage prediction accuracy of the distribution system of the ELM model, for this reason, it is proposed to use the genetic algorithm to optimize the ELM model, to achieve the modeling of the new distribution system prediction model based on the ELM-GA algorithm. Using the model constructed in this paper, panoramic perception and prediction analysis of the new distribution system is carried out. When the BiGRU model is deployed in the new distribution system, the BiGRU network’s system perception accuracy and error rate are 95.00% and 5.00%, respectively, which fully meets the user experience requirements of the new distribution system, and the relative errors of fault voltage and fault current prediction based on the ELM-GA algorithm for the new distribution system are less than 5%, which indicates that the ELM-GA distribution system prediction model has the characteristics of high robustness and high accuracy.
This paper analyzes public interest litigation and its salient features, and organizes the audit rules for the electronic transformation of litigation evidence. Aiming at the phenomenon of varying text length in litigation evidence, a joint CTC-Attention decoding model (HCADecoder) based on bigram hybrid labeling is proposed. Based on the existing research on computer vision technology for target number prediction, the stacked object occlusion problem existing in special scenes is proposed, and an algorithm for predicting the number of stacked objects combining planar density map and depth map is proposed. Combined with the public interest litigation evidence document corpus dataset, we analyze the recognition of basic elements of litigation evidence by text label recognition algorithm, and select the commonly used precision rate P, recall rate R and F1 value to evaluate the recognition results of basic elements. Subdivide the text length of litigation evidence and analyze the recognition accuracy of each algorithm on different text lengths. Bring the text label recognition algorithms into real cases to analyze the element extraction. For this paper, we propose monocular image target counting algorithm, which is brought into different scenarios for performance testing. This paper proposes text label recognition algorithm with evidence image target counting algorithm for litigation evidence text image recognition with mean value at 80%.
Innovation and entrepreneurship, as an important part of social and economic activities, has received more and more widespread attention. Based on the characteristics of the digital era, the study uses artificial intelligence to empower innovation and entrepreneurship education in colleges and universities. Optimize the allocation of innovation and entrepreneurship education resources in colleges and universities through multi-objective optimization algorithm. Construct an optimization model of resource allocation for innovation and entrepreneurship education in colleges and universities, and verify its resource optimization and allocation performance. Taking 13 colleges and universities in C city as the research object, the optimization of their innovation and entrepreneurship education resource allocation is processed. The MSS cumulative values of this paper’s multi-objective optimization model on the CPLX problem and the MATP problem are -1.400 and -1.033, respectively, which are the smallest among all models, with the best performance and ranked the first in resource allocation efficiency. After optimization, the resource allocation level of innovation and entrepreneurship education in all 13 colleges and universities has been improved, and the resource allocation among the colleges and universities is more balanced.The resource utilization efficiency of innovation and entrepreneurship education in the 13 colleges and universities has been improved by 17.02% on average.
The article aims to accelerate the growth and progress of young teachers in private applied colleges and universities and improve their teaching ability, combining with the knowledge graph, and designing a recommended algorithm based on deep reinforcement learning to improve teachers’ ability. Firstly, the growth and progress process of young teachers in private applied colleges and universities is defined as a dynamic development process, i.e., for different latitude abilities such as teacher ethics, professional knowledge, preteaching preparation, communication and cooperation, teaching ability training needs to be carried out gradually and in a certain order. Then the Knowledge Graph Teacher Competency Enhancement Recommendation Algorithm (KGDR) based on deep reinforcement learning and knowledge graph algorithm is constructed by combining deep reinforcement learning and knowledge graph algorithm. When performing top-𝑘 recommendation, the diversity value of the model at 𝑘 = 20 is 0.7876, and the model can provide more diverse paths for teacher ability improvement. After the application of the dynamic development mechanism of young teachers’ competence based on KGDR, the competence improvement of young teachers is significant and can reach the grade of “excellent”. The mechanism designed in this paper can be used as a reference for other colleges and universities.
The purpose of this study is to evaluate the comprehensive ability of students objectively by constructing the evaluation system of compound music talents based on multi-objective planning, so as to promote the quality improvement and excellent cultivation of compound music talents in higher vocational colleges. The selected evaluation indexes of composite music talent cultivation are empowered by using the combination assignment method, and the construction of multi-objective planning model for cross-border composite music talent cultivation is realized based on the setting of objective function, constraints and model solving method. The article forms an index system covering 6 dimensions and 24 indicators, successfully divides the interval length of five evaluation levels, and obtains the distribution of students in each level, with the largest proportion of students in level 3, which is 40.77%. In addition, the ratings of the level 1 indicators are 2.47 to 3.31, which are in the middle to lower level. According to the student groups of different grades and the evaluation results of the indicators, we can clarify the level of student cultivation, improve the music talent cultivation system, coordinate and improve the elements and resources of each dimension, and promote the cultivation of cross-boundary composite music talents in higher vocational colleges and universities.
The article uses the appropriate equipment for research data, designing the face and physiological signal emotion recognition network respectively, and putting its recognition features into the random forest classifier for training in order to realize the construction work of emotion recognition model. In-depth interpretation of the random forest algorithm based emotion recognition model in the application of information systems, combined with the research data, respectively, the emotion recognition model and system safety performance testing assessment. The emotion recognition model of this paper based on the 25% retention method has a recognition rate of 96.16% for the 14- dimensional B emotion features, which has the highest recognition efficacy and can well meet the system emotion recognition needs. The experimental group is found to be significantly different from the control group, and it is concluded that by introducing the emotion recognition model into the traditional information system, all three security performance indicators of the system are significantly improved.
In enterprise operations, multi-objective optimization involves multiple conflicting objectives such as cost escalation control, customer satisfaction, and production efficiency. Based on reinforcement learning algorithm, the article deals with multi-objective optimization problem in enterprise operation through the interactive learning between intelligent body and environment, for which a multi-objective operation efficiency improvement path for enterprise based on Q-learning scheduling is designed. The simulation data is utilized to generate the PDR tree structure, and subsequently, the intelligent body is prompted to complete the multi-objective operation learning of the enterprise through several iterations. On this basis, the intelligent body completes all the actions and generates scheduling strategies to improve operational efficiency. The model proposed in this paper can predict the demand changes of enterprises in the future time window and make the best decision to improve the operational efficiency. Under the model of this paper, the mean values of pure technical efficiency as well as scale efficiency of 10 firms in 2024 are 0.9 and 0.933, respectively, and they are predicted to continue to grow in 2025. The model reduces the firms’ average operating costs and administrative expenses, while employee compensation and fixed assets increase by 49.58% and 19.48%. Since the survey period, the TFP index of all 10 companies is greater than 1, which indicates that, the application of the model in this paper improves the operational efficiency of the companies.
This paper establishes a specific path for the realization of AI-enhanced learning on the content of Civic and Political Education, starting from the relevance, quality, novelty and intuitiveness of the teaching content. Through HTML parsing and other crawler technology to obtain the Civics education data on the news network, and extract the data characteristics of the Civics material, using the clustering rule algorithm, to classify the material. Decision tree calculation based on random forest is performed to dynamically expand and integrate the material, on this basis, using reinforcement learning recommendation algorithm, the Civic and political education content recommendation model is constructed, and the recommendation results of the algorithm are verified using simulation experiments. The experimental results show that the average success rate of the research-designed recommendation algorithm in the last 10 groups of experimental data is 25.218%, which is higher than that of the MK recommendation algorithm (18.03%), and the average time of the research-designed recommendation algorithm in the last 10 groups of data is 5.095s, which is more efficient than that of the MK recommendation algorithm (11.903s). After integrating the enhanced learning content recommendation in the Civics education, the students’ humanism scale score was 100.56±12.364, with a p-value of less than 0.05, which was significantly higher than that before teaching.
In response to the greening and decarbonization of economic development and in search of a path to improve the corporate efficiency of resource-consuming enterprises, the study explores the impact of the financial sharing model on the efficiency of resource-consuming enterprises. The research hypothesis is formulated after the preliminary analysis of related theories such as financial sharing and accounting information. After completing the selection of research samples and data collection, the research variables are defined, the regression analysis model of the impact of financial sharing model on enterprise efficiency is constructed, and empirical analysis is conducted. The research hypotheses proposed in the previous section are verified through regression analysis. Monte Carlo method is used to simulate the financial sharing model and resource-consuming enterprise efficiency, and the net present value of resource-consuming enterprises is simulated during the construction period and the operation period of the financial sharing model, respectively, so as to understand their enterprise efficiency. The results of the empirical study show that financial sharing can realize the improvement of enterprise efficiency. Enterprise efficiency can increase with the improvement of accounting information transparency and accounting information consistency. During the construction and operation periods of the financial sharing model, the mean enterprise NPV after five years of operation is $608.4 and $2,327.4 million, respectively, and the probability of positive NPV is 68% and 94%, respectively.
Supported by the theory of economic growth convergence, this paper takes the eastern, central and western regions as the research object during 2010-2020, analyzes the economic growth convergence of the eastern, central and western regions of the country, and verifies the relationship between the regulation of fiscal policy and the high-quality development of the regional economy. Analyze the relationship between regional economic development, fiscal policy and economic convergence, and put forward the analytical view that fiscal policy affects regional economic convergence. The combination of dynamic panel model and absolute convergence analysis is used to derive the results of the absolute convergence test of regional economic growth. Convergence role test for fiscal expenditure variables, transfer payment variables. It is brought to the western region to analyze the role of government expenditure in the western development policy on the convergence of the western region’s economy. Convergence as well as absolute convergence is conducted for each of the eight comprehensive economic zones, and the regional economic high-quality development policies are adjusted. Relative to 2010-2015, there is no convergence in economic growth in the western region in 2016-2020, and there is a tendency to divergence, which suggests that the fiscal policy of western development has limited effect on economic convergence among regional provinces. Absolute convergence exists for the whole country and the eight comprehensive economic zones, and the convergence coefficient is significantly negative at the 1% level. However, the speed of convergence varies for high-quality economic development.
Currently, the development of cultural tourism has become a new trend of urban development, and how to use modern technology to realize the innovative development of urban cultural tourism has become a key issue to be considered in the process of urban construction. The research combines the Web domain ontology to construct a multi-level user portrait master model, which mainly includes four sub-models: retailer static attribute vector model, retailer domain dimension model, retailer marketing ability model and retailer social dimension model. The FCM algorithm based on the improved AP algorithm is utilized to cluster the user portraits, and the user portrait clusters obtained by the method studied in this paper perform well with an average number of iterations and an average time consumed of 21.3 and 60.35 compared with the traditional K-Means algorithm, the improved KMeans algorithm, and the traditional FCM algorithm, respectively. Then a personalized recommendation method for tourism products based on MAGFM is proposed, which achieves Top-N recommendation of tourism products by calculating the total interest value of users and the comprehensive similarity of tourism products. And test and analyze in the tourism e-commerce platform, the results show that the recommendation algorithm proposed in this paper has higher effectiveness compared with the traditional recommendation algorithm. Finally, the research content builds a personalized recommendation system for tourism cultural and creative products.
Energy level fluctuations in Distributed Generation (DG) systems and Electric Vehicles (EVs) sometimes exceed the carrying capacity of typical distribution network topologies, which may lead to inefficiencies and lack of reliability. Based on this, this paper introduces a new Levy flight-electric eel foraging optimization (LF-EEFO) method for adapting network topology reconfiguration for new power systems. The DG output power, EV charging power, distribution network loss power, and switch lifetime cost cost are taken as the objectives, and the tidal current, voltage, branch power, network topology, and switching action are set as the constraints, in order to construct a multi-objective optimization model for distribution network topology reconfiguration. In the optimization phase, a Levy flight strategy is used to optimize the local search capability of the EEFO algorithm to obtain the optimal solution of the multi-objective optimization model for distribution network topology reconfiguration. In order to ensure the efficiency of the LF-EFO algorithm in optimizing the distribution network topology reconstruction model, an IEEE-33 node test system was established for simulation analysis. The results show that this research can significantly reduce the operating cost and improve the operational reliability of distribution networks, while promoting the development of electric vehicles.
Music therapy is the treatment of college students’ psychology through various techniques and methods of music, and this paper focuses on researching and analyzing the improvement effect of music therapy on college students’ mental health in the context of cultural education. Students’ physiological data are collected and denoised, and machine learning models are used to realize the multimodal fusion of all kinds of physiological signal features to obtain the objective psychological state assessment values of college students. The subjective assessment results of the mental health assessment scales were then combined to analyze the improvement effect of mental health of college students in the music therapy intervention. The analysis of the psychological health status of the students before and after the intervention experiment revealed that the objective assessment values of the psychological state of the college students in the intervention group gradually tended to be positive with the music therapy, and the subjective assessment results of the psychological health scales of the students in the intervention group were signiϐicantly better than those of the nonintervention group after the experiment (P<0.05). Music therapy has a signiϐicant role in intervening in the mental health of college students and resolving their psychological malaise, which is of great practical and guiding signiϐicance in improving the psychological tolerance and health of college students.
For national grid power line infrastructure construction construction, quality management and control can ensure improved safety standards, long-term reliability and cost savings through avoiding rework. In this paper, a high-definition image of a transmission line is collected from multiple viewpoints by a UAV, and a model for recognizing surface defects on infrastructure lines is proposed to reduce the computational complexity to improve the YOLOv8 algorithm. The model uses ResNet50 as the feature extraction backbone network and fuses convolution and attention mechanisms to enhance global and local feature extraction. A multi-scale feature aggregation diffusion module is added to the neck network of the model to enhance the detection of small targets on infrastructure lines. Finally, the classification loss function combined with the PIOU bounding box loss function is introduced to further enhance the recognition accuracy of infrastructure line surface defects. The experimental results show that the mAP of the infrastructure line surface defect recognition model is up to 0.935, which is 2.41% higher than that of the baseline model, and the performance is significantly better than that of some of the current mainstream defect recognition models. Therefore, from the computational complexity, combined with the target detection YOLOv8 algorithm can realize the accurate recognition of surface defects on infrastructure lines, and provide reliable data support for improving the timely repair of grid infrastructure lines.
In recent years, with the development of science and technology, image enhancement has become a very important topic in scientific research, become an indispensable part of machine vision, and has a wide range of applications in various fields of computer vision. In this paper, the image gradient enhancement algorithm is first improved based on the image gradient field, and its enhancement effect on low quality (low resolution) images is found to be poor through experiments. For this reason, the study constructs a multi-scale feature image enhancement model (LIEN-MFC) by convolutional neural network to further optimize the image enhancement effect. By comparing with different algorithms, the average PSNR of the model is 21.80 and the average SSIM is 0.8767, and it outperforms other compared algorithms in both PSNR and SSIM. In addition, the ablation experiments demonstrate that the enhancement effect of the LIEN-MFC model is further improved on the basis of the improved image gradient enhancement algorithm. The results show that the image enhancement model algorithm with multi-scale features proposed in this paper has a significant image enhancement effect and the improved image gradient enhancement in image enhancement of convolutional neural networks improves the model performance to some extent.
The expansion of English vocabulary is the foundation of college students’ English learning and the key to improve English learning. This project centers on the quantitative analysis of college English vocabulary learning efϐiciency improvement, through the questionnaire survey to understand the use of English vocabulary learning strategies of students. The inϐluencing factors of English vocabulary learning efϐiciency improvement are selected, correlation analysis is carried out, and then multiple regression model is used to explore the role of each variable on the improvement of English vocabulary learning efϐiciency. The results show that students most often use the metacognitive strategy of preplanning (3.674), and that students who are good at learning are more inclined to adopt the metacognitive strategy to control vocabulary learning from a macro perspective. Multiple selfelements and environmental elements together positively affect the improvement of English vocabulary learning efϐiciency (p < 0.01), with the most signiϐicant effects of learning strategies (0.482), teaching methods (0.457) and learning strategies (0.416). It is recommended to promote the efϐiciency of English vocabulary learning through innovative teaching methods, combining word class memorization, expanding the scope of reading, and vocabulary association learning.
In order to better realize the automatic classification and change detection of remote sensing images, this paper proposes an automatic remote sensing image classification model based on CNN and migration learning, and constructs a remote sensing image change detection model by combining CNN and Transformer network. In the remote sensing image classification model, DenseNet network and Inception network are used as the backbone network, combined with the new channel attention module to mine the image features of remote sensing images, and then realize the accurate classification of remote sensing images. In the remote sensing image change detection model, the convolution operation of CNN with different sizes of void rate and expansion rate is utilized to better guide the feature map to focus on local information. Combined with the dynamic deformable Transformer to provide more accurate remote sensing image location information and detail information, to reduce the impact of background interference on remote sensing image change detection, and to improve the model’s ability to recognize the target of remote sensing images. The parameter count and floating-point computation of the remote sensing image classification model are 7.69MB and 1.89GB, respectively, which are smaller than the parameter count and floating-point computation of the single network model. The RSICD models mF1 and mIoU are 1.66% and 0.58% higher than the optimal ones. Through the effective integration of convolutional neural networks and many different types of deep learning techniques, automated classification and change detection of remote sensing images can be realized.
The development of globalization has contributed to the increasing demand for cross-language communication, and machine translation, as an effective language conversion tool, has improved the quality and efficiency of English translation. The article discusses the syntactic optimization and semantic reconstruction strategies for English translation based on machine learning. The machine translation model of English syntax optimization and semantic reconstruction based on EM algorithm is constructed by using key technologies such as EM algorithm and multi-head attention mechanism. The model adopts a joint learning method, combining the Transformer model with the EM algorithm. The dependency between any two words in the input sequence is captured using the multi-head attention mechanism, and the new translation corpus is generated by multi-task joint training algorithm. The training phase of this paper’s model has good translation effect, and the model of this paper gets the highest BLEU score of 32.86 when the number of multi-head attention layers is 1. The distribution of semantic features of translation reconstruction under this paper’s method is basically consistent with the simulation results, and the error elimination rate of semantic reconstruction is 99.64% when the number of samples is 500. The method in this paper is more effective in syntactic structure optimization, with the highest BLEU scores on “Chinese to English” and “English to Chinese”, and the syntactic correctness rate on English long sentences of different topics reaches 88.69%~96.57%.
This topic is centered around temperature and stress, and describes the theory of electric power thermal characteristics. There are usually two methods for thermal coupling analysis, for direct coupling and sequential coupling. Considering that the stress field of the cable does not have much influence on the temperature field, it is proposed to use the sequential coupling method for the calculation of the thermal characteristics of the cable. The calculated and solved cable temperature and stress distribution values are put into the Lap-ML-ELM algorithm for training. When the contact coefficient k=1, 4, 7, 10, 13 and 15, the cable joints and surfaces produce a monotonically increasing law of temperature, and the stress exhibits the same situation.During the training of the model on the thermal characteristics of the cables, it is found that the accuracy curve of the thermal characteristics detection of the Lap-ML-ELM algorithm is higher than that of both the RNN network and the CNN network, which shows that in the detection of the thermal characteristics of cables, the Laplace Multilayer Extreme Learning Machine fusion algorithm performs more obviously.
Students have the problems of insufficient self-control, insufficient learning motivation and unplanned and unsystematic for independent learning of university French. In order to solve this problem effectively, this study proposes the reform of French blended education model guided by POA theory. In this paper, we design a hybrid intelligent teaching mode of university French guided by the output-oriented approach, improve it based on the mutation operation in the genetic algorithm, propose the adaptive mutation genetic algorithm, and optimize the BP neural network with this algorithm. The GA-BP neural network is trained through simulation experiments to verify the performance of the algorithm. Using SEM structural equation modeling, the measurement model of six dimensions, namely, learning effect, teaching effect, learning input, objective learning conditions, subjective learning factors and learning ability, is established, integrating factor analysis and path analysis, and relevant research hypotheses are proposed. The feasibility of the hypotheses is verified one by one through empirical research. The path coefficients between each variable in the model and the path coefficients of the factor loadings are all at the significant level of 0.000, and all of them are positive, the path coefficients’ validity is within the acceptable range, and the hypotheses proposed in this paper are all supported. Compared with the default path, 69.78% of the students in the recommended path for learning French think that the knowledge of the recommended learning path is easy to understand, and the learning path constructed on the basis of the educational resources of the output-oriented method can better satisfy the learning needs of the students compared with the default learning path.
The reasonable division of power supply grid plays an important role in the feasibility and stability of power grid operation. This paper mainly explores the feasible methods of power supply grid division under the dynamic change of grid load. The grid load prediction model is constructed by the improved long and short-term memory network algorithm (ILSTM) based on expert rules to visualize the dynamic changes of the grid load. Based on the study of hierarchical architecture of power supply grid, the objective function is constructed using hierarchical recursive method, and the power supply grid division model is constructed with adjacent connection as the basic constraint. The power consumption information of JH urban area is selected as the data source of this paper, and the method of this paper is used to forecast the grid load of JH urban area and perform the power supply grid division. The power supply network in JH city can effectively meet the objective function and constraints set in the model, and the average number of faults in the power supply network decreases by 94.8% compared with that before the grid demarcation, which fully ensures the safety and reliability of the power supply network operation.
In this paper, for the influence of non-metallic inclusions on the contact fatigue performance of steel, based on the finite element method and rolling contact fatigue theory, the contact fatigue model of U26Mn2Si2CrNiMo bainitic austenitic steel containing non-metallic inclusions is established. The characteristics of non-metallic inclusions and U26Mn2Si2CrNiMo bainitic austenitic steel are analyzed. To investigate the changes in the composition, density and size of each inclusions during the production steps of U26Mn2Si2CrNiMo bainitic austenitic steel by using the inclusions detection technique in steel, the stress and strain response algorithm and the thermodynamic calculations (deoxidization equilibrium calculations of the steel liquid). To analyze the range of fatigue damage concentration caused by non-metallic inclusions by characterizing the distribution of subsurface fatigue damage in the RCF process of U26Mn2Si2CrNiMo bainitic austenitic steel. Explore the effect of the distribution depth of individual non-metallic inclusions on the contact fatigue life of U26Mn2Si2CrNiMo bainitic austenitic steel, and the role of the angle of arrangement of dual nonmetallic inclusions on the properties of U26Mn2Si2CrNiMo bainitic austenitic steel. When circular alumina inclusions with a radius of 5 m are located at different depths of the bainitic austenitic steel, the von Mises stress reaches a maximum value of 770.0 MPa at a depth of 0.53 mm (0.67 Hb ) of inclusions, which is increased by 18.5% compared to the case without inclusions (650 MPa). When the spacing of the two inclusions is 2.5 r (12.5 m ) and the depth is 0.5 mm, the arrangement of the nonmetallic inclusions affects the predicted fatigue life, and the two inclusions reduce the predicted fatigue life around them to different degrees.
Corporate ESG disclosure quality is a key condition to optimize industrial structure and a realistic path to reach sustainability performance. Based on the theoretical knowledge of Bayesian network model, the research program of corporate ESG disclosure quality and sustainability performance influence path is designed. According to the current status of enterprise development, 11 research variables are set, which contain explanatory variables, interpreted variables, and control variables. Mathematical statistics and Bayesian network modeling are adopted to parse the mutual influence mechanism between the two. In the forward Bayesian inference, the probability of enterprise sustainability performance being in a good state is 49.3%, and the probability of the explanatory variables being in a good state is increased to 58.7% by changing the state probability of other variables. In order to provide a comprehensive overview of the relationship, backward Bayesian inference was also performed, and when the probability of sustainability performance being in a good state was 100%, the probability of the board concurrent position being in a good state was the highest with a value of 72.3%. This study enhances the most effective corporate ESG disclosure quality control program for companies to maximize the possibility of sustainability performance.
In this paper, with the help of the real-time state observation property of the Kalman ϐilter method, we propose to use the Kalman ϐilter method for channel estimation of OFDM wireless communication system. The linear interpolation method is used to deal with the fading process of data symbol positions, and the Kalman ϐilter estimation expression of the fading process is obtained. And considering the computational complexity of the channel estimation algorithm, the channel estimation is optimized by adding the 1st order AR model into the channel model. The Doppler frequency is used as the simulation parameter to analyze the operational performance of the Kalman ϐilter channel estimation method under different Doppler frequencies. To further broaden the applicability of the proposed method in this paper, a MIMO-OFDM system is introduced, and numerical simulations are conducted to analyze the relationship curves between the outage probability and the SNR performance under the OFDM channel processing module for both the random channel and the random channel with OFDM modulation. In the massive MIMO multipath random transmission channel, the better the SNR performance of the channel, the smaller the probability of generating interruptions. Meanwhile, in the presence of the same non-ideal factors (hardware impairments, interference noise) interruption probability impairments of the channel, the SNR in OFDM-ideal state is about 10 dB more than the OFDM-hardware impairments simulation value.
This study analyzes the aerodynamics of fluttering flight of birds through their body structure characteristics. A convolutional neural network is combined with a bird-like flight aerodynamic model. By analyzing the symmetric and asymmetric motion laws of birds in flight, the three-dimensional model and equations of motion of the wing-fluttering motion are established, the aerodynamic simulation study of bird wing-fluttering flight under Computational Fluid Dynamics(CFD) and train it by convolutional neural network. When the model trained to 12 rounds, the loss values on both the training and validation sets converge to about 3.5%, the training effect is good. The predicted values of the lift-to-drag ratio by the model in this paper are close to the CFD calculated values, and the average relative errors of the validation set test set are 0.483% and 0.486%, respectively. In addition, the model predicts the pressure coefficient of the flow field better, and the prediction error of the vast majority of the positions is less than 1.2%. In conclusion, the convolutional neural network can significantly improve the performance of bird flight aerodynamic simulation model.
The environment near substations is complex, and electrocution accidents of operators occur from time to time during on-site operations, and the development of safety detection models for substation operations has received more and more attention. The article proposes a safety distance detection model for substation operation, which is mainly composed of binocular stereo matching perception model and safe area detection model. The binocular stereo matching perception is based on the PSMNet network model, combined with the parallax regression calculation to obtain the threedimensional coordinates of the operation area in the process of substation operation, and the threedimensional reconstruction of the substation operation process. The spatial context inference algorithm is utilized in the safe region detection model to detect the edge of the safe region, and the image segmentation of the safe region of the substation operation scene is performed by the improved OTSU algorithm. Then the three-dimensional coordinates obtained from binocular stereo matching perception and the three-dimensional coordinates of safe region detection are solved for the Euclidean distance, and then the safe distance detection of substation operation is realized. The EPE result accuracy of binocular stereo perception matching on the dataset is reduced by 0.71px compared with CRL, and the resulting mismatch pixel rate is between 0.83 and 1.48%. The average time-consuming image segmentation of the improved OTSU threshold segmentation method is 6.34ms, and the average relative error of the safety distance detection for substation operation is only 0.85%, and the maximum absolute error of the safety distance detection is only 0.13 m. Combining the spatial contextual reasoning algorithm with the deep learning technology can realize the effective detection of the safety distance for substation operation in multiple scenarios, and fully ensure the operation of the substation workers’ safety.
At present, digital twin technology has been developed in many fields and plays a very important role. In this study, digital twin technology is applied to remote control of power system to build a set of remote control system of power system, which contains perception layer, data layer, operation layer, function layer and application layer. In order to make the power system remote control system more reliable and effective, a power system fault diagnosis method based on MRPSODE-ELM is proposed using deep learning technology. The method combines PSO algorithm and DE algorithm to construct a multiple stochastic variation particle swarm differential evolution algorithm, and it is used for the optimization seeking ability of the number of neurons in the hidden layer of the limit learning machine. The experimental results show that the MRPSODE-ELM model performs superiorly in detecting different fault types in terms of accuracy, recall and F1 score, with the results of each index above 95%, and the fault diagnosis accuracy is improved by 4.77% and 3.36% over SVM algorithm and DNN algorithm, respectively, and possesses a smaller training time consumption. The fault detection method proposed in the study can be applied to the remote control of power systems based on digital twins.
The study proposes a multi-stage dynamic resilient recovery strategy based on multiple energy storage to cope with distribution network failures after a disaster in a coastal city, and the post-disaster recovery of the urban distribution network is planned in phases, which is divided into the first stage of emergency response, the second stage of energy storage recovery and the third stage of economic optimization. Then the post-disaster defense measures of the coastal city are improved by optimizing the recovery strategy. After the calculation example design, the post-disaster recovery and resource scheduling effects of this paper’s multi-stage dynamic recovery model are examined through simulation experiments. The multi-stage dynamic recovery model of this paper takes 261 minutes to recover the urban distribution network, which is shorter than the 273 minutes of the traditional recovery model, and the post-disaster resilience is improved. The proposed dispatching scheme based on the multi-stage dynamic recovery model in this paper uses only 13 vehicles, which is the least number of vehicles among all dispatching schemes. The traveling path of mobile emergency resources of this paper’s scheme is most consistent with the post-disaster restoration scenario. The combined level of load reactive power and active power restoration of this paper’s scheme is optimal.
According to the decision-making process of power grid investment, this paper sets the objective function and constraints, realizes the construction of optimization model, and selects genetic algorithm as the solution algorithm of optimization model. Under the requirement of evaluation index principle, 16 secondary indexes and 4 primary indexes are screened, thus forming the evaluation index system of power grid project investment efficiency. The experimental conditions are set to evaluate and analyze the optimization of investment decision and multidimensional benefits of power grid project respectively. Along with the reduction of voltage data, the diversity of optimal solutions for grid project benefits begins to materialize, and the diversity of optimal solutions of GA algorithm is higher than that of PSO algorithm, indicating that the use of genetic algorithm to calculate optimal solutions for grid investment benefits is more effective. In addition, the closeness of the seven projects to the optimal solution is 0.4613, 0.5044, 0.4681, 0.5398, 0.6342, 0.5759, 0.4116, respectively, of which project 5 has the best investment benefit.
The rapid development of the electric power market makes the scientificity and rationality of grid investment decision-making particularly important. In this paper, firstly, we design a grid investment benefit assessment method based on fuzzy comprehensive evaluation. And taking the grid investment benefit of M city in 2022 as an example, the fuzzy comprehensive evaluation method is used to quantitatively evaluate the grid investment benefit. Based on the evaluation results, the weaknesses of power grid investment in M city are found. Then the multi-level optimization strategy of grid investment is further proposed to achieve the maximization of investment benefits. The strategy considers the objectives and constraints of different levels, such as grid structure, power supply reliability, operation efficiency, and power sales revenue, and coordinates the interests between all levels by establishing a multi-objective optimization model to achieve the global optimization of the grid investment decision. Finally, after adjusting the allocation ratio and the allocation amount by the multi-level optimization strategy, the overall evaluation of the city’s grid investment efficiency in 2023 is improved from “average” to “excellent”. It shows that the multilevel optimization strategy designed in this paper can provide scientific guidance for grid investment decision-making.
Aiming at the shortcomings of traditional relay protection, an adaptive multi-area protection coordination model is studied and designed. Firstly, combining different control strategies such as master-slave control and sag control, a method of AC/DC distribution network trend calculation and network loss analysis based on the alternating iteration method is proposed and realized to ensure that the adaptive relay protection can act correctly. The proposed method is analyzed for AC/DC hybrid distribution network trend calculation, and the alternating iteration solution method is used for trend analysis and calculation, and the effectiveness of the proposed method is veriϐied by two examples of AC/DC hybrid distribution networks. Then the adaptive Agent with reinforcement learning is introduced, and its constructed multi-agent system has more system adaptive capability. The adaptive current interruption protection is compared with the traditional current interruption protection, and its protection principle and protection scope are analyzed, on the basis of which an adaptive coordinated protection method based on MAS grid is proposed to realize the MAS adaptive current interruption protection, and its simulation is veriϐied. The experimental results show that the method of this paper can signiϐicantly improve the ϐlexibility, effectiveness and stability of AC and DC distribution network operation.
As a key link in international trade, the price volatility of container transportation has a profound impact on the global supply chain, and uncertainty shocks are one of the main causes of price volatility. With this topic, this paper measures the level of uncertainty at the policy level through the uncertainty index construction method, which lays the foundation for subsequent research. Dynamic correlation and impulse effect analyses of container transportation market prices under uncertainty shocks are conducted using DCC-GARCH and SVAR models. China’s economic policy uncertainty index showed four stages of significant increase in 2001, 2008, 2015 and 2019. The overall price volatility of the container transportation market shows an upward trend, and in 2023, the transportation price is 23,835 yuan. Container transportation prices are affected by the uncertainty of China’s economic policies as well as China’s trade policies, with correlation coefficients ranging from -0.69 to 0.60. The influence of China’s economic policy uncertainty index on container transportation price does not have a long time lag effect.
This paper studies the application of numerical simulation in visual communication design from two perspectives of artistic expression and technical application, and explores the facilitating effect of numerical simulation method on the intersection of artistic expression and technical application. Based on the improved K-means method, the extraction of the main color of the image is completed, and the extraction results are input into the color matching model integrating visual aesthetics as the label of the color palette. The visual communication design method is constructed based on image processing technology, and the method is realized through numerical simulation, so as to test the effectiveness of the technology application in visual communication design. Compared with other algorithms, the improved K-means algorithm in this paper can effectively realize the extraction of the main color of the image. The visual aesthetics score in the color matching model ϐluctuates within the range of [1.10,7.09], and the main color extraction result of the improved K-means algorithm combined with this score as a parameter can realize the coordinated matching of colors. At the same time, the visual communication design method based on image processing technology shows superior performance in terms of communication success rate and communication consumption time. According to the role of numerical simulation method in artistic expression and technical application, this paper explores the intermingling of artistic expression and technical application, highlighting the important inϐluence of numerical simulation method in the process of intermingling.
People’s performance requirements for air conditioning along with people’s requirements for indoor air quality also continue to improve, air conditioning heat exchanger as an important part of the refrigeration system in the air-conditioning products in the largest proportion of space. Therefore, this paper is based on ϐinite element analysis of air conditioning heat exchanger optimization design, oriented to the needs of air conditioning heat exchanger, heat transfer to the mechanism of depth analysis. The ϐinite element analysis is used to study the heat transfer simulation theory of air conditioning heat exchanger, and the heat transfer optimization design method is proposed, and the heat transfer model based on ϐinite element analysis is constructed. Through the physical model and its numerical simulation method for veriϐication, the numerical simulation value and experimental value of the pressure drop and convective heat transfer coefϐicient error of ± 6.50W/m² ℃ and ± 12.7Pa, respectively, which veriϐies the model of this paper and the feasibility of numerical simulation method for. Comparing the performance of the optimized air conditioning heat exchanger, the optimized heat exchanger in this paper improves the cooling capacity by 0.04~0.50kW and the total pressure drop by 11.19~50.84kPa compared with the comparative models, which proves that the optimized heat exchanger in this paper has better performance and can meet the performance and reliability index requirements of engineering applications.
Currently, the severity of information leakage is increasing, and attacks and protection against cryptographic devices have become a research hotspot in the ϐield of information security. In order to increase the security of SM4 algorithm structure against side channel attack, the paper focuses on the protection scheme of adding masks to cryptographic circuits to resist DPA attack, and proposes a cipher algorithm design method of ϐinite domain additive coding. Experimentally, it is proved that the additive coding SM4 algorithm used in this paper can correctly and efϐiciently perform encryption, and the encryption efϐiciency is improved by 56.54%~82.42% than the general SM4 algorithm. Meanwhile, it has the security against 1st-order and 2nd-order side-channel attacks, and the success rate against attacks reaches 93.67%, which is higher than that of the compared algorithms by 5.34%~21.00%. It also proves that the scheme has high security against side channel attacks and can provide a reliable solution for the information security of wireless LAN.
Ceramics have many applications, covering scientiϐic research, medical, industrial, jewelry, etc. Ceramic materials are stable and have a silk-like touch. Ceramic 3D printing technology is based on laser curing molding as a rapid manufacturing technology. This paper proposes a personalized design strategy for ceramic artwork, determines the degree of inϐluence of ceramic process parameters on the quality of laser 3D printed ceramic artwork by calculating the Pearson’s correlation coefϐicient, and adopts numerical simulation to obtain the ceramic 3D printing quality data, calculates the error of the number of printed layers, and controls the quality of the printed ceramic artwork. The ceramic quality parameter optimization model is established. Five algorithms of SVR support vector regression, BP neural network, RF random forest, RBF radial basis function, and Kriging model are used to set up the relevant parameters of 3D printing, input the six ceramic process parameters that have been processed by the uniϐied magnitude, and complete the optimization of the quality ceramic process parameters of laser 3D printing. Through the investigation and analysis of the effect of ceramic artwork design, the ceramic color designed in this paper makes the user generate positive emotions; a total of 235 positive emotions were generated, accounting for nearly 60%. The mean value of user preference for ceramic samples is analyzed. The samples with the highest user preference are sample 4, sample 6, and sample 1, and the mean values of preference are 3.425, 3.245, and 3.148, respectively.
Graph neural networks are widely used in image recognition. This paper introduces a two-node graph neural network DouN-GNN model based on a traditional graph neural network. By constructing two nodes, the features in the sample image that are difficult to extract by the shallow embedding network are extracted so that the network model can incorporate more multi-dimensional information about the sample image, thus enhancing image recognition accuracy. Aiming at the problem of the overall performance of the DouN-GNN model not reaching the ideal state, this paper adds three optimization modules to improve the DouN-GNN model and form the IGNN model. The optimized IGNN model is trained, tested, and applied to real-world scenarios such as agricultural weed recognition, natural resource enforcement, and video surveillance to explore the performance of the IGNN image recognition model constructed in this paper in real-world applications. The model achieves the highest accuracy of 98.39% in agricultural weed image recognition, and the classification accuracy for weeds is also high. In natural resources law enforcement and video surveillance, the model in this paper performs better than other image recognition models and can effectively meet the requirements of image recognition in practical application scenarios.
In the current information age, image tampering detection technology is crucial to ensure the integrity and authenticity of digital media, and remote image tampering detection technology combined with deep neural networks has become a research hotspot. This paper adopts convolutional neural network as the main detection tool, and on the improved DPN network model, the feature fusion module based on the attention mechanism is used to fuse the two features in this paper. In this way, the image tampering detection technique based on dual-stream feature fusion is proposed in this paper. The precision, recall and F value of the detection algorithm in this paper are better than the comparison algorithm. When the image compression quality factor is reduced to 20, the precision rate, recall rate, and F value of this paper’s algorithm do not appear to be greatly reduced, and the reduction is only 0.028, 0.041, and 0.042. This paper’s image tampering detection algorithm, which fuses the frequency domain branching module and the attention mechanism feature fusion module, has a higher detection efficiency. And the Accuracy rate, Recall rate and F Value of this paper’s algorithm on image level detection are 17.8%, 15.3% and 16.3% higher than that of DCT algorithm respectively. In conclusion, the remote image tampering technique combined with deep neural network provides an effective solution to ensure the authenticity and integrity of images.
In order to improve the impact toughness and service life of GF/EVE composites, this paper applies the thermoplastic nonwoven fabric structure to the preparation of GF/EVE composites. The thermoplastic polyurethane was used as the raw material, and the meltblown method was used for the preparation of thermoplastic nonwoven fabrics, and then the prepared thermoplastic nonwoven fabrics were used for the preparation of GF/EVE composites through the VARTM device. For the properties of GF/EVE-TPU composites, specific test methods are given to define the moisture absorption rate and the erosion performance based on the consideration of the stress change of its hygrothermal properties, and the determination of the interlaminar fracture toughness is given.The critical damage threshold load of GF/EVE-TPU composites is 1.57kN, and its contact force increases with time, and the composites are aging in After 60 days, its moisture absorption and erosion weight loss in alkaline environment were 0.736% and 81.19%, respectively.The optimum fracture toughness value of 9g/m² thermoplastic nonwoven structure incorporated into GF/EVE composite was 0.97kJ/m², and the GIIC value of GF\EVE-TPU30 was increased compared with the GF/EVE material without interlaminar toughening by 183.83%. Combining the thermoplastic nonwoven fabric structure with GF/EVE composites can enhance the erosion resistance and interlaminar toughness of the composites and improve the service life of GF/EVE composites.
With the continuous development of virtual reality technology, its application in the digitization of cultural heritage has been constantly emphasized and applied, which has an important role and significance for the protection and inheritance of cultural heritage. This paper proposes a rendering algorithm that combines LOD algorithm and occlusion rejection algorithm. The article firstly carries out theoretical research on the relevant theories and rendering processes of LOD algorithm and occlusion removal algorithm, and finally takes the cultural heritage of Shennongjia as the research object to analyze the performance of this paper’s algorithm in rendering different landscape scenes of the cultural heritage of Shennongjia. This paper concludes that in the high configuration machine, the algorithm of this paper improves the rendering performance by 587% in the resolution of 1280*720, and improves the rendering performance by 1061% in the resolution of 1920*1080. In the low configuration machine, the algorithm in this paper improves the performance by 653% in 1280*720 resolution and 770% in 1920*1080 resolution. Rendering frame rate LOD combined with occlusion culling algorithm (132.65fps) > occlusion culling algorithm (79.88fps) > LOD method (18.02fps) > without any optimization algorithm (5.32fps). The total number of rendering triangles is without any optimization algorithm (55.65) > LOD algorithm (16.78) > occlusion culling algorithm (3.64) > algorithm of LOD combined with occlusion culling (1.05).
Teaching digitalization and integration of industry and education are developing deeply in the field of education, this study designs and constructs the digital practical training system, innovates the teaching mode of school-enterprise collaboration, and applies it to the teaching practice of tourism specialty. The performance of the digital training system for tourism majors is tested by concurrency test, business success rate test and target system thing test. Design teaching experiments to verify the teaching effect of the digital practical training system and the school-enterprise collaboration model by comparing the gaps and changes between the experimental group and the control group in the competitiveness of students’ employment, the utilization rate of resources, the tourism market research, the tourism marketing, the results of the digital practical training, and the development of tourism projects. The maximum number of users in concurrent testing of the digital practical training system for tourism majors is 20, the average number is 10.182, and all the operations of users are processed, achieving good test results. Before the experiment, there is basically no difference between the two groups in the six aspects of employment competitiveness, resource utilization, tourism market research, tourism marketing, digital practical training results and tourism project development. After the experiment, the two groups showed large differences. The scores of the experimental group were higher than those of the control group in all 6 dimensions, and the difference in the scores of each dimension was more than 5 points. The teaching effectiveness of the experimental group rose more than 4.9 points in all 6 dimensions. And the score difference between the pre- and post-test of the control group is not more than 0.5 points. In this paper, digital practical training system and schoolenterprise collaboration model have better teaching effect.
The study firstly introduces the reinforcement learning theory, and proposes a decision-making method based on reinforcement learning to build a robot for autistic children, centered on autonomous human-robot interaction, with the purpose of serving the task of concentration training for autistic children. Among them, the goal task in the current environment is formulated based on imitation learning in the high level, and the robot’s action selection is realized based on interactive Qlearning in the low level. The decision making based on reinforcement learning to build a robot is applied to train the robot to interact with the training, and the simulation results verify the effectiveness and generalization of the designed algorithm in solving the concentration training path. Using the KANO model to analyze the needs of autistic children, based on which we design a multimodal human-computer interaction system for autistic children’s concentration training, and carry out a personal concentration intervention containing academic tasks for an 8-year-old autistic child, to verify the effectiveness of the multimodal human-computer interaction system in intervening in the concentration behaviors of autistic children, and the results of the study show that: the children’s concentration behaviors of the academic tasks in the intervention period are significantly improved compared with the baseline period compared with the baseline, and the mean value increased to 88.42%.
This paper takes the native vegetation in Hanzhong City as the research object, and constructs a multiobjective linear programming model to optimize the distribution of the suitability of the native vegetation in Hanzhong City. The ArcGIS software was used to test the sample consistency and screen the environmental variables of the native vegetation data in Hanzhong City represented by alfalfa, and the model in the software was used to predict the distribution of alfalfa’s suitability area. Based on the prediction results, this paper constructs a multi-objective linear planning model with economic and ecological benefits as the objective function and the land area of different utilization types as the decision variables to optimize the distribution of the suitability of native vegetation in Hanzhong. At the same time, the fuzzy mathematical planning method was used to solve the constructed model. After the model optimization, the area of fitness distribution of native vegetation in Hanzhong City increased significantly, and the growth of the fitness distribution area of each vegetation by 2080 was 49.61%, 35.51%, 36.41%, 28.11%, 15.36%, 24.75%, 27.92%, 28.40%, 31.22%, and 31.52%, respectively. In addition, the optimization of the distribution of native vegetation suitability using the model of this paper can produce obvious economic and ecological benefits, which fully demonstrates the effectiveness of the model of this paper.
Intelligent thermoregulation clothing as a new type of functional clothing, the design and development of which is receiving more and more attention. PID algorithm, as a kind of classical control algorithm, realizes the precise control of the clothing temperature regulation system by adjusting the three parameters of proportionality, integration and differentiation. The control system is firstly constructed according to the principle of PID control. Then the PID controller parameters are optimized by BP neural network to improve the response speed and stability of the temperature control system. Finally, the intelligent thermoregulation garment with physical therapy and health care and portable storage is designed. Experimental verification of the parameter self-tuning PID control based on BP neural network, the BP neural network can make the temperature better maintained near the set value, the control effect is more satisfactory. The final design of the smart thermoregulation garment has a body surface temperature retention rate of 98.35% after 30 minutes at -10°C and with the heating function on. The thermal sensation evaluation of the intelligent thermoregulation garment by the subjects in different states is concentrated between “0-2”, indicating that the garment can play a more ideal temperature control effect.
Since the introduction of fractal geometry, it has set off a wave of research in the scientific community, and it has been widely used in many fields. This paper firstly introduces the landscape modeling and generating technology based on fractal geometry, and proposes the virtual landscape generating method based on fractal geometry through the study of the regular characteristics of fractal geometry. Combined with the game development of virtual landscape generation diversity, complexity needs, in the fractal Brownian motion model on the basis of the proposed optimization of the generation process for game development. In the simulation experiments of virtual landscape generation, the NME value of virtual landscape generation under the method of this paper is the smallest, which is distributed between 3 and 6, and the generation time is reduced by 31ms and 38ms compared with the average time of the traditional generation method and the SEM method, which shows that the designed virtual landscape generation is able to generate the virtual landscape more realistically. The study concludes with strategies and recommendations for the application of fractal geometry to virtual landscape generation in game development, with a view to contributing to the promotion of virtual generation technology.
The argument of the article comes from the rapid development of digital technology and the urgent need for the digital protection and restoration of traditional paper horse art. For this reason, this paper proposes a method of digital protection and restoration of traditional paper horse art based on graphics processing technology. The traditional paper horse art image is collected, the image is denoised using mean filtering, the paper horse image is decomposed in gray scale through spatial conversion, and then its double histogram equalization is processed to obtain the color-enhanced image. Combined with the convolutional image restoration strategy, the paper horse art is digitally displayed. The method of this paper can enhance the color of the paper horse art image and retain the original details, and at the same time, in terms of the clarity effect, the method of this paper improves the comparison method by 25.27%~339.39%. In addition, the method in this paper has better image restoration quality with subjective evaluation rating ≥ 4 and higher PSNR and SSIM. What’s more, the scores on the evaluation dimension of digital preservation and restoration effect ranged from 4.02 to 4.48, and the overall effect performance was relatively good.
Under the dual background of the construction of the “new liberal arts” and the digital wave, the interdisciplinary practice of combining humanities and technology continues to develop. Taking a number of Chinese language and literature works as examples, this paper selects language features from the vocabulary and sentence levels, analyzes the syntactic structure of the selected Chinese language and literature works with the help of natural language processing technology and numerical measurement method of language features improved TF-IDF method, and realizes the discussion of the lexical categories of literary works, such as word length, word frequency, word class distribution and word density, as well as the study of sentence categories such as average sentence length, sentence dispersion and sentence class distribution. It is found that most of the utterances of the selected literary works are monosyllabic words and polysyllabic words, the cumulative proportion of both of them is more than 90%, the highest frequency of occurrence is nouns and verbs, both of them are more than 22%, the average sentence length and sentence dispersion do not differ much, and the overall readability of the selected literary works is better, with a free change of syntactic structure and a stronger narrative of the text.
Teacher-student interaction, as the most important way of classroom interaction, its level directly affects the quality of classroom teaching. The study selected three English listening classes, three English reading and writing classes, and three English exercise classes, totaling nine English classes in a university for video recording. With the help of the Improved Flanders Interaction Analysis System (iFIAS), the study utilized classroom observation and multiple regression analysis to investigate the effectiveness of teacher-student interactions in the classroom and their influencing factors. It was found that the average value of students’ classroom discourse ratio (40.3%) was smaller than the average value of teachers’ classroom discourse ratio (48.1%), and that a reasonable structure of teacher-student language ratio was more conducive to the formation of benign interactions in the classroom and the enhancement of the overall classroom effectiveness. In addition, teaching ability, learning style, learning motivation and classroom environment all positively affect the effectiveness of English teachers’ classroom interaction in colleges and universities. Therefore, it is necessary to start from these four aspects to adjust the language ratio structure, create a positive classroom atmosphere, and enhance the integration of information technology and the classroom.
The traditional English teaching mode in colleges and universities has many problems in cultivating students’ language ability. This paper introduces information technology into task-based English teaching in colleges and universities and constructs a task-based English teaching mode based on SPOC technology. With the orientation of improving students’ language ability, it implements the improvement of English teaching mode in colleges and universities. Using principal component analysis to comprehensively evaluate the relevant indicators of students’ language proficiency in the process of task-based English teaching in colleges and universities, and quantify the effect of the combination of information technology and task-based English teaching on the improvement of students’ language proficiency. Ten classes of students majoring in English in a university were selected and divided into experimental and control groups, and the data related to students’ language proficiency were collected and analyzed at the end of the experiment. The data were downscaled using principal component analysis, and the principal components were extracted according to the eigenvalues and cumulative contribution rate. The comprehensive score of students’ language proficiency is calculated by the comprehensive evaluation function of students’ language proficiency constructed in this paper. The language proficiency of students in the experimental group and the control group is significantly different after the experiment, and the comprehensive scores of students in the experimental group are 53.96% and 61.96% higher than those before the experiment, respectively. It reveals that the introduction of information technology into task-based teaching of English in colleges and universities has a significant effect on the enhancement of students’ language proficiency.
In educational research, more and more scholars recognize the importance of teaching interaction network for learning, and they find that “interaction” is not only the method of learning, but also the learning process itself. Social network analysis provides a new way to study teaching interaction. Through the study of social network analysis, this paper proposes the construction method of teaching interaction network for physical education. In this paper, we take four real physical education courses in L school as the research object to conduct in-depth research, obtain the physical education classroom teaching interaction behavior data, and construct the teaching interaction network. The results of the study show that in the interaction network of the four physical education teaching courses, the teaching behaviors of the community network of physical education classroom 1 are significantly concentrated in B3, B4, B5, and B6, course 2 is concentrated in B4, B5, B6, B9, and B10, the teaching interactive behaviors of physical education classroom 3 are significantly concentrated in B2~B6, and the significant physical education teaching interactive behaviors of course 4 are concentrated in B2, B4, B5, and B6.From the degree-centeredness analysis, there are 33 marginal learners with the number of stored interactions less than or equal to 2 in physical education teaching interactions, which indicates that in this paper’s study of physical education teaching interactions, teachers do not pay enough attention to teaching interactions in a comprehensive way. By summarizing the theoretical basis and practical significance of teaching interaction and social network analysis, it proves that the network construction of teaching interaction in this paper is effective, and at the same time, it also provides a new idea for physical education teaching courses.
In order to strengthen the construction of network security defense system and effectively respond to new types of threat attacks appearing in the network environment, this paper constructs a network security threat prediction model using data mining algorithms. The network security threat posture needs to be assessed before the security threat prediction. Accordingly, this paper assesses the four security threat postures of services, vulnerabilities, weaknesses, and hosts on the basis of the quantitative assessment method of hierarchical security threat posture. After that, a network security threat prediction model is constructed based on the support vector mechanism, and a genetic algorithm is used to optimize the parameters of the model. The three evaluation index values of MAE, RMSE and MAPE for the GA-SVM-based cybersecurity posture prediction method proposed in this paper are 0.0106, 0.0133 and 0.0222, respectively, which are better than those of the ABC-SVM-based and PSO-SVM-based prediction methods. It indicates that the method in this paper has smaller error and higher accuracy in cyber security posture prediction. This shows that the method in this paper usually achieves better accuracy in cyber security threat posture prediction.
In this paper, we understand the shortcomings of the current mainstream IoT privacy protection methods through analysis, and in this way, we propose an evolutionary and signaling game model for IoT privacy protection. The model analyzes the stabilization trend of IoT platform penalty coefficients on privacy protection and provides protection strategies. Combining the implications of the signaling game model, the degree of IoT privacy protection is measured using the Bayesian equilibrium solving algorithm. Simulation experiments are conducted to evaluate the specific effect of the model on IoT privacy protection. The increase in the detection rate of the model accelerates the convergence of the probability of malicious nodes, e.g., when the detection rate increases from 0.7 to 0.9, the convergence time is reduced by about two stages. The larger the penalty amount of the IoT platform, the model recommends more aggressive protection strategies, and the probability increases from 0.16 to about 0.4. The game parameters of the model reflect the malicious behavior in IoT, and the trust level affects the game parameters. The model in this paper reduces the attack gain by 4% to 10% compared with the comparison model when the fixed defense gain is 1500, which can better reflect the influence of protection signals on the attacker’s actions.
This project focuses on the classroom interaction of college English and proposes a framework for optimizing college English classroom interaction by integrating big data. Taking the behavioral analysis layer as the entry point, using PSO’s improved K-mean clustering algorithm, we focus on analyzing the specific application of data mining technology in students’ learning behavior. Then we conduct experiments on two classes of students in a university, design classroom behavioral coding to analyze classroom interaction behavior, and explore the application effect of this English classroom interaction optimization pathway. The students were divided into six categories through cluster analysis, with focused learners (22%) and continuous learners (36%) having the highest fidelity scores and the largest proportion, and the analysis of students’ learning behaviors can provide a reference for teachers’ classroom teaching. The composition of the English interaction optimization classroom changes from teacher-led to student-led in the traditional classroom, the teacher-student speech curves intersect each other and both appear four peaks, showing good classroom scope and teacher-student interaction effect, and the path of interaction optimization in the English classroom based on big data is practicable.
Vocal singing is a key art form of many stage singing arts, specifically including acting and singing. The study firstly is to introduce the detection principle of YOLOv5 target detection algorithm, on the basis of which the original YOLOv5 algorithm is improved by reconstructing the backbone network with the use of SENet and GhostNet, then the original YOLOv5 algorithm and the improved YOLOv5 algorithm are tested for comparison, and the test results show that on the target detection dataset Precision, Recall and mAP values reach 85.75%, 72.34% and 78.48% respectively, which are all improved compared with the original algorithm. Secondly, a high-resolution human posture estimation network incorporating multiple attention mechanisms is proposed to further extract multi-scale feature information and global feature information, and validated on publicly available datasets, CDLNet has an AP value of 0.662 and an AR value of 0.731 on the vocal singing posture estimation dataset, comparing with similar methods, the method has an MPJPE in Human3.6M The lowest is 44.6, which is suitable for use in vocal singing posture estimation in vocal singing scenarios. Finally, an action recognition model based on multi-granularity spatio-temporal graph convolutional neural network designed in this paper is used to analyze the singing gesture action recognition for singing action categories, and experiments show that the average recognition rate of MGstgcn can reach 86.5% on the HSiPu2 dataset, which meets the demand of vocal singing gesture action recognition analysis.
With the rapid development of the regional economy, the urbanization process is gradually accelerated, and the ecological safety problems of the urban water body network gradually appear, so this paper is based on linear planning to optimize the ecological landscape water body network. The study first gives a detailed description of the linear planning theory and highlights the gray linear planning model used. Based on the ecological constraints of the landscape pattern quantity optimization research, the “top-down” gray linear planning model from six aspects to build ecological constraints and objective function, through the simplex method to solve, resulting in different scenarios of the total amount of control of the optimization program. Three practicable optimization scenarios are obtained through repeated debugging of the optimization results, and the three scenarios achieve different results in terms of economic and ecological values. In this paper, effective optimization schemes are proposed for different optimization purposes, which on the one hand make the optimization results more realistically reflect the changes of the ecological landscape water body network, and on the other hand provide an optimal model for the management and development of the ecological landscape water body network, and promote the sustainable development of the region.
Distributed energy storage technology can effectively solve the load peak-to-valley difference and voltage quality problems faced by distribution networks. Reasonable and efficient scheduling of distributed energy storage in distribution networks is an important means to play its role. The study proposes a power prediction-based optimized scheduling strategy for distributed energy storage in distribution grids with hierarchical zoning. Firstly, power prediction is carried out using GWO-EEMDBP neural network. Then partition optimization is carried out according to distributed power and load distribution, and the energy storage scheduling strategy is formulated based on the energy storage power prediction interval. Finally, experiments and arithmetic examples are analyzed based on the data related to the distribution system of the IEEE-33 distribution node. The predicted SOC values based on GWO-EEMD-BP neural network are basically consistent with the real SOC values. After applying the energy storage scheduling strategy designed in this paper, the system power loss decreased by 260.86 kW∙h and the load volatility decreased by 67.5%. In addition, this strategy has significant advantages in terms of system operation economic efficiency and voltage quality improvement, and it is capable of scheduling distributed energy storage in the distribution network in a reasonable manner.
Ethnic folk dance in Southwest China is known for its unique regional characteristics and cultural background, and the optimization of its movement choreography strategy is especially crucial for the inheritance and development of this artistic influence. In this study, an optimized graph neural network model is used to choreograph the movements of folk dances in Southwest China. The model is equipped with multi-feature fusion, spatial modeling and temporal modeling modules, which can maximize the recognition performance of the graph neural network model. Based on the model, a framework for automatic generation of folk dance movements is designed, and the model is trained and validated using Laban-16 and Laban-48 dance movement datasets. The experimental results show that the method of this paper is well tested, and the loss value and accuracy convergence algebra of the training set and the test set are basically the same, reaching 0.25 and 96%, respectively. The lower limb motion recognition rate on Laban-16 dataset is improved by 5.21%~15.81% compared with the comparison model. Under the music of different rhythms, a variety of dance movements can be reasonably choreographed to, and the feasibility score of the model by experts is between 85 and 95, indicating that the model in this paper has practical value.
This paper constructs a three-dimensional model of energy storage power station through threedimensional visualization technology, and builds a virtual simulation environment of energy storage power station by inputting realistic environmental parameters. Four different energy storage technology routes, namely lithium-electronic battery energy storage, lead-acid battery energy storage, pumping energy storage and air compression energy storage, are selected, and the energy storage performance of the four technology routes is explored in depth based on the constructed virtual environment. At the same time, the energy storage performance of four different technology routes in the virtual environment of the energy storage power station is compared using the energy storage capacity and energy storage efficiency as the measurement indexes, and the energy storage technology routes suitable for the environment of this paper are highlighted based on the comparison results. In the energy storage simulation, the net energy storage capacities of the four technology routes in the virtual environment of this paper are 728.99MW, 724.18MW, 461.50MW and 393.45MW, respectively. Compared with the other three energy storage technology routes, the lead-acid battery energy storage capacity fluctuation is smaller, and the energy storage capacity is higher, with a higher degree of adaptability to the virtual simulation environment in this paper. At the same time, the average energy storage efficiency of lead-acid battery in four quarters is 99.71%, compared with the next highest efficiency of lithium-electronic battery energy storage efficiency increased by 14.29%, which further indicates that the lead-acid battery energy storage technology route in this paper builds the best performance of the virtual simulation environment of the energy storage power station.
Inheriting national sports culture plays an important role in promoting national culture and national spirit. The digitization technology’s has played a role in promoting the development and dissemination of traditional sports culture. Based on the digitization model of traditional sports and related research materials and audience comments, the article extracts effective data to construct a model of the influence factors of digitization of traditional sports. Trust, cultural confidence, inheritance willingness, perceived usefulness, perceived ease of use, inheritance resistance and technology anxiety are taken as eight latent variables. Relevant hypotheses are proposed for the relationship among the eight variables. Through the questionnaire method, 321 valid data collected were validated and analyzed. Including the use of structural theory model for reliability analysis, correlation analysis, path coefficient test, etc., it is finally concluded that the mediating effect of perceived usefulness and willingness to pass on is obvious, while the mediating effect of perceived ease of use is relatively insignificant, and it cannot play a significant role of acceptance between resistance to passing on to audience acceptance. Trust, cultural confidence, perceived usefulness, perceived ease of use and inheritance willingness are positive feedback relationships to audience acceptance, and inheritance resistance to inheritance willingness and technology anxiety to audience acceptance are negative feedback relationships.
The stability of the supply chain has a significant impact on both the strategic deployment and operational efficiency of the enterprise, in order to optimize the supply chain management model for the enterprise and resolve the major supply chain risks, this paper realizes the optimization management and risk assessment of the supply chain through Monte Carlo simulation algorithm and SVM. Taking the newsboy problem as an entry point, a supply chain management optimization model is constructed, and Monte Carlo simulation algorithm is used to solve it. Using SVM regression and assessment ideas, supply chain risk regression assessment is carried out by C-SVR. Applying the supply chain management optimization model, it is concluded that when the optimal inventory of prefabricated components of the selected construction unit is 2.55×10³m³, the enterprise profit is the largest, which is 902.31×10³m³ yuan. Supply chain risk assessment of a port, the training error and prediction error of the assessment model in this paper are only 0.043% and 1.76%, which are significantly better than the BP neural network assessment method. Therefore, it proves that the work in this paper achieves the optimization and risk assessment of enterprise supply chain management model through simulation algorithm.
This study aims to explore how Hunan higher vocational colleges and universities can build a new ecosystem of industry-education integration through linear programming optimization strategies under the guidance of the strategy of developing the country through science and technology. The article evaluates and analyzes the ecosystem of industry-teaching integration in Hunan higher vocational colleges under the strategy of developing the country through science and education using linear programming method, and proposes relevant optimization strategies using the dyadic model of linear programming. The main factors affecting the efficiency of industry-teaching integration are identified through multiple regression analysis, including industry-teaching resources, incentive mechanism and management system. According to the linear programming model for maximizing the efficiency of industry-teaching integration in higher vocational colleges and universities, it is calculated that the efficiency of industry-teaching integration is maximized when Hunan higher vocational colleges and universities invest 3.6 million yuan, 0.3 million yuan and 0.5 million yuan in the resource consumption of industry-teaching resources, incentive mechanism and management system respectively. And it is proposed to build a new ecology of industry-education integration from three aspects of platform construction, cooperation docking and parenting path, respectively.
In this paper, the evaluation system of college students’ innovation and entrepreneurship education is constructed and the indexes are assigned by combining the hierarchical analysis method. After that, PSO algorithm is introduced in the optimization of weights and thresholds of BP neural network, the neural network model using particle swarm optimization (PSO-BP) is constructed, and the process of PSO algorithm optimization of BP neural network is described. It was found that the combined weight of five indicators, namely, “examination results of innovation and entrepreneurship courses, entrepreneurial experience, participation in centralized entrepreneurship training camps, obtaining financial support from entrepreneurship funds, and participation in innovation or entrepreneurship clubs”, accounted for more than 10%, while the combined weight coefficients of the rest of the indicators were all below 0.1. Compared to the BP model, the PSO-BP model has better network performance and its training samples have higher correlation with the test samples. In addition, the PSO-BP model can be used for predicting data prediction after 9 iterations of training, and the maximum relative error between the actual value and the expected value of the model network test output is very small (<1.4272%), which makes the model ideal. After PSO optimization PSO-BP model has almost no prediction error (<0.34%), which can improve the evaluation efficiency and accuracy.
Behavior of different types of English learners tends to follow different patterns and characteristics, and the analysis of behavioral data is one of the directions for improving English learning and teaching. This study designs a set of behavioral analysis methods based on machine learning for English teaching and learners. The learners’ behaviors are firstly operated with feature extraction and quantification, and the behavioral data are clustered by using the systematic clustering method (HCM) to improve the SOM model. 1DCNN is used to process the learning time-series data and enhance the data mining and performance prediction ability by BiLSTM and attention mechanism, respectively. This paper distinguishes five categories of English learners, such as excellent, diligent, average, procrastinating and negative, and filters out the factors that are highly correlated with English performance, such as the download of learning resources and the number of times of teaching viewing. Comparison experiments show that the ACC of this paper’s achievement prediction model = 0.53, which is better than other comparison methods. Therefore, the idea of this paper based on machine learning methods to analyze the behavior of English teaching and learners has feasibility.
In this paper, some important algorithms in the field of target detection and tracking are optimized. Firstly, Gaussian modeling is performed in the color space for the dynamic background, and the priority is set for ranking. Then introduce adaptive Gaussian component number mixing, adaptively change the weights, and adaptively change the number of mixed Gaussian components according to the pixel color change in the scene to improve the convergence speed of the complex scene. Finally, Kalman filtering and mean drift algorithm are combined to ensure the robustness of target tracking in complex scenes. The single-frame detection time, accuracy, and average tracking error of the algorithm designed in this paper are examined on the dataset, and it is found that the time consumed by the algorithm in this paper in the three scenarios is 242ms, 323ms, and 274ms, respectively, with the highest accuracy of 96.9% and the average tracking error of only 1.5 pixels. The optimization algorithm designed in this paper is able to adapt to the slight disturbance of the background scene and overcome the influence of noise and ambient lighting, which is a target detection and tracking algorithm with good robustness.
The service efficiency of intelligent customer service robots affects the service operation efficiency of enterprises and plays an important role in maintaining customer resources. This paper applies multimodal interaction technology to intelligent customer service system, takes multimodal big language model Qwen-VL as the core, proposes a two-stage relationship multimodal relationship extraction framework based on big language model, realizes multimodal relationship extraction with the help of high-quality auxiliary knowledge, integrates dynamic semantic features and static structural features to complete the multimodal emotion polarity prediction, and constructs multimodal retrieval Q&A system to improve the performance of smart robot performance. Applying the intelligent customer service system in this paper for service practice, the conversation between the intelligent customer service robot and the customer usually ends in about 50 rounds, and the service efficiency is relatively efficient. In the face of customer emotional sentences labeled as happy, complaining and angry, the recognition accuracy under multimodal sentiment analysis is greater than 99%, and the behavior of “notification” and “confirmation” service behavior accounts for the largest proportion of behaviors, and the number of behaviors reaches 560,365 times, 365976 times, which is in line with the expected service behavior of intelligent customer service robots.
This paper carries out a research on the quantitative evaluation of classroom behavior based on the maximum information entropy model, explains the theory related to information entropy and analyzes the concept of information entropy. The improved iFIAS interactive analysis system was used as the main analysis tool, and S-T analysis and time series analysis were used as auxiliary analysis methods to analyze the classroom teaching behaviors. Classroom teaching behaviors were coded and sampled, from which classroom teaching behavior related data were obtained, through which teaching behavior information entropy, redundancy, interaction mode, teaching mode and behavior category frequency were analyzed. After analysis, the overall classroom teaching characteristics of the psychoeducational quality lesson examples are teacher behavior and student behavior as the main, psychoeducational and teaching materials as the secondary, and the teacher’s behavior in the early part of the classroom accounts for a high proportion in order to drive the students into the classroom. The proportion of student behavior rises in the middle and late stages. The proportion of hybrid and dialogic teaching mode is 95.83%, which is dozens of times more than the proportion of lecture. It reveals the teaching mode of quality psychology classroom teachers, i.e., focusing on the interaction with students and replacing the pure teacher lecture student acceptance mode with interactive counseling student active learning. The teaching analysis of quality classroom teaching behaviors with the maximum information entropy model realizes the establishment of an innovative model of psychological teaching and clarifies the direction for the future development of psychoeducational classrooms.
With the rapid expansion of the Internet and e-commerce, and the rapid revolution of the consumption mode, customer reviews have become the most important feedback means for customers’ preference and satisfaction level of products nowadays. In this paper, hotel customer reviews are used as the basis for predicting hotel customer satisfaction, and the TF-IDF feature word extraction method is proposed to extract review feature words. Based on deep neural network, we propose the sentiment analysis technology of hotel customer reviews, use BERT neural network to construct aspect term extraction model, realize the sentiment recognition and quantification of hotel customer reviews, and combine the fuzzy comprehensive evaluation and IPA analysis as the prediction and analysis model of hotel customer satisfaction. Taking 25837 customer reviews of XC Hotel as a research sample, we explore and analyze the satisfaction of XC Hotel customers. The secondary and primary features of the reviews were extracted by review feature words, and the themes were extracted by LDA theme mining model, which concluded that the evaluation items of concern for XC Hotel lie in location, facilities, hygiene, service, price, and food and beverage. The prediction results showed that 69.59% of customers were satisfied, 18.42% felt average and 11.99% were dissatisfied. IPA analysis of satisfaction and importance of XC Hotel and its visualization were conducted, and the intelligent service management model of the hotel was constructed based on the results of IPA analysis, and the optimization strategy of intelligent service of the hotel was proposed.
Variable speed running training method is an efficient training method targeting the improvement of athletes’ agility. In this paper, 30 male students from two badminton special classes of physical education majoring in a college of 2021 were selected as experimental subjects, and hexagonal ball reaction test, hexagonal jump test, repeated straddle test, standing bench press test, closed-eye in-situ step test, and low gravity center of gravity quadrangular running test were chosen as the evaluation indexes of agility quality. A new high-resolution multi-scale feature fusion network was designed for running stance estimation, and the effects of variable speed running training method and conventional agility training on the agility quality of young badminton players were analyzed. The performance curve of the RHPNet designed in this paper has low convergence difficulty and high recognition accuracy, which tends to 0.83, and performs much better than the LSTM network. The intergroup data after the experiments of the experimental group and the control group show that there are significant differences in the performance of the hexagonal jump test, the 20s repeated straddle test, the hexagonal ball reaction test, and the closed-eye in-situ step test. It verifies the effectiveness of the network designed in this paper in the estimation of athletes’ movements during running, and also shows that the training effect of variable speed running training is better than that of conventional agility training.
Disease prevention has always had an important impact on the development of human life and health. The integration of complex network theory and disease has become one of the major trends in epidemiologic research. However, aspects such as individual vaccination behavior and vaccination costs are affected by social capital investment. Based on this, the article investigates a reinforcement learning model of social capital investment on disease prevention. Based on the mechanism of infectious disease dynamics on complex networks, the article investigates the Markov decision process and composition of the reinforcement learning model, and utilizes the theory related to infectious disease dynamics and reinforcement learning to study the mechanism of voluntary vaccination based on epidemic perception. It was found that when the ratio of two kinds of investment (partial investment and full investment) reaches the set maximum value, the full investment policy of targeting selection is more effective in reducing the scale of disease infection in the whole social network and reducing the total social cost, followed by partial investment, and the full investment policy of random selection brings the smallest effect. However, the results may differ for different population investment ratios and partial investment ratios, and both the full investment policy and partial investment policy can effectively control disease prevention, which is conducive to the healthy and prosperous development of the whole society.
Virtual reality technology, as a trend of the development of the new era, has a profound impact and influence on traditional art design teaching. This paper combines virtual reality technology to construct a classroom teaching interactive analysis system to help art design teaching reform. All the objects required for the art design teaching scene are modeled in 3Ds Max, the mapping of each object in the scene is beautified using Photoshop, the FBX format file with animation effect is output, and Unity3D is used for the design and development of the VR part. Subsequently, a quantitative coding form for classroom observation, methods and rules for behavioral data collection, and a classroom migration matrix for analysis were designed to analyze the teaching interaction behaviors in the smart classroom classroom from the micro level. The characteristics of the teaching model in the art and design classroom were captured at the macro level based on the S-T analysis. A teaching experiment was conducted in a school’s art design program after the FIAS analysis. The improved art design classroom interaction increased for the blended teaching mode, and the average of the pre and posttest scores of the experimental class applying this model were 74.18 and 84.37 respectively, and there was a significant difference, which was a significant improvement over the control class. This study provides new ideas and methods for the teaching reform of art design majors in higher education institutions.
In this paper, information theory and information metrics are used to obtain an approximate estimation of linguistic information entropy. After that, the binary model of large-scale corpus and foreign language words is established, N-Gram model is constructed, and the information entropy of modern foreign language speech is estimated. Finally, the N-Gram model was utilized to statistically analyze the results of interpreting information loss, comparing the rate of information transfer in foreign language speeches and the subjects’ interpreting performance. The results showed that the phenomenon of information loss was prominent, with many types of loss, high frequency, and serious loss situations. T assertions had 8.61%-18.95% of propositional information loss, 3.0%-7.6% of constituent information loss, and 49.68% of overall loss. The data on the information loss of each language component showed that TPO and SPE presented the most and the least frequency among the 6 propositional information losses, which were 67 and 1 times, respectively. Among the 13 types of information component loss, TFLS presented the highest frequency and TLE and SFLO presented the lowest, with their losses of 55, 1, and 1 times, respectively. In the interpreted material of English speech, the rate of narration was 2.25 words per second and the average rate was 13.45 bits per second. Among the T assertions, numbers S7, S4, and S9 have the highest propositional untranslated rate (21.8%), propositional mistranslated rate (23.5%), and propositional information loss rate (44.5%), respectively; the corresponding lowest values are at S4 (2.7%), S5 (1.8%), and S4 (2.8%).
Target tracking is a fundamental task in the field of computer vision, which has a wide range of applications in real-life video image signal processing. This paper proposes target tracking optimization technique based on the principle of multi-scale convolutional neural network and multitarget tracking algorithm. The basic structure is designed using VGG16 network, the ROI align method is used to reduce the number of features for feature fusion, and the improved Hungarian algorithm is adopted to associate the fused features and obtain the target tracking results. In the tracking performance experiments, the target tracking optimization technique in this paper is more discriminative in terms of extracted features, and also has higher tracking results under challenging factors such as background clustering (BC), scale variation (SV), and out-of-view (OV). As for the target tracking experiments on mobile network video images, the average tracking accuracy and average tracking success rate of this paper’s method are 97.89% and 96.02%, which are better than DS_v2 and FFT16, and the average error between the target tracking results and the target’s actual motion trajectory is 4.12mm, while possessing the smallest error amplitude.
The traditional Japanese language teaching mode in colleges and universities has been unable to meet the requirements of Japanese language majors in various industries, and colleges and universities should use certain methods to carry out a reasonable reform of the teaching mode of Japanese language majors. Firstly, an error correction model based on UniLM model framework is proposed, using natural language processing technology to extract features, and fine-tuning training for the model after initialization. The model framework based on UniLM+CRF and the seq2seq model framework based on UniLM are built to realize the Japanese text grammar error annotation task and the Japanese text grammar error correction task respectively. Then a multi-task learning error correction method is proposed to integrate the grammar error labeling task and the grammar error correction task, so as to improve the accuracy of the error correction model. Finally, a specific Japanese grammar error correction system architecture is designed, a Japanese language knowledge base is established, and utterance synthesis rules are formulated to realize the innovative teaching of Japanese language in colleges and universities. The average grammatical error correction precision, recall, and F1 value of the model in this paper reached a good level in the students’ Japanese composition correction. The error between the average score of teacher correction and the average score of model correction is only 0.19 points, and the related experiments show that the innovative teaching model studied in this paper can effectively improve students’ mastery of Japanese syntactic ability. The above data illustrate that the Japanese error correction system based on UmiLM framework designed in this paper has certain application value and can realize the innovation of Japanese language teaching mode.
With the arrival of the aging society and the continuous improvement of human civilization, people pay more and more attention to the quality of existence, quality of life and happiness index, and the elderly service is becoming a hot issue of social concern. The article proposes a set of intelligent monitoring system for the elderly based on ROS service robot in the context of big health. The system is based on the machine vision following module to design the neural network-based fall detection module and the monitoring module of power consumption abnormality to realize the remote contact method between the elderly and the guardian. The article measures the quality of life and happiness index of 600 elderly people in old age through questionnaires, and systematically understands and comprehensively grasps the influence and effect of the monitoring system proposed in this paper on the quality of life and happiness index of the elderly from seven target levels and several index levels, including the quality of healthy life, economic quality of life, family quality of life, social quality of life, cultural quality of life, personal value realization and sense of identity and belongingness , with more than 97% of the elderly believing that the quality of cultural life has been improved by utilizing this AI intelligent machine.
With the access of multiple renewable resources to virtual power plants, hundreds of millions of power time series data are generated every day. A sparse learning-based power data compression and reconstruction processing method is designed in the study, which effectively solves the problems of low computational efficiency in the data processing centre of the virtual power plant and the waste of storage resources. According to the vector principal component analysis method, the power data are compressed. Then the data reconstruction network model is constructed based on sparse learning to achieve the reconstruction of power data. The experimental test results show that the median absolute errors of reconstruction of active and reactive power data are 4.05 MW and 0.885 Mvar, respectively, and the percentages of absolute errors are not more than 5%, which makes the reconstruction performance highly stable. The method achieves high-quality power data compression and highprecision reconstruction processing, which is of great significance for improving the computational efficiency of the virtual power plant data centre and accelerating the digital transformation of the power grid.
In this paper, the modelling and fault monitoring methods of virtual power plants are investigated. Aiming at the risks faced by the virtual power plant, a virtual power plant dynamic model based on BPNN is proposed, which uses neural networks to establish the relationship between the uncertainty factors and the technical parameters of the virtual power plant, and adjusts the technical parameters of the virtual power plant in real time according to the size of the uncertainty factors. The technical parameters of the virtual power plant are optimised to obtain the parameters that maintain the optimal performance of the virtual power plant. At the same time, in order to be able to comprehensively monitor the failure of the virtual power plant, play a role in early warning, starting from the real-time database of the equipment, the data from a variety of sources to the equipment as the centre of the fusion. Multiple state parameters of the equipment are tracked in real time and displayed in the form of trend graphs, which completes the analysis of the parameters of the fault characteristics in the database and achieves a nonlinear mapping from characteristics and signs to the cause of the fault and the type of fault. Based on the BPNN dynamic model, the SMAPE is 6.51%, and after using the model constructed in this paper to monitor the virtual power plant, the failure rate of the virtual power plant decreases month by month, and the failure rate is much smaller than that before the model is used. It verifies the good performance of the method of this paper, and also shows that the method of this paper has a broad application prospect in the field of fault monitoring and warning of virtual power plant.
This study aims to quantitatively analyze the impact of agricultural scientific and technological progress on rural economic growth. The contribution rate of agricultural scientific and technological progress in place A is measured through beyond logarithmic function model setting, data collection and processing. An agricultural carbon emission measurement model was built, in order to analyze the dynamic changes of total carbon emissions in place A. In addition, the gray correlation analysis algorithm was used to rank the correlation between agricultural science and technology indicators and economic growth in place A. Finally, a regression model is designed to analyze the impact of scientific and technological progress on rural economic growth. The coefficient of the t2 term of the contribution rate model of scientific and technological progress is 0.0013, which is greater than 0, indicating that there is scientific and technological progress in 2017-2023 in place A. The carbon emissions in place A decrease year by year with scientific and technological progress. All indicators in agricultural science and technology inputs can promote agricultural economic growth, and the gray correlation value in descending order is, T3>T9>T8>T1>T6>T4>T7>T2>T5. Scientific and technological progress has a different degree of promotion for the rural economic growth in place A.
In the development process of China’s power system, automatic monitoring mode has become an important development direction. In this context, how to achieve real-time monitoring of power data in the system has become an urgent problem. In this paper, considering the current range, the controller with input current is selected to collect voltage and current data signals and detect their circuits. Through metadata integration, the semantic integration of data expression is solved to achieve the management of electric power metadata. The collected data are sequentially accessed, handled and processed, the calibration of the voltage and current signals is agreed upon, the AD-converted values are read and the electrical parameters are calculated. Using the communication protocol IEC61850, the processed electric power data is uploaded into the server to complete the electric power data reading and monitoring tasks. The real-time management platform of intelligent maintenance power box constructed in this paper is used to monitor the abnormal power data. The abnormal power data appeared at different times, and the peak value of abnormal value 1 appeared at 14:00, and the peak data was 0.91 w. The evaluation value interval of the security threat in the transmission of power data is between 100-200 g, and the energy interval fluctuates around 1000 c. The results obtained are more reasonable, and the security of the data is guaranteed.
Based on digital simulation technology, this paper proposes a food packaging design model and a food production efficiency improvement model with food production as the research entry point. Establish the overall structure of the virtual reality design environment, the parameters of the packaging design process is converted into basic parameters to describe the problem, and the data is fed back to the CAD system to realize the design work. Design the hybrid optimization genetic algorithm based on annealing principle, adjust and optimize the production process and initialize the operation, simulate the annealing genetic algorithm process, and complete the production and processing scheduling sequence. Take A Food Co., Ltd. as the research object to carry out food packaging design and production efficiency improvement practice. The egg cake product packaging design scheme constructed by using the packaging design method in this paper obtains the total attention time of the subjects to be 149.3s, and the subjective score value reaches 85 points, which is better than the original packaging design. And in the real simulation of production using the production efficiency improvement method of this paper, the total production process operating time percentage is reduced from 73.8% to 35.1%, and the food production capacity is steadily increased by about 6%.
Based on the wide application of satellite remote sensing in the field of ecology and environment, the study builds a remote sensing monitoring system for marine ecological reserve, processes the marine remote sensing data by using the technologies of GeoTools, NetCDF and GeoServer, builds a WebGIS system, and collects, transmits and integrates and processes the marine ecological data through the data collection module and the visualization query module. Selecting Laizhou Bay as the study area, the system of this paper was used to collect and process the ecological remote sensing data within the study area during the period of 2003-2022, and to carry out multidimensional analyses including the factors of sea surface temperature and photosynthetically active radiation, sea surface salinity and degree of eutrophication, and so on. A marine ecological health assessment index system was constructed to assess the ecological health of Laizhou Bay and explore its spatial and temporal distribution characteristics. During the period 2003-2022, sea surface temperature and photosynthetically active radiation (AWEI) in the Laizhou Bay region showed an overall increasing trend, and sea surface salinity showed a slight decreasing trend. The total area of mariculture and the total area of zizyphus culture were generally on the rise, and the eutrophication of the water body in Laizhou Bay was most serious in 2013, with the AWEI reaching the maximum value (1.00), which was mitigated after 2013, and the AWEI was reduced to 0 in 2022.The integrated health index (IHI) of the ecosystem of Laizhou Bay increased gradually in 2003, 2012 and 2022, and the health status changed to “healthy”, and the area of healthy zone expanded to “healthy”. In 2003, 2012 and 2022, the integrated health index (IHI) of the Laizhou Bay ecosystem gradually increased, the health status changed to “healthy”, and the area of the healthy zone expanded by 54.54%.
With the wide application of renewable energy sources, the impact of distributed power access units (DPGUs) on the stability of the power grid is becoming more and more important. This study aims to analyze the impact of DPGU control command issuance on the variation of grid node parameters through simulation. An advanced power system simulation software is used to construct a complex distribution grid model containing multiple DPGUs, including a simulation and analysis method of complex distribution network current characteristics and a voltage hierarchical coordinated control strategy based on inverter regulation for low- and medium-voltage distribution networks, and the process of issuing commands under different control strategies is simulated. By comparing and analyzing the voltage, frequency and power changes of each node before and after the execution of control commands, the specific influence mechanism of DPGU control on grid stability is revealed. The experimental results show that a reasonable control strategy can significantly improve the stability of grid node parameters and reduce fluctuations and mismatches. The results provide a theoretical basis for the optimization of the DPGU control strategy, which is of great significance for realizing the efficient and safe operation of the power grid.
The emergence of multimedia technology has brought unprecedented changes to the education industry, and it is of practical significance to effectively utilize multimedia technology in red culture education. After analyzing the application of multimedia technology fusion in red culture education, the study takes 240 students in a university as the research object and uses questionnaire survey to explore the application level of multimedia technology in red culture. Subsequently, the influence factors of red culture education integrating multimedia technology on the expansion of ideological education were refined, and the interrelationship between the multimedia integration practice of red culture education and the expansion of ideological education was investigated through the multiple linear regression analysis to study the regression effect among the variables.More than 70% of the students believed that the red culture classroom applying multimedia technology had better interactive effect and learning effect, but the teachers’ understanding of the However, teachers’ use of multimedia technology and proficiency still need to be improved. The red culture education integrating multimedia technology has a significant effect on the goal of moral education, timeliness of education and playing the role of technology in the expansion of ideological and political education (p < 0.001), and the effect of the goal of moral education is the most obvious. The multimedia technology integration practice of red culture education has a promoting effect on the expansion of ideological and political education.
This paper explores the extent and direction of the impact of digital technology-enabled rural cultural tourism development by constructing fixed-effects models, GMM models, spatial Durbin models, Moran indexes, mediation effects and other models. Using the hierarchical analysis method to construct measurement indexes from two aspects of scale and benefit, and then using the coupling coordination degree model to measure the level of digital rural tourism development, to summarize the level of development and coordination of cultural tourism industry in rural areas. The results show that the difference between the minimum value and the maximum value of the rural digitalization level is about 8, and the large gap also reflects to some extent the uneven development of digital rural tourism in different regions. Digital rural tourism has a significant impact on rural economic development and non-rural non-farm employment level, and its impact coefficient is 0.138 and 0.784 respectively. in the data measurement of 2017 and 2022, the more significant the degree of aggregation is, the faster the level of development of digital rural tourism becomes. the comprehensive evaluation index of digital rural tourism in the sample area in 2021 is 0.82, which is at a high level, and the sample region’s service quality satisfaction is also at a high level. Therefore, this paper analyzes from various aspects that the development of digital tourism can promote the sustainable development of rural economy and realize rural revitalization.
There is a close quantitative relationship between college management and college students’ employability. This paper adopts Adaboost integration algorithm to construct an employment management system that integrates graduates’ personalized recommendation. And it divides graduates according to their personal situation and analyzes the relationship between their personal ability and employment recommendation. In addition, the relationship between the management based on the system of this paper and the employment ability of graduates in colleges and universities is quantitatively analyzed by logistic regression model. A questionnaire survey is taken to assess the changes in graduates’ employability as a result of the employment management activities organized by colleges and universities. The recommendation system constructed in this paper has a higher accuracy rate of 6.92% and 16.32% than the comparison system 1 and 2 respectively when the number of job recommendations is 60. And its recall rate and F1 value are also consistently higher than the comparison system. In this paper, the system divides the sampled 200 graduates into 5 categories to provide more accurate employment recommendation for graduates of different categories. The results of regression analysis show that universities organize employment management activities can improve the employability of graduates. For example, for every unit of “Interview practice”, the employability of graduates increases by 0.349. The results of the questionnaire survey show that the employability of graduates, both individually and as a whole, improves to different degrees after participating in the management activities organized by colleges and universities. In conclusion, the construction of employment management system in universities and the organization of employment management activities can improve the quality and ability of graduates’ employment.
This paper firstly studies the current situation of rural e-commerce development in China, and then collects the gross output value of agriculture, forestry, animal husbandry and fishery, express delivery volume, rural delivery routes and so on through consulting the relevant official data of the National Bureau of Statistics, which provides an effective and reliable data basis for the construction of econometric model. Through the establishment of a fixed-effects model to analyze the empirical results, to explore the role of rural e-commerce platform development on the promotion of the economy. Finally, with the help of the spatial Durbin model to measure the spatial spillover effect, analyze whether the development of rural e-commerce can reduce the urban-rural income gap. The results show that the number of Taobao villages, kilometers of rural delivery routes, and 10,000 rural broadband access users are the explanatory variables, and the gross output value of agriculture, forestry, animal husbandry and fishery is the explanatory variable, and the coefficients are 0.0156, 0.0781, and 0.0442, with the p-value less than 0.01. Therefore, the better the development of rural ecommerce, the better the economic development is. And the increase in the level of economic development can significantly reduce the urban-rural income gap with an estimated parameter of – 0.022.
The integration of science and education is conducive to promoting the integrated development of education, science and technology, and talents, and is a key path for the high-quality development of vocational education and serving the strategy of a strong education nation. This paper explains the necessity of integrating science and technology with education, and realizes the path design of vocational education quality improvement based on the new concept of science and education integration. Then, the quality of science and education integration in vocational education is evaluated using hierarchical analysis and fuzzy comprehensive evaluation. Then, a comparison test is designed and independent sample t-test is applied to verify the practicality of the path in this paper. In the criterion layer of the established evaluation index system, the weight of industry-university-research integration is the largest, which is 23.91%, indicating that industry-university-research integration is particularly important in the path of vocational education quality improvement. In the indicator layer, the research team building has the largest weight, 10.17%, which needs to be emphasized in the implementation of the integration of science and education in vocational education. The overall rating of the quality of science and education integration in H higher vocational colleges implementing the path of this paper is 84.638, which is between good and very good, and is at a high level. And the two sided Sig value of the T-test of the evaluation score of the quality of science and education integration in the higher vocational colleges and universities using this paper’s pathway and those using the traditional education model is 0.000<0.05, which is a significant difference. It indicates the practicality of this paper's path for improving the quality of vocational education based on science and education integration. This paper provides a path paradigm for improving the quality of vocational education using science and education integration.
Ideological and political education has the teaching characteristics of keeping pace with the times. In this paper, the nonlinear support vector machine is used as the ideological and political text data classification algorithm, combined with the text mining technology to collect and screen the ideological and political education data, and the ideological and political text data is divided into various clustering centers of ideological and political education, which are reflected in the research themes of ideological and political education, the hot spots of curriculum ideological and political research, and the teaching methods of ideological and political teachers. This paper analyzes the acquisition of ideological and political education resources from the perspective of students, and explores the matching degree between the acquisition of ideological and political education resources and the individual needs of students. The research objects and research hotspots of ideological and political education are divided, and the optimization strategy of ideological and political education is proposed. In the classification of research topics, the frequency of “college students” was the highest, which was 12568, and the calorific value of the research content “ideological and political education” and the research object “college students” was 8654, indicating that ideological and political education mainly revolved around “college students”. The matching degree between ideological and political education resources and students’ individual needs was 69.37%. Combined with the results of nonlinear analysis, ideological and political education can improve the effectiveness of educational content, strengthen the coupling degree between research content and research object, and strengthen the teaching factor of teachers.
In the era of information technology in education, accurate analysis of individual characteristics becomes the key to personalized learning and tailored teaching, which is of positive significance to the exploration of teaching reform paths. This paper constructs a cognitive map of college English courses under the guidance of cognitive theory, and establishes a reform model of college English teaching in combination with the cognitive map, so as to realize students’ self-knowledge and cognitive construction in the teaching process. The idea of fuzzy set theory is used to quantitatively analyze the knowledge ability level of college students, and then the Logistic model and Bernoulli distribution function are used to calculate the students’ cognitive level of mastering each knowledge point and their scores of answering the questions in the college English course. The analysis of the effect of the teaching model after practice found that the students’ mastery and cognitive level of subjective and objective knowledge points in the college English course were significantly improved and higher than the ideal reference value. The correct rate of answering composition questions in subjective questions increased by 30.44% compared with that before the teaching mode was carried out. The informatization teaching mode proposed in this paper lays a foundation for the teaching reform of college English and provides an effective path for students to improve their knowledge mastery and cognitive level.
With the development of artificial intelligence technology, the learning mode of “artificial intelligence + education” has become the direction of the times. Through a questionnaire survey on students’ vocabulary learning strategies and taking students of a middle school as the research object, the study explores the level of strategy use in English vocabulary learning in terms of the frequency of strategy use and the differences in strategy use among students of different levels. On this basis, the way of English word sense processing with the assistance of artificial intelligence is summarized and the word association memory model is proposed. And two classes in a middle school are selected for teaching experiments to apply the word association memory model to English vocabulary learning and explore the effect of the model on students’ word memory. Overall the cognitive strategy (3.489) and resource strategy (3.477) of English vocabulary learning are used more frequently. The English vocabulary level model of the students in the experimental class increased after the teaching experiment, which was 8.05 points higher than that of the control class and still 5.118 points higher than that of the control class in the delayed test, reflecting the vocabulary learning effect and durability of the word association memory model. Students can improve their language cognitive learning skills in three aspects: metacognitive strategies, cognitive strategies, and communicative/influential strategies, which further promote the development of English proficiency.
With the rapid development of informatization technology, the security of network data is more and more emphasized. In this study, ECDSA digital signature algorithm and PBET consensus algorithm are adopted to construct a network data security model based on blockchain technology. The system in this paper consists of three functional modules: application interaction client, federation chain Fabric module and data storage module DHT, which are further logically divided into ϐive parts: initialization, identity registration, uploading data, querying data and permission revocation. The average CPU occupation of each component of the system ranges from 0.02% to 39.96%, which consumes low resources, and the maximum value of the time used by the system for data encryption and decryption and signature authentication is no more than 41ms, which is a relatively fast operation speed, and it can support the operation of the network data security system, and the designed system has relatively high security in resisting the attack of the authentication process, and it utilizes the decentralized characteristics of blockchain to resist the attacks of the distribution process, and it utilizes the blockchain to resist the attacks of the distribution process. Centrality to resist distributed denial of service (DDoS) attacks and replay attacks. This study provides lessons and references for the application of blockchain technology in network data security.
With the accelerating process of urbanization development, it is urgent to optimize the national land spatial planning to promote the coordinated development of urbanization. Based on the image recognition technology, this study uses the kernel density gradient algorithm to segment the image samples of the national spatial layout and the GWO-SVM classiϐication model to classify the land use types of the national spatial layout, and ϐinally combines the Markov-FLUS model to predict the future planning of the existing national spatial layout. The research analysis found that the segmentation and classiϐication accuracy of the kernel density gradient algorithm and the GWO-SVM classiϐication model for the homeland spatial layout samples both reached more than 90%. The classiϐication accuracy using the GWO-SVM classiϐication model is improved to a greater extent than that of SVM, GA-SVM, etc. The Markov-FLUS model also maintains an accuracy of more than 80% for the prediction of future territorial spatial planning. In terms of land use types, the Markov-FLUS model shows that the proportion of residential land and industrial land will decrease after 10 years compared with 5 years, while the proportion of public facilities land will increase by about 8% after 10 years compared with 5 years. The optimization of national spatial layout is of great signiϐicance to the development of urbanization in China, and the research in this paper will promote the development of national spatial layout planning in a more reasonable direction.
With the development of sharing economy, educational resource sharing has become the focus of experts and scholars to explore and practice. In this paper, from the perspective of resource sharing, a smart teaching management platform is successfully designed by combining artiϐicial intelligence technology. This research adopts YOLOv5s algorithm for face recognition and prediction in the design process, which is convenient for teaching management. Relying on the Hadoop cloud resource base, the teaching resource sharing database is designed, and the system computing logic is optimized by the distributed ϐile system HDFS. It is analyzed that the maximum number of interactions per second of the intelligent teaching management platform designed in this paper can reach 207, and the maximum interaction response time is about 68ms, and the load performance is completely better than that of the traditional teaching resource platform. At the same time, the intelligent teaching management platform can accommodate nearly 300 people to study online at the same time, which is far more than the previous mode of learning in the classroom. With the use of the intelligent platform, the development of “Internet + education” is greatly promoted.
Artiϐicial intelligence plays an increasingly important role in contemporary education, and it provides new possibilities for the innovation of physical education teaching mode. This paper constructs a college sports teaching integration model based on artiϐicial intelligence from ϐive aspects: educators, learners, teaching methods, educational resources and teaching feedback and evaluation. It focuses on designing a precise teaching model PLRSM based on personalized learning resource recommendation by combining learner portrait and learning resource portrait, and takes the recommendation of physical education teaching resources for physical education students as a case study to verify the effectiveness of the proposed algorithm. The results show that compared with the traditional baseline algorithm, the PLRSM algorithm still maintains a better recommendation performance when the data set co-occurrence matrix is extremely sparse, and its correct rate of physical education teaching resources recommendation is 0.80. In addition, compared with the traditional teaching model, the AIbased college physical education teaching fusion model can signiϐicantly improve the learners’ knowledge of physical education subject and course teaching, and its post-test score is higher than the pre-test score 11.525 to 15.436 points. The study provides theoretical support and practical guidance for the application of artiϐicial intelligence in physical education teaching, and provides a useful reference for promoting the innovation of physical education teaching mode.
The modernization and development of industrial chain supply chain in the era of digital economy is an important content to cultivate new quality productivity, maintain industrial competitiveness and realize industrial modernization. After the promotion effect of digital economy on the modernization and development of industrial chain supply chain, this paper takes China’s digital economy data from 2012 to 2022 as the research object, designs the evaluation index system of the development level of digital economy, and measures the development level of digital economy by using entropy value method and Kernel density estimation method. The overall situation of China’s digital economy development level is analyzed, and the dynamic evolution trend of digital economy development level is explored. Then, based on the threshold regression model, the benchmark regression and threshold effect analysis of the relevant inϐluencing factors of the digital economy-enabled industrial chain supply chain modernization and development are carried out. 2012-2022 China’s digital economy shows a steady upward trend, and its average annual growth rate reaches 1.8%, and the Kernel Density value decreases from 0.0474 in 2012 to 0.0425 in 2022, with the digital economy of each region level gap decreases. For every 1% increase in the level of digital economy development, the level of industry chain supply chain modernization and development is increased by 1.407%, and there are two threshold effects of economic double cycle and digital technology level for digital economy-enabled industry chain supply chain modernization and development. Enhancing the level of digital technology promotes the enhancement of the level of international and domestic economic double cycle, which in turn improves the level of modernization and development of industrial chain supply chain.
Under the background of the development of digital economy industry, more and more enterprises begin to make attempts of digital change. After constructing the financial performance index system of pharmaceutical enterprises, the study selects 30 pharmaceutical listed companies as the research samples, and evaluates their financial performance by using the principal component analysis method and the collected relevant data. On this basis, the study selects indicators of digitalization degree and puts forward research hypotheses, explores the influence of digitalization degree on the financial performance of pharmaceutical enterprises through correlation analysis, multiple regression analysis and time lag effect analysis, and then puts forward the path of digitalization development of pharmaceutical enterprises in combination with the results of the analysis. The results show that the financial performance of the sample pharmaceutical enterprises is at a medium level, with an average composite score of 0.520, among which pharmaceutical enterprises E10, E6 and E22 have the best performance, with scores above 0.9. The degree of digitization has a negative impact on the financial performance of enterprises at the 1% level, but the coefficient of digital capital investment turns from negative to positive after the lag two period, and there is a time-lag effect of digitization on the financial performance of pharmaceutical enterprises. It is recommended to promote the digitalization of pharmaceutical enterprises by encouraging the cultivation of digital talents, improving the law and cultivating thinking, and building a digital platform.
With the rapid development of technology and online social networking, the popularization of smartphones has promoted the research and development of sentiment analysis of contemporary literary texts. In this paper, the CBOW model based on Hierarchical Softmax algorithm is used to extract text sentiment features. The classification mechanism of sentiment lexicon, machine learning, and deep learning methods supported by sentiment features is discussed. According to the discussion results, a 5-layer sentiment analysis model based on CNN-BiLSTM-ATT is built based on text preprocessing, and the model design of different layering is proposed. Meanwhile, the analysis method of text themes is proposed based on LDA. In the long story dataset, the model recall rate of this paper is 83.91% and the precision rate is 83.86%, the values are higher than the other six models; the MacroF1 mean value is 83.16%, which proves that the fused and improved CNN-BiLSTM-ATT model of this paper possesses excellent performance in the sentiment analysis task. In the short story dataset, the accuracy, precision and recall are not less than 98%, and the loss rate is the lowest 34.11%, which are lower than the other six models. The model in this paper can be applied to text analysis systems and has superiority in parsing the sentiment of contemporary literature.
With the booming development of large-scale open online courses, blended teaching, which combines traditional closed teaching and online open teaching, is increasingly favored by colleges and universities. In this paper, from the perspective of blended teaching of English in colleges and universities, based on the LSTM model to predict the relevant learning data in English teaching in colleges and universities, and based on the density optimization K-mean algorithm to cluster the student subjects with different learning behaviors, and then use the Apriori algorithm to study the correlation rules of the learning effectiveness and behaviors, to provide ideas for English teaching in colleges and universities. The clustering results show that the average learning scores of the first, second and third categories of learners are 92.35, 83.57 and 64.96 respectively. The results of association rule analysis show that routinely, the more active learners are in each learning session, the greater the possibility of getting better learning outcomes. The LSTM learning prediction model Precision, Recall and F1 assessment indexes trained with 4-month behavioral data are 0.899, 0.785 and 0.833 respectively, which are all greater than the corresponding index values of SVM, MLP and RF models, and have a significant advantage in prediction effect. This study provides lessons and references for improving the effectiveness of English teaching in colleges and universities.
With the rapid development of science and technology, in the face of the needs of social development, colleges and universities undoubtedly need to shoulder the important task of talent training and education reform in innovation and entrepreneurship. In this paper, an intelligent learning model is constructed by using artificial intelligence technology. The model takes the subject knowledge graph as the core support, and combines the learning path recommendation algorithm to provide digital and intelligent support for innovation and entrepreneurship education. On this basis, the objectives of innovation and entrepreneurship education are formulated, and the framework of innovation and entrepreneurship education system is established based on the intelligent learning model in this paper, and the cycle model of innovation and entrepreneurship education based on the intelligent learning model is proposed, and the model is experimentally studied. The AUC values and F1 values of the proposed algorithm in the three datasets are higher than 0.85 and 0.80. Compared with the traditional model, the average value of recommendation bias decreased by 8.56, and the evaluation satisfaction increased by 0.126. In the teaching experiment, the overall average score of the innovation and entrepreneurship education model based on this paper was 4.364, which was 1.129 higher than before. Compared with the traditional innovation and entrepreneurship education, it is increased by 0.693, indicating that the innovation and entrepreneurship education model in this paper can promote the all-round development of students’ ability level and play a positive guiding role in the development and reform of innovation and entrepreneurship education.
In the field of artificial intelligence education, teaching emotion, as the main assessment basis for teaching evaluation, profoundly affects the teaching method, classroom atmosphere and teaching effect of teachers. This thesis proposes a combined network structure, CRNN, by taking advantage of CNN for speech emotion feature extraction and RNN for sequence modeling, and realizes emotion recognition of classroom discourse through DenseNet neural network to realize the crosstalk between each layer and other layers, and LSTM neural network to complete the task of speech emotion classification. On this basis, the open classroom video of the sixth grade of an elementary school is analyzed for sentiment, and the teaching practice of the application of speech emotion recognition model is carried out to study the optimization effect of the model application on the classroom atmosphere of the elementary school. The overall sentiment value of the classroom interaction video floats in the range of 0~1.9, showing a trend of first increasing and then decreasing, reflecting the feasibility of applying the speech emotion recognition model of this paper to classroom sentiment analysis. Through the teaching experiment, the positive emotional performance of the experimental group is more obvious than that of the control group, and 95.46% of the students agree that the application of the model can improve classroom interaction and the overall atmosphere. The speech emotion recognition model studied here can mobilize the classroom atmosphere, and has more important classroom guidance and application significance.
With the continuous development of the rail vehicle business, high-speed rail, locomotive, subway, light rail and other railroad transportation industry to reach the prosperity of the previous scene, the wheelset is an important support and walking parts of the rail train, so the detection of its geometric parameters and tread quality of the safe operation of the vehicle is of great significance. In this paper, based on the principle of binocular measurement vision, the mathematical model of bilinear structured light is used to calculate the three-dimensional coordinates of the spatial points of the wheel pairs of high-speed railways. The collected point cloud data are filtered and smoothed to eliminate the noise contained in the data. Integrate the two point data under the same coordinate system, perform data fusion on the overlapping part to complete the alignment of the point cloud. And extract its eigenvalues to realize the point cloud coordinate transformation. Through testing experiments, the accuracy of high-speed rail wheel pair data measurement and other indicators are studied and analyzed. The measurement accuracy of the journal diameter of the HSR wheelset has a deviation of about 0.003 mm compared with the CMM, meanwhile, the fluctuation range of the HSR wheelset diameter data in the left and right directions is within 0.04 mm and 0.03 mm, respectively, and the stability of the measurement data of the model is good. The point cloud rotation error is between -1.09° and 1.09°, and the first quadrant angle error is between -1.114° and 0.829°, and the model controls the error to be around 1°, and the verification of the pairing accuracy is passed, which can meet the requirements of the production and operation activities.
This paper discusses the application of the neural machine translation model based on language modeling technology in British Victorian literature and its linguistic adaptation. Firstly, the linguistic features of Victorian literary works are analyzed, including thematic content and social background. Then the neural machine translation model based on language modeling technology is designed, and the text style migration method based on style representation is proposed to reproduce the linguistic features of the literary works. The performance of the translation models under the three fusion style methods is compared with five baseline systems, and the BLEU value, style migration accuracy, and style migration fluency of the machine translation model using the text migration decoding module are 37.49, 0.978, and 3.59, respectively, which are all higher than those of other models. Taking the translation of Wuthering Heights as an example, there is not much difference between this model and the human translation in terms of language adaptation evaluation. It shows that the machine translation model designed based on language modeling technology in this paper has better language adaptability for translating Victorian literature.
In today’s deepening education reform, promoting the deep integration of technology and education has facilitated the process of informatization of school education. Vocational education shoulders the important responsibility of cultivating “high-quality laborers and technical talents”, and the reform of informatization of vocational education has gradually become the focus of attention. In this study, we construct a prediction model of learning achievement based on machine learning to optimize the vocational teaching curriculum system. In this paper, before constructing the prediction model, the basic information data and learning behavior data of students are firstly subjected to feature extraction and feature selection. Then CNN combined with BiLSTM and Attention is used to construct the student performance prediction model CNN-BiLSTM-Attention. Finally, based on the performance prediction model, this study proposes the optimization path of the vocational education curriculum system to solve the problem of student employment. The model in this paper achieved the best prediction results in the performance comparison with both the single model and the integrated model, and the indicators were 0.961, 0.953, 0.985, 0.966, and 0.957, respectively. Moreover, it was found that the model had better prediction results in the process of vocational education courses at 80% and above. Among the features, the importance of the relevant features about honor acquisition is higher, all of them are above 0.8, which is an important factor affecting students’ performance. In the actual application of grade prediction, only one student had only 61.6 points in the final semester’s grade prediction, which had the risk of not being able to successfully graduate and proceed to employment. The study shows that the prediction model based on machine learning in this paper has good performance and can provide a strong basis for the reform and optimization of the vocational education curriculum system and promote the informatization process of vocational education.
The application of modern information technology in track and field training has become an important means to improve the training effect. The study analyses the application of smart wearable devices in track and field training, takes the real-time feedback data of smart wearable devices as the index observation point, constructs the evaluation index system of track and field training based on smart wearable devices, and explores the application of factor analysis and fuzzy comprehensive evaluation method. On this basis, teaching experiments are carried out using smart wearable devices and the evaluation system to explore the effect of smart wearable devices on the enhancement of track and field training in athletic performance. The track and field training of the students in the sample colleges and universities was of medium level, with a total score of 73.71, in which the development of students’ will quality and teachers’ grasp of the training situation still need to be improved. After training with smart wearable devices and assessment system, the practicing students got 4.09%~5.01% improvement in standing long jump, 50m run and 800m run, and there was also a significant difference in training interest with the control students (P<0.05). The smart wearable device and evaluation system can achieve real-time data monitoring and training feedback, which can help coaches and students adjust training in time and improve the effect of track and field training.
Writing skills not only promote the learning of other English skills such as listening, speaking and reading, but also effectively promote the internalization of language knowledge, laying the foundation for further improving the development of students’ comprehensive language skills. In this paper, with reference to the application path of information technology in English literacy teaching, we design a SCN-LSTM-based language model, and on this basis, we adopt a bidirectional recurrent network as the language model, and propose an improved SCN-BiLSTM network, which can effectively obtain the contextual relationship of the input sequence. Through the linear interpolation of the language model, the cached language model adaptation is obtained, and the teaching scene corpus is utilized to train the model, and the teaching context-oriented language model adaptation is obtained. Construct ANFIS model to improve the evaluation of English literacy teaching. After the empirical research experiment, the average English reading score of the students in the experimental class after the experiment is 53.631, which is 11.942 points higher than that before the experiment. The writing score is 8.45, which is 0.97 points higher than before the experiment. The application of the adaptive model of English reading and writing based on SCN-LSTM network is very effective.
The era of big data in education has come, data-driven intelligent decision-making has become the development trend in the era of big data, and precise teaching has become the keyword in the era of big data. This paper establishes a real-time dynamic teaching strategy adjustment decision-making model based on the learning characteristics in the process of industry-teaching integration practical training in higher vocational education, and uses Markov decision-making and Q-learning algorithms to solve the optimal teaching strategy in each stage of practical training and learning, which assists the teachers in decision-making and precise intervention. The results of the practical training teaching experiment found that the students in the experimental group, after the dynamic adjustment and intervention strategy implementation of the industry-teaching integration practical teaching, the scores of the practical training theory and application knowledge test were significantly improved (P<0.05), and the students' self-efficacy control sense, sense of effort, and sense of competence were all improved to different degrees. In addition, the scores of depth of understanding (P=0.000) and strategic approach (P=0.000) in practical training learning competencies also increased significantly. The strategy proposed in this study is able to capture the dynamic characteristics of educational data and use the multi-stage dynamic decision-making method to study the development of teaching strategies, which can provide stronger support for accurate teaching decisions and industry-teaching integration of practical training learning.
Prediction of legal decisions using machine learning and artificial intelligence techniques has gradually become an important part of smart court technology. In addition the crime prediction and law recommendation also face the problem of easily confusing crimes. In order to solve these problems, this paper unites multi-task learning models and proposes a model fusion legal verdict prediction model. An attention neural network fusing Transformer Encoder and DPCNN encodes the key semantic information in the case description. The TF-IDF algorithm and TextRank algorithm are applied to extract the keywords of the charge, and the forward propagation network is used as a classifier to constitute a multi-task learning legal verdict prediction model. Using 9 CAIL2018 legal datasets as experimental data, the metrics performance of the multi-task learning legal judgment prediction model proposed in this paper is measured on three subtasks (offense prediction, legal provision prediction, and punishment duration prediction) in LJP. Combining real case information for legal verdict prediction as well as charge differentiation. The verdict prediction results on the CAILBig-Multi dataset show that the mean MP value of the comparison algorithms is 82.925% in the charge prediction. And the MP index of the charge prediction of the multitask learning legal verdict prediction model proposed in this paper is 89.13%, which is significantly higher than the mean value of the comparison algorithms. And the multitask learning model incorporating the keyword information of charges in case analysis can effectively solve the problem of confusing charges.
As the main link of international trade, logistics plays a pivotal role in the entire international trade transactions, and choosing the appropriate logistics path is conducive to cost savings for enterprises. This study combines the traditional logistics model with the actual situation of international trade to select the headway transportation, overseas warehouses and tail distribution as the main elements of enterprise logistics cost optimization in international trade. Based on the cost calculation of the main elements, we design the objective function and constraints of enterprise logistics cost optimization, build the optimization model, and obtain the optimal solution by iterative analysis using the fitness function and genetic operator in genetic algorithm. The empirical analysis shows that after applying the optimization model, the total logistics cost of enterprise D is reduced from US$99,373,500 to US$72,653,400, indicating that the model is effective in optimizing the logistics cost of enterprise D in international trade. This study provides an effective method for the optimization of cross-border enterprise logistics costs, which has a positive role in promoting the development of international trade.
Research on event extraction and constraint encoding of legal cases, using Lawformer as a pre-trained language model for legal sentence prediction model, constructing MJP-Law model to predict the sentence of legal cases. The HAN encoder in the model is utilized to extract the inter-sentence relations in the legal case and construct the relations among the law, the charge, and the sentence period. Compare the performance of this paper’s MJP-Law model with other prediction models on law, charge, and sentence period, and explore the effects of the three subtasks of law, charge, and sentence period on the model through ablation experiments, and compare the prediction effects of a single MJP model and the MJP-Law model on low-frequency charges. In this paper, the MJP-Law model outperforms other prediction models in terms of prediction performance on statute, offense, and sentence. The four models of “MJP-Law”, “MJP-Law_law”, “MJP-Law_SG” and “MJP” had the same prediction performance, which were 95.54%, 89.86%, 89.73% and 89.81%, respectively. “MJP-Law” and “MJP-Law_law”, “MJPLaw_SG” and “MJP” have the same performance in law prediction. After removing the sentencing guidelines and legal sentences, the macro F1 values of the MJP-Law model all showed a decrease.The predictive performance of the MJP-Law model on low-frequency offenses was better than that of the single MJP model.
This paper defines doctor-patient interaction from the perspectives of interaction form and maintenance of patients’ health respectively, and also constructs a doctor-patient interaction discourse model. Based on the data mining technology to obtain the research data, the acquired data are preprocessed and stored in the form of dataset. Bi-LSTM is used to extract topic sentence features from the dataset, and the unsupervised pattern is transformed into a self-supervised pattern through the training and learning of auxiliary tasks to complete the construction of the discourse model of doctor-patient interaction based on topic structure. Combined with the processing flow of natural language processing and semantic technology, the communication strategy generation system for doctor-patient interaction discourse is designed, and finally the communication strategy based on natural language technology is researched and analyzed. There are significant differences between the experimental group and the control group in terms of expression ability and cognitive level (P<0.05), which concludes that compared with the traditional discourse model, the doctor-patient interactive discourse model has a higher priority, and it can effectively improve the expression ability and cognitive level of the patients' medical terminology. On the CMedQA2.0 dataset, the average performance of this paper's model is improved by 46.34% compared with the baseline model GPT-2, indicating that this paper's model has excellent performance. Under the condition of Chinese participle and topic extraction fusion, the average accuracy of this paper's system is as high as 85.02%, which indicates that the system can provide doctors with precise communication strategies based on patients' medical-related information, thereby effectively enhancing the discourse communication skills in doctor-patient interactions.
In response to cybersecurity threats such as security breaches, data leakage, supply chain attacks, and ransomware viruses in digital network environments, more reliable cybersecurity architectures are needed to address these challenges. The article builds a zero-trust firewall applied to network security protection based on zero-trust architecture by integrating SPA single-packet authorisation technology and authentication scheme. Then SPA single packet authorisation technology with SM3 hash algorithm and SM4 algorithm for fully nominal encryption processing is constructed as a network security protection scheme, and the authentication protocol and trust evaluation algorithm are established by using hash and different-or function. In the simulation verification results, the communication volume of SDP client to complete one authentication is 981B, which reduces 27.17% compared to WaverleySDP overhead. The server in the SDP+SPA scenario still retains a certain amount of legitimate data after DDOS attacks and Web attacks, and receives only 53.47% of the traffic of the SDP scenario. The CPU usage of the client deployed with SPA is only 11.47 percentage points higher than that without SPA mechanism. The combination of SPA single-packet knocking technology and zero-trust architecture can achieve network security protection, and can also effectively deal with DDoS and Web attacks, and improve the performance of network security protection.
Consumer data is an important support for analysing and observing consumer behaviours in the era of digital marketing, and constructing models to predict consumer purchasing behaviours. In this paper, we select the Retailrocket consumer behaviour dataset based on real shopping websites, analyse the distribution of various types of consumer behaviour over time and other data characteristics, and gain insights into the behavioural habits of consumers when shopping. Based on the XGBoost algorithm in machine learning, a prediction model of consumer behaviour is constructed, and the genetic algorithm is used to optimize and improve the XGBoost algorithm.The XGBoost prediction model has a significantly better prediction performance than the LSTM prediction model and the LR prediction model when facing the data under the under-sampling data balancing method and the improved random under-sampling method based on the K-means algorithm. . The performance of the GA-XGBoost prediction model optimised by the genetic algorithm is significantly improved compared to the XGBoost prediction model, and substantially better than the LSTM prediction model and the LR prediction model. The accuracy and F1 value of the GA-XGBoost prediction model in the data under the improved stochastic undersampling method are 0.90865 and 0.92435, respectively, which are improved by 14.69% and 17.26% relative to the XGBoost prediction model. Meanwhile, the stability of GA-XGBoost prediction model is also significantly improved compared to XGBoost prediction model.
The digital era requires enterprises to pay attention to technological innovation and optimise ESG performance in the development process, so as to achieve high-quality development. Based on this, this paper proposes the hypotheses related to enterprise ESG, technological innovation and enterprise high-quality development. And construct the regression model of enterprise ESG performance and high-quality development. Basic statistics and correlation analysis are used to provide a preliminary description of enterprise ESG performance and high-quality development. Through the total effect test, the role of enterprise ESG performance on high-quality development is clarified. Through the mediation effect test, the role played by technological innovation between corporate ESG and highquality development is clarified, and the proposed hypotheses are verified, and the property rights, geographic and industry differences in the impact of corporate ESG performance on high-quality development are further explored by using robustness test and heterogeneity analysis. Finally, corresponding recommendations are made. Most of the enterprises selected in this paper have low levels of high-quality development, unsatisfactory ESG performance, and large overall gaps in technological innovation.The correlation coefficients of ESG performance (ESG) with corporate highquality development (LnTFP) and technological innovation are 0.402 and 0.335, respectively, and all of them are significantly and positively correlated at the 1 per cent level. Hypotheses H1, H2, and H3 are all valid.ESG performance and technological innovation have more significant effects on the highquality development of state-owned enterprises, eastern regions, and high-pollution enterprises.
Measurement and verification play a crucial role in flexible production, and with the development of technology, advanced measurement systems in flexible production systems gradually integrate fault diagnosis and prediction techniques to improve production efficiency. In this paper, a deep confidence neural network model, combined with the ISSA-VMD feature fusion model, is used to model fault diagnosis and prediction in flexible production of power systems. The training effect, prediction performance, feature extraction and fault diagnosis of this paper’s model in flexible production are evaluated and analysed through simulation experiments. The Loss value of this paper’s model converges to about 0.05 after 15 rounds of training, and has a good fitting effect on the training and test sets. The RMSE, MAE and R² of the model in this paper are 0.613, 0.371 and 0.988, respectively, which show good prediction performance. And the prediction results in the measurement system of power generation in flexible production are also more close to the real results. In addition, the DBN model incorporating ISSA-VMD feature fusion can completely separate the five fault signals, and the overall fault identification accuracy reaches 98.53% for the fault test set selected in this paper, which has strong diagnostic effect. This study provides more scientific and effective technical support for metrological verification in flexible production.
With the rise of major e-commerce, how to make more customer groups choose to buy items in their own websites is the goal that major e-commerce platforms have been relying on. Therefore, a set of personalised recommendation system that can intelligently explore customers’ needs comes into being. In this paper, a graph neural network model is used to sort out the multi-path fusion neighbourhood relationship among three objects: user, product and query. The utility matrix is established and the collaborative filtering algorithm is used to derive the user’s preference situation for commodities. Subtractive clustering is combined with fuzzy C-means to obtain the clustering centre of gravity and cluster e-commerce users. Graph neural network is introduced to ensure that the data sparsity of the user dataset is within a reasonable range. The practical application effect of the model is evaluated through simulation experiments and empirical analysis, respectively. In this paper, according to the age of the users, the users are clustered and analysed, and three clustering centres of gravity are obtained, which are (3.16, 32.73), (45.35, 40.25), and (14.03, 52.89), so the users are classified into three clusters, and the analysis of simulation experiments is carried out. The training effect of this paper’s model is fitted, and the adjusted R² = 0.8292, which shows that the accuracy of personalised recommendation is high. Meanwhile, comparing with other algorithms, this paper’s method reaches a recommendation satisfaction level of 100% when the number of learning times is 60, which is significantly better than other algorithms.
Supply chain finance innovation has a significant impact on regional economy. In this paper, blockchain technology is applied to supply chain finance business to improve the technology and security of traditional supply chain finance business. Drawing on relevant research results, we construct a blockchain-based supply chain financial innovation efficiency evaluation index system and measure the supply chain financial innovation efficiency using Malmquist index. A spatial econometric model is used to test the spillover effect and spatial synergy between supply chain financial innovation and regional economic growth, and to demonstrate the promotional effect of blockchain-based supply chain financial innovation on regional economic growth.The centres of the distribution curves of the kernel density function of the logarithmic value of GDP and supply chain financial innovation of the 30 provinces and regions are all shifted to the right, and the height of the main peak rises gradually.The 2013-2023 regional Moran’s index of economic growth and supply chain financial innovation are both significantly positive. The regression coefficients of supply chain financial innovation under the two spatial weights are significant at the 1% level, which provides strong data support for the view that supply chain financial innovation can promote regional economic growth in this paper.
The load of power supply has been increasing in recent years, and the scale of the power grid has been expanding. The impact of electromagnetic radiation on the lives of residents is also increasingly visible, and the electromagnetic environment around high-voltage AC transmission equipment has attracted great attention. Based on the principle of electromagnetic induction and Gauss theorem, this paper proposes the calculation method of electromagnetic radiation to evaluate the distribution law of spatial electromagnetic field around high-voltage AC transmission lines. Then the risk analysis of the electromagnetic environment around the high-voltage AC transmission line is carried out from the height from the ground and the presence of woods according to the measured data. Finally, according to the electromagnetic law of high-voltage transmission lines, the safety control technology to reduce the environmental impact of electromagnetic fields is proposed, mainly by raising the vertical height of the arc of the transmission line from the ground and reasonably designing the distribution of forest planting in the vicinity of the transmission line. When the vertical height of the conductor’s arc height from the ground was increased from 10m to 40m, the electric field strength and magnetic induction strength were reduced by 2.9kV/m and 2.35µT correspondingly, and at the same time, the electric field strength in the vicinity of the building was reduced by 71% at the most. The study proposes measures to effectively mitigate the electromagnetic impact by reasonably analysing the electromagnetic environment in the area where the UHV transmission line is located.
Aiming at the demand for scientific training of athletes in college sports education, this paper integrates data mining technology to propose athlete training and optimisation methods, and constructs an athlete training quality monitoring system and intelligent recovery assessment system. The traditional Apriori algorithm is improved by using multidimensional association rules, and multidimensional attribute mining is carried out on the collected data of athletes’ training data to search for frequent item sets and output strong association rules, so as to achieve the monitoring of training quality and adjustment of training programmes. Using the improved fuzzy decision-making method to filter out the optimal feature subset, and integrating the improved whale algorithm and random forest to achieve intelligent recovery effect evaluation. By carrying out the practice of training and recovery optimisation, it can be seen that the total score of physical fitness test of track and field athletes increased from 18.19 to 19.8 before the experiment, and the training quality was significantly improved. Various health indicators such as heart rate, blood lactate, serum creatine kinase, etc. gained significant improvement in adopting the recovery optimisation method of athletes in this paper. The mean values of training status, coaching factors, and personal situation satisfaction evaluation dimensions were 4.35, 4.425, and 4.38, respectively, and the training and recovery plan of this experiment was well received by the subject athletes.
Through the examination and calculation of each link of the dairy industry chain, we analyze the benefit distribution pattern of the dairy industry chain and highlight the necessity of optimizing the benefit distribution strategy of the dairy industry chain. The Shapley value method of the equilibrium of interests in game theory is chosen to study the benefit distribution strategy of each subject in the dairy industry chain under the cooperative game, and the model is revised by using the input factor, the risk factor and the correction factor, so as to further improve the rationality of the benefit distribution strategy. The research data were obtained by visiting the dairy industry chain in Xilingol League through field investigation, and the modified Shapley values of the herdsmen, middlemen, milk processors and retailers were finally obtained as 3976.43 yuan, 3839.31 yuan, 4175.53 yuan, and 3977.47 yuan after the modeling calculation, respectively. The comprehensive cost profit margin of each subject after correction is 2.17%, 1.82%, 7.43%, 7.68%, respectively, and herdsmen and milk processors are compensated in the benefit distribution strategy of this paper, and the amount of benefit distribution and the comprehensive profit margin of all the subjects in the dairy industry chain have been improved compared with that before the cooperation.
In this study, we construct an unmanned vehicle path optimization model based on fast extended random tree, and after kinematic modeling of unmanned vehicles, we introduce the artificial potential field method to improve the fast extended random tree algorithm, and apply it to the path optimization of unmanned vehicles. According to the swarm intelligence perception decision-making algorithm, the end-to-end unmanned vehicle decision-making model based on vehicle-circuit collaboration is constructed. The effectiveness of this paper’s driverless path optimization and decision-making model based on vehicle-circuit collaboration is examined. The waiting time for red light of this paper’s model is shorter than other path planning schemes, and the vehicle passing benefit at intersections is the highest. The passing benefit values of this paper’s model are 70.3% and 46.8% higher than Maxband scheme and Synchro scheme, respectively. In the right-turn simulation experiments, the main vehicle speed change shows a tendency to accelerate and the path is basically overlapped with the edge of the lane without offsetting the center of the lane. In the normal driving speeds of [14,38], the fuel consumption of the driverless vehicle shows an up and down trend, and the carbon dioxide emission varies with the fuel consumption. The total cost of traveling decreases with increasing speed.
High-fidelity modeling of complex surfaces is the basis for accurate characterization of surface quality and realistic analysis of performance in the fields of digital process design of products and digital twin. This paper proposes to improve the new polynomial interpolation algorithm to improve the effect of the polynomial interpolation algorithm fitting in complex surface modeling through the center variable, and combines the moving least squares approximation function with the new polynomial interpolation algorithm to further optimize the effect of the complex surface modeling through the regular moving construction of the fitting surface by the local approximation method. It is found that the overall average error and standard deviation between the turbine blade surface roughness modeled based on the new polynomial interpolation algorithm and the roughness meter measurements are within 1.7 μm (0.7580-1.6715 μm), and the error is within the acceptable range. It is also found that using the method of this paper can save a lot of time and realize the rapid modeling of complex surfaces of the body. It also has good smoothness, which provides convenience for the subsequent processing of complex surface modeling. The new polynomial interpolation algorithm proposed in this paper provides a new idea for the research in the field of complex surface modeling, and can be applied to the actual production to assist the design and production of related products.
Container and cargo matching is a key issue to realize the construction of container and cargo supply and demand matching platform, through the intelligent matching of cargo and container information, improve the efficiency of container and cargo matching, which is conducive to the integration of resources, and improve the platform professional services. In this paper, we analyze the process of container cargo matching and transportation distribution center operation, put forward the two-stage container cargo model assumption in accordance with the basic principle of distribution optimization, and complete the establishment of container cargo matching model under the demand of cargo owners. Optimize the container and cargo matching and vehicle path model respectively, derive the optimized combination mathematical model, and solve the combination optimization model through genetic algorithm. Simulation experiments are designed to analyze the effectiveness of the model. The results of the analyses of the algorithms show that when the crossover probability is increased from 0.6 to 0.8, the average value of the RV value decreases from 1078.76 to 915.76, and the recommended value of the crossover probability is obtained as 0.8. After optimization, the average vehicle load and average loading volume of the recommended scheme of the combined model reach 98.436% and 87.963%, respectively, with a total mileage of 23.456km for distribution, and the total cost of distribution in the region is 1246.489 yuan, which achieves the optimal container-cargo matching and path scheduling scheme.
In this paper, the weights of different risks in the management process of e-commerce platforms are calculated on the basis of hierarchical analysis. After that, with the help of fuzzy comprehensive assessment algorithm, the risk level is divided. Finally, with the assistance of decision tree, simulation is carried out to simulate the risk of the first-level indicators affecting the risk control of e-commerce platform. According to the survey results, reasonable countermeasures are given to the management of e-commerce platform risks. Among the first-level indicators of the five major risk categories, the business model risk belongs to the high-risk category of Class I, with a fuzzy comprehensive evaluation score of >4.5. The rest belong to the risk category of Class II, with fuzzy comprehensive evaluation scores ranging from 3.5 to 4.5. Among the Level II indicators, there are 6, 6 and 3 Level II indicators rated as high risk category, medium risk and low risk respectively, with their fuzzy composite scores ranging from 4.7495-5.6370, 3.6807-4.4988 and 3.1356-3.2435 respectively Between. In the comprehensive risk simulation prediction of the case-based e-commerce platform, only the logistics model risk belongs to the medium risk control strategy with a risk value of 4.8614 (day 60). The simulation results for the remaining four risk types were all low risk, and their risk values decreased (3.5 points) when the simulation time was day 60. The experimental results provide a prediction for the change of risk and provide reasonable countermeasures and suggestions for the risk control of ecommerce platforms.
Anaerobic biological treatment of wastewater is an important technology in environmental engineering and energy engineering, and it is one of the methods for powerful treatment of highly concentrated organic wastewater. The study was conducted to design an optimal control strategy based on the anaerobic digestion model ADM1. Taking the maximisation of total gas production as the control objective, the Composite Intelligent Optimised Extreme Value Control Algorithm (CIOEC) was designed by combining the extreme value search control method with the model-free optimisation algorithm. The effectiveness of the proposed algorithm is verified by a combination of simulation tests and empirical analyses, and the CIOEC algorithm can maintain fast convergence and relative stability under both stable and changing input materials, and obtain the highest real-time gas production. Among them, the average daily gas production of the ADM1 system with the addition of the CIOEC algorithm can reach 873.9 mL, which is an increase of 124.3% compared with the original system. It shows that the algorithm proposed in this paper can enhance the total gas production and optimise the treatment effect in performing anaerobic digestion of high concentration organic wastewater.
Due to the continuous increase of housing prices in recent years, many special groups of low and middle income do not have enough financial ability to pay for the high housing prices, and the problem of living environment is becoming more and more prominent. Based on the utility function in economic theory, this paper constructs a utility function model under the constraints of household budget income and price, and determines the income line of housing security households. The distributional efficiency of the implementation of the guaranteed housing policy is estimated through both in-kind rent allocation and rent subsidy. Based on the empirical distribution characteristics and public opinion surveys, a rational distribution model for the current stage of sheltered housing is proposed. Taking Singapore’s guaranteed housing policy as a case study, combining empirical evidence and simulation experiments, the effect of improving the living environment of special needs groups under the framework of social security is explored. The results show that: using a 10% allocation ratio of subsidised housing (5% each for affordable housing and public rental housing), the vacancy rate of public rental housing shows an oscillating state in the period of 7~16. In the period from 16 to 20, it shows a gradual increase. Therefore, this guaranteed housing policy should be gradually adjusted or cancelled around period 16.
In order to improve the efficiency of agricultural irrigation industry and ensure the economy and environmental protection in the production process. The study proposes an agricultural water-saving irrigation path optimisation method based on the NGSA-III algorithm, and establishes a multiobjective water-saving optimal allocation model for the agricultural water source irrigation system. The NGSA-III algorithm is used to obtain the optimal solution of the model and achieve the path optimisation of agricultural water-saving irrigation resources. The results show that the running time of the article method to get the optimal path result is 0.31s, which can improve the economic and environmental benefits of the agricultural irrigation industry, the model in this paper can achieve the effect of smaller environmental objectives when the economic objectives are larger, and three solutions are selected to trade-off the analysis of economic and environmental objectives. Among the three different optimal solutions, the decision maker can choose the decision scheme according to the actual situation, which provides reference for agricultural water saving path planning.
With the development of big data, cloud computing and 5G digital technology, smart finance has emerged. The use of modern information technology to create a smart financial management system to transform and upgrade the original financial management system of the hospital has become an indispensable part of the effective operation and management of public hospitals. The article focuses on the current problems in the development of smart finance in public hospitals, plans the smart finance space from the front, middle and back office, and proposes a financial resource allocation mechanism from the perspective of smart finance. In the performance evaluation analysis of smart financial construction, the weights of the professional level of accounting personnel, financial accounting, comprehensive budget management situation, medical revenue management, outpatient satisfaction, and the standardisation of data sets are 0.1067, 0.0857, 0.0670, 0.0630, 0.0512, and 0.0476 in that order. The weights of cultivating human resources, consolidating the hospital’s financial foundation work, strengthening comprehensive budget management, promoting data standardisation and enhancing patient satisfaction are important ways to promote the development of smart financial construction in hospitals. The purpose of this paper is to provide reference and reference for the financial revenue management of public hospitals, to help hospitals optimise the management process, to improve the quality of service and to ensure financial security.
Underground cable tunnels are important infrastructures to maintain the normal operation of cities, and problems such as cable insulation aging and discharge can easily cause fires or even explosions, so the requirements for maintenance are high. In this study, the DGPS positioning method is used to optimise the positioning system of the intelligent inspection robot for underground cable tunnels, and the LQR controller is used to realise the deviation correction of angle and position in the motion path of the intelligent inspection robot. Then the inspection robot and UHF sensor are used to detect and accurately locate the defects in the cable tunnel, and finally the deviation correction and defect detection methods are integrated to design an intelligent management system for underground cable tunnels. The results of simulation experiments and field surveys show that the proposed method can correct the deviation of the robot in the inspection process in a timely manner, avoiding the problems of hitting the obstacles and the path around the long distance, and the average time consumed in the simulation map scenario is only 6.89 s. The communication scheme of the intelligent management system is practicable, and it can effectively detect and identify the defects and the specific location of the defects in the underground cable tunnels. The system proposed in this paper is able to detect defects and faults in time in practical applications, providing a new solution for the inspection of underground cable tunnels.
In this paper, the development of blast and shock engineering technology problems using linear algebra’s measure analysis is used to make expected judgements through the performance of the data. The problem can be simplified and the frequency stability of the communication transmission system can be optimised by using the data as a benchmark through linear transformations, eigenvectors, matrices and other arithmetic methods. Regularisation and quantisation process the image to improve the science and accuracy of large-scale image restoration algorithm operation. It has been shown that the optimised prediction formula is very consistent with the experimental results in blasting experiments with a building as the object of study. The frequency drift of the optimised laser is reduced from 850 MHz to 160 MHz. the acquired noise intensity is optimal at different communication transmission moments, and the highest noise intensity acquired at frequency is 0.097 dB. The stability is optimal at different times of communication signal switching. The regularisation optimised ship navigation images have the largest values of structural similarity and information entropy metrics.
With the deep development of digital transformation, the field of environmental art design is experiencing unprecedented changes. In this study, under the 3D scene reconstruction algorithm, the feature points of environmental art design images are collected and extracted using the camera selfcalibration algorithm, and the shape and topology of the point cloud dataset interpolated surfaces are explored using the triangular meshing algorithm. The rotation matrix is obtained by optimising the internal and external parameters of the camera using the essential matrix, basis matrix and Kruppa’s equation to clarify its effect on the efficiency of digital feature extraction of images in the process of environmental art design. The results show that the mesh surfaces constructed by the algorithm proposed in this paper make better use of the point cloud data when the number of cloud points input for environmental art design is the same. The rotation matrix algorithm used in this paper can increase the correct matching point pairs of the data, reduce the false matching point pairs, reduce the false matching rate, reduce the matching time, and eliminate more false matching points. And the triangular grid formed by this method is more uniform, and the quality of the grid is improved. In addition, the average satisfaction ratings of the subjects on the nine secondary test indicators are 4.45, 4.95, 4.75, 4.18, 4.70, 4.60, 4.44, 4.50 and 4.40, respectively. It can be seen that the effect of the application of the digital transformation of the 3D model proposed in this paper has been affirmed.
Shallow loess landslides, as one of the widely distributed and high-frequency geologic hazards, have brought great economic losses and ecological damage to human society. In this study, Qinzhou District, Tianshui City, Gansu Province, is taken as the study area, and the Scoops3D model is used to predict the occurrence of loess landslides in the area based on the DEM data of the area. Bishop’s simplified method and box search method were used to calculate and analyze the landslide stability in the study area. The landslide prediction results of the Scoops3D model of this paper are compared and analyzed under different DEM data resolutions. Subsequently, local environmental data are collected to study the correlation between environmental impact factors and shallow loess landslides. Finally, the prediction accuracy of the shallow loess landslide prediction model based on Scoops3D in this paper is tested by comparing the difference between the prediction results of the Scoops3D model of this paper and other prediction models with the actual results. The resolution of the DEM data has an important influence on the prediction results of the Scoops3D model, and the accuracy of the high-resolution DEM prediction results is higher than that of the low-resolution prediction results. There is a significant correlation between landslide displacement and humidity and cumulative precipitation, and the difference between the predicted and measured values of the GA-BP and GA-Elman models is within 8 mm, and the difference gradually increases. The difference between the predicted and measured values of the Scoops3D model in this paper is between 0.00 and 2.30 mm, and the prediction effect is optimal.
The inheritance and protection of urban cultural heritage faces the dilemma of narrow coverage and lack of change in form, and to solve this dilemma, we need to find a breakthrough in cultural creation and animation design, and carry out creative activities and popularisation among all people. The article proposes a feature extraction model that integrates multi-scale features and housing element information mining, and applies it to the feature extraction of housing elements in urban cultural heritage. A hybrid attention module is embedded in the ResNet-18 backbone network to enhance housing element features and suppress redundant information, and a CEB module and learnable parameters are combined to filter out the background information of the low-level features, so as to obtain finer architectural housing element features. The extracted housing elements are used as the basis for the design of creative products and animation scenes, and the feasibility of the programme is investigated through questionnaires. The overall evaluation mean value of the research respondents on the design of cultural and creative products for the housing was 7.64 points, and more than 95% of the evaluation respondents indicated that the housing elements were more suitable for the animation scene design. Relying on modern technology to extract housing elements from urban cultural heritage and realising the innovative application of cultural heritage in the form of cultural creation and animation provides a new path for the revitalisation and inheritance of urban cultural heritage.
According to the connotation of traditional and modern design elements in rural landscape beautification, multi-dimensional data cube mining method is adopted to construct the research data set of this paper. According to the ratio of 2:8, the data set is divided into test set and training set. The data of traditional and modern design elements are used as inputs, substituted into the decision tree model for training and classification, and the CART algorithm is used to construct a decision tree model for traditional and modern design elements in rural landscape beautification. Combining the dataset and the model in this paper, the simulation analysis of traditional and modern design elements in rural landscape beautification is carried out. The data show that based on the Gini index calculation formula of CART algorithm, it is concluded that the Gini index of X9 (0.9581) is the largest, so X9 is chosen as the root node for decision making, and the decision tree is derived downward until the leaf node, and the decision tree oriented to the countryside landscaping is obtained, and the rural landscape beautification scheme is induced based on the results of the analysis and the effect of the rural landscaping is found to have the difference between the before and after mean values of 3.36 ( 20.11-16.75=3.36), while there is a significant difference between the two, similarly, there is also a significant difference in the building living comfort above. This study enhances the effect of rural landscape beautification, which is of great significance in promoting rural revitalisation and architectural design development.
In today’s increasingly stringent sewage discharge standards, the construction of a new generation of wastewater treatment plants more and more urgent. This paper adopts MBBR as the main process to treat wastewater, the pretreatment process of wastewater treatment plant adopts coarse and fine grating + cyclone sand sedimentation tank, and the secondary treatment process selects AAO process. Through the reasonable calculation of water volume and hydraulics, and then calculate the size of each structure. Based on the ASM2 model, combined with the conversion rate equation of the AOO reaction tank, the kinetic model of the wastewater treatment system was constructed. Analyzing the inlet and outlet water quality monitoring data of the high-efficiency wastewater treatment plant for one year of operation, it was found that the average values of inlet and outlet water COD concentration in one year of operation were 255.437 and 10.556 mg/L, respectively, and the annual average removal rate was 94.37%. The average values of ammonia nitrogen in and out of the water for the whole year were 32.085 and 1.107mg/L, and the average ammonia nitrogen removal rate was 96.98%. All the effluent indicators have reached the “urban sewage treatment plant pollutant discharge standards” level A discharge standards and environmental protection departments on the effluent indicators, indicating that the overall operational efficiency of the research-designed high-efficiency wastewater treatment plant is good, and has reached the expected goals, with significant environmental and social benefits.
Optimizing regional economic resources is a crucial aspect of the Belt and Road initiative. This paper develops a multi-objective optimization model to objectively evaluate the development level of regional economic resource optimization in Belt and Road countries and to identify the key influencing factors. The model maximizes regional economic and social benefits under constraints of resource availability, output capacity, and coordinated regional development, and it incorporates a synergy measure to ensure robust progress. Our findings show that the regional economic benefits index increased from 0.264 in 2017 to 0.575 in 2023 (a growth rate of 117.8%), while social benefits grew by 14.29%. Additionally, panel regression analysis reveals that merchandise trade, foreign direct investment, road traffic mortality, and industrial development all have significant negative impacts on the optimization of economic resources, at the 1% significance level.
Alzheimer’s disease (AD) is a progressive neurodegenerative condition that affects the elderly population. The early detection and diagnosis of AD is critical for achieving effective treatment, as it can greatly improve the patient experience. AD can be viewed through imaging techniques like MRI, PET, and SPECT, providing valuable information about structural and functional changes. These findings are important in understanding this area. However, each imaging modality offers a different perspective. This information can be better collected from several of the other modalities as well as from some others to improve accuracy and reliability in AD detection. By combining information from different imaging modalities, such as MRI, PET, DTI, and fMRI, automated multimodal medical image frameworks aim to create a fused representation that preserves the relevant features from each modality. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), among other deep learning techniques, have been prevalent in these frameworks for learning discriminative and informative features from multi-modal data. In this paper, The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is used for experimental analysis. The proposed work gives 98.94% of accuracy and 1.06% of error which is greater than the existing approaches.
The power of the public key cryptosystem based on Paley graphs is due to several mathematical problems namely quadratic residuosity, local equivalence, and identification of the graphs induced by a sequence of local complementations of the Paley graphs. The classification in terms of degree of these induced graphs can be useful in the cryptanalysis part of the proposed public-key cryptosystem based on these algebraic graphs. This work aims to give the exact value of the minimum and maximum degree by local complementation, then the possible classifications in terms of degree to the graphs induced by a sequence of local complementations of Paley graphs of degree p less than or equal to 13 and some information about the equivalence problem.
Given a graph \(G \), a set is \(\Delta \) convex if there is no vertex \(u\in V(G)\setminus S \) that forms a triangle with two vertices of \(S \). The \(\Delta \)-convex hull of \(S \) is the minimum \(\Delta \)-convex set containing \(S \). This article is an attempt to discuss the Carath\’eodory number and exchange number on various graph families and standard graph products namely Cartesian, strong and lexicographic products of graphs.
Directed strongly regular graphs were introduced by Duval in 1998 as one of the possible generalization of classical strongly regular graphs to the directed case. Duval also provided several construction methods for directed strongly regular graphs. In this paper, an infinite family of directed strongly regular graphs is constructed, as generalized Cayley graphs.
With the need of international dissemination of Chinese culture, the problem of translating traditional Chinese texts gradually emerges. The study embeds a computer semantic model into the English translation of The Analects of Confucius, and constructs a natural language understanding model based on S-LSTM network through semantic representation of natural language processing. In order to explore the performance of the S-LSTM model, it is compared with RNN, LSTM, I-LSTM and other models in terms of training time and accuracy, so as to validate the superiority of the S-LSTM model in this paper. This paper deeply explores the philosophical connotation of the character “body” in The Analects, and studies the structural complexity of the translation of the character “body” through the S-LSTM model. Finally, the English translation strategy of The Analects and other classics is proposed. Among all the comparison models, the S-LSTM model has the fastest training speed and the highest accuracy. The translation of the word “body” in The Analects and the local complexity of the ministry are characterized by complication. The local complexity of the noun and the subject in the source English language, and the overall complexity of the “be-passive” structure have obvious effects on the structure of the translated Chinese character “body”.
Along with the development of the times, online classroom teaching activities have been carried out in different degrees and frequencies in various schools, and the gradual advancement of education informatization has improved the software and hardware environment of online classroom and other forms of teaching. The study designed a 21-item questionnaire related to English online classroom learning and selected all the students who participated in English online classroom teaching in a school for the survey. After collecting the questionnaire data, factor analysis and multiple stepwise regression model were used to conduct multivariate statistical analysis on the English online classroom data. And on this basis, the teaching plan was adjusted according to the actual learning behaviors of the high, medium and low risk level students themselves respectively to achieve personalized teaching. The results show that students’ satisfaction with the English online classroom is high, and that pre-course homework analysis, group learning, formative learning evaluation, students’ independent learning ability and online learning resources are the key positive factors affecting the learning effect of the English online classroom, with the influence coefficients of 0.036, 0.055, 0.048, 0.044, and 0.062, respectively. At the same time, after the optimization of teaching strategies, the students’ logged-in learning behavior, participation rate in interactive test questions and grades were significantly improved, proving the effectiveness of the strategy.
In today’s era, the transformative power of computing is highlighted, and computational thinking has become the core literacy and essential ability of learners, while computer education is an effective carrier for cultivating computational thinking. The article firstly researches the theory related to collaborative filtering and generative adversarial recommender system. Then it combines SeqGAN with traditional CF algorithms, proposes to use sequence generative adversarial network for missing data prediction, and makes appropriate improvements to SeqGAN to make it suitable for generating scoring data, and then further designs a computer teaching system based on this model. The article launches performance testing experiments on Ali’s real dataset UserBehavior, and conducts experiments on the effect of computer education with the students of computer application major in a secondary school as the research object. The results of the study show that in the comparative analysis of the pre-test and post-test of computational thinking of the experimental class, the mean of the total score of computational thinking of the experimental class in the pre-test and post-test is 71.17 and 78.35, respectively, and the post-test is more than 7 points higher than the pre-test. It can be concluded that the teaching model of multilevel computational modeling designed in this paper promotes the development of students’ computational thinking and academic performance, improves students’ learning attitudes, and increases classroom participation.
With economic globalization and the increasing complexity of inter-enterprise business linkages, corporate financial systems have gradually taken on the characteristics of complex networks. This paper firstly gives an overview of the complex network and introduces its basic topological properties, such as clustering coefficient and path length. After that, through the principal component analysis method, the enterprise financial risk early warning indicators are identified, and the key indicators are screened to improve the early warning accuracy. Based on these properties, the financial risk conduction network model of complex enterprises is constructed, the characteristics of the network are analyzed, including network density, centrality distribution, etc., and the effect of financial efficiency enhancement of complex enterprises under the optimization of topology computation is verified in real cases. The results show that most of the financial risk indicators of enterprises have strong correlation, and the degree of centrality of 9 indicators such as “gearing ratio and quick ratio” is more than 50%. In addition, the indicators of “current asset turnover ratio, interest coverage multiple, net profit growth rate” can play the role of intermediary and bridge, and the risk transmission effect among the indicators is high. The threshold value of 0.65 is the watershed of the changes in the financial structure of enterprises, and most of the financial risks in the network have a high degree of similarity in the financial structure when the degree value is 70, and it is negatively correlated with the coefficient of agglomeration, and the coefficient of agglomeration decreases with the increase in the intensity of the points.
In order to explore the deficiencies in the teaching process of marketing majors in higher vocational colleges and further improve the teaching quality of marketing majors in higher vocational colleges. This paper utilizes the improved ID3 algorithm to construct the SLIQ data mining algorithm to improve the teaching quality of teachers of marketing majors in higher vocational colleges and universities. Using ID3 algorithm to build a decision tree to get the portraits of teachers and students, at the same time, in order to reduce the computational complexity of ID3 algorithm and the problem of multi-value bias, the concept of sample structure vector similarity is introduced, and the degree of information gain is optimized to get a more reasonable decision tree. On this basis, based on the improved ID3 data mining algorithm, a teaching quality assessment system for senior marketing majors based on SLIQ algorithm is designed, which identifies important factors affecting teachers’ teaching quality by mining a large amount of data in the teaching process.The AUC value of the SLIQ data mining algorithm is 0.98, which can effectively improve the algorithm’s generalization ability, and it has an excellent performance in the teaching quality assessment task. The performance is excellent. In this paper, we systematically identify “the principles of marketing” and “the degree of seriousness of teachers’ homework correction” as the key factors to improve the teaching quality of marketing teachers. It provides a scientific basis for improving the quality of teachers’ teaching.
Visual communication design requires that feeling information and exchange of information must be conveyed efficiently and accurately. In this paper, we design a robust principal component sub-analysis visual enhancement algorithm based on improved Retinex. The algorithm transforms the image to the logarithmic domain so that it satisfies the decomposition condition of RPCA. After the RPCA decomposition model to get the low-rank component and sparse component, and will use adaptive gamma correction algorithm for the low-rank component for contrast enhancement, the two components are combined and then inverse transformed in the logarithmic domain to get the enhancement results. To avoid color distortion, the input image is converted to HSV color space to separate illumination information from noise. The model uses the inexact augmented Lagrange multiplier method (IALM) to solve the optimization problem, which leads to a significant improvement in the decomposition speed. The performance of the designed algorithm is verified on the dataset, and it is found that after the color equalization process for overexposed images, the gray value distribution is more uniform, and the image shows a better sense of brightness and visual effect after the contrast is increased. The algorithm scores 0.4648 and 0.7577 in UCIQE and UIQM respectively, which are ranked first among all algorithms and have better visual effect and information communication efficiency.
In recent years, China’s research investment in colleges and universities has gradually increased, but not much research and exploration has been done on the construction of the evaluation index system for the integration of industry and education. The state, society, industry and so on have brought rare opportunities for the implementation of in-depth integration of industry and education, which also indicates the imperative of the development of integration of industry and education. Based on the practical significance of educational evaluation, this paper applies the CIPP model to the construction of the quality evaluation system for collaborative education and training in university modern industrial colleges in view of the high degree of fit between the CIPP model and the process of university-industry-industry fusion activities in university modern industrial colleges. The recursive hierarchical structure is established according to the established index system, and the weights of the index system are calculated through the consistency test. The factor loading matrix of the first three principal components is constructed, and the modern industrial colleges are evaluated according to the principal components, and the mean values of the principal components 1, 2, and 3 are 0.27, 0.096, and -0.0186, respectively.In the calculated quality evaluation results of the integration of industry and education in modern industrial colleges, the score of educational and teaching achievements of the modern industrial colleges in Zhejiang Province is relatively low at 85.8439, which indicates that there is a gap in educational and teaching achievements, and there is a need to further improve the education and teaching achievements of modern industrial colleges. In addition, there are differences in the evaluation of the quality of industry-education integration in different modern industrial colleges in Zhejiang Province.The results of this study indicate that it is necessary to further optimize the construction path to meet the actual needs of industry-teaching integration in Zhejiang Province.
Lung cancer is the most common malignant tumor in humans and the leading cause of cancer-related deaths worldwide. In this study, we focused on the immune cells in the microenvironment of lung cancer at the protein expression level by IHC as well as mIHC techniques to explore the spatial distribution characteristics of immune cells within the tumor. To predict the prognosis of NSCLC patients and their potential response to immunotherapy, a machine learning-based immune-related prognostic model for lung cancer was constructed by combining Cox regression analysis, random survival forest and XGBoost algorithm, and the effect of the prognostic model was verified on the relevant dataset. The results showed that there were some differences in the immune cells between lung adenocarcinoma and lung squamous carcinoma in the lung cancer microenvironment, and the spatial distribution heterogeneity of CD3+ T cells and MHC class II antigen-presenting cells was higher in lung adenocarcinoma (P<0.05).The overall survival of high-risk patients was lower than that of the low-risk group in both LUAD and LUSC (P<0.01), and the immuno-associated prognostic model of lung cancer had a stable performance in the AUC value in multiple independent cohorts with stable performance, and the IRS model maintained high accuracy and stable performance in the training set and test set, which indicates that IRS has great potential for clinical application.
Rhythm matching of music and dance is an important research area in cross-modal analysis. In this paper, a music and dance rhythm matching algorithm based on time series analysis is proposed to extract the time series features of music and dance, and a genetic algorithm is used to determine the correspondence between music and dance movements to reflect the degree of correlation between changes in music and dance rhythm movements. In order to improve the matching and smoothing degree between the dance movement time series and the music time series, a constraint-based dynamic programming algorithm is introduced. The experimental results show that the model performs well in the matching degree and matching efficiency enhancement between dance movement time series and music time series, and its matching efficiency is 2-3 times of the traditional method. It shows high practicality in dance choreography and music matching, and can match any music clip with smooth and beautiful dance movements. The research in this paper provides new technical means for dance choreography and music matching, which will further optimize the transition harmony between music time series and dance movement time series.
This study takes the physical properties of high temperature devices as a starting point and the experimental apparatus used to obtain the study samples. The heat transfer process can be categorized into heat conduction, heat convection and heat radiation depending on the mode of contact. Under the theoretical support of the first law of thermodynamics, the nonlinear partial differential equations of the heat transfer characteristics of the high temperature devices are determined, and the above equations are analyzed by numerical simulation with the help of ANSYS software. When the thickness of the device is 1um, 8um and 15um, the heat transfer temperature and the power of the heat source show a monotonically increasing trend, in addition, when the thickness of the device is a fixed value, the spacing of the heat source and the heat transfer temperature show a nonlinear monotonically decreasing, and the present study has an important practical significance for improving the heat transfer performance of high temperature devices.
In order to explore the relationship between multi-source terrain features and lightning activity in Inner Mongolia, monitoring data and digital terrain elevation data of thunderstorm activity in Inner Mongolia from 2014 to 2025 were collected, and the spatio-temporal data mining method of mathematical and statistical analysis was used to analyze the distribution characteristics of lightning activity in Inner Mongolia. Based on the selected terrain feature factors, the machine learning method of multiple regression analysis is used to establish a research model of multi-source terrain features and lightning activity for quantitative analysis. The results show that the frequency of ground flashes in Inner Mongolia is mainly concentrated in May-October, accounting for more than 92% of the whole year, and the seasonal characteristics of its ground flash activities are significant, and the current intensity is mainly concentrated in the range of 20-40 kA. Correlation analysis reveals that multiple features of multi-sourced terrain are positively and negatively correlated with the frequency of lightning ground flashes and the current intensity (p < 0.05), and the prediction error of the constructed regression model for the ground flashes' frequency and the current intensity is 7.31%. The prediction errors of the constructed regression model on ground flash frequency and current intensity are 7.31% and 5.08%, which can provide a reference for lightning disaster prevention and mitigation in Inner Mongolia.
In response to the rapidly developing market demand, this paper proposes the use of genetic algorithms in industrial product design optimization under simulation environment. Design the product base gene coding, use the fitness function to determine the fitness value of different individuals, the genetic operator to support the optimization of industrial product design, by clarifying the optimal individual in the population in order to determine the optimization of industrial product design to meet the conditions. Then build up the industrial product design system based on genetic algorithm, plan the functional modules such as product information collection and coding, genetic generation of product solutions, and formulate the system process and function realization method. Exploring the performance of this paper’s industrial product design model in the simulation environment, this paper’s model in the operation efficiency, convergence speed and other aspects of performance are better than its other comparison model, in the iteration to about 300 times to achieve convergence. In the application practice of this paper’s design system, the values of this paper’s system are close to 1, and the RMSE values of each design parameter are lower than 0.5, and the average product quality score reaches 0.157, which is excellent in real-world applications.
The double bass, as the instrument with the lowest timbre and the largest volume in the string section of a symphony orchestra, is the “mainstay” of the orchestra’s acoustic effect, and grasping the bass performance mode in double bass performance is a problem that all double bass players need to explore in depth. A cluster-weighted multi-view kernel k-means clustering model (CWK2M) is proposed to study the local quality differences of the bass performance score views at the cluster level. The proposed weighted multiview clustering algorithm is then compared with several multiview clustering algorithms on several real multiview data for experiments and analysis of pitch change patterns. The experimental results show that, on the whole, the proposed algorithm in this paper obtains a relatively good clustering effect on each multiview data, especially on the Sens IT dataset of bass performance scores, the performance of each metrics is significantly improved, and the precision, recall, F1 value and NMI metrics are 0.632, 0.653, 0.687, and 0.713, respectively.In addition, the algorithm of this paper is utilized for the three bass playing patterns such as TaS1, Py11 and Mla1 are further analyzed, which further validates the universality and performance effect of the improved weighted clustering algorithm proposed in this paper for the analysis of pitch change patterns in bass playing.
The energy consumption problem of building complexes has become increasingly prominent along with the acceleration of urbanization. In order to achieve efficient energy saving in building complexes, this study proposes a Bayesian network-based uncertainty modeling in decision-making system for energy consumption management. By analyzing the uncertainty factors in the energy consumption data, a Bayesian network model is constructed to predict and analyze the energy consumption. And the uncertainty factors are used as decision variables to construct the energy consumption management decision-making system based on Bayesian network. The experimental results show that the uncertainty model and decision-making system constructed in this paper have more favorable performance compared with other benchmark methods, and exhibit smaller measurement errors in experimental tests. At the same time, the application of this paper’s decision-making system for energy consumption management of building complexes can significantly reduce management costs, and obtain the double benefits of reducing energy consumption and saving costs.
Based on the demand of load balancing in distributed system scenarios, this paper introduces the concept of dynamic priority in the algorithm and designs the dynamic feedback load balancing (DFLB) algorithm for numerical analysis. Through the closed-loop process of collection-feedback-utilization-collection, the overall performance of the system is realized. The Mininet tool and the Floodlight controller are used when building the load balancing system experimental environment to verify the reliability of the algorithm from the response delay, throughput and other indicators. The study shows that the DFLB algorithm reduces the response time of the system by about 20% compared with the static deployment method, and the DFLB algorithm reduces the load variance, saves computational resources, and makes the load of the system more balanced and efficient. The average throughput of the DFLB algorithm is improved by about 10% compared with the PALB algorithm and DALB algorithm, and 6% compared with the PALB algorithm and DALB algorithm, respectively. Starting from 1000 concurrent connections, the DFLB algorithm has a higher access rate. Thus, the algorithm leads to an improvement in the overall performance of the system.
The field of machine translation has made significant progress in recent years, but how to improve translation accuracy and context consistency is still an urgent challenge. In this paper, a context-aware translation accuracy improvement strategy based on deep reinforcement learning is proposed for English translation. Based on CNNs neural machine translation model, the multi-intelligence deterministic deep policy gradient algorithm is utilized to combine the output of the translation model with the human evaluation index (BLEU), and the reward function is constructed to guide the model learning. In addition, in order to enhance the context-awareness of the model, the study introduces a context encoder in the deep reinforcement learning framework to capture sentence-level contextual information and incorporate it into the translation process. The experimental results show that the optimized model has better training performance, with 40 epochs of iterations, the Loss converges to 0.135 up and down, and its English translation F1 value is 94.95%. And as the number of encoder layers rises, the number of semantic high-level features increases. The N-GRR difference between the generated translation and the standard translation of the model in this paper is the smallest, and the over-translation phenomenon is less. The number of out-of-set word interference is more than 6, and the BLEU value of this paper’s model is improved by 17.89% to 55.55% compared with the comparison model. And the algorithm has good translation performance, with METEOR scores of 0.562~0.803 on different topics. The research results fully verify the effectiveness of deep reinforcement learning based on deep reinforcement learning to improve the accuracy of English machine translation.
This paper proposes a risk indicator system for mental health management of college students that takes individual developmental status, social environment, human-computer interaction, and negative emotions as the first-level indicators, and clarifies the path of obtaining mental health management monitoring data, the weights of the indicators, and the safety warning interval of mental health management. Because of the uncertainties in the mental health management of college students, fuzzy logic is introduced to deal with the uncertainties of environmental changes, student behavior and other factors in the mental health management, and to improve the level of mental health management in colleges and universities. A fuzzy logic-based risk warning model for mental health management of college students is designed. The mental health status of students is further refined by the SCL-90 scale, and the mean score level of each factor of the scale is compared with the youth norm and adult norm. Input the fuzzified student mental health data in the fuzzy logic risk early warning model, and output the risk score of the fuzzy logic model for mental health management of college students. When the set threshold is 60, the fuzzy logic risk early warning model can effectively identify the abnormal values of students’ mental health, and the early warning model has practical utility.
How to communicate with users in a timely and effective manner and determine the intentional purpose of customers plays an important role in promoting continuous user interaction and improving service efficiency in the power marketing industry. The article firstly researches on a single-round natural language understanding algorithm based on intent-slot bi-directional interaction, which adopts a bi-directional information flow to realize the bi-directional information interaction between intent and slot. In the intention recognition layer, the interaction attention mechanism is utilized to introduce slot context information. Then the overall design scheme for the construction of an intelligent customer service system for power marketing from dialogue state keeping, multi-round question and answer, model storage to answer visualization is proposed, and the potential functional requirements are analyzed exhaustively. Finally, experiments from various aspects prove the effectiveness of the proposal in this paper. In the comparison experiments on MixATIS with MixSNIPS dataset and DSTC4 dataset, the metrics are improved by 0.3%, 1.5% and 0.5% respectively when comparing GL-GIN model on MixATIS dataset. This leads to the feasibility of the intelligent customer service system for power marketing constructed in this paper.
In recent years, socio-economic development and the process of massification of vocational education have been accelerating. The article surveys the current situation of the articulation between vocational education and undergraduate education through questionnaires. On this basis, in order to better realize the cultivation of employment-oriented talents, it designs a teaching resource acquisition method based on computational optimization, constructs a crawler search method by fusing genetic algorithm and ant colony algorithm, and realizes automatic clustering by using a clustering algorithm based on the combination of K-mean and particle swarm algorithm in random search direction. The results show that only 23.3% of the students think that there is no duplication of content between vocational and undergraduate education, 89.6% of the students want to set the teaching content according to different needs, and the current talent cultivation for the articulation of vocational and undergraduate education suffers from poor wholeness and monotonous tendency. The proposed crawler search method and automatic clustering method show superior performance and can accurately extract teaching resources and process structured information. Finally, the employment-oriented talent cultivation model is proposed to actively explore the path of integrating vocational and undergraduate education and promote the development of vocational education.
Key frame extraction is an important research content for human motion capture data analysis and processing, for this reason, a key frame extraction method for motion capture data based on quantum particle swarm optimization algorithm is proposed, which can either extract a definite number of key frame sequences or extract key frame sequences according to the objective function. In this paper, the spatio-temporal graph convolutional network is selected as the benchmark network for tap dance action recognition, and the dance action recognition is realized by combining adaptive and attention mechanisms. The comprehensive index of tap dance is introduced and used as a constraint, and the golden section algorithm is used to optimize the training path of the dance action to obtain an ergonomic training path. The experimental results of this paper show that the key frame extraction method of motion capture data based on quantum particle swarm optimization algorithm meets the need of real-time compression of motion capture data. By constructing the validation dataset, the accuracy improvement of AAST-GAN algorithm and the effect of gesture extraction are compared and verified, and the recognition accuracy reaches more than 86%, which is a good recognition accuracy for each tap dance action. The dance movement training path proposed in this paper ensures the effectiveness and comfort of tap dance movements.
Dance Anatomy is a basic theory course for university dance majors, which reveals the structure and function of various parts of the human body and their important roles in dance training through an in-depth interpretation of dance anatomy. Using relevant equipment and instruments, we will set up a data acquisition environment for data acquisition and pre-processing. For the problem of coordinating music rhythm and dance movement, a time-series autoregressive model is used to realize music-driven dance synthesis, and the model loss function is clarified. Combining the above model, data, and modeling software, the task of modeling the human dance movement mechanism is completed, and the cosine similarity is adopted to analyze the problem of coordinating music rhythm and dance movement. In both the training and test sets, the music-driven dance sequences and the original sequences fluctuate within a certain range (-8, 13), and the scoreRatio value of this paper’s method (1.505) is much better than that of the other four sets of models, which verifies the efficacy of its model in the application of the task of modeling the mechanism of human dance movement, and also verifies the reliability of cosine similarity method. This will enable better implementation of human movement mechanisms in dance anatomy into practical scenarios, help trainers to better perform dance training and performance, reduce dance injuries and prevent occupational diseases.
Aiming at the many problems in research resource management in private universities, this paper takes the integration of research resources in international business discipline of Xiamen Institute of Technology as an example, proposes a global integration and dynamic allocation model of research resources in distributed computing environment based on mobile agent (DCMA), and designs a dynamic bidirectional matching method of tasks and resources (DBMM) in order to improve the effectiveness of distributed computing. Experiments show that the proposed DBMM algorithm outperforms the LDCP algorithm and the hierarchical node sorting algorithm (SNLDD) in three metrics, namely, scheduling length, acceleration ratio and computational efficiency. Compared with LDCP and SNLDD, the scheduling length of DBMM algorithm is shortened by an average of 19.89% and 11.81%, the acceleration ratio is improved by an average of 19.77% and 9.26%, and the computational efficiency is increased by an average of 10.74% and 3.72%, which further improves the resource utilization rate of distributed computing system. Experiments were conducted using the research resource integration model, which achieved better efficacy in terms of probability value, goodness-of-fit, and stability of research resource integration in international business disciplines compared with the gray correlation analysis method. This paper provides an example reference for distributed computing system to realize research resource integration and efficiency improvement.
Aiming at the dilemma of corpus-based intelligent English translation, the article proposes an English neural machine translation method based on depth-separable convolution, which combines with the dynamic computation method to improve the semantic consistency of the translation system for semantic alignment and fusion. In order to verify the training effect of the proposed convolutional neural network model combined with the dynamic computation method, comparison experiments with one-way and two-way network models and baseline model with different cut-off granularity are conducted respectively. In order to better examine its performance in practical translation applications, online translation, machine translation and systematic methods are utilized for comparison. The BLUE values of this paper’s model for Chinese-English data translation in four different granularities of words, syllables, subwords and characters are 21.41%, 21.91, 29.25% and 20.40%, respectively. In 100,000, 200,000 and 500,000 training English-Chinese bilingual parallel corpus, the training time consumed by the model in this paper is 9.58 h, 15.94 h and 32.69 h. In practical application, the decibel range of the noise reduction of the translation system method designed by the research is distributed in [1.62 ~ 1.89], the average value of coherence is 91.1%, and the average compression rate and the average stability of the BLEU scores are 93.84% and 98.38%, respectively, and the results are better than the comparison methods.
This paper constructs a set of models for monitoring and evaluating the effect of Civics education through the research on the evaluation of Civics education based on educational big data environment. First, based on distributed gray cluster analysis, it analyzes and researches students’ Civics learning behavior, and explores learners’ learning characteristics by mining meaningful behavioral features for cluster analysis. The second is to design the Civics teaching quality evaluation model using principal component analysis, test the effects of population size and convolution kernel number on the performance of the Civics teaching quality evaluation model, and optimize the teaching quality evaluation model by using the dimensionality-reduced evaluation data. Distributed gray cluster analysis gets four clusters according to the characteristics of students’ learning behaviors, which are divided into excellent, diligent, average, and negative students.PCA selection of evaluation indexes found that the cumulative contribution rate of the first 10 principal component indexes to the evaluation of the quality of Civic Teaching in colleges and universities has reached 95.63%, which indicates that these 10 indexes can adequately evaluate the quality of Civic Teaching in colleges and universities. When the number of population size is taken as 31 and the number of optimal convolution kernels is taken as 19 values, the RMSE of the evaluation model is 0.01973, and the test time consumed is 0.0783ms, which is the best performance. The constructed Civics education effect monitoring model can effectively assess students’ learning behavior and efficiently and accurately evaluate the quality of Civics teaching.
This paper proposes knowledge representation based on knowledge graph embedding (TransE model) and based on deep wandering (DeepWalk model) to enhance the level of intelligent recommendation of knowledge points. Synthesize and construct a knowledge graph-based Civic Education model. Analyze the node centrality specifics of the model. Carry out a controlled experiment of model application and investigate student satisfaction on this basis. The three nodes with the highest node centrality are “life view and values”, “morality and law” and “patriotism and nationalism”. The average score of the test questions in the experimental class is 71.25, and the correct rate of the six types of test questions is higher than that of the control class. Most of the students’ satisfaction level with the intelligent teaching mode combined with the model was between 65 and 100 points. 92% of the students found the teaching mode interesting at a level between (75,100]. 90% of the students’ content mastery satisfaction level was between 85 and 100 points. Intelligent teaching using the knowledge graph-based Civics education model can help students improve their interest in learning Civics knowledge and construct Civics knowledge system.
English children’s literature has strong application value in educational content selection. This study takes classic English children’s literature texts as the research object, and constructs a semantic theme mining model based on the implicit Delicacy Distribution (LDA). Through keyword weight analysis and theme probability distribution calculation, multi-dimensional theme clustering and visual characterization of literary works are realized. According to the 2378 English children’s literature collected in the corpus, the LDA model was used to extract five core themes: “Adventure and Fantasy”, “Friendship and Teamwork”, “Growth and Self-Identity”, “Family and Affection”, and “Nature and Animals”.A semester-long controlled experiment was conducted with third-grade students in an elementary school in Guangdong Province, designing graded English teaching content based on the results of topic distribution. Through the questionnaire survey, vocabulary test and reading ability assessment, it was found that students in the experimental group significantly outperformed the control group in terms of active interest in learning (12.42% increase in mean value) and independent learning ability (15.67% increase in test scores) (p<0.05). The study shows that the educational content adaptation method based on the LDA theme model can effectively optimize the selection strategy of teaching resources, and provide a theoretical basis and practical path for the precise matching of literary themes and cognitive development stages in children's English teaching.
In this paper, finite element analysis is applied to the mechanical characterization of the foot. A finite element simulation model of the foot is constructed and its material properties are defined. Finite element analysis is applied to calculate the stresses on various tissues of the foot under different touchdown modes. Set up controlled experiments to verify the advantages of FEA technology in sports. The material property values of each tissue in the simulation model differed greatly, which was in line with the actual situation of biological tissues. In the 2 touchdown modes, the change curves of flexion and extension angles of the supporting foot were generally similar in the latter 75% of the supporting phase, and the differences were concentrated in the first 25%. The movement of the foot on the coronal plane showed a general tendency toward eversion. There were 2 peaks in the vertical ground reaction force variation in the heel-touch mode and only 1 peak in the non-heel-touch mode. The resistance impulse and power impulse ratios varied widely. The time of occurrence of the maximum contact stress on the talo-heel joint surface varied. P<0.05, the experimental group was better than the control group in terms of skill level, learning interest and initiative of the two groups of students after the experiment. The use of finite element analysis to assist physical education teaching can enhance students' enthusiasm and skill level.
The automation system is gradually applied to many fields because of its intelligent and efficient characteristics, and its energy control makes the equipment work in the optimal efficiency zone, however, the actual control effect needs to be further optimized. This paper explains the energy control problem of automation system for its control process, and uses the weighted residual value method to transform the original system into a system dynamics model. On the basis of this model, the optimal control is solved by the variational method, and the energy control algorithm based on the variational method is built by combining Lie algebra. The algorithm of this paper is used to establish the energy optimal control strategy and simulation experiments are carried out as a prerequisite for constructing the driving cycle. In the simulation experiments, the energy optimal control strategy based on this paper’s algorithm saves 4.77% of fuel, which shows that the energy control of the automation system under this paper’s algorithm is better and in line with the environmental protection needs.
Graph neural networks are widely used in educational research, and have strong application potential in the prediction of students’ comprehensive development and recommendation of personalized educational resources. In this paper, the information and characteristics of students are mined from massive learning data, and the prediction method of multi-topology graph neural network is used to realize the effective prediction of students’ comprehensive development. Through the graph neural network, knowledge graph and cluster search algorithm and other technologies, the personalized learning path planning and optimization are completed, and the personalized learning path is designed. The research shows that the data accuracy of the student development trend prediction model in this paper reaches the qualifying value of 0.1, and the absolute maximum value of the error does not exceed 0.17, so the model constructed in this thesis is effective and robust. It can fulfill the task of student development direction prediction. The usage frequency of generating learning paths are more than 60%, so the learning path generation method proposed in this paper is practical. And the average grade of the users who use this method is 6.17 points higher than the average grade of the users who do not use this method.
Industrial Internet based on distributed computing and cloud computing platform forms a “cloud-edge-end” cooperative system. Facing the problem of computing task offloading for machine-type communication devices in industrial Internet scenarios, this paper transforms the task offloading problem into a Markov decision process problem, proposes an online task offloading algorithm based on deep Q neural network (DQN), and designs an optimal scheduling method based on iterative optimization for industrial Internet resources. Simulation experiments are conducted by comprehensively considering the network environment and server state during the task offloading process, and compared with other resource optimization scheduling strategies. The results show that the DQN algorithm converges in about 9000 steps and has good convergence performance. The offloading strategy based on the DQN algorithm can effectively reduce the delay, energy consumption and total overhead of the computational task offloading system in the economy.
Currently, digital libraries face challenges in piracy and illegal distribution, data and privacy security, digital content identification and traceability. In this paper, we design a blockchain-based copyright protection system for digital libraries to provide true and reliable digital copyright information for libraries and users, and to ensure the security of data information stored in the digital copyright registration system. Firstly, we classify blockchain and analyze in detail the three core technical principles of consensus mechanism, cryptography principle, and hash algorithm. Then design the copyright registration protection system that contains the functions of unique authentication of digital work copyright, IPFS distributed storage, and privacy data encryption. The designed algorithm is tested for performance and the service performance of this paper’s scheme is analyzed in real applications, and it is found that the throughput performance of this paper’s algorithm when the number of nodes ranges from 4 to 20 is on average 36.19% more than that of the PBFT consensus algorithm, and 55.92% more than that of the RBFT consensus algorithm. When there are 5000 digital resource feature vectors in the system database, the time required for similarity retrieval is only 0.523s, which meets the requirements of the system’s non-functional needs for similarity retrieval runtime, and realizes a good balance between the operational efficiency of digital libraries and security. The research has practical reference significance for the application of blockchain technology in the field of digital copyright protection.
Acupuncture has been recognized by more and more experts as a treatment method to relieve various pains in human body, but the association between specific acupuncture treatments and diseases is still unclear, which affects the long-term development of acupuncture treatment. In this paper, we abstract the knowledge of acupuncture points as ontologies in the knowledge graph, and propose a method to improve the RoBERTa-WWM-BiGRU-CRF model to optimize the knowledge extraction of the knowledge graph by combining the SoftLexicon technique and the adversarial training method. Based on the knowledge graph of acupuncture points, the collaborative filtering model is introduced, and the original similarity matrix construction method is replaced by the co-occurrence matrix construction method based on the association characteristics of acupuncture points and diseases, which improves the operational efficiency of the association search and realizes the design of the association search technology of acupuncture points and diseases. The average consultation time in the acupuncture outpatient departments of the experimental and control groups applying this paper’s technology for acupuncture visits was faster than that of the full outpatient clinic by 0.32 min, showing a significant difference (P<0.05). Patients in the experimental group who received acupuncture treatment assisted by the technology of this paper were higher than those in the control group in the dimensions of acupuncture treatment experience, such as physiological reflections, treatment emotions, and treatment effects and treatment feeling dimensions, which were 2.22, 3.57, 2.2, and 1.33, respectively.
In-depth investigation of the combination of innovation and entrepreneurship education and computing technology is of great theoretical and practical significance for the continuous promotion of innovation and entrepreneurship education in colleges and universities. In this paper, after clarifying the three elements of environment, subject and behavior in the design of innovation and entrepreneurship education courses, we design an innovative teaching model for innovation and entrepreneurship education courses based on computing technology and digital learning environment, and adopt similarity metrics and questionnaires to count the frequency of students’ on-line learning behaviors and the level of cultivation of their innovative spirit and ability respectively. The results of teaching practice show that the practice of innovation and entrepreneurship education courses based on computing has a facilitating effect on the cultivation of students’ innovative spirit and ability. The Spearman correlation coefficients of the learning behaviors in the online teaching platform of innovation and entrepreneurship education courses and the dimensions of innovation spirit and ability show medium-high correlation (r>0.3), and its regression model can effectively explain more than 60% of the variance of innovation spirit and ability. The research in this paper provides an effective reference for the innovative development and practice of innovation and entrepreneurship education programs, and lays the foundation for promoting more effective and innovative development of dual innovation education in colleges and universities.
Natural language processing (NLP) is developing very rapidly in the field of artificial intelligence, and has become an important direction in the development of computer science field and artificial intelligence industry. In this paper, in order to realize the efficient construction of natural language processing model in low-dimensional embedding space, firstly, a word vector learning model is constructed based on matrix decomposition for word vectors in natural language processing. On this basis, in order to further realize the efficient construction of natural language processing models, this paper designs the Semantic Discarding Network (SDN) and Semantic Fusion Alignment Method (SFA) for the problem of interfering semantics of the model and the problem of a single way of fusion of local inference results. Finally, the SDF-NN natural language processing model is proposed and a multi-view subspace clustering (DLTE) method based on deep low-rank tensor embedding is proposed. The results of the research experiments show that the average performance index of this paper’s word vector model for each task in three corpora ranges from 71.55 to 89.11, and the performance is stable and the time overhead in the three corpora is 3.93, 7.29, and 13.42 minutes, respectively, and the speed of the model has been significantly improved and the overall performance is better. In addition, the natural language processing model (SDF-NN) constructed in this paper achieves the best performance in the comparison test with strong competitiveness, which further validates the performance of the matrix decomposition-based natural language processing model in this paper, and provides the method and direction for its efficient construction in low-dimensional embedding space.
The risk of financial aspects intuitively reflects the development status and operating results of enterprises, enterprises must control the financial risk of this key link, so that the financial risk of a safe landing, to protect the stability and health of the enterprise. This paper selects the financial data of listed companies, and comprehensively analyzes the level of the company’s financial performance from four aspects, namely, profitability, operating capacity, growth capacity and solvency indicators. Using Benford’s law to test the quality of each data of each financial indicator, the Benford factor is introduced as a new explanatory variable, and combined with the company’s financial risk early warning indicators to establish a random forest early warning model. The results show that profitability and growth capacity are the strengths of listed companies, while operational capacity and solvency are the weaknesses. The results analyzed by K-means clustering algorithm show that the sample companies are divided into 5 categories. And compared with the basic random forest model, the random forest model based on Benford’s law can improve the accuracy of financial risk warning. Finally, the model with the best prediction effect is used to judge the financial status of G listed companies, get the early warning results, verify the accuracy and applicability of the model and put forward corresponding countermeasure suggestions.
The development and utilization of shale gas is the main path to solve the current high carbon dioxide emissions, and this paper proposes to use the LEAP model to explore the role of shale gas development and utilization on carbon emission reduction in all aspects. Under the principle and definition of LEAP model framework, shale gas development and utilization scenarios and parameters are determined to facilitate the research and analysis work, and in order to realize the intelligent monitoring of carbon emission reduction work, the neural network two-layer carbon emission reduction prediction model is constructed. With the support of research data and LEAP model, the relationship between shale gas development and utilization and carbon emission reduction is studied and analyzed, and the carbon pulse analysis and prediction model validation model of LEAR simulation results are also supplemented. Although all three scenarios have different contributions to carbon emission reduction, the green scenario is the most obvious means of carbon dioxide emission reduction, with a total of 52.87 from 2010 to 2050, and the prediction model in this paper is able to satisfy the current demand for carbon dioxide emission reduction work, and provide a guiding reference for urban carbon emission reduction.
The rapid development of artificial intelligence technology has made its application in the field of education increasingly widespread. The purpose of this paper is to design and implement a personalized vocal music teaching system based on artificial intelligence algorithms to solve the problems of single teaching method and lack of personalized guidance that exist in traditional vocal music teaching. The overall architecture of the system is constructed by analyzing the demand for vocal music teaching and combining deep learning and other artificial intelligence technologies. The key algorithms involved in the system are elaborated in detail, including the personalized recommendation algorithm of the learning path fused with the long and short-term memory network (LSTM) and the attention mechanism, and the intelligent evaluation algorithm that includes the evaluation of pitch, rhythm and timbre. Through practical application cases, it is verified that the system in this paper can effectively improve the teaching effect of vocal music and students’ vocal music professionalism, providing an important auxiliary role and key ideas for the innovative development of vocal music teaching.
This study firstly introduces the working principle of deep learning-based neural machine translation model (NMT) and its recurrent neural network translation backbone network, which enhances the semantic characterization capability through Glove word embedding layer. A tree-to-sequence based attention mechanism is innovatively introduced at the encoder side, and a tree-based encoder is appended to the traditional sequence encoder to construct syntax-aware context vectors. On the decoder side, the syntactic tree structure information is integrated into the sequence-to-sequence model (seq2seq), and this model is used to explore the knowledge transfer effect of the English translation teaching process. The results show that the accuracy rates of the neural machine English translation models incorporating syntactic information proposed in this paper are all above 90%. The experiment on the effect of English translation teaching shows that the mean values of students’ scores on the post-test of long sentence translation and composition translation in the reading section of the experimental class increased by 11.022 and 12.5388 points respectively compared with those of the control class, with significant differences between the scores of the two groups of students (p<0.05), and the same significant differences are presented between the scores on the pre-test and post-test of the students' scores on the long sentence translation and composition translation in the experimental class. It can be seen that the application of the model can effectively promote knowledge transfer and help students better understand and utilize translation skills.
In order to realize the intelligent calculation of cost management during the implementation of construction projects, this paper proposes a methodological architecture based on Multi-intelligent Reinforcement Learning (MARL) and Building Information Model BIM. The construction cost management problem of the project is analyzed with examples in order to optimize the construction cost management and construction time management of ZZYH comprehensive business building. The results of the study show that a reasonable rebar path can be found through 40 independent simulation verifications, and the final convergence reaches 100%. Compared to manual savings, the collision-free rebar design using the computational framework of BIM and multi-intelligence saves roughly 90% of the time. In terms of optimizing the construction cost management of civil engineering, installation engineering, cable engineering, and overhead line engineering, the total amount of cost savings of the project amounted to 382,320,000 yuan.
Based on the material demand forecasting model using BP neural network and particle swarm algorithm, the study builds the material whole chain response efficiency calculation model under dynamic multi-objective optimization by comprehensively considering the demand level weights of the affected area, and adopts genetic algorithm to assist the model solution in finding the optimal and decision-making. Taking an earthquake as a case for example analysis, the model in this paper can give the Pareto frontier, and combined with the weight coefficients after the transformation of the model solving results are more scientific and feasible, the demand satisfaction rate of the original model and the transformed model are 73.43% and 74.28% respectively, and the demand satisfaction rate of the affected points is improved by 4.24%, and this paper introduces the material allocation model of the demand level weights to be able to obtain better response efficiency of the whole chain of materials, which can provide important theoretical and practical guidance for the whole chain distribution of materials.
With the rapid development of digital technology, the inheritance and dissemination of folklore sports culture have ushered in new opportunities and challenges. This paper constructs a digital educational resource management platform for China-ASEAN folklore sports culture based on Knowledge Graph. The knowledge system of folklore sports culture is systematically constructed by using Knowledge Graph, the data related to China-ASEAN folklore sports culture are collected and organized, and the construction of the corpus of China-ASEAN folklore sports culture domain is completed. Then we extracted knowledge from the data of folklore sports culture domain and stored the obtained knowledge in Neo4j graph database. The China-ASEAN Folklore Sports Culture Digital Education Resource Management Platform, which includes several modules such as login and registration, courses, personal center, institutions and teachers, and backstage management, was designed. The construction and application of the platform gained 91.2% satisfaction from students, enhanced students’ interest in learning folklore sports culture, helped to protect and pass on the rich China-ASEAN folklore sports cultural heritage, and also promoted in-depth exchanges and communication between the two sides in the field of sports and culture to build a community of human destiny.
In this study, generative adversarial network is used as the basic architecture, and the multi-head attention mechanism is introduced to enhance the model’s ability to perceive and process image features. The image generation process is optimized by bilinear interpolation to further enhance the detail expression of character design. The generation efficiency of the model and the quality of the IP image are improved by the improved network structure. A personalized recommendation model with implicit feedback and explicit feedback is also used to achieve targeted placement of IP image characters for agricultural and sideline products cartoons. The study combines the local characteristics of Jilin Province, taking Jilin rice as an example, and designs two rice brand IP images with regional characteristics, “Rice Xiaoji and Rice Xiaoling”, which have a good migration effect. When the recommended list length is Top=10 and 20, the recommendation effect of internal diversity of Jilin rice brand reaches 83.47% and 89.09% respectively, and the recommendation effect of overall diversity reaches 88.43% and 95.31% respectively. It can be seen that the method of this paper can improve the market competitiveness of agricultural and sideline product brands in Jilin Province, which provides a technical path and practical reference for rural revitalization in Jilin Province.
Supply chain inventory forecasting and control is an integral part of supply chain management system, and it is a focus that industries must pay attention to in their operation and management. In this paper, the supply chain inventory demand forecasting model is constructed from the perspective of supply chain end, combined with the Transformer model in AIGC technology. The DL-Informer model is used to improve the Transformer model, realize the feature fusion of graph convolutional neural network, design and solve the feature graph adjacency matrix and complete the information fusion of each feature subgraph to improve the prediction accuracy. Aiming at the problems faced by supply chain inventory demand forecasting, the traditional algorithm with strong local optimization ability is combined with the genetic algorithm, and the hybrid genetic algorithm (HGA) is proposed to solve the nonlinear optimization problem. In the supply chain inventory forecasting practice, when the forecast length is 12, the MSE, MAE and RMSE index values of this paper’s forecasting model are 0.202, 0.174 and 0.416, respectively, which have more stable long-term forecasting performance compared with other models. And in the nonlinear simulation optimization experiments, the HGA algorithm shows good convergence and outstanding optimization effect in the nonlinear problem of supply chain inventory.
Under the guidance of relevant theories and techniques, this project binarizes and segments red cartoon images, and then extracts their contour features. Neural network classifiers are used to identify and classify the outline features to realize the acquisition of visual symbols of the revolution in the history of Chinese red cartoons in the past 100 years. With the help of Pierce semiotics, the system of revolutionary visual symbols is constructed, and the system is explored in depth. Compared with other models, this paper has a high superiority on the recognition of revolutionary visual symbols in Chinese centuries-old red cartoons, and seven items of revolutionary visual symbols are extracted, specifically, flag, badge, gear, pentagram, wheat ear, hammer and sickle. In addition, the visual symbol system of the revolution has a high degree of recognition, for example, the CMYK value of the flag is 0, 100, 100, 0, and its color is red, which symbolizes the red of “passion and revolution”, which well reflects the “red years” of China’s development and the fruitful results of the revolution and construction. The fruit of construction.
In this paper, first of all, the data preprocessing of ethnic patterns is carried out through image segmentation and grayscaling processing methods, and then the image processing technology is applied to the feature extraction of ethnic dress patterns, and the improved SIFT algorithm is used for the feature extraction of images. The original DCGAN algorithm feature extraction ability is weak generates style picture fuzzy, the effect of the problem of poor, proposed the use of 32-layer deep neural network with residual structure instead of the original 5-layer shallow feature extraction network, significantly enhanced the algorithm’s feature extraction ability, enhance the model of the style migration effect. By introducing the objective evaluation index PA of the improved SIFT algorithm, the algorithm was compared with other algorithms, and the segmentation algorithm experiments were carried out with the local patterns of several images, and the results of pixel accuracy PA were obtained to be greater than 0.95, which confirmed that the improved SIFT algorithm was able to realize the accurate extraction of the contours of local patterns. In terms of pattern quality evaluation dimension, the subjective average scores of the amateur group and the expert group are 4.87 and 4.89 respectively, indicating that the ethnic patterns generated by the algorithm of this paper have reached a high standard in quality.
With the accelerated pace of society and the increasing pressure of competition, the issue of mental health has received increasing attention. Especially in the field of education, students’ mental health status directly affects their student outcomes and overall development. The aim of this study is to design a mental health status monitoring system based on large-scale data streaming computation, to realize dynamic real-time monitoring of individual mental health through multi-source data acquisition and efficient algorithm processing, and to explore its application in educational scenarios. Sliding window algorithm and Hidden Markov Model are used to analyze and process the collected multi-source data such as physiological signals, and the experimental results show that the system is able to significantly test the difference between people with high and low scores on psychological test scales in the monitoring of mental health status, and it can provide educators with valuable decision-making support and help students’ mental health education and intervention.
Solving the health problems of key populations such as people with disabilities is an important way to realize universal health coverage and promote social equity. Sports as the main means of rehabilitation for disabled people at present, this study clarifies the concept of disabled people and sports rehabilitation, and uses empirical investigation to analyze the plight of cruel sports, laying a realistic and theoretical foundation for this paper based on high-dimensional rehabilitation data optimization. In the process of sports rehabilitation exercise for the disabled, it is generated through the modular control of multi-skeletal muscles by the central nervous system as well as the regulation of neural oscillations. And the surface EMG signal is the combined effect of superficial muscle EMG and electrical activity on the nerve trunk on the skin surface, which belongs to the high-dimensional data characteristics. For this reason, this paper constructs a rehabilitation exercise muscle synergy model using matrix decomposition for the rehabilitation of disabled people’s sports. The data were refined in the time domain by adding time windows, and then the data were decomposed into targets based on the non-negative matrix decomposition method to extract the muscle synergy features in each time window, so as to analyze the muscle synergy differences in different exercise processes and different feature frequency bands, and to further obtain the muscle synergy law during exercise and the physiological change mechanism of the nervous system during exercise control. Finally, the experiments were carried out in both non-electrical stimulation and electrical stimulation modes, and the results showed that the number of muscle synergism in wrist flexion and extension was the same in both modes, which was 3. However, the number of synergistic pairs of muscles in the electrical stimulation mode was significantly increased. It also proves the effectiveness of the method of this paper on the analysis of muscle synergy of multi-channel surface EMG signals, which provides a new method for exploring the muscle synergy characteristics and the control mechanism of rehabilitative movement in the process of disabled people’s sports.
In order to cope with the damage of urban electricity and the dilemma of residents’ electricity consumption caused by flooding disaster, we study the dynamic planning of intelligent operation and maintenance equipment scheduling and distribution network restoration under flooding disaster. Consideration is given to both pre-disaster deployment and post-disaster scheduling levels, while dynamic planning is carried out for collaborative repair and energy storage scheduling to construct a scheduling model with multi-source collaboration. Based on this, a multi-resource cooperative post-disaster recovery strategy for distribution networks is further proposed. The usability of this paper’s multi-source cooperative strategy is studied in depth through case analysis. Among the six Cases of the simulation experiment, the total cost in Case 1, which is operated and restored according to the strategy proposed in this paper, is the lowest, which is only 257080.2 RMB. The maximum, minimum, and average values of the solution time of the multi-source cooperative strategy are much faster than those of the comparison methods, and it has obvious advantages in fast decision making. The multi-source synergy model in this paper is able to recover all the loads within 285 min, while the finite synergy model takes 330 min. The multi-source synergy model was able to recover 7,500 kW of load, while the limited synergy model was only able to recover 6,850 kW. The multi-source cooperative model has strong applicability.
Organic light-emitting diodes (OLEDs), as a new generation display and lighting technology, are critical for charge transport and luminescence efficiency enhancement. The study determines the potential, electron and hole concentrations in OLED devices based on Poisson’s equation and current continuity equation, and fits the charge transport process in the devices with the drift-diffusion model. The differential equations are solved by improved Euler’s method and iterative solution method to simulate the operating state of the OLED device. In conjunction with experiments, the enhancement effects of the OLED devices optimized based on the differential equation model in terms of charge transport and luminescence efficiency are analyzed. The optimized device and the comparison device exhibit the same partial pressure and a largely overlapping luminescence curve at 450~460 nm, but the optimized device lifetime and brightness are better than the comparison device. The charge transfer efficiency of the optimized device exceeded 99.99%, while that of the comparison device was lower than 95%. In addition, the light extraction efficiency of the optimized device is more than 20% higher than that of the comparison device, and it has the highest current efficiency, i.e., the optimized device has a better luminescence efficiency. The differential equation model is used in OLED devices to describe the processes of charge transport, optical properties, etc. The model can be used to systematically optimize the material properties and improve the overall efficiency of OLED devices.
This paper proposes to design the power meter based on TMR current sensor, screen the chips that meet the requirements of the power meter, and stipulate the technical specifications and technical parameters of the power meter based on TMR current sensor. Design the system structure of power meter with TMR current sensor including MCU module, storage module, communication module and so on. And design the main and vice system clocks in the single-phase energy meter with TMR current sensor. Analyze the design of signal acquisition module, bias adjustment and temperature compensation module, communication module and circuit protection module in the current monitoring system. According to the characteristics of the TMR sensor, establish the objective function, improve the GWO algorithm, and optimize the design of the multi-stage magnetic ring structure current sensor. The performance parameters of the TMR sensor are analyzed, and the DC current test and AC current test are conducted to verify the performance of the TMR current sensor measurement module. The accuracy, precision and linearity of the current measurement module are tested, and the relative error between the actual current value and the theoretical current value derived from the formulae in the DC current test and the AC current test are controlled within 5% in the TMR current measurement system. The measurement system based on TMR current sensor meets the current measurement requirements.
Artificial intelligence technology can effectively improve the quality and efficiency of industrial design and manufacturing, so the study takes Shuangdun Carved Symbols of cultural products as an example, utilizes the generative adversarial neural network to carry out style migration processing in the design of Shuangdun Carved Symbols and their products, and constructs the DCGAN model to assist the design and generation of Shuangdun Carved Symbols of cultural products. After semantic analysis of the color symbols of Shuangdun Carved Symbols products generated with the aid of DCGAN model in this paper, quantitative and qualitative measurements are carried out. Users of Shuangdun Carved Symbols products rated the products after the style migration significantly higher than before the migration in terms of volumetricity, distance, emotion, character, and texture.CycleGAN and DCGAN models achieved the best overall results in terms of PSNR, SSIM, FID, and KID indicators. The DCGAN model with added spectral normalization and Res2Net outperformed the CycleGAN model in the ablation experiments. The overall user rating of the Shuangdun Carved Symbols product designed by the DCGAN model in this paper is 4.24, and the product has obtained more satisfactory evaluation results.
Under the new situation of continuous and stable development of China’s economy, large products have extremely high requirements on transportation safety due to their high price, complex transportation technical requirements, which determines that large products should be delivered to customers in the safest and most economical way, which poses a difficult problem for decision makers to choose the optimal path. In this paper, we constructed an intelligent approval framework for bulky transportation, made technical and economic analysis of transportation routes, and established a multi-objective optimization mathematical model for path selection of bulky transportation vehicles. A hybrid genetic algorithm incorporating greedy strategy is proposed to solve the problem, which strengthens the ability of the algorithm to jump out of the local extremes and selects the optimal chromosome in the final population as the resulting optimal solution. The results of the approval and optimal route planning for bulky transportation are verified by the method of example experimental analysis. The volume of bulky transportation increases with the increase of years until 2023, and the GDP, value added of tertiary industry, total population, and road mileage are 1015987.54, 553948.15, 140563, and 536.48, respectively. In the instances where the number of orders is 2000 or more, the transportation distance, the maximum number of service bundles of orders on the route, and the maximum service hours of vehicles the mean values are 50, 3.56, and 14.33, respectively. According to the constructed mathematical model, the optimal line for the bulky transportation scheme is 0→2→4→7→8, and the total transportation cost is 670,500,000 yuan, of which the transportation costs are 116,500,000 yuan, 320,000 yuan, 151,000,000 yuan, and 83,000,000 yuan, respectively.
Aiming at the problems of unfixed switching frequency and complicated calculation in the control of permanent magnet synchronous motor, a permanent magnet switch FNN-PID control strategy based on deep learning technology is proposed. Based on the vector control of permanent magnet synchronous motor, the resonant pole inverter is combined with permanent magnet switch control, and then the fuzzy neural network and incremental PID algorithm are used to construct the optimization strategy of permanent magnet synchronous motor switching frequency FNN-PID control. And combined with the finite element simulation software, the permanent magnet switch finite element model is constructed, and the effectiveness of the FNN-PID control strategy is illustrated by verifying the permanent magnet switch control strategy and the temperature rise curve change. When using the FNN-PID control strategy, the electromagnetic torque quickly reaches stabilization near the given torque of 9 N-m after 0.03 s of startup, and the permanent magnet switch frequency of the FNN-PID control strategy is reduced by 24.04%. The difference between the measured maximum winding temperature and the calculated maximum temperature under rated operating conditions is less than 9°C, and the permanent magnet switching loss is reduced by about 35% with the FNN-PID control strategy compared with the traditional MTPA control strategy. Therefore, the combination of deep learning technology and finite element analysis can explore the optimization effect of PM switches from the strategy and application dimensions and provide research ideas for the stable operation of PM switches.
The ring network cabinet of the distribution network is an important part of the urban power system, and its operation state directly affects the stability and reliability of the power system. In this paper, a deep learning algorithm is used to analyze and process the partial discharge signal, and a permanent magnet fast ring main unit partial discharge detection and fault identification model based on improved DBN-LSTM is proposed. By analyzing a large amount of local discharge signal data under normal operation and fault conditions of ring main cabinet, and using these data to train a deep learning-based fault prediction model. The performance of the improved DBN-LSTM model is tested by combining the defect spectrograms of four typical ring network cabinet partial discharge models and compared with other algorithms. The proposed model has good effect on fault identification of ring network cabinet, with a combined identification accuracy of 98.41%, and the overall identification performance is better than both BP neural networks and SVM classifiers. The prediction accuracy of the fault prediction model also reaches 88.52%, and the experimental results of the method in this paper are more satisfactory.
Syntactic analysis is a basic work in the field of natural language processing, which explores the syntactic structures and their interaction relations in sentences. This paper first describes the basic approach of syntactic analysis, and explores the computational method of Chinese syntactic structure classification from large-scale corpus construction. Then, a grid-based large-scale corpus construction and distribution model is constructed. And the word embedding model BERT is used as the pre-trained language model, and the captured semantic features are input into the Bi-LSTM model to extract the contextual bidirectional sequence information, and the results of Chinese syntactic structure classification are obtained by the Conditional Random Field (CRF) processing. Through manual proofreading as well as the calculation of confidence level, the average correct rate of syntactic structure classification of the final Chinese canonical corpus is increased from 94.21% to 99.06%, which is an improvement of 4.85%. The syntactic structure classification accuracy of the BERT-Bi-LSTM-CRF1 and BERT-Bi-LSTM-CRF2 models with “complement structure” and “object structure” were higher than those of the BERT model, the Bi-LSTM-CRF model and the BERT-Bi-LSTM-CRF3 model with all syntactic structures. Meanwhile, the accuracy of the syntactic structure annotation method of BERT-Bi-LSTM-CRF model + manual differs from that of manual annotation by only 0.66%, and the average time spent is reduced by 37.04%, which reduces the workload of the annotators and improves the efficiency of the annotation, which verifies the validity and practicability of this paper’s model in automatic classification of Chinese syntactic structures.
The construction of dual prevention mechanism is a necessary way to solve the problem of “not recognizing, not thinking, not managing well” in the field of enterprise safety production. This paper combines the elements involved in the theoretical framework of the dual prevention mechanism, constructs two evaluation index systems of safety risk classification and the operation effect of the dual prevention mechanism, and then establishes an evaluation model based on the multi-level analysis method and the fuzzy comprehensive evaluation method, to explore the operation effect of the dual prevention mechanism in the enterprise. The evaluation results show that after the dual prevention mechanism of safety risk classification and hidden danger investigation and management strategy is operated in S enterprises with higher safety risk level (1.50 points), the awareness of safety production and the level of intrinsic safety of the enterprises have been significantly improved, and the average value of the evaluation of the operation effect of the dual prevention mechanism in enterprises is 3.91 points, which reaches a good level. The research results of this paper not only have strong guiding significance and practical help for the optimization of risk management of production safety in enterprises, but also can be used by the same type of enterprises and even other enterprises in optimizing the risk management of production safety and the management of hidden danger investigation.
Digital auditing has become the key to the transformation and upgrading of the auditing field. Financial audit data anomaly detection needs to combine multiple aspects of information, and it is of great practical significance to utilize the existing technical means to discover financial anomalies in the limited content. In this paper, based on the limitations of the weighted KNN deep neural network algorithm, a multi-branch deep neural network is proposed and a cost-sensitive loss function is designed. Combining the qualitative and quantitative methods of risk assessment, the enterprise audit risk assessment index system is constructed, the indexes are standardized, and the results of enterprise audit risk assessment are analyzed. The specific application effect of the assessment model is analyzed from the aspects of industry status and key financial performance, and the relevant strategies for corporate audit risk response are proposed. In the 1st risk assessment, 8 of the 20 enterprises are above higher risk, 6 are medium risk, and 6 are below lower risk. The results of the 2nd audit risk assessment have varying degrees of reduction between -0.3663 and -0.0119. From 2017, the overall net profit growth rate of enterprises is decreasing year by year, especially in the period from 2019 to 2020, and the net profit growth rate of the industry in 2020 is -24.87%, which predicts that the future development of the industry is not optimistic.
With the rapid development of blockchain technology, consistency assurance of distributed database has become one of the key issues. In this paper, a blockchain distributed database consistency assurance mechanism based on the practical Byzantine fault tolerance (Rpbft) algorithm and its improved algorithm is studied in depth.The RPBFT algorithm combines the RSA algorithm and the PBFT consensus algorithm, and then performs the signature operation after message encryption in order to increase the system security. Aiming at the shortcomings of the master node selection mechanism of the original algorithm and the RPBFT algorithm, a master node selection mechanism that includes the time factor is proposed, which introduces the role of the recording node, so that the waiting time of the node can be adjusted dynamically. Meanwhile the algorithm changes the conditions of view switching and reduces the system consumption. Through simulation experiments to verify the performance of this paper’s R-PBFT algorithm and OmniLedger and RapidChain two programs in the same network conditions, this paper’s algorithm compared to the comparison algorithm can be more effective in guaranteeing the consistency of the distributed database, when the number of slices is 20, the transaction latency time is 13s, 25s lower than that of RapidChain and OmniLedger, respectively. When the number of shards is 20, the transaction delay time is lower than that of RapidChain and OmniLedger by 13s and 25s respectively, which provides strong support for the application of blockchain technology in the field of distributed database.
Urban spatial structure and three-dimensional perspective can express personalized city brand image, which is an important feature of city brand form. In this paper, computer graphics technology is applied to design a city 3D modeling algorithm based on point cloud fusion, which transforms city information into city spatial visual symbols, and then carries out the innovation of city brand image morphology. Firstly, on the basis of binocular stereo vision, tilted image generation modeling technology is utilized to realize texture mapping 3D dense point cloud structure network. Aiming at the lack of accuracy of the sparse point cloud and the existence of noise points and mesh voids due to the influence of occlusion and shadows, we design the stereo vision PMVS algorithm based on the faceted slice in order to realize the densification of the point cloud. The algorithm performance is tested on the dataset using standard 3D reconstruction evaluation metrics F-score, chamfer distance (CD), and the application analysis of segmentation and merging execution efficiency for building clusters, optimization effect of rectangle fitting, and height calculation of building clusters, and the study finds that this paper’s algorithm is ahead of the baseline model in 13 categories. When the number of regions reaches 70,000, the traditional RAG method takes 26.9 seconds, while this paper’s algorithm only takes 14.8 seconds. The time consumption reduction reaches more than 40%. The average score of the aesthetic assessment of the city brand design is 83.47 points, and the 10 experts’ evaluation of the spatial aesthetics is above 90 points, and the design is unanimously recognized. The study makes a useful exploration for the innovation of city brand image under the conditions of cutting-edge information technology.
The study of the impact of climate change on permafrost and the response mechanism in the Upper Irtysh River Basin can help to comprehensively understand the impact of climate change and grasp the development of coping strategies. In this paper, the one-dimensional heat conduction equation is used as the core to propose a model for calculating the distribution of permafrost in the upper Irtysh River Basin and the boundary conditions for solving the model, and the model is simulated and solved by using the general form of partial differential equations in the COMSOL Multiphysics finite element analysis software. Subsequently, the simulation results and regression equations are combined to investigate the driving effect of meteorological data changes on permafrost depth distribution changes. The simulation results found that the meteorological factor regression model could explain 30.6% of the variation in maximum permafrost depth, with mean annual relative humidity driving permafrost depth to the greatest extent (Beta = -0.251). This paper finds that the driving effect of meteorological factors on permafrost depth change provides a new perspective for understanding the dynamical mechanism of permafrost change in the upper Irtysh River Basin, and also provides a scientific basis for predicting and responding to the impact of future climate change on permafrost.
In this paper, the basic structure of fuzzy integral-based multi-classifier fusion model is used as a reference to construct Choquet integral vectors, measure the similarity of English sentences, and construct a fast retrieval algorithm for English sentences based on Choquet expectation. Determine the algorithm threshold and compare the running time of similar retrieval algorithms. Deploy the algorithm into the English sentence retrieval model for dataset training and comparison experiments. Verify the model robustness and determine the chosen K value for the model. Further use the test set to compare the retrieval effectiveness of the model with the traditional semantic retrieval model. The algorithm threshold is set to 6 to improve English sentence recall. The running time consumption of the algorithm is 0.827s and 1.941s, which is lower than the other three similar retrieval algorithms. In the dataset comparison experiments, the algorithmic model of this paper scores better than the comparison model in all 5 evaluation metrics. The model has the best robustness when k takes the value of 15. The model check accuracy and check completeness are higher than the semantic retrieval model LM by nearly 8 percentage points. The fast retrieval algorithm for English sentences based on Choquet expectation can improve sentence retrieval timeliness and retrieval accuracy, and reduce retrieval energy consumption.
The development of digital technology provides more possibilities for the inheritance of Chinese excellent traditional handicrafts. This paper takes Chinese movable type printing as the research object, and develops and designs a user-oriented virtual experience system by combining its handicraft characteristics. In order to optimize the rendering of real-time images and video frames of the virtual scene in this system, this paper takes the deep learning oversampling algorithm as the basic framework, and uses two major types of neural network structures, namely convolutional neural network (CNN) and recurrent neural network (RNN), to carry out the rendering reconstruction, and at the same time, it uses the texture enhancement oversampling algorithm to recover the image texture details, improve the edge sharpness of the image, and comprehensively build the DLSS model. The performance of the DLSS model constructed in this paper and the virtual experience system of movable type printing is tested successively. The average score difference between the pre- and post-tests of the virtual experience system of this paper is 34.46, which is much higher than that of the traditional form of knowledge mastery of 20.76, indicating that the virtual experience system supported by this paper’s algorithms can effectively carry out the inheritance of traditional handicrafts.
Phishing has become an increasing threat on online networks with evolving Web, mobile device and social networking technologies. Therefore, there is an urgent need for effective methods and techniques used to detect and prevent phishing attacks. In this paper, a phishing detection model based on decision tree and optimal feature selection is proposed. An optimal feature selection algorithm based on a newly defined feature evaluation metric (f_Value), decision tree and local search is designed to prune out negative and useless features. The overfitting problem in the process of training neural network classifiers is mitigated. The optimal set of sensitive features for feature selection and the optimal structure for training the neural network classifier are constructed by tuning the parameters. Experiments on CART-based phishing detection system and comparative experiments based on different phishing detection models are also conducted. The experimental results show that the model precision, accuracy, and recall of the improved decision tree-based algorithm proposed in the article are 92.7%, 96.5%, and 88.3%, respectively, on the dataset of phishtank, and the three metrics are 98.3%, 99.1%, and 99.5%, respectively, on the datasets of Vrbanˇciˇc-small and show that the proposed CART has a higher performance than the many existing method models.
Image segmentation, as an important direction of computer vision, is gradually being applied to a variety of fields, however, the existing image segmentation methods still need to be improved in terms of segmentation accuracy and effect. In this paper, the variational level set method is used as the level set image segmentation method, and its theoretical basics and solution method (gradient descent flow method) are described in detail. For the problem of insufficient gradient vector flow in the traditional parametric active contour Sanke model, a global gradient vector flow model that can overcome the noise interference is given to obtain a more accurate gradient field, thus combining with the variational level set method to build an image segmentation model based on global gradient vector flow (GGF Snake). In the comparison experiments with three commonly used image segmentation algorithms, the DSC value of this paper’s algorithm reaches more than 96.00%, and the time used is less than 15s, which is better than the remaining three algorithms, and verifies the superiority of this paper’s algorithm.
Jiangnan gardens have become a valuable cultural heritage of China with its elegant garden style. The article proposes a binocular visual recognition system by analyzing the composition of the garden spatial elements and performing feature fusion based on scene-driven coefficients. Ablation experiments are conducted on each part of the constructed data enhancement framework for generating the design of the Jiangnan garden plan, which is applied to generate a set of high-quality datasets and apply the data to image segmentation for generating the design of the Jiangnan garden. The algorithm training is carried out by applying the generated design plan dataset. On this basis, the data from the actual Jiangnan garden research and the spatially quantized feature data are used to do the correlation analysis between the design elements and the aesthetic mood. The data enhancement framework constructed in this paper improves the IOU of ST elements to 0.537, and the average intersection and merger ratio MIOU is 0.389. It shows that the data evaluation framework based on visual recognition is suitable for the study of plan generation of Jiangnan gardens. The correlation coefficients of connection value, spatial control value, average depth value, and integration degree regarding aesthetic context with the data of Jiangnan garden design elements are 0.173, 0.301, -0.278, and 0.325, respectively, which indicate that there is a significant correlation between all of them.
Based on the concept of “user-centered”, this paper designs a product form optimization model based on ant colony algorithm. Through mining the online reviews of the products, we determine the perceptual imagery of users, and categorize the perceptual imagery and determine the weights from the perspective of user satisfaction. Combining the factor analysis of perceptual imagery and the contribution value of morphological features on perceptual imagery, the product morphology optimization fitness function is constructed. Solve the model according to the basic principle of ant colony algorithm, and study the decision-making method to assist product optimization. Take a brand A model forum word-of-mouth data as an example to analyze, obtain users’ perceptual imagery through SO-PMI algorithm, and assign values to perceptual intention weights with the help of cluster analysis. Determine the contribution value of morphological features through the SD investigation of product morphological differences. Genetic algorithm is introduced to carry out comparative experiments to verify the superiority of ant colony algorithm in optimizing model solving. Finally, the application effect of the predictive model solving scheme is analyzed through user satisfaction survey. The results show that the output of the product optimization design model based on ACO algorithm Model A is 8. 23.11% of the users are very satisfied with the optimized Model A, 65.55% of the users are satisfied, and 85.72% of the survey respondents are very willing and ready to buy the optimized Model A.
The evaluation of English course goal attainment is an important basis for colleges and universities to judge whether the goal of cultivating foreign language talents has been achieved. This paper proposes a method for quantitative assessment of course goal attainment according to the OBE concept. Calculating the importance of attributes about classification, the decision tree algorithm based on rough set is proposed, combined with association rules for deep mining of educational data. Collect quantitative educational data and questionnaire data of a university, modeling relying on SPSS Modeler 14.2, and outputting decision tree of influencing factors. Using the evaluation of course goal achievement to analyze the achievement of A4 course goals, and exploring the association rules of influencing factors based on the decision tree. The traditional decision tree algorithm is introduced as a control group to evaluate the performance of the rough set-based decision tree algorithm. The results show that the achievement degree of each sub-objective of A4 course is higher than 0.70, and students who have the achievement degree of A4 course objective greater than 0.7, the nature of their major is foreign language and they have passed the Grade 4 test have a higher possibility of achieving the final foreign language talent cultivation goal of the university. The precision of the assessment method based on rough set decision tree is maintained at about 88%, and the accuracy rate is basically maintained at about 90%.
Frequent outbreaks of cyanobacterial blooms in Lake Taihu are undoubtedly a great threat to the economic development of its neighboring areas and the safety of drinking water of its residents. This paper takes Taihu Lake as the study area and analyzes its geographic location information and development status. Then, based on the remote sensing data from MODIS and Landsat 8 satellites, the normalized vegetation index is improved to identify the blooms, and the dynamic detection method of cyanobacterial blooms is constructed by combining with the remote sensing inversion of water temperature. At the same time, the spectral performance of each band is integrated to excavate the characteristic information of cyanobacterial bloom, and the algorithm in this paper is used to process the satellite remote sensing data of cyanobacterial bloom in Lake Taihu to analyze its spatial and temporal distribution characteristics, which is used as the basis of the dynamic warning model for early warning. Then the LightGBM method is introduced to realize the all-weather spatial and temporal continuous monitoring of cyanobacterial blooms in Lake Tai. Analyzing the monitoring data of this paper’s model on the intraday change process of cyanobacterial bloom in Lake Taihu, it is found that the trend of intraday change in the area of cyanobacterial bloom in Lake Taihu in different seasons is relatively consistent, with the highest area of the bloom in autumn, accounting for 21% of the area of Lake Taihu’s water body. The study pointed out that after entering the fall, extra attention should be paid to the monitoring, prevention and control of cyanobacterial bloom in Lake Taihu.
In this paper, we design and implement a model network for English writing style generation using UNet network as well as ViT for encoding and decoding, and PatchGAN to enhance the identification speed. Based on the CRF-NLG model to identify and extract professional English terms, and design a special loss function to optimize the quality of writing style generation. The F1 value is used to evaluate the model recognition ability, and the writing style generation effect is explored by controlled experiments of the proposed model and three baseline models. The practical application results of the proposed model are visualized from four perspectives: overall evaluation, style strength, content preservation, and fluency, to verify its practical application effect. The results show that the proposed model exhibits the strongest performance in the two levels of content preservation and fluency, which are improved by 12.71% and 39.11%, respectively, compared with the existing GAN-based style generation model. Of the 119 modifications 92 (77.3%) were better, 17 (14.3%) were average, and only 11 (9.2%) were worse.
With the rapid development of science and technology, the traditional mode of teaching is inefficient and difficult to flexibly respond to the needs of knowledge updating, and generating content and applications based on AI has become an important way to solve this problem. According to the form of interaction in the digital exhibition hall, the article proposes SinGAN model and uses the multi-head self-attention mechanism to coordinate the overall features and detailed features in the generated adversarial network image, and to deal with the large range of dependencies in the image. The proposed AI-generated content and SinGAN image processing method are applied in the teaching of practical courses using the course “Digital Electronics Technology and Application” of a university in Guangdong Province, which specializes in electronic information and engineering, as an experimental object. The experiment shows that the percentage of content with a content quality score of 0.6 to 1.0 reaches 75.7%. As the course progresses, the keyword coverage rate reaches 0.996, and AI-generated content is efficiently applied in the course. The student performance of the experimental class with AI-generated content and image processing method teaching mode and the regular class with traditional teaching mode were 80.75 and 67.91 respectively, and the sample t-test for the significance of the student performance of the two classes was P=0.006, which showed a significant difference in the students’ performance between the two teaching modes. Students’ satisfaction with the new teaching mode is high, indicating that the AI-generated content and image processing methods proposed in the article have been well applied in education reform.
With the arrival of the big data era, the demand for massive data storage is growing, and distributed storage systems have become a key technology to solve this problem. The traditional HDFS system has a large storage overhead, this paper in order to improve the storage efficiency of massive data, the introduction of corrective deletion code (RS code) technology, to ensure the reliability of the data at the same time significantly reduce the cost of storage. In order to improve the storage efficiency of massive data, this paper introduces the corrective censoring code (RS code) technology, which ensures the data reliability and significantly reduces the storage cost. In addition, to address the problems of low coding efficiency and high repair overhead in the practical application of RS code, this paper further introduces the local repair code (LRC) technology, which reduces the data repair overhead, and compares and analyzes the application effect of optimization model (RS-LRC-HDFS). The experimental results show that after RS-LRC optimization, the time overhead of the HDFS storage system in the write process and read process is significantly improved by 81.12% and 93.01%, respectively, compared with the pre-optimization period, and the repair time of massive file data is reduced by 87.25%. It can be seen that it provides an efficient and reliable solution for massive data storage.
This paper intends to introduce the multi-intelligence of digital resources in cultural and tourism industry in reinforcement learning. In order to scientifically evaluate digital resource allocation, the index system characterizing resource allocation is constructed using hierarchical analysis. From there, a multi-objective collaborative optimization allocation model of digital resources in cultural and tourism industry based on reinforcement learning and multi-intelligent body system is established. Through empirical analysis, it can be seen that referring to the observation of the development of the comprehensive level of digital resource allocation, there is an imbalance in the development level of N province. The indicator system is refined to consist of 4 guideline level indicators and 26 indicator level indicators. Before and after the multi-objective synergistic optimization, the total amount of digital resource procurement for the cultural and tourism industry in province N was reduced by 460,742 yuan. After optimization, the comprehensive efficiency of resource allocation in area a improves by 0.03136, area b improves by 0.03275, and area h improves by 0.02799. Moreover, all of them tend to be in equilibrium. Therefore, the multi-objective synergistic optimization allocation model in this paper can improve the efficiency of digital resources in cultural tourism industry and reduce the differences between districts and counties.
Based on the Delphi method and relevant definitions, this paper determines the evaluation index system of college students’ employability, adopts the hierarchical analysis algorithm (AHP) to calculate the weights of the evaluation indexes, and for the weights of the evaluation indexes do not satisfy the consistency test, adopts the Adaptive Gradient Algorithm (AdaGrad) to adjust the weight parameters so as to make them satisfy the consistency test, and arrives at the adjusted values of the evaluation indexes weights. The weights of the adjusted evaluation indexes are derived. Using the fuzzy comprehensive evaluation theory, a comprehensive assessment model of college students’ completion ability was constructed, and then the research sample was evaluated and analyzed with the help of this model. It is calculated that the affiliation vector of the evaluation of college students’ employability is (1.9466, 1.2539, 1.1123, 0.9752, 4.714), and the maximum affiliation value is 4.714, which can be inferred that the students of this university have good comprehensive ability of employment and can well face the employment pressure in the current society.
Micro-landscape is a kind of green landscape designed to enhance the local landscape environment of the city along with the renewal of urban green space and the transformation of old city. The article adopts Hadoop technology and utilizes the Hadoop distributed computing framework to preprocess the data, constructs the urban micro-landscape greening evaluation system, and carries out research on four evaluation levels, namely, building façade landscape, multimedia landscape, water landscape, and landscape facilities. At the same time, based on the principal component analysis and factor analysis method for comprehensive evaluation, it is determined that the multimedia interaction factor is the most important factor affecting the effectiveness of micro-landscape greening. Then use SWMM model to design a city urban area, through SWMM model simulation to get the actual average annual runoff control rate of the demonstration area in 2023 is 59%, and the overall long-term goal of urban micro-landscape greening planning in 2020-2030 there is a gap, based on which put forward the urban micro-landscape greening design program.
The basic genetic algorithm suffers from problems such as precocity and low search efficiency when solving multi-objective optimization problems in large-scale computing environments. Aiming at these problems, this paper introduces various improvement strategies such as neighborhood operation, adaptive strategy, chaos optimization and cooling into the classical genetic algorithm, and designs an improved genetic algorithm process that organically combines various improvement strategies. The improved genetic algorithm and other existing large-scale multi-objective optimization algorithms are tested using LSMOP test problems, and the improved genetic algorithm has better convergence and diversity than other algorithms on both two-objective and three-objective LSMOP test problems. The PF curves of the seven algorithms are plotted separately for the two-objective on LSMOP6 and the three-objective on LSMOP5 when the decision variable is 200, and the images show that the improved genetic algorithm has the most uniform population distribution. The experimental results confirm the effectiveness of the improved genetic algorithm in solving large-scale multi-objective optimization problems.
With the development of virtual reality and computer vision technology, the demand for virtual scenes of music performances is becoming more and more prosperous, which brings new development opportunities for music performances and music teaching. In this paper, we use the beam leveling method to determine the camera parameters in the virtual scene, implement the calibration process and parameter solving for the camera, and implement the segmentation process for the virtual scene image through the GrabCut algorithm, formulate the model constraints and objective function, construct a virtual scene for music performance, and design a virtual scene system for music performance. Based on the virtual scene of music performance, the interactive learning model of music is proposed, and the virtual roaming mode is formulated by combining human-computer interaction technology to realize the interactive learning roaming of music learners in the virtual scene of music performance. The PSNR and SSIM values of the music performance virtual scene constructed by this paper’s technology are 25.8291db and 0.9396 respectively, which are higher than those of the virtual scene construction algorithms such as VSRS and JTDI as a comparison. Carrying out music teaching experiments, the experimental class that applies the interactive learning model of this paper for music interactive learning roaming has higher mean values of all dimensions than the control class in both music learning ability and music listening ability, showing significant differences (P<0.05).
Text is the carrier of language, and language is the carrier of cultural soft power, if you want a country’s soft power to be enhanced, it will certainly start from the dissemination of the native language. This paper constructs a complex social network J-SEVIR model for the dissemination of Japanese text information with the help of complex network theory combined with the information dissemination model using graph theory as the technical support. The data about Japanese text information on Sina Weibo is used as the research object, and the data analysis is carried out through the dimensions of model simulation, real data comparison, and information dissemination enhancement strategies. The study shows that the peak number of Japanese text message dissemination nodes is 1.987*107, which is 41.32% and 28.94% higher than the peak number of dissemination nodes in the traditional SEIR model and BCIR model, respectively, and the peak number of disseminators of the Japanese text message dissemination enhancement strategy designed by the J-SEVIR model can be up to 0.62, whereas the number of Japanese text message dissemination counterattackers is only 0.12. Therefore, the number of Japanese text message dissemination counterattackers is only 0.12. Therefore, the establishment of Japanese text information dissemination paths with the help of complex networks based on graph theory can be used to provide new research perspectives for optimizing the effect of Japanese text information dissemination.
The prediction of the scale of big data talent training in colleges and universities belongs to an important content in the field of big data talent research in colleges and universities. The article uses the primary exponential smoothing method in the time series and the gray model prediction method to predict the scale of college big data talent training and talent demand respectively, and then uses the Lorenz curve and the Gini coefficient to study the matching degree of education in the field of big data. There are experimental results can be obtained, the degree of matching between the professional settings of colleges and universities and the trend of the demand for big data-related positions in enterprises needs to be strengthened, in order to adapt to the future demand for big data-related positions in enterprises, and to further output talents that are in line with the enterprises, the article proposes a model of big data talent cultivation civic and political education in colleges and universities based on the KSAO model. Based on the KSAO model, the ideological education mode of big data talent cultivation in colleges and universities can be implemented at six levels: “theory + project” curriculum system, promoting the dual strategy of “on-campus simulation + off-campus practice”, establishing the KSAO multi-dimensional practice assessment system, strengthening the coordination of the industry-teaching cooperation model, building a cloud learning platform with the help of information technology, and implementing the top-down education design.
This study focuses on library data mining scenarios and proposes an optimization method for the deficiencies of existing knowledge discovery algorithms in terms of efficiency, accuracy and interpretability. The method first uses principal component analysis to downscale library high-dimensional data to extract the main features and improve the data mining efficiency. Then, the fuzzy clustering algorithm is used to cluster the dimensionality reduced data to more accurately identify the user groups, resource categories and other implicit knowledge. The clustering results are interpreted and analyzed to provide data support for knowledge discovery in library data mining. The algorithm in this paper demonstrates better performance in data dimensionality reduction at the level of memory usage as well as time consumption, and identifies three major components with cumulative contribution of more than 80%. In addition, the algorithm achieves an average purity of 95.45% for book data clustering and a clustering time consumption of 3.47s with a data stream of 300unit k, both of which are better than the comparison algorithms. The comprehensiveness weight of a university’s book resources is 0.17, which is the highest performance, while the practicality and standardization are the next highest, 0.155 and 0.152, respectively. It can be seen from the clustering that the book category with the highest borrowing rate is science and technology, and the lowest one is literature, which reflects the user’s demand for knowledge of a specific field.
Under the impetus of computer technology, the creation of digital art continues to develop, and computer-assisted creation has gradually become the mainstream of artistic creation. This paper is oriented to digital art innovation, in-depth exploration of computer-assisted art creation and its integration with the development of digital media. Through the in-depth analysis of computer-assisted art creation, this paper constructs an improved CycleGAN art pattern generation model by introducing the self-attention mechanism in the CycleGAN model on the basis of pattern generation. In the generation experiments of the improved CycleGAN model, the SSIM and PSMR values of the improved model in this paper are 0.721 and 17.563, and in the number of in-parameters, the model size, and the running speed are reduced compared with the traditional model, and the overall performance of the improved model is excellent. At the same time, the works based on the computer-aided art creation method of this paper compared with the traditional art creation works of the comprehensive average score increased by 11.40 points, further illustrating the more advantageous in computer-aided art creation. The study concludes by analyzing the path of the combination of computer-aided and digital media, and proposes a path for the integration and development of the two from multiple perspectives, which provides directions and ideas for the research on the integration and development of computer-aided and digital media technologies.
In order to overcome the shortcomings of traditional physical education teaching quality assessment methods, this paper proposes a hybrid online-offline physical education teaching quality assessment method based on the assignment method. The method utilizes the hierarchical analysis method (AHP) to initially assess the quality of hybrid physical education teaching, and introduces the improvement of the pull apart step method (ISD) to improve the assessment accuracy of the hierarchical analysis method. The AHP and ISD methods are weighted to form a comprehensive integrated assignment method to construct a hybrid physical education teaching quality assessment model. Finally, the accuracy of the teaching quality assessment model was tested by the plain Bayesian classifier (NBC). The questionnaire data from teachers and students of an engineering university were collected and applied to the model of this paper, and the final results show that the model of this paper can effectively realize the grade assessment of hybrid physical education teaching quality according to the obtained data. The simple Bayesian classifier used in this paper has obvious performance advantages compared with multiple linear regression (MLR) models. The application of the method in this paper can effectively meet the needs of teachers and students in mixed physical education teaching and learning, and at the same time, it can significantly improve students’ physical education performance, which is highly welcomed by teachers and students in schools.
This paper draws a framework for constructing user demand modal information, uses crawler technology to obtain online review text information, processes the text information, and mines relevant consumer demand information. The LDA topic model is used to extract the topics of consumer concern from the online comments, identify the topics of consumer demand and clarify the concern degree of each demand. The KANO model is proposed to establish a consumer demand classification method based on the KANO model by combining product characteristic attributes and consumer demand information. Examine the theme discrimination performance of the LDA model on the hotel category, footwear category, and food category datasets. Combine the preprocessed user demand data to statistically quantify user demand for quantitative Kano transformation. Classify user demands into Kano categories and calculate the priority order of user demands to get the product optimization strategy. The weighted order of consumers’ demands for automobiles is footrest, cigarette lighter, antenna, window, low beam, key, etc. in order. It can be found that automobile consumers pay more attention to the needs of antenna, cigarette lighter, pedals, and enhancement of accessory functions. As a result, automobile manufacturers should increase the seat comfort, improve power, enhance the flexibility of shifting such aspects of the whole vehicle handling experience, in addition to improving the lights, keys and other car quality related needs.
Answering the spatial relationship between ESG ratings and total factor productivity of enterprises can provide a reference for the high-quality development of macroeconomy and the sustainable and healthy development of enterprises. In this paper, the improved K-means algorithm-PCA-K-means is used to measure the principal component data corresponding to the economic development level of 26 central cities, based on which and cluster analysis is conducted to classify the regions and city types of East, Central and West China. Furthermore, benchmark regression and spatial heterogeneity analyses were conducted using a fixed-effects model. The study shows that ESG ratings have a significant positive relationship on firm-wide factors. Observing the PCA-K-means clustering results, it can be found that there is no significant positive effect between the economic development speed and the ESG ratings of enterprises, which indicates that there is a difference in the impact of ESG ratings on the total factor productivity of enterprises in different regions. Therefore, the spatial heterogeneity analysis shows that the correlation coefficients of ESG rating performance in the central and western regions are 0.0163 and 0.0275, respectively, and ESG rating performance has a greater impact on enterprises in the central and western regions compared with the eastern region. The effect of ESG rating on total factor productivity of enterprises in resource-dependent cities and old industrial bases is not significant.
Due to the development of advanced information technology such as artificial intelligence, the traditional marketing profession is being transformed and upgraded in the direction of intelligent higher vocational marketing, and the requirements of marketing positions on the knowledge, quality and ability of practitioners have changed. The article analyzes students’ cell phone online behavior in different classrooms based on DBSCAN clustering algorithm by collecting students’ campus network usage data, according to which the results can provide an effective basis for school management. By introducing the Interpretive Structural Model (ISM) and analyzing the interrelationships between courses, the article proposes a course cluster division scheme for marketing majors, which provides methodological support for the division of clusters in the construction of course clusters for professional teachers, as well as the selection and organization of the courses within the clusters. Finally, investigate the differential judgment of students from different places of origin about the influence of teaching environment, teacher quality, teaching process, teaching tools and resources on the teaching effect of marketing courses, the data show that the influence factors of marketing course teaching have obvious differences in the influence of the teaching effect of the course, improve the ability of professional teachers to educate people, optimize the teaching process of the marketing course, and deepen the reform of classroom teaching.
In the current fields of quantum information processing and quantum computing, fast and accurate quantum state manipulation and preparation have been of keen interest to researchers, and their potential applications are mainly in quantum measurement, quantum information, quantum communication, and quantum sensing. In this paper, the Hilbert space of a bipartite state system is unfolded by four Bell state entanglement bases and the result is projected to the subsystem to obtain a mixed state. A quantum approximation algorithm is proposed to provide a solution to the combinatorial optimization problem, and based on the workflow of the quantum approximation optimization algorithm, an improvement is proposed to the quantum approximation optimization algorithm to solve the constrained problem using the quadratic unconstrained binary optimization method. Based on the theory of cavity magnetism, the hybrid quantum system model is constructed, and the calculation method of Hamiltonian quantity is proposed. Combined with the quantum entanglement optimal path calculation of UQAOA algorithm, the optimal value of time-microwave entanglement is obtained at r=0.234, so the compression parameter r=0.2 is used in the calculation. Based on the UQAOA algorithm for the analysis of the transmission characteristics of the generated OMA wave in air and the transmission optimization problem, the simulation obtains the reflection coefficient is slightly lower than that of the test, and the maximal error error is controlled at ±7.5dB around, and the two results are basically in agreement.
The organic combination of traditional rule of law culture and Civics education in colleges and universities is a breakthrough to improve the effectiveness of Civics education. Focusing on the Civic and political education that integrates traditional rule of law culture, the article introduces virtual reality technology and differential evolution algorithm to explore the course effect optimization method of Civic and political virtual reality teaching, and obtains the optimal content applied to the corpus through differential evolution algorithm according to the content characteristics of Civic and political education. On this basis, the evaluation index system is constructed to assess the course optimization effect of Civics virtual reality teaching. Example validation shows that the Civics corpus based on differential evolutionary algorithm and the proposed Civics virtual reality teaching method achieve better Civics course optimization effect, with an overall score of 3.833, and have the ability of practical application. Students of different genders and grades show significant differences (P<0.05) in the evaluation results of most of the first-level indicators. The application section of virtual reality technology promotes the teaching effect of traditional rule of law culture into the ideological education of colleges and universities.
The research in this paper mainly focuses on the design of the quality assessment system of Ideological and Political Education to realize the innovation of Ideological and Political Education mode. The principal component analysis algorithm is used as the core algorithm of the assessment system, and combined with the system architecture model of hierarchical design, it realizes the collection, processing, analysis and assessment of the data on the quality of Ideological and Political classes. The research results show that the assessment system based on principal component analysis algorithm in this paper has a higher accuracy rate of education quality assessment compared to the evaluation system based on a single deep learning algorithm such as RBF neural network. At the same time, the system in this paper also has a higher assessment accuracy than the evaluation system using a combination of algorithms, and shows excellent stability performance when assessing the educational quality of 150 teachers. Using this system to assess the quality of Ideological and Political Education of 8 teachers, the comprehensive ranking is more reasonable than the original ranking. The Ideological and Political education quality assessment system designed based on the principal component analysis algorithm in this paper has a far-reaching impact on the innovation and intelligent development of the Ideological and Political Education model in the digital era.
The development of electronic and electrical architectures towards domain centralization makes it difficult for traditional distributed control architectures to meet the functional needs and performance requirements of increasingly complex intelligent devices. This study utilizes a multi-model adaptive control algorithm to assist the domain controller to adjust the control parameters in real time according to the state of the device and environmental changes, and to realize the optimization of the control of the device. The wi-fi wireless networking communication technology is chosen to transmit the real-time data acquired by the sensors to the web page. The electrical and electronic architecture composed of the two combined with each other is carried to the intelligent control platform to realize the functions of sensing, positioning, planning and decision-making of the equipment platform. The study shows that: the algorithm selected in this paper can reach the target speed of the motor within 0.2s in the process of no-load and loaded operation, and the time required for balancing to the load torque is significantly reduced compared with the comparison algorithm. In this paper, the maximum throughput and CPU occupancy of the domain controller + wireless sensor architecture are lower than that of the traditional distributed architecture. And the platform constructed accordingly has no packet loss when the number of packets sent is less than 10000, and the average communication delay is between 0.65 and 1.2ms, which meets the requirements of vehicle wireless control and communication. Through the domain controller based on adaptive control algorithm to regulate the vehicle speed in real time, to ensure the safety distance between the rear vehicle and the front vehicle.
Learning path optimization aims to generate and optimize a knowledge learning sequence for learners that best meets their knowledge needs. This study focuses on the important role of online learner behavior in personalized path planning. By constructing a knowledge point difficulty model and a learning behavior prediction model based on online learning behavior, together with a user-based collaborative filtering recommendation algorithm, a personalized learning path is proposed comprehensively. The MOOC websites “College English 1” and “Xuedang Online” are selected as sample data to analyze the online learning behavior of English learners and verify the learning effect of the learning path proposed in the article through the change of students’ online time. The personalized teaching model based on the learning path is investigated in practice by taking the college English course in school A as an example. Compared with the traditional teaching mode, the optimized learning path shows a significant difference of 0.01% in the dimensions of learners’ “knowledge and skills”, “process and method” and “affective attitude”. The mean values of the optimized blended teaching mode are 4.12, 4.33 and 4.07 respectively, which are all better than the traditional teaching mode. It shows that the English learning path proposed in this paper is conducive to enhancing students’ personalized learning needs and provides a reference for promoting the effective implementation of personalized learning in the information technology environment.
As the key driving force to promote the development of new quality productivity, the internal logic of the integration of production and education is to provide core support for the development of new quality productivity by training high-quality workers, providing high-quality labor elements and creating an efficient innovation platform. However, at present, the integration of middle and teaching in undergraduate education faces challenges such as “school hot and enterprise cold”, school-enterprise cooperation obstacles, and imperfect mechanism. This paper analyzes the current situation of the integration of production and education in undergraduate education, constructs the corresponding mathematical model. And uses genetic algorithm to solve the optimization objectives of curriculum design and teaching resource allocation under the integration of production and education, include the incorporation of enterprise elements, such as the proportion of enterprise practice courses, enterprise mentors, joint research and development data. Based on the above, the feasibility of GA optimization algorithm is tested from three perspectives: comparison of the same kind, practical application and student satisfaction. In order to effectively enable the development of new quality productivity, it is necessary to optimize the education major setting in accordance with industrial changes, deepen the learning situation and customize practical courses, deepen the school-enterprise cooperation and development platform, strengthen collaborative innovation, and improve the incentive mechanism, so as to form an effective connection between the education chain, the talent chain, the industrial chain and the innovation chain, and jointly promote the high-quality development of undergraduate education.
Teaching and correcting athletes’ techniques by analyzing and referring to the performance of professional tournament players can improve the teaching level and quality of wushu movements. In this paper, the performance of college students in UFC tournaments is taken as the research data, and the multilayer perceptron algorithm is used to process the images and carry out the global modeling of wushu fighting action images. The network coding design is used to improve the data transmission rate of the algorithm, and the activation function is used as the nonlinear expression method of the algorithm. The Tanh_Softsign activation function is improved to counteract the noise interference of the dataset images, in order to construct the multilayer perceptual machine algorithm and develop the learning of martial arts fighting action scores. After optimizing the learning of UFC martial arts action scores by this algorithm, this algorithm shows a high correlation between the performance scores of students and the professional teachers’ scores of an elective class of martial arts in a university with P>0.05, which indicates that the algorithm in this paper can accurately assess the students’ action performance.
Based on the status quo of Sanya Digital Intelligence Tourism Economy, this paper puts forward the strategy of intelligent teaching change under the dual-leader cultivation mode of colleges and universities. Relying on clustering analysis technology to achieve the mining processing of the whole process data of the wisdom teaching platform, to promote the optimization of the process of wisdom teaching change. The catechism data of the basic course of tourism management of a smart teaching platform is collected, and z-score and PCA principal component analysis are utilized to eliminate the quantitative influence of the data. The best cluster values were determined by hierarchical cluster analysis, and the learners were divided into three cluster groups with the help of K-Means clustering algorithm. One-way ANOVA was introduced to compare the achievement data before and after smart teaching of the three groups of students to explore the effect of smart teaching. The results showed that among the paper grades, category 2 students had the greatest change in the mean value of their grades. In practical grades, the mean value of category 2 students’ practical grades was 95.63, which was 20.18 and 26.75 points higher than those of category 0 and category 1 students, respectively. p-value of 1.56951E-17 was less than 0.05, which indicated that the grades of the three categories of students showed significant differences.
This paper combines the necessary functional requirements for teaching system generated by teaching activities in the context of mobile Internet, designs the general framework of the system, users and their rights management, and constructs a set of teaching system. Subsequently, the traditional PSO algorithm is introduced, and the processing scheme of the scheduling problem is defined as particles to form an initial particle swarm, while the particle swarm position in the algorithm is updated by drawing on the crossover idea of the genetic algorithm, so as to optimize and obtain the scheduling algorithm based on DPSO. Then we test the teaching system of this paper from three levels of pressure bearing, response delay and stability performance to ensure the operating environment of the scheduling algorithm of this paper. The courses of three colleges of a university are used as experimental data to analyze the performance of the scheduling algorithm in this paper. In the comparison of course arrangement in different colleges, the adaptability of this paper’s scheduling algorithm is above 0.900, while the highest adaptability of manual scheduling is only 0.8147, which indicates that compared with manual scheduling, this paper’s scheduling algorithm is able to make a more reasonable course arrangement.
Aiming at the potential risks existing in the power market transaction under the new power system, and considering the temporal attributes of the information, this paper proposes to use dynamic Bayesian network to construct the risk monitoring and early warning model of the power market transaction. The dynamic Bayesian network is utilized to calculate the correlation between different risk factors, estimate the risk value of power market transactions, and classify the warning level. Taking the southern regional electricity market as the research object, the relationship between electricity price and transaction volume is explored based on the experimental dataset. A credit grading system is introduced to carry out transaction prediction simulation experiments, relying on the prediction data to determine the link between electricity price and transaction volume. The results show that overall power price and transaction volume show a negative correlation, but in June, when the power price is 0.4370 yuan per kWh, the transaction volume still reaches 19.65 million kWh, and the inverse relationship between the transaction volume and the price is not obvious. The use of dynamic Bayesian network to construct the power market transaction risk monitoring and early warning model can dynamically update and adjust the risk monitoring with the passage of time, making the power market transaction early warning more flexible and real-time.
In this paper, the semantic description framework is used to standardize the extraction of semantic information of non-legacy images. The SIFT algorithm is chosen to calculate the key feature points of non-legacy images. The integrated semantic description framework and SIFT algorithm construct a model to extract the non-heritage image features globally and process them locally, and add the attention feature fusion module to fuse the features that are inconsistent in semantics and scale, so as to realize the accurate extraction of features. Use the algorithmic model of this paper to extract the color features of She ribbons. Develop a website for she color band design and verify its usability. Collect website evaluation data from target users to study the role of digital translation of non-heritage elements. The color feature extraction results are richest and most detailed when the number of She ribbon feature colors extracted is 21. The website usability scale score was 50.31, rating B+, with usability. 65% of the users thought that the website embodied the cultural characteristics of She ribbons. 71% of the users thought that the website was very helpful for understanding the ethnic graphic culture. 88.16% of the users thought that the digital design of She ribbons could effectively promote the dissemination of the ethnic graphic culture.
Increasing urbanization has led to large changes in residents’ consumption behavior, but due to a variety of factors, the overall level of residents’ consumption is low, so that it cannot play its role in promoting economic growth. This paper selects the panel data of China Household Finance Survey from 2011 to 2019, and empirically analyzes the impact and path of changes in residents’ consumption behavior on consumption upgrading by constructing structural equation model and fixed effect model combined with STATA software. The study shows that changes in residents’ consumption behavior and its dimensions can promote residents’ consumption upgrading, and there is regional heterogeneity in the impact of changes in residents’ consumption behavior on residents’ consumption upgrading, and its promotion effect on residents’ consumption upgrading is stronger in the developed regions in the Middle East. Based on the above findings, this paper puts forward feasible suggestions on how to optimize residents’ consumption behavior to better promote residents’ consumption upgrading.
This paper takes the integration of AI technology into piano teaching as the starting point, generates accompaniment rhythms through AI computation, adopts deep learning model to generate accompaniment, and builds a multi-level accompaniment effect generation mechanism. Taking the MuseFlow model as the base model, the generative adversarial network and variational autoencoder are introduced to optimize the structure in a limited arithmetic environment. Quantitative and manual evaluations are used to measure the accompaniment generation effect of the proposed mechanism, and controlled experiments are designed to explore its practical application effect. The results show that the improved MuseFlow model generates accompaniment with an average pitch distance of 0.92, which is 0.15 smaller than that of MMM, and the overall score reaches 4.18. The scores of the experimental group in all six abilities are significantly higher than those of the control group, the degree of students’ positive response to each ability increases to some extent, and the number of students who consider the ability of melodic creation to be at a satisfactory level is 18 more than that of the pre-experiment after the experiment.
Network teaching has become an important way of teaching reform in current higher education and has been applied in the education of various courses. This paper proposes a kind of intelligent auxiliary teaching system based on P2P mode, and researches the realization of the system with the example of Civics course. The construction of “Civics Course Teaching Evaluation System” is systematically discussed by using the fuzzy comprehensive evaluation method, and the weights of the indicators are calculated by entropy weighting method and hierarchical analysis method. Taking the teaching of Civics and Political Science in a university in Guangdong Province as the research object, the intelligent teaching system proposed in this paper is applied to evaluate the interactive effect of teaching with the evaluation system constructed in this paper. The evaluation analysis shows that the school’s evaluation results of all indicators are above 80 points, and the overall teaching rating of its Civics and Political Science course is 86.33, in which social merit, teaching equipment, teaching expression, and professional ethics have the highest scores of 94.37, 92.32, 89.02, and 88.52, respectively. It shows that the intelligent auxiliary teaching system for Civics proposed in the article is well applied in actual teaching.
Aiming at the bridge project in the construction of the development of the status quo of the overdevelopment, maintenance and management level lagging behind, this paper, under the premise of ensuring the safety of the bridge, the bridge surveillance monitoring and risk early warning launched a study to solve the problems of its operation and repair and maintenance. For bridge monitoring and safety monitoring, this paper is based on the vibration acceleration of bridge structure damage identification. On this basis, the damage recognition model constructed by using common neural networks convolutional neural network (CNN), long short-term memory network (LSTM) and deep autoencoder (DAE), and the recognition effect of the three models is compared. This for, for the bridge risk problem, this paper utilizes the Extreme Learning Machine (ELM) and Firefly Algorithm (GSO), constructs the implementation of the GSO-ELM algorithm model for early warning of the bridge safety risk, and the experimental results show that the model proposed in this paper has good effect, which provides support for the future development of the bridge structural safety facilities should be developed in the direction of digitization, automation, and networkization.
A scientific, comprehensive and effective evaluation system of asset management performance of public colleges and universities in the context of high-quality development in the new era contributes to the “asset power” for the construction of high-level and high-quality development of colleges and universities. This paper takes 20 public colleges and universities in Province Y as research samples, and analyzes the asset management performance of public colleges and universities and its influencing factors through the super-efficiency DEA model and SFA model. The results show that the asset management performance of 13 public colleges and universities has reached DEA effective, and the rest of them are DEA ineffective. Human and material inputs have a significant positive effect on the asset management performance of public universities in terms of inputs, and both research income and number of patents have a significant positive effect on the asset management performance of public universities in terms of outputs at the 1% level. Relying on the scientific evaluation index system of asset management performance of public universities, establishing a high-level asset management team and clear budgeting and audit management are effective means to improve the asset management performance of public universities.
With the rapid development of the sports industry, quality traceability and credibility issues have become important issues in the sports industry chain. Traditional quality traceability and credibility assurance methods have shortcomings in efficiency and accuracy, and emerging technologies need to be adopted to solve them. Blockchain technology is considered an important means to address quality traceability and credibility issues in the sports industry chain due to its decentralized, transparent, and tamper resistant characteristics. This article proposed a quality traceability and credibility assurance system based on blockchain technology to address the issues of quality traceability and credibility in the sports industry chain. The system adopted blockchain technology to achieve quality traceability and information credibility assurance in the production, circulation, and consumption processes of sports products. The system adopted distributed ledger technology to record the production, circulation, and consumption records of products, and achieved automated quality inspection and transaction verification through smart contracts. The experiment in this article showed that using this system can improve efficiency and reliability by 80% -95%. The research on methods and systems for enhancing the credibility of quality traceability in the sports industry chain through blockchain can effectively improve the quality traceability ability of the sports industry chain, thereby safeguarding consumer rights and market stability.
As the economy develops, the tourism ecological environment (TEE) has been gradually damaged. The ecological environment is the basis of human life, and the sustainable development of the ecological environment is of great importance to promote the stable development of society. China has rich grassland tourism resources. However, as a result of the rapid development of tourism, some scenic spots have been overdeveloped and commercialised, leading to the destruction of natural landscapes, damage to ecosystems and the gradual sanding of large tracts of grassland. The desert grassland used for tourism development is located between the grassland and the desert, and is the barrier that ensures the entire grassland ecosystem. To carry out environmental management of tourism ecology, it is necessary to construct a statistical monitoring index system for tourism ecology. However, traditional ecological environment monitoring is mainly based on manual sampling survey, which is cumbersome. The monitoring data is not accurate enough. In this paper, remote sensing technology (RST) was used to obtain remote sensing images of desert grassland, and intelligent image processing (IIP) technology was used for feature recognition. Compared with the traditional ecological environment statistical monitoring method, it showed that: In desert grassland A, the average monitoring accuracy of the traditional ecological environment statistical monitoring method and the ecological environment statistical monitoring method based on IIP were 90.12% and 94.56% respectively; in desert grassland B, the average monitoring accuracy of the traditional ecological environment statistical monitoring method and the ecological environment statistical monitoring method based on IIP were 88.20% and 92.60% respectively. Therefore, statistical monitoring of TEE based on IIP can improve the monitoring accuracy of ecological environment indicators.
In order to analyze the reading behavior and its meaning of readers in blockchain online reading platforms, this article conducted research on reading emotion recognition. This article utilized the characteristics of blockchain technology to analyze the reading mode of blockchain internet platforms. By using audio and image bimodal recognition methods, the recognition of readers’ reading emotions can be achieved. After feature extraction of speech and facial images, hidden Markov models (HMM) can be used for speech emotion recognition. Support vector machines (SVM) can be used for facial image emotion recognition, and decision level fusion can be used for bimodal emotion recognition. This article obtained the final emotion recognition results to analyze and predict user reading behavior. Analyzing the psychological state of readers based on emotional recognition results can achieve more intelligent reading information push. Experimental results on the effectiveness of reading bimodal emotion recognition showed that the accuracy of reading bimodal emotion recognition based on decision level fusion was much higher than that of single modal emotion recognition. The bimodal method has an average accuracy rate of over 85% in emotion recognition and has a high effect in emotion recognition. Reading bimodal emotion recognition based on audio and image can accurately identify readers’ emotions, adjust information push content in a timely manner, and achieve the regulation of readers’ emotions, which has high application value.
At present, there are differences in the building of information in various career institutions. The degree of implementation of management, teaching and services is uneven, and educational resources are limited and unevenly distributed. The construction of educational resources includes the overall layout, structure and quantity of resources, information mode, service impact, etc, all of which require systematic planning. Under the above background, this paper conducted research on the topic of building a model of co-construction and sharing of digital ideological and political resources for embedded courses based on artificial intelligence algorithms, and considered the insufficiency of the existing digital ideological and political resources in the allocation efficiency and insufficient system sharing, as well as creatively used artificial intelligence algorithms to improve the previous system. In the algorithm, the texture mapping of the system was carried out, and the duty cycle of each columnar area was specified. In the experiment, the number of resources in the digital resource platform was investigated, and the input of different types of colleges and universities in digital ideological and political resources was collected. The explanation of experimental data: 83% of 985/211 colleges and universities used the database designed in this paper, and 17% of them actively built the database; 57% of the general undergraduate schools used the database designed in this paper, and 20% were under construction, as well as 13% were still preparing. This showed that in general undergraduate schools, a small proportion of the digital ideological and political resource sharing model was used, and the 985/211 colleges and universities had relatively good investment in the construction of digital ideological and political resources.
The rapid expansion of tourism across the world necessitates constant innovation and development in the services offered to visitors in order to assure their comfort and happiness while on the road. Travelers’ experiences may be greatly enhanced by providing them with basic and essential conveniences such as optimal route identification and suggestion technology. In this paper, we use data mining to investigate the effect of scenic site clustering and group emotion on tourist route choosing. It is common for traditional route selection algorithms to just examine the impact of picturesque locations on route design. Many people choose the Chimp optimization algorithm (ChOA) because of its straightforward idea, simple implementation, and high level of resilience. With the goal of solving practical challenges in mind, this study uses real-world geographic data to build a discrete ChOA for the tourism route planning problem, which may be applied in practice. Simulation experiments are done, and outcomes data are studied and assessed. The assessment findings show that the ChOA is suitable for mass tourist data mining. The smart machine’s final best tour routes are directly tied to the requirements, interests, and habits of visitors and are completely connected with geospatial services to ensure accuracy. The ChOA algorithm serves as a good example of how data mining may be used in the field of mass tourism.
Due to the deepening reform of quality education, the requirements for physical education teaching in colleges and universities have become increasingly strict. In this era of rapid renewal and development of multimedia information technology, in order to make the traditional sports basketball teaching keep up with the pace of the trend and to search for the future development direction of college public sports basketball teaching, this paper studied the application of multi information data fusion technology in college public sports basketball teaching. The remote sensing technology and global positioning system in the multi information data fusion technology were used to conduct real-time detection and statistics on the sports effects of students in basketball teaching, and the relevant experimental scheme was designed. The data results recorded by manual recording and multi information data fusion technology were compared. The experimental results showed that when three student representatives and remote sensing technology simultaneously counted the times of passing and touching, the success rate of passing and the scoring rate of throwing for four sports members, the accuracy of remote sensing technology was higher; the Global Positioning System (GPS) system could effectively record the running distance, average speed and heart rate of 4 athletes. The average speed of No. 3 athlete was 9.1 m/s; the passing rate and shooting rate were both 50%, and the average speed of No. 4 athlete was 7.85 m/s. The pass success rate was 50%, and the shooting rate was only 33.3%. These data were conducive to teachers’ timely understanding of students’ personal conditions and basketball level, which could improve the efficiency of college sports basketball teaching and also increase the quality of students’ sports. At the same time, the questionnaire survey method was also used to study the results of the introduction of multi information data fusion technology. The findings shown that multi-information data fusion technology might increase students’ passion for learning basketball courses, hence improving the quality of sports, by altering their interest and attitude. In order to provide guidance for the future development of college public sports basketball instruction, this study offered a reference value for the application of multi-information data fusion.
Artificial intelligence (AI) and multimedia technology (MT) provide a new platform for college physical education (PE), which plays a positive role in promoting college PE. Combined with the actual situation, some discussions are made on the application of multimedia teaching technology in college PE teaching, in order to better serve the MT teaching of college PE teaching. The popularization and wide application of multimedia teaching technology in education and teaching have caused a series of changes in teaching concepts, teaching design, teaching methods, creative teaching, etc., preparing for the development of teaching. Starting from the teaching quality evaluation methods, the existing problems in the evaluation process were analyzed. These problems are reflected in the retrospective evaluation method, which is not scientific enough to summarize the evaluation results, and it is difficult to track and improve the teaching ideas. Teaching evaluation is a complex system that includes classroom teaching, sports facilities, sports activities, classroom teaching, physical health, supervision and management and many other aspects. Modern educational philosophy generally holds that the classroom teaching process should include formulating clear teaching objectives, selecting the most appropriate teaching methods and using scientific evaluation methods to collect information about correct answers. According to the construction of a comprehensive evaluation system of intelligent algorithm and AI technology, the quality of teaching evaluation has been improved by 21.4% after calculation.
Although human motion form capture is widely used in multiple fields, it often requires a significant amount of time and cost to learn how to operate the device during use. Therefore, this article attempted to apply computer vision (CV) technology and image segmentation algorithms to human motion form capture technology, simplifying the operation scheme and improving recognition accuracy and efficiency. This article provided an in-depth analysis of human motion form capture technology. Firstly, it identified several parts of the current human motion form capture technology that can be optimized, and introduced the effects of these optimized parts on human motion form capture in sports training. This article took the form capture of aerobics athletes as a sample and extracted 50 keyframe images containing aerobics scoring actions from 100 aerobics activity videos. The extraction interval for these keyframe images was at least 10 seconds. Next, this article used histogram equalization to enhance the image, while segmenting and recognizing the human motion forms of the five types of actions in the keyframe images, highlighting the level of action standards of athletes in aerobics. Finally, this article selected 6 key frame images containing different movements of aerobics athletes for comparative experimental analysis. In this experiment, both commonly used optical unlabeled capture techniques and motion morphology capture techniques combining CV and image segmentation algorithms were used to capture the human body in the image. The addition of CV technology and image segmentation has improved the overall performance of human motion morphology capture technology by approximately 26.02%. The integration of CV technology and image segmentation algorithms into human motion form capture technology has greatly improved image processing efficiency. At the same time, CV technology and image segmentation algorithms have also enabled better image processing accuracy in human motion form capture.
Image hiding is a technique for transmitting secret information under the cover of a digital image. It usually conceals sensitive information into images for the purpose of encryption. Currently, high embedding capacity and information security remain important research aspects of the image hiding. In this study, a secret image sharing scheme based on a reference matrix is proposed to enhance embedding capacity and verify data integrity. In the proposed scheme, a hill matrix is designed as a reference matrix and a location table is generated. Moreover, a location pair table is generated to ensure the uniqueness of data hiding locations. Then, leveraging the processing of the location pair table, as well as the mapping of the reference matrix and the location table, each pixel pair is exploited to conceal eight secret bits. Furthermore, based on the special construction of the hill matrix, a deception recognition mechanism is designed. This mechanism can detect deceptive behavior and identify tampered images by means of data hiding locations. The experimental results indicate that the proposed scheme achieves a higher embedding capacity and better deception recognition performance than that of most of existing schemes.
Upon the arrival of the sharing consumption model, guaranteeing the authenticity of products and the transparency of transactions has emerged as fundamental challenges hindering the industry’s progression. This paper explores the selection and optimization of blockchain technology implementation methods within the shared supply chain. Through a comparative analysis of non-blockchain, private blockchain, and distributed application models, our findings reveal that distributed application generates higher profits when consumers exhibit high sensitivity to blockchain performance and when such performance adheres to specific standards. Conversely, the private blockchain is more suited to customized requirements. Blockchain technology not only increases prices and transparency but also enhances consumer trust, particularly within the distributed application framework. Performance plays a crucial role in decision-making, with the private blockchain relying on corporate investment for optimization and distributed application being constrained by the limitations of the public chain. Based on these findings, it is recommended that enterprises adopt a flexible approach in selecting the most appropriate mode according to their unique needs. Additionally, they should prioritize technological innovation, strive to improve blockchain performance, consider fostering consumer trust, and promote collaborative development throughout the supply chain. These strategies will collectively contribute to the healthy and sustainable growth of the industry.
“Internet + medical health” service is an important direction of current medical development. The high interactivity between doctors and patients in online medical services and the massive and dynamic nature of recommended information have brought new challenges to the platform’s analysis of patient perceived trust. It is difficult for the trust transfer model to process massive information in real time. Clustering massive recommended trust is an effective solution, but data clustering is difficult to process simultaneously with the perceived recommendation trust tendency, which brings about the problem of perceived recommendation trust clustering. How to measure the trust tendency reflected in the clustering of patient perceived recommendation trust is a difficult problem faced by the trust transfer model in the context of Internet medical health services. This paper proposes a two-stage research idea of ” conversion first, clustering later”. Intuitive fuzzy sets are used to measure the fuzziness of patient perceived recommendation trust, and combined with sentiment dictionary, density clustering method and other methods to cross and penetrate each other, a patient perceived recommendation trust clustering method is constructed in the context of Internet medical health services. Finally, data experiments were conducted using the real data of the top 17 doctors on the Haodafu online platform to verify the effectiveness of the method. This method can reflect the subjectivity and ambiguity of patients’ perceived trust, provide a solution for the processing of massive recommendation information, contribute to the research on the improvement of trust transfer method system, and provide method support for predicting and analyzing the trust measurement of patients in the context of Internet medical health services. The model proposed in this paper can be used as the core of the trust-based recommendation system in Internet medical care, and help Internet medical platforms formulate precise strategies for doctors.
This paper seeks to discuss focused prototype development of self-driving, autonomous, driverless, electric cars with emphasis on subsystem advancement constituting the progress of the technology. The introduction lays special emphasis on the increased role of autonomous technology in transforming transportation by underlining its potential to enhance safety, effectiveness, and sustainability. Some technical background is provided with the definition of what an autonomous car is and its evolution timeline. Electrical vehicle current advancement is also described in detail. At last, comparative analysis of further prototype developments and subsystems with respect to their usefulness and prospects is given. This assessment serves to contribute to the present discourse on self-driving vehicle technology, and the role that these vehicles will play in on-going transport modal shift.
Purpose – This study investigates the impact of career planning education on university students’ entrepreneurial intentions by examining the mediating roles of self-efficacy and perceived behavioral control, as well as the moderating effects of digital competency and risk propensity. Design/methodology/approach – Data were collected from 450 university students through a structured questionnaire. The research model was tested using structural equation modeling with bootstrapping procedures for mediation analysis and hierarchical regression for moderation effects. Findings – The results reveal that career planning education positively influences entrepreneurial intentions both directly ( =0.312, p<0.01) and indirectly through self-efficacy ( =0.178, p<0.01) and perceived behavioral control ( =0.133, p<0.01). Digital competency ( =0.156, p<0.01) and risk propensity ( =0.143, p<0.01) positively moderate these relationships. Practical implications – The findings suggest that higher education institutions should integrate digital skills development into career planning curricula and tailor educational approaches to students' individual characteristics to enhance entrepreneurial intentions effectively. Originality/value – This study extends the theory of planned behavior by incorporating digital competency as a crucial moderating factor and demonstrating the specific mechanisms through which career planning education influences entrepreneurial intentions in the digital era.
As a result of continuous economic development and accelerated urbanization, the agriculture development has had to change from the traditional mode of agricultural production to the modern mode of agricultural production. What kind of method can better help the development of modern agricultural production mode has become one of the current research topics that has attracted much attention. In response to this problem, the field of modern agricultural production models becomes highly relevant for research. With the in-depth study of modern agricultural production, the research on Internet of Things (IoT) technology in rural characteristic ecological agriculture (ECO) is gradually carried out, and its functional advantages are of great significance to promote the development of modern agriculture. This paper aimed to study the application of IoT technology in the development of rural characteristic ECO. The analysis and research of IoT and ECO enables it to be applied to the construction of an ecological farmland information monitoring system to address the problem of enhancing the ECO development with rural characteristics. In this paper, IoT technology, information detection and ECO were analyzed; the performance of the method was experimentally analyzed; the relevant theoretical formulas were utilized for interpretation. The outcomes demonstrated that the incidence of pests and diseases in field A using the IoT-assisted information monitoring system was 31.11% lower than that in field B, and the use of pesticides was reduced by 15.69%. It can be learned that IoT technology can meet the needs of enhancing the development level of rural characteristic ECO, and the level of agricultural development and work efficiency have been greatly improved.
With the rapid development of society, the emergence of society and people’s daily life have put forward higher quality requirements for power supply. The original distribution system cannot monitor and control the circuit condition in real time. The power grid operation efficiency is low, and the loss of electric energy in the transmission process is large, resulting in the unstable power supply to users. With the development of smart grid, distribution automation has become the goal of Power System (PS) development. There are many noise data in the process of medium voltage distribution communication. In this paper, the medium voltage high-speed analog Communication Technology (CT) was applied to distribution automation. By modulating the signal and other operations, automatic power distribution can be realized, which can effectively shorten the maintenance time of fault circuits and quickly share power data resources. This paper compared the traditional medium-voltage distribution with the distribution automation based on the medium-voltage high-speed analog CT. The experimental results showed that the average power supply reliability of the traditional medium-voltage distribution and distribution automation was 88.90% and 95.56% respectively in the 10 kV voltage. In the 20 kV voltage, the average power supply reliability of traditional medium-voltage distribution and distribution automation was 90.24% and 97.04% respectively. Therefore, the application of medium-voltage high-speed analog CT in distribution network to distribution automation can effectively improve the reliability of power supply.
Financial digital management is a new type of financial management method. Through information technology, the financial management process has been digitized, and with the help of technical means such as data analysis and artificial intelligence, financial management automation has been achieved. Traditional financial management methods often require a large amount of manual intervention and processing, which is prone to problems such as cumbersome data processing, time-consuming and labor-intensive, and prone to errors. With the development of computer technology and network technology, digital management has become a new trend in financial management. This article analyzed the application of blockchain and cloud computing technology in financial digital management, and selected 12 enterprises as the research objects. The traditional financial management model and the financial digital management model of blockchain and cloud computing technology were respectively adopted to compare the differences in financial process efficiency, data accuracy, labor cost savings, digital management, and financial risk management between the two models. The experimental results of this article indicated that under the financial digital management mode using blockchain and cloud computing technology, the processing time of the revenue and expenditure process was 4.45 hours in terms of financial process efficiency. In terms of data accuracy, the accuracy rate of accounting was 99.7%. In terms of labor cost savings, the labor cost was 1.505 million yuan/year. In digital management, the data processing efficiency score was 92. In financial risk management, the accuracy score of risk assessment and prediction was 93, which was better than traditional financial management models. The adoption of blockchain and cloud computing technology in financial digital management can significantly improve multiple key indicators such as financial management efficiency, data accuracy, and security. This model has important value and significance for enterprises.
The popularity of the Internet and mobile smart terminals has changed many forms of learning, and the mobile learning model was born in this environment. As a new learning mode, mobile learning has brought certain development opportunities for college English writing teaching. In the current educational environment, many students hold various mobile devices, which also motivates them to have a strong willingness to learn on mobile. It can be said that the application of mobile learning to English writing is quite suitable. At present, the application of mobile learning in college English writing is not mature enough, and there are often a series of problems such as shortage of resources and network freezes, which also reduces students’ enthusiasm for learning. In order to further improve the fluency and maturity of the mobile learning mode, this paper has combined the wireless network to study the new mobile learning mode of college English writing. By building a mobile learning framework based on wireless network, innovating mobile learning writing content and computing learning resource categories, a new mobile learning mode of college English writing has been finally formed. The experimental results have shown that the new model has mobilized students’ enthusiasm for learning and further improved the writing efficiency. Compared with the old model, the efficiency has increased by 6.73%.
Traditional power load forecasting (PLF) usually uses statistical models or time series analysis methods, but they often only consider historical load data and ignore the impact of meteorological, temperature, humidity and other factors on load, resulting in inaccurate load forecasting. Moreover, traditional methods have limited real-time performance in power load data transmission and cannot respond to changing load demands in a timely manner, which limits the real-time and accuracy of PLF. Wireless networks (WN) and intelligent sensing technology (IST) were used to obtain real-time charge data, and these data were intelligently analyzed to improve prediction performance. WN and IST were used to improve the transmission efficiency and prediction accuracy of PLF. This article studied the transmission delay and integration delay of power load data in WN, and conducted experimental tests on the root mean square error (RMSE) of CER Electricity Data, REFIT Power Data, and Umass Smart Data Set datasets using an intelligent sensing algorithm based on sensors to study their predictive effect on power load. As the number of users continues to increase, the transmission delay and integration delay of power load data were also increasing. During the process of increasing the number of users from 0 to 500, the transmission delay increased from 389ms to 735ms; the integration delay increased from 568ms to 1086ms. The power load prediction algorithm based on intelligent perception technology had average prediction RMSEs of 0.2885, 0.2716, and 0.2618 for CER Electricity Data, REFIT Power Data, and Umass Smart Data Set datasets, respectively. In WN, the transmission delay and integration delay of power load data are relatively small, and with the increase of the number of users, the impact of this delay is relatively small, which can have the effect of supporting the transmission and integration of power data for a large number of users. The power load prediction algorithm based on intelligent perception technology has good prediction results for different datasets and can accurately predict power loads.
This research presents an innovative machine learning framework for predicting library space utilization patterns through the integration of multi-modal deep learning architectures and ensemble methodologies. The proposed system combines Long Short-Term Memory (LSTM) networks with attention mechanisms and sophisticated feature engineering techniques to achieve superior prediction accuracy while maintaining computational efficiency. The methodology encompasses three primary contributions: (1) development of a comprehensive feature extraction pipeline incorporating spatial, temporal, and environmental data streams; (2) implementation of a novel LSTM-Attention hybrid architecture with adaptive learning rate optimization; and (3) integration of ensemble learning techniques for robust prediction performance. The framework demonstrates significant improvements over existing approaches, achieving 96.8% prediction accuracy across diverse operational scenarios. Experimental validation, conducted using an extensive dataset comprising 2.1M samples collected over 33 months from multiple library facilities, demonstrates the framework’s effectiveness. The proposed model achieves a Mean Absolute Error (MAE) of 0.142 and Root Mean Square Error (RMSE) of 0.186, representing a 39.8% reduction in prediction error compared to baseline approaches. The system’s computational efficiency is evidenced by an average processing time of 45.3ms per prediction, with a memory footprint of 512MB. The research contributes to the field of intelligent library management systems by establishing a theoretically grounded and practically implementable solution for space utilization prediction. The framework’s superior performance in capturing complex spatial-temporal patterns, combined with its computational efficiency, makes it suitable for real-time applications in resource-constrained environments. These advances provide a foundation for enhanced space management strategies in modern library systems.
Amidst the digital economy and ESG policy frameworks, digital transformation emerges as the prime strategy for high-tech companies to enhance their corporate performance. The research investigates the impact of high-tech organizations’ digital transformation on their performance, utilizing data from A-share listed tech firms in Shanghai and Shenzhen spanning 2018 to 2022.The research indicates that digital transformation enhances the performance of high-tech firms in the context of ESG. The modulating mechanism shows that executive compensation will weaken the impact of digital transformation on enterprise performance. The intermediary mechanism demonstrates that internal control and cost effect contribute to the mediating influence on the relationship between enterprise performance and digital transformation. Each of them has successfully cleared multiple tests for robustness. At the same time, there is a certain heterogeneity in the influence of high-tech enterprises on firm performance, and the improvement effect on firm performance is significant in the east and the growth and maturity period. The research presents new empirical evidence and acts as a benchmark for understanding how digital transformation affects high-tech companies’ performance.
Purpose – This study aimed to explore the internal structure of sustainable employability of liberal arts college students in China and develop a comprehensive scale to facilitate research on this topic and establish a theoretical framework for cultivating sustainable employability of liberal arts college students in China. Design/methodology/approach – Through theoretical derivation and open questionnaire and the Delphi method, the main dimensions of sustainable employability of liberal arts college students are explored. The components elments of each dimension are explored through a text analysis of 189 job advertisements. Through 392 questionnaires and statistical analysis techniques, a scale is developed for measuring the sustainable employability of liberal arts college students. Findings – This study found three dimensions characterizing the sustainable employability of liberal arts students in China: attribute characteristics, general ability of employment, and innovation-driven ability. Additionally, the attribute characteristics encompassed five attribute elements, the general employment ability included six, and the innovation-driven ability included four. This study also developed a 34-item scale for measuring the sustainable employability of liberal arts students that demonstrated good reliability and validity. Originality/value – This study was among the first to investigate the internal structure of sustainable employability of liberal arts students in China.
This study introduces a new methodology for the configuration and optimization of algorithm-driven strategies in the digital economy. It puts forward a hybrid optimization algorithm for the efficient handling of complex resource allocation problems. The proposed approach combines adaptive learning mechanisms with traditional optimization methods, showing significant improvement in convergence speed, solution accuracy, and stability of the system. Through extensive experimental validation conducted on a range of benchmark functions and real-world contexts, this algorithm proves to be outstanding at a 48.7% reduction in convergence time, as well as a solution quality enhancement by 66.4% compared with the traditional methods. Robustness analysis confirms consistent effectiveness under all diverse noise conditions and retains high success rates, even in demanding environments. This result greatly contributes to advancing algorithmic optimization approaches for digital economic systems and paves the way toward concrete applicative implementations.
With the rapid development of artificial intelligence technology, the education sector is undergoing unprecedented changes. Personalized learning has become a key method to enhance teaching quality and learning outcomes. This paper aims to explore the application of artificial intelligence technology in personalized learning resource recommendation for students, by constructing user profiles, multidimensional models, and personalized recommendation algorithms, in order to provide precise learning resource recommendations for students. This paper proposes a personalized learning resource recommendation algorithm based on a one-dimensional convolutional neural network (1D-CNN). The algorithm first extracts local features of the sequence through convolutional operations, then uses pooling operations to extract long-term features of the sequence, and combines the two features through weighted addition to obtain the user feature information, which allows for the comprehensive extraction of both local and long-term features. Subsequently, the user feature information is multiplied by the linearly transformed sequence information to introduce temporal information. Additionally, student learning records, class performance, and incorrect question records are collected and integrated as user feature information. These user features are passed through a feedforward network to achieve nonlinear transformation and cross-dimensional interaction enhancement. Finally, the user feature vector and item feature vector are computed to obtain their relevance, which is then used for recommendations. Experimental evaluations validate the effectiveness and feasibility of the proposed method, with the aim of providing valuable insights for educational reform and development.
The rapid development of digital technology and artificial intelligence has made the improvement and optimization of intelligent warehousing and automated distribution systems important topics for research in modern logistics management. With this as the background, the current study uses a systematic approach to explore critical factors, innovative ways, and implementation strategies related to these factors and their role in improving the effectiveness of intelligent warehousing systems. The study adopts a mixed-methodological approach, establishing a comprehensive evaluation index system including operational efficiency, technical performance, and economic benefits, and simultaneously verifying the implementation of the system through empirical analysis. According to the findings, the intelligent warehousing system increased the efficiency of operations in relation to order processing time and had reduced it by 71.7%, and enhanced the accuracy of picking to 99.8%. The intelligent warehouse system by use of machine learning and meta-heuristic algorithms had greatly improved the efficiency in resources utilization and energy as storage utilization increased by 19.3% while energy consumption dropped by 31.4%. A cost-benefit analysis shows that, despite the significant up-front financial investment, the system achieved a 186% return on investment over three years. This research deepens the theoretical understanding of intelligent warehousing and, at the same time, provides optimization strategies applicable to industry practice. Future research directions should focus on exploring the applications of multi-agent digital twin technology and researching how intelligent warehousing systems contribute to supply chain resilience and sustainability.
Power metering system is directly related to the production and operation level and benefit of power supply enterprises, and even has a close relationship with the national economic development and people’s life. Numerous scholars have applied deep learning to the field of fault diagnosis. Accordingly, this paper proposes a fault diagnosis method for power metering system based on stacked autoencoder (SAE) algorithm. The deep learning data samples are formed by comprehensively collecting the historical operation data of the system and the feature data provided by the third-party manufacturers. And the fault diagnosis model is designed with the SAE algorithm, and the training and optimization fine-tuning of the algorithm model is realized by BP neural network. Finally, the model is trained using explicit test data samples, and the BP neural network can reach the set accuracy after 3804 training sessions with the output error. Compared to Elman neural network iterations are less and converge faster. Using the trained fault detection model of power metering system for fault diagnosis, the model can successfully classify the faults and achieve the expected diagnostic effect.
This paper presents an AdaBoost-DNN (Adaptive Boosting-Deep Neural Network) model for the detection of anomalous electricity consumption in power grid users. Initially, the k-means SMOTE (Synthetic Minority Oversampling Technique) technique is employed to enhance the sample set of the original anomalous consumption data to address the issue of data imbalance. Subsequently, an ensemble learning model based on AdaBoost-DNN is designed for the detection of anomalous consumption. To validate the effectiveness and superiority of the proposed AdaBoost-DNN model, comparative experiments are conducted with three traditional algorithms.
This study aims to construct a corporate demand model of brand design for financial central enterprises based on grounded theory, providing a systematic theoretical framework to help financial central enterprises effectively meet their brand design needs. The method of grounded theory is used to extract relevant information from public information and in-depth interviews, and to demonstrate the rationality of the corporate demand model by combining quantitative research. Through an in-depth analysis of the subsystems of demand motivation generation, demand factor analysis and demand design realisation, a feasible theoretical framework is provided for financial central enterprises to achieve effective satisfaction of brand design corporate demands. The problem of accurately grasping requirements in the practice of brand design for financial central enterprises is solved. Useful reference and support are provided for the brand design of financial central enterprises. The innovative practice of brand design is promoted for financial central enterprises.
This study investigates the application of artificial intelligence techniques in coal mine gas monitoring and prediction, aiming to construct more efficient and accurate gas concentration prediction models to reduce the risk of gas explosion in coal mine production. Due to the limited performance of traditional prediction methods in dealing with high-dimensional and dynamic three-dimensional mining environments, this study employs a fusion model based on temporal convolutional network (TCN) and temporal generative adversarial network (TimeGAN), TCN-TimeGAN, to predict the gas concentration. The model combines the interval sampling advantage of TCN and the time series characteristics of TimeGAN, and through four processes of embedding, recovering, by generating and discriminating gas concentration time sequences, the time-dependent features of gas concentration data can be effectively captured, thus improving the prediction accuracy and timeliness. In this study, gas concentration data from September 2020 through December 2021 were used as the basis, through data cleaning and outlier processing, it is found that the gas concentration data has obvious time-dependence, which is suitable for using time series modeling. Embedding and Recovery Networks via TCN-TimeGAN Modeling, the gas concentration data are mapped to a low-dimensional feature space, a generative network is then used to generate new time series data from random noise, and the model parameters are optimized by combining the discriminative network in order to improve the quality and consistency of the generated data. In particular, to cope with the problem of gradient instability of generative adversarial networks during training, In this paper, Wasserstein distance is introduced to optimize the loss function and a gradient penalty term is added during the training process to improve the stability of model training and the realism in the samples generated. In addition, this study also explores the prediction performance of combining LSTM networks for gas concentration. The standard recurrent neural network (RNN) faces the problem of gradient vanishing in the processing of long time-dependent data, whereas the improved LSTM overcomes this problem through memory cells and gating mechanism for real-time prediction task of gas concentration. In this study, the LSTM is further extended to three-dimensional spatial input data, experiments demonstrate the prediction accuracy of the improved LSTM. To verify the validity of the model, this paper adopts a hierarchical K-fold cross-validation method, which divides the data into a training set and a validation set to ensure that the model can be generalized. Experimental results indicate that TCN-TimeGAN and improved LSTM significantly outperform traditional methods in gas concentration prediction. By analyzing the training and validation accuracies, the models showed high prediction accuracy (89.1% to 93.8%) after 20 epochs, verifying the stability and applicability of the models.
In conclusion, this study shows that the gas prediction model based on TCN-TimeGAN and improved LSTM can more accurately predict the gas concentration in coal mines, improve the intelligence level of coal mine gas monitoring, and provide technical support for safe production in coal mines. Meanwhile, the methods and models in this study also provide new thoughts and methods for time series data prediction in other fields.
This paper presents an innovative optimization framework aimed at data mining in social networks, guaranteeing solutions for some of the basic challenges of computational efficiency, scalability, and accuracy. This work presents a precise approach that integrates state-of-the-art algorithmic enhancements with dynamic resource management techniques. Extensive experimental validation using real and synthetic datasets has marked the significant performance gains achieved within the framework. These results point to a 70.2% reduction in processing time and a 71.2% saving in memory consumption, all while maintaining accuracy rates above 95%. This optimization framework is very stable under different operation conditions, since its responses have always remained below 85 ms under peak loads of up to 245,000 requests per second. The empirical evaluation of the framework across diverse social networking platforms bears testimony to the fact of practical efficacy and has emerged strongly while dealing with dynamic network architecture with extensive data processing needs. The application results in significant improvement in resource utilization efficiency, providing sub-linear increase in memory consumption for maintaining consistent performance under fluctuating load scenarios. The present study extends the scope of social network analysis by proposing a scalable, efficient, and reliable optimization framework that might be of vital importance in both research and practical implementation contexts.
With the unprecedented growth of technological advancement, effective technological transfer has become increasingly important in all dimensions of human lives. Technological transfer is a multi-level and complex ecosystem network with complicated inter-relational elements and effective fac-tors. This complexity raises the question of how to rearrange the elements of the technology transfer to improve its positive performance. To address this issue, this study aims to compare the perfor-mance and gaps of the three modes of technology transfer, which are technology entrepreneurship, technology licensing, and technology shareholding, by evaluating the three participants, which are universities/research institutes, corporations, and intermediary agencies, using related attributes. This study applies a hybrid multiple attribute decision-making (HMADM) model including the DE-MATEL for constructing the INRM, DANP for computing influence weights, modified VIKOR for evalu-ating the performances and gaps among the three technology transfer modes so that to develop sus-tainable and systemic improvement strategies. At the macro level, the results show that, the technol-ogy transfers modes receive an overall positive effect, especially universities/research institutions. At the micro level, the technology licensing has not only the highest performance but also the largest gap. According to this finding, technology licensing is the most feasible way to cater to technology transfer at the macro level from the micro level. The findings suggest decision makers pay attention to the role of universities/research institutes as the main factor influencing technology transfer ef-fectiveness. Also, they should focus on influential attributes such as researcher participation and technical collaboration ability for reducing the gap.
With the reforms in competition rules and equipment by the International Table Tennis Federation (ITTF), the number of rounds in table tennis matches has increased, placing higher demands on athletes’ abilities to transition between technical and tactical offensive and defensive strategies, as well as on their physical and psychological qualities. Therefore, this study employs methods such as the strength difference evaluation, competition performance (CP), and multiple regression. Using 48 international important matches in which Player W (anonymous) participated as case studies, the study evaluates and predicts the competitive performance of W when facing athletes of different world rankings. The results indicate that in matches against athletes with different skill rankings, the phases where the technical strength difference significantly affects competition performance (CP) are the attack-after-serve phase and attack-after-receive phase, followed by the rally phase, and finally, the rally phase Ⅱ. The competitive level in serve rounds is superior to that in receive rounds. The Kruskal-Wallis test results reveal significant fluctuations in the competition performance (CP) during the rally phase Ⅱ, demonstrating highly significant differences (P < 0.01). In matches, Player W has a very low probability of winning when not holding an absolute advantage in key techniques (the first four strokes) – particularly pronounced when facing athletes ranked in the world top 20. The multiple regression model for the technical strength difference in table tennis matches plays a certain role in predicting the performance of athletes in terms of technical and tactical indicators during matches, offering a clear reflection of the effectiveness of these indicators.
The presented article develops the detailed analysis of battery performance degradation profiles for EVs, based on operational data collected in real-world use. Based on data points gathered for 150 vehicles over 24 months, we have developed and then validated an integrated degradation prediction model incorporating several degradation mechanisms. Our study applies a novel hybrid approach that will combine physics-based principles with data-driven methods for outlining the battery aging profile. The model proposed in this paper realizes a better prediction performance of 94.3% under different operational conditions and thus proves to be considerably superior to the existing techniques. Indeed, the change of temperature and charging behavior becomes the main influence factor with the correlation coefficient of 0.85 and 0.78, respectively. After applying the proposed model to a fleet management system, there are 32.4% maintenance cost reduction and 15.8% increasing of the cycle life for batteries. It represents in detail the continuous degradation assessment and predictive maintenance framework, validated on different vehicle platforms under varying operational conditions. These findings provide valuable inputs related to the improvement of battery management strategies and life extension of a battery in electric vehicle applications, hence benefiting theoretical understanding and practical application in electric vehicle battery management.
It has identified and presented a unified machine-learning-based malware defense system that can handle dynamic features in cyber-security challenges. This approach will leverage recent deep learning models, ensembles, and automatic generation of defense strategies to construct an effective and adaptive framework for malware detection and mitigation. These results tend to indicate significant gains compared with traditional signature-based approaches, whereby known malware detection rates reached 99.2%, and zero-day vulnerabilities reached 87.5%. The system also recorded an extra 68% reduction in false positives after one month of operations due to the adaptive learning component, while real-time detection features yielded less than a one-second response time for 95% of the threatened records. The generated defense strategy module can demonstrate a 92% success rate in the automated mitigation or containment of identified threats. The paper further presents that even with such advances, much potential still exists for optimizing resource use, enhancing model interpretability, and building more robust defenses against adversarial attacks. It enhances the area of cybersecurity and adds a new dimension by showing the capability of AI-enabled methodology to create much more efficient, agile, and flexible malware protection systems-thereby paving the way for more advanced cybersecurity innovations.
This paper presents a hardware encryption system based on FPGA (Field-Programmable Gate Array) implementing the elliptic curve cryptography algorithm. Using FPGA as the core control unit, IoT (Internet of Things) data transmission terminals are connected to FPGA-specific external interfaces via USB/SPI interfaces. Data collected into the FPGA undergoes encryption and decryption using the FPGA’s internal hardware resources. The encrypted data is then converted into TCP/IP protocol packets and transmitted to a cloud server through the FPGA’s internal Ethernet interface circuit module. A detailed analysis and design of the hardware implementation of the elliptic curve encryption algorithm are provided. Simulation validation of the point multiplication algorithm was conducted on a computer platform with a quad-core 3.2GHz processor and 8GB of memory, using the Xilinx 5vlx20tff323 chip. The simulation results indicate that the maximum execution frequency reached 372.686 MHz, with a single point multiplication operation completed in 3328 . This significantly enhances the processing speed of the algorithm, bearing significant theoretical value and practical implications for advancing the security of the IoT ecosystem.
Aiming at the current problems of low level of intelligent development and backward infrastructure in the countryside, this paper proposes a multi-objective optimization model for rural construction. According to the overall principle of optimization and the current situation of rural infrastructure construction, model assumptions, objective functions and constraints are determined. Facing the problem of calculating the optimal values of the four objective functions, NSGA-II method is chosen to solve and analyze the problem. NSGA-II algorithm is calculated in 100 iterations, and the optimal solutions of the four objective functions are 0.813, 0.943, 0.852, and 0.886, which are better than NSGA and GA algorithms in terms of performance. In order to improve the intelligent development of the countryside, two targeted development proposals are put forward.
With the progress of modern technology, smart wearable devices have been gradually applied in the field of sports. This paper focuses on the experiments of motion recognition of the main joints realized by convolutional neural network-assisted smart wearable devices. Using smart wearable devices to feature extraction of a variety of sports signals, using GAF algorithm for sports signal image coding, and using convolutional neural network and gated recurrent unit, a CNN-GRU-based motion recognition method is proposed. Through the training and evaluation experiments of the model, it is found that the average accuracy of the CNN-GRU model training and testing is higher than 96%, and the loss value is lower than 1.5%, and the performance of sports recognition is better than that of CNN and CNN-LSTM models. Meanwhile, it presents excellent performance in the recognition of sports with different classifications and different signal durations, reaching 97.02% and 92.63% accuracy in the recognition of three and four types of sports, respectively, and the distribution of the values of human body indexes in different sports in the case study presents a certain degree of regularity, which verifies the effectiveness and feasibility of the CNN-GRU model in different application scenarios. It also shows that the method has great development potential in the field of intelligent sports.
This paper constructs a heterogeneous network adjacency matrix containing multiple user relationships from the connotation of professional organizations and other guides to individual behaviors covered by the take-read mechanism. The GAT algorithm is used to learn the embedding of its heterogeneous network in order to obtain the embedding vectors of user nodes, which serves as the basis for the analysis of the spreading influence of group behavior. An event recognition method based on word embedding and hierarchical cohesive clustering is proposed to analyze the recognition and evolution of social media essay-carrying behavioral events (group behavioral events) for complex networks. We point out that the distribution of group behavior affects the dynamics of information dissemination, set the adoption threshold parameter of the group, and analyze the dissemination pattern of individuals’ (individual information) participation in essay-reading behaviors. Analyze the emergence and evolution of thesis-reading behavior in social media, and explore the influence of individual’s own attributes and the attitude of neighboring nodes on the evolution of group behavioral events in complex networks. The spreading degree analysis is conducted for different relational social media bandwagon behaviors. When =0.6 and =0.8, the individual’s decision is supported by the neighbor’s viewpoints, and the users who have already participated in the paper band-reading activities have a strong attraction to the individual. When the strong degree increases to a certain value, the individual decides to participate in the dissertation banding activity, at which point the individual is no longer influenced by the external environment. The degree of the initial node for the propagation of thesis banding behavior in random networks and small-world networks is linearly and negatively correlated with the percentage of the information audience.
This paper constructs an improved Changsha city brand image communication model on the basis of the traditional contagion model, and studies the communication effect of Changsha in the process of city brand image transformation from “online star city” to “long-term famous city”. By summarizing and analyzing the current situation of Changsha’s city brand image communication, the evaluation index system of Changsha’s city brand image communication effectiveness is constructed, and the collected evaluation index data are downscaled using principal component analysis. The support vector regression machine combined with differential evolution algorithm is used to quantitatively analyze the communication benefits of Changsha city brand image. The improved city brand image communication model in this paper has a higher accuracy compared with the traditional contagion model, and can accurately grasp the communication effect of Changsha city brand image. The average relative error of the support vector regression machine model in the quantitative analysis of communication benefits for the test samples from 2020 to 2023 is only 1.53%, which is 27.86% lower than that of the BP neural network model. It strongly demonstrates the effectiveness of the regression model selected based on the communication big data in this paper, and provides a useful reference for accurately measuring the communication benefits of Changsha’s city brand image.
Under the background of carbon peak carbon neutrality, the competition among ports is not only the competition among terminal scale, throughput, and service level, but also the competition of low energy consumption and low pollution, and with the development of China’s carbon trading mechanism, the cost of carbon emission has become more and more a part of the enterprise that cannot be ignored. In this paper, the berths and shore bridges of the port are taken as the target variables, and the fuel consumption in the process of ships traveling to the port is inferred according to the assumed conditions, and the BAP model under the carbon peak carbon neutrality is deduced, and the relevant constraints are proposed. The initial population is randomly generated, and the first generation of offspring population is obtained through the selection, crossover and mutation operations of multi-objective genetic algorithm, which then continues until the end conditions of the program are satisfied. Through the empirical method, comparing the effect of carbon cost optimization scheme generated by multi-objective genetic algorithm and traditional method, the value of the objective function under the multi-objective genetic algorithm model decreased by 10.48%, the operation cost of the port decreased by 4.54%, the cost of the ship’s in-port time decreased by 24.9%, and the ship’s average in-port time decreased by 11.01%, as compared with the traditional allocation scheme. The multi-objective genetic optimization model of berth shore bridge considering carbon cost can shorten the ship’s time in port, which reduces the carbon emission from the side and achieves the promotion purpose of green port. In the model sensitivity analysis, with the increase of carbon trading price, the four indicators F, F1, F2 and T also showed linear growth, with the growth rate of 17.24%, 18.44%, 14.37% and 18.02%, respectively, and the model sensitivity is good.
Participatory culture, as one of the characteristics of audience performance in the current communication environment, provides imaginative space for stimulating the power of audience participation in the communication of non-heritage culture, and at the same time provides new thinking direction and inspiration for the current communication of non-heritage culture. In this paper, we mainly apply recurrent neural networks to model sequence data, and control the flow of information by adding special gating structures, so as to be able to effectively memorize and process long sequence data. Self-attention is constructed so that the network can better focus on the important parts of the sequence while ignoring the irrelevant information in the sequence. Identify non-heritage communication behaviors based on time-series data, and model non-heritage cultural communication behaviors based on the length of time the behaviors occur under the framework of situational awareness. The research experimental model is designed, relevant hypotheses are proposed, and examined through empirical evidence. The number of borrowings by visitors under 18 years old, which is the main group of visitors, declined from 737 in 2016 to 357 in 2022, with an overall decline of 51.56%, and the overall visiting behavior also showed a declining trend. In order to test the mediating role of perceived value in the relationship between interactive behavior and the communication effect of intangible cultural heritage, the benchmark model M3 model was constructed with the communication effect as the dependent variable and gender and whether the only child was the controlling variable, and the independent variables “interactive behavior” and “perceived value” were added on this basis, and the perceived value had a significant positive impact on the communication effect, β=0.485, p<0.001. The influence of interactive behavior on communication effect remains significant, at this time the β-value is 0.487 and p<0.001, the mediating role of perceived value between interactive behavior and non-heritage culture communication effect.
Science and technology innovation talents are the center of gravity of the national strategic power, which is crucial for promoting social development and scientific and technological progress. The purpose of this paper is to study the scientific and technological innovation talents of power grid enterprises, build the evaluation index system of scientific and technological innovation talents with reference to the CIPP model, select a power grid enterprise to analyze the examples, and use the fuzzy AHP model to evaluate its scientific and technological innovation talents training. Then build the role mechanism model of science and technology innovation talent cultivation, conduct regression analysis of the influence factors of science and technology innovation talent cultivation, and verify the research hypothesis. The evaluation results of the STI talents of the sample grid enterprises range from 3.6 to 4.0 points, and the evaluation grades are all good, confirming the practicality of the proposed STI talent evaluation method. Except for years of education, high focus in research field and teamwork, the selected personal factors, organizational factors and environmental factors have positive and significant effects on the quality of STI talents training. It is suggested that power grid enterprises improve and promote the development of the training system of scientific and technological innovation talents by building a training and development channel, developing a layered training model, innovating training methods as well as building a research platform.
Artificial intelligence digital tools are widely used in teaching scenarios. This study designs a digital learning tool capable of personalized learning resource recommendation and applies it to tourism English education to improve teaching quality. The study first establishes a set of nearest-neighbor user selection scheme based on clustering algorithm and analyzes the overall user behavior in a collaborative filtering way, so as to provide the target users with learning materials pushing service with high accuracy. Then a personalized teaching model for tourism English education is designed based on this system. Finally, the model is applied to actual teaching, and the application effect of this AI digital tool in tourism English education is verified through teaching practice. The students’ performance in tourism English teaching using the personalized learning resources recommendation system increased by 13.59 points compared with that before using the system, which is a significant difference. It shows that the personalized learning resources recommendation system has value in tourism English education.
The electric power industry is an important basic industry of the country, and among all the electric power equipment, the distribution lines are directly facing the end-users, which is an important infrastructure to serve the people’s livelihood. In this study, we first transformed the distribution line engineering quality defect acceptance problem into a sequential decision-making problem, and constructed an improved reinforcement learning network model DDQN based on it, and introduced a reward function into the model to improve the intelligent adjustment ability of the intelligent bodies in the model to the data related to the distribution line, so as to improve the detection performance of the DDQN model in the distribution line engineering quality defect acceptance. The results show that the improved DDQN model is highly feasible and effective in the detection of quality defects in distribution line engineering compared with other comparative models. The simulation test of distribution line engineering quality defects found that the accuracy of the DDQN model-based distribution line engineering quality defects acceptance technique in detecting line quality defects is 95%. It is verified that the accurate and reliable distribution network line engineering quality defect acceptance technology based on the improved DDQN model is conducive to guaranteeing the safe and stable operation of the power grid system.
In this paper, OpenCV technology is used to produce the distribution network defects dataset, which can be used as a training set, validation set, and test set in the ratio of 6:2:2. Combining the dataset and the Transformer framework, the S-Transformer based distribution network key quality defect identification model is constructed together. At this level, the degree of equipment deterioration is fitted, the distribution network intelligent operation and maintenance optimization strategy is formulated, and the experimental method is applied to evaluate the distribution defect identification and intelligent operation and maintenance. The identification rate of S-Transformer network for the six collected distribution network equipment defects is 0.9~0.95, which accurately controls the potential dangers, and is conducive to the subsequent intelligent equipment operation and maintenance of the distribution grid and its management and control, compared to the Compared with the traditional operation and maintenance program, the operation and maintenance program in this paper can reduce the operation and maintenance time by 52 hours per month, which greatly provides the efficiency of operation and maintenance labor.
Financial sharing has become an important trend in the process of enterprise development in the era of big data. This topic centers on the research of the application of cloud computing technology in financial shared services, and introduces machine learning algorithms into financial risk early warning. Financial and non-financial indicators are selected to construct the financial analysis index system, K-tuning and mean value algorithm is used to realize the risk level division, SVM algorithm is used to construct the financial risk early warning model, the parameters are continuously adjusted according to the model accuracy rate, and the model is applied to the benefit analysis. Dividing the samples into four financial risk levels of none, low, medium and high can more accurately reflect the specific situation of enterprise finance. It is proved through experiments that the financial risk prediction performance of SVM model in this paper far exceeds the logistic regression model and Gaussian plain Bayesian model, the accuracy rate is improved by 9.7% and 18.6% respectively, and the average accuracy rate in the test set reaches more than 93%. Therefore, it is feasible as well as of great research value to apply cloud computing technology in artificial intelligence to the research field of risk warning of financial shared services.
The construction of ecological civilization is a fundamental plan related to the sustainable development of economy and society, and the dispute settlement mechanism of environmental damages is its innovative and important content. Starting from the environmental legal dispute resolution mechanism, the article analyzes the legal basis of environmental dispute mediation and the process related to pre-litigation mediation. Considering environmental legal dispute resolution as a kind of multi-objective decision-making optimization problem, a multi-objective decision-making optimization model for environmental legal disputes is constructed with the objective functions of legal effectiveness, legal applicability and subject interest rate. Then adaptive inertia weights and dynamic image Pareto solution set updating strategy are introduced to improve the multi-objective particle swarm algorithm, and combined with information entropy-based TOPSIS decision-making to realize the optimal solution selection for environmental legal dispute resolution. In the multi-objective decision-making optimization model, the improved multi-objective particle swarm algorithm achieves the optimum for a total of 15 data, and the simulation time in solving the optimal solution of the 10*10*5 case problem is only 2.314s, and the optimal solution of environmental legal dispute resolution can be obtained based on different objective functions. Environmental legal dispute resolution needs to aim at effectiveness, applicability and subject’s interests, introduce appropriate punitive damages, realize the effective connection between administrative law and criminal law, and promote the high efficiency of environmental legal dispute resolution.
This paper improves the deep residual network, proposes 3DResNet network and carries out particle swarm optimization, constitutes the PSO-3DResNet model, and designs the coal mill fault diagnosis model based on PSO-3DResNet model. The technical parameters, common fault types and fault characteristics of the coal mill are analyzed, and the relationship between the input and output parameters of the coal mill is decomposed by the residual-based condition monitoring method. Combining the numerical simulation model of coal mill and historical operation data, the typical fault condition monitoring of coal mill is constructed. Compare the classification accuracy of each model on the working state of blast furnace wind mouth, and get the anomaly detection performance of each model. The PSO-3DResNet model is analyzed to monitor the normal operating state of the coal mill, and the model is tested using the historical current and outlet wind temperature anomaly data of the coal mill. When the coal mill is in an abnormal state, the estimated residuals of the current abnormal condition fluctuate within [-16,3] with a small range, and the weighted average residuals of the current abnormal condition index remain within [-4,1].
Artificial Intelligence Generated Content (AIGC), as a computer technology mainly characterized by intelligent content generation, has caused significant changes in film and television performances and creations, and has greatly broadened the creation and development space of film and television performances. In this paper, we use motion capture technology to obtain the character movement data in film and television performances, and combine it with the skeletal motion data generation algorithm to realize the mapping of skeletal motion data. Using ResNet-122 as the backbone network, a 3D action pose estimation model is constructed by combining multi-view and multi-feature fusion networks. Based on the 3D action pose estimation sequence, the character animation generation model is constructed by combining GAN and action detail attention mechanism, and the action detail feature loss function is designed to improve the generalization ability of the animation generation model. In order to verify the effectiveness of the above method, data analysis is carried out through simulation verification. The average value of PCP3D index of the 3D action pose estimation model is 98.37, which is 0.28 percentage points higher than the sub-optimal model, and the average joint position error is only 16.07 mm. The animation generation model combining GAN and the action detail attention mechanism has the values of animation generation diversity and richness index of 5.104 and 3.997, respectively, and the animation generation diversity and richness indexes of the animation generation model combining GAN and the action detail attention mechanism are 5.104 and 3.997, respectively. 3Ds MAX software can map the generated animation sequences into the virtual space, providing assistance for optimizing the motion design of film and television performances.
The emotional curve of a story is the core embodiment of the reading value of a novel, and good novels tend to have similar patterns of emotional changes, which are explored in novels by combining artificial intelligence technology. After collecting modern Chinese novel texts, Chinese word segmentation and de-duplication are performed to complete the novel text preprocessing. In view of the limitations of convolutional neural network (CNN) and recurrent neural network (RNN) in text feature extraction, this paper proposes a multi-channel convolutional and bi-directionally gated recurrent unit (BiGRU) deep learning model, Pt-MCBGA, to mine the emotional polarity in the text and analyze the emotional trend of modern Chinese novels. After a series of comparison experiments, it is demonstrated that the model performance achieves a relatively excellent performance, and the recall rate on the two datasets is improved to 83.53% and 83.69%, respectively. According to the Pt-MCBGA model, the sentiment analysis of the modern Chinese novel The Legend of the Eagle Shooting Heroes finds that the novel is dominated by positive sentiment, with both positive and negative sentiment values being relatively high, and that the characters are rich in emotions and have great emotional ups and downs.
In wireless sensor networks in industrial control systems, wireless communication security is challenged due to the broadcast nature of the wireless channel, where information is more easily eavesdropped by illegal nodes on the network. The article establishes a secure communication system based on ZigBee wireless communication technology applied to wireless sensor networks in industrial control systems. In order to improve the secure communication performance of wireless sensor networks, this paper combines the Merkle tree with the μTesla protocol to establish a key management scheme for wireless communication. Then from the node trust degree, the node two-way authentication mechanism for data transmission is constructed by combining the digital signature algorithm. For the effectiveness of the secure communication mechanism of wireless sensor networks, this paper carries out data analysis through performance testing. The key management scheme takes about 17.37 μs and 3.24 μs to add and revoke a key, respectively, and the local optimal value of user time consumption is 7.26 s when the connectivity frequency is 12 min and the revocation threshold is 60. The average value of the node bidirectional authentication mechanism can reach 96.17% for the accuracy of identifying the malicious nodes in the wireless sensor network, and the bit error rate is lower than 0.5 % for the communication transmission with the mesh topology. The bit error rate is less than 0.1%. The introduction of Merkle tree and digital signature algorithms into the construction of secure communication mechanisms in wireless sensor networks can significantly improve the data transmission security performance of industrial control systems.
Tang poetry, as a treasure of ancient Chinese literature, contains a wealth of natural imagery, which not only add to the picture sense of Tang poetry, but are also important carriers of the poet’s emotions and thoughts. The study outlines the nature imagery from the perspective of Tang poetry, as well as the key elements and intrinsic connections among them, and borrows k-means clustering to categorize the nature imagery groups. In addition, the study improves the principal component model by using index homogenization, homogenization, and entropy weighting, so that it achieves the best dimensionality reduction effect while guaranteeing the integrity of the data of Tang poetry text.The F1 value of SVM and KNN classifiers for classifying the natural imagery and emotional expression of Tang poetry text is more than 0.9 after dimensionality reduction of the method in this paper, which is a good classification performance. Cluster analysis divides the natural imagery of Tang poetry into astronomical imagery, landscape imagery, and animal imagery, which account for 38%, 53%, and 9%, respectively. “Old times – bright moon”, “Thinking – slanting sun”, “Looking back – west wind”, “the end of the world – west wind” natural discourse is more likely to form word clusters in the natural imagery of Tang poetry. The analysis of principal component model shows that poets are more willing to express their emotions through natural imagery, and the proportion of neutral emotional expression is 5.17% to 7.43%.
This paper proposes a vocal music teaching system architecture integrating multimedia technology, aiming to enhance the intuitiveness, interactivity and personalization of vocal music teaching through technical means. The system is equipped with virtual reality and voice interaction technologies to realize the digital presentation of the functional modules of the architecture. In addition, in order to evaluate the teaching effectiveness of the system, a number of evaluation indicators are designed. The fuzzy comprehensive evaluation algorithm is used as the main method, supplemented by hierarchical analysis method, to comprehensively evaluate the teaching effectiveness. Multimedia technology can improve students’ vocal ability and mastery of theoretical knowledge, in which the vocal ability is improved by 5.98% to 10.48% compared with the control class, and at the same time, there is a promotion effect on students’ positive interest in vocal learning. The students’ recognition of the system in terms of technology application, learning interaction experience, learning content and process, and teaching effect ranged from 4.077 to 4.608, with a high degree of recognition. The experts’ comprehensive evaluation of the classroom effectiveness of vocal music teaching under the system of this paper is 93.437, which is highly satisfactory. This study not only provides new technical support for vocal music teaching, but also provides a scientific assessment method for teaching evaluation, which is of great significance to improve the level of vocal music teaching.
With the progress of the times, the scientific and reasonable planning of physical education infrastructure and resources is an important way to realize the fair development of education. Firstly, a physical education resource input-output evaluation index system and a multi-objective optimization model of resource allocation to improve the utilization rate of physical education resources are constructed for the integration of physical education resources in Wuhan private colleges. In order to achieve the effect of enhanced spatial traversal ability, the collision range of raindrops is expanded by adding the hybrid collision strategy and introducing the adaptive collision factor, and the artificial raindrop algorithm with the introduction of hybrid collision and stretching is proposed on the basis of the original artificial raindrop algorithm. The improved artificial raindrop algorithm is compared with different optimization algorithms for simulation comparison experiments and model solving. The results show that the improved artificial raindrop algorithm converges faster and with higher accuracy, while the multi-objective optimization model proposed in this paper achieves the balanced development goal of physical education resources integration and allocation in Wuhan private colleges and universities.
This study aims to construct an effective pathway for students’ career planning and innovative industry education by integrating support vector machine algorithm with big data analysis technology. By effectively integrating multi-source data and combining the improved genetic algorithm for feature selection and extraction of student data, the support vector machine algorithm is used to conduct in-depth analysis of the data related to students’ career planning and innovation and entrepreneurship education, to provide students with accurate and personalized career and entrepreneurship guidance, and based on which, the career planning and innovation and entrepreneurship education path is constructed. Experimental analysis of the classification prediction performance of the support vector machine algorithm and comparison with other classification prediction algorithms show that the support vector machine algorithm used in this paper has the highest classification accuracy in the assessment of students’ career planning and innovation and entrepreneurship ability, and the model performance is the most stable. The results of the educational experiment show that after using the educational path proposed in this paper, the students’ satisfaction with career planning and the mean value of the assessment score of innovation and entrepreneurship ability increase by 70.89% and 170.73%, respectively. The above results fully demonstrate the effectiveness of the educational path constructed in this paper, which provides a useful reference for efficient education and teaching reform.
This paper measures the international trade efficiency of developing countries based on the data envelopment analysis (DEA) model, and explores the impact of digital transformation on trade efficiency differentiation using regression analysis. Relevant data of 19 developing countries, including China, are selected, and the trade efficiency at each stage is calculated separately using the three-stage DEA model in this paper. The regression model is constructed to quantitatively analyze the impact of digital transformation in the differentiation of trade efficiency of developing countries. From 2011 to 2020, the trade efficiency of each developing country shows a wave-like upward trend, and the average value of the comprehensive average efficiency in the third stage is 0.728, but only China, Peru and Colombia have a higher than average level of trade efficiency, which intuitively demonstrates the trade efficiency differentiation of developing countries. Differentiation. The overall regression results show that the elasticity coefficient of digital transformation on the international trade efficiency gap is -0.274, indicating that digital transformation has a greater effect on narrowing the trade efficiency gap than widening it. And in the subregional regression, the elasticity coefficient of digital transformation in Asia is 1.398, and the elasticity coefficients in Africa and Latin America regions are -0.953 and -0.603 respectively, and the digital transformation has significantly different impacts on trade efficiency differentiation in different regions.
The continuous improvement of judicial construction has led to the emergence of a large amount of judicial data on the Internet, and how to make full use of judicial data to promote judicial openness, fairness and efficiency has become an important issue in the construction of judicial informatization. In the article, the word vector generation technique is used to obtain the annotation sequence of legal text, and then the BiLSTM model is combined with the CRF model to realize the recognition of legal text entities, and the Adam algorithm is used to optimize the training of the model, so as to improve the recognition effect of the model on legal text entities. The GCN model in the graph representation learning algorithm is introduced, and the legal text entity recognition results are used as inputs for the construction of sequential and semantic relationships, and the GCN-BiLSTM model for legal text entity relationship extraction is constructed by combining the graph representation attention network and the BiLSTM model. Based on the self-constructed legal text dataset, the validation analysis of the above model is carried out through simulation experiments.The accuracy of the BILSTM-CRF model in legal text entity recognition is 85.67%, which is 7.35% higher than that of the single LSTM-CRF model. The GCN-BiLSTM model improves its accuracy by 2.14 percentage points compared with the CasRel model in extracting the entity relationships of legal texts with multi-entity overlapping. Combined with the legal text entity relationship extraction results, the knowledge map of legal cases can be constructed to provide accurate knowledge relationship support for sorting out the veins of legal cases.
Under the accelerated process of economic globalization and the booming development of Internet technology, cross-border e-commerce, as a new mode of international trade, is becoming a new driving force for the transformation and upgrading of foreign trade with its high efficiency and convenience, low cost and high benefit. This study uses data cleaning and missing value filling methods to preprocess user behavior data and merchandise sales marketing data in cross-border e-commerce Wish platform, and discretizes user behavior data using rough set method. Then, we select the merchandise sales and user behavior as the dependent and independent variables to construct a multiple nonlinear regression model in order to analyze the influence of user data on sales in cross-border e-commerce Wish platform. The results of the multivariate nonlinear regression model show that user behavior in cross-border e-commerce Wish platform has a significant effect on merchandise sales (P=0.005243). It is also found that the sales strategy adjusted according to the regression results can improve the sales and promotion effect of enterprises in cross-border e-commerce platform. The research results of this paper enrich the theoretical and practical research on the optimization and adjustment of cross-border e-commerce enterprises’ sales strategies, provide theoretical basis and decision-making reference for the subsequent adjustment of cross-border e-commerce enterprises’ sales strategies, and help cross-border e-commerce enterprises to go global.
In this paper, based on the knowledge graph, word vectors and other personalized path generation related technologies, based on the graph convolutional neural network to complete the construction of the English knowledge graph model, to generate a personalized English knowledge graph, drawing on the data structure in the graph, to generate a personalized learning path, in order to make the generation of personalized learning path is more reasonable, in accordance with the difficulty value of the exercises for the exercises to be sorted. Simulation experiments are designed to evaluate the difficulty level of the generated exercises. The difficulty level of most of the English exercises generated by the personalized recommendation path is concentrated in the easy and general levels, and there are a total of 2,229 questions in these two difficulty levels, so the difficulty level of the generated questions is moderate. After a period of personalized path-generated English learning, six teaching activities were carried out, and the average score of the first post-test of the experimental group was higher than that of the control group, and the Sig values were all less than 0.05, indicating that the difference in the scores of the two groups of students was significant, which side by side reflected the accuracy of personalized path-generated English teaching.
In the era of digital economy, the digital transformation of enterprise financial management has become an important topic that needs to be studied and solved at present. In this paper, based on analyzing the internal and external drivers on the digital transformation of enterprise financial management, the financial data of 3,498 Shanghai and Shenzhen A-share listed enterprises were obtained using Python technology. Then a fixed effect model was constructed by combining the multiple linear regression model to analyze the degree of influence of internal and external drivers on the level of digital transformation of enterprise financial management. Policy support, digital technology environment, leadership support, team awareness, and digital technology investment all have a significant effect at the 1% level on the level of digital transformation of enterprise financial management. Among them, the influence of digital technology investment is the largest, that is, every 1 percentage point increase in the enterprise’s digital technology investment in financial management, the level of digital transformation of enterprise financial management will increase by 0.204 percentage points. And there is significant regional and equity heterogeneity in the level of digital transformation of enterprise financial management, and the effect of digital transformation of financial management is stronger in the eastern region and state-owned enterprises. Therefore, in the era of digital economy, enterprises need to build a digital financial management system, strengthen cross-departmental collaboration and communication, and combine composite talents to realize the digital transformation of financial management.
In this paper, the financial structure is defined as two parts, asset structure and capital structure, with respect to the mechanism of enterprise financial management on the economic performance of enterprises. The multivariate regression model of asset structure and business performance is constructed with the dimensions of asset turnover efficiency and asset structure ratio. In order to represent the operating performance, total return on assets and return on net assets are chosen as the measures of operating performance and as the explanatory variables. It is proposed that there is a linear correlation between capital structure and corporate profitability, and the linear model between capital structure and corporate operating profitability is constructed. Combined with empirical tests to verify the relationship between asset structure or capital structure on business operations. The curve estimation method of the regression model is used to analyze the effects of inventory ratio, money fund ratio and fixed asset ratio in asset structure and capital structure on the total return on assets and return on net assets. The coefficients of fixed asset turnover on performance are 0.033 and 0.025 respectively, i.e., for every increase of 1 in fixed assets, total return on assets and return on net assets increase by 0.033 and 0.025. Similarly, the fixed asset turnover, inventory turnover, and the ratio of long term financial assets are positively correlated with the performance of the enterprise. The correlation coefficients of equity ratio and state-owned ratio of enterprise capital structure are positive, which bring positive impact on enterprise operating profitability.
Six historical building clusters in the main city of Changchun, namely People’s Street, Xinmin Street, the Palace of the Forged Manchus, the South Square, the First Automobile Manufacturing Plant, and the Kuanchengzi Station of the Middle East Railway, with a total of 2,501 historical building sites, are taken as the research objects. Using ArcGIS software, the morphology and spatial distribution pattern of the historic building clusters in the main city are discussed based on the perspective of spatial layout by invoking spatial measurement methods such as kernel density, standard deviation ellipse, algebraic geometry, and spatial correlation, etc. The results are summarized in the following table. The results show that the spatial distribution of historic buildings in the main city of Changchun is dominated by a “single center (People’s Square)” agglomeration, with a maximum kernel density of 0.9950. At the same time, the periphery also appeared to diffuse re-agglomeration, hierarchically showing a “two-axis” diffusion pattern. Among them, the main axis resides in the center of the city and extends infinitely from north to south. The secondary axis is the administrative office and center of the pseudo-Manchukuo State, which is the pseudo-Manchu Imperial Palace and Xinmin Street respectively. Finally, from the perspective of planning and design, it tries to put forward the strategy of protection and utilization, including environment, function, and culture, etc., to provide methods and bases for the holistic protection and utilization of Changchun’s historical buildings.
Students’ mental health problems are increasingly becoming an important part of the educational and teaching process in colleges and universities. In this paper, we collect students’ psychological data through the students’ mental health early warning system and preprocess the data through data cleaning and other data. The features of the processed mental health data are extracted using Global Chaos Bat Based Algorithm (GCBA). Construct a mental health early warning system for college students and build a decision tree model into the system for categorizing students’ mental health status. The performance of the decision tree model in this paper is verified by evaluating the finger with other models and comparing the actual classification prediction results, constructing the decision tree model with the psychological condition of interpersonal relationship of college students as an example, and conducting the visualization analysis of the decision tree. Independent sample t-test is conducted on three measures such as using the mental health early warning system constructed in this paper, and according to the results, the application of the system in this paper highlights the role of the enhancement of the level of students’ mental health and the significant improvement of depression and other psychological conditions.
This paper points out that dance movements can be regarded as the carrier of the fusion of traditional cultural elements and styles, and ethnic folk dance movements are used as the dynamic expression of inheriting traditional cultural elements and styles. Analyze the characteristics of non-negative matrix decomposition algorithm, and use the non-negative matrix decomposition algorithm to reduce the dimensionality of dance action images. In order to optimize the classification effect of the classifier on the data after dimensionality reduction, SVM algorithm is selected to form a dance movement recognition method based on matrix decomposition technology and SVM classifier. By adjusting the values of penalty factor and kernel parameter , the effectiveness of matrix decomposition algorithm for image dimensionality reduction is verified. Analyze the feasibility of the dance movement recognition method based on matrix decomposition technique and SVM classifier by selecting different data sets. Establish the dance movement evaluation model based on matrix decomposition technology, compare the evaluation model scores with the dance expert scores, and test the effect of matrix decomposition technology on the classification of dance movement styles. The Spearman’s correlation coefficient between the expert’s score and the model’s score remains above 90% in the evaluation of different dance movements. Combined with the evaluation guidance of dance experts, the dance style movement evaluation model proposed in this paper can effectively evaluate and analyze dance movement styles.
AIGC-driven development and innovation of regional education has become an important issue, and in the context of the era when AIGC technology has triggered profound changes in education, the traditional education model is experiencing a paradigm shift from the transmission of knowledge to the cultivation of innovation ability. Based on this, we first construct a model of influencing factors in the application of AIGC in course management based on the rooting theory, and verify the proposed hypotheses to provide a theoretical basis for the construction of course management optimization and multi-level decision-making model. Then we optimize the course management of foreign language teachers in colleges and universities by relying on the all-round and multi-level innovation of AIGC in the field of education, and construct a multi-level decision-making model. In the teaching application practice, the scores of the experimental class on learning interest, learning attitude and learning motivation are all higher than 75 points after practice, and the average score is 8.87 points higher than that of the control class, and the P is less than 0.05. The learning achievement of the experimental class is increased from 73.95 to 80.95 (P < 0.05), and the optimized multilevel decision-making model of this paper has a significant effect on improving students' learning interest, learning attitude, learning motivation and learning achievement, learning attitude, learning motivation as well as learning achievement, which further validates the application effectiveness of the multilevel decision-making model and provides case references for researchers of AIGC-based instructional decision-making.
The research selects the documents related to the legal regulation of civil abuse of rights of action as the research object, crawls the central and local legal regulation database through Python, and uses the social network analysis method to quantitatively analyze the dimensions of the subject of legal regulation from the composition of the subject of legal regulation, the density of the network, the centrality, and the cohesive subgroups, etc. The data preprocessing is carried out on the valid data obtained. Secondly, we pre-processed the acquired valid data, extracted high-frequency words using the improved TF-IDF algorithm, and obtained the probability distribution of the subject strength of “document-subject” and “subject-phrase-item” by calculating the degree of perplexity and utilizing the LDA subject model, and obtained the probability distribution of the subject strength at different stages of civil abuse litigation. In order to obtain the themes and evolution characteristics of the legal regulation of civil abuse of rights of action at different stages, the research results are combined with the results of the study from multiple dimensions. Finally, the research results are combined to design the strategy of legal regulation of civil abuse of rights of action from multiple dimensions.
With the deepening of education modernization, improving teachers’ digital literacy has become the key to promoting the digital transformation of education. The growing demand for professionals in modern society has made the digital literacy of physical education teachers in vocational undergraduate colleges more and more important. This paper defines digital literacy and the digital literacy of vocational undergraduate teachers in turn, explores the four connotations of digital literacy, and proposes strategies to improve the digital literacy of physical education teachers in vocational undergraduate colleges. The entropy value method was used to measure the digital literacy level of physical education teachers in vocational undergraduate colleges, determine the weight of teachers’ digital literacy evaluation indexes, and select and analyze the influencing factors of teachers’ digital literacy. Pearson correlation analysis was conducted on teachers’ digital literacy and influencing factors, as well as various dimensions and influencing factors, and multiple linear regression models were constructed to analyze the improvement path. The measurement results show that in the dimension of digital awareness, the mean values of digital willingness, digital cognition, and digital will are 4.4269, 4.3484, and 4.3748, respectively, indicating that the subject vocational undergraduate physical education teachers are highly willing to learn and use digital technology resources. The correlation coefficients between the dimensions and influencing factors of digital literacy were roughly in the range of 0.4~0.7, and the P values were all < 0.01, indicating that there was a significant positive correlation between them. The path coefficients of "TS→DA", "TE→DA" and "TM→DA" were 0.0533, 0.0796 and 0.0789, which did not reach the significance level, while the other paths reached the significance level (P<0.05), indicating that there was a significant positive impact.
The application of big data in modern enterprise finance is becoming more and more common, and the research adopts the random forest algorithm to explore the enterprise financial risk status, so as to make personalized financial decisions. Construct the enterprise financial risk early warning model based on random forest and construct the financial risk early warning index system. The performance of the random forest model is tested by comparing the financial risk early warning effect of the random forest model with other models. Taking M company as an example, by analyzing its financial risk situation from 2019 to 2023, it puts forward targeted financial decision-making suggestions. The random forest model performs best in the financial risk early warning performance experiment, far outperforming other models. The financial risk status of Company M in 2019-2023 is dangerous, sub-safe, general, dangerous, and general. Although it has been improved in general, it is still in a fluctuating state and the development status is unstable. For the specific financial risk status of Company M, financial decision-making suggestions are proposed for the three aspects of solvency, operating capacity and development capacity.
This paper studies the 3D target modeling method under multi-view video based on deep convolutional network. Through the detailed exposition of the basic theory of 3D target modeling technology and the complete derivation of non-uniform rational B spline curve, this paper establishes technical support such as camera coordinate system for the generation of 3D target model. According to the basic structure of Deep Convolutional Network (DCNN), a DCNN network model suitable for the research scenario of this paper is established, and the model is utilized for feature extraction of images in multi-view videos. The softargmin algorithm is used to generate the parallax map for parallax estimation in the parallax calculation stage. According to the parallax map, voxel-based 3D reconstruction of the target in the multiview video is performed, and the surface reconstruction of the voxel model is performed using the Marching Cubes algorithm, and after obtaining the surface model of the target object, texture mapping is performed to enhance the realism of the model. The deep convolutional network based 3D building method in this paper can effectively realize the feature extraction of target objects in multi-view video. In 3D target modeling, the model in this paper achieves good results on both public and measured datasets, and has obvious performance superiority and generalization ability compared with other methods.
According to the principle, characteristics and use of CCD, this paper designs a laser beam quality measurement program using CCD as a beacon light capture detector and proposes a laser spot detection method based on CCD. The experimental steps and calculation steps for laser beam width measurement and laser power measurement by CCD camera are proposed respectively. The beacon light is used as a light source, and the spot image is processed according to the principle of gray-scale image thresholding to capture the beacon light and present it in the form of a spot on the CCD image sensor. Then, through binarization processing, the spot of the beacon light is distinguished from the background, so as to realize the spot position detection of the beacon light beam. The image data are collected to experimentally detect the laser spot position detection algorithm based on CCD image sensor proposed in this paper, respectively. In the fine-tracking spot position detection, the spot is adjusted in the range of ±9.25mrad, and the solution value is set to be determined every 0.78mrad. The spot center is kept in the range of ±9.05mrad, and centering is carried out every 0.003mrad according to the fine-centering algorithm. The experimental results show that the spots after fine centering are all within the range of ±0.78mrad, and the change trend is consistent with the simulation results, so the laser spot position detection algorithm proposed in this paper is feasible in fine tracking spot position detection.
Driven by the core qualities of the Civics discipline, the requirements of curriculum reform and the needs of teaching practice, the optimization of teaching strategies has become particularly urgent in the field of Civics education. The article introduces the Markov decision-making process and basic elements of reinforcement learning, combines the Q learning algorithm with neural networks, and constructs a deep reinforcement learning model (IDQN) for multiple intelligences with collaborative scheduling. Based on this, a numerical simulation experiment of deep reinforcement learning strategy in Civics teaching was designed and implemented. Through experimental analysis: when the recommended path is 30, the IDQN model has the best learning path recommendation effect, with an IKL of 0.477. The model also has excellent performance in the allocation of teaching resources, with the accuracy, recall and F1 value of 5 tests above 90%. After the numerical simulation of Civic Education teaching, the learning interest, attitude, and motivation of students in the experimental group increased by 27.52% to 34.49%. Under this influence, combined with the learning path and resource allocation provided by the IDQN model, students in the experimental group showed a significant improvement in their learning effect, and the average score of Civic Education Theory was 6.06 points higher than that of the control group.
The continuous development of digital informatization has opened the era of intelligent education in the field of education. Higher education has accumulated a huge amount of data, but it is not fully utilized, and in-depth mining and analysis of these data can reveal the students’ learning and life status and provide powerful support for teaching management. Therefore, the research of using clustering algorithm to build a hierarchical management model for English teaching is very necessary. Clustering algorithm provides an effective way for the analysis of students’ learning behavior, and for the research needs of English teaching, this paper proposes a multi-factor improved K-means clustering algorithm and compares and verifies its clustering effect. For the problem of stratified division of student groups, firstly, the clustering index system of students’ book borrowing behavior and English course learning behavior constructed is used. Then, the improved K-Means clustering algorithm is used to cluster and mine the data of each student’s behavior to discover the student groups under different behaviors, so as to realize the hierarchical clustering of students in hierarchical management. Finally, for English teaching, a student stratification management model is established from three aspects: student stratification, teaching goal stratification and teaching process stratification, which provides important decision support for student stratification determination in English teaching and provides a more rationalized management model for student management workers.
Image alignment is a fundamental problem in the field of computer vision and an important prerequisite for carrying out many other tasks. Firstly, the theoretical basis and realization method of image alignment as well as the process and the method of alignment are introduced to provide alignment ideas. Subsequently, an image alignment method based on the union of multi-scale features is proposed, and a new loss term is introduced to the small-scale features therein, which further improves the distinguishability of the small-scale feature descriptors while guaranteeing the invariance of the large-scale feature descriptor matching therein. Three common alignment algorithms (RIFT algorithm, HAPCG algorithm, and SAR-SIFT algorithm) are selected for stability assessment and quantitative evaluation on the dataset, and an image enhancement algorithm with histogram equalization is used to enhance the dataset. The results show that the feature stability of this paper’s method is described as 99.1%, which is better than other algorithms. Meanwhile the desired effect is achieved on the dataset.
At present, the evaluation of spoken English in domestic universities is affected by the evaluation teachers’ personal cognition, preference, time, energy and other factors, and it is difficult to unify the standard of oral evaluation in the implementation, and the evaluation frequency and timeliness are insufficient to meet the students’ willingness to improve their oral language. In this paper, multimodal speech recognition technology is utilized to firstly collect students’ speech signals through microphone arrays, secondly extract acoustic and linguistic features of speech, and construct multimodal feature vectors by combining visual information such as students’ lip movements and facial expressions. Subsequently, the feature vectors are input into a deep neural network model for training and recognition, fusing LSTM network with attention mechanism to analyze the speech emotion and capture the emotional changes in speech. Meanwhile, the interaction behavior in speech is analyzed by combining temporal convolutional network. Construct a deep reinforcement learning model, introduce a user item interaction layer, design a user interaction simulator, and obtain user feedback on the smart English classroom. Using multimodal speech recognition technology, the temporal waveform of classroom speech is analyzed for sound pressure value, and the normalized sound pressure value range fluctuates around [-1.5,1.5].The average recognition rate of the six emotions rises to 67.86% with the joint effect of LSTM and attention mechanism. By comparing the experiment, analyzing the difference between the experimental class and the control class before and after the reading aloud ability, the average score of the experimental class is 23.945, and the average score of the control class is 21.464, at the same time, the post-test of reading aloud ability corresponding to the experimental class and the control class P=0.005<0.05. It can be seen that the intelligent interactive classroom of English language constructed in this paper has a facilitating effect in the process of teaching reading aloud in the aspect of reading aloud ability of students The classroom can be seen that the intelligent English interactive classroom constructed in this paper has a promoting effect in the process of teaching reading aloud in terms of students' reading ability.
Intellectualization of agricultural machinery can effectively improve the efficiency and quality of operations, and has an important role in promoting agricultural development. Based on AR technology, this paper introduces the key technology to build the interactive control system of agricultural machinery, uses NURBS to realize virtual agricultural machinery modeling, uses VRML technology to design a prototype of the scene environment of interactive farmland virtual reality, and details the methods of virtual modeling, virtual roaming, interactive control and collision detection in the process of system development. A four-degree-of-freedom simulation test bed is established to realize the simulation of the tractor’s attitude when walking in the field. The position information of the crop rows is extracted from the virtual scene, and the control signals are given according to this information to carry out the speed, direction and balance control of the traveling of the agricultural machine, so that the tractor travels along the crop rows. The maximum deviations of the roll angle, pitch angle and yaw angle are within 0.36°, and the maximum deviations of the elevation and traveling speed are 2.11 mm and 0.14 km/h. The simulation analysis and the physical test show the feasibility of the interactive control system of the farm machine.
In this paper, we use a large language model for business English translation and context analysis, and propose an adaptive parameter unfreezing method based on the quantization difference between adjacent layers within the decoder to fine-tune the layers of the language model related to the translation task, and to understand the behavior of the model in the relevant layers. Then the method of combining different encoders is proposed as a dual encoding-decoding framework on top of the traditional encoding-decoding framework, which is applied to the task of context analysis in business English translation. The fine-tuning method in this paper significantly improves the text translation quality of the language model, especially in the English-X tri-lingualization, which improves the COMET and BLEU metrics by 3.22 and 2.58 points respectively. In addition, the dual encoding-decoding model proposed in this paper is applicable to the task of contextual analysis in business English translation, which significantly improves the performance of contextual analysis in business English, and the F1 value on the HIT-CDTB dataset is improved by 11.60% compared with that of Rutherford’s model. The experiment proves that the proposed method of text has made progress in the research of the task of analyzing textual contextual relations in business English.
In this paper, the 3D reconstruction of the finite element model of the knee joint is completed by first generating and editing the 3D images of the martial arts movements through Mimics software. After that, Hypermesh and Abaqus software are used to pre- and post-process the properties of materials in the knee joint biomechanical finite element model. Visual 3D software and low-pass filter smoothing technique were used to obtain and process the kinematic and kinetic data of the martial arts maneuvers, and the processed data were used as boundary and loading conditions to import the data of the three martial arts maneuvers, namely, horse stance, lunge stance, and servant stance into the finite element model for calculating and comparing the biomechanical responses of the articular cartilage and meniscus. The results showed that the movement pattern of horse stance has a larger knee range of motion and a smaller peak ground reaction force compared to the lunge and servant stance movements in the martial arts maneuvers. Finite element simulations showed that the straddling knee stance produced smaller peak contact stresses on the knee cartilage and meniscus, and the peak stress area changed more during the movement. Three-dimensional finite element simulation analysis obtained four characteristic moments, namely: the first peak ground reaction force moment, the maximum external rotation-external rotation moment, the maximum dorsiflexion moment, and the second peak ground reaction force moment, which corresponded to a greater difference in ground reaction force values. Therefore, it is recommended to wear protective equipment in advance for the injury-prone areas to reduce the risk of injury before the wushu performance.
Under the background of big data era, big data mining technology is widely used, through data mining technology, deeper exploration of data, discovering the relevance of data, can provide decision support for decision makers. This paper analyzes the Internet big data of college students’ employment decision-making based on big data mining technology, uses Apriori algorithm to mine the influencing factors of college students’ vocational skills generation, meanwhile applies ID3 decision tree algorithm to analyze the college students’ tendency of vocational choice, and explores the relevant factors affecting college students’ employment through correlation analysis and clustering analysis. The results of the study show that students’ personal, family and school have strong correlation with students’ vocational skills generation, which affects the improvement of students’ personal job-seeking ability. Meanwhile, the ID3 decision tree algorithm is applied to the employment consulting service for graduates to construct a career decision tree for individual college students, which visualizes their career choice paths under the influence of career values and helps them make more appropriate career choices. In addition, qualification certificates, social practice experience, academic performance, expected salary, ideal employment unit and other factors will affect the employment choice of college students, and there are individual differences among different students.
With the continuous promotion of the integration of industry and education, constructing a quality evaluation system for the integration of industry and education in vocational education has become a key issue to improve the level of vocational colleges and universities’ curricula. Based on the CIPP model, the article builds a quality evaluation system of vocational education industry-teaching integration that includes 4 first-level indicators, 12 second-level indicators and 34 third-level indicators, and empirically analyzes the quality of industry-teaching integration in three higher vocational colleges, H1, H2 and H3, using the fuzzy comprehensive evaluation method through the questionnaire survey from the viewpoint of empirical application. According to the results of the fuzzy comprehensive evaluation, the quality of industry-teaching integration in H1 and H2 higher vocational colleges and universities belongs to the good level, and its comprehensive judgment value is 78.2 and 78.395 respectively.The comprehensive judgment value of the quality of industry-teaching integration in H3 higher vocational colleges and universities is 82.037, which belongs to the excellent level. The three sample higher vocational colleges have achieved outstanding results in the integration of industry and education, providing an example for the development of integration of industry and education for higher vocational colleges in the region.
In the digital campus network security construction, the existence of potential security vulnerabilities can easily cause serious threats to campus information security, resulting in significant losses. In order to prevent and mitigate the risk, the article designs a security vulnerability identification system. Firstly, the URL similarity is compared by machine learning in order to scan the vulnerability information. The SeCF embedding layer is utilized to improve the input speed and the discard layer is designed to improve the overfitting problem during the training process. Finally, TextACBL security vulnerability identification model is proposed by combining CA, 1D-CNN and BiLSTM techniques and analyzed numerically. The average recognition rate of this paper’s method is as high as 80% for 10 common security vulnerabilities, which achieves better security vulnerability recognition results compared with existing methods such as cppcheck, deepbugs, flawfinder and vuldeepecker. The experimental results verify the effectiveness and feasibility of the method in this paper, which provides ideas for safeguarding campus network security during the construction of digital campus.
This paper focuses on the characteristics of multilevel information extraction, based on the convolutional neural network model (CNN), introduces the multi-scale feature fusion and multilevel feature fusion strategy to study the multilevel information extraction method, and proposes the full convolutional neural network based on the attention mechanism and residual connection to form the multilevel information extraction model. Aiming at the gradient disappearance and saddle point problem of convolutional neural network, an activation gradient (AG) algorithm is proposed to optimize its training, which is improved to a class of activation gradient convolutional neural network (AG-CNN). The practical application effect of the multilevel information extraction model in this paper is verified by the information extraction work of net-pen culture in river-type reservoirs. Compared with the classical models such as UNet and ResUNet, the intersection and integration ratio (IoU), recall rate, precision rate, and F1 score of this paper’s model reach the highest 80.28%, 91.02%, 87.18%, and 89.03% among all the models, which possesses a stronger extraction capability. And in the multilevel information extraction experiments on Cifar100 and Caltech256 datasets, when the number of batch training data is greater than 100, the accuracy rate and performance of the experimental group basically remain stable.
Scientific and efficient curriculum design and teaching activity plan is the key to the quality of teaching in higher vocational colleges and universities. Based on the principle of SPOC segmented teaching, this paper proposes a “two-line hybrid” language teaching model. Combined with the implementation process of blended teaching, a blended teaching quality evaluation index system for higher vocational colleges is constructed, which includes the dimensions of rule of law and ethics, professionalism, learning ability, skills and technology. Using the standardization principle of hierarchical analysis, the judgment matrix was constructed by comparing two by two to achieve the empowerment of the indicator system. Introducing cloud model comprehensive evaluation, combining the weights of indicators from the forward cloud generator to get the cloud diagram, and derive the evaluation results. The initial matrix is constructed according to the scores of experts, and all the items passed the consistency test, which verifies that the index system has high reliability and validity. The obtained cloud diagram shows that the cloud model parameter Ex = 5.462, in which the A rule of law ethical dimension Ex is about 5.58, closest to the medium level. This paper makes a useful exploration for actively promoting the teaching reform of higher vocational discipline courses.
This paper first outlines the theoretical method of parametric modeling of BIM technology in building structural design, and introduces Revit and Dynamo software to ensure the interactivity and sharing of data while parameterizing the influencing factors of the building structure and automating the extraction of data. Multiple linear regression analysis and the least squares method are used to quantitatively analyze the building energy consumption and the enclosure structure, and to construct a calculation model for the overall structural energy consumption of the building. In order to maximize the comfort of the users and minimize the source consumption of Huizhou architecture, NSGA-III algorithm is introduced to design the multi-objective optimization model of Huizhou architecture. Finally, the optimization effect of the model is verified through simulation and emulation tests. The results show that: the proportion of time that the internal temperature of the antechamber of the building is in the thermal comfort zone is the highest throughout the year (38.29%), and the thermal insulation performance of the building is insufficient; the average illuminance of the compartment space does not meet the lighting requirements (52.07 Lux), and there is a lack of diversity in the lighting design; and it is necessary to optimize the thermal insulation performance of the building enclosure structure to ensure the comfort and livability of the indoor environment. In addition, between the optimal solution and the worst solution interval of the annual energy consumption value and the absolute comfort value of Huizhou architecture, the maximum difference between the energy consumption and comfort indexes is 1.051×107kwh and 0.807, respectively, which can be used for the intuitive analysis of the BIM model and the comparison of the solutions.
With the development of the Internet, public safety public opinion events have gradually become an important part of social public opinion and an important content of government response. In order to establish a standard system for evaluating the response effectiveness of the public safety public opinion incident response system, this paper, in accordance with the current status of the government’s public safety public opinion incident response system and the literature, selects four indicators, namely, serviceability, dynamics, timeliness and legitimacy, as the criterion layer of the evaluation system. Hierarchical analysis method and TOPSIS method are used to evaluate the public security public opinion incident response system. Finally, in order to verify the reasonableness of the AHP-TOPSIS method for evaluating the response effect of the public security public opinion event response system to public opinion events, 80 cases were selected, which were clustered and analyzed and the proximity scores between the samples and the positive ideal solutions were calculated, and the clustered samples were ranked to obtain the response effect ratings of the system to different events. The analysis of the data shows that timeliness has the most significant effect on the evaluation of the public security public opinion event response system, and the public security public opinion event response system responds best to government-led policy-oriented major public opinion events. The legal compliance framework can be constructed from three aspects: improving the existing laws and regulations on public security public opinion events, strictly enforcing the existing laws and regulations, and carrying out in-depth legal publicity, so as to lay the foundation for the implementation of the legal review work.
Pre-school education, as a key stage on the path of children’s growth, plays a vital role in their overall development. Based on the independent sample t-test method, this paper explores the gender differences in preschool education. It also takes digital media education methods as an example, and utilizes Pearson correlation coefficient, linear regression model, and systematic clustering algorithm comprehensively to quantitatively assess the impact of education methods. The results of the study showed that there were extremely significant differences (P<0.01) in the five dimensions of language ability, creativity, social interaction ability, critical thinking ability, and independent learning ability between male and female toddlers, indicating that there are significant gender differences in preschool education effectiveness. The correlation coefficients between the frequency and duration of use of digital media education methods and language skills, creativity, social interaction skills, critical thinking skills, and independent learning skills ranged from 0.47 to 0.75, with significant positive correlations, and were associated with higher scores on each of the competencies as well as higher levels of satisfaction. This paper reveals in depth the gender differences in preschool education and the important role of digital media in preschool education, which is of great value for the optimization of teaching methods in preschool education.
With the continuous development of the network environment, the traffic data in the network increasingly presents high-dimensional, huge and complex characteristics, and the network threat is also increasing, the network information security threat prediction and defense mechanism plays an irreplaceable position in network security. Based on the general process of network anomaly detection, combined with deep learning algorithms, the article proposes a network anomaly detection method based on data enhancement to improve the detection accuracy of network anomaly detection model. Self-attention mechanism is embedded in the neural network framework to accomplish the improved SA-GRU network information security threat prediction method. In the performance index comparison experiments of network security posture values predicted using different prediction models, the average absolute error of the training data of the results predicted by this paper’s model is 0.00266, and the average absolute error of the test data is 0.00369, and the prediction accuracy of this paper’s model prediction is significantly higher than that of other deep learning methods. This verifies the effectiveness of the method proposed in this paper. Finally, based on the experimental results, the network information security defense mechanism is proposed from the three levels of data encryption, the use of secret keys and intrusion detection.
In the joint electrical drive system of industrial robots, the optimization and improvement of robot motion control is one of the hotspots of current research, and this paper proposes a method of optimizing the joint electrical drive control of robots using multilevel genetic algorithm. An improved PID control method is used to fuzzify the robot motion, and the robot trajectory fuzzy PID controller is optimized according to the idea of multilevel genetic algorithm. The rise time of each joint of the robot is about 5ms, 55ms, and 75ms, respectively, and the overshooting amount is smaller, and the optimized joint electrical drive system of the industrial robot is more stable in speed control in both the acceleration and deceleration phases, and shows a good dynamic control capability of the motion. It can be seen that the work in this study effectively optimizes the control performance of the industrial robot drive system using multilevel genetic algorithm.
Aiming at the learning path recommendation problem, which is the key in personalized teaching, this paper takes the personalized learning path recommendation model as a guide, and researches and gives a method that combines the learning path recommendation model with the NFSBPSO algorithm. The learning path recommendation model based on the two-dimensional features of learners and learning resources is constructed, the population is initialized using the chaos strategy, and the optimal and worst particles in the iteration are optimized using the particle optimization strategy to obtain the optimal solution of the learning path. In order to verify the effectiveness of the personalized learning path recommendation optimization model in this paper, simulation experiments are carried out, and the teaching prototype system of a higher education institution in F city is seen as the experimental platform, and the model in this paper is applied to carry out personalized learning path recommendation practice. The first group of experimental subjects who learn according to the recommended path of this paper have an average test score of 83.6 and an average learning time of 371.7 minutes, which is better than the second group of experimental subjects who learn according to the default path. Most of the values of the recommended matching degree of personalized learning paths are between 0.64-0.9, and most of the adaptation degrees are between 0.11-0.21, which proves that the learning paths recommended by this paper’s model to the users have a high degree of accuracy and adaptability.
In order to improve the planning efficiency of urban landscape, this paper proposes a combination design method of urban landscape construction based on grid division and a spatial optimization model of urban landscape based on particle swarm algorithm to optimize the spatial and pathway layout of urban landscape that takes both economy and ecology into account. The original landscape image was mapped with 3D remote sensing image to generate a 3D image model, and the gradient decomposition method was used for image sampling. Then the multi-dimensional dynamic feature distribution model of urban landscape was constructed, on which the urban landscape area grid was divided to realize the landscape construction combination design. Using particle position to simulate the meta-space layout results of landscape type raster images, the optimization of landscape pattern space and path is completed. The experiment proves that the algorithm in this paper reduces the influence of multiple types of perturbations on the landscape layout results, and the spatial optimization model of urban landscape pattern based on particle swarm algorithm realizes the organic coupling of quantitative and spatial optimization, which not only improves the utilization rate of the urban land, but also substantially reduces the risk index of the urban landscape, and meets the design expectations.
This paper first describes the basic theoretical knowledge of supply chain inventory control and analyzes the existing supply chain inventory control strategies. For the relationship between safety stock and customer service level and inventory cost, the safety stock factor is used as a decision variable, and a supply chain multilevel inventory control model is established under (t,s,S) inventory replenishment strategy. Secondly, the selection operator, crossover operator and mutation operator of the traditional genetic algorithm are adaptively improved, and an improved multi-objective adaptive genetic algorithm is proposed, and this algorithm is used to solve the inventory optimization with the two objectives of supply chain inventory cost and customer service level. The simulation results of the algorithm show that the improved genetic algorithm has better convergence and the obtained Pareto optimal solution set is closer to the real optimal frontier. When the IGD value is minimized and kept constant, the convergence speed of this paper’s algorithm (34 times) is 38.18% lower than that of the traditional genetic algorithm (55 times), and the model converges faster while its Pareto solution set is more uniformly distributed. Example results also show that using the model in this paper can reduce the inventory of each node in the supply chain system and reduce the transportation cost.
This paper takes 2020-2022 Shanghai main board listed companies as the research object, and empirically examines the relationship between factors such as the establishment of internal audit department and the quality of internal audit empowered by new quality productivity, with the effectiveness of the quality of internal control as the explanatory variable, the degree of separation of two powers and so on as the explanatory variable, and the corporate governance structure as the control variable to carry out a via gradient descent Logistic regression analysis optimized by gradient descent algorithm. On this basis, to address the problem that internal audit is prone to bias or falsehood due to management’s self-interest, the fsQCA method is combined to analyze the influencing factors of the choice of auditing policy (capital item or expense item) for general R&D expenditures. It is found that there is a significant positive relationship between companies with an internal audit department and a higher hierarchical level of affiliation and obtaining a standard audit opinion, and the regression relationship holds at the 0.05 level of significance, with a positive correlation with a regression coefficient of 3.745, and an OR value of 40.099. However, the effect of the company’s twofold separation of powers governance structure on the quality of the audit fails the significance test. Firms with lower profitability levels, higher R&D intensity, higher debt levels, lower tax benefits for R&D additions and deductions and lower external audit quality are more likely to capitalize R&D expenditures. The study uses cutting-edge algorithms to accurately analyze new quality productivity-enabling internal audit quality factors and innovate corporate compliance internal control paths.
At the present stage, the staff of mental health center in colleges and universities have a heavy workload, fatigue work and low work efficiency, and it is urgent to explore new paths to alleviate the severe situation of mental health work in colleges and universities. In this paper, we first start from the students’ mental health assessment data and use data mining technology to analyze the students’ mental health status. Then, students’ behavioral characteristics are digitally represented to construct a prediction model of students’ mental health status based on PDNN neural network. Finally, the design method of psychological intervention system in colleges and universities is proposed. In the collected mental health assessment data, the age distribution is skewed toward the younger population, and nearly 55% of these students show a tendency toward psychological abnormality. And the average accuracy and high group recall of the prediction model of students’ mental health status established using PDNN neural network were 88.95% and 87.44%, respectively, which verified the feasibility of the modeling method in this paper. Using the psychological intervention system designed based on the method of this paper for the intervention experiments, there is no significant difference between the experimental group using the system and the control group not using the system in the factors before the intervention (p>0.05), while after the intervention the experimental group scored significantly lower than the control group in the total mental health score, interpersonal relationship sensitivity, depression and anxiety factor items. This proves the validity of the intervention system design method in this paper, which can be applied in psychological intervention methods in universities.
The article firstly outlines the concept of parametric design and modeling techniques and processes, then expresses the relationship between customer needs and functional design parameters of smart home products with discrete sensitivity matrix, and introduces the fuzzy pairwise comparison method to calculate the importance of customer needs. The correlations in the dataset are mined on the Rough Set (RS) tool. AGO and IAGO are used to predict the customer demand importance and design parameter importance in the future cycle, and the parametric product family optimization model is solved by combining the non-occupancy sorting genetic algorithm with congestion distance. In this paper, the optimization ranking and core parts of the functional modules of the smart flowerpot are obtained through the parametric smart home design method, and the functional rankings of the modules are automatic irrigation function, intelligent light replenishment function, monitoring function, and human-computer interaction function; the core parts include temperature and humidity sensors, light sensors, water tanks, and single-chip microcomputer parts, and so on. In the intelligent flowerpot product family design, this paper finds that the efficiency of this paper’s optimization method increases significantly (4.23%-9.12%) and the weight of the product decreases significantly (0.1141kg-0.617kg), both in the known platform mode and in the unknown platform mode. The results of this paper are extremely important for the development and design of parametric product families based on platforms.
This paper realizes the detection of changes in physical fitness of track and field athletes in different training cycles by monitoring their sports training functions. The method used is the time series model ARMA. The athletic training function time series data were preprocessed to fit the ARMA (p,q) model, and the optimal time series fitting model was selected by examining the coefficient of determination, AIC criterion, and SC criterion. Four biochemical indexes, hemoglobin, urea, creatine kinase, and testosterone, were selected as the content of training monitoring for track and field athletes, and the ARMA(1,1) model was selected to analyze the changes in physical fitness of track and field athletes in different training cycles. Taking the hemoglobin index (HB) as an example, through the numerical simulation of the time series of HB levels of 16 track and field athletes preparing for the 15th National Games in Guangdong Province, it can be learned that the change trends of male and female track and field athletes are basically the same throughout the whole year training cycle. From the first cycle, the athletes’ Hb levels began to decrease, fell to the lowest level in the third cycle, and rebounded in the fourth cycle, reaching the highest Hb level in the winter training period.
This paper designs a multimodal data mining and learning behavior analysis model for civic education, uses improved clustering and association rule algorithms to analyze the multimodal data obtained from students, mines the basic consumption, learning and life behavior characteristics, and carries out analysis of the students’ civic situation in order to take targeted civic education measures. Aiming at the problem that traditional clustering results are greatly affected by the selection of initial clustering centers, Gaussian density function is used to determine the initial clustering centers, and Euclidean distance is replaced by density-sensitive distance to avoid sensitivity to noise and anomalies, which improves the accuracy of the clustering results of students’ behaviors. Then we use the FP-Growth association rule algorithm to improve the Apriori construction, recursively and iteratively construct the frequent pattern tree and get the final frequent item set, which improves the efficiency of student behavior data mining. After analyzing the processed student data of a university, it is found that most of the students have low interest in borrowing books, 38.22% of the students borrowed only 2.19 books on average, and the total number of times of book borrowing is only 5.4 times, and the average number of days of single borrowing is 62.3 days, and the school library needs to increase the promotion of students’ reading, which can be done through the way of offline book fairs and e-recommendations to improve students’ interest in reading books. Reading interest. The study makes a useful exploration for the informatization and intelligentization of ideological education in colleges and universities.
This paper puts forward countermeasures to maximize the ecological benefits of agroforestry resources from the perspective of sustainable development of urban agroforestry resources. Taking the maximization of ecological benefits as the goal, the optimal allocation of agricultural and forestry resources is carried out. Based on the results of the optimal allocation of water resources, the planting structure of crops in the irrigation area is adjusted with the water allocation of irrigated crops as the constraint. The optimization model under the constraint of eco-efficiency objective was constructed based on the variational method and optimal control model, and the model was solved by the method of Pontryagin’s great value. After the model adjustment in this paper, the planting structure of crops in the irrigation area of city A was obviously optimized, and the planting area of potatoes accounted for the largest share of the planting area of all the crops in the irrigation area, which was about 40.61%, and the ecological benefits of potato crops were higher, which got the priority of the model, and at the same time, the model also reduced the planting area of the crops with low ecological benefits, and this reasonable allocation adjustment method satisfied the goal of maximizing ecological benefits.
This paper combines the project response theory to dynamically adjust and update the resources according to the learning effect and learning feedback in the process of Civic Education, so as to achieve the goal of matching the learners with the learning resources and realize efficient learning. The differential artificial raindrop algorithm based on perturbation mechanism is designed to realize the solution of multi-objective combinatorial optimization of learning resource allocation. Performance experiments show that the convergence curve of the resource allocation algorithm in this paper is gradually flattened, and the algorithm still has the evolutionary ability, the convergence curve is still decreasing, and the final characteristic difference value is also better than other BPSOR and GAR algorithms. In the case of the number of learning resources of 10, 20, 30, 50, 100, the time consumed is 207ms, 1602ms, 20506ms, 68430ms, 354687, all of which are the lowest, and the success rate is also the highest in the model. The optimal learning path is applied to an experimental class in a university for a 6-week teaching experiment, and the experimental class scores 87.2 points in the Civics test, which is much higher than the control class. This paper realizes the accurate capture of students’ Civics learning problems and the recommendation of targeted teaching resources, which can improve the quality and effect of Civics teaching.
Due to differences in lifestyle, cultural capital and social support, foreign immigrants often have difficulty integrating into the ecology of their native communities and are limited in their space for development. To solve this difficulty, this paper applies the principle of regularization to obtain a logistic regression model by categorizing the factors affecting the social integration of foreign immigrants. The algorithms of log-likelihood function and negative Hessian matrix are used to optimize the parameters of the model, construct the multivariate logistic regression model based on the social integration of foreign immigrants, and analyze the regression results among various factors. The success rate of foreign immigrants’ local integration is higher when the immigration-related system is more perfect, the foreign immigrants’ cultural identification with the local area is higher, the cognitive deviation between foreign immigrants and locals is smaller, and the community integration structure is more appropriate. The highest correlation between the factors affecting the social integration of foreign immigrants is the formation of ethnic networks that are not embedded in the community by foreign immigrants who “embrace the group”, and the cognitive bias of local residents towards foreign immigrants, with a correlation coefficient of 0.9214, and the correlation coefficients of the rest of the indicators are less than 0.9. This paper classifies the migrants into “migrants of work nature” and “migrants of employment nature” in accordance with the purpose of their migratory activities. In this paper, according to the purpose of migration, migrants are classified into four categories: “work migration, study migration, investment migration and shelter migration”, and the results of the multivariate logistic regression analysis are credible.
New biofuels, as a sustainable energy alternative to traditional fossil fuels, are attracting global attention. With the increasing awareness of environmental protection and the continuous growth of energy demand, biofuels offer the possibility of reducing greenhouse gas emissions and decreasing dependence on fossil fuels. In this paper, by introducing the Wasserstein distance, which is used to describe the objective function of the GAN model, the self-attention mechanism is applied to improve the discriminator structure of the traditional WGAN-GP to achieve more efficient generation of high-quality data samples. The WGAN-GP model is used to design a new biofuel combustion scenario, and based on the combustion data, the new biofuel is prepared in the scenario. The final data generation results of the model are evaluated based on relevant evaluation indexes. It can be seen that the trend of the generated data set is consistent with the trend of the actual output value of the power station, and the interval range formed by the generated 50 sets of data can include the real data in a more complete way, with a high data coverage, and the error between the generated value and the real value is in the range of ±250-±300. The new biofuel output scenarios generated by the WGAN-GP model were utilized for EMF synthesis experiments. PTFE@ACMS-SO3H samples showed strong absorption peaks at 759cm-1 and 54cm-1 , indicating that the acidic groups-SO3H were successfully loaded on the surface of the material and the preparation of the novel biofuel was successful.
This study utilizes the Apriori algorithm for association rule mining, aiming to deeply explore the intrinsic connection between college students’ physical health and sports performance. The relevant definitions of association rule mining and the application process of Apriori algorithm in this study are elaborated in detail, including data preprocessing, frequent item set generation, and association rule extraction. Through empirical analysis, various combinations of physical fitness factors affecting college students’ athletic performance and the corresponding association rules are revealed. For example, under the condition of support degree of 0.598 and confidence level of 0.709, when male students’ “stiffness upward grade” is passing, their athletic performance is also passing. By mining the correlation rules between college students’ sports performance and physical health, it provides scientific basis and targeted suggestions for physical education and students’ health management in colleges and universities.
With the rapid development of virtual reality technology, its application in the field of art and design is attracting increasing attention. Based on the perspective of user demand, the article combines the Kano model to analyze user satisfaction with virtual reality technology used in modern Chinese image culture design, and finds that its landing point is the desired attribute in the first quadrant, with the Better and Worse coefficients of 0.531 and -0.141, respectively, which indicates that users expect the application of virtual reality technology in the design of image art and culture. Then the evaluation index system of VR image art and culture design is constructed, and the principal component analysis method is used to assign weights and establish the quantitative model of VR image art and culture design. The analysis shows that the weight of the sensory level is the largest 0.3780, and users attach great importance to the aesthetic experience (0.3780) and emotional experience (0.2710) of VR image art and culture design. The application of virtual reality can draw on the results of the quantitative model to design optimization strategies, combine traditional and modern elements, use the interactivity of VR to enhance artistic expression, create an immersive experience, and create more in-depth and original works of video art and culture.
In recent years, the construction of education informatization has been comprehensively promoted, and the personalized learning recommendation model has brought a new direction for the development of intelligent learning platform for college English vocabulary. This study constructs the KCPE-SR model based on collaborative filtering algorithm and knowledge graph, generates and optimizes the suitable personalized learning paths for learners through the interaction between learners of college English vocabulary and resources, and develops a personalized college English vocabulary learning system based on this model. The analysis of the application effect of the system reveals that the experimental class students’ English vocabulary learning performance has been significantly improved with the help of the personalized learning system, and the students’ English vocabulary knowledge mastery (20.00 points) and vocabulary comprehensive application ability (20.49 points) have also increased. The personalized college English vocabulary learning path generation and optimization system proposed in this paper is able to achieve accurate personalized recommendation of learning resources and can meet the needs of college English vocabulary learning.
In recent years, the scale of the electric vehicle industry and social ownership are gradually growing, in the case that the charging facilities are not yet able to meet the demand for electric vehicle charging. Aiming at the situation described above, the research of charging station siting supported by variable neighborhood genetic algorithm is proposed. Based on the principle of charging station siting, the objective function and constraints are set, and the design of charging station siting model is realized. It is found that the traditional genetic algorithm, which has the problem of poor search ability, adopts the variable neighborhood genetic algorithm to solve the model. Calculated, this paper’s algorithm in the charging demand peak period scenario, to determine the optimal charging station site selection there are four, the two objective function value of 0.94, 0.98, both in the charging peak period or the low peak period, this paper’s method compared to the traditional genetic algorithm has a higher superiority.
The rapid growth in the scale of cross-border data flow has pushed the protection of personal information to become an important issue of global concern. This paper drafts a legal adjustment mechanism for the protection of personal information under cross-border data, and builds a data sovereignty practice system from the aspects of comprehensive strength construction and cross-border flow pilot. It utilizes civil law, criminal law and administrative law to protect personal information in cross-border data flow. Based on the numerical analysis method, the legal protection mechanism of personal information in cross-border data flow is discussed in depth. The numerical analysis results show that the probability of personal information exposure increases to about 0.35 when the ratio of malicious nodes under the legal mechanism of this paper is 0.5. The estimated accuracy of personal information protection effect increases by 65.16% to 80.52% when the enforcement strength of this paper’s mechanism is 0.7 and the sample size of companies is 300. Fixing the initial ratio of cross-border data information disclosure, the smaller the initial ratio of personal information protection, the faster the speed of personal information leakage under the legal mechanism. The investigators’ scores on the personal information risk indicators of a cross-border e-commerce platform are uniformly distributed between 1 and 2, and the sum of the overall scores is less than 10, demonstrating the effectiveness of the legal mechanism constructed in this paper on the protection of personal information.
County economic development directly affects the national economy, and the county economy of Henan Province has become the economic pillar of the province. The purpose of this paper is to analyze the county-level economic development of Henan Province and its economic influencing factors by using the quantitative evaluation method. From the time series, the level of economic development of 105 county units in Henan Province from 2000-2023 is analyzed from two perspectives, absolute difference and relative difference, using the indicator of GDP per capita. Screening of factors affecting the level of economic development of counties in Henan Province is carried out from the aspects of population, resources, policies, etc., and a four-aspect indicator system is constructed, namely, human capital, government regulation, industrial level, and economic vitality. A multiple linear regression model is established, and the regression model is fitted by the regression coefficients of each influencing factor, and the fit of the regression model is examined. Each county in Henan Province is divided into three development gradients: developed, generally developed and less developed counties. Panel data regression analyses were conducted on the overall county economy of Henan Province and the influencing factors of developed, generally developed and less developed counties respectively. In the overall economic development of counties in Henan Province, the degree of influence of physical capital investment and the structure of secondary and tertiary industries on the overall differences in county economies is particularly significant. It is manifested in the fact that for every 1% increase in the investment in fixed capital of the whole society, the output of GDP per capita increases by 0.09112% accordingly. Therefore, in order to improve the differences in the economic development of counties in Henan Province, local governments and enterprises should make efforts to improve the market and investment environment and adjust the structure of secondary and tertiary industries.
The construction of harmonious labor relations is of great significance in improving the quality of public services and promoting social harmony and stability. The study uses multi-period DID algorithm to construct a mathematical model of artificial intelligence application and labor dispute resolution, and conducts research on the influence relationship between the two. Aiming at the lack of preventive mechanisms for labor dispute resolution at present, principal component analysis and artificial neural network are used to establish a labor relations early warning model. The results show that artificial intelligence application has a significant positive impact on labor dispute resolution at the 5% level, and there is regional heterogeneity.The prediction accuracy of PCA-ANN model on labor relations in the training set and test set is 81.25% and 85.71%, respectively, which presents a good effect of early warning of labor relations, and it can be used to improve the mechanism of labor dispute resolution. Finally, based on artificial intelligence technology, the online labor dispute resolution mechanism is proposed to prevent the escalation of labor disputes and improve the effectiveness of labor dispute resolution by focusing on prevention, secondary control and subsequent resolution.
Data empowers educational evaluation, and blockchain technology aids in the governance of educational evaluation data. The union of big data and blockchain technology has prompted the development of educational evaluation toward digitalization and precision of educational evaluation. This paper combines the multifaceted governance utility of blockchain technology for educational evaluation data and proposes to improve the consensus mechanism in educational evaluation information sharing. The PBFT consensus algorithm is updated with node contribution reward and punishment mechanism, the consensus nodes are selected by Fibonacci function characteristics, and the consistency protocol is optimized, so as to design a practical Byzantine fault-tolerant algorithm NCG-PBFT based on node contribution grouping, and analyze the credit value, throughput, normal block out delay, and the number of communications of NCG-PBFT consensus algorithm. Build a comprehensive education quality evaluation platform and bring in the improved PBFT consensus algorithm to test the operation performance of the comprehensive education quality evaluation platform. When the request frequency tends to be stable, the education comprehensive evaluation system of NCG-PBFT consensus algorithm is able to improve the system throughput by 74.54% compared with the PBFT algorithm, which is able to meet the performance and stability requirements of the education comprehensive quality evaluation system.
The development of Artificial Intelligence has renewed the direction of art history, making the relationship between technology and art a matter of great interest once again. The application of artificial intelligence in the field of fashion design brings new tools to the designers’ way of designing and displaying. This paper researches artificial intelligence technology and analyzes the application of artificial intelligence as an auxiliary means in the field of art and design, and deeply researches the way of applying artificial intelligence in fashion design as well as its advantages. It also researches the intelligent image generation problem under the fashion big data environment, adopts the method of fusing the external features of fashion images and decoupling the internal features, and provides theoretical methods and bases for the controllable generation of fashion images based on the architecture of generative adversarial network. A multiconditional information fusion generative adversarial network architecture (MCF-GAN) is proposed, and the experimental results show that the image generation performance of the model in this paper is excellent, and better performance is obtained compared with other comparative methods. And it is applied to the actual fashion design for evaluation, the designer’s evaluation in all dimensions are more than 10 points, indicating that the method in this paper has a better application value in fashion design, and provides an effective path for fashion design optimization.
Aiming at the traditional pavement construction, there are problems such as poor construction conditions, limited quality inspection methods, backward control mode and incomplete management means. In this environment, the project in this paper (Gansu Road and Bridge Anlin Pavement Second Standard Project) uses multi-objective particle swarm optimization algorithm to establish a multi-objective machine group optimization configuration model based on quality constraints under the schedule – cost, and the first time to quote asphalt pavement to carry out the intelligent construction of unmanned machine group in Gansu Province. Analyze the intelligent unmanned machine group composed of auto-pilot paving technology and roller auto-pilot technology. Design the optimal configuration model of highway construction machine group, and use multi-objective particle swarm algorithm to design the cooperative operation of unmanned machine group. Combined with the optimal configuration of highway construction fleet problem itself, the standard particle swarm algorithm and fleet configuration model are also modified and improved. Simulate the highway pavement construction process, emphasizing the preparation of construction personnel, machinery, and management platform. The parameters of particle swarm algorithm are designed to solve the optimal construction machine fleet optimization configuration under quality constraints of duration-cost. The machine utilization and duration of scheme 2 are 15.23% and 10.96%, respectively. With the priority of duration, scheme 2 is selected as the machine fleet configuration scheme. Option 4 has the lowest machinery cost of 9.41%. With the priority to ensure the maximum profit, option 4 can be chosen as the machine swarm configuration scheme.
