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Facing the dual background of risk society and the fourth scientific and technological revolution, digital emergency management (DEM) research has attracted wide attention worldwide. Based on the comparative analysis of data from Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, the research draws the knowledge map by CiteSpace, in order to analyse the spatiotemporal distribution, hotspots, and trends of DEM research. The following are the results: (1) DEM research shows a spiral growth, with a significant increase in the number of papers. Existing highly cited references are mainly carried out from the perspective of management or technology. (2) The complex countries' cooperation network has been formed from the USA, China, and other countries; the complexity of the institutions' cooperation network must be strengthened, and the density of the authors' cooperation network is low; DEM research has significant interdisciplinary attributes. (3) The hotspots of DEM research can be divided into three categories: means, processes, and objects, but the research emphases between international and China are different. (4) The evolution of DEM research can be divided into three stages between international and China: embryonic, development, and deepening stages, and research contents in each stage show some differences. (5) The trends of DEM research between international and China are different. The former are information technology, artificial intelligence, and machine learning, whereas the latter are emergency decision making, scenario analysis, and public health. [ABSTRACT FROM AUTHOR]

With the rapid development of modern science and technology, the speed of digital media to disseminate information is also accelerating, and the forms of communication become more and more diversified. In order to make digital media art better innovate and develop, designers should actively explore, so that art under digital media has the characteristics of intelligence, networking, and content diversification, so that it can be better applied in modern digital media communication and let visual art reach new development heights in the context of digital media. The current rapid development of artificial intelligence technology is being gradually applied to all fields of society. From the current point of view, China's digital media art will inevitably remain in the early development stage for a long time to face many difficulties and problems. In the context of the era of artificial intelligence, there will also be a large number of art and design talents for continuous research and exploration, and the emergence of these talents will certainly prompt the further development and enhancement of digital media art and artificial intelligence technology. In this process, it is especially worth noting that China's excellent cultural connotations should not be abandoned. Committed to digging more beneficial digital media art elements from traditional culture to make digital media art always develop in a more meaningful way. This paper takes the artificial intelligence era as the background, discusses the core of the development of the mutual integration of artificial intelligence technology and digital media art, analyzes the current development status of digital media art and technology, as well as the innovative development direction and future trends, proposes a digital media art design algorithm based on the convolutional neural network, and finally proves the effectiveness of the method in the relevant data set. [ABSTRACT FROM AUTHOR]

Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent. [ABSTRACT FROM AUTHOR]

The development of network computer technology and artificial intelligence technology promotes the generation of software agents. However, online shopping has inherent information asymmetry due to the anonymity and liquidity of C2C (Customer to Customer) transactions. In the virtual environment, buyers cannot see the physical products, and they do not experience online shopping in person. They can only select products through the seller's pictures and descriptions. After payment, there may be some problems, such as sending the wrong product, the seller does not deliver the goods after receiving the goods, or the seller does not receive the payment after delivery, etc. Therefore, the credit problem is the bottleneck for the development of online shopping business. Therefore, in order to ensure the transaction security of buyers and sellers, major C2C online trading platforms at home and abroad have established a credit evaluation mechanism. However, due to the reliability of e-commerce technology, the legal environment of e-commerce, the ethical environment of e-commerce, and the problems of existing credit evaluation indicators. The development of e-commerce in China is very slow. Therefore, actively exploring the credit model of Chinese e-commerce is of great significance to promoting the development of Chinese market economy. In this paper, AI technology analyzes the credit evaluation management method of C2C e-commerce website for transaction participants. This paper summarizes some key factors that affect the establishment of C2C credit, and further finds that there are some problems in the credit evaluation model involved in the e-commerce process. These problems stem from the inability to properly deal with the current development of e-commerce in China. There are problems with its evaluation system and honest transaction process. In order to better promote the development of e-commerce, in view of the above problems, this paper proposes a new model of C2C e-commerce credit system based on the game theory model of e-commerce buyers and sellers of AI technology, and further improves the evaluation model and related measures to completely solve the problem. Credit value generally shows a growth trend, but the trend is not obvious. [ABSTRACT FROM AUTHOR]

In Chinese colleges, teachers' teaching level and proficiency in business knowledge are an important part of maintaining the knowledge system of colleges and universities. Therefore, in order to improve the teaching level of Chinese colleges and improve the knowledge reserve of college students, we should spare no effort to develop and improve the professional level of each teacher. The research on teacher training mode based on the development of teachers' teaching ability is of great significance to the development of colleges and the improvement of teaching quality. The new generation of artificial intelligence plays a pivotal role in the training of college teachers. This paper randomly selects 897 undergraduate teachers from 10 private colleges in a certain region of China who participate in higher teacher training for research. It mainly studies the application effect of several typical artificial intelligence technologies in evaluating higher teacher education and training. Specifically, the paper uses artificial intelligence as a carrier to predict the performance indicators of the three main modules involved in higher teacher education and training. The prediction results of several artificial intelligence techniques show that the support vector machine method based on immune algorithm optimization corresponds to the largest coefficient of determination and the smallest root mean square error. Therefore, it predicts the best. Furthermore, the comparison results between the predicted data and the measured data show that the artificial intelligence technology has a better prediction effect on the three module indicators. At the same time, there is a good exponential function relationship between the prediction data corresponding to the two main modules of organization and personnel. [ABSTRACT FROM AUTHOR]

In order to improve the development of China's public cultural service supply field, this paper introduces the public culture of weather services with the support of network intelligence services and smart devices. This article examines the purpose of public cultural cloud services to identify current public service public culture and future development models; The results show that the average consistency indexes determined by the weight of evaluation indexes based on AHP are 0, 0, 0.514, 0.894, 1.118, 1.248, 1.344, 1.421, and 1.461, respectively. Their importance order is judged according to the degree of relative importance. According to the hierarchy and diversity of users' needs, a "multiple" development model of public digital culture cloud service is constructed. This data provides a measure of resistance to innovations in public cloud culture, data advocacy to support public climate change in four areas: the reform of service strategies, the integration of public culture, the development of specialized resources, and the development of service teams and customer capabilities. [ABSTRACT FROM AUTHOR]

As China's economy continues to grow of informational technology and mobile Internet industry, the online tourism industry has received more and more extensive attention and use. However, as an emerging industry, users often need to spend a lot of time to choose travel services that match their needs because of the complex amount of relevant information. Under such circumstances, this paper studied the recommendation method in travel platform. First, the big data is used to extract user data. Secondly, the current online travel business recommendation for users has the problem of low accuracy. The reason is that the services provided are still in traditional recommendation algorithm. In this paper, the Bayesian network is used to evaluate the user's attribute preference and generate a data model, using effective methods in artificial intelligence algorithms to improve collaborative filtering algorithms and finally generate hybrid recommendation algorithms. Compared with the traditional recommendation method, the experimental results showed that the research can improve the recommendation accuracy of tourist attractions by 6.55%, increase the user's satisfaction for the platform, and enhance the visit rate and retention rate of the tourist attraction recommendation platform. [ABSTRACT FROM AUTHOR]

In recent years, the development of intelligent iteration technology and the use of big data processing technology have set off an upsurge, and the analysis and application of artificial intelligence algorithms have been paid more and more attention. In order to face the challenges of Arab migration and international trade, this paper constructs the basic structure of the Arab migration action imitation model. In this paper, a simulated servo clustering algorithm based on big data and intelligent iteration is used. Then, through the analysis of pseudo servo clustering algorithm, an optimization model is established, and a big data analysis system is formed. This paper focuses on the wide application of big data statistics to solve the construction of Arab immigration and entrepreneurship data system. This paper studies and applies the big data statistics and intelligent iterative algorithm of Arab immigration behavior, focuses on the annual ladder degree of Arab immigrants, and constructs a pseudo servo cluster system based on Intelligent iterative algorithm. Finally, the simulation experiment verifies whether the clustering model can accurately retrieve the behavior of Arab immigrants in China. The era of big data provides good development opportunities for Arab immigrants and international trade, but it also faces severe challenges. The study provides a reference for strengthening the analysis of international trade, and puts forward perfect countermeasures in combination with the actual situation, so as to improve the efficiency of international trade management and promote the better implementation of international trade. [ABSTRACT FROM AUTHOR]

The digitalization of the automobile industry must depend on the information technology industry. The coupling development of the two creates conditions for the realization of industry 4.0 and intelligent manufacturing. The purpose of this paper is to make an empirical study on the possibility of coupling and the coupling degree, correlation degree, and innovation ability of China's automobile industry and information technology industry. This paper takes the intelligent manufacturing of the automobile industry as the research object. According to the current situation of China's automobile industry, this paper uses data to analyze and predict the development level of China's automobile industry and the gap with the foreign automobile industry. Firstly, it summarizes and analyzes the development of China's automobile industry and information technology industry. Then, the gray correlation analysis method is used to explore the factors affecting the innovation of China's automobile industry. It is found that the relationship between capital investment and innovation is the most significant in "industrial innovation investment." Finally, the coupling development model of the automobile industry and information technology industry is established, and the coupling degree of the two is analyzed by using the analytic hierarchy process and the Delphi method. The research of this paper mainly focuses on the automobile industry. Under the wave of internet, other industries such as the commerce and service industry also have a strong coupling relationship with the information technology industry, which will provide a new research direction for the follow-up research of experts and scholars. [ABSTRACT FROM AUTHOR]

As China's economic level and industrial volume continue to develop and expand, the traditional methods of business management and resource scheduling demonstrate significant limitations and misalignments with China's daily economic activities. Traditional industrial and commercial management resource scheduling is highly reliant on manual labor. When confronted with the requirements of large-scale project management, the pure manual mode cannot track the real-time progress of the project flexibly, effectively, and in a timely manner, resulting in the inability to complete the corresponding resource-scheduling work efficiently, which will affect the overall operation progress of the project. Deep neural networks have a high-application potential for solving extremely complex and highly nonlinear optimization problems. Artificial intelligence and deep learning research have advanced at a rapid pace over the past few decades, thanks to the efforts of numerous researchers. Combined with GPU technology, the deep learning framework can provide an extremely complex optimization problem with a practical and feasible optimization scheme and corresponding solution path in a very short amount of time. Therefore, this paper explores the potential application of deep learning technology to industrial and commercial resource-scheduling management. By analyzing the benefits of deep learning technology and the bottleneck issues of existing industrial and commercial resource scheduling, a real-time optimized industrial and commercial resource-scheduling model based on deep learning technology is developed. The model is evaluated using the respective data set. The test results demonstrate that the resource-scheduling model proposed in this paper has strong real-time and high-operation efficiency and can assist engineers in completing the corresponding resource-scheduling tasks. [ABSTRACT FROM AUTHOR]

With the continuous development of information technology, China has officially entered into the era of big data, the enterprise business management has realized intelligent business management based on information technology, and the arrival of the era of big data has to some extent reduced its management and risk costs and improved its work efficiency and economic benefits, but at the same time, it also faces a series of challenges, such as with the continuous expansion of business scope. As the business scope continues to expand, its current management model cannot meet the development needs. The integration of artificial intelligence technology and enterprise business management has become the main trend of future development. The application of big data technology to corporate business management enables the production of products and the creation of product value in the direction of socialization, increases the mutual influence between companies and consumers, and enhances the fairness, openness, and justice between corporate development and consumer activities. In this paper, after a detailed understanding of the current situation of the combination of artificial intelligence technology and enterprise business management, we focus on the application path of artificial intelligence technology in enterprise business management and finally elaborate the future development direction of artificial intelligence technology in the field of enterprise business management, hoping to further deepen the relevant personnel's understanding of artificial intelligence. Analyzing the current situation of business management development in China, this paper proposes applying big data technology and artificial intelligence technology to enterprise business management work and proposes a new model to bring new development opportunities and development directions for enterprise business management work. The big data technology proposed in this paper is based on cloud platform services, while artificial intelligence based on neural network is embedded in the cloud platform for enterprise business management services. [ABSTRACT FROM AUTHOR]

Environmental public interest litigation is a category of modern litigation that aims to protect the rights and welfares of the public environment. In China, environmental public interest litigation is a common mean to protect the environment and has a vital role in protecting the environment. This paper aims to study how to use the advantages of the artificial intelligence era to study the definition of environmental public interest litigation. This paper proposes how to define environmental public interest litigation and proposes a data mining classification algorithm based on the era of artificial intelligence to analyze the definition of public interest litigation. The experimental results of this paper show that with the promulgation and implementation of the new environmental protection law in 2016, the number of environmental public interest litigation cases nationwide has increased significantly compared with previous years. There are 43,589 environmental public interest litigation cases nationwide, and the difficulty of work is 38%. It can be seen that public interest litigation has become one of the judicial channels generally accepted by the public. It has become a powerful weapon for safeguarding the interests of the public environment. The use of the data mining classification algorithm based on the era of artificial intelligence can make the case definition of environmental public interest litigation easier, thus improving work efficiency. [ABSTRACT FROM AUTHOR]

As the presentation form of the web, web pages contain a lot of important information in web design. As many information carriers, such as sound, graphics, and text, web pages have been integrated and have played a good role in visual transmission. It inherits many features of traditional graphic design, it can be said that it extends graphic design, and web design is richer in visual performance. At present, the research on web design in China is mainly background research. There is little research on the front-end aspects of web pages, especially in the aspect of plane vision. The lack of research in this area limits the development of web design. This paper introduces the relevant situation of web design and then analyzes the relevant elements in web design, such as visual elements, and their innovative applications. In addition, it focuses on the presentation form of visual elements in web design and summarizes the development trend of visual elements in contemporary web interface design. It proposes the strategy of innovation and application of visual elements in web design. The author hopes that, through the research of this paper, it will lay a theoretical foundation for the development of web design in the future. At the same time, this paper uses the NCMF algorithm in artificial intelligence to identify the visual elements of web pages, thereby improving the efficiency of web page design. Experiments show that the Chaos accuracy rate of the NCMF algorithm in the CETD dataset is 73.3482%. [ABSTRACT FROM AUTHOR]

At present, artificial intelligence technology has developed rapidly and has been gradually applied to various fields of society. With the popularization and development of computers, under the influence of artificial intelligence technology, digital media art and artificial intelligence technology merge with each other, which makes modern culture bloom with greater charm. Based on the era of artificial intelligence, this paper discusses the development core of the integration of artificial intelligence technology and digital media art, analyzes the current development status of digital media art and technology, and finally puts forward the innovative development direction and future trend. At present, China's artificial intelligence technology has gradually entered a prosperous stage in the continuous development and has become one of the high and new technologies in the new era, bringing many changes to people's work and life. With the development of society, modern people are pursuing the two-way needs of material and spirit, and art, as a special field, has attracted much attention. By involving artificial intelligence in the art field, it has become a new challenge to be faced at present, which will promote art and technology to present different forms of expression. Of course, the artistic content of digital media art will be richer, and the attention will be higher. Under the background of artificial intelligence era, the integration of artificial intelligence and digital media is the core industry at present, which promotes the development of China's economy in a better direction. [ABSTRACT FROM AUTHOR]

With the current upsurge of innovation and entrepreneurship in China, this paper objectively analyzes the importance of designers' professional quality and scientific awareness in response to the need for the integration and development of innovative skills and artistic spirit in environmental design. This paper emphasizes that the level of intelligent operation of science and technology should be improved in design, the rigor of intelligent utilization in creative expression should be improved, and the problem of evaluation criteria of artistic design value should be solved based on professional development. This paper gives the design scheme of intelligent monitoring module based on Lora technology and designs the hardware circuit of the intelligent monitoring module with MSP430F2272 as the core. It includes a microcontroller circuit, a Lora module interface circuit, an input signal detection module, a control output module, and the like. This paper focuses on the content, representation, and implementation of the associated configuration table, and compares four analog input signals with variable thresholds. The sensor detects 800Lux, and the error is 500Lux, which is higher than errmax, so it outputs -10 brightening every time to generate analog input logic. The 4 digital inputs are combined with the analog input logic to generate output control signals by associating operations. [ABSTRACT FROM AUTHOR]

High-quality vocational education is a vital basis for China's efforts to develop high-quality teachers and technical and skilled workers. The increasing development of contemporary vocational education is given higher importance in the new era. We proposed the stick to moral education while carefully integrating it with the requirements of changes in technology and modernization, continuous improvements in the quality of college vocational education, and the development of more conceptual and skilled talented individuals with both political integrity and the capacity to build modernization. Using artificial intelligence technology to challenge these difficulties in the context of social informatization can play important role in enhancing the teaching quality of high vocational education. With the advent of the Internet era, students and parents have an increasingly strong demand for online independent vocational English reading materials, although there is some English learning software that provides online vocational English reading materials in the education market. However, most of this software only changes the reading form of paper vocational English reading materials but fails to completely meet the real demand of students for vocational English reading materials, so how to carry out personalized recommendation reading according to the real reading situation and reading preferences of students is a vital problem to be resolved. In this background, this paper studies the personalized recommendation technology, in the use of recommendation algorithms on the based association rules to obtain higher vocational English reading content recommended the strongest association rules, on the based combination of user-based collaborative clarifying recommendation algorithm. The linear regression model combined with the users reading interest and personalized recommendation of higher vocational English reading process is optimized, improving the accuracy of English reading recommendation in vocational colleges. [ABSTRACT FROM AUTHOR]

Urban digital governance represented by digital city and smart city is becoming an important direction of urban agglomeration development. It is necessary to reexamine the internal relationship mechanism between local government digital action and regional governance behavior. Based on the theory of collective action and network governance, this paper discusses how the economic development level of local government interacts with the cooperative network status of urban agglomeration and affects the tendency of digital governance strategy of local government. By constructing the configuration matrix of "economic development level" and "cooperative network status" of local governments in urban agglomerations, this paper extracts the possible "center" and "follow" governance strategies that local governments in different configurations may adopt in the digital field. The ant colony optimization algorithm proposed in this paper consists of two parts—ACO initialization and ACO optimization. ACO initialization describes how to map the path of ants to the solution of the problem, that is, the combination of orders. In addition, ACO initialization will also define and initialize the pheromone and heuristic information model compatible with the solution. ACO optimization updates the pheromone model to heuristic guide ants to build paths, so as to iteratively find the optimal solution. The case study of 26 cities in the Yangtze River Delta of China shows that most of the digital governance strategies of local governments are in line with the selection logic of their configuration status of "economic level network status". The study results proved that the proposed model has provided effective accuracy of 96%. However, driven by selective returns, there are still some "abnormal" governance tendencies. [ABSTRACT FROM AUTHOR]

With the widespread application of IoT technology in the world, the new industry of IoT finance has emerged. Under this new business model, commercial banks and other financial institutions can realize safer and more convenient financial services such as payment, financing and asset management through the application of IoT technology and communication network technology. In the cloud computing model, the local terminal device of IOT will transmit the collected data to the cloud server through the network, and the cloud server will complete the data operation. Cloud computing model can well solve the problem of poor performance of IoT devices, but with the increasing number of IoT terminal devices and huge number of devices accessing the network, cloud computing model is constrained by network bandwidth and performance bottleneck, which brings a series of problems such as high latency, poor real-time and low security. In this paper, based on the new industry of IoT finance which is developing rapidly, we construct a POT (Peaks Over Threshold) over threshold model to empirically analyze the operational risk of commercial banks by using the risk loss data of commercial banks, and estimate the corresponding ES values by using the control variables method to measure the operational risk of traditional commercial banks and IoT finance respectively, and compare the total ES values of the two. This paper adopts the control variable method to reduce the frequency of each type of loss events of operational risk of commercial banks in China respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy and the continuous improvement of people's living standards, people pay more and more attention to environmental problems. It faces great challenges in energy, resources, and environmental protection. In recent years, people have realized that environmental pollution is a global problem. Therefore, the protection of ecological resources is an important topic in today's society. Based on wireless communication and artificial intelligence, this paper studies the role of environmental law in the protection of ecological resources. Firstly, this paper explains and introduces the concept and purpose of environmental rule of law, then expounds the importance of ecological resource protection, studies the wireless communication and artificial intelligence technology, designs the wireless communication and framework and the structure of artificial intelligence system, and adopts the questionnaire survey method. This paper studies and investigates an ecological resource protection area in China, and tests wireless communication technology and artificial intelligence technology. Finally, the test results show that the resources in the ecological nature reserve are evenly distributed, but there are still problems of overexploitation of resources and poor management. Each module of the ecological resource protection system based on artificial intelligence operates normally, the delay time and operation time are between 1 and 3 s, and the amount of memory is normal. In addition, the feasibility, effectiveness, and real-time performance of each module are more than 90%. All these show that the system can meet the requirements of normal operation. [ABSTRACT FROM AUTHOR]

During the last two decades, as computer technology has matured and business scenarios have diversified, the scale of application of computer systems in various industries has continued to expand, resulting in a huge increase in industry data. As for the medical industry, huge unstructured data has been accumulated, so exploring how to use medical image data more effectively to efficiently complete diagnosis has an important practical impact. For a long time, China has been striving to promote the process of medical informatization, and the combination of big data and artificial intelligence and other advanced technologies in the medical field has become a hot industry and a new development trend. This paper focuses on cardiovascular diseases and uses relevant deep learning methods to realize automatic analysis and diagnosis of medical images and verify the feasibility of AI-assisted medical treatment. We have tried to achieve a complete diagnosis of cardiovascular medical imaging and localize the vulnerable lesion area. (1) We tested the classical object based on a convolutional neural network and experiment, explored the region segmentation algorithm, and showed its application scenarios in the field of medical imaging. (2) According to the data and task characteristics, we built a network model containing classification nodes and regression nodes. After the multitask joint drill, the effect of diagnosis and detection was also enhanced. In this paper, a weighted loss function mechanism is used to improve the imbalance of data between classes in medical image analysis, and the effect of the model is enhanced. (3) In the actual medical process, many medical images have the label information of high-level categories but lack the label information of low-level lesions. The proposed system exposes the possibility of lesion localization under weakly supervised conditions by taking cardiovascular imaging data to resolve these issues. Experimental results have verified that the proposed deep learning-enabled model has the capacity to resolve the aforementioned issues with minimum possible changes in the underlined infrastructure. [ABSTRACT FROM AUTHOR]

Today, with the development of the times and the advancement of science and technology, informatization has gradually become an important standard for measuring the state of national development in the course of national development. The artificial intelligence multimedia technology has been developed under the background of informationization and has gradually become an important means to achieve national development and regional economic progress. The impact often depends not only on the economic attributes of the industry but also on the economic attributes of urban planning. Countries and regions with high levels of regional integration and industrial economic development have begun to inject economic knowledge and methods into regional integration and industrial economic processes, which are used to solve or alleviate problems and contradictions in the process of urban and rural construction and development. It can be seen that industrial economy and regional integration are not only a spatial phenomenon but also an economic phenomenon. Based on the needs of the development of urban planning discipline theory and the era of national industrial economic integration, this paper puts forward the research methods and ideas of this paper and combines current multimedia backgrounds and their development trends to analyze key innovations and research frameworks, incorporating the main theoretical research elements into the concept of "combination" of traditional Chinese philosophy, aiming at the main problems and contradictions in the process of China's industrial economic development, respectively, the cultural economy and industrial economy, and the integration of public economy and environmental economy, land economy, and housing economy, a new urbanization development strategy of population economy and regional economic competition. [ABSTRACT FROM AUTHOR]

In today's society, the continuous deepening of international cultural integration has become the background of the times. China has become more and more closely connected with the world, and many physical or online news media have become a platform for China to receive world information and spread Chinese culture. Business English translation is therefore valued by translation researchers and translators. Aiming at the shortcomings of current business English translation research, this paper designs and develops a business English translation architecture based on artificial intelligence speech recognition and edge computing. First of all, considering the relevance and complementarity between speech and text modalities, this paper uses the deep neural network feature fusion method to effectively fuse the extracted monomodal features and perform speech recognition. Secondly, adopt the edge computing method to establish the business English translation system architecture. Finally, the simulation test analysis verifies the efficiency of the business English translation framework established in this paper. Compared with the existing methods, our proposal improved the accuracy than others at least 10% and the time of model building also decreased obviously. The purpose of this research is to discuss how to deal with the many differences between the source language and the target language, and how to enhance the readability of the translation and meet the reader's cultural cognition and needs. [ABSTRACT FROM AUTHOR]

With the acceleration of global integration, the demand for English instruction is increasingly rising. On the other hand, Chinese English learners struggle to learn spoken English due to the limited English learning environment and teaching conditions in China. The advancement of artificial intelligence technology and the advancement of language teaching and learning techniques have ushered in a new era of language learning and teaching. Deep learning technology makes it possible to solve this problem. Speech recognition and assessment technology are at the heart of language learning, and speech recognition technology is the foundation. Because of the complex changes in speech pronunciation, a large amount of speech signal data, the high dimension of speech characteristic parameters, and a large amount of speech recognition and evaluation computation, the large volume of speech signal processing requires higher requirements of hardware and software resources and algorithms. However, traditional speech recognition algorithms, such as dynamic time-warped algorithms, hidden Markov models, and artificial neural networks, have their advantages and disadvantages. They have encountered unprecedented bottlenecks, so it is difficult to improve their accuracy and speed. To solve these problems, this paper focuses on evaluating the multimedia teaching effect of college English. A multilevel residual convolutional neural network algorithm for oral English pronunciation recognition is proposed based on a deep convolutional neural network. The experiments show that our algorithm can assist learners in identifying inconsistencies between their pronunciation and standard pronunciation and correcting pronunciation errors, resulting in improved oral English learning performance. [ABSTRACT FROM AUTHOR]

With the development of The Times, social events are increasing, and emergency management has gradually become the main helper to solve the crisis in the public domain. By observing the current situation of many countries and regions, we can find that various types of public crises often occur in many countries and regions in the world, which have severely affected people's daily life, lives, and property. Through long-term research and analysis, it can be known that the emergency management mechanism currently established in China has certain shortcomings. The communication problem of emergency information is likely to cause the emergency work to not proceed smoothly. In addition, problems in the communication channels of emergency information are likely to cause problems in the cooperation of various departments when people carry out emergency management work, and the efficiency of the government in dealing with problems will also be reduced in real scenarios. In order to improve the efficiency of emergency information management, this paper aims at the various problems existing and facing in the construction of emergency management system. On this basis, the integration of various relevant emergency information management plan models is analyzed and sorted out, and based on the research and integration of the development of artificial intelligence algorithms. The main research results of emergency information management at home and abroad are comprehensively studied and evaluated. Finally, a QG algorithm based on more model fusion is developed. In the process of analysis, this article uses artificial intelligence algorithms to build a prediction model of multiple modes and collects the data needed to build the model by random extraction. Through the analysis of different data sets, it is used as the basic training data for prediction. Through comprehensive analysis, the model constructed in this paper can promote the sharing of emergency information among departments to a certain extent. [ABSTRACT FROM AUTHOR]

Innovative product design is the core problem of modern industry and the source power of enterprise development. With the continuous improvement of China's material level, product innovation design has changed from functional needs to emotional needs, and the product design method centered on users' emotional needs has attracted much attention. With the emergence of new technologies such as artificial intelligence and big data, the automation and intelligence of advanced algorithms and innovative design methods for online product decision driven by multimodal multimedia data in e-commerce have become inevitable trends in the field of product design, and it is a big challenge to improve the innovative design ability of products by using information technology. In order to solve this problem, based on the research of BP neural network technology, this paper puts forward an innovative product design method based on image-Kempi method. The color features of images are reconstructed and integrated, and then transferred to product modeling, so as to generate new products with both content image modeling features and style image color features, which brings visual inspiration to users. The results show that KENPI method combines the theories of Kansei engineering, convolutional neural network, and neural style transfer, and establishes a mapping model between product modeling elements and product semantics. Convolutional neural network and neural style transfer model are used to extract, reconstruct, and integrate the color features of style diagrams, and transfer them to product modeling to design new product images. By evaluating the image quality and comparing the product semantics before and after the migration, the validity and feasibility of the migration are verified. Based on BP neural network, a nonlinear mapping model between product attribute space and product semantic space is constructed, and the generalization ability of the model is evaluated, which verifies the feasibility and effectiveness of this method. The research results have important theoretical guiding significance for improving the innovative design ability of enterprises, enhancing product competitiveness, and customer satisfaction. [ABSTRACT FROM AUTHOR]

At present, China's economic development continues to progress and foreign exchanges are also increasingly frequent. Learning to master the world's universal pronunciation, English, has become a more important link. However, people's living habits make it difficult to carry heavy desktop devices for a long time, so mobile English learning platform meets the development needs of people's English teaching. Based on the existing English mobile learning platform, this paper puts forward the concept of integrating artificial intelligence and deep learning technology into it. Through deep learning, the learning status and learning situation of students in the process of English learning are extracted, so as to analyze the needs and learning interests of each student in English learning and then push corresponding English materials to each student, which improves the efficiency of English learning. In addition, deep learning can also model the data of students' behaviors and build a language vector feature extraction mechanism and translation quality evaluation model, so as to carry out certain intelligent auxiliary correction and correction on students' English grammatical expressions and spoken English. The research in this paper has achieved good results through practice. The practice results show that the integration of deep learning into the existing mobile English teaching platform can optimize the functions of the existing platform, provide more ideas for the development of online English learning, and has good theoretical and practical value. [ABSTRACT FROM AUTHOR]

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on the BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of the BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301, respectively, which is much higher than Google translation. A model of machine translation evaluation based on the neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on the neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation. [ABSTRACT FROM AUTHOR]

With the advent of the era of artificial intelligence, a technological revolution has begun in the field of education, which has injected new momentum into moral education. Today, civic education is developing rapidly in countries around the world, while civic education in China is still relatively lagging behind. In China, we need to optimize civic education by building a comprehensive civic education system, implementing the concept of people-oriented, gathering the joint efforts of the whole society, expanding diversified civic education methods, and selecting and enriching the contents of civic education. Artificial intelligence technology enables higher education institutions to carry out civic education, enrich teaching contents, broaden teaching methods, and optimize teaching evaluation. However, technology-related innovations are dualistic in nature, with essential, technical, and value risks that hinder their effectiveness in civic education. This paper proposes strategies for optimization in terms of improving digital core risk response capabilities, establishing reliable technology risk response mechanisms, and maintaining a foundation for civic education response costs. Thus, weakening the risk factors of AI in civic education facilitates the transformation of AI and technology to the advantage of knowledge-based civic education. This paper discusses strategies and methods for optimizing smart education using AI devices and establishes a corresponding smart education platform to help students with their ideological education. Based on the analysis of actual cases, the paper discusses the effectiveness and advantages of the program. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy and the continuous optimization and improvement of Internet information technology, a new thing, new media, has emerged. On this basis, a kind of artistic behavior of creating, carrying, spreading, and appreciating is also called new media art. New media art has developed for a long time in foreign countries, but its development and popularization are relatively late in China, and it is still in a stage of improvement. As far as visual communication design is concerned, the emergence and development of new media have strongly promoted the development of its work and the dissemination of various works. Developing visual communication design based on new media art has become a key issue for all visual communication designers. Nowadays, deep learning is widely used in different fields in different ways, and image art representation is a hot research field in computer vision. Therefore, this paper mainly studies and analyzes the symbolism of new media art expression forms and proposes a new media art symbolism model based on artificial intelligence big data. In order to provide a method of real-time representation of art images, the deep learning method is used to model the artistic expression of images, and spatial reorganization of content features and style features is carried out. This paper designs simulation experiments to verify the effectiveness of the model, which can provide some help for the development and improvement of new media art in China. We compared the results of our model with a number of other models to test its effectiveness. We achieved a matching accuracy of 98.4% which is higher than or equal to the results of the existing models. [ABSTRACT FROM AUTHOR]

With the development of the times, the exchanges between countries are increasing. China is becoming a superpower, and the number of international cities is increasing. This requires the communication level of Chinese people to be improved. English, as the second largest communication language in China, should be better understood and studied. This paper makes an in-depth discussion on the optimization and evaluation of oral English CAF based on artificial intelligence and corpus and makes an experimental analysis. The results are as follows: (1) introducing oral English teaching based on artificial intelligence and oral English based on a corpus, so as to deepen the public's cognition of both and make oral English more deeply rooted in people's hearts and get attention. (2) Analyzing the algorithms of phoneme errors in spoken English. Errors in spoken English are very common. Algorithms can be used to identify them better. When evaluating spoken English, algorithms are needed to evaluate them more accurately. (3) There are many examples of the benefits of artificial intelligence to oral English teaching. By comparison, it is found that the method of evaluating using artificial intelligence is more accurate, a corpus can improve oral English, and CAF optimization is also of great help to oral English. [ABSTRACT FROM AUTHOR]

Based on the development background of the interweaving and integration of computer technology and Internet technology, China's artificial intelligence industry is quietly rising. In the social life of the information age, the artificial intelligence industry represented by machine deep learning is playing a very important role. This study in combination with the background of the new health reform, in view of the reform of the medical industry, analyzes the connotation of financial wisdom based on the important role of the hospital financial development problems, puts forward the development direction of artificial intelligence hospital financial wisdom development measures, designed to meet the changing external environment demand, reduces human costs, and improves the overall efficiency of hospital financial fund management. Based on the evaluation results, this paper proposes the correct direction for the development of hospital intelligence finance by using the BP neural network model. After the analysis, it is found that the development of artificial intelligence is an important measure to promote the development of hospital intelligent finance. In other words, hospital intelligent financial management is the product of the continuous progress of artificial intelligence technology. At the present stage, the intelligent financial management problems of hospitals are mainly as follows: (1) lack of financial informatization, (2) lack of perfect financial risk early warning system, and (3) the phenomenon of "information island" in the financial system. After analyzing the above problems, the research believes that the development of hospital intelligent finance based on artificial intelligence needs to solve the above thorny problems, so as to improve the outcome of hospital intelligent finance development. The following work should be done: (1) strengthen the design of information sharing module, (2) intelligent control of the cost of hospitals, and (3) intelligent treatment of hospital accounting. Combining the development of artificial intelligence and hospital intelligent finance theory, combined with the actual trend of financial intelligence development under the background of artificial intelligence development in the new era, it provides scientific basis for the development of hospital intelligent finance. [ABSTRACT FROM AUTHOR]

The research and application of artificial intelligence (AI) have changed the way people live and produce. This paper discusses the factors affecting the development of RE in the context of artificial intelligence, analyzes the industrial status of 31 provincial administrative regions in China from the three dimensions of industrial influence, industrial relevance, and industrial efficiency, and uses gray correlation analysis to evaluate the system and measure. [ABSTRACT FROM AUTHOR]

At present, music education in colleges is in a period of rapid development in China. At the same time, music education in universities is facing innovation and reform of teaching modes. How to improve music education in colleges and universities has become an important issue for music teachers in colleges. Music teaching and management activities for students in general universities can enrich students' talents and expand their knowledge. It can also help them develop a positive emotional psychology and develop positive and healthy character characteristics, both of which are vital for college students' healthy development. Innovation and modification in music teaching and management activities for students is the only way to increase the quality and effectiveness of music teaching for nonarts students and to promote the overall quality of students. Based on this, this paper proposes an innovative research method of college music teaching based on artificial intelligence technology. The method introduces a fuzzy evaluation algorithm to establish a two-level teaching evaluation index system and calculates the weights of each index based on fuzzy mathematical theory. In data processing, the SVM algorithm in the field of data mining is used to classify all collected teaching evaluation data in advance through supervised learning, which significantly improves the efficiency of data processing. The experimental results show that the model in this paper can well assess the quality of music teaching in colleges and universities and play a role in promoting the progress of music teaching in colleges and universities. [ABSTRACT FROM AUTHOR]

In today's society, China's social market continues to develop, which has caused the whole society to attach great importance to the professional ethics of Chinese citizens and put forward new requirements and expectations for it. This paper aims to research and discuss the diversified integration and integrated application of ideological and political teaching resources based on artificial intelligence model algorithms. This paper first analyzes and discusses the feasibility of the integration and application of moral education resources in ideological and political textbooks. Political classrooms provide favorable support for the integration and application of moral education resources, and political classrooms have become a practical carrier for the integration and application of moral education resources. And then it introduces the artificial intelligence model algorithm-artificial neural network algorithm. Artificial intelligence is a new intelligent machine that can make intelligent responses similar to human actions according to human actions. Finally, it studies the use of social teaching resources for ideological and political education in city A and conducts a comprehensive analysis on the integration and utilization of moral education teaching resources for ideological and political courses in colleges and universities. In the survey of "Whether the introduction of local social resources in the political class of city A is helpful to students' learning," 84 teachers thought it was very helpful, 12 teachers thought it was helpful, and only 3 teachers thought it was not helpful. It can be found that most teachers think that it is helpful for students to introduce social resources in political lessons. At the same time, in order to better improve the reliability and validity of moral resources and to internalize social moral norms into students' hearts, it is only by combining moral resources with students' moral growth. [ABSTRACT FROM AUTHOR]

The snowboarding project has the characteristics of high risk and high technical level. The current publicity level is not high, and the number of participants is also very limited. Another potential advantage medal breakthrough project that is expected to be achieved in the Winter Olympics has received a lot of attention, creating favorable opportunities for the promotion and development of this project in China. The event requires good special physical support, skeletal muscle contraction is the body to produce motor function, and special physical training and recovery are key factors for athletes to obtain excellent results in the competition. This article is aimed at performing ultrasonic quantitative analysis on the skeletal muscles of skiers after exercise based on artificial intelligence and complex networks and at studying the skeletal muscle conditions of snowboarders after exercise, so as to provide a certain theoretical basis for coaches in future scientific training. Based on a large amount of literature, this paper uses variational optical flow calculation and split Bregman method to solve the typical HS model, L1-L2 model, and L1-high-order model, respectively, and uses the motion estimation method to describe the movement of muscles. An experiment was designed to collect ultrasound images of the gastrocnemius and quadriceps muscles during contraction. In addition, a motion target positioning algorithm was used to obtain some motion parameters, which provided direct help for athletes in rationally arranging training load and scientific training. The experimental results in this paper show that the muscle motion features extracted from the ultrasound sequence images can quantitatively express a lot of important information about the skeletal muscle motion form and function and have potential practical application value. And the different invariants of each type of ski trajectory vary greatly, floating between 1.5429 and 7.6759. [ABSTRACT FROM AUTHOR]

In the new era of rapid development of intelligent information, sports tourism and cultural industry have become a new goal to improve China's urban competitiveness and realize the development of urban industry. Based on the integration of sports tourism industry and cultural industry, this paper establishes the relevant information data interface mode. This paper constructs the information coupling system model of sports tourism industry and cultural industry under the background of artificial intelligence era and obtains the corresponding data results by using the invisible statistical logic computer independent judgment (IDIJA) method of artificial intelligence system under the condition of incomplete data. The application of the coordinated development system of coupling industry can realize the rapid development of sports tourism and cultural reconstruction mode and achieve the new effect of intelligent sports tourism cultural comprehensive experience. It can not only improve the people's quality of life but also provide feasible suggestions for improving the continuous development of the coupling and coordination of sports tourism and cultural industry. [ABSTRACT FROM AUTHOR]

This paper uses Northwest China as an example to present an in-depth study and analysis of river valley cities' spatial structure and city form in the context of artificial intelligence. The paper focuses on alleviating the "core-edge" spatial structure locking effect, achieving the "industry-space" scale effect and cooperation, solving the problem of endogenous dynamics to promote regional and urban development, and promoting various main functional areas based on the derivation discussion and spatial growth model selection. The study focuses on the exploration of paths to alleviate the "core-edge" spatial structure's locking effect, realize the scale effect and cooperation ability of "industry-space," solve the problem of endogenous power to promote regional and urban development, and promote the new urbanization path of different main functional areas based on the discussion and spatial growth mode selection. We measure the evolution characteristics of industrial and spatial elements and the level of intersystem synergistic development to address the spatial growth and industrial transformation of the northwest region urban agglomeration in the special development period, discover the defects of the northwest China urban agglomeration's spatial growth, and select the appropriate spatial growth model in the foreseeable spatial and temporal scope. The new city spatial form in the northwest region, which strengthens regional characteristics, is the spatial form combined with the new city plan form in two dimensions. This paper investigates the relationship between regional characteristics and urban spatial carriers by developing spatial planning design ideas and methods that reflect the regional characteristics of new cities in the northwest region, in order to improve the spatial quality of new cities in the northwest region and their sense of belonging to the city, provide a new spatial expansion idea for small cities in river valleys in the Qinling Mountains of southern Shaanxi, and enrich the spatial quality of new cities in the northwest region. [ABSTRACT FROM AUTHOR]

This paper attempted to study the cultural characteristics and intelligent inheritance of Chinese Xiangjin brocade. Qualitative data was collected by in-depth interview method, and more than 1000 pieces of Xiangjin brocade of Suzhou Archives in China were measured for analyzing artistic features of their pattern design, color matching skills, and weaving technology, the study of which illustrated artistic features of modern Xiangjin brocade in detail and provides an important reference for its cultural and weaving inheritance. In addition, the artificial intelligence (AI) and the inheritance of Xiangjin art characteristics are integrated, the traditional handicrafts and digital art are docked, and the corresponding digital art protection mode is selected according to the characteristics of traditional handicrafts. The intelligent inheritance of cultural heritage not only changes the way of inheriting the artistic characteristics of Xiangjin, but also changes people's general cognition of the cultural characteristics of Xiangjin. As a result, the integrity, authenticity, reliability, and comprehensiveness of the technological process of Xiangjin can be inherited, the patterns of Xiangjin can be preserved and applied, and the craft culture of Xiangjin can be inherited, protected, and developed. [ABSTRACT FROM AUTHOR]

As a new tourism industry model, the ice and snow sports tourism industry has played an increasingly important role in the development of China's entire tourism industry. This paper analyzes the structural optimization of the ice and snow sports industry chain based on sensor network communication and artificial intelligence and aims to fully apply sensor network communication and artificial intelligence technology to the development of the ice and snow sports industry. Let us look for an optimized method to promote the development of the ice and snow sports industry chain structure. This paper conducts a research on the optimization of the ice and snow sports industry chain structure based on sensor network communication and artificial intelligence. In the method part, this article explains the architecture of sensor nodes and networks, uses the long-term monitoring of wireless sensor networks and the flexibility of the data acquisition structure, combined with the superiority of artificial intelligence in information processing and analysis, to obtain and analyze the experimental data of this research, and introduces the specific content of artificial intelligence and the ice and snow sports industry. In the algorithm, the grey relational analysis method is introduced. In the part of the experimental results, experiments were carried out on the ice and snow city resources, competitive advantages, the quarterly average value of the ice and snow sports enterprise prosperity index, ice and snow sports venues, GDP, and the speed comparison and structure of the development of the sports industry, and simulation experiments were carried out from the perspective of sensors. On the whole, the development curve of China's ice and snow sports industry basically conforms to the economic development curve. With the slowdown of GDP growth rate, the growth rate of the sports industry slowed down simultaneously. However, despite the continued decline in economic growth, the sports industry has ushered in a peak of development, with a growth rate of 89.45%, a year-on-year increase of 69.3 percentage points. This phenomenon shows that the sports industry has sufficient endogenous development momentum and huge development potential. It is an important driving force and emerging growth point of the economic society and modern services. It has a comprehensive driving role in stimulating urban consumption, promoting economic transformation and upgrading, and driving the development of the service industry. [ABSTRACT FROM AUTHOR]

China, the United States, the United Kingdom, Germany, and other major AI superpowers as research objects, this paper establishes the assessment index system with the diamond model, weights the international competitiveness indexes of AI industry in the four countries based on positive and negative ideal points, and applies the simulated annealing (SA) algorithm to obtain the final weights. Then, the TOPSIS assessment method is used to score and rank their international competitiveness of AI industry. It is concluded that the United States is on the leading position in the human factor, knowledge factor, capital factor, enterprise strategy structure/competitors, and policy laws and regulations. China has a complete infrastructure system and huge market demand. The leading of these indicators is also the reason why China can catch up in the development of some industries when it participates in the global value division. Europe also performs well in knowledge factors, capital factors, strategic structure/competitors, and policies and regulations but needs to invest more in AI industry infrastructure. Finally, this paper analyzes the advantages and disadvantages of the countries and reasons to provide comparative reference among different countries for AI industry. [ABSTRACT FROM AUTHOR]

This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application. [ABSTRACT FROM AUTHOR]

This paper discusses the method of fossil digitization combining paleontology and art by using data mining. The aim of the study is to increase the creativity and vitality of museum exhibits and promote scientific exchange. The purpose of the study is to discuss how the interdisciplinary approach will benefit the communication of science and to realize the cooperative development of creative science popularization, art science popularization, dialogue science popularization, and communication science popularization through the cooperation between museums and institutions disseminating and researching science. The research method takes the Jehol Biota in western Liaoning Province of China as an example to explore the methods and approaches to transform museum resources into creative products. Through artificial intelligence data mining and digital reconstruction, data can be used for archaeological research but also for scientific popularization and scientific dissemination of the resources of art transformation. Research results: as the cornerstone to consolidate the digital development of society and economy, "the new infrastructure of artificial intelligence" will start a comprehensive digital transformation. Through the integration of multiple fields and disciplines, museums will change from resource dependence to focus more on market creation, which will promote museum's industry development. [ABSTRACT FROM AUTHOR]

Industry 4.0, also known as the Fourth Industrial Revolution, is characterized by process automation and digitization and the use of technologies such as the Internet of things, cloud, big data, artificial intelligence (AI), and embedded systems, among others. The application of automation technology based on Industry 4.0 contributes to the realization of a sustainable food security regime. Based on the policy guidance of the "No.1 Document" of the Chinese government on seed industry innovation and the quantitative analysis of the application, authorization, research, and development data of new varieties of agricultural plants in China in recent years, as well as the data of food import and export, this paper studies the risks and challenges facing China's food security at present and explores the significance, existing problems, and specific countermeasures of resolving food security risks through seed industry innovation. [ABSTRACT FROM AUTHOR]

Intercultural communication not only promotes the emergence and development of multiculturalism but also promotes cultural exchanges on a global scale. China has rich resources and a long history. Because of its unique national characteristics, it is even more famous and unique in the world today. However, with the transformation of society, cross-cultural dissemination and blending are inevitable. Intangible cultural heritage is an important cultural form, and its international dissemination is increasingly attracting people's attention. The spontaneous inheritance mode of intangible cultural heritage, which is mainly based on oral and heart teaching, has been unable to adapt to the development of society and has gradually moved away from people's sight. How to better protect and inherit intangible cultural heritage is an important topic in the field of current cultural exchanges. However, due to the particularity of intangible culture, especially its abstract nature, it is difficult to show it to the public, which makes it difficult to protect and promote intangible cultural heritage. The application of the advantages of artificial intelligence technology to the dissemination of intangible cultural heritage is to solve these problems, which can not only make it innovative in protection but also promote its sustainable development to a certain extent. This paper discusses the application of artificial intelligence technology in the protection and dissemination of intangible cultural heritage from multiple perspectives and points out that the advantage of artificial intelligence is that although it cannot be creatively inherited, it can be reproduced in a historical moment, so as to meet people's needs for intangible cultural heritage. It can also be found in the questionnaire for the visitors that 66% of the visitors prefer to learn about the intangible cultural heritage through experience and feel that the personal experience is more intuitive and interesting. Artificial intelligence technology has great advantages in disseminating intangible cultural heritage. [ABSTRACT FROM AUTHOR]

Since the beginning of the 21st century, with the continuous growth and improvement of the comprehensive strength of our national economy and the continuous improvement of our modern industrial machinery and equipment, the level of safety technology, production level, and quality management level see continuous steady improvement, resulting in the rapid economic growth of chemical industries in China in recent years. In recent years, chemicals have been widely used and demand is increasing. However, the physical properties of some chemical substances are very unstable and vary according to process conditions, making it difficult to control. At the same time, safety issues have become a major "red warning" for industrial enterprises. Various safety incidents have attracted social attention, and the chemical industry has also learned from them, sounding the safety alarm for itself and reviewing its own shortcomings, and taking improvement measures. Therefore, it is obviously necessary to always emphasize the concept of "safety first, prevention first, and comprehensive management" to prevent enterprise safety accidents. It is necessary to regularly investigate and analyze the situation of safety accidents to summarize and continuously improve. This is the most important thing for enterprises to prevent the continuous occurrence of safety accidents in a good and effective way. Regularly analyzing, summarizing, and improving safety issues are the best way to prevent accidents. Deep learning mainly relies on letting computers or other electronic devices perform iterative learning on a large number of data samples according to the rules defined by the program. The computer after learning will have the analysis ability similar to humans, but its analysis ability is limited to the range of the sample data to be learned. Artificial intelligence technology realizes an operation system that simulates human thinking through the definition of algorithms and programs. The use of deep learning and artificial intelligence in industrial enterprise safety production supervision can realize enterprise safety production supervision, early warning, and intelligent operation. Without the security of the production chain, a business cannot develop. Based on the basic theory of safety supervision and early warning in the field of enterprise safety production and related knowledge of artificial intelligence, this paper studies the work safety in the supervision and early warning mechanism abides by the selection principle of the management system, safety production indicators and indicator system, and conducts work based on the production work data of industrial plants. Reasonable safety supervision and early warning mechanism provide technical assistance for effectively controlling safety accidents in production and management measures to improve enterprise safety. [ABSTRACT FROM AUTHOR]

With the vigorous development of China's market economy, a new generation of artificial intelligence technology has also developed rapidly, which has strongly promoted the optimization and improvement of China's electrical automation control system. The use of artificial intelligence technology in power automation systems mainly has the following two advantages: it can reduce the manufacturing time of power products, thereby increasing the number of workers manufacturing power products and promoting enterprises to conduct larger-scale machine manufacturing, thereby improving the comprehensive economic benefits of enterprise machine manufacturing, but also improve the quality of manufactured products of electric power products. Since the machine control of artificial intelligence is more efficient than manual manufacturing, it reduces the manufacturing problems caused by the subjective factors of manufacturing workers to a certain extent and thus improves the qualification rate of machine production. Electrical engineering plays an important role in China's urban development, and automatic control is an important part of electrical engineering, which can have a direct impact on the quality of electrical engineering. Therefore, in order to promote the healthy development of cities in China, this paper studies the application of artificial intelligence technology in electrical engineering automation by analyzing relevant data, in order to provide a reliable basis for relevant personnel to carry out their work. [ABSTRACT FROM AUTHOR]

Since China's reform and opening up, the social economy has achieved rapid development, followed by a sharp increase in carbon dioxide (CO2) emissions. Therefore, at the 75th United Nations General Assembly, China proposed to achieve carbon peaking by 2030 and carbon neutrality by 2060. The research work on advance forecasting of CO2 emissions is essential to achieve the above-mentioned carbon peaking and carbon neutrality goals in China. In order to achieve accurate prediction of CO2 emissions, this study establishes a hybrid intelligent algorithm model suitable for CO2 emissions prediction based on China's CO2 emissions and related socioeconomic indicator data from 1971 to 2017. The hyperparameters of Least Squares Support Vector Regression (LSSVR) are optimized by the Adaptive Artificial Bee Colony (AABC) algorithm to build a high-performance hybrid intelligence model. The research results show that the hybrid intelligent algorithm model designed in this paper has stronger robustness and accuracy with relative error almost within ±5% in the advance prediction of CO2 emissions. The modeling scheme proposed in this study can not only provide strong support for the Chinese government and industry departments to formulate policies related to the carbon peaking and carbon neutrality goals, but also can be extended to the research of other socioeconomic-related issues. [ABSTRACT FROM AUTHOR]

Neural network (NN) is among the most important and vital form of artificial intelligence which are utilized for the classification of data, information, or images. Moreover, NN has been extensively utilized in various research domains throughout the world, and it is because of overwhelming properties. Painting is a form formed by China's long history and culture, and a large number of paintings reflect the living conditions of China in different periods, which is of great value to the development of China's culture. Image classification has become a key research content in the field of image in the stage of rapid development of information technology, and the content of art painting image classification has also developed rapidly. At present, most traditional image classification methods are formed on the basis of shallow structure learning algorithm, and there are many types of image features that can be extracted, but some features will be lost when extracting, and we need to master the basic painting knowledge. As a result, this extraction process is not general, which explains why traditional Chinese art picture classification is not ubiquitous. The fast development of big data technology and neural network algorithms in recent years has the potential to speed up the categorization of art painting images. As a result, this research investigates the use of neural networks to classify art painting images. The painting image classification method based on artistic style is used to determine the styles of distinct creative works, and the painting image classification algorithm based on saliency is then used to categorize the picture semantics. Finally, a dataset for testing the categorization impact of art painting pictures is developed. The results show that the neural network algorithm can significantly improve the classification effect of art painting images with higher accuracy. [ABSTRACT FROM AUTHOR]

With the acceleration of industrialization and urbanization in China, a large amount of waste in industrial parks has become the main cause of regional environmental pollution. In order to solve this problem, this paper relied on artificial intelligence's prediction technology and image recognition technology to intelligently upgrade the traditional industrial waste planning management system and designed a waste intelligent classification center with intelligent prediction and intelligent classification capabilities. So, as to realize this new intelligent classification center and explain its value, this paper explains the key implementation technology of this intelligent classification center and validates it by constructing a multitarget location model that considers both economic and environmental benefits. [ABSTRACT FROM AUTHOR]

Today, artificial intelligence and deep neural networks have been successfully used in many applications that have fundamentally changed people's lives in many areas. However, very limited research has been done in the meteorology area, where meteorological forecasts still rely on simulations via extensive computing resources. In this paper, we propose an approach to using the neural network to forecast the future temperature according to the past temperature values. Specifically, we design a convolutional recurrent neural network (CRNN) model that is composed of convolution neural network (CNN) portion and recurrent neural network (RNN) portion. The model can learn the time correlation and space correlation of temperature changes from historical data through neural networks. To evaluate the proposed CRNN model, we use the daily temperature data of mainland China from 1952 to 2018 as training data. The results show that our model can predict future temperature with an error around 0.907°C. [ABSTRACT FROM AUTHOR]