Frontier Computing on Industrial Applications Volume 2

In marketing, can not leave the sup. of modern information technology means. Through the Internet database, enterprises use big date mining (DM) technology to analyze the massive data collected, and provide scientific data sup. For marketing decision-making. DM will also help enterprises to achieve more accurate segmentation and positioning of customers, and accurately obtain the needs of potential customers. In this article, we will take the most classical Apriori algorithm of association rules as the research object, and the classical algorithm has also completed the parallelization implementation in the MapReduce framework. This article draws on the domestic and foreign marketing research theory, analyzes the inside and outside environment of Xiaomi smart mobile phone, starts from the marketing 4P theory, studies the current marketing strategy, and on this basis, formulates targeted optimization plan, and has been preliminarily confirmed. At the same time, in view of the current domestic market mobile phone product homogeneity phenomenon is serious, consumer demand diversification and other status quo. Combined with the Apriori algorithm under the association rules, this article deeply studies the influence of DM on the consumer behavior and personalized marketing of Xiaomi smart mobile phone customers, as well as the new marketing strategy.

This paper proposes a short video creation mode based on an interactive multi-objective optimization algorithm. By adopting a multi-objective optimization algorithm, this mode can balance multiple objectives to obtain the optimal balance point. At the same time, it also uses interactive feedback, allowing users to choose and adjust optimization solutions to achieve a better user experience. Specifically, the mode includes steps such as determining optimization objectives, generating initial optimization solutions, user feedback, and repeating the optimization process. The advantage of this mode is that it fully utilizes users’ personalized needs and creativity to achieve better creative results and user experience. Moreover, it can continuously improve the optimization solution through iterations to achieve the optimal solution.

Accurate and efficient detection of each earthquake is an important foundation for earthquake work. However, at present, due to the lack of observation data information, the accuracy of parameter estimation is low, especially for seismic detection of major projects, the current situation of sparse station layout makes it impossible to use multiple averages like dense network seismic detection to reduce the discreteness of parameter estimation, and the accuracy of seismic detection parameters based on a single station needs to be improved. In recent years, there has been an increasing focus on how machine learning can be used to improve seismic detection performance. In this paper, the principle of AI seismic detection and waveform mask matching is analyzed, and then a new deep learning method, TransQuake, is proposed based on the frontier sequence model Transformer for seismic wave detection. TransQuake combines STA/LTA algorithms for feature enhancement of seismic waveform data and interpretable model learning using a multi-head attention mechanism. To validate the performance of the model, this paper conducts an extensive evaluation on the 2008 Wenchuan MW7.9 earthquake aftershock dataset. The results show that TransQuake can achieve the best detection performance beyond the baseline of leading-edge algorithms.

Traditional quantitative trading strategies are widely used in stocks, futures and other financial markets, but the manual extraction method of features makes it lack the ability to effectively adjust strategies dynamically, and deep reinforcement learning can effectively simulate complex market environments and solve dynamic quantitative trading problems. Based on the development status of the financial industry, this paper introduces the deep reinforcement learning algorithm into the field of stock trading to build an intelligent trading model. Its goal is to discover the laws of the market in the learning of massive data, so as to carry out effective transactions, effectively avoid market risks and improve investors’ returns. On the basis of the traditional DQN algorithm, corresponding to the actual requirements, we propose RB_DRL deep reinforcement learning algorithm model to improve the network structure. The experimental analysis results show that the improved model also shows good results in multi-group comparative experiments.

Maternal and infant nursing students must master sufficient knowledge and skills and have good professional competencies, especially before graduation, the assessment should be based on the requirements of maternal and infant health nursing professional competencies to assess the competencies that nursing students should have. The aim of this paper is to investigate the design and implementation of a realistic training system for maternal and child health care based on MR virtual technology. A complete imitation training system is constructed under the context-aware mechanism of maternal and infant health care, the workflow of the system is illustrated, and the design strategy of the maternal and infant health care imitation training system is proposed from the environmental context and task context. In the design strategy, a more detailed design guideline is proposed for the mother-infant health care imitation training system based on the multi-camera collaborative tracking technology of MR glasses. The experimental results show that the design solution is feasible.

In the financial field with the background of modern Internet, the core part of CBEC (Cross border e-commerc) system has become electronic payment. China's financial industry has serious problems in third-party platform payment, such as fund circulation and monitoring, lack of credit reporting system, privacy disclosure, etc., and should use various science and technology to fill the gap in Internet financial supervision and management. To ensure the safety of consumers and the companies to be consumed, eliminate the security risks in the gray zone, the standardization platform will firmly control the scale of personal information disclosure, create a healthy consumption and investment environment for consumers and investors, reduce and solve many risks and problems such as cross-border payment credit risk, technical risk, liquidity risk, privacy protection and supervision and management difficulties. This paper mainly studies how to use blockchain technology to discuss the risks of CBEC payment. At the same time, this paper first introduces the risks of the underlying blockchain technology and CBEC payment, and then applies the underlying blockchain technology to CBEC payment risk resolution, which reduces the risk of payment. Finally, it analyzes the actual application performance of the underlying blockchain technology. The experimental results of the article show that the questionnaire of CBEC companies and staff can show that there are great risks in e-commerce payment. Therefore, using the underlying technology of blockchain to reduce the risk of CBEC online payment is a topic worthy of study.

Brand visual identity system is not an isolated system, it is a very systematic whole. The block vision system is closely connected with the city and belongs to a part of urban planning. The overall and unified VRS reflects the good operation of all departments in a city, and also shows the neat style of the city. In this paper, the design of brand visual identity system based on urban brand image design, taking the construction of urban commercial block visual identity system as an example, discusses the process of urban brand design and 2 matters needing attention in the design of brand visual identity system. By means of questionnaire survey, this paper analyzes the satisfaction of temporary residents and long-term residents of city a to the city's brand image and their awareness of the city's regional culture. The test results show that the most satisfactory of the temporary residents is that the “cultural atmosphere” accounts for 47.2%, the “urban environment” is nearly 28% lower than the “cultural atmosphere”, and the “cultural heritage” accounts for more than 80% of the temporary residents and long-term residents, of which the “local folk customs” account for 11.4% and 9.3% respectively, and the “industrial culture” accounts for less than 6%. From this, we can see that the most attractive place of a city is the urban culture, that is, the design of urban brand image is very important; A the cultural heritage, local folk customs and industrial culture in urban regional culture have distinct characteristics. When shaping the visual image of urban brand, we should organically combine the three. Only in this way can we truly reflect the spiritual outlook, value orientation and deep cultural connotation of a city. It is very necessary to design a brand visual identity system based on urban brand image design.

Data quality assessment is mainly to analyze, evaluate and calculate the collected original information, and then to obtain the decision results. At present, there are many research methods for data quality. Through density clustering of data, we can get some parameters. This paper proposes a new data quality evaluation model to improve the performance of products and services. This paper mainly analyzes the data quality evaluation model by using experimental comparison and density clustering algorithm analysis. The experimental data shows that because the weight of the precision rule set in the evaluation is 0.18, the precision evaluation value in the final evaluation value is 14.99.

The relationship between the packet loss rate and queue length of the main queue management in the computer network connection process, through the analysis of the main queue management algorithm in the computer network packet loss process unreasonable problems, in order to effectively solve this problem, this paper through the construction of data information processing model, the use of the new model of computer network transmission process packet loss rate analysis, according to the quadratic function model. The new packet loss rate calculation formula is composed, and this formula is used to upgrade and optimize the network blocking process queueing management process packet loss algorithm, which can ensure that the main queueing management algorithm is more reasonable for packet loss management, which can make the fairness and stability of the system in the application process improved and the stable operation of the system is stabilized.

With the rapid development of modern information society, all types of advertisements emerge in an endless flow, where ambient media that emphasizes the multi-sensory experience of users rise as the times require. In this paper, based on the price stepping algorithm, a new ambient media advertising model based on O2O that integrates offline large-screen resources is proposed to further promote the development of ambient media in modern society. The practice demonstrates that the penetration of ambient media advertising into the living environment allows consumers to remember them more quickly.

Face recognition began in the 1960s, by virtue of its rapidity, it has been widely used in various fields. However, face recognition technology still has a large room for progress, at the same time, the exposed data security problem is also one of the key defects concerned by the public. With the development and application of face recognition technology, the problem of secure storage of face data has been paid more and more attention. The leakage, tampering and damage of facial information caused by system vulnerabilities will cause huge losses to the personal and property security of users. Therefore, this paper proposes a face recognition system which is based on data box security mechanism to realize the secure storage of face information. At the same time, this paper also puts forward a variety of face recognition algorithm fusion, can further reduce the error of face recognition, improve the accuracy of face recognition.

The rapid development of technologies such as big data, cloud computing and AI has laid the foundation for solving contemporary urban governance problems, and at the same time, has enabled the development of “smart cities” in a deeper way. The “wisdom” of a smart city is mainly expressed in the interconnection of various factors within the city, making the city an organic whole and enabling more accurate and intelligent decision-making. In today's information age, smart cities are seen as a new way of thinking about urban governance and the future direction of urban construction. The aim of this paper is to investigate smart city development strategies based on artificial intelligence. From a “people-centred” perspective, the development strategy of smart cities and artificial intelligence technologies are analysed. The project's practical results show the effectiveness of the platform design and implementation.

Imbalance modeling of financial risk control refers to a situation where the sample in the data set is unbalanced due to various factors, such as the credit level and repayment ability of different financial customers. In order to evaluate and control risks more accurately, it is necessary to model unbalanced data. This article mainly designs models for unbalanced data sets of financial risk control, and uses different algorithms to compare and analyze the computational capabilities of the algorithms. It studies the financial risk control of benchmarking management optimization algorithms. Experimental data show that the maximum AUC value and accuracy value obtained by the benchmarking management optimization algorithm in the risk control of different financial enterprises exceed 0.85. When creating a model, it is necessary to consider the characteristics of unbalanced data and apply appropriate algorithms and techniques to ensure the reliability and stability of the model. At the same time, it is necessary to continuously optimize and improve the model to adapt to different risk scenarios and customer needs.

Green consumption is a key development strategy outlined in China's “14th Five-Year Plan” and the 2035 Vision Outline. Research reports indicate that 80% of consumers are in favor of sustainable consumption practices. Despite this, several studies suggest that there exists a noticeable gap between intention and behavior in consumers’ eco-friendly consumption patterns. Utilizing web crawler technology, this study collected data on 8,020 home appliances that were energy-efficient and screened 1.481 million customer opinions associated with these products on Jingdong platform. The study measured consumer energy-saving attention through word frequency statistics. The research concluded that consumers’ attention towards energy-saving behaviors when utilizing energy-efficient home appliances was low, accounting for merely 4.279% of their energy consumption. This outcome highlights the observed gap between consumers’ environmental intentions and their actual energy-saving behaviors. Besides, based on descriptive analysis and Kruskal-Wallis test results, the study discovered significant differences in consumers’ levels of energy-saving attention towards various categories and energy efficiency levels of eco-friendly home appliances. As a solution, the study recommends that businesses can guide customers to improve their energy-saving attention through effective customer service guidance, offers such as gift coupons, augmented yet meaningful experience cards, or even through setting up a dedicated energy-saving comment section. The proposal aims to amplify other consumers’ comprehension of energy-saving information and usage experience of ecological products.

Traditional shallow learning radar target image recognition ways usually depend on complex artificial feature extraction process, which needs a lot of human expertise. to effectively extract the characteristic information of enemy combat units, and realize the type recognition of the UAV radar, which is very important to ensure the UAV’s combat capability and combat victory. Aiming at the problem that the shallow learning method of radar target image recognition is difficult to extract advanced features and the small number of enemy unit radar image samples available in practical engineering has a great effect on the diagnosis accuracy of the deep neural network model, this paper uses the transfer method in deep learning. The learning way is to fine-tune the pre-trained hotspot deep CNN convolutional network (GoogleNet) respectively, which are used for the recognition and classification of radar target images. The noise reduction radar target figure is used as input to train a constructed model, Then, the trained model can realize radar target image recognition and classification. The model are validated using the MSTAR dataset gived by DARPA/AFRL, and the results show that model achieve high diagnostic accuracy, proving the effectiveness of the proposed way in radar image recognition of enemy combat units.

With China's scientific and technological level continuously improving in recent years, computer digital technology has become widely used in a variety of industries. People's lives are enriched by textile art. Using computer digital technology can make textile art patterns and patterns more novel and diverse, laying a solid foundation for the advancement of textile art design level. When compared to traditional textile art, the use of computer digital technology can increase work efficiency, save time, and improve the overall benefits of businesses. The application of computer digital technology has had a significant impact on textile art design, resulting in a significant change in the textile art design process. It has challenged people's preconceived notions and ways of thinking, infusing new life into the design community.

With the development of the times, information technology is gradually becoming popular, and multimodal information technology is gradually being used in intelligent construction. Through multimodal information technology, the quality and efficiency of building management can be improved, and the cost of building intelligence can be saved, thus saving resource utilization efficiency. However, there are still some shortcomings in the use of multimodal information technology in intelligent construction. This article studied the application of multimodal information technology based on Building Information Modeling (BIM) technology in intelligent construction, aiming to continue improving the quality of intelligent construction through BIM technology. This article tested the cost reduction of intelligent construction using multimodal information technology after using BIM technology through experiments. The data showed a minimum reduction of 10% and a maximum reduction of 19%. This proved that BIM technology had good results in intelligent construction of multimodal information technology.

Since the 1980s, world economic integration, the financial industry has become the core of social development, and the accompanying financial risks have become a relatively difficult problem for countries. The complexity and diversity of financial business and the rapid increase of uncertainty factors have led to a significant increase in risks and may lead to financial crisis when accumulated to a certain extent. Once a financial crisis breaks out, it will lead to social and even political crisis in addition to economic recession. Its main purpose is to provide investors with timely information about financial market risks, so that they can make better decisions and thus increase profits.

The charge point design based on the rectangular self convolution window algorithm data measurement is to use the rectangular self convolution window to measure the electric field strength in a certain area. This method can simultaneously measure electric field strength, voltage and current. The main advantage of this method is that it can be applied to any type of charging equipment at any location without changing its form or structure. In addition, the method has no effect on the charging system itself; Therefore, it can be used to test different types of charging systems. In this paper, the main circuit units of electric vehicle charging pile are designed, including rectifier unit, power conversion unit and human-computer interaction unit, and the rectifier unit and power conversion unit are modeled. For the rectifier unit, VIENNA rectifier is used to replace the common power frequency rectifier to provide DC with good power quality. Then DC-DC full bridge power conversion is performed on the output DC current, and the output is adjusted by phase shifting control. Finally, the human-computer interaction unit is designed, and the work flow chart of the human-computer interaction system is given.

The role of system design in university management is very important, but there is a problem with the unreasonable design. The B/C structure does not solve the security problems in university management, and the data integrity rate is low. Therefore, this paper proposes an NFC technology to construct a management system model. First, the information management knowledge is used to classify the management data, design according to the system development standards, and realize the standardized processing of the system data. Then, NFC technology is used to form an information management set and optimize the system design. MATLAB simulation shows that under specific management requirements, the management data analysis accuracy and system design time of NFC technology are better than those of the B/C structure.

Financial sharing is a phenomenon that people share unused resources in order to reduce financial burden. Financial sharing has been widely used by students, housewives, the elderly and others. Many researchers have investigated this human behavior, such as Yang (2012). Yang's research shows that there are three factors that affect the frequency of financial sharing: personal characteristics, social relations and economic conditions. In addition, Huang et al. (2018) provided a new perspective on financial sharing from the perspective of technological evolution and its impact on human life in the 5G era. And its peak theoretical transmission speed can reach tens of gigabits per second, providing full support for innovative applications of blockchain technology. Starting from the characteristics of blockchain technology in the 5G network background, this paper expounds the influence of blockchain technology on financial sharing in the 5G network background, and further analyzes the application in financial sharing in the 5G network background.

Since the reform and opening up, with the modernization of our country's socialist market economy, the development of science and technology has also changed rapidly. Under the current international situation, competition among countries has become more intense, and various industries are vying for hegemony. In order to obtain more economic benefits, it is necessary to reduce the necessary working hours in society and improve work efficiency. The experimental results can be drawn that the pso algorithm with initialization function not only converges quickly, but they can also maintain the diversification of ant colony data in a small range, making it similar to or even better than the zn-pso algorithm.

Power engineering drawing recognition technology based on improved joint region detection algorithm is a new development in the field of artificial intelligence. It uses the concept of depth learning to identify objects with high accuracy and speed. The construction level of engineering drawings has been significantly improved. However, due to the large amount of engineering drawings and the complex types of engineering drawings, management needs to take into account multiple aspects, and it is difficult to take into account the whole. The development of the technical management of power engineering drawings is not smooth. Based on the consideration of the importance of the technical management of power engineering drawings in the development of modern electrical enterprises, what this paper studies is the identification technology of power engineering drawings that improves the connectivity detection algorithm.

Traditional robot trajectory planning algorithms rely on kinematic models and are unable to adapt to the production requirements of dynamic changes in the environment. However, reinforcement learning algorithms do not need to build complex mathematical models and directly train agents to interact with the environment through data, which is highly flexible and more adaptable to the environment. The focus of the art is to design an effective control law for the mobile manipulator, which can be used to perform manipulation tasks such as picking up objects or moving objects in space. The main idea behind this method is to use a model free method called reinforcement learning (RL) to design the optimal control law of the robot, so that it can learn how to move according to the desired trajectory without knowing the dynamics of its environment in advance.

This article proposes a method for optimizing the routing and wire size of distributed photovoltaic access distribution networks using multiple genetic algorithms. This method can effectively integrate photovoltaic power into the existing power grid, while minimizing power loss and improving network reliability. This article provides a detailed description of the proposed method, including two stages: route selection and wire size determination, and provides evaluation results using a testing system.

This article introduces the research of a fault diagnosis system for mine hoists based on BP algorithm. The system aims to improve the reliability and safety of mine hoists. This article collected operational data of mine hoists and extracted fault data as the training set of the model. Then, a BP neural network model was established on the MATLAB software platform, and the model parameters were optimized and adjusted. Finally, use this model for real-time monitoring.The contribution of this study is to propose a fault diagnosis system for mine hoists based on BP algorithm. This system can achieve real-time monitoring of mine hoist data, while greatly improving the quality of diagnosis system’s judgment and prediction of mine hoist status. At the same time, the system has the function of data visualization, which enables users to understand the running status of the system more intuitively, and provides a more convenient and efficient operating platform for engineering technicians and managers.The controller is used to control all components to achieve stable operation; The sensor detects abnormal conditions in real time, such as overload or fault; It also provides information about the operation status and other related parameters. Finally, after receiving the data from the sensor, it will automatically adjust its parameters according to different operating modes.

The history of animation began in the late 19th century. At that time, it was a new way to present films. In those days, there was no screen or projector. The only way to watch movies is through magic lanterns and shadow plays. These are very simple ways to display images on paper and cloth, without sound effects. In this era, people’s lives are changing rapidly due to the progress of technology. Animation is an art form that uses drawings, models, pictures, videos, or other methods to create motion without using camera motion or movie recording techniques, such as live action movie production. As technology develops and becomes more complex, so does technology. The most important thing everyone uses is the visual representation of things, because without it, they cannot easily understand. In this paper, we will introduce the research and application of animation visualization based on HTML5 algorithm.

Logistics service integrators usually charge a certain proportion of transaction costs from subcontractors according to the assigned order value, set the transaction costs as a linear function of the transaction amount, and constructed a new mixed integer programming model for order allocation optimization of logistics service supply chain. The optimization goal is to minimize the transaction costs, procurement costs, the quantity of logistics capacity of short service and delayed supply. It is a research on the practical teaching design of applied university mental health curriculum in the virtual simulation scene. The main purpose of this study is to explore how to improve teaching methods and technologies through the application of new technologies such as virtual reality (VR) technology, which has been widely used in the field of education, such as learning games and training projects. In addition, it aims to study whether VR can be used as an effective tool to improve students’ mental health during their study. Researchers believe that virtual reality will help us better understand human psychology and behavior, and better understand various psychological barriers.

The role of fault location in single-phase grounding systems is very important, but there is a problem of poor accuracy. The original positioning method solves the fault location problem in single-phase grounding system, and the diagnostic ability is low. Therefore, this paper proposes a low-current grounding diagnosis method to analyze the talents of single-phase grounding system. Firstly, the big data theory is used to evaluate the talents, and the fault location requirements are divided to reduce the interference factors in the fault location. Then, the big data theory requires the single-phase grounding system to form a fault location scheme, and conducts comprehensive fault diagnosis on the fault location requirements 。 MATLAB simulation shows that under the condition of stable power flow of the power grid, low-current grounding diagnosis method are better than those of PLD teaching mode. Determine the location of the failure.

Intelligent integration technology is used to monitor urban rail transit system. Intelligent integration technology can provide real-time information about the operation of the whole urban rail transit system, including its safety and reliability. What is the application of intelligent integration technology in automatic monitoring of urban rail transit engineering? It is a system that provides real-time data about the performance and operation of urban rail transit system. Automatic monitoring is to continuously and real-time measure and record the data of various parameters related to the performance and condition of urban rail transit system. The information obtained from these measurements will be used for maintenance planning, system optimization, traffic management and other operational activities.

In logistics distribution systems, dynamic multi-agent algorithms can monitor and adjust the delivery process in real time, and optimize delivery paths, vehicle scheduling, and other issues based on real-time information, improving the timeliness and accuracy of logistics distribution.In terms of specific applications, using dynamic multi-agent algorithms to optimize express delivery needs to consider multiple factors, such as the number of deliveries, delivery address, weather, traffic flow, and so on. By comprehensively considering these factors, we can build a fitness function of dynamic multi intelligence algorithm, optimize the distribution scheme, and make real-time adjustments according to real-time data to achieve the optimal distribution effect.In the post pandemic era, logistics distribution systems face more severe challenges. The application of dynamic multi-agent algorithms can improve the efficiency and quality of logistics distribution, promote the digital and intelligent development of the express delivery industry, and also provide better support and guarantee for the development of the social economy. Therefore, the application of dynamic multi-agent algorithms has important significance and broad application prospects in the field of express delivery. This article aims to develop an effective method for designing and implementing dynamic multi-agent algorithms, which can serve as a tool to improve the performance of logistics distribution systems in the post pandemic era.

CNC machine tool is a high-precision machine tool, which can perform complex machining operations with high accuracy. CNC machine tools are widely used to produce various engineering products, such as aircraft parts, automobile parts, etc. In recent years, the demand of Chinese industry for CNC processing technology has increased rapidly. Therefore, its main function is to carry out complex and accurate cutting operations for various types of materials (such as wood, plastic, metal, etc.). The equipment has been used in the manufacturing.

Kruskal algorithm is a data reduction method Kruskal algorithm and PCA with different component numbers. Through the comprehensive evaluation of the existing rural economic information system, it can provide governments at all levels and relevant departments with scientific evaluation basis for the construction status, service level and benefits of the economic information system. On the one hand, it can avoid inefficient operation and resource waste caused by repeated construction; On the other hand, we found the weak links in the construction and development of rural economic information system through evaluation, and strengthened the software and hardware construction of the system itself.

The role of management in the study of public social activities is very important, but there is a problem of low management level. Traditional management methods cannot solve management problems in the study of public social activities, and the level of management is low. Therefore, this paper proposes a computer technology method for management work analysis. First of all, the organization theory is used to study management work, and the public social activities are deeply excavated to reduce the Irrelevant management factors. Then, form the final management work set. MATLAB simulation shows that the management work accuracy and management work of computer technology method is known under the condition that the management requirements are known time is better than traditional management methods.

The intelligent service platform for big data technology is a new approach to infectious disease management. It aims using big data technology. It integrates epidemiological, clinical and laboratory information as well as social network analysis tools, so as to detect and prevent the epidemic as early as possible by predicting its occurrence or spread to public health authorities. The project aims to develop an intelligent service platform to support the rapid collection of big data from various sources such as health departments and other relevant institutions across China, so as to promote the management of epidemics. It will also provide real-time information sharing between local governments and other relevant parties. In addition, it will provide customized services for local governments, such as epidemiological forecasting tools and national systems for monitoring disease outbreaks throughout the country.

Multi joint serial industrial robot is widely used in industrial production because of its convenient operation, accurate positioning, flexible execution and other advantages. The working principle of industrial robot is mainly to remember the running track through manual teaching, and use control to make it reach the specified position and pose. Generally, industrial robots only follow one or more fixed routes in actual pipeline work. Dynamic path planning is a method types of robots, such as wheeled mobile robots or underwater robots (UUV). The key idea behind our method is to use the depth map generated by a stereo camera, rather than simply measuring the line of sight distance between two points as traditional methods. The depth map provides information about the relative position between two objects, which is crucial for finding a free path.

Face detection and recognition technology has always been a hot research direction, face detection and recognition technology has received extensive attention from society. The detection and recognition rate and running speed of the face detection and recognition system directly affect the user’s experience, so it is very important to realize a fast and accurate face detection and recognition system. The adaboost method uses a set of classifiers (also called decision trees), which are trained for different subsets of data points (called splits).

Using AR technology for tourism products research and development and application, especially should pay attention to the feeling of tourists experience, with cultural creativity as the starting point and the foothold, using AR technology, stimulate tourists a variety of feelings, stimulate their excitement, make they produce close feeling, constantly produce consumption desire, meet the actual needs of tourists.

The research of distribution network monitoring and fault location based on edge computing is a research, focusing on the design of low power consumption, high reliability and high fault tolerance distributed systems. The main purpose of this research is to develop a distributed system architecture for monitoring and locating faults in large-scale networks. The architecture consists of three components: fault location component (FLC), data acquisition component (DCC) and data analysis component (DAC). These three components are connected through an intermediate layer called the Service Interface Controller (SIC). SIC provides necessary interfaces between DCC, DAC and FLC.

The role of fermentation analysis in microbial research is very important, but there is a problem of low analysis accuracy. The real-world statistical method cannot solve the problems of yeast evolution and harmful bacteria identification in fermentation research, and the analysis accuracy is low. Therefore, this paper proposes a crowd intelligence algorithm to construct a fermentation simulation model. Firstly, the group theory is used to divide the fermentation process, and the method is selected according to the reading requirements to realize the preliminary observation of the fermentation data. Then, a collection of fermentation studies is intelligently formed and data mining analysis is performed on the yeast. MATLAB simulation shows that under certain requirements, the swarm intelligence algorithm's optimization degree and simulation stability are better than the realistic statistical method.

We also apply this method to our robots. Robot path planning is a task that requires a robot to move from one position to another, and then repeat the process. The robot must always know where it is, in which direction it should move, how fast it should go, and how far it needs to go. The research and improvement of robot path planning is the process of developing, testing and improving algorithms to effectively perform tasks. The main goal is to find the best solution and maximize the time spent on the task with the least errors. The main problem in this field is to find a strong enough solution to deal with unexpected situations or obstacles that may occur during path planning, but it is also fast enough to avoid wasting too much time on repetitive tasks, such as re planning when problems occur.

The cultural tourism industry is a pillar industry, and its development should keep pace with The Times. Therefore, the industry is actively building the 5G metauniverse, but the construction process is difficult and lacks a clear direction. In view of the current situation, this paper will conduct research on the basis of the improved AHP algorithm. First, it introduces the basic concept of the improved AHP algorithm, and puts forward practical strategies based on the improved AHP algorithm for the current situation of the construction of the cultural tourism industry 5G meta-universe. Through research, the improved AHP algorithm helps the cultural tourism industry find the direction of 5G meta-universe construction, so that the 5G meta-universe can play a full role.

The aviation industry is not only a huge economic engine, but also one of the most important sectors that play a key role in safety and security. The analysis of aircraft maintenance records and engine maintenance records is essential for airlines, airports, aviation authorities and manufacturers to ensure that their products are properly maintained. Although this task is important, an automation solution has not been developed. This paper presents a method to automatically recognize civil aviation maintenance records from images. The main task is to identify aircraft maintenance records, engines and airframe components being processed. To solve this problem, we use the depth learning algorithm, and use ground data for pre training. We test by training the model on the image dataset and using the test dataset to compare with the classification results of human experts.

The training process of medical and nursing health professionals is complicated and difficult, so the training efficiency and quality can not be guaranteed. In order to change this situation, the related fields believe that the talent training prediction model can be constructed by fuzzy neural network algorithm, and the goal can be achieved by relying on the model. This paper mainly discusses the basic concept of fuzzy neural network algorithm, and then establishes the prediction model combined with the training process of medical and nursing health professionals, and finally introduces the specific role of the model in talent training, and carries on the demonstration.

Renewable energy is the main energy in China. It has great development potential, but it also brings severe challenges to the operation and management of power system. Firstly, this paper combs the distributed robust optimization theory, specifically including the characteristics and applicable conditions of the distributed robust optimization model, classifies them according to the different methods of fuzzy set construction of uncertain parameters, and analyzes the advantages and disadvantages of each method of fuzzy set construction, laying a theoretical foundation for the subsequent construction of the distributed robust optimization scheduling model.

This article aims to study a steel plate surface defect classification algorithm based on an improved BP neural network. Firstly, we analyzed the classification of surface defects on steel plates and proposed an image segmentation algorithm based on shape features, which divides the steel plate surface image into multiple regions and extracts shape features from different regions. Then, we used traditional BP neural networks and improved BP neural network models to classify these shape features to determine the type of surface defects on the steel plate.Specifically, we propose an improved BP neural network model to address the issues of low classification accuracy and slow training speed that traditional BP neural network models face in dealing with multi class problems. The model uses momentum term and learning rate annealing technology to accelerate the network training process, and uses Sigmaid function instead of the traditional step function to improve the fitting ability of BP neural network.Through a large number of experiments, we compared and analyzed the performance of traditional BP neural network and improved BP neural network in the classification accuracy and training speed of steel plate surface defects. The results show that the steel plate surface defect classification algorithm based on improved BP neural network has significantly improved classification accuracy and training speed compared to traditional BP neural network. This algorithm has important application value for automatic recognition and classification of surface defects on steel plates.In summary, this article studies a steel plate surface defect classification algorithm based on an improved BP neural network. In the future, we will further optimize the performance of the algorithm and improve its application scenarios to improve its practicality and universality.

Binary association rule extraction algorithm is a machine learning method, which is used to extract rules from text data. Text data can be in any form, such as documents, news articles, or social media posts. The rules extracted by this algorithm are called associations. These associations are then used to predict future events. This paper introduces how to use binary association rule extraction algorithm to predict students’ employment competitiveness according to their performance in SAT/ACT/NAPLEX and other college entrance examinations. Use the above algorithm to extract the employment competitiveness of college students. The binary association rule extraction algorithm is applied through the following steps: Step 1: input the data into the computer and import it into the database; Step 2: Search for rules in the database and extract them; Step 3: Combine all extracted rules into a rule set; Step 4: Calculate the score of each rule set according to its accuracy, and then compare it with the scores of other rule sets.

The research direction is the dynamic scheduling method of aerospace surveying and mapping system based on chaotic genetic algorithm. This is a new technology to solve complex problems in aerospace systems and has been proved to be very effective in solving these problems. The dynamic scheduling algorithm, which can be used as a tool to improve aircraft performance by reducing turnaround time. This will help airlines reduce operating costs and increase profits. This paper aims to design a dynamic scheduling method for aerospace T/C system based on chaotic genetic algorithm. It also includes simulation results and performance analysis of the proposed method in different parameters (such as number of jobs, number of machines, number of processors, etc.).

Since the 1980s, economic globalization has gradually become the trend of world economic development. Economic globalization is gradually affecting our lives. More and more enterprises have started transnational operations to expand their business scope in order to seek broader markets and resources. With the continuous expansion of enterprises. The integrity verification algorithm of accounting information cloud data in the sharing mode is used to verify the correctness of data in the distributed system. It uses a set of rules to check the validity and consistency of the information stored in each node. This algorithm is useful when there are multiple nodes with different version information, such as when a node has been updated by another node or even after an update from another source.

Soil moisture prediction is a technique for predicting soil moisture content. It is used in agriculture and hydrology to design irrigation systems, predict crop yields, monitor the impact of weather on crops, etc. The main purpose of soil moisture prediction (SPM) based on past data and climate information. SPM uses machine learning techniques, such as neural networks, k-means clustering, support vector machines, etc., to predict soil moisture at different time points in the growing season. The purpose of this technique is to classify soils into specific categories at an accurate and high-precision rate. It provides us with information about the moisture content of various soils, which is helpful to understand their characteristics and applicability for different purposes such as agriculture, engineering and construction. We can find that there are many methods that can be used to predict soil moisture, such as mathematical models, but they cannot provide accurate results samples from different locations.

The design and practice of based on machine learning is the process of designing a system that can make decisions using data, knowledge and rules. Decision Support System (DSS) is a computer-based tool that helps decision-makers make better decisions by providing them with information, knowledge and analysis tools. DSS is used in the field of environmental management to support decision-making on issues such as water resources development, pollution control and protection. The main objective of DSS is to provide decision-makers with a set of comprehensive data, analysis tools and models for use in the planning process.

In recent years, represented by smart phones and tablets have been greatly developed. The mobile terminal color management module is a system that can be used in the field of mobile terminals. It has a function that can control the display mode, color space and other parameters of each application through its own API (application programming interface). Here are some applications: (1) Camera calibration on mobile phone screen; (2) Use external devices such as digital cameras and webcams to calibrate the screen; (3) Color correction of images taken by smart phones or tablets; (4) Image processing using image recognition technology; (5) According to different video formats.

At studying the key technologies of virtual reality (VR) system development, construction and operation. Its main objective is to develop virtual reality technology in order to reconstruct the ancient city based on archaeological evidence, which is difficult or impossible to reconstruct by physical methods. The project will also provide a basic understanding of how virtual reality technology is applied to Archaeology and related fields. The main purpose is to carry out experimental research on three topics: 1) the development of virtual reality technology; 2) The construction method of virtual reality system; 3) Application of virtual reality system. A series of advanced methods such as 3D scanning and photogrammetry are used to study the key technologies of image-based virtual restoration of ancient buildings. The results obtained from this method will be used in our further work to improve the quality and efficiency of these technologies.

The relationship between camera position and reconstruction accuracy is analyzed, and the weight value is estimated on this basis. The application of weighted least squares algorithm in machine vision system is a method for estimating unknown model parameters. The weight is calculated by using some known data, and then applied to unknown data for estimation. This method is also called supervised learning or regression based on training set. Here, we use all the features in the image and train them with available data sets containing preprocessed and normalized training sets so that they can be easily compared with other images in the same category.

Internet finance is a new financial market formed by the interaction of traditional financial markets and emerging financial markets. In this article, we will focus on how to use AI technology to analyze security risks. Artificial intelligence (AI) is an intelligent technology that can make decisions based on certain knowledge or rules without human intervention. AI can help us reduce human error. The application of artificial intelligence technology in internet finance is becoming increasingly widespread, including risk control, customer service, intelligent investment advisory, and other fields. Among them, artificial intelligence algorithms can help financial institutions determine whether there are risks based on users’ personal information and behavior patterns, improve risk management level, and improve service quality. At the same time, artificial intelligence technology can also provide personalized investment advice to customers, helping them achieve better returns. However, the application of artificial intelligence technology in internet finance also brings some security risks. For example, hackers can exploit algorithmic vulnerabilities, attack artificial intelligence systems, and steal users’ personal information and funds. In addition, artificial intelligence systems are susceptible to human manipulation and erroneous indications, resulting in misjudgment and bias. This requires financial institutions to adopt strict security measures, improve their technological level, ensure the safety and privacy of customers, and ensure the sustainable and healthy development of artificial intelligence technology.

In recent years, with the significant improvement of China’s economic development level, the tourism industry has ushered in a period of rapid development, and health care tourism has gradually become one of the hot spots of the people’s attention. In the process of carrying out health tourism, the utilization and protection level of the traditional culture of health tourism destinations can be further improved. Based on this, the article is based on the Android platform, and the paper deeply discusses the design and realization of the traditional culture utilization and protection system of health tourism destination, hoping to help the protection and development of the excellent traditional culture of health tourism destination.

Weak target detection in the process of target scale, background noise, causing weak target image visual transmission effect is unsatisfactory, to improve the weak target image visual transmission effect, now propose a weak target image visual transmission design scheme: using guide filter technology to enhance image characteristics, using partial differential equation to obtain image background baseline, to achieve better weak target image background suppression effect;Histogram equalization algorithm is used to improve image quality and obtain good image enhancement effect, and image feature extraction technology and particle swarm algorithm are used to complete the tracking and visual transmission of weak target images. Experimental analysis proves that the proposed design scheme in this study can effectively reduce the influence of interference factors of weak target detection, produce a good visual transmission effect of weak target images, and meet the actual needs of weak target detection and tracking.

This method includes two steps: (1) The first step is to use neural network to extract depth from the collected hyperspectral images, and then use it to classify the hyperspectral images. (2) The second step is to use the classification result obtained in step 1 to detect the target in the collected hyperspectral image. we need more algorithms, such as classification algorithm and data mining algorithm, which can help us achieve our goals. In this technology, the training data is divided into two parts: one part contains the original images captured with different spectral bands; The other part contains the same original image after processing with various algorithms. The final HIC model consists of a set of convolutional neural networks, which are trained to predict.

The research on automatic control reliability of intelligent robots is a research project carried out by the Institute of Automatic Control, Chinese Academy of Sciences in recent years. The main purpose is to study the reliability and robustness of the intelligent robot control system. In this paper, we will focus on the design and analysis of reliable and robust controllers for intelligent robots. We also discussed some practical applications related to our work. In this paper, aiming at the problems of frequent maintenance and low work efficiency caused by the accuracy of automatic robots in the actual working environment, the calculation method of automatic control reliability of intelligent robots is studied. Starting from the use of tools for mechanical accuracy reliability analysis, analyze the calculation process of mechanical motion reliability, establish a virtual prototype, and discuss the variables. The experimental results show that the reliability calculation method of robot automation designed in this paper is effective.

Dunhuang Mogao Grottoes is an art museum in the desert, and the architectural paintings on the murals are a treasure house of architectural history research. The development of big data in recent years has greatly promoted the research of architectural history. The launch of the Dunhuang Digital Museum has shown its advantages. The mathematical study of Dunhuang architectural paintings can also blaze a trail for the study of architectural history and the promotion and development of traditional culture. In 2017, the State Council issued the “Opinions on the Implementation of the Inheritance and Development Project of Chinese Excellent Traditional Culture”, emphasizing the inheritance and development of Chinese excellent traditional culture. Through digital research on architectural paintings, big data and digitization provide technical support for the “protection, inheritance, dissemination and exchange” of traditional culture. Through the digitalization of architectural paintings, the blueprint of architectural historical research can be protected, and architectural paintings can be excavated on the basis of in-depth development, laying the foundation for cultural inheritance and modern communication. This paper conducts a special research on architectural paintings in Dunhuang murals, combs the development status of architectural painting digitization, and the technical process of realization to provide specific directions for later research and development.

Distribution network planning is an important guarantee for power grid construction and transformation, which can ensure the reliable, stable, economic and flexible development of the system. With the increasing capacity of distribution network, the amount of data to be processed and analyzed in distribution network planning has increased dramatically. Especially for the high voltage, medium voltage and low voltage superior power supply, distribution network structure and operation status, the workload is huge and the complexity is high. If the planning process completely depends on the planners to analyze and calculate, it is easy to have calculation errors, incomplete analysis or other uncertainty errors. Based on the improved convolutional neural network (CNN) algorithm, a new identification algorithm is proposed to improve the measurement accuracy of distribution network planning. The proposed method uses CNN to extract the first k important variables, and then combines them with the previous two methods, one uses nonlinear regression, the other uses linear regression. In addition, we propose a new metric method. In order to evaluate our results, this article uses a large number of real data sets.

The role of fault section location in the grounding diagnosis is very important, but there is a problem of low diagnostic accuracy of small current. The GPS positioning method does not solve the problem of fault section location in the grounding diagnosis of the distribution network, and the small current diagnosis ability is low. Therefore, a multi-terminal synchronous waveform method is proposed to analyze the grounding diagnosis. Firstly, the synchronous theory is used to judge, and the fault section positioning standards are divided according to the fault section positioning standard to reduce the fault section positioning Disturbing factors. Then, the synchronization theory forms grounding diagnostic standards and synthesizes the fault section positioning standards OK. The fault segment positioning accuracy and fault section location time of the multi-terminal synchronous waveform method is superior to the GPS positioning method.

The role of information management in universities is very important, but there is a problem of low management level. The management system cannot solve the management problem of multiple types of information in the university system, and the rationality. Therefore, decision tree method to construct an information management optimization model. First of all, the information knowledge is used to classify the university information, and the university information is selected according to the degree of importance to realize the standardized processing of data.Then, the information knowledge is classified according to importance, forming an information optimization collection and iteratively analyzing the scoring content. MATLAB simulation shows that the decision tree method’s optimization degree and optimization time are better than that of a single management system when the system is fixed.

The analysis continuous calculation in energy measurement, this consortium chain technology, which uses technology, chain metering method and alliance chain rules to test the relevant measurement in energy, and inserts the energy string method in the blockchain to improve the comprehensive computing ability of energy measurement. At reasons for the interference of measurement, summarizes the characteristics of energy measurement, puts forward the design ideas of blockchain technology, and tests the correctness, fit of energy measurement and degree of energy measurement through actual cases. The simulation results of MATLAB show that the energy measurement ability of the alliance chain technology is better, the energy measurement degree is more than 90%, and the comprehensive computing power continues to rise, and the continuous improvement effect is better, which is better than the traditional statistical method. Therefore, the blockchain-based energy metering technology suitable for the optimization of energy metering.

The role of in construction is very obvious, but there is a problem that the investment accuracy is not high. Previous audit investment methods could not solve the problem of accurate investment, and the evaluation ability projects was low. Therefore, this to improve the fuzzy neural reasoning method and construct an evaluation model for projects. Firstly, the fuzzy theory is used to plan the data, and the evaluation and collection division according to the project funds are used to reduce the uncertainty factors of investment analysis. Then, fuzzy theory will form the power grid investment planning, form an investment project evaluation set, and evaluate the data in the set for inference evaluation. MATLAB simulation shows that under the condition of a certain scale of investment projects, the evaluation accuracy and evaluation time of the improved fuzzy neural reasoning method are better than the previous audit evaluation methods.

The role of computer very important, but there is a problem with the low note-matching rate. Traditional music teaching methods cannot solve the problem of note-matching rate in music teaching, and the matching degree is less. Therefore, this paper proposes computer technology to construct a matching model for information and music teaching. Firstly, the music score knowledge is classify the musical notes, and the set is divided according to the score standard to realize the quantification of music information Processing. Then, the notation knowledge classifies the note match rate, forms a music match set, and iteratively analyzes the matches. MATLAB simulation shows that under the condition of certain teaching standards, the accuracy of music score analysis and note-matching rate of computer technology is better than traditional music teaching methods.

The role of computer technology in garden plant configuration is very important, but there is a problem of low plant allocation rate. Traditional teaching methods cannot solve the problem of plant configuration in gardens, and there is less rationality. Therefore, this paper proposes an online teaching method based on computer technology to construct a matching model of plant configuration and teaching. Firstly, the garden knowledge is used to classify the plant configuration, and the configuration collection is divided according to the teaching standards to realize the quantitative processing of the garden plant configuration. Then, garden knowledge classifies plant configurations, forms configuration collections, and iteratively analyzes the configurations. MATLAB simulation shows that under the condition of certain teaching standards, the configuration analysis accuracy and rationality of online teaching methods based on computer technology are better than those of traditional teaching methods.

With the continuous development of science and technology, a variety of advanced science and technology and equipment have been gradually started in education and teaching, which can improve the teaching efficiency and quality to a certain extent. This paper uses a virtual technology to build an English teaching system. The integration of virtual environment and foreign language courses can provide a new environment for foreign language education, make the content of foreign language teaching in an approximate way, improve the way of teaching and learning and the interactive mode of Teachers and students, and provide autonomy for foreign language learning. The learning environment of inquiry, inquiry and cooperation enables foreign language learning to be carried out in a relaxed and pleasant environment in a task driven manner.

With the online teaching has become a new type of education, which is based on computer technology, communication, and is university English teaching and welcomed. At the some new media tools have gradually emerged in English higher education to assist students in learning and acquiring information. With the technology, the online teaching platform of college English has also ushered in a new round of development opportunities. Based on this, this paper tries to build a college English Internet online teaching system as a new development of English online teaching mode, so as to provide relevant help for it.

With the continuous development of computer technology, multimedia technology has been widely applied in the field of teaching. As a highly practical subject, physical education teaching requires students to possess various sports skills. Therefore, the use of multimedia technology in physical education teaching can create a more vivid and interesting teaching environment, which helps to enhance students’ learning interest and enthusiasm. This article designs a sports teaching platform based on multimedia technology, aiming to provide students with more effective sports teaching methods. This platform utilizes digital technology to organically integrate sports knowledge, skills, and teaching resources, forming a complete sports teaching content, including multimedia teaching resources such as videos, images, and audio. These resources are very beneficial for students to review and learn independently after class, while also greatly reducing the teaching burden of teachers and improving teaching efficiency. This platform adopts a layered design architecture, where the data layer, application layer, and display layer each undertake different functions. The data layer is mainly responsible for storing physical education teaching resources and classifying them appropriately; The application layer provides users with various teaching services; The presentation layer provides users with a visual interactive interface, allowing them to freely browse and learn related content.

This paper discusses the application and effect of nursing practice qualification examination counseling and learning management system (hereinafter referred to as human health and nursing examination system) developed by the people’s Health Publishing House in nursing teaching. Using the methods of convenient sampling and objective sampling, taking nursing students and teachers, the human health examination system was applied in the teaching of internal medicine nursing course and the guidance of nurse practice qualification examination (nursing examination). After the course teaching and nursing examination counseling, the students’ scores were evaluated, questionnaires were issued to investigate the teaching effect, and the students’ scores of internal medicine nursing examination at the end of the semester were compared.

The role of phrase translation in English reading is very important, but there is a problem of poor translation accuracy. Manual proofreading increases the workload of teachers and is less efficient. Therefore, proofreading system for English proofreading. First, AI technology is used to proofread the phrase of English reading requirements, and the horizontal division is carried out according to the phrase proofreading standard to reduce it Distractors in phrase proofreading. Then, AI technology proofreads English phrase translation to form phrase proofreading results, and performs continuous phrase proofreading for transactional phrase proofreading requirements. MATLAB simulation shows that under a certain amount of reading, the evaluation accuracy and phrase proofreading time of the computerized intelligent proofreading system are better than those of manual proofreading.

The ITER (International Thermonuclear Experimental Reactor) ELM (Large Amplitude Essential Mode) is an important problem for the terrain change on the surface of the Divertor target plate during the operation of ITER. The main function of the Divertor target plate is to filter out high-energy particles and ions in thermonuclear reactants and maintain the stable operation of subsequent devices. Due to the overload caused by ITER ELM and local extremely high heat emissions, significant terrain changes may occur on the surface of the target plate, leading to operational issues. In this paper, the surface topography of Divertor target under ITER ELM is studied by theoretical simulation and experimental research. Based on experimental measurement results and theoretical simulation predictions, the terrain changes were analyzed and explained. The research shows that ITER ELM operation will cause local high temperature and thermal stress on the surface of the target plate of the Divertor, and the surface will undergo significant deformation and distortion. In addition, the experimental results also indicate that the terrain changes on the surface of the target plate show significant differences not only in the longitudinal and transverse directions, but also in different areas of the target plate surface. Through theoretical simulation and experimental research, this paper obtained the experimental results and theoretical predictions of the terrain changes on the surface of the Divertor target under the operation of ITER ELM, which has important reference value for understanding the lubrication and preventing radiation pollution in the field of thermonuclear fusion.

The design of intelligent terminal dance movement supplement and optimization system can systematically analyze the 3 D digital form of dance. And in the subsequent process of folk dance style presentation, we can better understand the characteristics of similar artistic style. This study analyzes the intelligent terminal action supplement technology and dance damage control technology, and shows that the motion capture technology can effectively prevent various dance style problems, produce strong economic benefits in the process of 3 D digitalization promotion, and realize the presentation and optimization of the subsequent dance art style content.