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The spatial layout and optimization of social facilities for sports are related to many factors such as urban economy, transportation, population, and urban planning. With the rapid development of artificial intelligence today, deep learning came into being. It also provides a lot of methods for our in-depth research on it. To solve the problem of unbalanced layout of urban social facilities for sports and the difficulty in meeting the needs of residents under the current background, this paper adopts the methods of M-P model, loss function, and activation function. Taking Hangzhou, a sports city that is about to host the Asian Games, as an example, it carried out an experiment to optimize the layout of urban public sports services. Through various tests and data, it divides the accessibility level of social facilities for sports in Hangzhou into four levels: high accessibility point (1.0-1.5), high accessibility point (0.5-1.0), the accessibility is average (0.2-0.5), and the accessibility is poor (<0.2). It also concluded that the areas with high accessibility (1.0-1.5) are Shangcheng District and Binjiang District, and the layout of sports facilities is optimal. According to the minimum facility point model, Xiaoshan District and Yuhang District need to add 6 large-scale social facilities for sports. Through this experiment, this paper can also provide a reference for other urban layouts. This has significance for the optimization of sports facilities and the optimization of public facilities. [ABSTRACT FROM AUTHOR]

In order to improve the reference value of film review data, this paper combines clustering analysis technology to construct a film review clustering visualization research system to improve the visualization effect of film reviews. Moreover, this paper analyzes online reviews based on the method of type-2 fuzzy sets and determines the interval of type-2 fuzzy sets of each factor set of commodity status in commercial stores by considering both the language and specific numbers of keywords in online review texts. In addition, this paper analyzes intelligent control through practical application and sets the expected target of the control object. Finally, according to the fuzzy rules and word calculation, this paper determines the interference or improvement measures that can be referenced to form a closed-loop control. The experimental research shows that cluster analysis technology can play a certain role in the visual analysis of film review data. [ABSTRACT FROM AUTHOR]

Cartoon rendering of images is a challenging nonphotorealistic image rendering task, which aims to transform real photos into cartoon-style nonphotorealistic images while preserving the semantic content and texture details of the original photos. Based on the understanding of the characteristics of cartoon images and analysis of the defects of established approaches, this paper improves existing methods. The convolutional neural networks' powerful image processing ability and attention mechanism are utilized to get more fine-grained image features, making the result of rendering more realistic. This paper mainly studies how neural networks can better process cartoon pictures, adjust parameters, and choose training methods. Furthermore, the article proposes a new solution of cartoon-style image rendering based on deep learning. The actual test results of real original images have shown that the model is suitable for cartoon-style image rendering. [ABSTRACT FROM AUTHOR]

In order to explore the teaching efficiency of online intelligent courses and improve the quality of online teaching, this paper builds a classroom intelligent auxiliary management model based on the grid simplification method. Moreover, this paper formulates corresponding teaching strategies through the recognition of student state features, uses a target detector to detect all detection targets from the scene, and then counts the number of detection targets, identifies specific individuals, and judges the individual state. Simultaneously, this paper intercepts candidate subregions from the scene image and then inputs the subregion image to the detector to determine whether the candidate region image is a detection target and formulate corresponding countermeasures. In addition, on the basis of the existing 3D mesh model stitching and editing method, this paper proposes a grid splicing and fusion method based on the idea of partial reuse of the model to calculate the result of the target. Finally, this paper designs experiments to verify the performance of the model. The research results show that the model constructed in this paper is effective. [ABSTRACT FROM AUTHOR]

In this paper, a better particle filter algorithm is put forth to address the issues of particle filter sample exhaustion and weight degradation. The algorithm frames the received signal and separates the signals in two steps based on the slow-varying properties of system parameters in practical applications, such as phase shift and transmission delay. In addition, the network model and energy consumption model are built while the sensor data is being collected and processed using the industrial IoT's communication mechanism and algorithm. The repeater is chosen as the node with the lowest transmission energy consumption, and the industrial field's sensor data is gathered via the fog server node. The simulation results demonstrate that the proposed algorithm's accuracy rate is 95.54 percent, higher than that of the comparison algorithm. The enhanced algorithm suggested in this paper can simultaneously achieve improved parameter estimation performance and achieve signal separation with low bit error rates. Additionally, the communication system and algorithm can efficiently gather the sensing information from the industrial field, and the indicators like energy consumption and the first dead node are better than other algorithms. It offers an innovative method for enhancing industrial field application. [ABSTRACT FROM AUTHOR]

Automated guided vehicle technology has become a hot area of scientific research due to its increasing use in manufacturing and logistics. Its main features are programming and control, remote computer eye tracking, command receiving and execution, autonomous route planning, and autonomous driving execution of tasks, with the advantages of high intelligence and flexibility. In this work, a simple vehicle model is used to study the route planning and tracking control of automatic guided vehicles. This paper uses wireless communication to find the optimal route planning problem. Using geometric methods, we develop a model of the working environment of the mobile automatic guided vehicle and develop a route finding algorithm. Based on the kinematic model, an advanced routing controller is designed to conduct experimental simulation of two trajectories and verify the effectiveness of the trajectory tracking controller. When the time is after 2 s, the position error is almost completely zero. In the path planning, when the number of iterations is greater than 10, the path length remains constant, verifying the effectiveness of the method in this paper. [ABSTRACT FROM AUTHOR]

In volleyball, the correct approach and start (including the number of steps and stride speed) are a prerequisite for all technical movements to attack. It can not only improve the horizontal speed of the athlete, but also properly convert the total speed into vertical speed, so that the hitting point is improved and the ball speed is accelerated. To explore the biomechanical characteristics of lower limb movements in the run-up and take-off stage of volleyball spiking, this paper takes four male volleyball players from the Physical Education College of X University as the research objects to analyze the kinematics and dynamics of the run-up process and the take-off process. This paper uses the precise recognition method under the background of deep learning to accurately capture the movements of the research object. This paper discusses the effects of time, speed, distance, knee, and hip parameters (angle, joint muscle torque, and power) on the effect of spiking techniques. It is expected to provide reference for the diagnosis, guidance, and muscle strength training of this special technical movement. The research results show that the horizontal speed of No. 2 athlete is 3.62 m/s and the vertical speed is 2.71 m/s when he takes off. The landing time is 0.375 s and the lift-off time is 0.16 s. The torque and power of the knee joint changed greatly during the take-off process, and the change of the hip joint was small. [ABSTRACT FROM AUTHOR]

China's development is inseparable from the reform of education, so ECE is very important in the development of Chinese society. Based on this reason, this paper introduces the three reform waves of communist Chinese society under the influence of western culture. Because the professional level of kindergarten teachers does not match the concepts and requirements of the three preschool curriculum reforms in Chinese society, this paper analyzes the solutions based on this problem and holds that the leadership of kindergarten teachers should be vigorously developed. [ABSTRACT FROM AUTHOR]

Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the general methods that support machine understanding of text content. They play a very important role in the text information processing system including recommendation and question and answer systems. There are many researches in the field of NLU such as Bag of words, N-Gram, and neural network language model. These models have achieved a good performance in NLU and NLG tasks. However, since they require lots of training data, it is difficult to obtain rich data in practical applications. Thus, pretraining becomes important. This paper proposes a semisupervised way to deal with math word problem (MWP) tasks using unsupervised pretraining and supervised tuning methods, which are based on the Unified pretrained Language Model (UniLM). The proposed model requires fewer training data than traditional models since it uses model parameters of tasks that have been learned before to initialize the model parameters of new tasks. In this way, old knowledge helps new models successfully perform new tasks from old experiences instead of from scratch. Moreover, in order to help the decoder make accurate predictions, we combine the advantages of AR and AE language models to support one-way, sequence-to-sequence, and two-way predictions. Experiments, carried out on MWP tasks with 20,000+ mathematical questions, show that the improved model outperforms the traditional models with a maximum accuracy of 79.57%. The impact of different experiment parameters is also studied in the paper and we found that a wrong arithmetic order leads to incorrect solution expression generation. [ABSTRACT FROM AUTHOR]

In the agricultural industry, wireless sensor networks (WSNs) can be an important tool to promote economic growth. Using network devices in agriculture has the potential to enhance the production process. One of the key challenges WSN faces is energy efficiency. A model based on the water pipeline method is proposed in order to efficiently utilize sensor nodes in agricultural production and water distribution. A water pipeline serves as an important structure for transporting potable water across a distance for consumption or irrigation. In contrast, the biggest transportation problems of water pipelines are leaks. So, water resources may be lost as a result. These pipes need real-time monitoring to prevent such problems. The wireless sensor network technique, however, is considered one of the best solutions available today for monitoring water pipelines. A detailed analysis of agriculture is provided by the model. Aspects of WSN are discussed and their agricultural use is expounded. Moreover, this paper describes the various types of applicability of existing sensor networks in the field of agriculture, along with some technical perspectives. To achieve the best power consumption and communication for the two types of range, ZigBee wireless protocols are utilized. As such, high-performance information that provides a platform for WSNs to better support agricultural production is also included in our proposed model in order to address the shortcomings of existing WSNs regarding energy efficiency. This paper presents the improvements of the proposed solution compared to other techniques in the context of energy conservation in wireless sensor networks and in the monitoring water-saving process. [ABSTRACT FROM AUTHOR]

Under the present background, optimizing the existing urban rail transit network is the focus of urban rail transit construction at present. Based on DL, this paper constructs the optimization algorithm of urban rail transit network route planning. According to the current urban layout and urban planning, build a suitable rail transit network line form; according to the function, the types of urban rail transit stations are divided, and the optimization of urban rail transit network lines is realized. In addition, according to the K short path algorithm, this paper calculates the effective path between any stations of rail transit and, according to the model, allocates the passenger flow to each path. Experimental results show that the accuracy of real-time traffic flow prediction by this algorithm can reach 94.98%, which is about 9% higher than other methods. This algorithm can effectively optimize the route planning of urban rail transit network. This verifies the effectiveness of the route planning optimization algorithm proposed in this paper. Using the algorithm in this paper for line planning can get good real time, rationality, and optimality. [ABSTRACT FROM AUTHOR]

The main goal of speech recognition technology is to use computers to convert human analog speech signals into computer-generated signals, such as behavior patterns or binary codes. Different from speaker identification and speaker confirmation, the latter attempts to identify or confirm the speaker who uttered the speech rather than the lexical content contained in it. The short-term idea is that it should be able to record the musical sound played by the user with a certain musical instrument, then extract the note and duration information from it, and finally generate the corresponding MID file according to the MIDI standard, which can set the type of musical instrument in advance to complete the function of musical sound transformation, such as playing with a harmonica, and playing the MID at the end is the piano sound. With the rapid development of the mobile Internet, fields such as machine learning, electronic communication, and navigation have placed high demands on real-time and standard text recognition technology. This paper merges the sound of visual music into text-based data set training, uses the exported scanner features for model training, uses the model to extract features, then uses the features for prior training, and then uses pretraining. DNN results show that the combined training of target prevention and expansion plans, by replacing long-term and short-term memory networks, end-to-end speech recognition programs, and behavioral tests organized by mobile devices, can provide a larger receptive field combined with expanded convolution instead of long and short periods. The experimental results show that when the input sampling point is 2400, it can be seen that the convergence speed of the model becomes slower with more than 90 iterations and the loss of the model on the verification set increases with the increase in the number of iterations. This shows that the model in this paper can fully meet the needs of speech recognition in piano music scenes. [ABSTRACT FROM AUTHOR]

The IT system of manufacturing enterprises usually has many problems, such as complex industrial software, different development languages, diverse communication protocols, and complex operation environment. Cloud service bus (CSB) technology based on service model encapsulates various applications existing in enterprises by means of the integration of cloud service bus and micro services, which can realize the rapid cloud migration and deployment of heterogeneous industrial application software. Theoretically, after the production system is connected to CSB, it can be arranged arbitrarily by service choreography technology to produce any possible products. However, in the context of industrial Internet, the production system connected to CSB corresponds to the equipment, materials, personnel, and other resources on the production line one by one. These production nodes need to consider the production capacity and cost of production service nodes and the output value of the whole production network, and cannot be combined arbitrarily. Therefore, when integrating production service nodes, we should not only consider the technical integration, but also consider whether the production conditions support this integration. To solve the problem of production node integration in CSB, a dynamic collaboration model of production network based on cloud service bus is proposed in this paper. The model takes the capacity, cost, and production relationship of production nodes as constraints, and the overall efficiency of production network as the optimization goal. The model can calculate the new creation, modification, deletion, and other scheduling operations of production line services in real time and give the production plan with the highest resource utilization and the greatest value in the current production network. The model can improve the rationality and economy of service choreography and give full play to the value of production network. Taking an enterprise with 11 production nodes and 5 production lines as an example, this paper discusses in detail how to use this model to calculate the optimal production organization scheme and the maximum output value of the enterprise. [ABSTRACT FROM AUTHOR]

Technology is the means by which all arts, including woodblock prints, are realized. The "kinship" with modern science and technology makes the development history of woodcut art that can also be understood as a technology history. The purpose of the texture expression produced in the creation of contemporary woodcut is to explore the rich texture expression forms made by contemporary representative painters using special material materials and tools in artistic creation, form a painting technique of personalized words, add new aesthetic meaning to art, and lay a foundation for the formation of unique style of Contemporary Art and the creation and development of woodcut texture. With the development of the times and the change of the public's aesthetic taste, the traditional pattern of printmaking needs to be properly transformed if it is to adapt to the modern humanistic environment, which also involves the importance of screen layout and pattern analysis of Chinese traditional woodcut. Based on the analysis of texture and color texture features in a few-sample environment, this paper proposes an automatic classification method for vignetting texture pictures by extracting the corresponding vignetting coefficients, and through experiments to verify that the proposed SILCO has good generalization sex. In the algorithm designed in this paper, the experiment shows that the accuracy P has a 64.7% improvement effect, and the recall r has a 67.8% performance improvement. On the whole, the experimental data show that the comprehensive classification accuracy is more than 57.4%. [ABSTRACT FROM AUTHOR]

This paper aims to further solve the risks encountered by college students in the process of employment and entrepreneurship in rural areas and improve the success rate of employment and entrepreneurship. It presents a method of social environment analysis based on college students' rural employment and entrepreneurship. This method deeply understands the employment and entrepreneurship environment of college students, deeply analyzes and identifies the constituent elements of college students' rural employment and entrepreneurship environment, and takes the social environment of college students' rural employment and entrepreneurship as the research object. With the help of questionnaire survey and data analysis, the survey shows that, in the channel selection of job hunting in the future, up to 77.20% choose to apply for a job through job fairs, and 69.60% will use advertising media such as networks and newspapers; 26.50% will apply for jobs through the talent market; 31.30% will apply for a job through relatives and friends; 15.40% will apply for jobs through independent entrepreneurship. This paper constructs the evaluation model of entrepreneurship and employment environment from the perspective of subjectivity and puts forward the optimization strategy of entrepreneurship and employment environment from four fields: policy environment, capital environment, education environment, and cultural environment. [ABSTRACT FROM AUTHOR]

Face mask-wearing detection is of great significance for safety protection during the epidemic. Aiming at the problem of low detection accuracy due to the problems of occlusion, complex illumination, and density in mask-wearing detection, this paper proposes a neural network model based on the loss function and attention mechanism for mask-wearing detection in complex environments. Based on YOLOv5s, we first introduce an attention mechanism in the feature fusion process to improve feature utilization, study the effect of different attention mechanisms (CBAM, SE, and CA) on improving deep network models, and then explore the influence of different bounding box loss functions (GIoU, CIoU, and DIoU) on mask-wearing recognition. CIoU is used as the frame regression loss function to improve the positioning accuracy. By collecting 7,958 mask-wearing images and a large number of images of people without masks as a dataset and using YOLOv5s as the benchmark model, the mAP of the model proposed in the paper reached 90.96% on the validation set, which is significantly better than the traditional deep learning method. Mask-wearing detection is carried out in a real environment, and the experimental results of the proposed method can meet the daily detection requirements. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of its benefits of ease of implementation, high accuracy, and quick convergence, this algorithm has gained the attention of academics, and it has demonstrated its supremacy in addressing real issues. This paper aims to study the optimization of PSO in the field of computational intelligence, improve the matching degree of learning resource recommendation and learning path optimization, and improve the learning efficiency of online learners. This paper suggests intelligent education as the center, takes the PSO algorithm as the main research object, and expounds the related concepts of intelligent education and PSO algorithm. It uses swarm intelligence algorithms for intelligent education personalized services. He focuses on PSO algorithm and its work in intelligent education recommendation and learning path planning. Experiments show that the average value of the difference between the two obtained by the NBPSO algorithm is 1.13E + 02 and the variance 1.88E + 02 is the smallest. Therefore, PSO aids in improving the quality and consistency of online course resource development. In conclusion, the research results of this paper further demonstrate the advantages of PSO algorithm in solving the problem of personalized service in intelligent education. It can promote the in-depth application of swarm intelligence optimization algorithms in intelligent online learning systems. This effectively enhances the intelligent service level of the online learning system and increases the efficiency of online learning. [ABSTRACT FROM AUTHOR]

In order to enhance the competitiveness of enterprises, how to evaluate and enhance the competitiveness of B2B e-commerce enterprises and promote the orderly and healthy development of B2B e-commerce industry are discussed. This paper puts forward the research on the innovation of platform economic business model driven by BP neural network and artificial intelligence technology. BP neural network is used to study and evaluate the competitiveness of B2B e-commerce companies. According to the B2B e-commerce company competitiveness theory and BP neural network algorithm, combined with BP neural network and B2B e-commerce company competitiveness evaluation index system, a BP neural network model is designed to analyze the competitiveness of B2B e-commerce enterprises. Determine the expected value of network samples, select G1 method to determine the subjective weight, and select entropy weight method to determine the objective weight. With the help of the function in the MATLAB neural network toolbox, the neural network is trained. The results show that when the training times reach 3297 times, the sample mean square error is 9.9869e − 06, and the training network reaches convergence. The samples of three enterprises test the trained neural network and input the data of three test samples into the trained BP neural network, and the output results are 0.1531, 0.1371, and 0.1557, respectively. The network model constructed in this paper is effectively close to the training samples. The established BP neural network has good performance and can be used to evaluate the competitiveness of B2B e-commerce companies. Accelerate technological change and realize innovation. Technological capability is the inexhaustible driving force for the development of enterprises. Only with the innovation of keeping pace with the times can application-oriented e-commerce enterprises meet the needs of customers and the market, form the difference between goods or services, and then enable enterprises to win more customers and market share. [ABSTRACT FROM AUTHOR]

Based on ML algorithm, this paper puts forward a method that can search instructional resources through keyword indexing technology, and then cluster and recombine the related results and present them centrally. In this paper, the semantic processing of user query based on the subject index of educational resources is adopted to make up for the deficiency of query semantics, solve the problem of mismatch between query words and document words, and improve the recall and precision of resource retrieval. It is proposed to select the category feature items manually and establish the category feature model. In the environment of small sample set, the weight of category feature items is trained by ML method. The research shows that the user rating of this system is ideal, reaching 93.21% at the highest. In addition, the stability of the system can still reach 89.31% under the condition of relatively large usage, and its performance is excellent. This system can effectively solve the problem of scattered distribution of English instructional resources and make the presentation of knowledge more in line with the needs of users, thereby further improving the utilization rate of English instructional resources and users' satisfaction. [ABSTRACT FROM AUTHOR]

In order to improve the effect of e-sports training, this paper combines the intelligent gesture recognition technology to construct an e-sports training system and judges the training effect of players through the recognition of players' gestures. Moreover, this paper studies the commonly used feature extraction algorithms and proposes an improved SLC-Harris feature extraction algorithm, and the feasibility of this algorithm is verified by the experimental results on the EUROC data set. In addition, this paper uses the KLT optical flow algorithm to track the extracted feature points and calculates the pure visual pose through epipolar geometry, triangulation, and PnP algorithms. The experimental research results show that the electronic economic training system based on intelligent gesture recognition proposed in this paper has certain effects. [ABSTRACT FROM AUTHOR]

The arrival of the 5G era, along with the gradual acceleration of information technology development, makes all kinds of new media gradually integrated into all corners of people's life, work, and study. Communication systems are breaking the original boundaries and moving toward communion. "Convergence Media" is a general term of new media which is built on modern network technology and integrates various media forms. In this context, animation design not only is limited to two-dimensional or three-dimensional creation, but also formed a new form of animation expression, namely, MG animation. For MG animation, with its simple artistic modeling elements and flexible rhythm, people have gained new cognition. When the Internet information is transmitted, MG animation can output a large number of dynamic images and text to people in a very short time, which is similar to the current mode of new media communication. It can make information more attractive and appealing with the visual performance of MG animation. In order to improve the quality and efficiency of animation, convergence media technology is applied to the drawing and production of animation. The emergence and continuous improvement of software such as AE, PR, and PS and other software have a strong compatibility and can fully meet our various requirements for MG animation production, thus bringing the audience a different kind of animation experience. AE, a common software for video production, can create a variety of striking visual effects with high efficiency and precision, with powerful effects control and millions of plug-ins to achieve a wide range of animation effects. High-quality video rendering supports resolutions from 4 × 4 to 30,000 × 30,000 pixels including high-definition television (HDTV). Therefore, in terms of animation production, we can choose to mainly use AE for production and rendering. In terms of drawing tools, PS is the software of choice, with no delay in drawing, dithering correction, selection, and other features which are quite outstanding, and has a great advantage in drawing lines and coloring. Based on this, this paper mainly explores the design and production cases of MG 2D animation from the new media perspective using the intelligent design technology. The animation designed by the method in this paper can also be used for medical treatment management or lesion display in the medical field. [ABSTRACT FROM AUTHOR]

This paper constructs knowledge graphs of speech emotion feature to support the expression of rich semantic information due to their unique graphical structure and to provide new ideas for studying speech emotion recognition recommendations. Impulse-coupled neural networks, as a mathematical abstraction of the visual properties of the human eye, have been widely used in various fields of speech processing. In this paper, we take the classical impulse-coupled neural network model as the research object, aiming to explore, analyze, and study the method of constructing knowledge maps of speech emotion features based on the impulse-coupled neural network model, and propose several improved impulse-coupled neural network models, which are used in the fields of target detection, speech segmentation, and quantization compression. This paper further constructs various domain semantic concept maps and performs multidimensional semantic enhancement understanding of search text based on the concept maps and the constructed entity-relationship knowledge maps and proposes a knowledge-based interpretable recommendation method for cloud services and a generalized recommendation and sample enhancement method for cloud scenarios. The fusion algorithm based on multiscale analysis can decompose the source speech into subspeech at different scales and then fuse each subspeech separately, while typical objects in the real world are also composed of many components at different scales. Based on the factors of different degrees in the speech context, the imagery work ruler evaluation model is refined with the basic principles of risk perception and risk resolution in the context, and an exploratory design is carried out separately for the verbal description, graphics, text, color, animation, and sound signals in the audiovisual signal using the workshop format to obtain a large number of design samples and evaluate the important units in turn, finally integrating the context involved in the study. The perceptual design system is given as a specific contextual design method. [ABSTRACT FROM AUTHOR]

With the continuous development of physical education reform, the defects and deficiencies of physical education teaching in colleges and universities are increasingly exposed. The reform of the original physical education teaching thought, education system, teaching mode, and method has achieved little. At present, the research on the prediction of physical education teaching achievements is mainly aimed at the prediction of athletes and physical education teaching achievements or the prediction of the past data of college students. This paper studies the physical education under the decision tree under the background of big data and constructs the physical education management system. When the number of tests reaches 40, the qualified rate of long-distance running is 65.2%, that of basketball is 68.1%, and that of volleyball is 68.2%. The quality of physical education teaching determines the lifeline of the development of school physical education teaching. In the process of collecting and selecting teaching materials, this paper enriches teaching materials and teaching reflection, cultivates one's own understanding, and improves the artistic appeal and creativity of teaching. [ABSTRACT FROM AUTHOR]

Political and ideological online teaching is a new thing integrating political and ideological theory teaching and artificial intelligence information technology. In order to effectively improve the quality of political and ideological online teaching, this paper aims to make an in-depth study on the application of artificial intelligence technology in the political and ideological online classroom system under the background of big data. This paper first expounds the overall architecture of the political and ideological online system from the system hardware architecture and software architecture and then discusses in detail the functional design of the online system and the structure of the database. This paper proposes a data mining algorithm based on scale affine transformation and curriculum data information space reconstruction, constructs the data structure model of the political and ideological online classroom system, uses scale affine transformation to fuse the curriculum data, carries out spatial reconstruction of the fused curriculum data, extracts the high-order features of the curriculum data in the political and ideological online classroom system in the reconstructed space, and completes the data mining of political and ideological courses. Experiments show that the political and ideological online classroom system under the proposed method is better than the political and ideological online classroom system under the traditional method in terms of user satisfaction and teacher operation proficiency and can effectively improve the quality of political and ideological online teaching. [ABSTRACT FROM AUTHOR]

This paper analyzes and investigates the quality assessment of spoken English pronunciation using a cognitive heuristic computing approach and designs a corresponding spoken pronunciation quality assessment system for practical training. Using the general Goodness of Pronunciation assessment algorithm as a benchmark, the shortcomings of the traditional Goodness of Pronunciation method are explored through statistical experiments, and the validity of the overall posterior probability output from the speech model for pronunciation quality assessment is verified. For the analysis of rhythm, there is no common algorithm framework, but in this paper, the F0 similarity algorithm based on dynamic time regularization and the stop similarity algorithm based on forced alignment is proposed for the two main factors of rhythm, intonation, and pause, respectively. After framing, the Hamming window processing is used to make the signal smoother, reduce the side lobe size after fast Fourier transform processing, and solve the problem of spectrum leakage. Compared with the ordinary rectangular window function, the Hamming window can obtain a higher quality spectrum. And combined with CTC for speech recognition modeling, the recognition rates are comparable in the case of using BLSTM and bidirectional threshold cyclic unit BGRU as the hidden layer unit, respectively, and the training time is 23% less than BLSTM using BGRU; in addition, the BGRU-CTC model is improved by using a 2-BGRU-CTC model with 256 hidden layer nodes, so that the error rate of phoneme recognition is reduced to 33%. The effectiveness of the algorithm framework is also verified through experiments, which further proves the effectiveness of our proposed phoneme segment feature and rhyme similarity algorithm. [ABSTRACT FROM AUTHOR]

At the same time that my country has shifted from high-speed development to high-quality development, my country has also put forward new requirements for education development. Due to the limited study time during college, each student's study habits and learning process are also different, and the degree of connection between tennis lessons is high, so there will be polarization when learning tennis. With the development of science and technology, more and more technological innovations are integrated into the classroom, and traditional teaching methods can no longer keep up with the pace of the times. Tennis teaching is a subject of equal proportion between theory and practice. The traditional teaching method simplifies the theory, which makes students to have some bad phenomena when they practice. Aiming at this series of problems, this paper uses algorithms such as softmax function and threshold function to construct an application model of virtual image technology based on the artificial neural network in tennis teaching. The research results of the article show that: (1) the average accuracy rate of the method in this paper is 97.22%, and the highest accuracy rate is 99.17%. The average accuracy rate also tends to increase with the increase of sample size; the recall rate is the highest, and the highest recall rate is 99.36%. The average recall rate is 96.77%; the highest correct rate is close to 100% and is significantly higher than the other three methods; the average correct rate reaches 98.8%; the response time is the shortest; the average response time is 33 ms; and the response time increases with the increase of the sample size. (2) After using this model, tennis skills have been improved, with an average of 12 in situ flips, an average of 7 in situ rackets, an average of 5 in situ forehand draws, and an average of 3 in situ backhand draws. (3) The average forehand and backhand scores of the class after the experiment were 90 and 86; the average forehand and backhand stability were 8 and 7; and the average forehand and backhand accuracy were 31 and 29, respectively. The average depth of forehand and backhand is 36 and 32. (4) Most of the students are satisfied with this model, and they all choose to strongly agree and relatively agree, and the percentage of very agree that helps stimulate learning has reached 60.52%, and no students choose to disagree very much. [ABSTRACT FROM AUTHOR]

To improve the analysis effect of e-sports competitions, this paper studies the intelligent analysis methods of e-sports, compares the advantages and disadvantages of various multi-classification methods, and innovatively designs a two-layer SVM classifier structure for four types of motor imagery EEG signals. Moreover, this paper uses the competition data set to test the classification accuracy of the designed double-layer SVM classifier structure and compares it with the DAG-SVM multi-classification method. The research results show that the e-sports competition analysis system based on the intelligent analysis system proposed in this paper has a good e-sports competition analysis effect, and has a good effect in e-sports competition prediction. [ABSTRACT FROM AUTHOR]

Each Chinese region has its own ancient opera, which is a treasure of folk culture and a living fossil for studying the historical origins of a local culture. It has significant academic value and historical significance at the national and local levels, whether from the perspective of promoting and disseminating national culture or from the perspective of protecting the world's intangible cultural heritage. Based on this practical significance, this paper conducts a study using the particle swarm algorithm to manage the marketing archives of drama intangible cultural heritage. The article employs the PSO algorithm to test the particle swarm optimization algorithm's convergence and conditions. The effectiveness of the algorithm is analysed in the Sphere function, Rosenbrock function, Griewanks function, and Rastrigin noncont function, and then the algorithm is compared, including the calculation speed comparison between the algorithm in this paper and the three optimal fitness functions. The experimental results show that the PSO algorithm has the highest four items in the statistics of the Schwefel function experimental results. About 45.0379 is the best value and 70.5878 is the maximum precision. The optimal average value is 6.1524, while the average value is 56.15245. In comparison to the QPSO and PSO algorithms, the algorithm in this paper has a faster convergence speed and better search accuracy. The topic of the intersection of the disciplines of drama, intangible cultural heritage marketing, and archive management using the particle swarm algorithm is well-developed. [ABSTRACT FROM AUTHOR]

In the context dominated by Internet communication, people's various emotions can be clearly reflected through network public opinion, whether it is the view of political affairs, the preference for entertainment, or the demand for life. This also allows management or providers to meet their needs more specifically. Based on today's need to understand the trend of Internet public opinion, this paper describes a deep neural network (DNN). A deep neural network is a machine learning model that is the foundation of deep learning and has a strong ability to mine potential information in data. By improving the loss function of the neural network, this paper reduces the influence of unbalanced data on the classification results and improves the classification effect of the model on a small number of categories. Aiming at the different lengths of Internet text, a more robust model of text sentiment classification is proposed, which makes the HCB-Att model better extract the local information and contextual information of the text. Finally, through comparative experiments, the optimization model used in this paper is proved to be effective for the analysis of network public opinion sentiment. [ABSTRACT FROM AUTHOR]

With the development of computer software and hardware technology, film and television production has made a breakthrough. More and more film and television works have changed from the traditional plane two-dimensional space projection to three-dimensional space projection. Three-dimensional works give the audience an immersive feeling. Taking human posture, one of the most important components in film and television works, as an example, this paper discusses the method of reconstructing three-dimensional human posture from two-dimensional images in film and television production. Starting from the research significance of two-dimensional image reconstruction of three-dimensional human posture in film and television production, the existing three-dimensional human posture in film and television production is analyzed. The audience can watch the film and television works in an all-round way to keep the film and television works fresh to the greatest extent. This paper provides a certain reference value for 3D stereoscopic projection. [ABSTRACT FROM AUTHOR]

With the continuous development of network communication technology, the domestic network has covered the homes of ordinary people, but the indoor coverage system of the network cable network needs to be further improved. The optical fiber distribution system is a signal coverage system integrating GSM, TD-SCDMA, and other standards. It is mainly composed of system access unit, system expansion unit, and system remote unit. Wireless network is an important communication technology, which is easy to use and can be connected wirelessly over long distances. The purpose of this paper is to study an optical fiber distribution system for indoor coverage design of wireless networks, so as to achieve faster and more efficient coverage. In this paper, a wireless network signal processing algorithm is proposed, and a systematic method for designing the wireless network indoor coverage of the optical fiber distribution system is proposed, and the algorithms are compared and tested. The test results in this paper show that the signal strength obtained by the algorithm in this paper is −54.83 dB, while the traditional artificial design algorithm is only −45.97 dB. In addition, the wireless network base point of the algorithm in this paper is also 3 lower than the traditional one. Moreover, the coverage rate achieved by the algorithm in this paper is 3.17% higher than that of the network cable network base point layout given by the genetic algorithm, and the average received signal strength is 3.34 dB higher. In addition, the algorithm in this paper takes 9.1 s, 11.2 s, and 5.3 s for the three scenarios of underground parking lot, large shopping mall, and residential residential area, while the traditional manual design algorithm takes 87.9 s, 108.8 s, and 51.7 s, respectively. The algorithm in this paper is only about 1/10 of the traditional artificial design algorithm. Therefore, it can be concluded that the coverage effect of the wireless network indoor coverage system of the optical fiber distribution system designed in this paper is significantly improved. [ABSTRACT FROM AUTHOR]

At present, complex discrete dynamic systems are widely used in the field of medicine. The control system in the complex discrete dynamic model is gradually transformed into intelligent control. It has become the main research direction of researchers to improve the medical platform system by adding different modeling strategies. The traditional discrete modeling technology can only be used as the knowledge content of students' textbooks because it can no longer meet the needs of the development of human society. In order to improve the application of a discrete system in the public platform, this paper studies the improvement of the public health service platform system based on the complex discrete dynamic system. Firstly, a time-driven control strategy is proposed to study the output feedback control with random sampling in the platform. Then, the stability of random parameters and the addition of dynamic scheduling strategies are further studied. Compared with the traditional system, the optimized system greatly strengthens the data transmission problem of input and output channels. The results show that by improving the performance of the public health service platform system, the probability of problems in the process of data transmission is greatly reduced. After adding controllable and observable performance to the system, the stability of the whole system is further improved. The improved public health service platform system studied in this paper can store and transmit a large number of user data in the network environment, automatically maintaining the stability of the system and has a good social application value. [ABSTRACT FROM AUTHOR]

Blockchain (BC) maintains a continuously growing database in a "decentralized" way, and its impact on the financial auditing industry is becoming increasingly significant. This paper aims to study the research on financial automation auditing methods supported by blockchain technology and proposes the related concepts of blockchain technology, hash function, financial auditing analysis, and the impact of BP Neural Network (BPNN) and its algorithms on financial automation auditing methods. Simultaneously, this paper likewise disperses the poll overview to definite individuals, for example, endeavor, monetary work force, focus and ranking directors, university researchers, and specialists, who have pragmatic support in the execution and use of monetary review. The experimental results of this paper show that speculation based on the interconnected environment is the most basic natural factor for understanding this idea, and its score is also the largest at 4.36 points. [ABSTRACT FROM AUTHOR]

We conduct an in-depth study on the model construction of deep neural networks and design a model of painter's psychology and anime creation style to realize the study of the relationship between painter's psychology and anime creation style based on deep neural networks. This paper proposes an animation creation psychology classification algorithm that integrates human cognitive deep network structure optimization. The algorithm analyzes the connection between different convolutional layer features and animation characteristics through animation creation style CNN feature visualization. It interactively uses the knowledge of animation creation psychology expression techniques to optimize the network structure. This paper proposes a scene animation network based on spectral difference perception style. By analyzing the characteristics and differences in the spectrum between realistic and anime domain images, the generator is guided to learn the mapping relationships better to fit the style distribution of anime domain images. This paper uses a fully convolutional structure; the network is more lightweight and supports image inputs of arbitrary size, which can keep the semantic system of the background unchanged while highly deforming the five facial features, moving toward the goal of human-scene fusion for the animation task. [ABSTRACT FROM AUTHOR]

This paper proposes corresponding teaching methods and instructional modes based on predecessors' research on mathematics instructional mode and the current state of mathematics teaching. In addition, this paper constructs a teaching evaluation model based on DL algorithm based on an in-depth study of DL-related theories in order to accurately and scientifically analyze the problems that exist in mathematics teaching. This paper constructs an instructional quality evaluation index system based on rationality and fairness, and uses the BPNN evaluation model to train and study a set of instructional quality data. Finally, the experimental results show that this system has a high level of stability, with a 96.37 percent stability rate and a 95.42 percent evaluation accuracy rate. The results of this paper's evaluation of the mathematical instructional quality model are objective and reasonable. It can accurately assess instructional quality while also assessing problems in the teaching process based on the instructional quality scores and making reasonable recommendations for teaching improvement based on the weak links in the teaching process. It has the potential to provide a workable system for assessing instructional quality. [ABSTRACT FROM AUTHOR]

Multisource information mining systems and related business intelligence technology are currently a hot topic of research. However, the current commercial applications and applications are not ideal in terms of application. Because there is still much work to be done before decision support, it is best to transition to them only financially. This paper examines the multisource part of the information used in mining and introduces research hotspots in the fields of accounting informatization, the development status of intelligent financial analysis software, the research and application status of data warehouse, data mining, and decision support systems. This paper examines the specific composition and content of a financial information system using information mining to lay a solid foundation. Financial intelligent analysis, financial intelligent monitoring, financial intelligent decision-making, and financial intelligent early warning are the four parts of the financial intelligent system. It then examined the structure and processing of the financial intelligence system and proposed a financial intelligence system operation strategy. Financial intelligence low-risk integrated implementation strategies and ideal financial intelligence models, according to the current state of research and practical applications. According to the findings, the overall discrimination accuracy of the financial information system based on mining multisource information features is up to 95%, which is 42% higher than the traditional model. The development and use of financial information benefit from the realization and exploration of the financial intelligence system model. [ABSTRACT FROM AUTHOR]

The establishment of enterprise target inventory is directly related to the forecast of drug sales volume. Accurate sales forecasting can help businesses not only set accurate target inventory but also avoid inventory backlogs and shortages. In this paper, NN technology is used to forecast sales and is optimized using the PSO algorithm, resulting in the creation of a drug sale forecast model. The model optimizes the weights and thresholds of NN using the improved PSO optimization algorithm and predicts the periodic components based on time correlation characteristics, effectively describing the trend growth and seasonal fluctuations of sales forecast data. Furthermore, the model in this paper has been creatively improved according to the needs of practical application, which has improved the shortcomings of traditional NN, such as long training time, slow convergence speed, and ease to fall into local minima, to improve performance and quality, and has received positive results in application. The experimental results show that this model has a prediction accuracy of 96.14 percent, which is 12.78 percent higher than the traditional BP model. The optimized model can be used to forecast drug sales in a practical and feasible way. [ABSTRACT FROM AUTHOR]

The modernization of education necessitates the enhancement of instructional quality. This paper begins with a basic overview of the current state of film and television art education and then goes on to examine the current state of film and television art education and develop a virtual simulation-based teaching mode for film and television art. This paper develops an instructional quality evaluation model to assess the feasibility of this teaching mode. In this model, GA is used to optimize BPNN's initial weights and thresholds, reducing the time it takes BPNN to find the weights and thresholds that satisfy the training termination conditions. Improve BPNN's prediction accuracy and convergence speed in relation to instructional quality evaluation results. The evaluation accuracy of this algorithm can reach 94.31 percent, which is 6.35 percent higher than other methods. Furthermore, the use of virtual simulation technology in the teaching of film and television art in higher education institutions can have a significant impact and have far-reaching research implications. The findings of this study can be used to improve the teaching of film and television art in institutions of higher learning. [ABSTRACT FROM AUTHOR]

Monitoring and guiding instructional management require student performance evaluation. Traditional evaluation and analysis methods based on absolute scores, on the other hand, have certain flaws and are unable to fully reflect the information contained in student performance, thus limiting the impact of student performance evaluation on teaching and learning management. Data mining is regarded as the backbone technology for future information processing, and it introduces a new concept to the way humans use data. Schools must analyse and evaluate the performance of students in the same grade level and secondary school in a timely and staged manner. Clustering is a type of data mining that uses similarity rules to classify sample data into groups with a high degree of similarity. To address the difficulties caused by the wide variation in course difficulty in student performance evaluation, a method based on the K-means clustering algorithm is proposed. The K-means algorithm and the improved K-means algorithm with student information are investigated. The test results showed that the K-means clustering algorithm, the improved algorithm in this paper, and the fast global mean clustering algorithm all cluster the same randomly generated data set with noisy points, but the clustering time of the algorithm in this paper is only 0.04, which has obvious advantages. As a result, the clustering algorithm-based higher education management and student performance evaluation mechanism provides some insights for future research on student learning patterns. It is hoped that instructional administrators will gain a better understanding of students' learning characteristics so that they can better guide their teaching. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of emerging Internet of Things technology and short-range wireless communication technology, smart healthcare monitoring network technology has become a research hotspot. It provides convenience for people and enhances the development of people's own healthcare awareness. This paper aims to study how to make its application in the field of smart healthcare education more applicable through the use of related technologies in the Internet of Things era and few-shot learning. For this reason, this paper proposes to optimize and improve the new sensor technology and the algorithm of few-shot learning, and to adjust some parameters as a whole. At the same time, related experiments and analysis are designed for the improved algorithm to study and understand its performance. The experimental results in this paper show that the improved algorithm improves its application effect by 36.9% and is relatively more applicable than the unimproved algorithm. [ABSTRACT FROM AUTHOR]

This paper comprehensively reviews the literature related to disaster relief supply management in recent years by taking the perspectives of three critical decision-making issues, i.e., coordination issues, facility location decisions, and inventory decisions. For each decision-making issue discussed, we clarify the barriers of current research papers and identify the major challenges and critical factors that should be considered. In the following, we present the perspectives on the road of coordination between multiple relief actors, characterize the location decisions of relief facilities with a variety of optimization objectives, and emphasize the importance of relief supply varieties and critical factors in the decisions of disaster relief inventories. Future research directions are recommended for further discussions. [ABSTRACT FROM AUTHOR]

In this paper, through data analysis of multimodal knowledge graph optimized neural network and visual analysis of college students' sports performance, we use huge graph, a graph database supporting distributed storage, to store domain knowledge in the form of the knowledge graph, use Spring Boot to build a server-side framework, use Vue framework combined with vis.js to visualize relational network graphs, and design and implement a knowledge-oriented. This paper proposes a visual analytics system based on the theory of visual analytics. Based on the idea of visual analytics, this paper presents a visual analytics framework combining predictive models. This framework combines the automated analysis capability of predictive models with interactive visualization as a new idea to explore the visual analysis of student behavior and performance changes. Using relevant predictive algorithms in machine learning, corresponding models are built to refine the importance of features for visual analysis and correlate behavioral data with achievement data. In this process, multiple prediction algorithms are used to build prediction models. The model effects are analyzed and compared to select the optimal model for use in the visual analytics framework. The graphical analytic view is integrated. EduRedar, an optical analytical system for sports data based on the performance prediction model, is designed and implemented to support multidimensional and multiangle data analysis and visualize the changes in college students' sports and performance based on accurate campus exercise data. [ABSTRACT FROM AUTHOR]

The incidence rate of peptic ulcer is increasing gradually. Medical images can meet the needs of patients as an auxiliary diagnosis and treatment method for peptic ulcer. However, in the long-term treatment, the actual effect is average, and the diagnosis effect of gastrointestinal diseases will gradually deteriorate. In this paper, we use an image enhancement algorithm to study the mechanism of peptic ulcer from the perspective of a medical image. In this paper, 56 images of children with peptic ulcer were selected, and the gastroscopy based on the image enhancement algorithm provided technical support for the rapid diagnosis of patients with peptic ulcer. Experimental results show that the clinical features of peptic ulcer have different characteristics according to the age difference of patients, which can play a positive role in promoting the treatment of patients of different ages. [ABSTRACT FROM AUTHOR]

The rapid development of online shopping has contributed to marketing strategy. Social presence plays an important role in the field of marketing. Therefore, this paper studies the influence of social presence on online impulse buying. The key marketing strategy is that consumers make impulsive buying behavior in online shopping. This paper proposes a research scheme for an impulsive buying sharing model based on user features and an article studied before which has some factors presented. The model is evaluated on a data analysis based on SPSS24.0 and AMOS23.0. The results show that the main factors such as interactivity, vividness, and media richness, all have positive effects on social presence. Therefore, in the variable relationship, social presence has a direct impact on impulsive buying behavior. This result has a theory contribution on the marketing theory model which also has an important practice significance in marketing strategy for enterprises. [ABSTRACT FROM AUTHOR]

The forecast of capital demand has the characteristics of uncertainty. There are known and unknown information about the capital demand for regional economic development. In fact, there are also some in between, that is, uncertain. Consumption is the ultimate goal of production and a key link in realizing a virtuous circle of economic development. This paper uses the GM (1, 1) model to compare the predicted value of the test area with the actual value in 5 years, and the loudness is as high as 90%. Under the guidance of the profit model, the regional economic capital demand has a decisive influence on the regional economic development. The predictive analysis model of capital needs is conducive to fully mobilizing the impact of infrastructure construction of all parties and is an important factor affecting economic development. The mathematical model proposed in this paper is helpful for deepening the research on the management of regional economic development and enriching the theoretical system of regional economic development. [ABSTRACT FROM AUTHOR]

Training simulators have been gradually evolving in the direction of software, virtualization, networking, and multiplatform in recent years, with the continuous development of hardware and software technologies, particularly the maturity of VR (Virtual Reality) related technologies. The network intrusion program allows remote hackers to take control of the system, posing a serious threat to the network and computer security. As a result, this paper proposes a VR-based ID (intrusion detection) simulation training system. This paper proposes an ID model based on CNN (Convolutional Neural Networks) and LSTM to address these issues (Long Short-term Memory Networks). This model oversamples data from unbalanced data sets, reducing the difference in category data and thus improving the ID model's performance and existing detection methods to compensate for the flaw. 3DSMAX technology is used to simulate the process visualization scene, as well as some key equipment models and signal transmission simulations, during the system design and implementation process. The experimental results show that CNN LSTM outperforms BP, and the overall evaluation index F1 has significantly improved, particularly the F1 index of D4. CNN LSTM outperforms GA (genetic algorithm) by 12.75 percent and BP by 14.07 percent. The system essentially accomplishes the anticipated simulation training goal, and the simulation training effect is impressive. [ABSTRACT FROM AUTHOR]

The main contribution of this research paper is to summarize the results of Meta-analysis of moxibustion in the treatment of infantile diarrhea which is one the common disease and requires considerable attention from the research community and funding organizations. In order to verify that the proposed scheme has merits, a comprehensive searching methodology was adopted by considering various databases such as China Biomedical Literature Database (CBM), China National Knowledge Network Infrastructure (CNKI), Wanfang Database, Pub Med Database, Google Academic, and Cochrane Library. It is important to note that a powerful computer has been utilized to carry out this searching. Finally, only those literature contents are selected which meet the inclusion criteria. Likewise, exclusion criteria was used to exclude irrelevant contents of the literature. RevMan 5.3 was used to analyze the collected data and after reading the titles and abstracts, 29 well-designed studies were selected. Through searching the full text, reading literature, and quality evaluation, 17 papers were finally included. Response rates were reported in all 17 studies, and subgroup analysis was performed based on whether or not other therapies were combined. 7 studies compared the effectiveness of simple moxibustion and conventional therapy in the treatment of infantile diarrhea, and the results showed statistically significant differences [OR = 4.01, 95% CI (2.03, 7.84), P < 0.0001 ]; 10 studies compared the effectiveness of moxibustion combined with other therapies and conventional therapies in the treatment of diarrhea in children, and the results showed that the difference had general meaning [OR = 4.45, 95% CI (2.83, 7.10), P < 0.00001 ]. The funnel plot (in Figure) showed that the distribution of included studies was asymmetrical on both sides of the baseline, which could be considered as publication bias. Traditional Chinese medicine moxibustion could effectively relieve the symptoms of infantile diarrhea, and the effect was significant. [ABSTRACT FROM AUTHOR]

Folk dance is a very unique local culture in China, and dances in different regions have different characteristics. With the development of 3D digital technology and human gesture recognition technology, how to apply it in folk dance is a question worth thinking about. So, this paper recognizes and collects dance movements through human body detection and tracking technology in human gesture recognition technology. Then, this paper writes the data into the AAM model for 3D digital modeling and retains the information by integrating the manifold ordering. Finally, this paper designs a folk dance learning method combined with the Few-Shot learning method. This paper also designs a data set test experiment, an algorithm data set comparison experiment, and a target matching algorithm comparison experiment to optimize the learning method designed in this paper. The final results show that the Few-Shot learning method based on gesture recognition 3D digital modeling of folk dances designed in this paper reduces the learning time by 17% compared with the traditional folk dance learning methods. And the Few-Shot learning method designed in this paper improves the dance action score by 14% compared with the traditional learning method. [ABSTRACT FROM AUTHOR]

We look at the current state of ideological and political classroom teaching based on artificial intelligence and the Internet of Things. We also look at the problems with traditional ideological and political classroom teaching in order to improve the effectiveness of classroom reform. Also, this paper reforms teaching based on the real world and builds a modern, intelligent system for teaching political and ideological ideas in the classroom. The ideological and political education system is built on the Internet of Things. It has three layers: the perception layer, the network layer, and the application layer. The system collects efficient ideological and political teaching activity data in real time through the Internet of Things and wireless networks, sends the data to the data center through the Internet, and then uses the collected data as the original data for applications, data mining, and modeling simulation. Lastly, this paper proves through simulation experiments and teaching experiments that the system built in this paper can be used to reform ideological and political education. [ABSTRACT FROM AUTHOR]

With the rapid development of science and technology in recent years, more and more researchers began to explore the basic disciplines of sports, namely, biomechanics and physiology, and further update and improve the traditional sports training theory. However, the developed countries, i.e., Europe and the United States, have dedicatedly worked on the effective role of physical training particularly in middle-class farmers in sports powers. They have realized that the continuous improvement of the physical training evaluation system is directly related to the reforming in the evaluation and monitoring methods of physical training. At the same time, there is a demand for athletes' physical fitness evaluation, which requires a large amount of data support during the evaluation. These data collection and analysis are difficult and consume a lot of manpower. Therefore, this paper uses artificial intelligence technology to design the sports athletes' physical fitness evaluation information management system to improve the comprehensiveness of system data collection and the accuracy of data analysis. Through the physical attenuation calculation and physical analysis of the system developed in this paper, data accuracy is high. The coach can make a training plan according to the physical consumption of athletes in sports training or competition, which can greatly improve the ability of athletes. [ABSTRACT FROM AUTHOR]