Development Of Informatization Research Articles

Financial Risk Assessment of Enterprise Management Accounting Based on Association Rule Algorithm under the Background of Big Data

Under the background of the rapid development of data informatization, the evaluation and analysis of enterprise management accounting risk are the key link of enterprise sustainable development. Based on the theoretical basis of enterprise management accounting risk analysis, this paper selects 11 representative corporate financial risk indicators from financial risk indicators through correlation analysis and then selects 8 key indicators that affect corporate financial risk through association rules, to assess and analyze the company’s financial risks. Finally, this paper takes 20 ST companies in China as the research object to carry out empirical research. According to the assessment and analysis of financial risks in the company’s management accounting, it proposes measures from four modules to strengthen the company’s ability to resist risks.

Optimization of the Music Teaching Management System Based on Emotion Recognition.

Along with the development of social informatization, computer has been widely used in daily teaching and the technical support teaching management system, which can greatly improve the quality of students and teachers' information sharing and teaching, and is an important part of college information construction. Teachers' personal information, teaching information query, teaching file management, students' attendance, students' results query, teachers' evaluation, and so on constitute the music teaching information management system. Based on the Internet of things technology and emotion recognition, this paper mainly describes the design idea of the music teaching management system software, function design, and implementation scheme. The realization process of each subfunction module and hardware and software of the system is described in detail. In view of the characteristics of music teaching management itself, combined with the actual situation of music teaching, to form the system structure analysis, database design and data exchange, and other aspects of exploration, in order to establish a practical music teaching management system.

Application of Spatial Digital Information Fusion Technology in Information Processing of National Traditional Sports

The rapid development of digital informatization has led to an increasing degree of reliance on informatization in various industries. Similarly, the development of national traditional sports is also inseparable from the support of information technology. In order to improve the informatization development of national traditional sports, this paper studies the fusion process of multisource vector image data and proposes an adjustment and merging algorithm based on topological relationship and shape correction for the mismatched points that constitute entities with the same name. The algorithm is based on topological relationship. The shape of the adjustment and merging algorithm is modified, and finally, a set of national traditional sports information processing system is constructed by combining the digital information fusion technology. Experiments have proved that the method of national traditional sports information processing based on digital information fusion technology proposed in this paper is effective and can play a good role in the digital development of national traditional sports.

Self-Adaptive Telemedicine Specialist Recommendation Considering Specialist Activity and Patient Feedback.

Purpose: With the rapid development of medical informatization, information overload and asymmetry have become major obstacles that limit patients’ ability to find appropriate telemedicine specialists. Although doctor recommendation methods have been proposed, they fail to address data sparsity and cold-start issues, and electronic medical records (EMRs), patient preferences, potential interest of service providers and the changes over time are largely under-explored. Therefore, this study develops a self-adaptive telemedicine specialist recommendation method that incorporates specialist activity and patient utility feedback from the perspective of privacy protection to fill the research gaps. Methods: First, text vectorization, view similarity and probabilistic topic model are used to construct the patient and specialist feature models based on patients’ EMRs and specialists’ long- and short-term knowledge backgrounds, respectively. Second, the recommended specialist candidate set and recommendation index are obtained based on the similarity between patient features. Then, the specialist long-term knowledge feature model is used to update the newly registered specialist recommendation index and the recommended specialist candidate set to overcome the data sparsity and cold-start issues, and the specialist short-term knowledge feature model is adopted to extend the recommended specialist candidate set at the semantic level. Finally, we introduce the specialists’ activity and patients’ perceived utility feedback mechanism to construct a closed-loop adjusted and optimized specialist recommendation method. Results: An empirical study was conducted integrating EMRs of telemedicine patients from the National Telemedicine Center of China and specialists’ profiles and ratings from an online healthcare platform. The proposed method successfully recommended relevant and active telemedicine specialists to the target patient, and increased the recommended opportunities for newly registered specialists to some extent. Conclusions: The proposed method emphasizes the adaptability and acceptability of the recommended results while ensuring their accuracy and relevance. Specialists’ activity and patients’ perceived utility jointly contribute to the acceptability of recommended results, and the recommendation strategy achieves the organic fusion of the two. Several comparative experiments demonstrate the effectiveness and operability of the hybrid recommendation strategy under the premise of data sparsity and privacy protection, enabling effective matching of patients’ demand and service providers’ capabilities, and providing beneficial insights for data-driven telemedicine services.

Innovation and Development of University Education Management Informationization in the Big Data Environment

With the arrival of the big data era, the role of big data can be fully utilized, and the comprehensive data evaluation is carried out to perform education information evaluation. In the general circumstance of computerized learning, college teaching regulation has new features and novel orientations. Along with the speedy progression of sci-tech, the pace of dissemination of data is continuously quickening, and it is being increasingly easy for the public to interact and carry on with their lives. Because people use the network to access a variety of data information, the system cannot handle huge amounts of data; this is the cause of the era. In the process of the comprehensive approach of the information age, with the impact of the Age of Big Data, the administration of intelligence in universities has transformed. Big Data InfoTech has infiltrated into all realms of community lifestyle. How to cope with the transformation of informationization of university education management in the era is a serious challenge for relevant personnel. This study begins by discoursing the essential implication of big data, followed by a diagnosis of the questions involved in the establishment of computerization in high schools under the environment of big data, comprising the imminent solutions to the dilemmas in collegiate education maintenance, the issues raised by privatization in high schools, and the strategies for education management. This is followed by the proposal of the innovative educational administration informationization development trend in the big data context. In this research, there are 11 professors in the expert group, accounting for 44%, 8 associate professors, accounting for 32%, and 6 lecturers or below, accounting for 24%. This study makes full use of a large number of procedural data generated in educational management activities to achieve an efficient, comprehensive, accurate, and objective evaluation, which provides strong technical support for educational management informatization evaluation.

Research on Open Oral English Scoring System Based on Neural Network.

This study designs and implements a scoring system for open-spoken English using NN technology. The system scores the oral recording from the phonetic level and the text level, respectively, and can comprehensively evaluate its oral level. The system will separately score the spoken speech and the spoken content through different scoring models and add the scoring results as the final score, in which the spoken content is obtained by text transcription of the recording by an external speech recognition engine. An acoustic sensor is adopted to collect pronunciation signals of spoken English. Modern signal processing and automatic pattern recognition technology are used to distinguish the quality of spoken pronunciation. Similar semantic units are marked between acoustic feature sequences, which make use of the parallel algorithm processing mode of multi-computing cores of modern GPU and allow multiple units to independently execute the comparison algorithm at the same time. Experiments show that the model in this study achieves better comprehensive scoring performance. The scoring model is of great significance to the development of educational informatization and intelligence, and it also provides a reference for the construction of intelligent oral scoring system.

Construction and Optimization of Distributed Electronic Archives System Merging Control Messages

This paper analyzes the whole of the electronic archives management system, describes the basic features and functions of the system, and gives the flow of each functional module of the system. Techniques for implementing important functions in the system are discussed and compared. Using the object-oriented idea, this paper designs the electronic archives management system and gives the hybrid structure model of the system. According to the needs of the electronic office, an official document transmission module is established to realize the electronic transmission of official documents, and the specific implementation process is given. According to the characteristics of archives information management and utilization, SQL Server is used as the background database and the database design is carried out. This paper introduces the full-text retrieval technology that realizes the query and retrieval function of electronic archives and gives how to configure the full-text retrieval service in SQL Server. This paper proposes a K-means-based merging control message clustering algorithm to solve the problem of communication topology changes caused by large-scale, high-mobility merging control messages. Based on the idea of K-means clustering, the calculation method of distance in K-means is improved by combining the distribution of the initial electronic archives message, and the fitness function of the electronic archives is designed by using the information of the electronic archives in the cluster and the average distance to the neighboring electronic archives. According to the maximum fitness value, the cluster head is selected to establish clustering. The file management system is tested and analyzed in a simulated environment. Based on the functional positioning of the system and the business environment, the functional performance and performance of the analysis system are examined and analyzed, and the research and development results are inspected according to the system development goals to check whether the development results of the system have reached the advanced stage. At the functional level, it can automate the management of the core business of the distributed electronic archives management department and support the distributed storage management of massive archive data, which effectively improves the informatization level and development efficiency of archives management.

An Empirical Study on the Construction Path of Sports Public Service Platform under the Background of “Internet +” by Fuzzy Computing

The Internet is the product of the rapid popularization of high technology. With the higher and higher configuration of computers, the functions are more and more complete, and the scope of coverage is more and more wide. With the continuous development and improvement of the Internet, computer network technology has been widely used in sports. Based on the background of “Internet +”, this paper builds the framework of Internet sports public service platform and uses websites, mobile apps, WeChat, and other platforms to build sports public service platform. Communication system is the most critical path of Internet construction. Based on the application path of sports public service communication system, the basic content of sports public service system is analysed. Through the research on the practicability of constructing the path, the results show that the Internet channels and media terminals passively receive the content released by the information subject so that the information can achieve the node transmission with high sharing and provide theoretical reference and practical guidance for the informatization development of sports public service.

Resource Collection Algorithm for Entrepreneurship and Employment Education in Universities Based on Data Mining

Graduate unemployment is one of the serious challenges in China, including the graduates of a large number of public and private higher education institutions. The collection of entrepreneurial employment education resources in colleges and universities is a basic project and a key link to promote the rapid development of education informatization. Data mining has various applications in different fields such as health care, smart agriculture, smart cities, smart businesses, and education, but is playing a vital role in the field of education and businesses. The applications of data mining provide new technical tools and development directions to realize the common construction, sharing, and collection of entrepreneurial employment education resources in colleges and universities. The closed nature of teaching resources within colleges and universities leads to the inability of external search engines to search them, which hinders the search and access of teachers and students and seriously affects the smooth implementation of current innovation and entrepreneurship employment work. Aiming at the real demand of entrepreneurial employment education resource collection in colleges and universities and the characteristics of on-campus resources, this study proposes a data mining-based algorithm for entrepreneurial employment education resource collection in colleges and universities. The algorithm obtains entrepreneurial employment demands from the academic affairs system, collects on-campus online teaching resources through internal crawlers, and provides services for teachers, students, and employees through online teaching resource collection drive subalgorithm and quick recommendation subalgorithm. We also compared the proposed model with the CLR model. The case analysis and performance experiments show that the proposed algorithm has a good resource mining effect, high user satisfaction, and high recommendation efficiency, occupies fewer system resources, and shows high performance as compared to the CLR model.

An Improved Crop Disease Identification Method Based on Lightweight Convolutional Neural Network

Identifying crop disease fast, intelligently and accurately, plays a vital role in agricultural informatization development, while existing methods are almost performed manually, which depends on expert experience, and thus the identifying result is inevitably influenced by personal preferences. To address these issues, an improved crop disease identification method based on convolutional neural network is proposed to process images of crops for identifying diseases. Firstly, the original crop images were cut and normalized, and the irrelevant noises were removed by image data enhancement to improve the generalization ability and recognition accuracy of the training network. Then a neural network with nine convolutional layers is built to work on crop images, the first stage of training loads data samples, and divide the training set and verification set, and then set the learning rate, image intensifier, and optimizer and compile the training convolution model. Finally, it saves the loss and accuracy data during the training process and evaluates the accuracy of the model. In order to improve the training learning rate, Adam optimizer combining momentum algorithm and RMSprop algorithm is used to dynamically adjust the learning rate; the combination of the two algorithms makes the loss function converge to the lowest point faster. Then the feature map after each convolution is obtained by using transferred revolution, and the model is adjusted according to the feature map to further improve the effect of model recognition. Finally, validations were carried out by PlantVillage dataset, which consists of images of about 38 kinds of crops. The experiment result shows that the validation accuracy and the test accuracy are 95.7% and 94.3%, respectively; in addition, the recognition accuracy of apple, corn, grape, and other single classes is about 97%, which proves that the convolutional neural network in this paper has faster training speed and higher accuracy. In addition, the proposed method is less time consuming, which is of great significance to promote the development of smart agriculture and precision agriculture.

Network Education Resource Information Sharing System Based on Data Mining

To enter a new stage of development of education informatization, network education has become a global trend of education training, the development of lifelong education, and resource sharing platform for higher vocational colleges to some extent, with network technology and multimedia technology as the core of the rapid development of modern information technology. The sharing of network education resource sharing system and the application in the whole advancement provide the conditions, it promotes the construction and development of higher vocational education resource system. This study examines the problems that exist in the construction of higher vocational college education resources, such as unreasonable management of education resources, unclear goals of higher vocational colleges, and unclear school-enterprise cooperation, based on the research status and achievements of higher vocational college education resources at home and abroad. On the basis of combing and analyzing the related concepts, theoretical basis, and construction principles of higher vocational education resources, the paper designs the network sharing system of higher vocational education resources in detail from its design objectives, design ideas, user needs, overall framework system, hierarchical structure, and functional modules. The core part is the realization of system function modules, including the resource management function module, system management function module, and user management function module. At present, domestic scholars have carried out a lot of research on the construction of basic education network resources. Although the research focuses on resource allocation, coconstruction and sharing mechanisms, and the current state of building and development methods, it also serves as a guide for the development of basic education network resources. However, most of these research results analyze problems from the qualitative perspective, and most of the research results are speculative research at the macro level, lacking practicality. With the rapid development of network education, the construction of network resource libraries is developing in the direction of intelligence and individuation to meet the different learning needs of learners. Based on the analysis of the problems encountered by learners in using the resource library for learning, this paper puts forward the application of data mining technology (DM) to guide the construction of the resource library and discusses how to improve the organizational structure of the resource library, improve the utilization rate of the resource library, and adapt to personalized learning. At present, the system is still in the system testing and experience stage, through the continuous testing of relevant functional modules, further improves the function of the sharing system, and constantly increases the scalability of the system function, in order to establish a perfect, comprehensive, and nationwide higher vocational education resource network sharing system.

Research on the Relationship between Intelligent Analysis and Weight of Keywords in English Test Questions

With the rapid development of educational informationization, Internet of Things, and other technologies, English education has been paid special attention to, and all aspects such as educational model, learning behavior, teaching philosophy, and teaching evaluation have been greatly influenced by educational informationization. Based on the experience of practical education, this paper explores and studies the connotation and characteristics of English test questions and its influence and application on modern English test questions. This paper constructs a systematic method to extract keywords from English test questions. It perfects the fair and reasonable index of English keywords, establishes the weight system, discusses the relationship of keywords, and conducts academic research on vocabulary, word frequency and word position, emphatically adopts BayesNet algorithm to extract keywords, and realizes the evaluation system of English test keywords based on intelligent analysis and weight relationship. The results show that (1) selecting the calculation method and weight relationship suitable for the text system to carry out intelligent analysis, the weight ratio exceeds 65%; that is, the text keyword retrieval is successful. (2) The average accuracy (%), average recall (%) and average F -measure (%) in weighted names are almost less than 70%. Only the BayesNet algorithm has 72.3% weight analysis in keyword extraction in reading comprehension. (3) KEA algorithm, PAT TERR algorithm, and BayesNet algorithm take 0-2.8 s, 0-2.6 s, and 0-2.1 s, respectively, and the BayesNet algorithm takes the shortest time. The calculation time of users is greatly saved. (4) According to the calculation results of CPT model, the sum of the weights of the three algorithms is equal to 1, and the BayesNet algorithm is dominant in extracting keywords with a weight analysis of 0.529 in verb translation.

Research and implementation of high priority scheduling algorithm based on intelligent storage of power materials

The Smart Supply Chain technology has been gradually established and improved as a popular power system over different application fields, which raises the development of informatization and intelligence in intelligent warehouse management. Intelligent scheduling systems are eagerly required to standardize warehouse operation management and intelligently schedule inbound and outbound tasks, maximizing the rational use of intelligent devices and realizing the automation unmanned operations in actual scheduling situations. Since the scheduling algorithm is the key component of the intelligent scheduling system that acts as the most powerful tool adding automation and intelligence to electric power material storage, this paper studies the principle of the high-priority based scheduling algorithm carefully and proposes a novel data model based on the algorithm to improve the execution efficiency and performance of intelligent scheduling system. The superiority of the proposed algorithm is verified by its principle analysis and comparing with other state-of-the-art methods, ensuring that our algorithm is perfectly applicable in actual scheduling systems. Additionally, we design an efficiency scheduling system satisfying the needs of intelligent warehouse management, and further demonstrate our algorithm works well in realizing the automatic loading and unloading operation in the storage of electric power materials under unmanned condition with simulation experiments.

Application of Product Life Cycle Management Method in Furniture Modular Design

With the rapid development of informatization and network technology, it has become an inevitable trend to use informatization to drive industrialization, industrialization to promote informatization, and high-tech to improve China’s traditional manufacturing industry. In order to conform to the trend of furniture development, combined with the characteristics of China’s furniture manufacturing industry, this paper analyzes the sustainability of multifunctional furniture based on the whole life cycle of the product, and improves the sustainable utilization rate of multifunctional furniture at each stage of the life cycle, and then apply the modular concept for structural design. At the same time, the paper studies the modular design of imitation solid wood frame cabinet furniture. This paper proposes a method of producing and designing imitation solid wood frame cabinet furniture by using the cabinet module of panel furniture. This design method makes it possible to change the shape and style through a wide variety of decorative modules, under the condition that the basic modules of the cabinet are the same as the panel furniture, to produce imitation solid wood frame furniture with different styles. The research results show that this paper redefines multifunctional furniture based on the product life cycle, proposes a new definition of multifunctional furniture under the sustainable concept, and proposes the design process of multifunctional furniture. At the same time, the application of a multifunctional furniture design process for furniture design can effectively improve the sustainability of furniture.

Information and Data Analysis Based on Big Data and Blockchain Technology in Promoting the Development of Cultural Tourism Industry

With the introduction of the reform and opening-up policy, the Chinese tourism industry has made considerable progress, but the current development is facing new development shortcomings and limitations. With the rapid development of technology and informatization, big data and blockchain technology provide new ideas and directions for the development of information and data analysis in the cultural and tourism industry. Under big data and blockchain technology, an in-depth research is conducted on the statistical system and GDP accounting data of the two industries of culture and tourism using coupling coordination model, model setting, and proxy re-encryption technology. It coordinates the statistical system of cultural and tourism industries, forming a unified cultural and tourism statistical report and value-added accounting report. It uses blockchain technology to calculate and analyze that the total output value of the tourism industry and cultural integration development in the past two years has increased by 1,4677.5 billion yuan, with a growth rate of 42.79%. Under the integrated development of culture and tourism in various regions, the overall income has shown an upward trend, with the highest growth rate of nearly 50%. The deep integration and development of cultural and tourism industry will become the top priority of Chinese cultural and tourism work in the future. With the addition of big data and blockchain technology, the great development of cultural and tourism integration has been realized.

E-Commerce Precision Marketing Model Based on Convolutional Neural Network

With the rapid development of network and informatization of the consumer market in my country, the application and maturity of technologies such as the Internet, terminal equipment, logistics, and payment and the continuous improvement of people’s consumption concepts, online shopping has gradually become the mainstream purchase method for Chinese consumers, and e-commerce has gradually become one of the important driving forces to promote the sustained and vigorous development of China's economy. Under the traditional marketing model, companies do not fully understand the needs of users. The sales staff's thinking is only how to sell products to users. They do not know the specific consumer needs, so they can only focus on the product. Based on these foundations, this research uses convolutional neural networks and applies this model to precision marketing to obtain accurate portraits of consumers, thereby increasing the company's turnover. After comparing different models and conducting some experiments, it is concluded that (1) through the collection and analysis of W enterprise data, the training and testing conditions of the CNN model, LSTM model, LSTM attention model, and CNN + LSTM attention model are compared. It is concluded that the CNN + LSTM attention model and the LSTM attention model perform better, and the accuracy of testing and training is higher. (2) Through the fitting of the model, it is found that Sn(%) = 70.71, Sp(%) = 86.25, Acc(%) = 81.07, and MCC = 0.752 of the CNN + LSTM attention model are the best fitting models. The men and women stratification and gender stratification of users are predicted, and it is found that men in the W company are the main purchasing power, and in the age stratification, it is found that the population of 41–50 accounts for the highest proportion. (3) The average accuracy rate of the LSTM attention model is as high as 66.6%, the average recall rate is 82.3%, and the F1 score is 73.1%. This model has met expectations for precision marketing forecasts. (4) Using the CNN + LSTM attention model to predict the marketing input for the next year, it is found that the use of precision marketing will increase the profit of W company. The average annual data show that the monthly revenue of precision marketing has increased by 73.5%.

Implementation of the expansion of sensor information storage based on the Internet of Things and cloud computing

AbstractIn the era of rapid development of informatization, the Internet of Things (IoT) and cloud computing have also been born and developed, quietly changing our way of life. The application of sensors has also brought great convenience to our lives, but there are still shortcomings that need to be studied in depth. This article aims to study the application and realization of Internet and cloud computing in sensor information storage. Aiming at the problems of sensors with multiple sources of information, large amount of data concurrency and low efficiency, the single‐point data agent is clustered and clustered to achieve high availability of sensor information storage. After a series of experimental results analysis, the information storage system constructed in this article can input 12 700 pieces of information per second and output 134 pieces of information per second. Therefore, sensor information storage based on the IoT and cloud computing has higher work efficiency, safety performance and availability than other sensor information storage. The architecture of information storage based on the IoT and sensors made in this article has far‐reaching significance for the development of smart devices in the future, and has great research value.

Construction of Big Data Technology Training Environment for Vocational Education Based on Edge Computing Technology

With the rapid growth of the BD (big data) industry, many universities are focusing their professional development and expansion efforts on cultivating talent in related industries as well as building and developing big data disciplines. The BD technical training platform of vocational education based on IPE (Integration of Production and Education) is built in this paper, and the agreement signing, process management, effectiveness evaluation, big data analysis, and statistics of the integration of production and education are realized using edge computing technology, providing a solution for the informatization development of the integration of production and education. The access sequence was discovered among some knowledge points, as well as the sequence of knowledge points frequently viewed by students, and the teaching platform adjusts the content of web pages based on these rules to better provide personalized services for users.

The Application of Internet Technology in Vocational Education Informatization

Under the background of the new era, higher vocational colleges should improve education in line with the characteristics of social development, so as to improve education quality and promote the development of education informatization. In regard to this, teachers and teaching managers should strengthen the research on internet technology, improve the application level of internet technology, and appropriately modify the mode of teaching, so as to create favorable conditions for the application of internet technology in higher vocational education.

Wireless Communication-Based Design and Implementation of Digital Protection Platform on Folk Art

Numbers no longer only have a purely mathematical meaning but a code that can change the way of human existence, cognition, and thinking, and a basic element to convey information. With the advent of the “Internet+” era, the development of digital media technology, digitization, and informatization has been integrated into all walks of life and has a vital impact on the development of all walks of life, and also brought about the ideological change of fine arts. Nowadays, the digitization of fine arts is a new product of the computer age, and how to protect it is also an important topic. The continuous development of wireless mobile communications has promoted the rapid development of microprocessors. In this article, we will study the protective effect of microprocessors on the digitization of fine arts. This paper designs a microprocessor-based art digital protection website, with the purpose of preserving, inheriting, and disseminating folk art works through the website. The result of the experiment shows that the sampling rate of the microprocessor has reached the requirement of the signal. The largest number of users of the folk art digital website is young people aged 18–29, accounting for 32%; the highest average score for the website is those over 59 years old, with an average score of 90.