Business Logic Layer Research Articles

Design of emergency evacuation route for large buildings based on cloud computing and GIS big data

In order to ensure the safety of life and property in large buildings, the design of emergency evacuation routes for large buildings based on cloud computing and GIS big data is studied. Combining cloud computing and GIS big data, a command model for emergency evacuation of large buildings is built. Emergency evacuation functions are realized through the access layer, business logic layer, cloud computing layer and data layer. GIS big data of large buildings is stored in the model data layer. GIS geographic data is clustered through the MapReduce based parallel K-means clustering algorithm in the cloud computing layer. After clustering, the emergency evacuation road network of large buildings is constructed through GIS in the business logic layer. On the road network, the emergency evacuation route selection method combining Dikstra algorithm and ant colony algorithm is used to realize the optimal route selection of emergency evacuation of large buildings. Experiments show that this method can effectively select the best evacuation path in large buildings, and the evacuation speed of the selected path is fast, which can ensure the safety of people in buildings.

Design and implementation of popular quality analysis system for dry red wines originating from Northwest China

SummaryChinese wine, especially those from the Northwest region, has received worldwide attention and consumer recognition. However, mislabelling the year and origin of wine is a major fraud challenging the wine industry, such as the deception on year or origin. Inspired by the Appellation d'Origine Contrôlée (A.O.C) system, a feasible wine popular quality analysis system (WPQAS) has been established to supervise the selling. First, an investigation for all potential users was carried out to analyse their requirements for WPQAS. Then, the system framework was designed with the Browser/Server (B/S) 3‐tier architecture, including the data layer for saving data, the business logic layer for running the models, and the presentation layer for interacting with users. Meanwhile, a unified modelling language (UML) sequence diagram was used to monitor the dynamic behaviour of the WPQAS. The core for driving system operation was generated based on a series of machine learning algorithms, including K‐nearest neighbour (KNN) for detecting outlines, random forest (RF) for weighting the chemical indexes, and artificial neural network (ANN) for training the models. Finally, the WPQAS, implemented as a software, was applied to estimate the popular quality of 600 dry red wines from Northwest China. The mode of decoupling models from the system could ensure WPQAS simplicity and scalability, and make the models reusable. Thus, the present work proposes a lightweight system that can be easily popularised and applied in diverse scenarios, and would play a significant positive impact on the correct meeting the challenges associated with counterfeit wine.

Design of an enterprise accounting system based on decision tree model

Abstract The key to the enterprise is in management, and the focus of financial management is enterprise accounting, and the enterprise accounting management system is the core of financial management. Developing a set of accounting management systems has become an urgent problem to solve to improve the efficiency and accuracy of enterprise accounting. In this paper, we use the decision tree model to design the enterprise accounting system and establish an efficient accounting system, which is logically divided into 5 layers: database, data storage layer, business logic layer, business appearance layer and user interface layer. The effectiveness and applicability of the decision tree model for enterprise accounting systems are discussed. The results show that the decision tree model can improve the accounting error tolerance and efficiency and reduce accounting risk. The satisfaction rate of most evaluation indexes has increased to more than 50%, the satisfaction rate of accounting business operation compliance has increased from 6% to 55%, the reasonableness of the internal audit cycle has increased from 18% to 70%, and the timeliness of annual accounting statement disclosure has increased from 8% to more than 50%. The decision tree model is positive for using enterprise accounting and can meet the requirements of enterprise accounting system design.

Research on auxiliary training system of oral English pronunciation based on data extraction

Due to the limitations of the language environment and other aspects, students generally have problems such as inaccurate English pronunciation in oral English learning. To solve the English pronunciation problem, an English oral pronunciation auxiliary training system based on data extraction is designed. With the help of the B/S three-tier structure, the system designs the overall framework of the system, including the data layer, business logic layer, and display layer. In the hardware design of the system, the collection, transmission, and processing of oral English pronunciation audio are realized with the help of pickup, audio Bluetooth chip, and programmable logic controller. Three business processing programs are designed, including a pronunciation audio input processing program, pronunciation audio data extraction program, and pronunciation auxiliary training program, to correct oral English pronunciation. The experimental results show that the system can improve students' oral English pronunciation and improve students' oral learning performance.

Development of a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository.

To establish a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository. This study was approved by the Institutional Review Board. The steps of establishing the database are as follows: 1) According to the standards required for the intelligent diagnosis of HCC, it was attempted to design the corresponding functional modules after analyzing the requirements; 2) Based on client/server (C/S) mode, 3-tier architecture model was adopted. A user interface (UI) could receive data entered by users and show handled data. Business logic layer (BLL) could process the business logic of the data, and data access layer (DAL) could save the data in the database. The storage and management of HCC imaging data could be realized by the SQLSERVER database management software, and Delphi and VC++ programming languages were used. The test results showed that the proposed database could swiftly obtain the pathological, clinical, and imaging data of HCC from the picture archiving and communication system (PACS) and hospital information system (HIS), and perform data storage and visualization of structured imaging reports. According to the HCC imaging data, liver imaging reporting and data system (LI-RADS) assessment, standardized staging, and intelligent imaging analysis were carried out on the high-risk population to establish a one-stop imaging evaluation platform for HCC, strongly supporting clinicians in the diagnosis and treatment of HCC. The establishment of a HCC imaging database can not only provide a huge amount of imaging data for the basic and clinical research on HCC, but also facilitate the scientific management and quantitative assessment of HCC. Besides, a HCC imaging database is advantageous for personalized treatment and follow-up of HCC patients.

Retracted] Painting Color Editing System Based on Virtual Reality Sensor Technology

In order to solve the problem of large color matching error in color matching system, this paper proposes a painting color editing system based on virtual reality sensing technology. Apply virtual reality technology (technology) to the proposed system design, and design the system framework based on the three‐tier architecture, which includes data access layer, business logic layer, and presentation layer. The hardware includes spectrophotometer, central processing unit, storage device, and virtual reality somatosensory interaction device. The software part takes BP neural network algorithm as the core, trains and processes the three color values, constructs the automatic matching model of painting colors according to the training results, and realizes the logical operation and analysis of the system software. The experimental results show that although there is still a certain error between the painting color matching output by the system and the actual color matching of the test samples, the maximum absolute error of the matching is relatively small, which is less than 0.05. The error between 6 groups of actual matching samples and predicted matching samples is <1.5. Although the designed system has some errors with the expected color matching, the error is small, so the color difference does not affect the visual presentation effect.

Data-Driven Information Management Method of Power Supply Chains Using Mobile Cloud Computing

Based on the spring, spring MVC and MyBatis structures of the cloud platform SSM framework, an information management platform for power grid material supply chain is built. The data layer uses a variety of sensors to collect power grid material supply chain information, and the information is fed back to the data storage layer after being integrated by the logical reorganization function of the persistence layer. The data storage layer uses the multi-sensor supply chain information fusion method based on paste progress to fuse the information and store it in the database. The business logic layer calls the information in the database and uses the improved k-means clustering algorithm to detect the abnormal supply chain data information. After calculation and data control by the control layer, the data management results are displayed through the presentation layer. The experimental results show that the absolute error of data fusion is very low. It can effectively cluster data information and distinguish outlier anomaly information at the same time, and the effect of information management is good.

Vectorization Analysis of Cultural Creative Design Based on the Recognition Model of Virtual Reality Technology

In order to solve the problem of large color matching errors exhibited in the previous color matching system, this study proposes a special color automatic matching system for cultural and creative products based on virtual reality technology. In this study, virtual reality technology (Technology) is applied to the proposed system design. Based on the three-tier architecture, the system framework is designed. The architecture includes a data access layer, a business logic layer, and a presentation layer. The hardware includes a spectrophotometer, a central processing unit, a storage device, and a virtual reality somatosensory interaction device, and the four key devices are introduced in detail. The software part takes the BP neural network algorithm as the core, trains and processes the three color values, constructs the automatic color matching model of cultural and creative products according to the training results, and realizes the logical operation and analysis of the system software. The experimental results show that although there is still a certain error between the color matching of the cultural and creative products output by the system and the actual color matching of the test samples, the maximum absolute error of the matching is relatively small, both of which are less than 0.05, which proves the effectiveness of the research to a certain extent. The error between the 6 groups of actual matching samples and the predicted matching samples is <1.5, which proves that the color difference is related to human vision, indicating that the matched samples have no color difference and achieve the expected effect. Although there are some errors between the designed system and the expected color matching, the error is small, so the color difference does not affect the visual presentation effect.

Design and Application of the Digital-Oriented Interactive System for Teaching Preschool Art Education

In this paper, the design of a teaching interactive system for preschool art education is digitally studied and analyzed, and the teaching interactive system is designed to be applied to the teaching process of preschool art education. It is to design and implement a convenient and feasible art education study and research mutual aid system. The art teaching mutual study and research system is based on the three-layer architecture of NET as the main system structure. The main function of the representation layer is to process the data requested by users and realize the interaction between users. The data layer is mainly to realize the processing of data logic, through the call and operation of the database, to realize the interactive access function of the database. The business logic layer is a relationship in the middle and plays the function of carrying on the top and bottom, realizing the logical processing of the database, and then realizing the business objectives of the system. The design and implementation of the multitier mechanism are of great importance in terms of improving system reliability as well as system compatibility. In the framework of activity theory research, the concept of dynamic integration of digital learning resources is proposed in conjunction with relevant research on digital learning resource integration. The intermediary of the interaction between subjects and digital learning resources in the digital learning process establishes the connection between different subjects through the interaction between elements in the activity system, promotes the accumulation of the number of digital learning resources and the two-way interaction of effective information, and thus realizes the orderly organization and management of the digital learning resource system. The theoretical basis of dynamic integration of digital learning resources, including activity theory, knowledge organization theory, and feedback principle, is discussed in detail in the study to provide a basis for the construction of the theoretical model of dynamic integration of digital learning resources.

Exploration of micro-video teaching mode of college students using deep learning and human-computer interaction.

In order to improve the efficiency of teaching and learning in Colleges and Universities (CAUs), this work combines the Browser/Server (B/S) framework with Model View Presenter (MVP) technology to build a college student–oriented micro-video teaching system based on Deep Learning (DL) and Human–Computer Interaction (HCI) technology. Firstly, it makes an in-depth analysis of the problems in the classroom teaching of Chinese CAUs. Three functional modules are designed for the micro-video online teaching platform: video management, user learning, and system management. Then, it uses MVP technology to analyze the use-cases of these three functional modules in detail. Based on this, the micro-video online teaching platform is designed using the B/S framework. The teaching platform interface layer realizes the HCI between the platform and users. The business logic layer responds to the user requests submitted and returns the processing results to the interface layer. Finally, the function test and stress test of each module of the micro-video online teaching platform is carried out. The test results show that the response time of the proposed micro-video teaching platform increases with the number of users. Under the peak concurrent users, the system response time is 6 s, without abnormalities. Meanwhile, the proposed teaching platform has improved students' satisfaction with classroom teaching by nearly 15% and improved the compactness of the college classroom by nearly 12%. When the number of virtual users increases and the number of services increases linearly, the Random Access Memory and Central Processing Unit growth rate is significantly lower than that of the number of services. These outcomes indicate that many system resources are reused, and the system has good scalability, which can meet users' needs for the network video teaching system. The proposed teaching platform provides a new idea for applying DL and HCI technology in researching college students' micro-video teaching mode.

A Deep Neural Network-Based Method for Building a Professional Farmer Training Model

In this paper, we analyzed the deep neural network (DNN) model and proposed a DNN-based method to build the training model for professional farmers. This paper aims to construct the vocational farmer cultivation model and seek a better path for transforming farmers into new experienced farmers by analyzing each training body involved in the cultivation of vocational farmers and studying their respective problems and reasons. The conceptual and logical structures of the system database were designed, and MySQL was selected for database implementation to complete the information of professional farmers. The system network topology and logical architecture are created, and the functions of view, control, business logic and data access layers are divided. This paper combines the DNN with the vocational farmer training enhancement decision tree. The experimental results of this model are most intuitive and accurate. This paper reconstructs the neural network model using the global average pooling layer to better model the vocational farmer training model to replace the fully connected layer in the original convolutional neural network. At the same time, to make the network model produce a lower probability of overfitting, a dropout layer is added to the layer after the fully connected layer to improve the efficiency of the neural network further to enhance vocational farmer training. The experimental content of this paper provides a new research direction for the estimation of vocational farmer cultivation. The above modeling is used to improve the vocational farmer cultivation model and accelerate the process of vocational farmer cultivation.

Entity Relationship Modeling for IoT Data Fusion Driven by Dynamic Detecting Probe

To solve the problem of integrating and fusing scattered and heterogeneous data in the process of data space construction, we propose a novel entity association relationship modeling approach driven by dynamic detecting probes. By deploying acquisition units between the business logic layer and data access layer of different applications and dynamically collecting key information such as global data structure, related data, and access logs, the entity association model for enterprise data space is constructed from three levels: schema, instance, and log. At the schema association level, a multidimensional similarity discrimination algorithm combined with semantic analysis is used to achieve the rapid fusion of similar entities; at the instance association level, a combination of feature vector-based similarity analysis and deep learning is used to complete the association matching of different entities for structured data such as numeric and character data and unstructured data such as long text data; at the log association level, the association between different entities and attributes is established by analyzing the equivalence relationships in the data access logs. In addition, to address the uncertainty problem in the association construction process, a fuzzy logic-based inference model is applied to obtain the final entity association construction scheme.

Optimization of Classroom Teaching Strategies for College English Listening and Speaking Based on Random Matrix Theory

Online English teaching systems are more and more widely used in higher education teaching. Based on random matrix theory, this study constructs an optimization model of classroom teaching strategies for college English listening and speaking. By comparing and analyzing the universal properties of the research system and the random system matrix, the model can examine the random properties and special nonrandom properties within the system and solve the quantification problem of college English classroom education. First, based on the existing college English listening and speaking classroom teaching system, with the help of J2EE structure in information technology, the existing college English listening and speaking classroom teaching system is optimized, the customer layer, web layer, business logic layer, and student listening and speaking classroom learning effect management layer are designed, and the system is applied to college English listening and speaking classroom teaching. During the simulation process, a random matrix theoretical dynamics graph library and a subject library were written, so that combining the elements of each vector into a new matrix. Experiments show that the ensemble framework based on ranking learning can not only integrate multiple matrix factorization algorithm models to obtain better recommendation accuracy but also can more fully reflect the eigenvalues of each individual. The experimental results show that when the number of sampling points is 80 and the signal-to-noise ratio is −15 dB, the detection probability is close to 80%, which effectively improves the classroom random detection performance of English teaching and the effect of strategy optimization.

Construction of E-Commerce Economic Management Platform Based on Data Visualization Technology

In the age of information explosion, users are exposed to overwhelmingly visual information every day. The raw unprocessed data are not attractive enough to the human brain. Thankfully, humans are extremely sensitive to graphic image perception and can understand the meaning behind numbers from graphic images. For this reason, data visualization is becoming the best way to communicate with people. In the context of big data, how to display complex data efficiently, what kind of drawing technology to use, and how to improve user interaction with data are urgent problems to be solved by data visualization technology. On the other hand, researchers’ understanding of data visualization is not consistent; there is no clear standard; and it is relatively confusing. Researchers engaged in data visualization and executives engaged in related data visualization industries have different interpretations of it, and the core theoretical basis and research methods are still in the exploration stage. Firstly, I systematically introduce the design principles of data visualization, software development related technologies, data visualization research methods, basic charts, and characteristics of data visualization. Secondly, based on the software development process, I analyzed the key issues of data visualization for the e-commerce system, gave the objectives of the design plan, and designed the whole architecture and development framework of the project. In particular, I designed the class diagram and timing diagram of the control layer, service layer, and data access layer for the visualization of user purchase preferences in the data visualization and analysis module as an example. Then, with the structure and characteristics of the data in the visualization studied, the business logic layer and the front-end visualization layer are implemented, and a good user interaction function is provided for the users. The data visualization is loaded asynchronously and on demand to improve the performance of data visualization and user interaction experience. Finally, the system data is presented with the analysis of the user population’s region, the analysis of user preferences, and the analysis of the product price sales relationship, and the product category tree diagram and the results of the data visualization run are given, providing a comprehensive platform for shopping, data analysis, and data visualization for the next generation e-commerce system.

Hybrid IoT and Data Fusion Model for e-Commerce Big Data Analysis

This article is aimed at studying the e-commerce big data recommendation model based on data fusion and the Internet of Things. This article chooses an embedded system for the construction of an e-commerce platform and uses data fusion technology to collect, transmit, and filter useful information from various information sources. Then, the collected information and data are analyzed and integrated, and visualization algorithms are used to better present data analysis, and association rules and structural similarity methods for electronic comparison are uses. This article uses the B/S architecture to design the overall framework of the data access layer, business logic layer, and user presentation layer; collects, organizes, stores, and presents the acquired consumer information; and finally analyzes the e-commerce background, platform performance, and supply and demand analysis. The experimental results show that the average clustering coefficient of the platform (0.7559) is smaller than the average clustering coefficient value of 8 items in the store (0.811) and smaller than the average network diameter and average path length of the online store (3.86, 7.7). Store products are better than store products, and the diameter and length of the product should be larger (2.71, 5.75). The recall rate of the e-commerce big data platform and model matching review method designed in this paper is 5% higher than that of the word matching model method and has a better expected effect in terms of user supply and demand.

An Analysis of the Optimal Allocation of Core Human Resources in Family Enterprises Based on the Markov Model

As an important component of family business management, human resource allocation greatly affects all aspects of the company’s strategic direction and organizational structure. Therefore, the optimal allocation of core human resources in family businesses has become a topic of great practical significance. Based on the Markov model, this article is based on the B/S architecture, designed the three‐tier architecture of the presentation layer, business logic layer, and data layer adopted by the system, forming the basis of quality management, job management, performance management, and salary management‐oriented human resources optimization configuration system. Taking family business A as an example, the Markov chain is used to predict the flow of personnel in the company in the next three years. The results show that with the development of normalization and the improvement of the management level, the demand for personnel in the family business will decrease year by year. Competency analysis of human resources positions found that grassroots employees have higher requirements for competency, but at the same time, lower performance requirements, while company seniors need relatively low requirements for competence indicators and higher requirements for performance. In addition, through data calculation and prediction, it is found that the prediction accuracy of the model algorithm is 81.37% on average. Compared with other model algorithms, the Markov model algorithm has the highest accuracy in predicting the state, and the accurate prediction results make the recruitment work predictable. The system makes up for the shortcomings of traditional human resource management data, such as insufficient data, low accuracy, and inability to achieve homogeneity. It uses a centralized database to comprehensively and organically link human resource management‐related information to provide a basis for future family business human resource optimization.

Design and Implementation of an Intelligent Online English Learning System Based on Mobile Internet Technology

A predicament facing education is that the traditional teaching mode could no longer meet the requirements of modern society. The fast development of mobile Internet technology has brought online education platforms into existence, and these newborn things are very helpful to solve the mentioned contradiction. To deepen the research of the application of mobile Internet technology in modern education, this paper designed the functional modules of an intelligent online English teaching (IOET) system and implemented the proposed system. In our study, satisfying students’ learning requirements had been taken as the core principle when building the proposed system, and the database design had followed the principles of standardization, consistency, security, integrity, scalability, and ease of maintenance. The IOET system adopts a three-layer architecture, from top down, the system has a representation layer, a business logic layer, and a data service layer. The main management modules of the system include course notification module, visitor management module, and homework management module, etc. Through this paper, we hope to provide a piece of evidence for the basic research on the upgrade and improvement of intelligent online English teaching platforms.

An English Online Homework Tutoring System Supported by Internet Database

College English tutoring is an important content of current research, and how to provide more effective methods for teaching tutoring is currently a hot research topic. Therefore, this paper designs an Internet-based online tutoring platform for college English teaching, which is accelerated by using the K-means clustering algorithm. The data processing efficiency and processing methods to improve user satisfaction require more practical experience to summarize. In the business logic layer, the uploaded information is mined through the K-means clustering algorithm to form an online tutoring university English database to provide platform data support, realize the business logic judgment of the data, convert the data in the database, and return to the user interface of other formats, to provide users with browsing and consulting. In the data access layer, the data from the business logic layer is processed. After the processing is completed, data can be added, deleted, modified, and checked in the database. Finally, the operation result of the database is fed back to the business logic layer for processing. Experimental results show that the designed platform has good data mining performance, low connection speed and low response delay, good compatibility, low CPU usage, fast resource sharing speed, and high user satisfaction. It can be connected to different operating systems.

A deep network prediction model for heavy metal cadmium in the rice supply chain

Cadmium and its compounds are currently known as Class I carcinogens, and excessive intake can cause severe health damage to humans. Rice has a strong absorption effect on cadmium, and rice products with excessive cadmium content have caused several significant public health contamination incidents. It is essential to predict the development trend of cadmium hazards in the rice supply chain so that countermeasures can be formulated to reduce the hazards. This paper proposes a deep prediction model for cadmium hazards in the rice supply chain based on the regularization method. Firstly, a long and short-term memory network is used to build the depth prediction model by using the regularization method, and the noise penalty term is added to reduce the model fitting to the noise and prevent the over-fitting caused by the noise. Finally, the optimization of the model hyperparameters was carried out using a Bayesian optimization approach to develop the prediction performance. Then, early warning system for prediction of cadmium hazards in the rice supply chain is built based on the deep prediction model proposed in this paper with SOA architecture, including data resource, business logic, and application service layers. The proposed model performs well on an actual data set of cadmium hazards in the rice supply chain and fits the data well.

Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands

With the emergence of computers and networks, the social needs for English competence have presented a diversified and professionalized trend. The current single teaching model can no longer satisfy students’ needs. To cater to different demands of students and improve their level of satisfaction with personalized and automatically recommended teaching videos, an automatic recommendation sys-tem of college English teaching videos, which consists of interface layer, busi-ness logic layer, and data layer, was designed. The quality of recommended teaching videos was ensured through the strict management of English teaching videos within the system. The degree of interest in videos was calculated accord-ing to students’ browsing history of teaching videos. The content of teaching vid-eos that meet students’ personalized demands was established on the basis of the degree of interest. The Naïve Bayesian classification method was used to precise-ly, rapidly, and stably divide the teaching videos into two classes—interest and disinterest—according to the abovementioned information. Results show that the recall ratio and precision ratio of this system reach as high as 95.18% and 97.2%, respectively. The system recommendations averagely rank top, the recommenda-tion precision is high, and the recommended video contents are abundant, with an applause rate of 97.79%. This designed system can establish student-centered college English teaching methods, create a favorable language environment, and better promote the teaching of English among college students.