Research on Campus Education Information System Based on

Research ArticleResearch on Campus Education Information System Based onInternet of Things and Artificial Intelligence Decision

Hetiao Hong

Hangzhou Normal University, Hangzhou Zhejiang 311121, China

Correspondence should be addressed to Hetiao Hong; [email protected]

Received 27 July 2021; Revised 9 September 2021; Accepted 15 September 2021; Published 4 October 2021

Academic Editor: Rajesh Kaluri

Copyright © 2021 Hetiao Hong. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Because of the different reasons between regions, the distribution of educational resources is also different, the development ofeach school is unbalanced, and the degree of campus education informationization is different. The complex functionalstructure not only does not facilitate teachers and students but also leads to many problems: the prevention and prevention ofcampus life safety. It is difficult to keep and use multiple cards owned by one person. Software and education platform cannotbe seamlessly connected, and there are various barriers between data and data and people and data. The lack of learningmaterials leads to the inequality of information. There are no good feedback and solution between teachers and students. It isdifficult to manage accurately with a large number of people. This study will be based on the Internet and artificial intelligencetechnology, to explore how to study a large (or super large), concise and efficient, and excellent performance of campuseducation information system; this system can meet the teachers and students no matter what year, month, and day of a largenumber of visits. For some problems in the process of building the system, actively optimize and refine them. After functionaltesting and analysis of the system, the experimental results show that the interface function of the new system is stable, theusability test is better than the feedback experience of the original system, the response time is reduced by 21.6% on average,and the overall power consumption of the system is reduced by about 1.43% on average.

1. Introduction

With the rapid development of the times and the impact ofnew technologies, all walks of life have changed their faces,and the education industry is no exception. Especially inthe past two years, the global epidemic has seriously affectedthe academic process. Primary schools, junior high schools,senior high schools, and higher education institutionsshould build a platform integrating learning, living, moni-toring, safety, and other functions for teachers and studentsto use and facilitate command and arrangement in case ofemergencies. The Internet of Things and artificial intelli-gence technology are introduced into the campus, integratedwith educational information, and a variety of rich applica-tion scenarios are designed and applied to meet the dailystudy and life of teachers and students, accelerate the com-pletion of the modern process of educational informationi-zation, keep up with the pace of the times, and build anintelligent campus. The literature [1] realizes the virtual

scene simulation and multimedia information interactiondesign of multimedia information platform, intelligent class-room, library interactive platform, and student informationmanagement system on intelligent multimedia informationprocessing terminal. The literature [2] discusses the intro-duction of Internet of Things technology into intelligentmanagement and the combination of management conceptsand methods with artificial intelligence technology, whichhas an impact on life. On the basis of analyzing the currentsituation of medical device management, the literature [3]incorporates the innovative achievements of informationtechnology such as electronic signature technology, big dataanalysis technology, Internet of Things technology, artificialintelligence, and cloud services into the daily work of medi-cal device management. The literature [4] introduces thedomestic artificial intelligence popularization educationresources and the exploratory work of artificial intelligencetechnology popularization on this platform. The literature[5] systematically introduces the research status of industry

HindawiWireless Communications and Mobile ComputingVolume 2021, Article ID 8626890, 17 pageshttps://doi.org/10.1155/2021/8626890

https://orcid.org/0000-0001-5712-7721

https://creativecommons.org/licenses/by/4.0/

https://doi.org/10.1155/2021/8626890

standard system related to Internet of Things and introducesthe general standard classification method, hierarchicalarchitecture, conceptual model, and system table in UPIoTapplication layer. The literature [6], based on campus secu-rity management, is aimed at establishing an intelligentmobile information system in colleges and universities,which adopts Windows media player along RTP/RTSP pro-tocol. The literature [7] analyzes the application of Internetof Things in the construction of intelligent campus in col-leges and universities in detail. The literature [8] comparesthe campus information systems of three British universitiesand obtains the functions that should be possessed on anideal CWIS. The literature [9] introduces the applicationmethods and significance of big data and cloud computingtechnology in intelligent campus. The literature [10] intro-duces an intelligent sensor network and applies the technol-ogy of intelligent sensor network to the research of campusenvironmental management information system model.The literature [11] introduces the architecture design of bigdata platform of BI application system. This building hasoutstanding advantages in cost, scalability, and scalability,and the realization of BI analysis function plays an impor-tant role in the strategic decision-making of colleges anduniversities. The literature [12] eliminates the “isolatedisland” in university information system and provides effec-tive data basis for teachers and students’ work, study, anddaily life. The literature [13] proposes a new algorithm anduser support system based on users’ personal data and mul-tiattribute preferences and proposes an effective lecture allo-cation method. The document [14] was based on VRML andJavaScript, the client programming, design, and implemen-tation of online virtual campus system literature. The litera-ture [15] takes the design of virtual campus card systemunder microservice architecture as the goal and designs thevirtual campus card system. As an important educationallink in cultivating innovative talents, under the backgroundof the development of Internet and artificial intelligence,many colleges and universities are constantly applying newtechnological achievements, which must be reformed tomeet the requirements of cultivating innovative talents fac-ing the information society, establish and own their owncampus information systems, change the basic environmentof traditional education and teaching, and improve the over-all level and application of education and teaching. Thedevelopment of information society also provides environ-ment and conditions for this reform; under the backgroundof Internet and artificial intelligence, the construction ofeducational informationization has played a role in promot-ing the teaching, scientific research, and student training incolleges and universities, which will effectively promote theinformationization and modernization of higher education,which is also the requirement and inevitable result of educa-tional informationization. The method proposed in thispaper runs well in the system, but in the whole campus sys-tem, the system expands more. In this way, the performanceof the whole system will decline, and the adoption of intelli-gent systems requires better equipment requirements andnetwork environment. The key research work in the futureneeds to improve the system response time and server

throughput, especially in the context of wireless networks,using multiprotocol mechanism. When different networkstructures are accessed, the system is continuously accessedto improve the application effect of the whole system.

2. Theoretical Basis of System Development

2.1. Internet of Things Technology. Internet of Things [16,17] is a new technology in the 21st century, which has agood development trend in China and has gradually becomea very important technology in the information age. Itrequires sensors to collect and process information, and atthe same time, it exchanges information through the net-work, and the real and virtual worlds have completed a greatleap. The Internet of Things involves a wide range and hasmany application levels.

2.2. AI Artificial Intelligence Technology. Artificial intelli-gence [18], also known as AI, has never stopped the researchon intelligent machines, hoping to create advancedmachines similar to human intelligence and ability and evenput forward the theory of what should happen when intelli-gent robots surpass human development. In addition, peoplehave created a series of books and audio-visual works relatedto artificial intelligence to tell their expectations and worriesabout artificial intelligence. But so far, the development ofartificial intelligence is not ahead of schedule, there are manydefects and drawbacks, and the core technology needs fur-ther research.

2.3. Network Upgrade Technology. So far, 4G network basi-cally covers the whole campus. However, due to the limita-tion of 4G technology, with the passage of time, thenetwork gradually becomes stuck, and sometimes, even thenetwork is disconnected. Therefore, it is necessary to updatethe network. In this network upgrade, the latest 5G networktechnology will be used to upgrade the bandwidth to 5G, andthe specificity of the campus network will be strengthened toprevent unknown access and attacks from outside, and var-ious authentication methods [19] will be adopted to facilitateintegration with the new system.

2.4. ETL Technology. In the ETL technology, which ismature, we can understand it as the process of building abuilding. The actual application process is shown in Figure 1.

ETL technology, that is, data warehouse technology,extracts, cleans, transforms, and loads all kinds of data intodata warehouse. We can also use a metaphor to understandit as the process of building a building. Figure 1 shows thepractical application process, in which the functions ofETL are data extraction, cleaning and transformation, datasummary, data federation, data synchronization, data migra-tion, and data distribution.

2.5. Information Security Technology. Information security isa very important issue. Criminals often attack computersoftware or hardware in various ways all over the world:stealing network information, causing serious troubles tocountless people, spam calls and spam sale information,human flesh search, personal pictures and audio leakage,

2 Wireless Communications and Mobile Computing

Trojan virus, fraudulent links, and gambling propaganda.Our system needs to prevent malicious attacks, tampering,destruction, and rejection of individuals or computers thathave not been accessed through formal channels, so as toensure the confidentiality and security of information. Inorder to ensure information security, the system needstechnical personnel to maintain and supervise the systemfrequently and often make up for system loopholes. Forthis system, we will mainly use five major informationsecurity technologies: firewall technology, informationencryption technology, identity authentication technology,security protocol, and intrusion detection system. Figure 2shows the classification diagram of information securitytechnology.

As shown in Figure 3, the firewall operation diagram.

2.6. Apriori Association Algorithm. The Apriori algorithmuses a progressive search method to find the relation forma-tion conditions in the database, which is composed of con-nection and interruption processes. We set the minimumsupport (s) and the minimum confidence (C).

(1) Find all frequent itemsets, and the frequency isgreater than or equal to the minimum support (s)

(2) All frequent itemsets can be found by its frequentitemset

(3) The association rules should satisfy the minimumsupport (s) and the minimum confidence (C)

According to the Apriori algorithm, the purpose ofcampus education information system is to dig out hiddeninformation that is usually ignored by people and find thatteaching level and service need to be improved andimproved from a new perspective. In Figure 4, we discussthe time consumption of minimum support. The greaterthe minimum support, the less time consumption. InFigure 5, with other contents fixed, the larger the amountof data found, the less time it takes.

(1) Support:

A⟶ Bð Þ = P A ∪ Bð Þ, ð1Þ

S X, Yð Þ = P X, Yð Þ = num xyð Þnum all samplesð Þ : ð2Þ

(2) Confidence:

A⟶ Bð Þ = P BjAð Þ, ð3Þ

C xYð Þ = P xjYð Þ = P xyð ÞP yð Þ : ð4Þ

2.7. Decision Tree Algorithm. The decision tree algorithmclassifies information through a series of rules.

…Productionstorehouse

1

Productionstorehouse

3


n


2Data extraction

Cleaning conversionData aggregationData federation

Data synchronizationData migration

Data distribution

ETL

Datawarehouserepository

Reportquery

Dataanaylsis

Datamining

Metadata management

Figure 1: Practical application process of ETL.

Informationsecurity

technology

Fire wall technology

Information encryption technology

Identity authentication technology

Security protocol

Intrusion detection system

Figure 2: Classification of information security technology.

3Wireless Communications and Mobile Computing

Internet

Router

Intranet

Mail server

WEB server

FTP server

DMZregion

Firewall

Figure 3: Schematic diagram of firewall operation.

0

5

10

15

20

25

30

1000 5000 10000 15000 20000 25000 30000 35000 40000

Time consuming (s)

Figure 4: Time consumption of minimum support (s).

0

50

100

150

200

250

300

350

10 20 30 40 50 60 70 80 90 100

Time consuming (s)

Figure 5: Time consumption of data volume (d).


2.7.1. ID3 Algorithm

(i) Information entropy:

P x = xið Þ = Pi, i = 1, 2,⋯, n,

H xð Þ = −〠n

i=1log2pi:

ð5Þ

(ii) Conditional entropy:

H Y jXð Þ = 〠n

i=1piH Y ∣ X = xið Þ, pi = P x = xið Þ: ð6Þ

(iii) Information gain:

g D, Að Þ =H Dð Þ −H DjAð Þ: ð7Þ

2.7.2. C4.5 Decision Tree

(i) C4.5 which is an optimization decision tree definedas follows:

$$SplitInfo Dð Þ = −〠ν

j=1

D _J

��

Dj j ∗ log2Dj

��

Dj j

!

$$: ð8Þ

(ii) Definition of information gain rate:

$$GainRatio Að Þ = GainAð Þ

SplⅈtInf 0 að Þ $$: ð9Þ

2.7.3. CART Decision Tree. Carnegie Mellon University deci-sion tree is divided into classification tree and regressiontree.

(i) Gini index:

$$G Dð Þ = 〠yj j

k=1〠k′≠k

pkpk′ = 1 − 〠yj j

k=1p2k$$: ð10Þ

2.7.4. Continuous Value Processing

(i) For continuous attribute a:

Ta = aⅈ + aⅈ+1

2

��1 ≤ i ≤ n − 1

� �

: ð11Þ

2.7.5. CART Regression Tree

(i) Predictive regression continuous data:

D = x1, y1ð Þ, x2, y2ð Þ, x3, y3ð Þ,⋯ xn, ynð Þf g: ð12Þ

(ii) Selecting variable j and segmentation point s:

minj,8

minC1

〠xi

yi − C1ð Þ2 + minC2

〠xε

yi − C2ð Þ2" #

ð13Þ

(iii) Selecting variable and segmentation point ðj, sÞ:

R1 j, 8ð Þ = xjx jð Þ ≤ 8n o

, R2 _J , 8� �

= xjx jð Þ > 8n o

cm

= 1Nm

〠xi

yi, x ∈ Rm,m = 1, 2:ð14Þ

(iv) Dividing the input space into m regions to generatea decision tree:

f xð Þ = 〠M

m=1CmI x ∈ Rmð Þ: ð15Þ

(v) Solving the optimal output value with square error:

〠xi∈Rm

yi − f xið Þð Þ2: ð16Þ

3. System Architecture Design

The system is designed for data management, offline appli-cation, and online service.

(1) Unified data management refers to the platform forsummarizing various data on campus, which con-trols the flow and storage of data; most people donot have permission to view or use this part of data

(2) The offline application part of the system mainlyrefers to the use of infrastructure on campus, includ-ing campus security services and life convenienceservices, including subsystems such as payment


subsystem, face recognition subsystem for enteringand leaving the door, classroom, and laboratory usesubsystem

(3) Online services mainly refer to virtual campus ser-vices, which are mainly used through APP applica-tions and WEB interface

Subsystem has an independent logical structure, but inthe use of some subsystem functions, there will also be someother subsystem functions; this time we do not have to man-ually access to part of the subsystem permissions involved;the system will automatically judge and release the part ofthe data call and use of functions.

Offline application refers to the application of Internet ofThings technology and the use of physical equipment oncampus. Online service means that there is no entity at all,and all functions do not depend on physical devices on cam-pus but are mainly used through APP applications and webinterface. It can be distinguished according to whether phys-ical equipment is needed.

3.1. Data Management

3.1.1. Common Data Unified Platform. The original inten-tion of designing this unified public data platform is to pro-cess, clean, and integrate the shunted data of varioussubsystems and various basic original data, which need stan-

dardized and unified management, so as to effectively avoidthe confusion, complexity and failure of data. In the com-mon data unified platform, the data sources are processedand cleaned by the platform and finally diverted into the rel-evant information data of each subsystem after standardizedand unified treatment. There is a total score relationshipbetween data source and information data. It is also conve-nient to record and classify data when using it. Figure 6shows the architecture diagram of the platform.

3.1.2. Portrait of Student Behavior. A campus has many stu-dents, so it is difficult to manage. Therefore, we should usecertain tools and methods to collect and sort out students’abnormal data and analyze some abnormal behaviors andviolations of students. The system cannot illegally collectstudents’ personal information. Except for some technicians,ordinary people can only apply to view the analysis results.As shown in Figure 7, it is the related data analysis process.

As shown in Figure 8, it is the platform E-R diagram.

3.1.3. Keeping of Personnel Data Files. Data files record allaspects of a person. The preservation of archives is a veryconfidential job, which should be highly confidential. In gen-eral, except in exceptional circumstances, no one (especiallythe person himself) is allowed to view and read these mate-rials. The system will automatically update the personnel filecontent and, if there is an error, will have a high authority to

……

Common data unified platform

Core informationdata

Codemanagement

Servicemanagement

Modelmanagement

Backupmanagement

Logmanagement

Datamanagement

Business datasharing

Standard code

Data dynamics

Source database

Campus card Face recognition Personnel datafile

Geographicinformationnavigation

Figure 6: Architecture diagram of common data unified platform.


deal with the personnel. Finally, this subsystem will make asafe backup to avoid any missing or missing files. As shownin Figure 9, it is the structure diagram of the subsystem.

3.2. Campus Security Services. Whether it is primary school,junior high school, high school, or university, campus safetyis the key issue that has been emphasized again and again. In

recent years, due to the development of network technologyand the change of the times, data security and paymentantifraud security have gradually become more and moreimportant.

3.2.1. Data Security. It is far from enough to protect thesecurity of data only by virtue of the security system of the

Student portraits

Basic attribute

Gender

Age

Specialties

Department

Native

Grade Awards

Elective courses

Attendance

Books borrowing

Violations

Achievements

Learning status Living conditions

Consumption status

Access control

Participate inactivities

Sports health

Figure 8: Platform E-R diagram.

Database

InputData preprocessing

Data warehouse

Apriori associationModeling

Result analysis

Student's school data(study, consumption,access control, books,

etc.)

Data mininganalysis

Figure 7: Student behavior portrait-data analysis process.


system itself, so we will specially design a security frame-work. Many criminals in the world make viruses (Trojanvirus, Hack virus, Script virus, etc.) to steal informationand data for illegal activities. The most terrible thing aboutviruses is that they infect computers through various chan-nels and cause serious damage, which can lead to thedestruction of data files and the paralysis of the network.We must focus on data security issues, maintain and updatethe system for a long time, make up for loopholes, and pre-

vent the system from being attacked or crashing. The secu-rity architecture is shown in Figure 10.

3.2.2. Payment Security. The popularity of online shoppingpromotes the change of payment methods. Due to the devel-opment of information technology and communicationindustry in China, besides cash payment, credit card, orbank card payment, mobile phone scan code payment ortwo-dimensional code payment has become a mainstream

……

……

Personnel data file system

Educationalbackground

Politicaloutlook

Politicaloutlook

Moral styleOrganize

the transfer

workEvaluation File update

record

Workingconditions

Evaluation File updaterecord

Moral styleUnit

transferwork

Figure 9: Personnel data file system.

DMZ district Core application area Data district

Database, sensitive dataApplication service, logs,database password

Web services, logs, keys

https https

User

Administrator/O&M personnel

VPN+SSH/VPN+HTTPSVPN+SSH

VPN+SSH

tls/jdbc

Figure 10: Security architecture diagram.


trend. But the popularity of online payment also makescriminals eager to move. They have devised all kinds of net-work traps, which are difficult to capture telecom frauds anddifficult to guard against malicious applications. Many peo-ple were cheated out of a lot of money because they did

not pay attention for a while. Teachers and students on cam-pus are also highly deceived places. As shown in Figure 11, itis an electronic payment flow chart.

As shown in Figure 12, it is a design drawing about elec-tronic payment security technology.

Consumption of teachersand students

Buy merchandise

Provide services

Notice oftransaction results

Confirm transactioninformation

Send a paymentrequest

Payment result

Deduction request

Bank Payment gateway

Online payment

Merchant

Figure 11: Electronic payment process.

Information confidentiality Identityauthentication Integrity Non-repudiation

Application system layer

3DS agreementSecurity protocol layerSSL/TLS agreement

SET agreement

Symmetric/asymmetricencryption

Encryption technologylayer

Identity authenticationlayer

Network service layer

Firewall Network security software Kill virus software

Digital signature

CA certification

Figure 12: Electronic payment security technology.


3.2.3. Positioning System Usage. In order to ensure the safetyof school resources and the safety of teachers and studentswhen they are mobilized, the system will use the school posi-tioning system (generally placed on the virtual campus APPand various school infrastructures) to identify whether per-sonnel are in the safe area, and only after confirming theiridentities and authorities can various resources be called toensure personal safety. Figure 13 shows a flowchart of thepositioning system.

3.3. Convenience Services

3.3.1. Intelligent Control of Classroom Equipment. Old-fash-ioned classroom conditions are average, There are deficien-cies in many aspects, such as air circulation, difficultdebugging of projection equipment, too old-fashioned com-puters, and serious water and electricity consumption. After

the classroom is updated, a new intelligent integrated class-room will be built, which can easily meet the requirementsof teachers and students, and will be more conducive toteachers and students to carry out various teaching activitiesand even provide distance teaching services for students.The schematic diagram is shown in Figure 14.

3.3.2. Intelligent Control of Laboratory Equipment. Labora-tory is a very important part in scientific research, teaching,and research. Experimental equipment and utensils shouldbe more accurate and strict, and there should be a safety pro-tection system to ensure the safety and integrity of experi-mental equipment. Special personnel should regularlycheck and maintain the cleanliness of the laboratory andcheck the laboratory situation. The structure is shown inFigure 15.

Lighting control node

Infrared control of airconditioner

Automatic temperatureand humidity regulation

Smoke alarm

Broadcast

Access control

Other function nodes

Intelligent classroom

Coordinatorinductive

equipment

Master control equipment

Campus network InternetTerminal

(mobile phone, PC,TV equipment...)

Server

Figure 14: Functional structure diagram of intelligent classroom.

Begin

System positioning

Yes

No

Is it in a safearea?

End

Confirm one’sidentity Confirm permissions

Figure 13: Positioning system.


3.3.3. Application of Face Recognition. The traditional iden-tity authentication method, which is manually checked andapproved or card-type, consumes manpower and materialresources and is prone to flaws, which causes hidden dangers

to campus security. Therefore, using artificial intelligencetechnology and Internet of Things technology, we introducesystem equipment with face recognition application andplace it in school gates, dormitories, laboratories, studyrooms, libraries, and other access control places that needidentity verification. Teachers and students only need to

Table 1: Server requirements.

Hardware platform type PC server

CPU 6G

Memory 8G

Hard disk 60G

Operating system Windows or Linux

Database management system Oracle

DescriptionAccording to the requirements

of data exchange

Table 2: Development environment.

Category Describe

Operating system Win10

IDE 2021.1.1

JAVA JDK15

Database Oracle\MySQL

Application server Tomcat8.0

Backup Journal Feedback Error correctionVirtual campus

Authenticated login

Campus consumption

Course attendance

Personal data enquiry

Sports healthmanagement

Curriculum resourcesharing

Importantannouncement

Book borrowing

Site borrowing

Other functions

GIS geographicinformation navigation

Course lectureallocation

Figure 16: Virtual campus.

Lighting control node Infrared control of airconditioner

Automatic temperatureand humidity regulation

Smoke alarm

Monitoring

Broadcast Access control

Other function nodes

Intelligent laboratory

Campus Terminal

(mobile phone, PC,TV equipment...)

Attendance

Check

Laboratory maintenance

Record

Figure 15: Functional structure diagram of intelligent laboratory.


complete the step of “brushing their faces,” and they can usethe corresponding venues and equipment resources accord-ing to their respective authorities. In this way, the inconve-nience caused by numerous certificates is perfectly solved,and the work problems such as manpower, materialresources, and time cost are greatly reduced. For outsiders,the system will give corresponding registration or alarm,which greatly improves the security of campus.

3.3.4. Virtual Campus Services. Virtual campus servicesinclude personal data inquiry, important announcement,GIS geographic information navigation, campus consump-tion, sports health management, venue borrowing, courselecture distribution, course attendance, course resource shar-ing, book borrowing, and other services. Teachers and stu-dents can enjoy convenient services only by logging in toAPP or website on PC or mobile phone and authenticatingtheir identities. The specific system interface of virtual cam-pus service is shown in Figure 16.

Begin

Identify resources

Select resources

Yes

Yes

Yes

Yes

Is there anerror?

No

No

No

No

No

Is the utilizationrate high?

Is it saturated? �e survey foundthat

Are all resources checked?

End

Is the problemidentified?

Figure 17: USE method.

Table 3: UI testing.

Number Test content Test results

1 Home navigation position Normal

2Navigation bar content layout interface

layoutNormal

3 Interface layout Normal

4 Text display Normal

5 Font size Normal

6 Garbled code None

7 Hyperlink Normal

8 Color styleAccordwith

9 Shortcut key Normal

10 Options button Normal

11 Text box, dialog box Normal

12 Clarity Clear


4. System Optimization Research

The performance optimization of the system is not simply todetect one by one or to wait for feedback from users whenthey find problems. We should first automatically monitorperformance problems and analyze them according to data.This is a long-term work, which requires continuous optimi-zation research.

4.1. Caching. Caching technology can help us to develophigh-performance and high-availability applications, saveresources, and achieve optimization. We will cache dataand computation results, as well as domain name resolutionand resource objects themselves, which have been accessed,and they are likely to be used again in the future. This canreduce the waste or consumption of system resources andnetwork resources to a certain extent.

4.2. Inertia. In order to save system resources, the systemdelays the calculation to some extent. When necessary, elim-inate those parts that do not need to be calculated, and carryout the most precise calculation. This mode of operation can

make the system have a “gap” to rest. Instead of running athigh speed for a long time, it will cause irreversible damageto the life and performance of the system and cause a lotof troubles to maintenance personnel.

4.3. Code Quality. Excellent code is often efficient, usuallywith comments and code; code efficiency is very good, sav-ing a lot of unnecessary work and trouble. When doing someoptimization work, we should carefully modify our codeunless we have to, because a little change can cause seriousimpact. The quality of the code and the logic of the codeare important. Therefore, it is proposed that we should notoptimize too early or overoptimize.

5. System Function Test and Analysis

Using professional testing system tools to test the campuseducation information system, according to the experimen-tal results, the experimental analysis is given to determinethe future improvement scheme and optimize the perfor-mance and function of the detailed system.

Table 4: Compatibility testing.

Number Test caseTestresults

1 It can be installed and enabled normally on different platforms, without card machine or flashback phenomenon. Pass

2Login, page browsing, search, comment, and other interfaces have no deformation, occlusion, uncoordinated size, and

other problems and can be scaled and displayed at different resolutions.Pass

3 Verify that interactive controls such as text boxes and keys in the interface can click and respond normally. Pass

4Verify that the controls in the interface can load network content, and the icons and texts of a single table item/list

item have no distortion and occlusion.Pass

5 Verify that the font and resolution in the interface are scaled to a certain extent. Pass

Table 5: Response time of original system.

Concurrent request traffic 100 1000 1900 2800 3700 4600

Average response time per request (ms) of the original system 25 100 168 228 307 411

0 50 100 150 200 250 300 350 400 450

100

1000

1900

2800

3700

4600

Average response time per request (ms) of the original system

Figure 18: Response time.


5.1. Running Environment. The environment in which thesystem runs is shown in Tables 1 and 2.

5.2. USE Method for Performance Analysis. Check the utili-zation, saturation, and errors of all resources, as shown inFigure 17.

5.3. UI Testing. Through the Google Chrome browser man-ual operation identification detection, the main test platformoperation is interface rationality. The test results are shownin Table 3.

5.4. Compatibility Testing. Test the compatibility of the sys-tem with software and hardware, as shown in Table 4.

5.5. System Performance Comparison. We will simulate twosystems, one is the system before the transformation, andthe other is the newly designed campus education informa-tion system. Because it is a simulation system, we considermany factors, such as cost, time, and experimental condi-tions. According to past experience, we reduce the systemto an equal scale, which is convenient for the experiment.

Table 6: Response time of new system.

Concurrent request traffic 100 1000 1900 2800 3700 4600

Average response time per request for new system (ms) 10 57 123 196 249 336

100

1000

1900

2800

3700

4600

Average response time per request for new system (ms)

0 50 100 150 200 250 300 350 400

Figure 19: Response time.

0%

5%

10%

15%

20%

25%

30%

Original systemNew system

Stud

ent q

uart

ers

Cant

een

Libr

ary

Trai

ning

room

Teac

hing

bui

ldin

g

Labo

rato

ry

Who

le sc

hool

mon

itorin

g

Scho

ol g

ate e

ntra

nce g

uard

Syste

m ce

nter

Gym

nasiu

m

Infir

mar

y

Figure 20: Comparison of regional power consumption between the original system and the new system.


5.5.1. System Response Time. Response time refers to thetime taken to execute a request or task. Response time meanswhether the performance of the system meets the require-ments and whether it can bring smooth use effect. We setup six groups with visits of 100, 1000, 1900, 2800, 3700,and 4600.

(1) The data of the original system are shown in Table 5.

The average response time per request of the originalsystem is shown in Figure 18.

(2) Data for the new system are shown in Table 6.

The average response time per request for the newsystem is shown in Figure 19.

(3) The following is the analysis of experimental results:

Comparing the data of the original system with the newsystem, it is not difficult to find that after adopting theInternet of Things and artificial intelligence technology,the average response time of our system for each requestis obviously reduced, which is 21.6% less than that of theoriginal system, and the working efficiency of the systemis greatly improved.

5.5.2. Area System Power Consumption. In response to thenational call to save resources, the power consumed by thesystem is also an important index to test the systemperformance.

(1) The regional data of the original system and the newsystem are shown in Figure 20.


We can clearly see from the figure that most areas of thenew system consume less power than the original system,and the overall power consumption decreases by 1.43% onaverage. Only the school access control and system centerhave different increases in power consumption.

5.5.3. Usability Testing. There are 12 test points in this partof the test, which test whether the software, hardware, andnetwork conditions meet the requirements and the users’evaluation feelings. The test part is the user’s feeling afterusing the system for a long time (the time is set to 30 days):1 stands for very bad, 2 stands for very bad, 3 stands for bad,4 stands for average, 5 stands for good, 6 stands for excellent,and 7 stands for perfect.

(1) Data comparison between the original system andthe new system is shown in Table 7.

The comparison diagram between the original systemand the new system is shown in Figure 21.

(2) The following is the analysis of the test results:

As can be seen from the chart, users’ evaluation of theoriginal system is very bad, showing extreme and fluctuatingvalues. However, the evaluation of the new system is stable,

Table 7: Data of the original system and the new system.

Test point T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12

Original system 4 5 5 3 6 3 2 5 1 5 6 4

New system 5 6 6 5 5 7 5 5 7 4 5 6

0

1

2

3

4

5

6

7

8

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12Original systemNew system

Figure 21: Data comparison diagram.


the use effect is very good, and the fluctuation value is verysmall.

5.6. Apriori Algorithm Analysis. The experimental settingdata (D) is 100000 pieces, and the minimum confidence(C) is fixed at 0.75 without changing.

(1) Fixed data volume (D) changes minimum support (s):

The data are shown in Table 8.The time consumption of minimum support is shown in

Figure 4.

(2) Fixed minimum support (s) changes the amount ofdata (D):

The data are shown in Table 9.The amount of data consumes time, as shown in

Figure 5.


As shown in the figure, the more data is, the more time ittakes. When the amount of data is constant, the timedecreases gradually with the increase of the minimumsupport.

6. Conclusion

To sum up, the algorithms and technologies used in thisresearch have obvious progress and superiority. After thenetwork upgrade, compared with the old and backward sys-tems used in previous schools, the current new system hasstronger update iteration, stronger intelligent optimization,and stronger user experience. However, it should be notedthat our testing of the system is only in the initial stage. Ifwe want to put it into the market formally, there are stillmore details to be improved and optimized in the processof putting it into use. With the passage of time and theupdate of technology, there is still many work that needsto be adjusted to be studied by later generations.

Data Availability

The experimental data used to support the findings of thisstudy are available from the corresponding author uponrequest.

Conflicts of Interest

The author declares that there are no conflicts of interestregarding this work.

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Num/10000 10 20 30 40 50 60 70 80 90 100

Time consumed (s) 27 63 86 112 139 144 189 203 243 299

Table 8: Effect of minimum support (s) on performance.

Minsup 1000 5000 10000 15000 20000 25000 30000 35000 40000

Time consumed (s) 26 28 27 24 15 11 6 3 1


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