# Design and Optimization of University Management Information System Based on Internet of Things and Intelligent Computing Model

Authors: Qin Lei; Yuxi Li; Shi Yan
Journal: Journal of Sensors
 (2022)

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## Abstract

After the preliminary design of university management information system is completed, the related systems are optimized through IOT and intelligent computing. The data of the system is monitored and trained through IOT. Through the training data, it can be seen that the maximum difference between the actual output and the predicted output data of the Internet of Things is about 4. By calculating the error percentage of the predicted and actual output values, it is found that the maximum error percentage is about 0.0632, and less than 0.1 is a normal error. The IIA model is compared with MQT model, HAMT model, and SAR model in four different function states. Through the analysis of iteration times, it is found that the best value of IIA model is 12 and the average value is 14. Compared with the other three models, the convergence degree is higher, the calculation time is shorter, and the calculation results are more accurate. In order to explore whether the IIA optimization model proposed in this paper can run normally, the performance of the optimization system is tested by dividing the number of users into three groups: 100, 400, and 1000, and testing and analyzing the average response time, peak traffic response time, and test conclusion. According to the data, the average response time of IIA optimization model system is 2.031, 3.211, and 4.421. The average response time of related systems in traditional universities is 3.075, 4.563, and 5.097, respectively, and the test conclusions are all passed. According to the data analysis, it is known that the average response time of IIA optimization system is greatly reduced compared with the traditional system, which shows that this model has a good optimization effect for management information system. Finally, the success rate of IIA optimization model and traditional system is analyzed, which shows that performance is greatly improved after IIA optimization model is optimized. At present, the management information system is of great help to the entry of college students’ information. The management information system is used to enter, classify, and manage student information. The design and optimization of the system through IOT technology and intelligent computing-related models are helpful in improving the average response time of the system and improving the data processing capability of the system, which is convenient for register student information.


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## Body

## 1. Introduction

Through IOT technology with intelligent computing to design and optimize the university management information system, the design process includes admission, educational administration system, and related units. Through the data monitoring and training to improve the operation speed, through the system test to judge the optimization performance, this paper optimizes the system by proposing the IIA optimization model. After optimizing the IIA model, it analyzes and compares the performance-related data. Compared with other models, the IIA model has the advantage of improving the data computing ability and the success rate. To facilitate and the life of urban residents, IOT and other technologies are combined [1]. Through relevant research to achieve the Internet of Things for the later more convenient to meet people’s needs to prepare [2], create and analyze the physical world and the real-time state of applications. Involve intelligent devices in the work of related enterprises to blur the boundaries between virtual world and real world [3]. In order to explore the connection between the Internet of Things and many fields, we analyze the basic state, application, and characteristics of the Internet of Things and also study and analyze the sensor network structure which is related to the Internet of Things. Through a series of analysis, this paper makes analysis and planning suggestions for the related applications of the Internet of Things in order to maximize the use value of the Internet of Things [4]. The concept that the Internet of Things is a belated communication world view is obtained [5]. Through the research and analysis of intelligent computing, we can see that it plays an important role in medical planning, diagnosis, and treatment. Combining ICM algorithm with GA, ANN, FL, and other algorithms, a study illustrates the role of intelligent computing in the medical field. Through the combination of KBS and ICM methods, RBR-ANN and other algorithms are obtained. Therefore, it is concluded that this method is helpful to the development of medical field and novice researchers in most medical diagnosis [6]. The uncertainty of receiver position plays an important role in passive source location. In this paper, the joint location estimation model is introduced to analyze the location accuracy reduction caused by the uncertainty of receiver position. The calculation results show that this method is more accurate than other methods when the TDOA calculation error is large [7]. In this paper, the potential physical meaning and domain of tanh function and sigmoid function are studied and analyzed. Through correlation analysis, we know that ANN such as tanh function and sigmoid function cannot be deduced accurately in the range of data used for model calibration. Attribute recording and statistical verification for different data subsets [8]. Through the research of cable induction and LWD tools, the concept of inductive resistivity equipment is studied. This paper explores the potential benefits of electromagnetic dipoles by analyzing related models and finds that the research results are similar to those of previous studies by analyzing mandrel, borehole, and intrusion effects [9]. Combining GA model with initial model and optimizing parameters, the following conclusions can be obtained, which can be carried out without knowing any prior knowledge. Through the research, it is found that this method greatly optimizes the random and subjective problems of traditional methods. This experiment also lays a theoretical foundation for the next large-scale application [10]. An interaction model between rational and selfish agents for coding is called DAG model. Two models are used to deal with the problems encountered in modeling calculation in real life to design and show interesting modeling cases. At the same time, the two methods proposed in this paper are verified, and the results show that the results obtained by this model are novel [11]. The research goal is to improve enterprise information system and realize the new management mode to break through the development direction of enterprises to e-commerce. The updated enterprise information system can improve the accuracy of information, thus improving the cooperation satisfaction between customers and employees [12]. In this paper, IT technology is very useful in the knowledge, information, and other aspects of promotion. The improvement of university system under IT technology the improvement of multimedia technology is carried out in the fields of library service, teacher activities, and administration. In order to maximize the potential and utility of IT technology under the goal of system reform [13], this paper explains the information system through a series of studies. According to the research results, the successful operation of information system is supported by many fields such as data accessibility, reliability, consistency, and relevance. In order to understand the defects of the information system, 863 interviewees were investigated to improve the defects faced by the information system and related management problems [14]. In order to model the system design and optimization, and to minimize the variance of reliability estimation in the modeling process, four different models are used to demonstrate. The design problem is solved by four model designs, and the correlation is also considered [15].


## 2. Design of University Management Information System

### 2.1. IoT Structure

The structure of the Internet of Things includes three aspects: platform layer, transport layer, and physical layer [16]. The security problems faced by the platform layer are system failures and virus attacks, and the technical solution is to verify relevant identity information [17]. Transport layer security issues include network theft and data theft, and the related solution to its technology is to encrypt and protect the transmitted information through related keys to prevent information leakage. The security problems faced by the physical layer include the security problems of devices and terminals, and the technical solutions include physical protection and access control [18] as shown in Figure 1.Figure 1
Internet of Things structure.


### 2.2. Process of University Management Information System

The design of university management information is as follows. The design of university management information includes the admission of university students, and the admission information includes basic information, admission management, and accommodation management to report student-related information [19]. Information management for students in school includes student status management, related files, semester registration, student status change, and graduation processing. Financial assistance for poor students’ enrollment includes student aid processing, poverty subsidies, and student loans [20]. The educational administration system needs to manage the relevant student status information. For the employment management of graduates, it can efficiently manage the graduates’ leaving school by analyzing and summarizing the employment intentions of relevant units [21] as shown in Figure 2.Figure 2
Design of efficient management information system.For admission, you need to enter student information, colleges, departments, accommodation management, and other information.The educational administration system includes registering the student’s file status information.Relevant units mainly provide consultation on graduate employment issues.


## 2.1. IoT Structure

The structure of the Internet of Things includes three aspects: platform layer, transport layer, and physical layer [16]. The security problems faced by the platform layer are system failures and virus attacks, and the technical solution is to verify relevant identity information [17]. Transport layer security issues include network theft and data theft, and the related solution to its technology is to encrypt and protect the transmitted information through related keys to prevent information leakage. The security problems faced by the physical layer include the security problems of devices and terminals, and the technical solutions include physical protection and access control [18] as shown in Figure 1.Figure 1
Internet of Things structure.


## 2.2. Process of University Management Information System

The design of university management information is as follows. The design of university management information includes the admission of university students, and the admission information includes basic information, admission management, and accommodation management to report student-related information [19]. Information management for students in school includes student status management, related files, semester registration, student status change, and graduation processing. Financial assistance for poor students’ enrollment includes student aid processing, poverty subsidies, and student loans [20]. The educational administration system needs to manage the relevant student status information. For the employment management of graduates, it can efficiently manage the graduates’ leaving school by analyzing and summarizing the employment intentions of relevant units [21] as shown in Figure 2.Figure 2
Design of efficient management information system.For admission, you need to enter student information, colleges, departments, accommodation management, and other information.The educational administration system includes registering the student’s file status information.Relevant units mainly provide consultation on graduate employment issues.


## 3. Correlation Formula

### 3.1. IoT Algorithm Definition

#### 3.1.1. Establish

The range of D is [1,n−1].Admin stands for administrator; Fog stands for Fog Computing; Device stands for a sensing device.d is a random number key.
(1)PKAdmin=dAdminG,E,G,n,PSAdmin=dAdmin,(2)PKFog=dFogG,E,G,n,PSFog=dFog,(3)PKDevice=dDeviceG,E,G,n,PSDevice=dDevice,(4)Mx,y=x=Q∗KIDp+j,Qisalargeinteger,0≤j≤Qy2≡x3+ax+bmodp,TakeywithsmallerKIDpoffset,(5)EKIDp=EncPKFogM=rG,M+rPKFog=rG,M+rdFogG.


#### 3.1.2. Certification Stage

(6)Mx,y=DecPSFogEKIDp=DecPSFogrG,M+rPKFog=M+rdFogG−dFogrG=Mx,y,(7)KIDp=x/Q,x/Qisthelargestintegerlessthanorequaltox/Q,(8)r1=x1Pmodn,(9)s1=k1−1HTokenDevice1+r1PSDevice1modn=k1−1HTokenDevice1+r1dDevice1modn,(10)P′=U1G+U2PKDevice1=U1G+U2dDevice1G=s1−1HTokenDevice1+s1−1r1dDevice1G=s1−1HTokenDevice1+r1dDevice1G.IfP=P′, the verification is successful:
(11)P=k1G=s1−1HTokenDevice1+r1dDevice1Gmodn=P′.


### 3.2. Intelligent Computing

#### 3.2.1. Bayesian Optimization

(12)X∗=argmaxx⊆X⊆Rdfx,where x is the decision variable of ddimension [22].
(13)pf∣D1:t=pD1:t∣fpfpD1:tBayesian optimization theorem [23] is a method to search for the global extremum of function in random process and probability space.


#### 3.2.2. Probability Formula

(14)fX∼NmX,KX,XT,mX=EfX,KX,XT=EfX−mXfX−mXT.Covariance matrixK.
(15)pf∣X,θ=N0,Σ,(16)py∣f=Nf,σ2I,(17)py∣X,θ=∫py∣fpf∣X,θdf=N0,∑+σ2I.The Gaussian process is shown in(18)yf∗∼N0,∑+σ2IK∗K∗TK∗∗.


### 3.3. System Design and Optimization

#### 3.3.1. Regression Analysis

(19)y=a+bx,(20)yi=a+bxi+εiεi∼N0,δ2,(21)yk=a+∑i=1mbixik+εkk=1,2,⋯,n,where M is m independent variables.


#### 3.3.2. System Test

(22)C=nLT.The time of performance test [24] is expressed by Formula (23) and Formula (24).
(23)F=Nvu×RT,(24)R=TTs.User thinking time is expressed by Formula (24).
(25)Z=p+t+k.


## 3.1. IoT Algorithm Definition

### 3.1.1. Establish

The range of D is [1,n−1].Admin stands for administrator; Fog stands for Fog Computing; Device stands for a sensing device.d is a random number key.
(1)PKAdmin=dAdminG,E,G,n,PSAdmin=dAdmin,(2)PKFog=dFogG,E,G,n,PSFog=dFog,(3)PKDevice=dDeviceG,E,G,n,PSDevice=dDevice,(4)Mx,y=x=Q∗KIDp+j,Qisalargeinteger,0≤j≤Qy2≡x3+ax+bmodp,TakeywithsmallerKIDpoffset,(5)EKIDp=EncPKFogM=rG,M+rPKFog=rG,M+rdFogG.


### 3.1.2. Certification Stage

(6)Mx,y=DecPSFogEKIDp=DecPSFogrG,M+rPKFog=M+rdFogG−dFogrG=Mx,y,(7)KIDp=x/Q,x/Qisthelargestintegerlessthanorequaltox/Q,(8)r1=x1Pmodn,(9)s1=k1−1HTokenDevice1+r1PSDevice1modn=k1−1HTokenDevice1+r1dDevice1modn,(10)P′=U1G+U2PKDevice1=U1G+U2dDevice1G=s1−1HTokenDevice1+s1−1r1dDevice1G=s1−1HTokenDevice1+r1dDevice1G.IfP=P′, the verification is successful:
(11)P=k1G=s1−1HTokenDevice1+r1dDevice1Gmodn=P′.


## 3.1.1. Establish

The range of D is [1,n−1].Admin stands for administrator; Fog stands for Fog Computing; Device stands for a sensing device.d is a random number key.
(1)PKAdmin=dAdminG,E,G,n,PSAdmin=dAdmin,(2)PKFog=dFogG,E,G,n,PSFog=dFog,(3)PKDevice=dDeviceG,E,G,n,PSDevice=dDevice,(4)Mx,y=x=Q∗KIDp+j,Qisalargeinteger,0≤j≤Qy2≡x3+ax+bmodp,TakeywithsmallerKIDpoffset,(5)EKIDp=EncPKFogM=rG,M+rPKFog=rG,M+rdFogG.


## 3.1.2. Certification Stage

(6)Mx,y=DecPSFogEKIDp=DecPSFogrG,M+rPKFog=M+rdFogG−dFogrG=Mx,y,(7)KIDp=x/Q,x/Qisthelargestintegerlessthanorequaltox/Q,(8)r1=x1Pmodn,(9)s1=k1−1HTokenDevice1+r1PSDevice1modn=k1−1HTokenDevice1+r1dDevice1modn,(10)P′=U1G+U2PKDevice1=U1G+U2dDevice1G=s1−1HTokenDevice1+s1−1r1dDevice1G=s1−1HTokenDevice1+r1dDevice1G.IfP=P′, the verification is successful:
(11)P=k1G=s1−1HTokenDevice1+r1dDevice1Gmodn=P′.


## 3.2. Intelligent Computing

### 3.2.1. Bayesian Optimization

(12)X∗=argmaxx⊆X⊆Rdfx,where x is the decision variable of ddimension [22].
(13)pf∣D1:t=pD1:t∣fpfpD1:tBayesian optimization theorem [23] is a method to search for the global extremum of function in random process and probability space.


### 3.2.2. Probability Formula

(14)fX∼NmX,KX,XT,mX=EfX,KX,XT=EfX−mXfX−mXT.Covariance matrixK.
(15)pf∣X,θ=N0,Σ,(16)py∣f=Nf,σ2I,(17)py∣X,θ=∫py∣fpf∣X,θdf=N0,∑+σ2I.The Gaussian process is shown in(18)yf∗∼N0,∑+σ2IK∗K∗TK∗∗.


## 3.2.1. Bayesian Optimization

(12)X∗=argmaxx⊆X⊆Rdfx,where x is the decision variable of ddimension [22].
(13)pf∣D1:t=pD1:t∣fpfpD1:tBayesian optimization theorem [23] is a method to search for the global extremum of function in random process and probability space.


## 3.2.2. Probability Formula

(14)fX∼NmX,KX,XT,mX=EfX,KX,XT=EfX−mXfX−mXT.Covariance matrixK.
(15)pf∣X,θ=N0,Σ,(16)py∣f=Nf,σ2I,(17)py∣X,θ=∫py∣fpf∣X,θdf=N0,∑+σ2I.The Gaussian process is shown in(18)yf∗∼N0,∑+σ2IK∗K∗TK∗∗.


## 3.3. System Design and Optimization

### 3.3.1. Regression Analysis

(19)y=a+bx,(20)yi=a+bxi+εiεi∼N0,δ2,(21)yk=a+∑i=1mbixik+εkk=1,2,⋯,n,where M is m independent variables.


### 3.3.2. System Test

(22)C=nLT.The time of performance test [24] is expressed by Formula (23) and Formula (24).
(23)F=Nvu×RT,(24)R=TTs.User thinking time is expressed by Formula (24).
(25)Z=p+t+k.


## 3.3.1. Regression Analysis

(19)y=a+bx,(20)yi=a+bxi+εiεi∼N0,δ2,(21)yk=a+∑i=1mbixik+εkk=1,2,⋯,n,where M is m independent variables.


## 3.3.2. System Test

(22)C=nLT.The time of performance test [24] is expressed by Formula (23) and Formula (24).
(23)F=Nvu×RT,(24)R=TTs.User thinking time is expressed by Formula (24).
(25)Z=p+t+k.


## 4. Design and Optimization of Management Information

### 4.1. Data Monitoring in Internet of Things

#### 4.1.1. Data Training

Through the data training in the data monitoring under the related IOT, train the data multiple times, and the training results as shown in the figure are obtained. When the training times range from 0 to 16 times, the training variance results is 104 at first, and when the training times reach 4 times, the mean square error drops to about 3.4364, and when the training times reach 16 times, the mean square error is about 3.4364. Through the relevant data training test, it can be seen that the best training result is about 3.4364 mean square error, and when the training times exceed four times, the training result reaches the best training result. Through data investigation and test, we can see that the mean square error of the target is about 10-2, and there is a big difference between the training results. We should constantly train the data, so as to select the appropriate test times according to the training results to improve the data monitoring under IOT technology and realize the high efficiency of the university management information system. Improve the accuracy and applicability of the system through continuous training [25] as shown in Figure 3.Figure 3
Data training.


#### 4.1.2. Sample Data Comparison

By comparing and analyzing the training results of 30 groups of data tested by the management information system under IOT, the training effect under IOT can be obtained by comparing the predicted value with the actual value. Through chart analysis, it can be seen that in 30 groups of sample groups, the trend of actual output data and predicted output data is not much different. When the number of samples is 5 groups, the predicted output value is 69.833, and the actual output value is 71.229, and the comparison results are not much different. When the sample number is 10, the actual output value is 71.566, the predicted output value is 74.995, and the maximum difference value in 30 sample groups is controlled within 5. When the number of groups is 20, the actual output value is closest to the predicted output value. Through images and data, we can see that the training advantages of university management information system data under IOT are more prominent, which is basically consistent with reality, as shown in Figure4.Figure 4
Comparison of sample data.Through the analysis of the error percentage between the actual output and the predicted output value, we can see that the error percentage value is small in 30 groups of data through the trend of line chart image, which shows that the detection value is more accurate. In 0-5 groups, the highest error percentage is -0. 0231, the lowest is -0. 0227, and the numerical error percentage is relatively small. In 5-10 groups, the maximum error percentage is 0.0372 in 10 groups, and the error less than 0.1 is small. The highest error percentage in 10-15 groups is 0.0172. Among 15-20 groups of data, the maximum error value of 18 groups of data is 0.0632, which is the largest group in the whole group but still less than 0.1. The average error percentage of 20-25 groups is about 0.0031, which can be ignored. The highest error percentage in 25-30 groups is 0.0279. Through the analysis of the chart, it can be seen that the error percentage values are small; it shows that the difference between the actual value and the predicted value is small; and the value is more accurate. Therefore, the Internet of Things technology is feasible for data monitoring of university management information. Through this test, the numerical difference between the actual value and the predicted value can be reduced to provide a better performance university management information system as shown in Figure5.Figure 5
Error percentage analysis.The relevant system data is monitored and trained through the Internet of Things, and relevant conclusions are drawn through the analysis of the sample data and the error percentage. The Internet of Things can improve the calculation speed of the system data and improve the calculation accuracy. Therefore, IOT can improve the optimization of system data.


### 4.2. Intelligent Computing Information Management System Model of Colleges and Universities

#### 4.2.1. Comparison of Optimization Structures of Different Models

Through the four different models in four different function states for the best value and average value of comparative analysis, under the condition of F1 function, the best value and average value of IIA model are the lowest, which shows that the global ability of IIA model is better. The optimum value of IIA model is 6.1503, and the average value is 20.0537 in F2 function state. The optimum value and average value of IIA model in F3 function state are 0 and -4.0532, respectively; the optimum value of IIA model in F4 function state is 0, and the average value is 0.573. Compared with MQT model, HAMT model, and SAR model, the optimal value and average value of IIA model are analyzed by relevant numerical analysis, and the optimization ability of IIA model is higher, so it should be preferred for university management system under intelligent computing as shown in Table1.Table 1
Comparison of optimized structures.
FunctionIIAMQTHAMTSAROptimum valueAverageOptimum valueAverageOptimum valueAverageOptimum valueAverageF1000-2.873101.018717.11487.625F26.150320.0537-1.9217.631-0.00935.842-0.900313.532F30-4.053204.875-0.27381.43274.281610.007F400.5731.2570.7932.5740.5350.5520.983By comparing the average optimal values of IIA model, MQT model, HAMT model, and SAR model in F1-F4 states, we can see that the average value of IIA model is lower, the accuracy is more accurate, the independence is better, the calculation time is shorter, and the accuracy is higher in F1, F3, and F4 states, so IIA optimization model should be preferred as shown in Figure6.Figure 6
Comparison of model optimization average value.


#### 4.2.2. Comparison of Iteration Times of Different Methods

By analyzing the iteration times of IIA model, MQT model, HAMT model, and SAR model in four different function states, the best value of IIA model is 12, and the average value is 14 in F1 function state, which is the lowest in comparison, while the best value of SAR model is 60, and the average value is 95, which is the highest among the four models. The optimum value and average value of IIA model are the lowest in the state of function F2-F4, which shows that the number of iterations is less, the operation time is lower, and the operation stability is better as shown in Table2.Table 2
Comparison of iteration times.
FunctionIIAMQTHAMTSAROptimum valueAverageOptimum valueAverageOptimum valueAverageOptimum valueAverageF11214245740936095F21447199380963356F351602881100100100100F44651100100100100100100The average value of IIA model is lower, the lowest is 14, and the highest is 60. The highest average value of MQT model is 100, and the lowest is 57. The highest average value of SAR model is 100, and the lowest is 56. Through the numerical analysis of the mean value, we can see that the IIA model has the lowest mean value, the least iteration times, the shorter operation time, and the higher accuracy and is superior to the other three models in convergence degree as shown in Figure7.Figure 7
Comparison of average iteration times.Comparing the models through the number of iterations, the data related to the IIA model is better in comparison, indicating that the convergence of the IIA model is better in comparison, so the IIA model has excellent optimization for the management information system of colleges and universities.


#### 4.2.3. Convergence Curve Analysis

The convergence curve shows that the convergence of IIA model is very fast, about 10 generations of convergence, MQT model about 40 generations of convergence, and HAMT model about 60 generations of convergence, and SAR model convergence is poor. The convergence of the four models from high to low is IIA model, MQT model, HAMT model, and finally SAR model. The convergence of IIA model is the fastest compared with other models, so IIA model should be preferentially selected to optimize the system under intelligent computing and improve the system performance and computing ability as shown in Figure8.Figure 8
Convergence curve analysis.


### 4.3. Performance Test of System Optimization Model

The university management information system under IIA model is compared with the system under blockchain and traditional management information system to test optimized system model.


#### 4.3.1. Test of University Management Information Blockchain

Through the research and test, the test conclusions are all passed, and the response time of peak traffic is slightly higher than the average response time. When the number of users is 100, 400, and 1000, the average response time is 0.965, 1.374, and 2.209, respectively, which is faster as shown in Table3.Table 3
Blockchain test.
Number of usersAverage response timeCorresponding time of peak flowTest conclusion1000.9651.098Pass4001.3741.895Pass10002.2092.873Pass


#### 4.3.2. IIA Optimization Model System Test under IOT and Intelligent Computing

IIA optimizes the system model under IOT and intelligent computing to detect three groups of experiments with 100, 400, and 1000 users, respectively, and the detection conclusions are all passed, with average response times of 2.031, 3.211, and 4.421, respectively, which are higher than those in blockchain scenarios as shown in Table4.Table 4
IIA model testing.
Number of usersAverage response timeCorresponding time of peak flowTest conclusion1002.0312.213Pass4003.2113.401Pass10004.4214.765Pass


#### 4.3.3. Testing of Traditional Information Management System

Three groups of data with 100, 400, and 1000 users are tested under the traditional university management information system, and the test conclusions are all passed. The average response time is 3.231 when the number of users is 100, 4.563 when the number of users is 400, and 5.097 when the number of users is 1000 as shown in Table5.Table 5
Traditional system testing.
Number of usersAverage response timeCorresponding time of peak flowTest conclusion1003.0753.231Pass4004.5634.771Pass10005.0975.192PassBy comparing and analyzing the average response time of university management information systems under blockchain environment, IIA optimization model, and traditional system, it can be seen from the image that the average response time of the system under IIA optimization model is slightly higher than that under blockchain environment, but it is greatly improved compared with the traditional system, which shows that IIA model under IOT and intelligent computing has obvious advantages for system optimization, which improves the average response time of the system and optimizes the system performance as shown in Figure9.Figure 9
Average response time analysis.According to the analysis of relevant data, the average response time under IIA optimization model is slightly higher than that under blockchain. The success rate is relatively low, but compared with the traditional university management information system, IIA model has greatly shortened the average response time and greatly improved the success rate. Compared with the traditional system, IIA model has better optimization ability, so IIA model has higher optimization ability for university management information system as shown in Table6.Table 6
Performance comparison.
SystemNumber of query requests1000150020002500300035004000BlockchainMean response time (MS)20325387120198255Success rate (%)100100100100100100100IIA optimization modelMean response time (MS)274263105144208298Success rate (%)10099.799.6299.599.4498.7999.25Traditional systemMean response time (MS)356287176263321397Success rate (%)10099.4399.2599.198.799.199By comparing and analyzing the average response time and success rate of blockchain, IIA optimization model, and traditional systems, the average response time and success rate of IIA optimization model are slightly lower than those of blockchain, but the overall performance is better than that of traditional systems. Therefore, through relevant system testing, the IIA model has an optimization effect on system analysis.The average response time of IIA model is obviously lower than that of traditional system through the query times of 1000, 1500, 2000, 2500, 3000, 3500, and 4000, so IIA model has strong optimization ability for this system and has passed the system optimization test as shown in Figure10.Figure 10
Average response time comparison.Through the analysis of the success rate in three states, we know that the success rate of IIA model is higher than that of traditional system, so IIA model also plays an optimization ability in the success rate, and improves the optimization ability and efficiency of management information system as shown in Figure11.Figure 11
Success ratio comparison.


## 4.1. Data Monitoring in Internet of Things

### 4.1.1. Data Training

Through the data training in the data monitoring under the related IOT, train the data multiple times, and the training results as shown in the figure are obtained. When the training times range from 0 to 16 times, the training variance results is 104 at first, and when the training times reach 4 times, the mean square error drops to about 3.4364, and when the training times reach 16 times, the mean square error is about 3.4364. Through the relevant data training test, it can be seen that the best training result is about 3.4364 mean square error, and when the training times exceed four times, the training result reaches the best training result. Through data investigation and test, we can see that the mean square error of the target is about 10-2, and there is a big difference between the training results. We should constantly train the data, so as to select the appropriate test times according to the training results to improve the data monitoring under IOT technology and realize the high efficiency of the university management information system. Improve the accuracy and applicability of the system through continuous training [25] as shown in Figure 3.Figure 3
Data training.


### 4.1.2. Sample Data Comparison

By comparing and analyzing the training results of 30 groups of data tested by the management information system under IOT, the training effect under IOT can be obtained by comparing the predicted value with the actual value. Through chart analysis, it can be seen that in 30 groups of sample groups, the trend of actual output data and predicted output data is not much different. When the number of samples is 5 groups, the predicted output value is 69.833, and the actual output value is 71.229, and the comparison results are not much different. When the sample number is 10, the actual output value is 71.566, the predicted output value is 74.995, and the maximum difference value in 30 sample groups is controlled within 5. When the number of groups is 20, the actual output value is closest to the predicted output value. Through images and data, we can see that the training advantages of university management information system data under IOT are more prominent, which is basically consistent with reality, as shown in Figure4.Figure 4
Comparison of sample data.Through the analysis of the error percentage between the actual output and the predicted output value, we can see that the error percentage value is small in 30 groups of data through the trend of line chart image, which shows that the detection value is more accurate. In 0-5 groups, the highest error percentage is -0. 0231, the lowest is -0. 0227, and the numerical error percentage is relatively small. In 5-10 groups, the maximum error percentage is 0.0372 in 10 groups, and the error less than 0.1 is small. The highest error percentage in 10-15 groups is 0.0172. Among 15-20 groups of data, the maximum error value of 18 groups of data is 0.0632, which is the largest group in the whole group but still less than 0.1. The average error percentage of 20-25 groups is about 0.0031, which can be ignored. The highest error percentage in 25-30 groups is 0.0279. Through the analysis of the chart, it can be seen that the error percentage values are small; it shows that the difference between the actual value and the predicted value is small; and the value is more accurate. Therefore, the Internet of Things technology is feasible for data monitoring of university management information. Through this test, the numerical difference between the actual value and the predicted value can be reduced to provide a better performance university management information system as shown in Figure5.Figure 5
Error percentage analysis.The relevant system data is monitored and trained through the Internet of Things, and relevant conclusions are drawn through the analysis of the sample data and the error percentage. The Internet of Things can improve the calculation speed of the system data and improve the calculation accuracy. Therefore, IOT can improve the optimization of system data.


## 4.1.1. Data Training

Through the data training in the data monitoring under the related IOT, train the data multiple times, and the training results as shown in the figure are obtained. When the training times range from 0 to 16 times, the training variance results is 104 at first, and when the training times reach 4 times, the mean square error drops to about 3.4364, and when the training times reach 16 times, the mean square error is about 3.4364. Through the relevant data training test, it can be seen that the best training result is about 3.4364 mean square error, and when the training times exceed four times, the training result reaches the best training result. Through data investigation and test, we can see that the mean square error of the target is about 10-2, and there is a big difference between the training results. We should constantly train the data, so as to select the appropriate test times according to the training results to improve the data monitoring under IOT technology and realize the high efficiency of the university management information system. Improve the accuracy and applicability of the system through continuous training [25] as shown in Figure 3.Figure 3
Data training.


## 4.1.2. Sample Data Comparison

By comparing and analyzing the training results of 30 groups of data tested by the management information system under IOT, the training effect under IOT can be obtained by comparing the predicted value with the actual value. Through chart analysis, it can be seen that in 30 groups of sample groups, the trend of actual output data and predicted output data is not much different. When the number of samples is 5 groups, the predicted output value is 69.833, and the actual output value is 71.229, and the comparison results are not much different. When the sample number is 10, the actual output value is 71.566, the predicted output value is 74.995, and the maximum difference value in 30 sample groups is controlled within 5. When the number of groups is 20, the actual output value is closest to the predicted output value. Through images and data, we can see that the training advantages of university management information system data under IOT are more prominent, which is basically consistent with reality, as shown in Figure4.Figure 4
Comparison of sample data.Through the analysis of the error percentage between the actual output and the predicted output value, we can see that the error percentage value is small in 30 groups of data through the trend of line chart image, which shows that the detection value is more accurate. In 0-5 groups, the highest error percentage is -0. 0231, the lowest is -0. 0227, and the numerical error percentage is relatively small. In 5-10 groups, the maximum error percentage is 0.0372 in 10 groups, and the error less than 0.1 is small. The highest error percentage in 10-15 groups is 0.0172. Among 15-20 groups of data, the maximum error value of 18 groups of data is 0.0632, which is the largest group in the whole group but still less than 0.1. The average error percentage of 20-25 groups is about 0.0031, which can be ignored. The highest error percentage in 25-30 groups is 0.0279. Through the analysis of the chart, it can be seen that the error percentage values are small; it shows that the difference between the actual value and the predicted value is small; and the value is more accurate. Therefore, the Internet of Things technology is feasible for data monitoring of university management information. Through this test, the numerical difference between the actual value and the predicted value can be reduced to provide a better performance university management information system as shown in Figure5.Figure 5
Error percentage analysis.The relevant system data is monitored and trained through the Internet of Things, and relevant conclusions are drawn through the analysis of the sample data and the error percentage. The Internet of Things can improve the calculation speed of the system data and improve the calculation accuracy. Therefore, IOT can improve the optimization of system data.


## 4.2. Intelligent Computing Information Management System Model of Colleges and Universities

### 4.2.1. Comparison of Optimization Structures of Different Models

Through the four different models in four different function states for the best value and average value of comparative analysis, under the condition of F1 function, the best value and average value of IIA model are the lowest, which shows that the global ability of IIA model is better. The optimum value of IIA model is 6.1503, and the average value is 20.0537 in F2 function state. The optimum value and average value of IIA model in F3 function state are 0 and -4.0532, respectively; the optimum value of IIA model in F4 function state is 0, and the average value is 0.573. Compared with MQT model, HAMT model, and SAR model, the optimal value and average value of IIA model are analyzed by relevant numerical analysis, and the optimization ability of IIA model is higher, so it should be preferred for university management system under intelligent computing as shown in Table1.Table 1
Comparison of optimized structures.
FunctionIIAMQTHAMTSAROptimum valueAverageOptimum valueAverageOptimum valueAverageOptimum valueAverageF1000-2.873101.018717.11487.625F26.150320.0537-1.9217.631-0.00935.842-0.900313.532F30-4.053204.875-0.27381.43274.281610.007F400.5731.2570.7932.5740.5350.5520.983By comparing the average optimal values of IIA model, MQT model, HAMT model, and SAR model in F1-F4 states, we can see that the average value of IIA model is lower, the accuracy is more accurate, the independence is better, the calculation time is shorter, and the accuracy is higher in F1, F3, and F4 states, so IIA optimization model should be preferred as shown in Figure6.Figure 6
Comparison of model optimization average value.


### 4.2.2. Comparison of Iteration Times of Different Methods

By analyzing the iteration times of IIA model, MQT model, HAMT model, and SAR model in four different function states, the best value of IIA model is 12, and the average value is 14 in F1 function state, which is the lowest in comparison, while the best value of SAR model is 60, and the average value is 95, which is the highest among the four models. The optimum value and average value of IIA model are the lowest in the state of function F2-F4, which shows that the number of iterations is less, the operation time is lower, and the operation stability is better as shown in Table2.Table 2
Comparison of iteration times.
FunctionIIAMQTHAMTSAROptimum valueAverageOptimum valueAverageOptimum valueAverageOptimum valueAverageF11214245740936095F21447199380963356F351602881100100100100F44651100100100100100100The average value of IIA model is lower, the lowest is 14, and the highest is 60. The highest average value of MQT model is 100, and the lowest is 57. The highest average value of SAR model is 100, and the lowest is 56. Through the numerical analysis of the mean value, we can see that the IIA model has the lowest mean value, the least iteration times, the shorter operation time, and the higher accuracy and is superior to the other three models in convergence degree as shown in Figure7.Figure 7
Comparison of average iteration times.Comparing the models through the number of iterations, the data related to the IIA model is better in comparison, indicating that the convergence of the IIA model is better in comparison, so the IIA model has excellent optimization for the management information system of colleges and universities.


### 4.2.3. Convergence Curve Analysis

The convergence curve shows that the convergence of IIA model is very fast, about 10 generations of convergence, MQT model about 40 generations of convergence, and HAMT model about 60 generations of convergence, and SAR model convergence is poor. The convergence of the four models from high to low is IIA model, MQT model, HAMT model, and finally SAR model. The convergence of IIA model is the fastest compared with other models, so IIA model should be preferentially selected to optimize the system under intelligent computing and improve the system performance and computing ability as shown in Figure8.Figure 8
Convergence curve analysis.


## 4.2.1. Comparison of Optimization Structures of Different Models

Through the four different models in four different function states for the best value and average value of comparative analysis, under the condition of F1 function, the best value and average value of IIA model are the lowest, which shows that the global ability of IIA model is better. The optimum value of IIA model is 6.1503, and the average value is 20.0537 in F2 function state. The optimum value and average value of IIA model in F3 function state are 0 and -4.0532, respectively; the optimum value of IIA model in F4 function state is 0, and the average value is 0.573. Compared with MQT model, HAMT model, and SAR model, the optimal value and average value of IIA model are analyzed by relevant numerical analysis, and the optimization ability of IIA model is higher, so it should be preferred for university management system under intelligent computing as shown in Table1.Table 1
Comparison of optimized structures.
FunctionIIAMQTHAMTSAROptimum valueAverageOptimum valueAverageOptimum valueAverageOptimum valueAverageF1000-2.873101.018717.11487.625F26.150320.0537-1.9217.631-0.00935.842-0.900313.532F30-4.053204.875-0.27381.43274.281610.007F400.5731.2570.7932.5740.5350.5520.983By comparing the average optimal values of IIA model, MQT model, HAMT model, and SAR model in F1-F4 states, we can see that the average value of IIA model is lower, the accuracy is more accurate, the independence is better, the calculation time is shorter, and the accuracy is higher in F1, F3, and F4 states, so IIA optimization model should be preferred as shown in Figure6.Figure 6
Comparison of model optimization average value.


## 4.2.2. Comparison of Iteration Times of Different Methods

By analyzing the iteration times of IIA model, MQT model, HAMT model, and SAR model in four different function states, the best value of IIA model is 12, and the average value is 14 in F1 function state, which is the lowest in comparison, while the best value of SAR model is 60, and the average value is 95, which is the highest among the four models. The optimum value and average value of IIA model are the lowest in the state of function F2-F4, which shows that the number of iterations is less, the operation time is lower, and the operation stability is better as shown in Table2.Table 2
Comparison of iteration times.
FunctionIIAMQTHAMTSAROptimum valueAverageOptimum valueAverageOptimum valueAverageOptimum valueAverageF11214245740936095F21447199380963356F351602881100100100100F44651100100100100100100The average value of IIA model is lower, the lowest is 14, and the highest is 60. The highest average value of MQT model is 100, and the lowest is 57. The highest average value of SAR model is 100, and the lowest is 56. Through the numerical analysis of the mean value, we can see that the IIA model has the lowest mean value, the least iteration times, the shorter operation time, and the higher accuracy and is superior to the other three models in convergence degree as shown in Figure7.Figure 7
Comparison of average iteration times.Comparing the models through the number of iterations, the data related to the IIA model is better in comparison, indicating that the convergence of the IIA model is better in comparison, so the IIA model has excellent optimization for the management information system of colleges and universities.


## 4.2.3. Convergence Curve Analysis

The convergence curve shows that the convergence of IIA model is very fast, about 10 generations of convergence, MQT model about 40 generations of convergence, and HAMT model about 60 generations of convergence, and SAR model convergence is poor. The convergence of the four models from high to low is IIA model, MQT model, HAMT model, and finally SAR model. The convergence of IIA model is the fastest compared with other models, so IIA model should be preferentially selected to optimize the system under intelligent computing and improve the system performance and computing ability as shown in Figure8.Figure 8
Convergence curve analysis.


## 4.3. Performance Test of System Optimization Model

The university management information system under IIA model is compared with the system under blockchain and traditional management information system to test optimized system model.


### 4.3.1. Test of University Management Information Blockchain

Through the research and test, the test conclusions are all passed, and the response time of peak traffic is slightly higher than the average response time. When the number of users is 100, 400, and 1000, the average response time is 0.965, 1.374, and 2.209, respectively, which is faster as shown in Table3.Table 3
Blockchain test.
Number of usersAverage response timeCorresponding time of peak flowTest conclusion1000.9651.098Pass4001.3741.895Pass10002.2092.873Pass


### 4.3.2. IIA Optimization Model System Test under IOT and Intelligent Computing

IIA optimizes the system model under IOT and intelligent computing to detect three groups of experiments with 100, 400, and 1000 users, respectively, and the detection conclusions are all passed, with average response times of 2.031, 3.211, and 4.421, respectively, which are higher than those in blockchain scenarios as shown in Table4.Table 4
IIA model testing.
Number of usersAverage response timeCorresponding time of peak flowTest conclusion1002.0312.213Pass4003.2113.401Pass10004.4214.765Pass


### 4.3.3. Testing of Traditional Information Management System

Three groups of data with 100, 400, and 1000 users are tested under the traditional university management information system, and the test conclusions are all passed. The average response time is 3.231 when the number of users is 100, 4.563 when the number of users is 400, and 5.097 when the number of users is 1000 as shown in Table5.Table 5
Traditional system testing.
Number of usersAverage response timeCorresponding time of peak flowTest conclusion1003.0753.231Pass4004.5634.771Pass10005.0975.192PassBy comparing and analyzing the average response time of university management information systems under blockchain environment, IIA optimization model, and traditional system, it can be seen from the image that the average response time of the system under IIA optimization model is slightly higher than that under blockchain environment, but it is greatly improved compared with the traditional system, which shows that IIA model under IOT and intelligent computing has obvious advantages for system optimization, which improves the average response time of the system and optimizes the system performance as shown in Figure9.Figure 9
Average response time analysis.According to the analysis of relevant data, the average response time under IIA optimization model is slightly higher than that under blockchain. The success rate is relatively low, but compared with the traditional university management information system, IIA model has greatly shortened the average response time and greatly improved the success rate. Compared with the traditional system, IIA model has better optimization ability, so IIA model has higher optimization ability for university management information system as shown in Table6.Table 6
Performance comparison.
SystemNumber of query requests1000150020002500300035004000BlockchainMean response time (MS)20325387120198255Success rate (%)100100100100100100100IIA optimization modelMean response time (MS)274263105144208298Success rate (%)10099.799.6299.599.4498.7999.25Traditional systemMean response time (MS)356287176263321397Success rate (%)10099.4399.2599.198.799.199By comparing and analyzing the average response time and success rate of blockchain, IIA optimization model, and traditional systems, the average response time and success rate of IIA optimization model are slightly lower than those of blockchain, but the overall performance is better than that of traditional systems. Therefore, through relevant system testing, the IIA model has an optimization effect on system analysis.The average response time of IIA model is obviously lower than that of traditional system through the query times of 1000, 1500, 2000, 2500, 3000, 3500, and 4000, so IIA model has strong optimization ability for this system and has passed the system optimization test as shown in Figure10.Figure 10
Average response time comparison.Through the analysis of the success rate in three states, we know that the success rate of IIA model is higher than that of traditional system, so IIA model also plays an optimization ability in the success rate, and improves the optimization ability and efficiency of management information system as shown in Figure11.Figure 11
Success ratio comparison.


## 4.3.1. Test of University Management Information Blockchain

Through the research and test, the test conclusions are all passed, and the response time of peak traffic is slightly higher than the average response time. When the number of users is 100, 400, and 1000, the average response time is 0.965, 1.374, and 2.209, respectively, which is faster as shown in Table3.Table 3
Blockchain test.
Number of usersAverage response timeCorresponding time of peak flowTest conclusion1000.9651.098Pass4001.3741.895Pass10002.2092.873Pass


## 4.3.2. IIA Optimization Model System Test under IOT and Intelligent Computing

IIA optimizes the system model under IOT and intelligent computing to detect three groups of experiments with 100, 400, and 1000 users, respectively, and the detection conclusions are all passed, with average response times of 2.031, 3.211, and 4.421, respectively, which are higher than those in blockchain scenarios as shown in Table4.Table 4
IIA model testing.
Number of usersAverage response timeCorresponding time of peak flowTest conclusion1002.0312.213Pass4003.2113.401Pass10004.4214.765Pass


## 4.3.3. Testing of Traditional Information Management System

Three groups of data with 100, 400, and 1000 users are tested under the traditional university management information system, and the test conclusions are all passed. The average response time is 3.231 when the number of users is 100, 4.563 when the number of users is 400, and 5.097 when the number of users is 1000 as shown in Table5.Table 5
Traditional system testing.
Number of usersAverage response timeCorresponding time of peak flowTest conclusion1003.0753.231Pass4004.5634.771Pass10005.0975.192PassBy comparing and analyzing the average response time of university management information systems under blockchain environment, IIA optimization model, and traditional system, it can be seen from the image that the average response time of the system under IIA optimization model is slightly higher than that under blockchain environment, but it is greatly improved compared with the traditional system, which shows that IIA model under IOT and intelligent computing has obvious advantages for system optimization, which improves the average response time of the system and optimizes the system performance as shown in Figure9.Figure 9
Average response time analysis.According to the analysis of relevant data, the average response time under IIA optimization model is slightly higher than that under blockchain. The success rate is relatively low, but compared with the traditional university management information system, IIA model has greatly shortened the average response time and greatly improved the success rate. Compared with the traditional system, IIA model has better optimization ability, so IIA model has higher optimization ability for university management information system as shown in Table6.Table 6
Performance comparison.
SystemNumber of query requests1000150020002500300035004000BlockchainMean response time (MS)20325387120198255Success rate (%)100100100100100100100IIA optimization modelMean response time (MS)274263105144208298Success rate (%)10099.799.6299.599.4498.7999.25Traditional systemMean response time (MS)356287176263321397Success rate (%)10099.4399.2599.198.799.199By comparing and analyzing the average response time and success rate of blockchain, IIA optimization model, and traditional systems, the average response time and success rate of IIA optimization model are slightly lower than those of blockchain, but the overall performance is better than that of traditional systems. Therefore, through relevant system testing, the IIA model has an optimization effect on system analysis.The average response time of IIA model is obviously lower than that of traditional system through the query times of 1000, 1500, 2000, 2500, 3000, 3500, and 4000, so IIA model has strong optimization ability for this system and has passed the system optimization test as shown in Figure10.Figure 10
Average response time comparison.Through the analysis of the success rate in three states, we know that the success rate of IIA model is higher than that of traditional system, so IIA model also plays an optimization ability in the success rate, and improves the optimization ability and efficiency of management information system as shown in Figure11.Figure 11
Success ratio comparison.


## 5. Conclusion

In order to design and optimize the management information system, the combination of IOT technology and intelligent computing model is adopted. Through IOT technology data training and sample data research and comparison, as well as error percentage analysis, IOT technology for university management information system data detection has a significant, obvious positive effect. Through the analysis of relevant data, we can see that the data error percentage under the Internet of Things technology is small, which shows that the technology has high accuracy and high efficiency. Through the comparison of IIA optimization model with MQT model, HAMT model, and SAR model in four different states, IIA model has the best advantage in operation time and efficiency. Finally, the IIA optimization model is tested to judge whether the optimization system can run normally. Through the test conclusion, we can see that the test of the system is through, but through the average response time and success rate of data analysis, we can see that IIA model has more advantages than the traditional system, which shows that IIA model has a good optimization ability for university management information system. Through the relevant performance tests, it is known that the IIA model proposed in this paper passes the performance tests and has good optimization ability. Therefore, for the management information system, the Internet of Things, intelligent computing, and IIA model should be combined to design and optimize the system to improve the computing and processing capabilities of the management information system.



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*Source: 1049535-2022-04-30.xml*