# Student Management Method of Private Colleges and Universities Based on Geographic Information System and WIFI

Authors: Dongmin Qiao; Huan Yang; Ying Liu
Journal: Advances in Multimedia
 (2022)

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## Abstract

In order to improve the student management effect of private colleges and universities, this paper combines geographic information system and WIFI to construct a private college student management system. In the structural design, this paper divides the platform into several modules according to the idea of modularization and narrates the function of each part. In the database design, this paper completes the construction of each function of the platform. In the process design, this paper determines the design process of the platform. Finally, this paper studies the key technologies involved in the system and evaluates students’ trajectory positioning and management effects in colleges and universities. The research shows that the student management system of private colleges and universities based on geographic information system and WIFI proposed in this paper can effectively improve the student management effect of private colleges and universities.


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## Body

## 1. Introduction

As an important part of China’s higher education, private colleges and universities have played a huge role in talent training. In recent years, private colleges and universities have made remarkable achievements, but since private education is still in the stage of development, there are inevitably some problems. In this case, it is necessary for the relevant personnel to strengthen the analysis and research on these problems and find out the solution to the problem.The administrators of colleges and universities should formulate a management system in line with the current situation of the school and make continuous adjustments to deal with changes in the school situation. In the selection of counselors, young teachers who are optimistic and have a serious work attitude should be selected. These teachers have just been put into teaching positions, and they are of the same age as the students. The generation gap is small, which facilitates the formation of a friendly relationship between teachers and students and facilitates the instructor to understand the students’ situation in a timely manner and conduct effective management. In the teacher training plan, on-the-job training for teachers is carried out regularly to guide teachers to actively improve teaching methods and teaching methods, so as to create a lively and interesting classroom. At the same time, it is necessary to maximize students’ learning efficiency, stimulate students’ enthusiasm for learning, transform classroom roles, turn students into classroom masters, realize autonomous learning, and effectively improve learning ability [1]. The faculty team of private colleges and universities is constantly growing, and many scholars and professors often come to private colleges to give lectures to students, broadening students’ horizons, which is of great significance for the improvement of students’ overall quality. Moreover, the student management work of private colleges directly determines the social reputation of private colleges and universities, and a good style of study is an important means to attract students. Therefore, exploring a path that is in line with the development of private colleges and universities is the best choice to solve the problem of student management [2]. College administrators should put student management on the agenda and focus on solving them and strengthen the quantitative management of students and the management of daily behavioral norms. In addition, private colleges and universities should strengthen the construction of teachers’ teams, improve teachers’ professional quality and professionalism, improve teaching quality and teaching level, strengthen students’ safety management and behavior development education, and create a good learning environment. At the same time, it is necessary to teach students according to their aptitude according to their behavioral characteristics, to cultivate outstanding talents for national construction, to further achieve the goal of rejuvenating the country through education, and to lay a solid foundation for China’s future economic and social construction [3].Quantitative management methods have been widely used in the management of students in private colleges and universities in my country and gradually formed a work pattern with quantitative management as the main line. The application of quantitative management methods is conducive to the scientific and standardization of student management work, which makes student management work well-founded and rule-based, and to a certain extent changes the chaotic state of randomness, head-scratching, and poor results in previous management work. It reduces the difficulty and cost of management and plays a good role in promoting students to develop the habit of self-education and self-management [4]. However, in the quantitative management of students, especially in the quantitative management of students in private undergraduate colleges, people often ignore a theoretical premise, which is the thinking on human nature. Workers engaged in management in enterprises or other fields unconsciously apply the theories of “human nature is good,” “human nature is evil,” “economic man hypothesis,” and “moral man hypothesis” in management practice. In student management, the theory of human nature should also be analyzed first, because students, as individuals, have natural and social attributes of human beings [5]. Because today’s world is in a rapidly changing information age, values and concepts are diversified, and social phenomena are complex. Individuals are not equally influenced by nature and society. The acceptance and understanding of quantitative management, and the effective implementation of quantitative management, require consideration of human assumptions. If the human nature is set to be good, it will often be overlooked in the quantitative management of students. Human interference in quantitative management will make quantitative management lose its fairness and credibility. This situation is precisely the concept that is generally accepted by school administrators. An important reason for questioning the impartiality of [6]. Following the research results of the human nature hypothesis of management, in student management, many schools put forward the concept of “people-oriented” and “life-oriented.” However, the implicit assumption of this concept is more based on the belief that human nature is inherently good, and it is difficult to truly effectively address student anomie behavior. However, if it is assumed that human nature is evil and the content of quantitative management is difficult to reflect complex specific situations and specific problems, it often simplifies the implementation of management, resulting in general resistance to management, making management ineffective or even out of control [7]. Unconditional abstract metaphysical assumptions of good and evil human nature are difficult to support quantitative management. Based on this, this paper first conducts an in-depth analysis of the theoretical development and achievements of the hypothesis of good and evil in human nature and then combines the special background and life development stage of students in private colleges and universities to put forward a reasonable judgment of human nature to provide theoretical support for the formulation and implementation of quantitative management [8].The emergence and development of private colleges and universities is to meet the actual needs of the society and students, but in the process of their work development and progress, due to their limited strength and level, teachers and related managers do not have enough energy to pay attention to student management work effectiveness. For the management of students in private schools, not only the attention of leaders and managers is required but also the cooperation of students. From the perspective of management, the implementation of a system and concept needs to be implemented from the top down or from the bottom up, so as to truly achieve the purpose of effective management [9]. For managers, if they can get a certain sense of achievement or superiority from effective management, they can better stimulate their enthusiasm for work and innovation. Therefore, within the school, the staff in charge of student management can be praised or encouraged in terms of work promotion and funding according to their achievements. However, from the current management of many private schools, they only formulate rules and regulations to require subordinates to implement them. Such an unclear division of labor and specific responsibilities will cause confusion in the management system and superficial regulations [10]. Due to the incompleteness of the previous management system or work, it is difficult for workers and students to change their habits, so the management work with students as the main body and really close to the needs of students should be carried out step by step [11]. However, many managers cannot make students truly understand the importance of self-management and restraint, nor can they communicate with students and understand their actual needs through managers, and they will not clarify the standards of reward and punishment for different behaviors among students. Such nonstaged and hierarchical management cannot really stimulate the enthusiasm of teachers and students to work and study and also cannot establish the effectiveness of self-management and promotion [12].Judging from the current scale and number of students in colleges and universities, if teachers only manage students mechanically, it will not only increase the workload of teachers and cause their academic research to fail to achieve the desired effect but also cannot really achieve the desired results. Facilitate comprehensive management of student work. Because students in colleges and universities already have the ability to distinguish right from wrong, they already have the awareness of independent thinking and making appropriate choices when encountering difficulties and tasks, so the development of student management can also be partially handed over to students [13]. In addition, because students already have the ability to be independent, the dogmatic content and system can no longer meet the needs of students, so the work of student management still needs to be regulated and constrained by people. To truly improve the effectiveness of student management work, it is necessary to construct the student management team accordingly.From the perspective of the management of private colleges and universities, managers should keep pace with the times, fully understand the times and the actual needs of students, and manage students with scientific concepts. First, administrators should always insist on students as the center of management. This also shows that managers should fully understand the actual needs of each student and respect their personality development, so as to be truly student-oriented [14]. Second, managers should establish a democratization concept. This philosophy suggests that administrators should take the actual needs of students into account when making decisions and making development plans and can learn about them by interacting with students. When the interests of the students are involved, the administrators should fully listen to the opinions of the students. Only in this atmosphere can the students truly feel the joy of democracy and truly implement the concepts of democracy and equality. Therefore, under the guidance of this student-oriented and democratized concept, students can develop better [15].Management methods and models will directly affect the effect of work, so schools should consciously strengthen the construction of management systems. According to the size of the school, the characteristics of teachers and students, and the management methods of other schools, we will improve the system of our school. This perfect process is not a simple imitation, but to fully reflect the school’s purpose and characteristics, so that teachers and students can achieve targeted management and development [16]. Of course, because counselors in colleges and universities have multiple roles, they also need to constantly communicate with students while carrying out student management work and truly become students’ mentors and friends. In addition, many schools currently adopt a straight-line management method. In this hierarchical model, the completion of tasks and orders tends to be more mechanized, which is not conducive to the effective development of student management. Therefore, the school can give managers the corresponding autonomy, so as to fully mobilize the enthusiasm of the work [17].In order to improve the student management effect of private colleges and universities, this paper combines the geographic information system and WIFI environment to construct the student management system of private colleges and universities.


## 2. System Key Technology

In the private college student tracking and positioning management platform, the key technologies used are map matching algorithm, MapInfo-based electronic map development, platform website server implementation, Eclipse-based Android application development, and MFC-based application development. In the following, each part is explained separately in this paper.When performing map matching, it is difficult to determine on which route the students of private colleges and universities must move. The system can only give “probable movement on a certain route” or “unlikely to move on a certain route.” If the system is required to give a more precise positioning result, a reasonable judgment must be made on this ambiguity. Therefore, this paper adopts a map matching algorithm based on fuzzy comprehensive evaluation and uses the advantages of fuzzy mathematics in this uncertainty judgment. This paper finds a balance between “certain” and “possible,” so as to find a matching road section as the current road section for private college students to collect data points according to GPS positioning. A schematic diagram of the algorithm flow is shown in Figure1.Figure 1
Schematic diagram of the algorithm flow.In the process of constructing the algorithm model, the concepts of membership and fuzzy comprehensive evaluation are involved. By using these two, a quantitative evaluation can be made on the fuzzy problem of map matching.


### 2.1. Membership

Membership function is a mathematical tool used to characterize fuzzy sets. At present, the method of determining membership function is not mature, and it is usually used to conduct survey and statistics based on the proposed fuzzy concept. We assume that the fuzzy setA determines the degree to which different elements belong to A through statistical experiments. The membership function is defined as shown in
(1)ThemembershipdegreeoftofuzzysetA=thenumberof∈AthetotalnumberofexperimentsN.Many experiments have proved that with the increase ofN, the degree of membership tends to be stable, and the stable value is called the degree of membership of μ0 to A.


### 2.2. Fuzzy Comprehensive Evaluation

The fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics. It can better solve many ambiguity problems in the real world. The fuzzy comprehensive evaluation method is to transform qualitative evaluation into quantitative evaluation according to the membership degree theory of fuzzy mathematics, that is, to use fuzzy mathematics to make an overall evaluation of things or objects that are restricted by various factors.When the line is a double line, the derivation model of the map matching algorithm based on fuzzy comprehensive evaluation is established as follows:(1)
The algorithm determines the factor setU=D,A, where D represents the shortest distance from the GPS positioning collection point to the road section D≥0 and A represents the angle between the movement direction of the GPS positioning collection point and the road section orientation 0°≤A≤180°(2)
The algorithm determines that the evaluation setV=v1,v2,v3,v4,v5, where vn is the evaluation conclusion level, which represents very small, small, medium, large, and very large. The value of vn represents the weight of each level, respectively. Since the influence of factors D and A is smaller, the map matching can be closer to the true value. Therefore, v1>v2>v3>v4>v5, and d is assigned as 0.4, 0.3, 0.2, 0.1, 0.05 in the research(3)
The algorithm performs the membership determination from the factor set to the judgment setThe evaluation result of each factor is shown in(2)R=r,D≥0aswellas0≤¯A≤¯180,0,D<0,A<0orA>180.Then, the membership degree matrixR is shown in
(3)R=R1R2=r11r12r13r14r15r21r22r23r24r25.The closer the membership function is to 1, the higher the degree to which a factor is closer to a certain judgment level. The triangular membership function is used here to make decisions. The scale of the triangular membership function is close to people’s actual thinking, improves the accuracy of judgment, and can accurately prioritize and sort programs and is widely used in engineering applications.The membership function of the factorD belonging to the V judgment set is shown in equations (4), (5), (6), (7), and (8). YD1D~YD5D corresponds to r11~r15, which, respectively, represent the degree to which element D belongs to each review conclusion level. Since the official positioning accuracy of GPS is within 0~15 meters, the value of D is generally within 15 meters, that is, 4d=15 meters, where d=3.75 meters.
(4)YD1D=1−Dd,0≤D<d,0,d≤D,(5)YD2D=Dd,0≤D<d,2−Dd,d≤D≤2d,0,2d≤D,(6)YD3D=0,0≤D≤d,Dd−1,d≤D<2d,3−Dd,2d≤D≤3d,0,3d≤D,(7)YD4D=0,0≤D≤d,Dd−2,2d≤D<3d,4−Dd,3d≤D<4d,0,4d≤D,(8)YD5D=0,0≤D<3d,Dd−3,3d≤D<4d,1,4d≤D.The membership function curve of factorD is shown in Figure 2.Figure 2
Membership function curve of factorD.The membership function of the factorA belonging to the V judgment set is shown in formulas (9), (10), (11), (12), and (13). YA1A~YA5A corresponds to r21~r25, which, respectively, represent the degree to which element A belongs to each review conclusion level. Since the angle between the course and the orientation of the road section is generally relatively small during the movement of students in private colleges and universities, the value of A is generally concentrated within 60 degrees, that is, 4a=60 degrees, where a=15 degrees.
(9)YA1A=1−Aa,0≤A<a,0,a≤A,(10)YA2A=Aa,0≤A<a,2−Aa,a≤A≤2a,0,2a≤A,(11)YA3A=0,0≤A≤a,Aa−1,a≤A<2a,3−Aa,2a≤A≤3a,0,3a≤A,(12)YA4A=0,0≤A<2a,Aa−2,2a≤A<3a,4−Aa,3a≤A<4a,0,4a≤A,(13)YA5A=0,3a≤A,Aa−3,3a≤A<4a,1,4a≤A.The membership function curve of factorA is shown in Figure 3.
(4)
The algorithm determines the weight vectora=a1a2 of the factor set U. a1 represents the proportion of D in the fuzzy comprehensive evaluation, and a2 represents the proportion of A in the fuzzy comprehensive evaluation. In the research, it is considered that the weight of the distance from the positioning point to the line should be larger than that of the orientation of the positioning point, so the values are a1=0.7 and a2=0.3(5)
The algorithm determines the comprehensive membership degree vector. The determination of the comprehensive evaluation vector is the result of the comprehensive evaluation setV, the weight vector a and the membership matrix R. Therefore, the index of fuzzy comprehensive evaluation is obtained as shown in(14)B=Va⋅RT.Figure 3
Membership function curve of factorA.After substitutingV,A,andR, a relatively simple formula can be obtained as shown in
(15)B=0.28YD1D+0.12YA1A+0.21YD2D+0.09YA2A+0.14YD3D+0.06YA3A+0.07YD4D+0.03YA4A+0.035YD5D+0.015YA5A.It can be known from the above formula that as long as theD and A from the location collection point to the road segment can be obtained in real time, the result curve of the fuzzy comprehensive evaluation during this period can be obtained, and then, the selection of the optimal matching road segment relative to the collection point can be completed.After finding the optimal matching road section based on the fuzzy comprehensive evaluation method, it is also necessary to correspond the positioning collection points to the line to complete the map matching.When the optimal matching line is determined or the line is a single line, the direction of the private college students’ running is defined as theX direction, and the direction of the vertical line is the Y direction. The algorithm model diagram in the Y direction is shown in Figure 4.Figure 4
Model diagram of the orthogonal projection algorithm.We assume that the running position of students in private colleges is pointA at a certain moment, and the GPS positioning collection point BXB,YB point, point C is the projection of point B on the OP line. The straight line where the OP is located is an analog line, and the coordinate values of point 0 and point P are also known. Because AB>AC, we use point C to locate point A to reduce the error.TheOP’s equation is shown in
(16)Y−YO=YP−YOXP−XOX−XO.Since lineBC is perpendicular to OP, the equation for line BC is shown in
(17)Y−YB=XP−XOYP−YOX−XB.By combining formulas (5) and (6), the coordinates of point C can be obtained as shown in
(18)XC=YB−YO+K1XO−K2XBK1−K2,YC=YB+K2XC−XB.Among them,YP−YO/XP−XO=K1, and XP−XO/YP−YO=K2.In theX direction, the fuzzy comprehensive evaluation method is used to determine the most probable X position on the line. The derivation of the algorithm model is established as follows:
(1)
The algorithm divides 5 possible sectionsV=0,v/2,v,3v/2,2v according to the historical speed(2)
The algorithm uses the existing positionL and historical data to determine the degree to which the existing position belongs to the running speed. The triangular membership function that is close to the actual thinking of people is also used for scaling. Since the kilometer mark is set at each kilometer from the starting point of the line, here the kilometer mark is used as the benchmark, that is, 4l=1 km, where l=250 m. The membership functions are shown in formulas (19), (20), (21), (22), and (23).(19)YL1L=1−Ll,0≤L<l,0,l≤L,(20)YL2L=Ll,0≤L<l,2−Ll,l≤L≤2l,0,2l≤L,(21)YL3L=0,0≤L≤l,Ll−1,l≤L<2l,3−Ll,2l≤L≤3l,0,3l≤L,(22)YL4L=0,0≤L<2l,Ll−2,2l≤L<3l,4−Ll,3l≤L<4l,0,4l≤L,(23)YL5L=0,0≤L<3l,Ll−3,3l≤L<4l,1,4l≤L(3)
The algorithm determines the weight vectora=a1a2 of the factor set U. a1 and a2, respectively, represent the proportions of V and L in the fuzzy comprehensive evaluation. However, for the selection of the optimal section on the single-track line in the study, the weight of the instantaneous velocity and the displacement is equivalent. Therefore, the value is a1=a2=0.5, or the weight vector can be ignored here(4)
The algorithm determines the comprehensive membership degree vector. The obtained index of fuzzy comprehensive evaluation is shown in(24)B=Va⋅RTIt can also be concluded that as long as the speed and displacement of the positioning collection point can be obtained in real time, the result curve of the fuzzy comprehensive evaluation during this period can be obtained, and then, the most probable positionK in the X direction can be obtained.In this way, pointC on the line is obtained by orthogonal straight line projection in the Y direction, and point K is the most likely matching point obtained by the fuzzy comprehensive evaluation method in the X direction. Finally, by calculating the average value of Xc and Xk, the optimal matching point is obtained, and the line correspondence of the positioning collection point is achieved, that is, the map matching requirement.


## 2.1. Membership

Membership function is a mathematical tool used to characterize fuzzy sets. At present, the method of determining membership function is not mature, and it is usually used to conduct survey and statistics based on the proposed fuzzy concept. We assume that the fuzzy setA determines the degree to which different elements belong to A through statistical experiments. The membership function is defined as shown in
(1)ThemembershipdegreeoftofuzzysetA=thenumberof∈AthetotalnumberofexperimentsN.Many experiments have proved that with the increase ofN, the degree of membership tends to be stable, and the stable value is called the degree of membership of μ0 to A.


## 2.2. Fuzzy Comprehensive Evaluation

The fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics. It can better solve many ambiguity problems in the real world. The fuzzy comprehensive evaluation method is to transform qualitative evaluation into quantitative evaluation according to the membership degree theory of fuzzy mathematics, that is, to use fuzzy mathematics to make an overall evaluation of things or objects that are restricted by various factors.When the line is a double line, the derivation model of the map matching algorithm based on fuzzy comprehensive evaluation is established as follows:(1)
The algorithm determines the factor setU=D,A, where D represents the shortest distance from the GPS positioning collection point to the road section D≥0 and A represents the angle between the movement direction of the GPS positioning collection point and the road section orientation 0°≤A≤180°(2)
The algorithm determines that the evaluation setV=v1,v2,v3,v4,v5, where vn is the evaluation conclusion level, which represents very small, small, medium, large, and very large. The value of vn represents the weight of each level, respectively. Since the influence of factors D and A is smaller, the map matching can be closer to the true value. Therefore, v1>v2>v3>v4>v5, and d is assigned as 0.4, 0.3, 0.2, 0.1, 0.05 in the research(3)
The algorithm performs the membership determination from the factor set to the judgment setThe evaluation result of each factor is shown in(2)R=r,D≥0aswellas0≤¯A≤¯180,0,D<0,A<0orA>180.Then, the membership degree matrixR is shown in
(3)R=R1R2=r11r12r13r14r15r21r22r23r24r25.The closer the membership function is to 1, the higher the degree to which a factor is closer to a certain judgment level. The triangular membership function is used here to make decisions. The scale of the triangular membership function is close to people’s actual thinking, improves the accuracy of judgment, and can accurately prioritize and sort programs and is widely used in engineering applications.The membership function of the factorD belonging to the V judgment set is shown in equations (4), (5), (6), (7), and (8). YD1D~YD5D corresponds to r11~r15, which, respectively, represent the degree to which element D belongs to each review conclusion level. Since the official positioning accuracy of GPS is within 0~15 meters, the value of D is generally within 15 meters, that is, 4d=15 meters, where d=3.75 meters.
(4)YD1D=1−Dd,0≤D<d,0,d≤D,(5)YD2D=Dd,0≤D<d,2−Dd,d≤D≤2d,0,2d≤D,(6)YD3D=0,0≤D≤d,Dd−1,d≤D<2d,3−Dd,2d≤D≤3d,0,3d≤D,(7)YD4D=0,0≤D≤d,Dd−2,2d≤D<3d,4−Dd,3d≤D<4d,0,4d≤D,(8)YD5D=0,0≤D<3d,Dd−3,3d≤D<4d,1,4d≤D.The membership function curve of factorD is shown in Figure 2.Figure 2
Membership function curve of factorD.The membership function of the factorA belonging to the V judgment set is shown in formulas (9), (10), (11), (12), and (13). YA1A~YA5A corresponds to r21~r25, which, respectively, represent the degree to which element A belongs to each review conclusion level. Since the angle between the course and the orientation of the road section is generally relatively small during the movement of students in private colleges and universities, the value of A is generally concentrated within 60 degrees, that is, 4a=60 degrees, where a=15 degrees.
(9)YA1A=1−Aa,0≤A<a,0,a≤A,(10)YA2A=Aa,0≤A<a,2−Aa,a≤A≤2a,0,2a≤A,(11)YA3A=0,0≤A≤a,Aa−1,a≤A<2a,3−Aa,2a≤A≤3a,0,3a≤A,(12)YA4A=0,0≤A<2a,Aa−2,2a≤A<3a,4−Aa,3a≤A<4a,0,4a≤A,(13)YA5A=0,3a≤A,Aa−3,3a≤A<4a,1,4a≤A.The membership function curve of factorA is shown in Figure 3.
(4)
The algorithm determines the weight vectora=a1a2 of the factor set U. a1 represents the proportion of D in the fuzzy comprehensive evaluation, and a2 represents the proportion of A in the fuzzy comprehensive evaluation. In the research, it is considered that the weight of the distance from the positioning point to the line should be larger than that of the orientation of the positioning point, so the values are a1=0.7 and a2=0.3(5)
The algorithm determines the comprehensive membership degree vector. The determination of the comprehensive evaluation vector is the result of the comprehensive evaluation setV, the weight vector a and the membership matrix R. Therefore, the index of fuzzy comprehensive evaluation is obtained as shown in(14)B=Va⋅RT.Figure 3
Membership function curve of factorA.After substitutingV,A,andR, a relatively simple formula can be obtained as shown in
(15)B=0.28YD1D+0.12YA1A+0.21YD2D+0.09YA2A+0.14YD3D+0.06YA3A+0.07YD4D+0.03YA4A+0.035YD5D+0.015YA5A.It can be known from the above formula that as long as theD and A from the location collection point to the road segment can be obtained in real time, the result curve of the fuzzy comprehensive evaluation during this period can be obtained, and then, the selection of the optimal matching road segment relative to the collection point can be completed.After finding the optimal matching road section based on the fuzzy comprehensive evaluation method, it is also necessary to correspond the positioning collection points to the line to complete the map matching.When the optimal matching line is determined or the line is a single line, the direction of the private college students’ running is defined as theX direction, and the direction of the vertical line is the Y direction. The algorithm model diagram in the Y direction is shown in Figure 4.Figure 4
Model diagram of the orthogonal projection algorithm.We assume that the running position of students in private colleges is pointA at a certain moment, and the GPS positioning collection point BXB,YB point, point C is the projection of point B on the OP line. The straight line where the OP is located is an analog line, and the coordinate values of point 0 and point P are also known. Because AB>AC, we use point C to locate point A to reduce the error.TheOP’s equation is shown in
(16)Y−YO=YP−YOXP−XOX−XO.Since lineBC is perpendicular to OP, the equation for line BC is shown in
(17)Y−YB=XP−XOYP−YOX−XB.By combining formulas (5) and (6), the coordinates of point C can be obtained as shown in
(18)XC=YB−YO+K1XO−K2XBK1−K2,YC=YB+K2XC−XB.Among them,YP−YO/XP−XO=K1, and XP−XO/YP−YO=K2.In theX direction, the fuzzy comprehensive evaluation method is used to determine the most probable X position on the line. The derivation of the algorithm model is established as follows:
(1)
The algorithm divides 5 possible sectionsV=0,v/2,v,3v/2,2v according to the historical speed(2)
The algorithm uses the existing positionL and historical data to determine the degree to which the existing position belongs to the running speed. The triangular membership function that is close to the actual thinking of people is also used for scaling. Since the kilometer mark is set at each kilometer from the starting point of the line, here the kilometer mark is used as the benchmark, that is, 4l=1 km, where l=250 m. The membership functions are shown in formulas (19), (20), (21), (22), and (23).(19)YL1L=1−Ll,0≤L<l,0,l≤L,(20)YL2L=Ll,0≤L<l,2−Ll,l≤L≤2l,0,2l≤L,(21)YL3L=0,0≤L≤l,Ll−1,l≤L<2l,3−Ll,2l≤L≤3l,0,3l≤L,(22)YL4L=0,0≤L<2l,Ll−2,2l≤L<3l,4−Ll,3l≤L<4l,0,4l≤L,(23)YL5L=0,0≤L<3l,Ll−3,3l≤L<4l,1,4l≤L(3)
The algorithm determines the weight vectora=a1a2 of the factor set U. a1 and a2, respectively, represent the proportions of V and L in the fuzzy comprehensive evaluation. However, for the selection of the optimal section on the single-track line in the study, the weight of the instantaneous velocity and the displacement is equivalent. Therefore, the value is a1=a2=0.5, or the weight vector can be ignored here(4)
The algorithm determines the comprehensive membership degree vector. The obtained index of fuzzy comprehensive evaluation is shown in(24)B=Va⋅RTIt can also be concluded that as long as the speed and displacement of the positioning collection point can be obtained in real time, the result curve of the fuzzy comprehensive evaluation during this period can be obtained, and then, the most probable positionK in the X direction can be obtained.In this way, pointC on the line is obtained by orthogonal straight line projection in the Y direction, and point K is the most likely matching point obtained by the fuzzy comprehensive evaluation method in the X direction. Finally, by calculating the average value of Xc and Xk, the optimal matching point is obtained, and the line correspondence of the positioning collection point is achieved, that is, the map matching requirement.


## 3. System Construction

With the support of the algorithm in the second part, the construction of the student management system of private colleges and universities based on geographic information system and WIFI is carried out. The monitoring system consists of monitoring cameras, switches, transmission equipment, and servers. Video surveillance equipment mainly includes H3C SIC235 540-line infrared integrated barrel network camera and H3C SIC335E 540-line infrared vandal-proof network dome camera. The monitoring equipment is mainly designed for remote monitoring video, remote monitoring on-site sound, supporting multiuser Web connection and NVR access and can realize the networking of super large-scale monitoring system. In addition, H3CSIC335E is based on embedded Linux operating system, which supports plug and play, reducing maintenance costs. The system network structure diagram is shown in Figure5.Figure 5
Structure diagram of the system network.The service objects of the monitoring system cover school management users and ordinary users. From the perspective of the overall design of a complete monitoring system, the system can be divided into display layer, application layer, support layer, and data layer according to the hybrid structure theory proposed in this paper, as shown in Figure6. Among them, the display layer is divided into C/S and B/S architecture. The data collection mainly includes monitoring equipment information collection, monitoring equipment geographic information collection, terrain data collection, video data collection, route information collection, and attribute data collection. The application modules of C/S mode mainly include video capture, video analysis, video storage, and video sharing modules. The application modules of B/S mode mainly include application management systems such as data analysis, PTZ control, user settings, log management, and expansion module management. The application support platform mainly includes data exchange, authentication, analysis and early warning, and remote management. The data layer is divided into spatial database, video database, model database, and public information database.Figure 6
System application structure.According to the actual situation of application and development in the system, this paper divides the system into five parts: basic map, advanced analysis, monitoring point management, user management, and auxiliary tool functions, as shown in Figure7. Among them, the basic map functions mainly include functions such as full-map display, map zoom-in, map zoom-out, map panning, and map navigation. The advanced analysis functions mainly include shortest path analysis, real-time video display, real-time video recording, video data retrieval, and video people counting functions. The monitoring point management mainly includes monitoring point query, monitoring point editing, monitoring point search, multifunction search, and buffer analysis. The user management mainly includes functions such as user addition, user deletion, user modification, and authority management. The auxiliary tools mainly include functions such as area calculation, distance calculation, bookmark function, and scene saving.Figure 7
System function design.After designing the above system model, the effect of the model is verified, the movement trajectory of students in a university is statistically identified, and the campus network WIFI is used for real-time positioning, and the positioning accuracy statistics are obtained as shown in Figure8.Figure 8
Statistical accuracy of students’ motion trajectories.The above verifies that the student management system in private colleges and universities based on geographic information system and WIFI proposed in this paper can effectively improve the accuracy of students’ trajectory positioning. On this basis, this paper verifies the student management effect of the private college student management system based on geographic information system and WIFI, and the statistical results are shown in Figure9.Figure 9
Student management effect verification of private college student management system based on geographic information system and WIFI.From the above research, it can be seen that the student management system of private colleges and universities based on geographic information system and WIFI proposed in this paper can effectively improve the student management effect of private colleges and universities.


## 4. Conclusion

At present, it is necessary to establish a system suitable for the academic conditions of private higher vocational colleges for various types of information of students, to check and input the information from the admission process at one time, and to query the performance of the students’ information in school through the card. In addition, it is necessary to use the point system to report the students’ usual performance online, and students who have not completed the quality points will not be allowed to graduate, so that the student management method can be found. At the same time, it is necessary to use the face recognition system to register and keep files for students entering and leaving the dormitory and classrooms and use the smart campus system to record all the activities of students on campus. In addition, it is necessary to track the consumption behavior of students in canteens and supermarkets run by schools and accurately assess poor students. In order to improve the effect of student management in private colleges and universities, this paper combines geographic information system and WIFI to construct student management system in private colleges and universities. The research shows that the student management system of private colleges and universities based on geographic information system and WIFI proposed in this paper can effectively improve the student management effect of private colleges and universities.



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*Source: 1993281-2022-09-22.xml*