# Construction of a Complex System Based on Big Data for the Intelligent Service System of Youth Physical Health

Authors: Xinwen Li; Chao Song; Christine A. Rochester; Chaobing Yan
Journal: Complexity
 (2021)

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## Abstract

The progress of social economy has created a better environment for the healthy development of young people, but the heavy schoolwork and life pressure have caused many students to ignore the scientific management of physical health. At this stage, people need a scientific physical health service system to help students understand their own health data, propose targeted exercise methods and health knowledge, and actively encourage and guide students to participate in physical exercise. The purpose of this article is to cultivate students’ good self-exercise awareness and improve their physical fitness and health. To this end, this article has designed a smart health service system for young people. This article introduces the various service functions in the health management service system and explains in detail the entry, induction, and analysis of student physical health data in the system. The essence of the health intelligent service system is to provide students with targeted healthy exercise strategies through data analysis. This paper studies the health intervention plan of the health intelligent service system. From the experimental data, the improved particle swarm algorithm in this paper increases the effectiveness of the system in adolescent health data mining from 80.5% to 92.19%, which undoubtedly optimizes the system. It helps a lot.


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## Body

## 1. Introduction

In recent years, the number of obese children in my country has increased, and the rate of adolescents’ myopia has continued to increase. Physical fitness indicators such as vital capacity, speed, endurance, and explosive power have continued to decline. The physical health of adolescents has increasingly become a concern of the whole society. To ensure that young people have a healthy body, they must establish the concept of scientific fitness and have a certain degree of understanding of the basic physiology of the human body and exercise methods. Scientific fitness guidance can help young people improve the effects of exercise and effectively improve their physical fitness. In this era of the rapid development of computer technology, it is a very good choice to spread fitness information and fitness guidance services through the Internet because the network can infinitely enlarge the capacity of stored information and provide more targeted fitness guidance for teenagers through big data intelligent calculation.In foreign countries, many scholars have conducted research on the management system to improve the health of young people. Laurson cross-validated fitness standards related to Hungarian youth health. He collected data from 400 teenagers using treadmills to conduct aerobic capacity tests to determine peak oxygen consumption and bioelectrical impedance assessment data. Combined with the FitnessGram standard, young people are divided into healthy groups and groups in need of improvement, and logistic regression is used to estimate the probability of metabolic syndrome. Judging from the experimental data, he believes that 62.3% of the 400 research subjects are in a healthy state of fitness. Of course, considering that there are only 400 people in the survey, this affects the persuasiveness of the data to a certain extent [1]. Welk hopes to establish an effective physical education program through the Internet to support and enhance the school’s physical education program. He combined with FitnessGram, an American youth physical fitness assessment system, to provide unique insights on how to effectively provide large-scale school-based physical exercise. But on the whole, when he formulated the physical test data management, the design of the evaluation system was not perfect, and there is still a lot of room for improvement [2].In recent years, the country has also begun to work on the organic integration of Internet technology and adolescent health management system. Wang believes that the overall health status of the youth in our country is on a downward trend at this stage, and a scientific health management system needs to be built for this. In his research, he elaborated on the information management plan of the leading thought of the system development and argued that testing is not the purpose, and collecting data should be to better judge the physical health of students and give further exercise suggestions. From the actual experimental results, the function of the system in terms of information feedback and supervision is not perfect [3].The research of this article mainly starts with the following parts. First, this article introduces various technologies and methods in the health intelligent service system, including big data visualization technology, genetic algorithm and particle swarm algorithm in data mining algorithm, optimal sequence diagram method, and multiple methods of juvenile health literacy evaluation. Next, this article describes in detail the experimental development and database development of the health intelligent service system based on big data. Finally, this article conducts an in-depth analysis of the construction of the complex system of the adolescent physical health intelligent service system. The research focuses on the service functions in the health management service system, the entry, analysis, and calculation of health data, and the health intervention based on the health intelligent service system.


## 2. Technology and Methods in the Health Intelligent Service System

### 2.1. Big Data Visualization

The physical fitness test of young people is passed because these tests can reflect the physical health of the test subjects to a large extent. The big data visualization technology helps the test results to be displayed in a clearer way, allowing the young people to intuitively realize the health problems in their bodies and find the direction of fitness exercise [4]. The development of technologies such as big data storage, the Internet, and the Internet of Things has created better conditions for the processing of youth physical health data. Because physical health data has the characteristics of large data scale, multiple data types, low value of individual data, and fast data update, if you want to make an accurate comparison of the physical health of adolescents, the accuracy and reliability of data processing are very high requirements [5].


#### 2.1.1. Distance-Based Visual Clustering Technology

The visual layout technology proposed in this paper takes the parallel coordinate visual layout as the prototype, and the visual layout technology based on indicator health data enhances the visualization effect, making the visual analysis more clear and convenient for users [6]. The original parallel coordinate visualization layout does not enable users to quickly understand the distribution of data records as a whole. The distance-based visualization clustering method introduced in this section can make the data categories of different distribution characteristics in the data records clear [7]. Figure 1 is a schematic diagram of distance-based visual clustering.Figure 1
Schematic diagram of distance-based visual clustering.In Figure1, n represents the number of attribute axes, and d represents the distance between the two lines on the attribute axis, and they satisfy the following formula:(1)di=Pix−Piy.Among them,pix,piy represents the coordinate position of the two polyline segments on the i-th attribute axis. Then the average distance d¯ formula and vertical coordinate calculation formula of two polyline segments satisfy the following:(2)d¯=d1+d2+d3+⋯+dnn,yp′=Fa⋅yp+1−Fa⋅ym.In parallel coordinate visualization, each data record is connected to each other to form a polyline segment according to its position on each attribute. As the amount of data increases, the staggering phenomenon of parallel coordinate line segments becomes more and more obvious [8]. Set p0,p2 to be the vertical coordinates of the two ends of the curve, and p1 to be the vertical coordinates of the quadratic interpolation point, then the intensity factor of the attraction within the class and the quadratic spline curve formula satisfy the following:(3)Fa=e−dh/ym−yp⋅cosα⋅Δ,Bt=1−t2p0+2t1−tp1+t2,t∈0,1.


#### 2.1.2. Visual Aggregation Technology Based on Membership Degree

The biggest advantage of scatter plots over other visualization layouts is that they can quickly get the distribution information of the data, such as the main distribution range of the data, the type of data, the characteristics of various data, and other information [9]. However, users can only get rough data distribution information through the points scattered in the graph, and the observation of the scattered point distribution in the three-dimensional scatter plot is not clear. The visual aggregation technology based on membership degree can better enable users to more quickly obtain the distribution characteristics of the data records, thereby effectively enhancing the visualization effect of the scatter plot [10].This method uses the objective function as the benchmark for clustering, and the objective function is as follows:(4)J=∑i=1c∑j=1nuijmxj−cj2.At the same time, there are constraints on the objective function:(5)∑i=1cuij=1,j=1,2,3,…,n,where n is the number of scattered points, c is the number of classifications, ui,jm is the membership degree of the jth scattered points relative to the ith category, m is the fuzzy weighting coefficient, ci is the center coordinate of the ith category, and xj is the jth scattered point coordinates. Then the distance between two points and ci satisfy the following formula:(6)L=xj−ci,ci=∑j=1nxjuijm∑j=1nuijm.It can be seen that in order to obtainci, the value of the membership matrix must be calculated first, then uij and the radius of the sphere satisfy the calculation formula:(7)uij=1∑k=1cxj−ci/xj−ck2/m−1,di=∑k=1nxik−cin.


### 2.2. Data Mining Algorithm

The main job of the health intelligent service system is to collect various physical information data of the youth group, judge the physical health of the students based on intelligent analysis, and give targeted exercise opinions [11]. However, the human body’s health data is not only diverse in types but also very fast to update. In addition, there are still many data with low reference value, which brings great adjustments to data analysis. Faced with this situation, it is necessary to find valuable information contained in a large amount of data through data mining technology to improve the efficiency and accuracy of data analysis [12]. Genetic algorithm (GA) and particle swarm optimization (PSO) are two very commonly used data mining algorithms.


#### 2.2.1. Genetic Algorithm

The essence of a genetic algorithm is to simulate the genetic mechanism in nature and the theory of biological evolution. It has a very good performance when searching for the optimal solution randomly [13]. The genetic algorithm can be calculated as long as it has the information data of the objective function and will not be disturbed by spatial continuity. In addition, the genetic algorithm can also solve complex nonlinear problems and multidimensional space optimization problems, so it has a very wide range of applications in various fields [14, 15]. For a health management system based on big data, it is an excellent choice to analyze adolescent body data through genetic algorithms. Generally speaking, the genetic algorithm and the initial population can be formally described as follows:(8)GA=P0,N,I,S,g,p,f,t,P0=a10,a20,…,aN0∈IN.The basic operation steps of genetic algorithm are as follows: After clarifying the basic information of the objective function and variables of the problem, the variables can be coded, and the initial grouppopit can be randomly generated according to the established coding method.(9)popit,t=1,2,3,…,N.Individual fitness value is a concept used to evaluate and select individuals for various genetic operations. Generally speaking, fitness can reflect the excellence of each individual. The individual fitness value satisfies the following formula:(10)fi=fitnesspopit.When performing genetic algorithms, the value of individual fitness will directly affect operator selection in subsequent operations. Combining the value of the individual fitness function, it can be calculated that the selection probability of each individual satisfies the following formula:(11)Pi=fi∑i=1Nfi,i=1,2,3,…,N.Some excellent individuals are randomly selected from the original target group to complete genetic operations to form a new group of the next generation. The basic expression is as follows:(12)newpopt+1=popjt,q=1,2,3,…,N.


#### 2.2.2. Particle Swarm Algorithm

Particle swarm algorithm is a kind of swarm intelligence algorithm. The inspiration of the swarm intelligence method comes from the research on the behavior of various biological groups in nature [16]. Compared with individual biological individuals, a large number of biological groups gathered together can more clearly reflect the behavioral characteristics of species. The steps of the particle swarm algorithm mainly include initializing particles, evaluating the fitness value of the particle by the fitness function, updating the particle swarm, checking whether it is terminated, and finally outputting the optimal solution.Particle swarm optimization is also a population-based optimization algorithm like a genetic algorithm. It was originally used to solve continuous optimization problems and was specifically applied to function optimization and training neural network weights. The basic particle swarm algorithm satisfies the following formula:(13)vi=ω×vi+c1×rand×pbesti−xi+c2×rand×gbesti−xi,xi=xi+vi.Among them,ω is the nonnegative inertia factor, c is the acceleration coefficient, xi is the point in the D-dimensional space, vi is the particle velocity, pbest is the individual extreme value, and gbest is the global extreme value. Since the parameter settings of the particle swarm algorithm have a great influence on the performance of the algorithm, it is mainly suitable for the optimization of continuous space functions. When applied to the optimization of health data in this paper, certain improvements and adjustments to the particle swarm algorithm are required [17].


#### 2.2.3. Particle Swarm Optimization

At this stage, methods to improve the search accuracy of particle swarm optimization include dissipation theory, quantum update, chaotic update, etc. [18, 19]. Among them, quantum update and chaotic update have a good effect on the diversity of the population, and the dissipation theory can mainly improve the convergence speed and search ability of complex models, so these methods are beneficial to algorithm improvement. The following uses several standard mathematical model functions to test an improved real-number particle swarm algorithm and perform simple analysis of its performance.(14)f1=∑i=1n∑j=1ixj2.Search space1≤xj≤10,d=20, global minimum f1x∗=0,x∗=0,0,…,0.(15)f2=∑i=1n−1100xi+1−xi22+xi−12.Search space1≤xj≤10,d=20, global minimum f2x∗=0,x∗=1,1,…,1.(16)f3=14000∑i=1nxi2−∏i=1ncosxii+1.Search space1≤xj≤10,d=20, global minimum f3x∗=0,x∗=0,0,…,0.(17)f4=∑i=1nxi2−10cos2πxi+10.Search space1≤xj≤10,d=20, global minimum f4x∗=0,x∗=0,0,…,0.In order to improve the correlation between the test and the application effect of the physique test, each function model was randomly run 10 times, and the mean and variance of the running results were calculated. On the whole, quantum theory can effectively improve the search ability and search accuracy of the algorithm. For the effect of data mining in the health and intelligent service system, it is very necessary to improve the particle swarm algorithm [20].


### 2.3. Sequence Diagram

The principle of the optimal sequence diagram method is to compare and analyze the importance of each factor to the goal through the means of matrix illustration, and the purpose is to provide a basis for managers to make decisions [21, 22]. The optimal sequence diagram method is based on the number n of factors to be compared, and numbering the n factors. Let n be the number of comparison objects, and form a checkerboard pattern with n∗n spaces for n comparison factors. When comparing factors in pairs, users need to compare the importance or pros and cons of the two and use numbers. To simply express the comparison result.When judging the superiority or inferiority of each factor or the order of importance by using the superior sequence diagram method, it is necessary to check the validity of the results to ensure that the subjects are objective when comparing the various factors and will not appear because of the order in which the problems appear to be judgment deviation [23]. Assuming that the scores of each influencing factor are, respectively, A1,A2,…,Ai, then the weight of the ith influencing factor satisfies the following formula:(18)ai=Ai∑k=110Ak.


### 2.4. Adolescent Health Literacy Evaluation Method

The research of this paper is centered on the guidance service system of adolescent physical health based on big data. Since the construction of this system is based on the health needs of adolescents, it is necessary to comprehensively understand the health standards and scientific fitness needs of adolescents before proceeding with system development. To this end, this article has consulted a large amount of literature, combined with questionnaire surveys and interviews to collect basic personal information of some young people, their understanding of fitness knowledge, their acceptance of fitness guidance, and fitness needs. Since the satisfaction survey questionnaire used in this article is a qualitative factor questionnaire, it needs to pass theα reliability coefficient method to accurately reflect the consistency between the scores in the scale [24]. The calculation formula meets the following:(19)α=kk−11−∑s2ST2.In addition, this article also uses SPSS statistical software and EXCEL to enter and analyze the surveyed data. Perform statistical processing on the data results to obtain the basis for quantitative analysis. At the same time, on the basis of the paper’s interviews, questionnaire surveys and health management training camps, the collected data are analyzed and studied using logical thinking methods such as induction and deduction, analysis and synthesis, analogy, classification, and comparison [25].


## 2.1. Big Data Visualization

The physical fitness test of young people is passed because these tests can reflect the physical health of the test subjects to a large extent. The big data visualization technology helps the test results to be displayed in a clearer way, allowing the young people to intuitively realize the health problems in their bodies and find the direction of fitness exercise [4]. The development of technologies such as big data storage, the Internet, and the Internet of Things has created better conditions for the processing of youth physical health data. Because physical health data has the characteristics of large data scale, multiple data types, low value of individual data, and fast data update, if you want to make an accurate comparison of the physical health of adolescents, the accuracy and reliability of data processing are very high requirements [5].


### 2.1.1. Distance-Based Visual Clustering Technology

The visual layout technology proposed in this paper takes the parallel coordinate visual layout as the prototype, and the visual layout technology based on indicator health data enhances the visualization effect, making the visual analysis more clear and convenient for users [6]. The original parallel coordinate visualization layout does not enable users to quickly understand the distribution of data records as a whole. The distance-based visualization clustering method introduced in this section can make the data categories of different distribution characteristics in the data records clear [7]. Figure 1 is a schematic diagram of distance-based visual clustering.Figure 1
Schematic diagram of distance-based visual clustering.In Figure1, n represents the number of attribute axes, and d represents the distance between the two lines on the attribute axis, and they satisfy the following formula:(1)di=Pix−Piy.Among them,pix,piy represents the coordinate position of the two polyline segments on the i-th attribute axis. Then the average distance d¯ formula and vertical coordinate calculation formula of two polyline segments satisfy the following:(2)d¯=d1+d2+d3+⋯+dnn,yp′=Fa⋅yp+1−Fa⋅ym.In parallel coordinate visualization, each data record is connected to each other to form a polyline segment according to its position on each attribute. As the amount of data increases, the staggering phenomenon of parallel coordinate line segments becomes more and more obvious [8]. Set p0,p2 to be the vertical coordinates of the two ends of the curve, and p1 to be the vertical coordinates of the quadratic interpolation point, then the intensity factor of the attraction within the class and the quadratic spline curve formula satisfy the following:(3)Fa=e−dh/ym−yp⋅cosα⋅Δ,Bt=1−t2p0+2t1−tp1+t2,t∈0,1.


### 2.1.2. Visual Aggregation Technology Based on Membership Degree

The biggest advantage of scatter plots over other visualization layouts is that they can quickly get the distribution information of the data, such as the main distribution range of the data, the type of data, the characteristics of various data, and other information [9]. However, users can only get rough data distribution information through the points scattered in the graph, and the observation of the scattered point distribution in the three-dimensional scatter plot is not clear. The visual aggregation technology based on membership degree can better enable users to more quickly obtain the distribution characteristics of the data records, thereby effectively enhancing the visualization effect of the scatter plot [10].This method uses the objective function as the benchmark for clustering, and the objective function is as follows:(4)J=∑i=1c∑j=1nuijmxj−cj2.At the same time, there are constraints on the objective function:(5)∑i=1cuij=1,j=1,2,3,…,n,where n is the number of scattered points, c is the number of classifications, ui,jm is the membership degree of the jth scattered points relative to the ith category, m is the fuzzy weighting coefficient, ci is the center coordinate of the ith category, and xj is the jth scattered point coordinates. Then the distance between two points and ci satisfy the following formula:(6)L=xj−ci,ci=∑j=1nxjuijm∑j=1nuijm.It can be seen that in order to obtainci, the value of the membership matrix must be calculated first, then uij and the radius of the sphere satisfy the calculation formula:(7)uij=1∑k=1cxj−ci/xj−ck2/m−1,di=∑k=1nxik−cin.


## 2.1.1. Distance-Based Visual Clustering Technology

The visual layout technology proposed in this paper takes the parallel coordinate visual layout as the prototype, and the visual layout technology based on indicator health data enhances the visualization effect, making the visual analysis more clear and convenient for users [6]. The original parallel coordinate visualization layout does not enable users to quickly understand the distribution of data records as a whole. The distance-based visualization clustering method introduced in this section can make the data categories of different distribution characteristics in the data records clear [7]. Figure 1 is a schematic diagram of distance-based visual clustering.Figure 1
Schematic diagram of distance-based visual clustering.In Figure1, n represents the number of attribute axes, and d represents the distance between the two lines on the attribute axis, and they satisfy the following formula:(1)di=Pix−Piy.Among them,pix,piy represents the coordinate position of the two polyline segments on the i-th attribute axis. Then the average distance d¯ formula and vertical coordinate calculation formula of two polyline segments satisfy the following:(2)d¯=d1+d2+d3+⋯+dnn,yp′=Fa⋅yp+1−Fa⋅ym.In parallel coordinate visualization, each data record is connected to each other to form a polyline segment according to its position on each attribute. As the amount of data increases, the staggering phenomenon of parallel coordinate line segments becomes more and more obvious [8]. Set p0,p2 to be the vertical coordinates of the two ends of the curve, and p1 to be the vertical coordinates of the quadratic interpolation point, then the intensity factor of the attraction within the class and the quadratic spline curve formula satisfy the following:(3)Fa=e−dh/ym−yp⋅cosα⋅Δ,Bt=1−t2p0+2t1−tp1+t2,t∈0,1.


## 2.1.2. Visual Aggregation Technology Based on Membership Degree

The biggest advantage of scatter plots over other visualization layouts is that they can quickly get the distribution information of the data, such as the main distribution range of the data, the type of data, the characteristics of various data, and other information [9]. However, users can only get rough data distribution information through the points scattered in the graph, and the observation of the scattered point distribution in the three-dimensional scatter plot is not clear. The visual aggregation technology based on membership degree can better enable users to more quickly obtain the distribution characteristics of the data records, thereby effectively enhancing the visualization effect of the scatter plot [10].This method uses the objective function as the benchmark for clustering, and the objective function is as follows:(4)J=∑i=1c∑j=1nuijmxj−cj2.At the same time, there are constraints on the objective function:(5)∑i=1cuij=1,j=1,2,3,…,n,where n is the number of scattered points, c is the number of classifications, ui,jm is the membership degree of the jth scattered points relative to the ith category, m is the fuzzy weighting coefficient, ci is the center coordinate of the ith category, and xj is the jth scattered point coordinates. Then the distance between two points and ci satisfy the following formula:(6)L=xj−ci,ci=∑j=1nxjuijm∑j=1nuijm.It can be seen that in order to obtainci, the value of the membership matrix must be calculated first, then uij and the radius of the sphere satisfy the calculation formula:(7)uij=1∑k=1cxj−ci/xj−ck2/m−1,di=∑k=1nxik−cin.


## 2.2. Data Mining Algorithm

The main job of the health intelligent service system is to collect various physical information data of the youth group, judge the physical health of the students based on intelligent analysis, and give targeted exercise opinions [11]. However, the human body’s health data is not only diverse in types but also very fast to update. In addition, there are still many data with low reference value, which brings great adjustments to data analysis. Faced with this situation, it is necessary to find valuable information contained in a large amount of data through data mining technology to improve the efficiency and accuracy of data analysis [12]. Genetic algorithm (GA) and particle swarm optimization (PSO) are two very commonly used data mining algorithms.


### 2.2.1. Genetic Algorithm

The essence of a genetic algorithm is to simulate the genetic mechanism in nature and the theory of biological evolution. It has a very good performance when searching for the optimal solution randomly [13]. The genetic algorithm can be calculated as long as it has the information data of the objective function and will not be disturbed by spatial continuity. In addition, the genetic algorithm can also solve complex nonlinear problems and multidimensional space optimization problems, so it has a very wide range of applications in various fields [14, 15]. For a health management system based on big data, it is an excellent choice to analyze adolescent body data through genetic algorithms. Generally speaking, the genetic algorithm and the initial population can be formally described as follows:(8)GA=P0,N,I,S,g,p,f,t,P0=a10,a20,…,aN0∈IN.The basic operation steps of genetic algorithm are as follows: After clarifying the basic information of the objective function and variables of the problem, the variables can be coded, and the initial grouppopit can be randomly generated according to the established coding method.(9)popit,t=1,2,3,…,N.Individual fitness value is a concept used to evaluate and select individuals for various genetic operations. Generally speaking, fitness can reflect the excellence of each individual. The individual fitness value satisfies the following formula:(10)fi=fitnesspopit.When performing genetic algorithms, the value of individual fitness will directly affect operator selection in subsequent operations. Combining the value of the individual fitness function, it can be calculated that the selection probability of each individual satisfies the following formula:(11)Pi=fi∑i=1Nfi,i=1,2,3,…,N.Some excellent individuals are randomly selected from the original target group to complete genetic operations to form a new group of the next generation. The basic expression is as follows:(12)newpopt+1=popjt,q=1,2,3,…,N.


### 2.2.2. Particle Swarm Algorithm

Particle swarm algorithm is a kind of swarm intelligence algorithm. The inspiration of the swarm intelligence method comes from the research on the behavior of various biological groups in nature [16]. Compared with individual biological individuals, a large number of biological groups gathered together can more clearly reflect the behavioral characteristics of species. The steps of the particle swarm algorithm mainly include initializing particles, evaluating the fitness value of the particle by the fitness function, updating the particle swarm, checking whether it is terminated, and finally outputting the optimal solution.Particle swarm optimization is also a population-based optimization algorithm like a genetic algorithm. It was originally used to solve continuous optimization problems and was specifically applied to function optimization and training neural network weights. The basic particle swarm algorithm satisfies the following formula:(13)vi=ω×vi+c1×rand×pbesti−xi+c2×rand×gbesti−xi,xi=xi+vi.Among them,ω is the nonnegative inertia factor, c is the acceleration coefficient, xi is the point in the D-dimensional space, vi is the particle velocity, pbest is the individual extreme value, and gbest is the global extreme value. Since the parameter settings of the particle swarm algorithm have a great influence on the performance of the algorithm, it is mainly suitable for the optimization of continuous space functions. When applied to the optimization of health data in this paper, certain improvements and adjustments to the particle swarm algorithm are required [17].


### 2.2.3. Particle Swarm Optimization

At this stage, methods to improve the search accuracy of particle swarm optimization include dissipation theory, quantum update, chaotic update, etc. [18, 19]. Among them, quantum update and chaotic update have a good effect on the diversity of the population, and the dissipation theory can mainly improve the convergence speed and search ability of complex models, so these methods are beneficial to algorithm improvement. The following uses several standard mathematical model functions to test an improved real-number particle swarm algorithm and perform simple analysis of its performance.(14)f1=∑i=1n∑j=1ixj2.Search space1≤xj≤10,d=20, global minimum f1x∗=0,x∗=0,0,…,0.(15)f2=∑i=1n−1100xi+1−xi22+xi−12.Search space1≤xj≤10,d=20, global minimum f2x∗=0,x∗=1,1,…,1.(16)f3=14000∑i=1nxi2−∏i=1ncosxii+1.Search space1≤xj≤10,d=20, global minimum f3x∗=0,x∗=0,0,…,0.(17)f4=∑i=1nxi2−10cos2πxi+10.Search space1≤xj≤10,d=20, global minimum f4x∗=0,x∗=0,0,…,0.In order to improve the correlation between the test and the application effect of the physique test, each function model was randomly run 10 times, and the mean and variance of the running results were calculated. On the whole, quantum theory can effectively improve the search ability and search accuracy of the algorithm. For the effect of data mining in the health and intelligent service system, it is very necessary to improve the particle swarm algorithm [20].


## 2.2.1. Genetic Algorithm

The essence of a genetic algorithm is to simulate the genetic mechanism in nature and the theory of biological evolution. It has a very good performance when searching for the optimal solution randomly [13]. The genetic algorithm can be calculated as long as it has the information data of the objective function and will not be disturbed by spatial continuity. In addition, the genetic algorithm can also solve complex nonlinear problems and multidimensional space optimization problems, so it has a very wide range of applications in various fields [14, 15]. For a health management system based on big data, it is an excellent choice to analyze adolescent body data through genetic algorithms. Generally speaking, the genetic algorithm and the initial population can be formally described as follows:(8)GA=P0,N,I,S,g,p,f,t,P0=a10,a20,…,aN0∈IN.The basic operation steps of genetic algorithm are as follows: After clarifying the basic information of the objective function and variables of the problem, the variables can be coded, and the initial grouppopit can be randomly generated according to the established coding method.(9)popit,t=1,2,3,…,N.Individual fitness value is a concept used to evaluate and select individuals for various genetic operations. Generally speaking, fitness can reflect the excellence of each individual. The individual fitness value satisfies the following formula:(10)fi=fitnesspopit.When performing genetic algorithms, the value of individual fitness will directly affect operator selection in subsequent operations. Combining the value of the individual fitness function, it can be calculated that the selection probability of each individual satisfies the following formula:(11)Pi=fi∑i=1Nfi,i=1,2,3,…,N.Some excellent individuals are randomly selected from the original target group to complete genetic operations to form a new group of the next generation. The basic expression is as follows:(12)newpopt+1=popjt,q=1,2,3,…,N.


## 2.2.2. Particle Swarm Algorithm

Particle swarm algorithm is a kind of swarm intelligence algorithm. The inspiration of the swarm intelligence method comes from the research on the behavior of various biological groups in nature [16]. Compared with individual biological individuals, a large number of biological groups gathered together can more clearly reflect the behavioral characteristics of species. The steps of the particle swarm algorithm mainly include initializing particles, evaluating the fitness value of the particle by the fitness function, updating the particle swarm, checking whether it is terminated, and finally outputting the optimal solution.Particle swarm optimization is also a population-based optimization algorithm like a genetic algorithm. It was originally used to solve continuous optimization problems and was specifically applied to function optimization and training neural network weights. The basic particle swarm algorithm satisfies the following formula:(13)vi=ω×vi+c1×rand×pbesti−xi+c2×rand×gbesti−xi,xi=xi+vi.Among them,ω is the nonnegative inertia factor, c is the acceleration coefficient, xi is the point in the D-dimensional space, vi is the particle velocity, pbest is the individual extreme value, and gbest is the global extreme value. Since the parameter settings of the particle swarm algorithm have a great influence on the performance of the algorithm, it is mainly suitable for the optimization of continuous space functions. When applied to the optimization of health data in this paper, certain improvements and adjustments to the particle swarm algorithm are required [17].


## 2.2.3. Particle Swarm Optimization

At this stage, methods to improve the search accuracy of particle swarm optimization include dissipation theory, quantum update, chaotic update, etc. [18, 19]. Among them, quantum update and chaotic update have a good effect on the diversity of the population, and the dissipation theory can mainly improve the convergence speed and search ability of complex models, so these methods are beneficial to algorithm improvement. The following uses several standard mathematical model functions to test an improved real-number particle swarm algorithm and perform simple analysis of its performance.(14)f1=∑i=1n∑j=1ixj2.Search space1≤xj≤10,d=20, global minimum f1x∗=0,x∗=0,0,…,0.(15)f2=∑i=1n−1100xi+1−xi22+xi−12.Search space1≤xj≤10,d=20, global minimum f2x∗=0,x∗=1,1,…,1.(16)f3=14000∑i=1nxi2−∏i=1ncosxii+1.Search space1≤xj≤10,d=20, global minimum f3x∗=0,x∗=0,0,…,0.(17)f4=∑i=1nxi2−10cos2πxi+10.Search space1≤xj≤10,d=20, global minimum f4x∗=0,x∗=0,0,…,0.In order to improve the correlation between the test and the application effect of the physique test, each function model was randomly run 10 times, and the mean and variance of the running results were calculated. On the whole, quantum theory can effectively improve the search ability and search accuracy of the algorithm. For the effect of data mining in the health and intelligent service system, it is very necessary to improve the particle swarm algorithm [20].


## 2.3. Sequence Diagram

The principle of the optimal sequence diagram method is to compare and analyze the importance of each factor to the goal through the means of matrix illustration, and the purpose is to provide a basis for managers to make decisions [21, 22]. The optimal sequence diagram method is based on the number n of factors to be compared, and numbering the n factors. Let n be the number of comparison objects, and form a checkerboard pattern with n∗n spaces for n comparison factors. When comparing factors in pairs, users need to compare the importance or pros and cons of the two and use numbers. To simply express the comparison result.When judging the superiority or inferiority of each factor or the order of importance by using the superior sequence diagram method, it is necessary to check the validity of the results to ensure that the subjects are objective when comparing the various factors and will not appear because of the order in which the problems appear to be judgment deviation [23]. Assuming that the scores of each influencing factor are, respectively, A1,A2,…,Ai, then the weight of the ith influencing factor satisfies the following formula:(18)ai=Ai∑k=110Ak.


## 2.4. Adolescent Health Literacy Evaluation Method

The research of this paper is centered on the guidance service system of adolescent physical health based on big data. Since the construction of this system is based on the health needs of adolescents, it is necessary to comprehensively understand the health standards and scientific fitness needs of adolescents before proceeding with system development. To this end, this article has consulted a large amount of literature, combined with questionnaire surveys and interviews to collect basic personal information of some young people, their understanding of fitness knowledge, their acceptance of fitness guidance, and fitness needs. Since the satisfaction survey questionnaire used in this article is a qualitative factor questionnaire, it needs to pass theα reliability coefficient method to accurately reflect the consistency between the scores in the scale [24]. The calculation formula meets the following:(19)α=kk−11−∑s2ST2.In addition, this article also uses SPSS statistical software and EXCEL to enter and analyze the surveyed data. Perform statistical processing on the data results to obtain the basis for quantitative analysis. At the same time, on the basis of the paper’s interviews, questionnaire surveys and health management training camps, the collected data are analyzed and studied using logical thinking methods such as induction and deduction, analysis and synthesis, analogy, classification, and comparison [25].


## 3. Big Data-Based Health Intelligent Service System Experiment

### 3.1. Experimental Background

The successful development and promotion of adolescent health management system in some European and American countries can not only effectively improve the physical health of students but also have a very good effect in disease prevention. From a macroperspective, ensuring the physical and mental health of young people can also have a positive impact on promoting social harmony and economic stability. On the whole, this article can learn from the advantages of foreign health management systems and draw lessons from them to further promote the development of my country’s physical health intelligent management system.


### 3.2. Experimental System Design

Adolescent fitness test and subsequent data processing and analysis are a series of tedious but meticulous work. The statistics of the data after the physical fitness test of all students, the results of data analysis, and the management of user data are not allowed to make mistakes. Therefore, this article needs to develop a system that can simplify the tedious work according to requirements. System administrators can use the system to conveniently manage information and data. Users can query their own information and physical test results through the system and obtain corresponding fitness program recommendations. Figure2 is a diagram of the functional modules of the juvenile physical health intelligent service system.Figure 2
Functional module diagram of adolescent physical health intelligent service system.According to the system module functions in Figure2, this article uses a lot of technologies in the development of the juvenile physical health intelligent service system, including Win8, Tomcat9, MySQL, Redis, and MAVEN; development tools include Eclipse Oxygen.la.Release, Navicat for Mysql and visual operation tools.This article divides the use of the system into four parts: data administrators, general administrators, teachers, leaders, and ordinary students. After logging into the system, people with different identities have different permissions and available functions in the system. The module authority of the data administrator is to enter the data of the student’s physical test into the system database and to manage and maintain the data on a regular basis. The general administrator can comprehensively manage the system. In addition to adding, deleting, and modifying data, it can also manage all user accounts, such as adding user IDs and modifying user passwords. The module permissions of teachers and leaders can query the physical test scores and various health scores of all students in their school as needed. Students can log in to the system with their own ID and password and inquire about their physical test scores and various health scores.


### 3.3. Experimental Database Design

The database is composed of many data tables. In the process of completing the database, it is necessary to write code to add configuration files required for reverse engineering of each data table. Among the more important data table diagrams in the database table include workbook, student mark, student info, body mass index, and record file info.This study collects and analyzes the physical data of adolescents in various ways, and finally obtains the complete data requirements of the system. Based on a series of surveys, collections, and analyses, this article confirms the various information data required by the system, including student information data sheet, a list of student physical test scores, physical test index weighting tables for different grades, single index score sheets, bonus index score sheet, and application form for exemption from execution.


## 3.1. Experimental Background

The successful development and promotion of adolescent health management system in some European and American countries can not only effectively improve the physical health of students but also have a very good effect in disease prevention. From a macroperspective, ensuring the physical and mental health of young people can also have a positive impact on promoting social harmony and economic stability. On the whole, this article can learn from the advantages of foreign health management systems and draw lessons from them to further promote the development of my country’s physical health intelligent management system.


## 3.2. Experimental System Design

Adolescent fitness test and subsequent data processing and analysis are a series of tedious but meticulous work. The statistics of the data after the physical fitness test of all students, the results of data analysis, and the management of user data are not allowed to make mistakes. Therefore, this article needs to develop a system that can simplify the tedious work according to requirements. System administrators can use the system to conveniently manage information and data. Users can query their own information and physical test results through the system and obtain corresponding fitness program recommendations. Figure2 is a diagram of the functional modules of the juvenile physical health intelligent service system.Figure 2
Functional module diagram of adolescent physical health intelligent service system.According to the system module functions in Figure2, this article uses a lot of technologies in the development of the juvenile physical health intelligent service system, including Win8, Tomcat9, MySQL, Redis, and MAVEN; development tools include Eclipse Oxygen.la.Release, Navicat for Mysql and visual operation tools.This article divides the use of the system into four parts: data administrators, general administrators, teachers, leaders, and ordinary students. After logging into the system, people with different identities have different permissions and available functions in the system. The module authority of the data administrator is to enter the data of the student’s physical test into the system database and to manage and maintain the data on a regular basis. The general administrator can comprehensively manage the system. In addition to adding, deleting, and modifying data, it can also manage all user accounts, such as adding user IDs and modifying user passwords. The module permissions of teachers and leaders can query the physical test scores and various health scores of all students in their school as needed. Students can log in to the system with their own ID and password and inquire about their physical test scores and various health scores.


## 3.3. Experimental Database Design

The database is composed of many data tables. In the process of completing the database, it is necessary to write code to add configuration files required for reverse engineering of each data table. Among the more important data table diagrams in the database table include workbook, student mark, student info, body mass index, and record file info.This study collects and analyzes the physical data of adolescents in various ways, and finally obtains the complete data requirements of the system. Based on a series of surveys, collections, and analyses, this article confirms the various information data required by the system, including student information data sheet, a list of student physical test scores, physical test index weighting tables for different grades, single index score sheets, bonus index score sheet, and application form for exemption from execution.


## 4. Construction of a Complex System Based on Big Data for the Intelligent Service System of Youth Physical Health

### 4.1. System Analysis Module of Health Intelligent Service System

#### 4.1.1. Current Status of Adolescents’ Health and the Construction of Management Service System

When analyzing the status quo of adolescents’ physical health, it can be considered from many aspects such as body shape, physical function, and physical fitness. Based on the survey data in recent years, the overall physical health of adolescents in my country has shown a downward trend. Figure3 shows the statistics on the changes in student obesity rates during the eight national student physical health surveys from 1985 to 2019.Figure 3
Statistics on the change in adolescent obesity rate from 1985 to 2019.It can be seen from Figure3 that the proportion of malnutrition and low weight among Chinese adolescents has continued to decrease over the past 30 years, which corresponds to the continuous increase in the number of overweight and obese students. In the past ten years, the obesity rate of Chinese adolescents has nearly doubled. Even taking into account the factors of average height growth, these data are still worthy of attention.There is no doubt about the positive effect of physical exercise on the physical health of young people. Maintaining three to four times a week of exercise is very helpful for students to improve their physical fitness and promote their physical health. Figure4 is a survey of the number of weekly exercises for students in a college.Figure 4
Investigation and statistics of students’ weekly exercise times.It can be seen from Figure4 that only 17.63% of students do physical exercises 3-4 times a week, and most students only do simple physical exercises during physical education classes. Therefore, the proportion of students who exercise 1-2 times a week is as high as 48.5%. In addition, there are even close to 30% of students who said they basically do not participate in physical exercise.As an important part of student health management, the physical education department is responsible for helping students prevent health risks. At the current stage, physical education in schools often ignores the pertinence and intensity requirements for the classroom. In this context, the top priority is to continue to innovate ways to promote the health of young people and to better provide systematic and scientific help and services for the health of young people.At this stage, the school’s physical examination simply records various indicators of the student’s body and cannot effectively help students to correctly understand their own health status and arouses students’ attention to strengthening their physical fitness. In the smart service system for youth physical health based on big data, based on the detection of student health information, the information data will be further summarized, analyzed, and evaluated, and targeted health guidance and intervention will be provided to help students improve their health. Comprehensive quality.


#### 4.1.2. Service Function in Health Management Service System

In the development of a youth physical health intelligent service system, this article unites the school physical education department, physical test center, school hospital, mental health center, and food and nutrition science health resource sharing platform to unify these advantageous resource platforms to realize the sharing of data resources. By establishing individual health management files for young people, we can better manage and serve the health of students. Table1 and Figure 5 show the percentage of data of the types of adolescent psychological counseling problems.Table 1
Proportion of types of adolescent psychological counseling problems.
Depression and anxiety (%)Suicide and other serious psychological problems (%)Insomnia and eating disorders (%)Personal growth problems (%)Learning pressure (%)Emotional problems (%)Proportion of troubled problems76.214.832.572.668.135.2Proportion of people willing to consult58.251.640.334.823.725.2Figure 5
Types of adolescent psychological counseling problems.It can be seen from Figure5 that the most common types of problems in adolescents’ psychological distress are depression and anxiety, problems in personal growth (lack of self-confidence and trust, etc.), and stress in learning. It is worth noting that although learning pressure accounts for 68.1% of the troubled problems, only 23.7% of students are willing to go for psychological counseling.To ensure the physical health of young people, it is very important to ensure that they have a healthy mind. While the pressure of social competition is increasing, the learning pressure that young people endure from an early age has also continued to increase. Excessive learning pressure is very detrimental to the physical and mental health of students. Therefore, the health intelligence service system developed in this article cooperated with the Mental Health Center and added a psychological counseling section to the platform. In the online environment, young people can conduct psychological consultation anonymously and seek help from professional psychologists to reduce the probability of mental illness.In the health intelligence service system, the school hospital undertakes various service responsibilities such as student body examination, physical fitness assessment, disease treatment, medical supervision, and health knowledge publicity. The prevention, diagnosis, and treatment of students’ physical diseases in school hospitals can not only alleviate the pain caused by diseases to students but also reduce students’ financial expenditure on medical treatment. School hospitals can help students strengthen their self-care awareness and improve their quality of life by carrying out regular health consultations, disease surveys, and promoting healthy living behaviors.The Academy of Nutritional Sciences provides scientific diet guidance and scientific diet promotion services for students’ health. In the platform, the system will provide corresponding scientific recipes based on student body data. For example, students who are underweight can add more protein and carbohydrates, and students who are overweight need to control their diet and reduce calorie intake. All in all, it is to help students develop healthy eating habits through a scientific diet.


### 4.2. Data Analysis Module of Health Intelligent Service System

#### 4.2.1. Entry and Processing of Physical Health Data

When entering adolescent physical health data in the health intelligence service system, the data administrator can choose the appropriate entry method according to the actual situation. The most traditional method is to collect paper reports of student test data, sort all the data, and enter them into the system database by class. In addition, there are now many advanced testing instruments and systems that can automatically record data and generate excel forms. Administrators only need to add Excel forms into the health service system.Using the health and wisdom service system, students can view various indicators in their physical fitness tests, and teachers can further understand the physical health of the student group by class. Table2 is a single item score table for girls’ body mass index (BMI). Figure 6 is a statistical chart of the body mass index of female students in a high school.Table 2
Female body mass index (BMI) single item score sheet.
GradeScoreFirst gradeSecond gradeThird gradeSenior oneSenior twoSenior threeUniversityNormal10014.8–21.715.3–22.116.0–22.616.5–22.716.9–23.117.1–23.317.2–23.8Underweight80≤14.7≤15.2≤15.9≤16.4≤16.8≤17.0≤17.1High weight21.8–24.422.2–24.822.7–25.122.8–25.223.2–25.423.4–25.723.9–27.9Obesity60≥24.5≥24.9≥25.2≥25.3≥25.5≥25.8≥28.0Figure 6
BMI statistics chart of high school female students.Compared with tables, statistical graphs can help teachers and leaders understand the overall health data of students more intuitively. It can be seen from Figure6 that the overall body mass index of female students in the 21 classes of the high school from Grade One to Grade Three is in a normal state, but they are bound to be lighter and heavier students. Big data does not mean a general reference to data information. Choosing the correct data comparison indicators can help people further clarify the physical changes of students. Table 3 and Figure 7 show the body mass index of girls in the 7 classes in the second year of school. Figure 7 clearly shows the number of students who are lighter, heavier, and obese in each class. After the data is systematically summarized and sorted out, the effective calculation and analysis of the data health data can be ensured.Table 3
BMI situation of girls in 7 classes of high school.
ABCDEFGNormal31162721261922Under weight3234413High weight5223654Obesity2132433Figure 7
BMI situation of girls in 7 classes of high school.


#### 4.2.2. Intelligent Calculation and Analysis of Physical Fitness Data

In order to eliminate the interference items in a large amount of physical health data and correctly evaluate the health status of students, this paper uses intelligent data mining algorithms in the system. Figure8 shows the change in accuracy of health data trait analysis before and after the improvement of the particle swarm algorithm.Figure 8
Optimization accuracy changes after particle swarm optimization improved.It can be seen from Figure8 that the improved particle swarm algorithm has increased its accuracy from 80.56% to 92.19% during the calculation, which is a good improvement. With the support of the improved particle swarm algorithm, the health intelligent service system based on big data can more accurately determine the health status of students from the physical fitness data of students and provide health intervention programs.


### 4.3. Health Intervention Based on Health Intelligent Service System

In the health intelligent service system, data collection, induction, and analysis are all for feature summary, and finally implement scientific health education intervention. In the smart health platform, people can not only understand the physical health of students through visual graphics but also harvest corresponding health education knowledge teaching and scientific exercise programs in the health advice column. Table4 shows the changes in various indicators after students receive scientific exercises.Table 4
Statistical table of changes in various indicators after students’ scientific exercise.
ProjectBefore exerciseAfter exercisetpPhysiological function68.22 ± 19.6376.23 ± 19.98−15.63<0.001Body pain72.53 ± 15.4881.42 ± 13.51−17.88<0.001Social function62.31 ± 25.8881.84 ± 24.02−30.45<0.001Energy77.69 ± 14.2184.21 ± 13.05−23.27<0.001Emotional function62.76 ± 31.2584.21 ± 24.83−21.20<0.001Mental health83.21 ± 16.0289.38 ± 11.37−15.32<0.001General health74.23 ± 16.8282.53 ± 16.01−33.21<0.001It can be seen from Table4 that the p values of all dimensions in the health survey summary table are less than 0.05, which indicates that the differences in the indicators of each dimension are significant after students listen to the health interventions given by the health smart service system. Comparing the various dimensions of the health scale before and after the experiment, it is not difficult to see that the students’ health in all aspects of the body has been significantly improved after scientific physical exercise. In the seven dimensions of the health survey summary table, the average value of each dimension has improved after the experiment than before the experiment. Especially in the dimensions of social function, emotional function, and physical pain. In terms of the overall health dimension, the average after the experiment also reached 86.31, which is a very high score, indicating that health management is of great help to the overall health of students. Figure 9 shows the changes in the average scores of female students after scientific exercise.Figure 9
Change of students’ average scores in physical examination.From Figure9, after a period of exercise through the scientific intervention program recommended by the system, the students’ performance in the physical test has been significantly improved. According to the comprehensive assessment standards, all students can exceed the passing line, and most students can get good grades and above. Figure 10 shows the survey data of students’ satisfaction with the health intelligent service system.Figure 10
Survey data of students’ satisfaction with the system.It can be seen from Figure10 that although traditional online fitness guidance also includes texts, pictures, videos, and other ways of guidance, it lacks understanding of students’ physical conditions and cannot give targeted guidance. Therefore, students are satisfied with it. The degree is also relatively general. In contrast, the juvenile physical health intelligent service system built with big data has a significantly higher rate of praise among students.Most youth associations lack a correct understanding of their changing physical conditions. At this time, scientific physical health indicators are needed to provide students with reference to keep students more concerned about physical exercise. In the growth process of young people, both the body and the mind are in a state of constant maturity. The health service system based on big data is dedicated to accurately understanding the health status of each student and provides targeted health guidelines through intelligent analysis.


## 4.1. System Analysis Module of Health Intelligent Service System

### 4.1.1. Current Status of Adolescents’ Health and the Construction of Management Service System

When analyzing the status quo of adolescents’ physical health, it can be considered from many aspects such as body shape, physical function, and physical fitness. Based on the survey data in recent years, the overall physical health of adolescents in my country has shown a downward trend. Figure3 shows the statistics on the changes in student obesity rates during the eight national student physical health surveys from 1985 to 2019.Figure 3
Statistics on the change in adolescent obesity rate from 1985 to 2019.It can be seen from Figure3 that the proportion of malnutrition and low weight among Chinese adolescents has continued to decrease over the past 30 years, which corresponds to the continuous increase in the number of overweight and obese students. In the past ten years, the obesity rate of Chinese adolescents has nearly doubled. Even taking into account the factors of average height growth, these data are still worthy of attention.There is no doubt about the positive effect of physical exercise on the physical health of young people. Maintaining three to four times a week of exercise is very helpful for students to improve their physical fitness and promote their physical health. Figure4 is a survey of the number of weekly exercises for students in a college.Figure 4
Investigation and statistics of students’ weekly exercise times.It can be seen from Figure4 that only 17.63% of students do physical exercises 3-4 times a week, and most students only do simple physical exercises during physical education classes. Therefore, the proportion of students who exercise 1-2 times a week is as high as 48.5%. In addition, there are even close to 30% of students who said they basically do not participate in physical exercise.As an important part of student health management, the physical education department is responsible for helping students prevent health risks. At the current stage, physical education in schools often ignores the pertinence and intensity requirements for the classroom. In this context, the top priority is to continue to innovate ways to promote the health of young people and to better provide systematic and scientific help and services for the health of young people.At this stage, the school’s physical examination simply records various indicators of the student’s body and cannot effectively help students to correctly understand their own health status and arouses students’ attention to strengthening their physical fitness. In the smart service system for youth physical health based on big data, based on the detection of student health information, the information data will be further summarized, analyzed, and evaluated, and targeted health guidance and intervention will be provided to help students improve their health. Comprehensive quality.


### 4.1.2. Service Function in Health Management Service System

In the development of a youth physical health intelligent service system, this article unites the school physical education department, physical test center, school hospital, mental health center, and food and nutrition science health resource sharing platform to unify these advantageous resource platforms to realize the sharing of data resources. By establishing individual health management files for young people, we can better manage and serve the health of students. Table1 and Figure 5 show the percentage of data of the types of adolescent psychological counseling problems.Table 1
Proportion of types of adolescent psychological counseling problems.
Depression and anxiety (%)Suicide and other serious psychological problems (%)Insomnia and eating disorders (%)Personal growth problems (%)Learning pressure (%)Emotional problems (%)Proportion of troubled problems76.214.832.572.668.135.2Proportion of people willing to consult58.251.640.334.823.725.2Figure 5
Types of adolescent psychological counseling problems.It can be seen from Figure5 that the most common types of problems in adolescents’ psychological distress are depression and anxiety, problems in personal growth (lack of self-confidence and trust, etc.), and stress in learning. It is worth noting that although learning pressure accounts for 68.1% of the troubled problems, only 23.7% of students are willing to go for psychological counseling.To ensure the physical health of young people, it is very important to ensure that they have a healthy mind. While the pressure of social competition is increasing, the learning pressure that young people endure from an early age has also continued to increase. Excessive learning pressure is very detrimental to the physical and mental health of students. Therefore, the health intelligence service system developed in this article cooperated with the Mental Health Center and added a psychological counseling section to the platform. In the online environment, young people can conduct psychological consultation anonymously and seek help from professional psychologists to reduce the probability of mental illness.In the health intelligence service system, the school hospital undertakes various service responsibilities such as student body examination, physical fitness assessment, disease treatment, medical supervision, and health knowledge publicity. The prevention, diagnosis, and treatment of students’ physical diseases in school hospitals can not only alleviate the pain caused by diseases to students but also reduce students’ financial expenditure on medical treatment. School hospitals can help students strengthen their self-care awareness and improve their quality of life by carrying out regular health consultations, disease surveys, and promoting healthy living behaviors.The Academy of Nutritional Sciences provides scientific diet guidance and scientific diet promotion services for students’ health. In the platform, the system will provide corresponding scientific recipes based on student body data. For example, students who are underweight can add more protein and carbohydrates, and students who are overweight need to control their diet and reduce calorie intake. All in all, it is to help students develop healthy eating habits through a scientific diet.


## 4.1.1. Current Status of Adolescents’ Health and the Construction of Management Service System

When analyzing the status quo of adolescents’ physical health, it can be considered from many aspects such as body shape, physical function, and physical fitness. Based on the survey data in recent years, the overall physical health of adolescents in my country has shown a downward trend. Figure3 shows the statistics on the changes in student obesity rates during the eight national student physical health surveys from 1985 to 2019.Figure 3
Statistics on the change in adolescent obesity rate from 1985 to 2019.It can be seen from Figure3 that the proportion of malnutrition and low weight among Chinese adolescents has continued to decrease over the past 30 years, which corresponds to the continuous increase in the number of overweight and obese students. In the past ten years, the obesity rate of Chinese adolescents has nearly doubled. Even taking into account the factors of average height growth, these data are still worthy of attention.There is no doubt about the positive effect of physical exercise on the physical health of young people. Maintaining three to four times a week of exercise is very helpful for students to improve their physical fitness and promote their physical health. Figure4 is a survey of the number of weekly exercises for students in a college.Figure 4
Investigation and statistics of students’ weekly exercise times.It can be seen from Figure4 that only 17.63% of students do physical exercises 3-4 times a week, and most students only do simple physical exercises during physical education classes. Therefore, the proportion of students who exercise 1-2 times a week is as high as 48.5%. In addition, there are even close to 30% of students who said they basically do not participate in physical exercise.As an important part of student health management, the physical education department is responsible for helping students prevent health risks. At the current stage, physical education in schools often ignores the pertinence and intensity requirements for the classroom. In this context, the top priority is to continue to innovate ways to promote the health of young people and to better provide systematic and scientific help and services for the health of young people.At this stage, the school’s physical examination simply records various indicators of the student’s body and cannot effectively help students to correctly understand their own health status and arouses students’ attention to strengthening their physical fitness. In the smart service system for youth physical health based on big data, based on the detection of student health information, the information data will be further summarized, analyzed, and evaluated, and targeted health guidance and intervention will be provided to help students improve their health. Comprehensive quality.


## 4.1.2. Service Function in Health Management Service System

In the development of a youth physical health intelligent service system, this article unites the school physical education department, physical test center, school hospital, mental health center, and food and nutrition science health resource sharing platform to unify these advantageous resource platforms to realize the sharing of data resources. By establishing individual health management files for young people, we can better manage and serve the health of students. Table1 and Figure 5 show the percentage of data of the types of adolescent psychological counseling problems.Table 1
Proportion of types of adolescent psychological counseling problems.
Depression and anxiety (%)Suicide and other serious psychological problems (%)Insomnia and eating disorders (%)Personal growth problems (%)Learning pressure (%)Emotional problems (%)Proportion of troubled problems76.214.832.572.668.135.2Proportion of people willing to consult58.251.640.334.823.725.2Figure 5
Types of adolescent psychological counseling problems.It can be seen from Figure5 that the most common types of problems in adolescents’ psychological distress are depression and anxiety, problems in personal growth (lack of self-confidence and trust, etc.), and stress in learning. It is worth noting that although learning pressure accounts for 68.1% of the troubled problems, only 23.7% of students are willing to go for psychological counseling.To ensure the physical health of young people, it is very important to ensure that they have a healthy mind. While the pressure of social competition is increasing, the learning pressure that young people endure from an early age has also continued to increase. Excessive learning pressure is very detrimental to the physical and mental health of students. Therefore, the health intelligence service system developed in this article cooperated with the Mental Health Center and added a psychological counseling section to the platform. In the online environment, young people can conduct psychological consultation anonymously and seek help from professional psychologists to reduce the probability of mental illness.In the health intelligence service system, the school hospital undertakes various service responsibilities such as student body examination, physical fitness assessment, disease treatment, medical supervision, and health knowledge publicity. The prevention, diagnosis, and treatment of students’ physical diseases in school hospitals can not only alleviate the pain caused by diseases to students but also reduce students’ financial expenditure on medical treatment. School hospitals can help students strengthen their self-care awareness and improve their quality of life by carrying out regular health consultations, disease surveys, and promoting healthy living behaviors.The Academy of Nutritional Sciences provides scientific diet guidance and scientific diet promotion services for students’ health. In the platform, the system will provide corresponding scientific recipes based on student body data. For example, students who are underweight can add more protein and carbohydrates, and students who are overweight need to control their diet and reduce calorie intake. All in all, it is to help students develop healthy eating habits through a scientific diet.


## 4.2. Data Analysis Module of Health Intelligent Service System

### 4.2.1. Entry and Processing of Physical Health Data

When entering adolescent physical health data in the health intelligence service system, the data administrator can choose the appropriate entry method according to the actual situation. The most traditional method is to collect paper reports of student test data, sort all the data, and enter them into the system database by class. In addition, there are now many advanced testing instruments and systems that can automatically record data and generate excel forms. Administrators only need to add Excel forms into the health service system.Using the health and wisdom service system, students can view various indicators in their physical fitness tests, and teachers can further understand the physical health of the student group by class. Table2 is a single item score table for girls’ body mass index (BMI). Figure 6 is a statistical chart of the body mass index of female students in a high school.Table 2
Female body mass index (BMI) single item score sheet.
GradeScoreFirst gradeSecond gradeThird gradeSenior oneSenior twoSenior threeUniversityNormal10014.8–21.715.3–22.116.0–22.616.5–22.716.9–23.117.1–23.317.2–23.8Underweight80≤14.7≤15.2≤15.9≤16.4≤16.8≤17.0≤17.1High weight21.8–24.422.2–24.822.7–25.122.8–25.223.2–25.423.4–25.723.9–27.9Obesity60≥24.5≥24.9≥25.2≥25.3≥25.5≥25.8≥28.0Figure 6
BMI statistics chart of high school female students.Compared with tables, statistical graphs can help teachers and leaders understand the overall health data of students more intuitively. It can be seen from Figure6 that the overall body mass index of female students in the 21 classes of the high school from Grade One to Grade Three is in a normal state, but they are bound to be lighter and heavier students. Big data does not mean a general reference to data information. Choosing the correct data comparison indicators can help people further clarify the physical changes of students. Table 3 and Figure 7 show the body mass index of girls in the 7 classes in the second year of school. Figure 7 clearly shows the number of students who are lighter, heavier, and obese in each class. After the data is systematically summarized and sorted out, the effective calculation and analysis of the data health data can be ensured.Table 3
BMI situation of girls in 7 classes of high school.
ABCDEFGNormal31162721261922Under weight3234413High weight5223654Obesity2132433Figure 7
BMI situation of girls in 7 classes of high school.


### 4.2.2. Intelligent Calculation and Analysis of Physical Fitness Data

In order to eliminate the interference items in a large amount of physical health data and correctly evaluate the health status of students, this paper uses intelligent data mining algorithms in the system. Figure8 shows the change in accuracy of health data trait analysis before and after the improvement of the particle swarm algorithm.Figure 8
Optimization accuracy changes after particle swarm optimization improved.It can be seen from Figure8 that the improved particle swarm algorithm has increased its accuracy from 80.56% to 92.19% during the calculation, which is a good improvement. With the support of the improved particle swarm algorithm, the health intelligent service system based on big data can more accurately determine the health status of students from the physical fitness data of students and provide health intervention programs.


## 4.2.1. Entry and Processing of Physical Health Data

When entering adolescent physical health data in the health intelligence service system, the data administrator can choose the appropriate entry method according to the actual situation. The most traditional method is to collect paper reports of student test data, sort all the data, and enter them into the system database by class. In addition, there are now many advanced testing instruments and systems that can automatically record data and generate excel forms. Administrators only need to add Excel forms into the health service system.Using the health and wisdom service system, students can view various indicators in their physical fitness tests, and teachers can further understand the physical health of the student group by class. Table2 is a single item score table for girls’ body mass index (BMI). Figure 6 is a statistical chart of the body mass index of female students in a high school.Table 2
Female body mass index (BMI) single item score sheet.
GradeScoreFirst gradeSecond gradeThird gradeSenior oneSenior twoSenior threeUniversityNormal10014.8–21.715.3–22.116.0–22.616.5–22.716.9–23.117.1–23.317.2–23.8Underweight80≤14.7≤15.2≤15.9≤16.4≤16.8≤17.0≤17.1High weight21.8–24.422.2–24.822.7–25.122.8–25.223.2–25.423.4–25.723.9–27.9Obesity60≥24.5≥24.9≥25.2≥25.3≥25.5≥25.8≥28.0Figure 6
BMI statistics chart of high school female students.Compared with tables, statistical graphs can help teachers and leaders understand the overall health data of students more intuitively. It can be seen from Figure6 that the overall body mass index of female students in the 21 classes of the high school from Grade One to Grade Three is in a normal state, but they are bound to be lighter and heavier students. Big data does not mean a general reference to data information. Choosing the correct data comparison indicators can help people further clarify the physical changes of students. Table 3 and Figure 7 show the body mass index of girls in the 7 classes in the second year of school. Figure 7 clearly shows the number of students who are lighter, heavier, and obese in each class. After the data is systematically summarized and sorted out, the effective calculation and analysis of the data health data can be ensured.Table 3
BMI situation of girls in 7 classes of high school.
ABCDEFGNormal31162721261922Under weight3234413High weight5223654Obesity2132433Figure 7
BMI situation of girls in 7 classes of high school.


## 4.2.2. Intelligent Calculation and Analysis of Physical Fitness Data

In order to eliminate the interference items in a large amount of physical health data and correctly evaluate the health status of students, this paper uses intelligent data mining algorithms in the system. Figure8 shows the change in accuracy of health data trait analysis before and after the improvement of the particle swarm algorithm.Figure 8
Optimization accuracy changes after particle swarm optimization improved.It can be seen from Figure8 that the improved particle swarm algorithm has increased its accuracy from 80.56% to 92.19% during the calculation, which is a good improvement. With the support of the improved particle swarm algorithm, the health intelligent service system based on big data can more accurately determine the health status of students from the physical fitness data of students and provide health intervention programs.


## 4.3. Health Intervention Based on Health Intelligent Service System

In the health intelligent service system, data collection, induction, and analysis are all for feature summary, and finally implement scientific health education intervention. In the smart health platform, people can not only understand the physical health of students through visual graphics but also harvest corresponding health education knowledge teaching and scientific exercise programs in the health advice column. Table4 shows the changes in various indicators after students receive scientific exercises.Table 4
Statistical table of changes in various indicators after students’ scientific exercise.
ProjectBefore exerciseAfter exercisetpPhysiological function68.22 ± 19.6376.23 ± 19.98−15.63<0.001Body pain72.53 ± 15.4881.42 ± 13.51−17.88<0.001Social function62.31 ± 25.8881.84 ± 24.02−30.45<0.001Energy77.69 ± 14.2184.21 ± 13.05−23.27<0.001Emotional function62.76 ± 31.2584.21 ± 24.83−21.20<0.001Mental health83.21 ± 16.0289.38 ± 11.37−15.32<0.001General health74.23 ± 16.8282.53 ± 16.01−33.21<0.001It can be seen from Table4 that the p values of all dimensions in the health survey summary table are less than 0.05, which indicates that the differences in the indicators of each dimension are significant after students listen to the health interventions given by the health smart service system. Comparing the various dimensions of the health scale before and after the experiment, it is not difficult to see that the students’ health in all aspects of the body has been significantly improved after scientific physical exercise. In the seven dimensions of the health survey summary table, the average value of each dimension has improved after the experiment than before the experiment. Especially in the dimensions of social function, emotional function, and physical pain. In terms of the overall health dimension, the average after the experiment also reached 86.31, which is a very high score, indicating that health management is of great help to the overall health of students. Figure 9 shows the changes in the average scores of female students after scientific exercise.Figure 9
Change of students’ average scores in physical examination.From Figure9, after a period of exercise through the scientific intervention program recommended by the system, the students’ performance in the physical test has been significantly improved. According to the comprehensive assessment standards, all students can exceed the passing line, and most students can get good grades and above. Figure 10 shows the survey data of students’ satisfaction with the health intelligent service system.Figure 10
Survey data of students’ satisfaction with the system.It can be seen from Figure10 that although traditional online fitness guidance also includes texts, pictures, videos, and other ways of guidance, it lacks understanding of students’ physical conditions and cannot give targeted guidance. Therefore, students are satisfied with it. The degree is also relatively general. In contrast, the juvenile physical health intelligent service system built with big data has a significantly higher rate of praise among students.Most youth associations lack a correct understanding of their changing physical conditions. At this time, scientific physical health indicators are needed to provide students with reference to keep students more concerned about physical exercise. In the growth process of young people, both the body and the mind are in a state of constant maturity. The health service system based on big data is dedicated to accurately understanding the health status of each student and provides targeted health guidelines through intelligent analysis.


## 5. Conclusions

This article analyzes the system analysis module of the health intelligent service system. When analyzing the physical health of adolescents, it can be found that the overall physical health of adolescents in my country is on the decline. Therefore, helping students to form a correct understanding of physical exercise and choose appropriate exercise programs has become an important task of the physical health intelligent service system. When constructing the system, this article worked closely with the school’s physical education department, physical test center, school hospital, mental health center, and the Academy of Food and Nutrition Sciences, and consolidated the data resources of each platform to establish the personal health of young people, managing files to provide scientific management and services for students’ physical and mental health.This article analyzes the data analysis module of the health intelligent service system. Based on the smart platform of big data, the most basic workflow is to provide powerful data support for people to make correct decisions through data collection, entry, induction, calculation, and analysis. In the growth stage, various physical data of young people are in the process of constant change. In order to accurately obtain the valuable part of a large amount of physical health data, this paper uses an improved particle swarm algorithm for data mining. From the experimental results, the improved algorithm increases the accuracy of data analysis from 80.5 to 92.19. Based on the data analysis results of the health intelligent service system, this article has conducted health education intervention and health exercise intervention for young people. After scientific exercise, the students’ various physical indicators and physical test scores have been improved to a certain extent. On the whole, the physical intelligence service system can play a very good effect in promoting the physical and mental health of students.Based on big data, this article has launched research on the construction of a youth physical health intelligent service system. This article uses database analysis and processing technology to collect a large number of young people’s physical health data, combined with intelligent algorithms to comprehensively analyze the data and provide scientific guidance and suggestions for students to perform physical exercises. Judging from the survey results, the system has achieved high levels of satisfaction among students, but on the whole, the platform still has many shortcomings. In future research, from the perspective of big data, we can realize the comprehensive management of youth physical health data, optimize the operation process of the system during work, and further improve the effect of data mining algorithms.



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*Source: 6635346-2021-02-03.xml*