# Dynamic Monitoring of Football Training Based on Optimization of Computer Intelligent Algorithm

Authors: Jin Gang
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied.


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## Body

## 1. Introduction

Computer intelligent dynamic monitoring technology is widely used, and then it has evolved various types of intelligent dynamic monitoring methods over time. Therefore, it is not a simple task to thoroughly master computer intelligent dynamic monitoring technology (Viviana et al., 2021) [1]. With the development of the computer intelligent dynamic monitoring system, many relevant researchers have made technical improvements and functional upgrades to this kind of system. With the development of society, computer intelligent dynamic monitoring technology has also been innovated in the research process. Nowadays, although the development of computer intelligent dynamic monitoring technology has not reached idealization, there are often difficult technical problems to overcome (Kwon et al., 2021) [2]. However, the development prospect of computer intelligent dynamic monitoring technology in today's society and even in the future society is very broad, and this technology can also promote the development of intelligent technology. The development of computer intelligent dynamic monitoring technology is also the focus of today’s society (Bai et al., 2021) [3]. This also makes the computer intelligent dynamic monitoring technology applied to more industries in today’s society.At present, relevant researchers who study computer intelligent dynamic monitoring technology are very mature in both theoretical cognition and specific means of use. Computer intelligent dynamic monitoring technology mainly originated in the 1950s, and its earliest monitoring form is data collection and analysis (Shao et al., 2021) [4]. After three stages of technical change, the traditional data monitoring has evolved into single machine data monitoring and finally into today’s networked computer intelligent monitoring mode. In the process of building the computer intelligent dynamic system, the system function is mainly to implement application requirements such as data acquisition, storage, processing, and analysis (Fan and Zhao, 2021) [5]. Compared with the stand-alone monitoring system, the networked computer intelligent dynamic monitoring system achieves the system functions of data integration and processing and long-distance data transmission (Li et al., 2021) [6]. At the same time, you can also analyze multiple types of data at the same time. Computer intelligent monitoring technology not only helps to improve the system’s ability to analyze data but also reduces the human labor force. This technology is also becoming more and more important in the application of various industries (Wang et al., 2021) [7].This paper mainly studies the dynamic monitoring system of football training based on the computer intelligent algorithm. Innovation contributions include as follows: (1) In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. In the upgrade of the system algorithm, it is mainly enhanced in the part of human condition monitoring. (2) Only by ensuring the correctness of monitoring data, we can reduce the workload of the system and ensure the stability of the whole system. In order to improve the correctness of monitoring data, feature extraction algorithm and state recognition algorithm are added. (3) The performance and stability of the intelligent dynamic monitoring system designed in this paper are verified by specific experiments. The experimental results also prove that the dynamic monitoring system optimized by the computer intelligent algorithm can be applied to football training.This paper is mainly composed of three parts. The first part is the application of computer intelligent monitoring technology in football training and the development status of computer intelligent monitoring technology in various countries. The second part is the design and research of the football training dynamic monitoring system under the computer intelligent algorithm and the intelligent algorithm optimization design of the football training dynamic monitoring system. The third part is the result analysis of the dynamic monitoring system of football training under the computer intelligent algorithm and the optimization result analysis of the intelligent algorithm of the dynamic monitoring system of football training.


## 2. Related Work

Computer intelligent dynamic monitoring system appeared before World War II. It was the most common dynamic monitoring system in the early stage of development. The traditional dynamic monitoring system mainly carries out a brief analysis of data manually (Shohei et al., 2021) [8]. In the middle stage of development, the form of technology stays in the single-machine mode. In this system mode, the data acquisition ability has been significantly improved, but it does not absolutely reflect the collection and sharing of data. The computer intelligent dynamic detection system in the later stage of development is integrated with network and wireless communication technology, which not only solves the data collection processing but also improves data sharing (Zhang et al., 2021) [9]. The development of the computer intelligent dynamic monitoring system also provides a solid development foundation for relevant researchers and enriches the internal functions of the computer intelligent dynamic monitoring system. Then, based on the development of computer hardware, the data tasks processed by the computer intelligent dynamic monitoring system are becoming more and more complex. After the emergence of the intelligent dynamic monitoring system, it is applied to monitor a bridge structure for the first time. Its main task is to monitor and evaluate the abnormal data.In the United States, computer intelligent monitoring technology is mainly used to monitor hospital patients’ heart rate, blood pressure, and other data information. With the popularity of medical devices, the United States is also developing in the field of medical industries. The medical industry itself is a special group serving the masses, and there are many patients with different diseases every day (Lund et al., 2021) [10]. It is impossible for the only service personnel in the hospital to pay attention to the patient’s data information all the time. With the advent of computer intelligent monitoring technology, relevant researchers have conducted a series of experiments (Xu et al., 2021) [11]. It is found that this technology can be combined with the data information of hospital patients and transmit the data summary results to the computer screen, which also reduces the task of medical staff.Germany mainly applies computer intelligent monitoring technology to the automobile manufacturing industry. Germany ranks first in the field of automobile manufacturing in the world, and the quality of cars made in Germany is very high. Besides, high-quality automobile production is inseparable from computer intelligent monitoring technology. Germany applied the computer intelligent monitoring technology to monitor the internal parameter data of automobile parts for the first time and achieved great success (Loogen et al., 2021) [12]. This success also makes the computer intelligent monitoring technology popular all over the German country. In the process of autoparts manufacturing, the data are automatically collected and classified by the computer intelligent monitoring system so that the relevant quality inspection personnel can observe more intuitively. The combination of computer intelligent monitoring technology and the automobile manufacturing industry not only reduces the expenditure but also reduces the emergence of unqualified parts.France has widely applied computer intelligent monitoring technology to the breeding industry. Nowadays, scientific breeding methods can not only bring more considerable income to farms but also reduce the probability of animal diseases. As we all know, animal husbandry in France accounts for a large proportion in the whole social industry (Xiang and Xiang, 2020) [13]. Part of the reason for the large proportion is that there are a lot of lawns in France, which also leads to a lot of farms and pastures in France. However, when computer intelligent monitoring technology is not applied to the breeding industry, there are often phenomena such as nonstandard feeding and large-scale illness of livestock [14]. Once this phenomenon occurs, it means that the farm will lose a lot of money. The emergence of computer intelligent monitoring technology can automatically record the daily food intake and exercise of individual livestock. Once the data deviation is large one day, measures can be taken in time to avoid capital loss, which also reflects the importance of computer intelligent monitoring technology.China applies computer intelligent monitoring technology to the field of logistics industries. Because computer intelligent monitoring technology has the function of automatic classification of data, it can quickly classify packages in the face of a large number of express packages. The classified package can accurately deliver the express to the buyer, and the delivery time of the express package is also shortened (Xian et al., 2020) [15]. Computer intelligent monitoring technology is also widely used in other fields in China and has brought positive results to various industries, which also makes computer intelligent monitoring technology valued by mankind.The above content is the development history of the computer intelligent monitoring system and the application of this technology in various countries.


## 3. Methodology

### 3.1. Design of Dynamic Monitoring System for Football Training under Computer Intelligent Algorithm

Nowadays, the dynamic monitoring system has achieved the systematic analysis of the information data generated in the process of sports. This paper mainly studies the systematic analysis of the relevant data of football training under the computer intelligent monitoring system. The purpose of designing the computer intelligent monitoring system is to better monitor people or things and then process the collected data information to get the final required data results. In the process of monitoring football training, it is essentially monitoring athletes. Therefore, the design of the dynamic monitoring system for football training under the computer intelligent algorithm in this paper combines dynamics and machine science. These two contents can be integrated with the dynamic monitoring system and can also give play to its internal expertise in information and data processing. Through the high-speed processing ability of data and information, the internal performance of the computer intelligent dynamic monitoring system is comprehensive. The structure of the dynamic monitoring system after combining the two learning methods is shown in Figure1.Figure 1
Structure flowchart of computer intelligent monitoring system.As can be seen from Figure1, the dynamic monitoring system mainly includes three modules: data acquisition, storage, and data analysis. The data acquisition module cannot be completed by the intelligent dynamic monitoring system itself. The sensor needs to be worn on the football player. The data information generated by football players in the training process will be output to the intelligent dynamic monitoring system through sensors. The data storage system included in the system can automatically store the input data. It can also perform simple data processing, such as deleting incomplete data and noise data. Then, the system analyzes the data information in the database one by one. The data analysis operation is multidimensional, and the data can be systematically analyzed from different angles. Finally, the analysis result data are displayed by the visual method, which can also make the system users have a good user experience.Each module in the system mainly relies on the network for real-time communication. After connecting through the network, the whole intelligent dynamic monitoring system is formed. In the data acquisition module, the main component used is the sensor. The main purpose of the sensor is to collect the data information of football players and transmit data information. Because the collected data type is a physical variable, the data transmission mode in this paper mainly adopts the wireless transmission mode, and the data collected by the sensor are also sent by the wireless controller. The sensor also processes the data, and the processing flow has its own characteristics. The flow of data processing in networked sensors is shown in Figure2.Figure 2
Specific design flowchart of system processing data under networked sensor.It can be seen from Figure2 that the sensor can collect data on temperature, pressure, and speed. All kinds of internal sensors can get high-precision sensing data and also have the characteristics of high temperature resistance and waterproof. The connection between the sensor and the controller is mainly based on the communication protocol inside the controller, which can keep the sensor in the mode of high-speed operation and high-speed transmission. The data transmission part inside the sensor is mainly connected with the intelligent dynamic monitoring system through the data interface.In the data storage module of the system, the main purpose is to put the obtained data information into the system. The data in the system cannot be completely correct, and the phenomenon of data missing often occurs. The internal intelligent algorithm of the system can complete or retrieve the missing data. In the data analysis module of the system, the main analysis methods are mechanical analysis and detecting abnormal data. In the process of mechanical analysis of data, it is to judge the force. The analysis of mechanics in the intelligent dynamic monitoring system is also the focus of this paper.In the process of physical and mechanical analysis, it is mainly to combine the collected element data related to mechanics. After the combined data model is obtained, the speed of physical and mechanical analysis data can be accelerated. The basic construction equation in physical mechanics is as follows:(1)ρi∂2uit∂T2=∫HfξtdVj+bi.From the above equation, the acceleration existing in the data element can be obtained, and the force used in this expression is mainly composed of many element nodes. The nodal volumes of elements are the same, which is also conducive to the calculation of acceleration. The damage function describing the lack of data is also added to the whole computer intelligent dynamic monitoring system. The damage function studied in this paper mainly adds polynomial damage function to the ordinary damage function, which also makes the whole research more convincing. Because the process of calculating the damage function will slow down the data processing speed of the whole system, an OpenMP shared thread parallel algorithm is added to solve this problem. With the addition of the algorithm, the running speed of the whole detection system has also been greatly improved. The above content is the overall design concept of the football training dynamic monitoring system under the computer intelligent algorithm.


### 3.2. Intelligent Algorithm Optimization Design of Football Training Dynamic Monitoring System

In the optimization process of the intelligent algorithm of the football training dynamic monitoring system, the optimization method is adopted for the algorithm in the dynamics module. Considering that the environmental factors of football players in the training process may affect the monitored data information, it is necessary to improve the accuracy and overall scale of data processing in the computer intelligent dynamic monitoring system. To meet the functional requirements of the intelligent dynamic monitoring system, we need to constantly find suitable algorithms so as to improve the overall work efficiency of the system. The above contents have given the dynamic construction equation. When applied in the system, the construction equation is changed to the following formula:(2)ρi∂2uit∂T2=∫Hfξt+1dVj+bi.It can be seen from the above formula that the internal time of the formula has specific significance. With the displacement generated inside the data node, the force contained in it will also change. After the introduction of time, the continuous analysis of data by the intelligent dynamic monitoring system can be truly realized. Different intelligent recognition algorithms will also affect the collected data. In order to avoid data deviation and result error, this paper identifies the motion state of football players. The specific identification algorithm is as follows:(3)SVM1=ax,t2+ay,t2+az,t2,in which, because this paper studies the monitoring and recognition of the motion state generated by the human body, the collection of data information of football players’ legs and feet is particularly important in this study. Because the data monitoring of football training itself has periodicity, the use of the above function can reduce the data coverage between the same data. In order to more accurately collect the data of legs and feet, the following formula is used:(4)CSVMt=MaxSVMt-MinSVMt−k.By bringing the parameters into the above formula, more accurate data information of football players’ sports state can be obtained. From the above formula, it can be seen that in the process of monitoring data, the change of acceleration during movement is the core of data acquisition. By calculating the change value of acceleration, the training state of football players can be judged. If the overall data are in a balanced trend, it is normal training; on the contrary, athletes are breaking through their own limits. In order to avoid the influence of environmental factors during monitoring, the following formula is added:(5)CSVMt+k≤0.2g.By solving the above equation, the influence of noise on the collected data can be reduced. Considering that human beings cannot always be in a static state, the static state can only be formulated by setting parameters. In this paper, through repeated numerical analysis, 0.2 is finally selected as the experimental parameter. After reducing the influence of environmental factors, the error of the computer intelligent automatic detection system in data processing can be reduced. The error comparison of the computer intelligent monitoring system is shown in Figure3.Figure 3
Error comparison diagram of computer intelligent monitoring system.As can be seen from Figure3, the computer intelligent monitoring system studied in this paper has greatly reduced the probability of error due to the adoption of new algorithms for human motion state recognition. However, the common dynamic monitoring system has a very high probability of error, and it is not within the standard error. From the comparison of error probability between the ordinary dynamic monitoring system in the figure above and the computer intelligent monitoring system studied in this paper, it can be seen that the computer intelligent monitoring system studied in this paper has good performance and can better ensure the accuracy of data analysis results.In the detection process of football training, a new human recognition algorithm is added to the intelligent dynamic monitoring system. As the name suggests, the recognition algorithm is to calculate the actions of the human body. Among them, the time series needs to be modeled and calculated within the system, and the relevant formulas are as follows:(6)Xt=ρ1Xt−1+ρ2Xt−2+...+ρpXt−p+ζt−1,Xt=ρt−1,tXt−1+ζt−1,J=∑k=1Nζ2t−1=∑k=1NXt−ρt−1,tXt−12.The above formula constructs the state space model of football players. Based on the total data collected by the system, the deviation derivative ofJ to ρ is calculated. The relevant formula is as follows:(7)ρJ∂ρ|ρ=ρ⌢=0,−2∑k=1NXt−ρ˜Xt−1Xt=0,∑k=1Nρ^Xt−12=∑k=1NXtXt−1.After calculating the deviation derivative of the data, the least square algorithm needs to be added to estimate the data. The formula is as follows:(8)ρ˜t−1,t=MTM−1MTZ.In the process of football training, players have been in motion. When training a specific action, the action will be repeatedly trained. This repetitive training can also regard the human body as a stable state. The accuracy of the monitoring system can be greatly improved by estimating the error. Since the monitoring data type is nonlinear, it is also necessary to establish a discrete state equation and calculate its state. The specific formula is as follows:(9)Xt=ρt−1,tXt−1ζt−1,Yt=HtXtVt.In the above three formulas, firstly, the state equation is established to calculate the state of the data; then, the type of state is replaced by the function according to the calculated state quantity, and finally, the data are classified by the Kalman filter recurrence formula. After optimizing the intelligent algorithm in the dynamic monitoring system, the system can accurately judge the action characteristics and state of players in the process of football training and monitor and calculate the action data.


## 3.1. Design of Dynamic Monitoring System for Football Training under Computer Intelligent Algorithm

Nowadays, the dynamic monitoring system has achieved the systematic analysis of the information data generated in the process of sports. This paper mainly studies the systematic analysis of the relevant data of football training under the computer intelligent monitoring system. The purpose of designing the computer intelligent monitoring system is to better monitor people or things and then process the collected data information to get the final required data results. In the process of monitoring football training, it is essentially monitoring athletes. Therefore, the design of the dynamic monitoring system for football training under the computer intelligent algorithm in this paper combines dynamics and machine science. These two contents can be integrated with the dynamic monitoring system and can also give play to its internal expertise in information and data processing. Through the high-speed processing ability of data and information, the internal performance of the computer intelligent dynamic monitoring system is comprehensive. The structure of the dynamic monitoring system after combining the two learning methods is shown in Figure1.Figure 1
Structure flowchart of computer intelligent monitoring system.As can be seen from Figure1, the dynamic monitoring system mainly includes three modules: data acquisition, storage, and data analysis. The data acquisition module cannot be completed by the intelligent dynamic monitoring system itself. The sensor needs to be worn on the football player. The data information generated by football players in the training process will be output to the intelligent dynamic monitoring system through sensors. The data storage system included in the system can automatically store the input data. It can also perform simple data processing, such as deleting incomplete data and noise data. Then, the system analyzes the data information in the database one by one. The data analysis operation is multidimensional, and the data can be systematically analyzed from different angles. Finally, the analysis result data are displayed by the visual method, which can also make the system users have a good user experience.Each module in the system mainly relies on the network for real-time communication. After connecting through the network, the whole intelligent dynamic monitoring system is formed. In the data acquisition module, the main component used is the sensor. The main purpose of the sensor is to collect the data information of football players and transmit data information. Because the collected data type is a physical variable, the data transmission mode in this paper mainly adopts the wireless transmission mode, and the data collected by the sensor are also sent by the wireless controller. The sensor also processes the data, and the processing flow has its own characteristics. The flow of data processing in networked sensors is shown in Figure2.Figure 2
Specific design flowchart of system processing data under networked sensor.It can be seen from Figure2 that the sensor can collect data on temperature, pressure, and speed. All kinds of internal sensors can get high-precision sensing data and also have the characteristics of high temperature resistance and waterproof. The connection between the sensor and the controller is mainly based on the communication protocol inside the controller, which can keep the sensor in the mode of high-speed operation and high-speed transmission. The data transmission part inside the sensor is mainly connected with the intelligent dynamic monitoring system through the data interface.In the data storage module of the system, the main purpose is to put the obtained data information into the system. The data in the system cannot be completely correct, and the phenomenon of data missing often occurs. The internal intelligent algorithm of the system can complete or retrieve the missing data. In the data analysis module of the system, the main analysis methods are mechanical analysis and detecting abnormal data. In the process of mechanical analysis of data, it is to judge the force. The analysis of mechanics in the intelligent dynamic monitoring system is also the focus of this paper.In the process of physical and mechanical analysis, it is mainly to combine the collected element data related to mechanics. After the combined data model is obtained, the speed of physical and mechanical analysis data can be accelerated. The basic construction equation in physical mechanics is as follows:(1)ρi∂2uit∂T2=∫HfξtdVj+bi.From the above equation, the acceleration existing in the data element can be obtained, and the force used in this expression is mainly composed of many element nodes. The nodal volumes of elements are the same, which is also conducive to the calculation of acceleration. The damage function describing the lack of data is also added to the whole computer intelligent dynamic monitoring system. The damage function studied in this paper mainly adds polynomial damage function to the ordinary damage function, which also makes the whole research more convincing. Because the process of calculating the damage function will slow down the data processing speed of the whole system, an OpenMP shared thread parallel algorithm is added to solve this problem. With the addition of the algorithm, the running speed of the whole detection system has also been greatly improved. The above content is the overall design concept of the football training dynamic monitoring system under the computer intelligent algorithm.


## 3.2. Intelligent Algorithm Optimization Design of Football Training Dynamic Monitoring System

In the optimization process of the intelligent algorithm of the football training dynamic monitoring system, the optimization method is adopted for the algorithm in the dynamics module. Considering that the environmental factors of football players in the training process may affect the monitored data information, it is necessary to improve the accuracy and overall scale of data processing in the computer intelligent dynamic monitoring system. To meet the functional requirements of the intelligent dynamic monitoring system, we need to constantly find suitable algorithms so as to improve the overall work efficiency of the system. The above contents have given the dynamic construction equation. When applied in the system, the construction equation is changed to the following formula:(2)ρi∂2uit∂T2=∫Hfξt+1dVj+bi.It can be seen from the above formula that the internal time of the formula has specific significance. With the displacement generated inside the data node, the force contained in it will also change. After the introduction of time, the continuous analysis of data by the intelligent dynamic monitoring system can be truly realized. Different intelligent recognition algorithms will also affect the collected data. In order to avoid data deviation and result error, this paper identifies the motion state of football players. The specific identification algorithm is as follows:(3)SVM1=ax,t2+ay,t2+az,t2,in which, because this paper studies the monitoring and recognition of the motion state generated by the human body, the collection of data information of football players’ legs and feet is particularly important in this study. Because the data monitoring of football training itself has periodicity, the use of the above function can reduce the data coverage between the same data. In order to more accurately collect the data of legs and feet, the following formula is used:(4)CSVMt=MaxSVMt-MinSVMt−k.By bringing the parameters into the above formula, more accurate data information of football players’ sports state can be obtained. From the above formula, it can be seen that in the process of monitoring data, the change of acceleration during movement is the core of data acquisition. By calculating the change value of acceleration, the training state of football players can be judged. If the overall data are in a balanced trend, it is normal training; on the contrary, athletes are breaking through their own limits. In order to avoid the influence of environmental factors during monitoring, the following formula is added:(5)CSVMt+k≤0.2g.By solving the above equation, the influence of noise on the collected data can be reduced. Considering that human beings cannot always be in a static state, the static state can only be formulated by setting parameters. In this paper, through repeated numerical analysis, 0.2 is finally selected as the experimental parameter. After reducing the influence of environmental factors, the error of the computer intelligent automatic detection system in data processing can be reduced. The error comparison of the computer intelligent monitoring system is shown in Figure3.Figure 3
Error comparison diagram of computer intelligent monitoring system.As can be seen from Figure3, the computer intelligent monitoring system studied in this paper has greatly reduced the probability of error due to the adoption of new algorithms for human motion state recognition. However, the common dynamic monitoring system has a very high probability of error, and it is not within the standard error. From the comparison of error probability between the ordinary dynamic monitoring system in the figure above and the computer intelligent monitoring system studied in this paper, it can be seen that the computer intelligent monitoring system studied in this paper has good performance and can better ensure the accuracy of data analysis results.In the detection process of football training, a new human recognition algorithm is added to the intelligent dynamic monitoring system. As the name suggests, the recognition algorithm is to calculate the actions of the human body. Among them, the time series needs to be modeled and calculated within the system, and the relevant formulas are as follows:(6)Xt=ρ1Xt−1+ρ2Xt−2+...+ρpXt−p+ζt−1,Xt=ρt−1,tXt−1+ζt−1,J=∑k=1Nζ2t−1=∑k=1NXt−ρt−1,tXt−12.The above formula constructs the state space model of football players. Based on the total data collected by the system, the deviation derivative ofJ to ρ is calculated. The relevant formula is as follows:(7)ρJ∂ρ|ρ=ρ⌢=0,−2∑k=1NXt−ρ˜Xt−1Xt=0,∑k=1Nρ^Xt−12=∑k=1NXtXt−1.After calculating the deviation derivative of the data, the least square algorithm needs to be added to estimate the data. The formula is as follows:(8)ρ˜t−1,t=MTM−1MTZ.In the process of football training, players have been in motion. When training a specific action, the action will be repeatedly trained. This repetitive training can also regard the human body as a stable state. The accuracy of the monitoring system can be greatly improved by estimating the error. Since the monitoring data type is nonlinear, it is also necessary to establish a discrete state equation and calculate its state. The specific formula is as follows:(9)Xt=ρt−1,tXt−1ζt−1,Yt=HtXtVt.In the above three formulas, firstly, the state equation is established to calculate the state of the data; then, the type of state is replaced by the function according to the calculated state quantity, and finally, the data are classified by the Kalman filter recurrence formula. After optimizing the intelligent algorithm in the dynamic monitoring system, the system can accurately judge the action characteristics and state of players in the process of football training and monitor and calculate the action data.


## 4. Result Analysis and Discussion

### 4.1. Result Analysis of Dynamic Monitoring System for Football Training under Computer Intelligent Algorithm

In order to further verify the dynamic monitoring system of football training under the computer intelligent algorithm, this paper analyzes the data by using different monitoring systems. We first select 200 groups of football training process data as sample data and then input the sample data into three monitoring systems, respectively. The purpose of this experiment is to judge whether the performance of the intelligent dynamic monitoring system studied in this paper is practical through the high and low data processing capacity of each system. In the process of this experiment, through the repeated analysis of 200 groups of sample data within the system and the cooperative operation between various modules within the system, the result data are finally transmitted to the computer PC. Through the above dynamic monitoring and processing of football training data under the computer intelligent algorithm, the best system can be clearly distinguished. The performance test comparison results of three types of monitoring systems are shown in Figure4.Figure 4
Performance test comparison diagram of three types of monitoring systems.As can be seen from Figure4, the computer intelligent dynamic monitoring system studied in this paper has the highest efficiency of 65% in processing football training sample data. Compared with a traditional monitoring system and an ordinary dynamic monitoring system, the computer intelligent dynamic monitoring system studied in this paper has better data processing ability and does not consume too much human resources in the whole process of the experiment. Therefore, the computer intelligent dynamic monitoring system has considerable social practicability in terms of data processing ability. This further proves that the computer intelligent dynamic monitoring system studied in this paper can be applied to football training and has good application performance.


### 4.2. Analysis of Intelligent Algorithm Optimization Results of Football Training Dynamic Monitoring System

After optimizing the intelligent algorithm, the football training dynamic monitoring system does reduce the phenomenon of data error. However, it is far from enough to judge the performance of the optimized intelligent dynamic monitoring system only from the error level. In order to further verify whether the new monitoring system studied in this paper can maintain or exceed the performance of the original system, thirteen groups of different sample data are selected in this experiment, which contains a variety of different types of data information. Compared with a single type of data information, using different types of data information as sample data can better reflect the excellent data processing ability of a system. During this experiment, the main purpose is to observe whether the system can maintain the stability and high-speed processing capacity of the system in the face of massive data. Finally, the system processes the overall trend of sample data transmission to the computer PC, as shown in Figure5.Figure 5
Data processing rate diagram of optimized detection system.As can be seen from Figure5, the results of the non-optimized system are very obvious compared with the optimized system. It can be seen from the picture that the whole system has a significant downward trend in the process of data processing. The main reason for this trend is the function disorder between modules in the system, which leads to the serious decline of data processing rate, which also proves that there is an unstable problem in the non-optimized system. On the contrary, the optimized intelligent dynamic monitoring system not only has high-speed data processing capacity but also the trend of data processing of the whole system in the figure is very stable. This also proves that the optimized intelligent dynamic monitoring system can still run smoothly in the face of massive data. It can also be seen that the dynamic monitoring system optimized by the computer intelligent algorithm has a wide development space in football training.


## 4.1. Result Analysis of Dynamic Monitoring System for Football Training under Computer Intelligent Algorithm

In order to further verify the dynamic monitoring system of football training under the computer intelligent algorithm, this paper analyzes the data by using different monitoring systems. We first select 200 groups of football training process data as sample data and then input the sample data into three monitoring systems, respectively. The purpose of this experiment is to judge whether the performance of the intelligent dynamic monitoring system studied in this paper is practical through the high and low data processing capacity of each system. In the process of this experiment, through the repeated analysis of 200 groups of sample data within the system and the cooperative operation between various modules within the system, the result data are finally transmitted to the computer PC. Through the above dynamic monitoring and processing of football training data under the computer intelligent algorithm, the best system can be clearly distinguished. The performance test comparison results of three types of monitoring systems are shown in Figure4.Figure 4
Performance test comparison diagram of three types of monitoring systems.As can be seen from Figure4, the computer intelligent dynamic monitoring system studied in this paper has the highest efficiency of 65% in processing football training sample data. Compared with a traditional monitoring system and an ordinary dynamic monitoring system, the computer intelligent dynamic monitoring system studied in this paper has better data processing ability and does not consume too much human resources in the whole process of the experiment. Therefore, the computer intelligent dynamic monitoring system has considerable social practicability in terms of data processing ability. This further proves that the computer intelligent dynamic monitoring system studied in this paper can be applied to football training and has good application performance.


## 4.2. Analysis of Intelligent Algorithm Optimization Results of Football Training Dynamic Monitoring System

After optimizing the intelligent algorithm, the football training dynamic monitoring system does reduce the phenomenon of data error. However, it is far from enough to judge the performance of the optimized intelligent dynamic monitoring system only from the error level. In order to further verify whether the new monitoring system studied in this paper can maintain or exceed the performance of the original system, thirteen groups of different sample data are selected in this experiment, which contains a variety of different types of data information. Compared with a single type of data information, using different types of data information as sample data can better reflect the excellent data processing ability of a system. During this experiment, the main purpose is to observe whether the system can maintain the stability and high-speed processing capacity of the system in the face of massive data. Finally, the system processes the overall trend of sample data transmission to the computer PC, as shown in Figure5.Figure 5
Data processing rate diagram of optimized detection system.As can be seen from Figure5, the results of the non-optimized system are very obvious compared with the optimized system. It can be seen from the picture that the whole system has a significant downward trend in the process of data processing. The main reason for this trend is the function disorder between modules in the system, which leads to the serious decline of data processing rate, which also proves that there is an unstable problem in the non-optimized system. On the contrary, the optimized intelligent dynamic monitoring system not only has high-speed data processing capacity but also the trend of data processing of the whole system in the figure is very stable. This also proves that the optimized intelligent dynamic monitoring system can still run smoothly in the face of massive data. It can also be seen that the dynamic monitoring system optimized by the computer intelligent algorithm has a wide development space in football training.


## 5. Conclusion

This paper mainly makes systematic experimental research on computer intelligent dynamic monitoring technology. Firstly, the history and development of computer intelligent dynamic monitoring technology are introduced. Secondly, the computer intelligent dynamic monitoring system is designed, and its structure construction and algorithm upgrading are described in detail. We understand that the first simulation test has its own meaning and function, and ensure that each module can run normally. In the upgrade of the system algorithm, it is mainly enhanced in the part of human condition monitoring. Compared with a traditional monitoring system and an ordinary dynamic monitoring system, the computer intelligent dynamic monitoring system studied in this paper ensures the correctness of monitoring data, reduces the workload of the system, and ensures the stability of the whole system. In order to improve the correctness of monitoring data, the feature extraction algorithm and the state recognition algorithm are added. Finally, the performance and stability of the intelligent dynamic monitoring system designed in this paper are verified by specific experiments. The experimental results also prove that the dynamic monitoring system optimized by the computer intelligent algorithm can be applied to football training. However, this paper still lacks a detailed description of intelligent dynamic monitoring system equipment. The research needs to change and monitor according to the changes of external factors such as environment, and the array synchronization operation needs to be further elaborated, which needs to be further supplemented in future research.



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*Source: 2199166-2022-02-28.xml*