# Distributed Virtual Environment Basketball Equipment Embedded Systems’ Research and Development

Authors: Yang Zhou
Journal: Mathematical Problems in Engineering
 (2021)

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## Abstract

With the development of social economy and the improvement of material level, people are paying more and more attention to their own health, and they are also constantly improving their fitness awareness. As far as the current sports equipment is concerned, the boring exercise methods can no longer satisfy people’s pursuit of higher quality fitness exercises. The embedding of the distributed virtual environment can incorporate its perceptual, immersive, and interactive characteristics, which increases the realization of multiuser interactive behavior in the field of sports equipment in the virtual scene. Therefore, this paper proposes the research and development of basketball sports equipment embedded system based on distributed virtual environment. First of all, this article adopts the literature method to learn the application of distributed virtual environment and embedded system in depth and, secondly, designs the embedded system architecture of basketball sports equipment suitable for distributed virtual environment and the software and hardware framework of the embedded system. Finally, the performance, delay comparison, system optimization test, and other aspects of the embedded system are analyzed. The average delay of interactive functions of embedded systems based on distributed virtual environment is 1.4 min, which saves 4 min compared with traditional interactive methods. And, the throughput rate of the system has also been greatly improved. The research on the embedded system of sports equipment based on the distributed virtual environment proposed in this paper will be beneficial to the development of society and economy, and it can also help more people to enrich the way of sports and enhance the fun of sports.


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## Body

## 1. Introduction

With the development of social economy, the development trend of sports equipment sales has performed well. On the one hand, people pursue a healthy body and a slim figure, so the demand for sports equipment is increasing. On the other hand, people's requirements for sports equipment are also increasing. Sports equipment, in addition to good quality, beautiful appearance, and other objective conditions to meet the basic functions of exercise, also has a high quality and high-tech level. This has also created a new challenge and motivation for the innovation of sports equipment.Long-term exercise is easy to make people feel boring. Single-function sports equipment combined with a distributed virtual environment embedded system can not only meet people’s needs for fitness but also bring people a sense of immersion and pleasure. Therefore, through the research of this project, the application of virtual reality technology is integrated into the embedded system of basketball sports equipment. While laying a solid foundation for the research and development of other sports equipment products, it can also improve the quality and level of domestic sports company products. This has an important role and value for the formation of new economic development points.Latoschik et al. study the performance and user experience of social virtual reality (SVR), with the goal of distributed, concrete, immersive, and face-to-face encounters. They showed the close relationship between scalability, replication accuracy, and resulting performance characteristics and the impact of these characteristics on users coexisting with larger groups of virtual others. System scalability provides a variable number of avatars in the same location and Al control agents with various appearances, including virtual people with real appearances generated by photogrammetric scanning. This article reports how to use today’s off-the-shelf technical solutions to meet the needs of embedded SVR as well as expectations in terms of features, performance, and potential limitations. Special attention is paid to the social signals embodied by reliable communication necessary to achieve low delay and sufficient frame rate. They proposed a hybrid evaluation method that consistently linked the results of technical benchmarks with subjective ratings and evaluation results. However, the delay of the system is still at a relatively high level in practical applications [1]. Arthur et al. summarized the distributed real-time (DRT) system is one of the most complex software systems to design, test, maintain, and develop. The existence of components distributed on the network often conflicts with real-time requirements, resulting in design strategies relying on domain-specific and even application-specific knowledge. The Distributed Virtual Environment (DVE) system is a DRT system that instantly connects multiple users through the network and shares a virtual space. The DVE system is different from the traditional DRT system in the importance of the end user experience quality. Although they put forward important analysis, it is a challenging problem in testing and evaluation when designing specific DVE, Open Simulator experimental lens and DVE system. They built their observations within six dimensions of well-known design concerns: correctness, fault tolerance/prevention, scalability, time sensitivity, consistency, and distribution overhead. In addition, they put the experimental work in a broader historical context, showing that these challenges are inherent in DVE, and proposed the direction of future research. However, there are too many uncontrollable factors in their experiment, and the results of the experiment need to be treated more correctly [2]. Zeng et al.’s traditional independent embedded system is limited in terms of functionality, flexibility, and scalability. Fog computing platform is characterized by pushing cloud services to the edge of the network and is a promising solution to support and strengthen traditional embedded systems. Resource management has always been a key issue affecting system performance. This article considers a software-defined embedded system that supports fog computing. Task images are stored on a storage server, and calculations can be performed on embedded devices or computing servers. Designing an efficient task scheduling and resource management strategy to minimize task completion time is of great significance for improving user experience. To this end, this article studies three issues. (1) How to balance the workload on client devices and computing servers, that is, task scheduling? (2) How to place task images on storage servers, that is, resource management? (3) How to balance storage server I/O interrupt request between? They are considered jointly and expressed as a mixed integer nonlinearity. However, they did not propose a specific solution to solve the above problems [3].Only innovation can continuously promote the development and progress of fitness equipment. Combining high-tech with fitness equipment is an inevitable trend in the development of this industry. The innovation of this article lies in the full application of distributed virtual reality technology, the design of the client embedded frame system, and the completion of the construction of immersive and interactive virtual fitness scenes under the formation of a specific fitness virtual environment. This paper makes full use of the combination of qualitative analysis and quantitative analysis and has been fully reflected in the analysis part.


## 2. Basketball Equipment Research and Development of Embedded Systems Based on Distributed Virtual Environment

### 2.1. Distributed Virtual Reality System

Distributed virtual reality refers to a virtual environment based on the network, and its application in sports can realize the panorama, virtuality, and interactivity of sports [4].Virtual reality is a computer system that can create a virtual world for experience. It uses computer technology to create an actual virtual environment with a combination of visual, auditory, and tactile perceptions [5]. Various interactive devices interact with entities in the virtual environment to generate interactive simulation and information exchange, giving people a new way of communication and contact [6]. Since its birth, virtual reality technology has shown great advantages in the fields of military simulation, urban planning, real estate development, industrial simulation, geographic information systems, games, education, and training. The three most promising technologies of this century are computer, multimedia, and VR technology. VR technology design disciplines are very extensive, including computers, artificial intelligence, and sensor technology. It only needs computer technology to realize the realistic environmental world and make people feel brand-new experience and interaction [7, 8].The virtual reality system emphasizes realism, vision, interaction, personal perspective, and rapid response. Therefore, the virtual reality system has four basic characteristics, namely, multisensitivity, engagement, interactivity, and autonomy [9]. Multisensory refers to the sense of sight, hearing, and touch in the virtual world. The sense of engagement refers to the user's degree of immersion and concentration in virtual reality, which requires the creation of the virtual world to attract him. Interactivity, that is to say, can realize independent communication and interaction between multiple users and has a complete sense of reality. Autonomy means that the objects in the virtual environment have the right to choose independently and can change related parameter settings and attributes by themselves [10, 11].The distributed virtual reality system mainly has the following three characteristics:(1)
Decentralized management and control: distributed in different regions and different levels of systems, it can not only ensure the sharing of network resources but also autonomously control its own data [12]. Most of the management and control of local conditions can be solved only on the local side. If it contains data from other websites, it needs to be managed through the Internet as a connectivity issue, for example, through local data entry, local query, and maintenance. At present, computer resources are very close to users, so communication costs can be reduced and response speed can be improved. However, the amount of data in other location databases is very small, which can greatly reduce information transmission to the network. At the same time, it can improve the security of local data [13].(2)
Multiuser interaction: with the development of network technology, the interaction of networks is indispensable for decentralized virtual reality systems [14]. The current comprehensive fitness equipment system does not have network interaction, and even if some networks have interaction, they have specific limitations. According to the decentralized virtual reality system, user groups geographically dispersed in different places can have real-time conversations. This user group can accommodate thousands of users at the same time [15].(3)
Real-time can also be regarded as dynamic [16]: because each user is displayed as a virtual image in a computer environment, the virtual image is to be explained by a user’s video or a description of autonomy. When users, virtual worlds, or objects created in the computer interact with other images, it is actually a real-time dynamic operation [17]. Because only real-time guarantee can make users feel that they are in a virtual environment.In addition, the decentralized virtual reality system has excellent scalability and is relatively easy to integrate with existing systems and easy to extend [18]. In the case of a comprehensive system based on a decentralized virtual environment, using distributed virtual reality technology to operate more than one gymnastics machine server can quickly expand the existing network system to form a decentralized system [19]. It is also simpler than building a large-scale system that saves time, money, material, and resources.


### 2.2. Bayesian Algorithm of Simulation Interactive Credibility

Interactive behavior is the basis for the simulation of distributed virtual environments, and credible interaction behavior is essential to the normal operation of the system. Large-scale distributed virtual environment simulation has the characteristics of large scale and various interactions [20], which intensifies the impact of network delay and middleware overhead on the credibility of system interaction, which may damage the accuracy of simulation results and system fairness, which leads to frequent simulation interruption, manual intervention, and even simulation failure [21].In a distributed virtual environment, to maintain the consistency of interaction between different nodes, it is necessary to keep the timing and spacing of events consistent. UseV=v1,v2,⋯,vn to represent the set of simulation nodes, where E=e11,e12,⋯,eij,⋯,enneij represents the maximum delay between simulation nodes ei and ej, O represents all interaction events, G(om) represents the node that generated the event Om, R(om) represents the collection of nodes that received the event, tgi(om) represents the time when the event Om was generated, tri(om) represents the time when the node vi received the event, and tei(om) represents the time when the event Om is executed on node vi and the timing is consistent:(1)∀om,on∈O,vi,vj∈Rom∩Ron,teiom≤teion⇒tejom≤tejon.Consistent spacing:(2)∀om,on∈O,vi,vj∈Rom∩Ron,teiom−teion=tejom−tejon.The interaction event that meets the requirements of consistent timing and consistent spacing at the same time is considered credible; if not, the interaction event is considered untrustworthy.Since the network conditions and operating load of the distributed virtual environment are constantly changing, whether the interactive event is credible can be regarded as a random event, and the interactive credibility can be regarded as a random variable. Here, we choose the distribution as the prior distribution of the interactive credibility evaluation and record the value of the interactive credibility as this; then, the prior distribution of the interactive credibilityπθ:(3)πθ=Γa+bΓaΓbθa−11−θb−1,0≤θ≤1,a>0,b>0.Among them,a and b are the hyperparameters of the prior distribution and Γ are the gamma function.If the meanθ¯ and variance Dθ of the simulation interaction credibility can be obtained based on the prior information, the hyperparameters of the prior distribution(4)a=1−θ¯θ¯2Dθ−θ¯,b=a1−θ¯θ.The prior information is mainly collected and analyzed based on historical data or simulation models. If any valid prior information cannot be obtained, the Bayesian assumption can be used, and the interaction credibility prior is considered to be uniform in the (0,1) interval distributed.According to the Bayesian formula, the joint density function of interactive credibilityθ is(5)px,θ=pX=x|θπθ.If in order to estimate the credibilityθ of the interaction, n independent interval consistency data collection and judgment are performed in a certain simulation, where the number of credible interactions is X; then, X obeys the binomial distribution b(n, θ), and the likelihood function is(6)pX=x|θ=nxθx1−θn−x,x=0,1,⋯,n.Then,(7)px,θ=Γa+bΓaΓbnxθa+x−11−θb+n−x−1,x=0,1,⋯,n,0<θ<1.Edge distribution:(8)px=∫01px,θdθ=Γa+bΓaΓb⋅Γa+xΓb+n−xΓa+b+nnx,x=0,1,⋯,n.In summary, the posterior density of interactive credibility is(9)πθ|x=px,θpx=Γa+b+nΓa+xΓb+n−xθa+x−11−θb+n−−x−1.We choose the posterior expectationθ as the Bayesian evaluation conclusion; then, the Bayesian estimate of the interaction credibility is(10)θ^=∫01θπθ|xdθ=a+xa+b+n.


### 2.3. Edge Fog Calculation

Fog computing is an extension of cloud computing. It is the cloud closer to the ground and is the last part of the network between the terminal and the data center, so this article also refers to the combination of edge computing and edge fog computing [22]. The service platform is composed of distributed servers, provides computer and network services for data storage and distribution, provides cloud preprocessing services, and provides analysis and feedback of local smart cards and other service terminal devices [23]. Its purpose is to reduce bandwidth pressure, reduce application delays, and reduce the computing load of the data center [24].The fog computing framework is shown in Figure1. This graphic shows that fog computing is located between the cloud server and the terminal device. All fog computer servers are connected to cloud servers and terminal devices in various regions [25]. All sensors and smart terminals (local devices, etc.) are located at the edge. As the executor of the cloud server and the decision maker of the terminal equipment, fog computing provides services for the upper and lower levels within the framework.Figure 1
Framework of fog calculation.Fog computing is a highly virtualized cloud computing platform that extends to the edge of the network. As an extension of the cloud platform, fog computing uses its new architecture to provide storage, computing, and network services, which means that fog computing is destined to have some unique characteristics, mainly including the following. (1) Edge location and low Latency: fog computing is deployed at the edge of the network. Compared with cloud computing, it is closer to the ground and closer to terminal equipment. It does not need to go through numerous routers and networks to connect to the server, and it can better support the calculation and application of terminal equipment. (2) Wide geographic area and high mobility: compared with more centralized cloud computing, fog computing has a wider deployment area. (3) Massive nodes and distributed computing: the number of network nodes in fog computing is huge, which is determined by the wide geographic distribution. (4) Real-time interaction and online analysis: fog computing revolves around the sensor network equipment, providing real-time interaction for it, supporting local online analysis and giving corresponding responses and feedback, and effectively improving the real-time performance of the application system. (5) Multitype protocols and heterogeneity: fog computing supports multitype communication protocols with different functions and supports diversified heterogeneous software and hardware devices in different form factors and different environments.The optimization of peak computing services must be based on specific programming strategies to meet the requirements of end users as much as possible [26]. The service optimization of edge fog computing is mainly based on a certain scheduling technology to minimize the cost of service providers under the condition of satisfying user requests to the maximum. This article mainly calculates the cost from the two perspectives of SLO violation penalty and resource node request processing terminal expenditure:SLO violation penalty function: let Penalty denote the total penalty cost for SLO violations; then, the penalty function for violations is defined as(11)Penalty=∑k=1qγk+η×tkstop−tkdeadline.Among them,k represents the sequence number of requests that have SLO violations due to service timeouts, q represents the total number of requests that have SLO violations, γk represents the basic penalty for the k th request that has SLO violations, and η represents the cost of the k th SLO violation request, resulting in penalty cost per unit time. At the k th request, t stop is the actual response time and t deadline is the request deadline response time.The cost function of edge fog computing when processing resource node requests: letvm be the total cost; then, the formula for calculating the resource cost function is(12)vm=∑i=1nvmci,vmci=∑j=1MiPi,jvm×ti,j.The equation on the left represents the total cost of processing requests for theith type of computing resource, Mi in the equation on the right represents the number of its virtual resource nodes, j represents a single virtual resource node with serial number j among virtual resource nodes of type i, and ti,j represents the time it takes for the virtual resource node with serial number j in the virtual resource node of type i to process the request. Combining the above two aspects, the total cost function of the service provider can be calculated as(13)cost=vm+Penalty.


## 2.1. Distributed Virtual Reality System

Distributed virtual reality refers to a virtual environment based on the network, and its application in sports can realize the panorama, virtuality, and interactivity of sports [4].Virtual reality is a computer system that can create a virtual world for experience. It uses computer technology to create an actual virtual environment with a combination of visual, auditory, and tactile perceptions [5]. Various interactive devices interact with entities in the virtual environment to generate interactive simulation and information exchange, giving people a new way of communication and contact [6]. Since its birth, virtual reality technology has shown great advantages in the fields of military simulation, urban planning, real estate development, industrial simulation, geographic information systems, games, education, and training. The three most promising technologies of this century are computer, multimedia, and VR technology. VR technology design disciplines are very extensive, including computers, artificial intelligence, and sensor technology. It only needs computer technology to realize the realistic environmental world and make people feel brand-new experience and interaction [7, 8].The virtual reality system emphasizes realism, vision, interaction, personal perspective, and rapid response. Therefore, the virtual reality system has four basic characteristics, namely, multisensitivity, engagement, interactivity, and autonomy [9]. Multisensory refers to the sense of sight, hearing, and touch in the virtual world. The sense of engagement refers to the user's degree of immersion and concentration in virtual reality, which requires the creation of the virtual world to attract him. Interactivity, that is to say, can realize independent communication and interaction between multiple users and has a complete sense of reality. Autonomy means that the objects in the virtual environment have the right to choose independently and can change related parameter settings and attributes by themselves [10, 11].The distributed virtual reality system mainly has the following three characteristics:(1)
Decentralized management and control: distributed in different regions and different levels of systems, it can not only ensure the sharing of network resources but also autonomously control its own data [12]. Most of the management and control of local conditions can be solved only on the local side. If it contains data from other websites, it needs to be managed through the Internet as a connectivity issue, for example, through local data entry, local query, and maintenance. At present, computer resources are very close to users, so communication costs can be reduced and response speed can be improved. However, the amount of data in other location databases is very small, which can greatly reduce information transmission to the network. At the same time, it can improve the security of local data [13].(2)
Multiuser interaction: with the development of network technology, the interaction of networks is indispensable for decentralized virtual reality systems [14]. The current comprehensive fitness equipment system does not have network interaction, and even if some networks have interaction, they have specific limitations. According to the decentralized virtual reality system, user groups geographically dispersed in different places can have real-time conversations. This user group can accommodate thousands of users at the same time [15].(3)
Real-time can also be regarded as dynamic [16]: because each user is displayed as a virtual image in a computer environment, the virtual image is to be explained by a user’s video or a description of autonomy. When users, virtual worlds, or objects created in the computer interact with other images, it is actually a real-time dynamic operation [17]. Because only real-time guarantee can make users feel that they are in a virtual environment.In addition, the decentralized virtual reality system has excellent scalability and is relatively easy to integrate with existing systems and easy to extend [18]. In the case of a comprehensive system based on a decentralized virtual environment, using distributed virtual reality technology to operate more than one gymnastics machine server can quickly expand the existing network system to form a decentralized system [19]. It is also simpler than building a large-scale system that saves time, money, material, and resources.


## 2.2. Bayesian Algorithm of Simulation Interactive Credibility

Interactive behavior is the basis for the simulation of distributed virtual environments, and credible interaction behavior is essential to the normal operation of the system. Large-scale distributed virtual environment simulation has the characteristics of large scale and various interactions [20], which intensifies the impact of network delay and middleware overhead on the credibility of system interaction, which may damage the accuracy of simulation results and system fairness, which leads to frequent simulation interruption, manual intervention, and even simulation failure [21].In a distributed virtual environment, to maintain the consistency of interaction between different nodes, it is necessary to keep the timing and spacing of events consistent. UseV=v1,v2,⋯,vn to represent the set of simulation nodes, where E=e11,e12,⋯,eij,⋯,enneij represents the maximum delay between simulation nodes ei and ej, O represents all interaction events, G(om) represents the node that generated the event Om, R(om) represents the collection of nodes that received the event, tgi(om) represents the time when the event Om was generated, tri(om) represents the time when the node vi received the event, and tei(om) represents the time when the event Om is executed on node vi and the timing is consistent:(1)∀om,on∈O,vi,vj∈Rom∩Ron,teiom≤teion⇒tejom≤tejon.Consistent spacing:(2)∀om,on∈O,vi,vj∈Rom∩Ron,teiom−teion=tejom−tejon.The interaction event that meets the requirements of consistent timing and consistent spacing at the same time is considered credible; if not, the interaction event is considered untrustworthy.Since the network conditions and operating load of the distributed virtual environment are constantly changing, whether the interactive event is credible can be regarded as a random event, and the interactive credibility can be regarded as a random variable. Here, we choose the distribution as the prior distribution of the interactive credibility evaluation and record the value of the interactive credibility as this; then, the prior distribution of the interactive credibilityπθ:(3)πθ=Γa+bΓaΓbθa−11−θb−1,0≤θ≤1,a>0,b>0.Among them,a and b are the hyperparameters of the prior distribution and Γ are the gamma function.If the meanθ¯ and variance Dθ of the simulation interaction credibility can be obtained based on the prior information, the hyperparameters of the prior distribution(4)a=1−θ¯θ¯2Dθ−θ¯,b=a1−θ¯θ.The prior information is mainly collected and analyzed based on historical data or simulation models. If any valid prior information cannot be obtained, the Bayesian assumption can be used, and the interaction credibility prior is considered to be uniform in the (0,1) interval distributed.According to the Bayesian formula, the joint density function of interactive credibilityθ is(5)px,θ=pX=x|θπθ.If in order to estimate the credibilityθ of the interaction, n independent interval consistency data collection and judgment are performed in a certain simulation, where the number of credible interactions is X; then, X obeys the binomial distribution b(n, θ), and the likelihood function is(6)pX=x|θ=nxθx1−θn−x,x=0,1,⋯,n.Then,(7)px,θ=Γa+bΓaΓbnxθa+x−11−θb+n−x−1,x=0,1,⋯,n,0<θ<1.Edge distribution:(8)px=∫01px,θdθ=Γa+bΓaΓb⋅Γa+xΓb+n−xΓa+b+nnx,x=0,1,⋯,n.In summary, the posterior density of interactive credibility is(9)πθ|x=px,θpx=Γa+b+nΓa+xΓb+n−xθa+x−11−θb+n−−x−1.We choose the posterior expectationθ as the Bayesian evaluation conclusion; then, the Bayesian estimate of the interaction credibility is(10)θ^=∫01θπθ|xdθ=a+xa+b+n.


## 2.3. Edge Fog Calculation

Fog computing is an extension of cloud computing. It is the cloud closer to the ground and is the last part of the network between the terminal and the data center, so this article also refers to the combination of edge computing and edge fog computing [22]. The service platform is composed of distributed servers, provides computer and network services for data storage and distribution, provides cloud preprocessing services, and provides analysis and feedback of local smart cards and other service terminal devices [23]. Its purpose is to reduce bandwidth pressure, reduce application delays, and reduce the computing load of the data center [24].The fog computing framework is shown in Figure1. This graphic shows that fog computing is located between the cloud server and the terminal device. All fog computer servers are connected to cloud servers and terminal devices in various regions [25]. All sensors and smart terminals (local devices, etc.) are located at the edge. As the executor of the cloud server and the decision maker of the terminal equipment, fog computing provides services for the upper and lower levels within the framework.Figure 1
Framework of fog calculation.Fog computing is a highly virtualized cloud computing platform that extends to the edge of the network. As an extension of the cloud platform, fog computing uses its new architecture to provide storage, computing, and network services, which means that fog computing is destined to have some unique characteristics, mainly including the following. (1) Edge location and low Latency: fog computing is deployed at the edge of the network. Compared with cloud computing, it is closer to the ground and closer to terminal equipment. It does not need to go through numerous routers and networks to connect to the server, and it can better support the calculation and application of terminal equipment. (2) Wide geographic area and high mobility: compared with more centralized cloud computing, fog computing has a wider deployment area. (3) Massive nodes and distributed computing: the number of network nodes in fog computing is huge, which is determined by the wide geographic distribution. (4) Real-time interaction and online analysis: fog computing revolves around the sensor network equipment, providing real-time interaction for it, supporting local online analysis and giving corresponding responses and feedback, and effectively improving the real-time performance of the application system. (5) Multitype protocols and heterogeneity: fog computing supports multitype communication protocols with different functions and supports diversified heterogeneous software and hardware devices in different form factors and different environments.The optimization of peak computing services must be based on specific programming strategies to meet the requirements of end users as much as possible [26]. The service optimization of edge fog computing is mainly based on a certain scheduling technology to minimize the cost of service providers under the condition of satisfying user requests to the maximum. This article mainly calculates the cost from the two perspectives of SLO violation penalty and resource node request processing terminal expenditure:SLO violation penalty function: let Penalty denote the total penalty cost for SLO violations; then, the penalty function for violations is defined as(11)Penalty=∑k=1qγk+η×tkstop−tkdeadline.Among them,k represents the sequence number of requests that have SLO violations due to service timeouts, q represents the total number of requests that have SLO violations, γk represents the basic penalty for the k th request that has SLO violations, and η represents the cost of the k th SLO violation request, resulting in penalty cost per unit time. At the k th request, t stop is the actual response time and t deadline is the request deadline response time.The cost function of edge fog computing when processing resource node requests: letvm be the total cost; then, the formula for calculating the resource cost function is(12)vm=∑i=1nvmci,vmci=∑j=1MiPi,jvm×ti,j.The equation on the left represents the total cost of processing requests for theith type of computing resource, Mi in the equation on the right represents the number of its virtual resource nodes, j represents a single virtual resource node with serial number j among virtual resource nodes of type i, and ti,j represents the time it takes for the virtual resource node with serial number j in the virtual resource node of type i to process the request. Combining the above two aspects, the total cost function of the service provider can be calculated as(13)cost=vm+Penalty.


## 3. Research and Development Model Design of Basketball Sports Equipment Embedded System Based on Distributed Virtual Environment

According to the characteristics of the distributed virtual reality system and the system’s function and performance requirements, this section proposes a fitness equipment embedded system architecture suitable for a distributed virtual environment and the software and hardware framework of the embedded system.The functional requirements of this system include with playback function, which can be connected to the card, and with a TV interface. The playing of the ship can increase the user’s fun in the fitness process, increase the user’s excitement, and help the user entertain in fitness and exercise in entertainment. The fitness system comes with a three-dimensional virtual scene, which can also be downloaded from a fixed website via the Internet. The application of three-dimensional virtual scenes increases the user’s sense of vision, and the virtual scenes downloaded on the Internet help users to obtain more virtual scenes according to their needs, so as to feel more fun. Multiuser interactive fitness is realized in virtual space. The interactivity of multiple users enables better communication between users and feels that fitness is a collective exercise, not just the user’s own exercise. You can get help from others to make your own fitness more effective. The performance requirements of this system include simple operation of the control panel, clear functions, and intuitive and convenient operation of each function key; users do not need to spend a lot of time to learn and familiarize themselves; they can use the virtual scene to draw fast, and the network bandwidth limit is low. The above remote users realize interactive fitness in the virtual space.


### 3.1. The Overall Structure of the System

The embedded system of basketball sports equipment in this distributed virtual environment chooses the client server mode. When the system is working, the operation triggered by the user on the client side is transmitted to the server. The server determines the client group to receive the operation according to a certain grouping strategy and then transmits this operation to the relevant client by multicast, and each client has the backup of the virtual scene and related computing capabilities.In order to reduce the amount of network information transmission and support the concurrent operation of users in the system when the system is running, the system adopts a data management method based on replication and transmits user operations between the client and server instead of status update information. While adopting the replication structure and transmission operation mode, the system must add physical time stamps to all operations in order to control all concurrent operations that occur in the system, and the premise of adding physical time stamps is the physical time of all nodes. Synchronize: in order to synchronize the physical clocks of all nodes in the system, the system runs a sleepy server on the server, runs clients on all clients, and synchronizes the physical clocks of all nodes at regular intervals. At the same time, the client has an independent communication module that can independently and randomly transmit the change information of the entities in the scene to the server, and it can also monitor the information sent from the server at all times and perform proper processing.While the fitness equipment servers in different regions are interacting, the interaction between the client and the server in the same region is relatively frequent. Then, the overall structure proposed in this article is conducive to the partition management of the virtual scene, and the load is balanced to each region. It greatly reduces the load of a single server and avoids the system “bottleneck” problem caused by the centralized structure, thereby improving the overall performance of the system. Due to the use of virtual scenes, the amount of data is relatively large, so in the process of transmission between the server and the client, the protocol is used to increase the response time and improve the real-time performance. In this structure, if a regional server fails, it will not affect other regions. At the same time, users can connect to other servers through the network to ensure the user’s real-time performance.


### 3.2. Server System Structure

The server side is composed of system management module, network communication module, user management module, information processing module, fitness scene management module, and object management module. The system management module is mainly used for console command processing, client request response, and system control. The network communication module is mainly used for information filtering and transmission of fitness scenes; the management module is mainly used for the management and processing of current scenes and scene libraries. The user management module is mainly for service management for users, including private information such as account numbers and passwords. Authority management includes role authority management and data authority management. The administrator can set authority settings and role definitions for newly added users. The information processing module is mainly used for information processing, grouping, and consistency control. For the consistency control problem in the information processing module, we solve the spatial consistency problem on the basis of analyzing the shortcomings of traditional consistency control methods.


### 3.3. Client System Structure

The client-side system structure is mainly composed of system management module, network communication module, user management module, information processing module, fitness scene management module, object management module, user perception module, and scene drawing module. The system management module is mainly used for client command processing, client request sending, response receiving, and system control. The network communication module is mainly used for information filtering and transmission. The user management module is mainly used for user-related information and transmission. The management information processing module is mainly used for information processing and consistency, the control fitness scene management module is mainly used for the management and processing of the current scene and the scene library, and the object management module is mainly used for the management and processing of objects in the scene. The scene drawing module is mainly used for collision detection, LOD control, and graphics drawing.The user perception module is mainly used for the perception and preprocessing of user behavior. The user conducts asynchronous full-duplex communication through the serial port between each sensor and the embedded virtual scene. Various general data collected from sensors, mainly various actions of users, are sent to the central computer through the serial port for processing. At the same time, it is necessary to send the topography and other data of the virtual scene to the controller and apply the output torque of the mechanical device. It simulates the changes of the real scene and sports field that people feel during the fitness process so that the user has a feeling of being in the virtual scene.


### 3.4. Framework Design of Client Embedded System

#### 3.4.1. Hardware System Frame Design

The system intends to adopt the embedded development version of the PC/104 standard, equipped with Intel XScale series of multimedia expansion technology, embedded processor that provides high-quality video, and 3D graphics playback functions, and the embedded processor controls the memory, fitness equipment controller, and a series of audio and video processors, which include NAND flash memory, SDRAM memory equipped with LCD frame buffers, video coprocessors to enhance the video processing capabilities of embedded processors, audio and video encoders for fitness equipment output devices, such as TV equipment, High-quality audio, and video output processors such as TFTLCD equipment and audio equipment use the universal input and output interface array GPIOS array, and asynchronous serial communication interface to communicate with the controller speed and slope of fitness equipment, as well as the interconnection of mileage, speed, and heart rate of many sensing equipment. Input devices such as keyboards and remote controls provide input of system commands and perform network communication through interfaces such as network adapters, Bluetooth, and GPRS.


#### 3.4.2. Software System Framework Design

In order to support the better development, deployment, operation, and management of distributed embedded systems, the application based on multilayer structure must be equipped with a software support platform for the development of application software, which is divided into 6 levels from hardware platform to application software.(1) System boot layer: in order to introduce an embedded operating system, a system boot and kernel startup module must be built to complete the communication function of the host computer and the hardware platform, the initialization function of the hardware platform, and the monitoring function of the hardware platform. (2) Operating system: under the guidance of the boot program, this layer is tailored and transplanted to the embedded operating system kernel. In order to cooperate with the LCD display module for efficient 3D graphics display, it is equipped with controller-related driver software and frame buffers. (3) Driver layer: after completing the operating system level support platform, develop various hardware device drivers on it, including audio drivers, fitness equipment controllers and sensor drivers, sensor monitoring drivers, and keyboard array drivers. (4) Media library: in order to support virtual environment applications such as virtual scene construction, 3D graphics support library and multimedia support library are built on the embedded GUI. (5) Application software layer: mainly, develop embedded audio and video playback, picture browsing, virtual scene construction, and network distributed interaction applications to meet the needs of users in entertainment. (6) Graphical user interface: in the graphic display system, the user needs to interact with the system through the embedded graphic user interface GUI, and 3D graphics also need to communicate with the hardware through it, so the user graphic interface GUI is essential. Figure2 shows the system software structure.Figure 2
System software framework.


## 3.1. The Overall Structure of the System

The embedded system of basketball sports equipment in this distributed virtual environment chooses the client server mode. When the system is working, the operation triggered by the user on the client side is transmitted to the server. The server determines the client group to receive the operation according to a certain grouping strategy and then transmits this operation to the relevant client by multicast, and each client has the backup of the virtual scene and related computing capabilities.In order to reduce the amount of network information transmission and support the concurrent operation of users in the system when the system is running, the system adopts a data management method based on replication and transmits user operations between the client and server instead of status update information. While adopting the replication structure and transmission operation mode, the system must add physical time stamps to all operations in order to control all concurrent operations that occur in the system, and the premise of adding physical time stamps is the physical time of all nodes. Synchronize: in order to synchronize the physical clocks of all nodes in the system, the system runs a sleepy server on the server, runs clients on all clients, and synchronizes the physical clocks of all nodes at regular intervals. At the same time, the client has an independent communication module that can independently and randomly transmit the change information of the entities in the scene to the server, and it can also monitor the information sent from the server at all times and perform proper processing.While the fitness equipment servers in different regions are interacting, the interaction between the client and the server in the same region is relatively frequent. Then, the overall structure proposed in this article is conducive to the partition management of the virtual scene, and the load is balanced to each region. It greatly reduces the load of a single server and avoids the system “bottleneck” problem caused by the centralized structure, thereby improving the overall performance of the system. Due to the use of virtual scenes, the amount of data is relatively large, so in the process of transmission between the server and the client, the protocol is used to increase the response time and improve the real-time performance. In this structure, if a regional server fails, it will not affect other regions. At the same time, users can connect to other servers through the network to ensure the user’s real-time performance.


## 3.2. Server System Structure

The server side is composed of system management module, network communication module, user management module, information processing module, fitness scene management module, and object management module. The system management module is mainly used for console command processing, client request response, and system control. The network communication module is mainly used for information filtering and transmission of fitness scenes; the management module is mainly used for the management and processing of current scenes and scene libraries. The user management module is mainly for service management for users, including private information such as account numbers and passwords. Authority management includes role authority management and data authority management. The administrator can set authority settings and role definitions for newly added users. The information processing module is mainly used for information processing, grouping, and consistency control. For the consistency control problem in the information processing module, we solve the spatial consistency problem on the basis of analyzing the shortcomings of traditional consistency control methods.


## 3.3. Client System Structure

The client-side system structure is mainly composed of system management module, network communication module, user management module, information processing module, fitness scene management module, object management module, user perception module, and scene drawing module. The system management module is mainly used for client command processing, client request sending, response receiving, and system control. The network communication module is mainly used for information filtering and transmission. The user management module is mainly used for user-related information and transmission. The management information processing module is mainly used for information processing and consistency, the control fitness scene management module is mainly used for the management and processing of the current scene and the scene library, and the object management module is mainly used for the management and processing of objects in the scene. The scene drawing module is mainly used for collision detection, LOD control, and graphics drawing.The user perception module is mainly used for the perception and preprocessing of user behavior. The user conducts asynchronous full-duplex communication through the serial port between each sensor and the embedded virtual scene. Various general data collected from sensors, mainly various actions of users, are sent to the central computer through the serial port for processing. At the same time, it is necessary to send the topography and other data of the virtual scene to the controller and apply the output torque of the mechanical device. It simulates the changes of the real scene and sports field that people feel during the fitness process so that the user has a feeling of being in the virtual scene.


## 3.4. Framework Design of Client Embedded System

### 3.4.1. Hardware System Frame Design

The system intends to adopt the embedded development version of the PC/104 standard, equipped with Intel XScale series of multimedia expansion technology, embedded processor that provides high-quality video, and 3D graphics playback functions, and the embedded processor controls the memory, fitness equipment controller, and a series of audio and video processors, which include NAND flash memory, SDRAM memory equipped with LCD frame buffers, video coprocessors to enhance the video processing capabilities of embedded processors, audio and video encoders for fitness equipment output devices, such as TV equipment, High-quality audio, and video output processors such as TFTLCD equipment and audio equipment use the universal input and output interface array GPIOS array, and asynchronous serial communication interface to communicate with the controller speed and slope of fitness equipment, as well as the interconnection of mileage, speed, and heart rate of many sensing equipment. Input devices such as keyboards and remote controls provide input of system commands and perform network communication through interfaces such as network adapters, Bluetooth, and GPRS.


### 3.4.2. Software System Framework Design

In order to support the better development, deployment, operation, and management of distributed embedded systems, the application based on multilayer structure must be equipped with a software support platform for the development of application software, which is divided into 6 levels from hardware platform to application software.(1) System boot layer: in order to introduce an embedded operating system, a system boot and kernel startup module must be built to complete the communication function of the host computer and the hardware platform, the initialization function of the hardware platform, and the monitoring function of the hardware platform. (2) Operating system: under the guidance of the boot program, this layer is tailored and transplanted to the embedded operating system kernel. In order to cooperate with the LCD display module for efficient 3D graphics display, it is equipped with controller-related driver software and frame buffers. (3) Driver layer: after completing the operating system level support platform, develop various hardware device drivers on it, including audio drivers, fitness equipment controllers and sensor drivers, sensor monitoring drivers, and keyboard array drivers. (4) Media library: in order to support virtual environment applications such as virtual scene construction, 3D graphics support library and multimedia support library are built on the embedded GUI. (5) Application software layer: mainly, develop embedded audio and video playback, picture browsing, virtual scene construction, and network distributed interaction applications to meet the needs of users in entertainment. (6) Graphical user interface: in the graphic display system, the user needs to interact with the system through the embedded graphic user interface GUI, and 3D graphics also need to communicate with the hardware through it, so the user graphic interface GUI is essential. Figure2 shows the system software structure.Figure 2
System software framework.


## 3.4.1. Hardware System Frame Design

The system intends to adopt the embedded development version of the PC/104 standard, equipped with Intel XScale series of multimedia expansion technology, embedded processor that provides high-quality video, and 3D graphics playback functions, and the embedded processor controls the memory, fitness equipment controller, and a series of audio and video processors, which include NAND flash memory, SDRAM memory equipped with LCD frame buffers, video coprocessors to enhance the video processing capabilities of embedded processors, audio and video encoders for fitness equipment output devices, such as TV equipment, High-quality audio, and video output processors such as TFTLCD equipment and audio equipment use the universal input and output interface array GPIOS array, and asynchronous serial communication interface to communicate with the controller speed and slope of fitness equipment, as well as the interconnection of mileage, speed, and heart rate of many sensing equipment. Input devices such as keyboards and remote controls provide input of system commands and perform network communication through interfaces such as network adapters, Bluetooth, and GPRS.


## 3.4.2. Software System Framework Design

In order to support the better development, deployment, operation, and management of distributed embedded systems, the application based on multilayer structure must be equipped with a software support platform for the development of application software, which is divided into 6 levels from hardware platform to application software.(1) System boot layer: in order to introduce an embedded operating system, a system boot and kernel startup module must be built to complete the communication function of the host computer and the hardware platform, the initialization function of the hardware platform, and the monitoring function of the hardware platform. (2) Operating system: under the guidance of the boot program, this layer is tailored and transplanted to the embedded operating system kernel. In order to cooperate with the LCD display module for efficient 3D graphics display, it is equipped with controller-related driver software and frame buffers. (3) Driver layer: after completing the operating system level support platform, develop various hardware device drivers on it, including audio drivers, fitness equipment controllers and sensor drivers, sensor monitoring drivers, and keyboard array drivers. (4) Media library: in order to support virtual environment applications such as virtual scene construction, 3D graphics support library and multimedia support library are built on the embedded GUI. (5) Application software layer: mainly, develop embedded audio and video playback, picture browsing, virtual scene construction, and network distributed interaction applications to meet the needs of users in entertainment. (6) Graphical user interface: in the graphic display system, the user needs to interact with the system through the embedded graphic user interface GUI, and 3D graphics also need to communicate with the hardware through it, so the user graphic interface GUI is essential. Figure2 shows the system software structure.Figure 2
System software framework.


## 4. Distributed Virtual Environment Basketball Equipment Research and Development of Embedded Systems Analysis

### 4.1. Performance Analysis of Embedded System

As shown in Table1, it is a parameter list of fog computing, cloud computing, and edge computing. Among them, 0–3 are cloud computing, 4-5 are fog computing, and 6 are edge computing parameters. The processing capacity of the system selected in this article is 800–5000 MIPS, the unit price is 3–6 CENTS, and the broadband settings are 1000 and 2000 MB.Table 1
List of parameters for fog computing, cloud computing, and edge computing.
Resource numberProcessing capacity (MIPS)Unit price (CENT/S)Broadband (MB)050006100012500510002250051000315004100041000420005100032000680031000It can be seen from Table2 and Figure 3 that, as the number of requests changes, the delay comparison of the three systems becomes more obvious. When the number of requests is 20, the latency of fog computing is 20 s, the latency of cloud computing IGA is 22 s, and the latency of cloud computing RRSA is 21 s, and the three are at the same level. However, when the number of requests reaches 200, the delay of fog calculation SLO is 121 s, while the delay of IGA reaches 168 s, and the delay of RRSA is 180 s, which are significantly higher than the delay of fog calculation. This shows that embedded systems based on fog computing have shorter time delays and perform better.Table 2
Delay comparison of embedded system under different number of requests.
Systems/Requests20406080100200Fog-SLO2035436077121Cloud-IGA2236456377168Cloud-RRSA2137496579180Figure 3
Delay comparison of the embedded system under different number of requests.It can be seen from Table3 and Figure 4 that, as the number of requests increases, the execution time of the three systems is rising, but the rate of increase is different. When the number of requests is 20, the execution time of fog calculation is 46 s, the execution time of cloud computing IGA is 47 s, and the execution time of RRSA is 48 s; when the number of requests is 200, the execution time of fog calculation is 200 s, and IGA is executed. The time is 246 s, and the execution time of RRSA is 247 s. It can be clearly seen that the execution time of IGA and RRSA exceeds the fog calculation, and the speed is significantly slower.Table 3
Comparison of system execution time under different number of requests.
Systems/requests20406080100200Fog-SLO4695146176180200Cloud-IGA47102149189192246Cloud-RRSA48103153190197247Figure 4
Comparison of system execution time under different number of requests.


### 4.2. Based on the Experimental Results of Sports Equipment Distributed Virtual Environment Embedded Systems

According to the province’s dynamic analysis report on economic development, selected sports equipment related data are shown in Table 4.It can be seen from Table4 and Figure 5 that, according to the current development trend of sports equipment, the sales of sports equipment will continue to rise in the future. In the case of treadmills, the annual demand is only 3 million units, and the sales are still increasing at an annual growth rate of 25.1%. The market growth is extremely good. The annual demand for basketball is also 1 million, and the annual market growth rate is 18.9%. If distributed virtual technology can be embedded in these sports equipment, it can better meet people's sports and relaxation needs. In the future, people's demand for embedded system sports equipment will show an exponential growth trend.Table 4
Sales of sports equipment.
Sports equipmentDemand (ten thousand)Output (ten thousand)Sales volume (ten thousand)Annual market growth rate (%)Treadmill30045040825.1Basketball10018011918.9Sport ware58129695.8Badminton1582132096.7Table Tennis1691871534.3Football761891025.9Figure 5
Sales of sports equipment.It can be seen from Table5 and Figure 6 that the system optimization test is compared by observing the hardware operation of the system. When dealing with different data packets, there is a big difference between tuning and an untuned system. After tuning and processing, the embedded system shows a better operating condition and a higher throughput rate.Table 5
System optimization test.
Throughput rateNo tuningCPU/memoryVirtio (multi-Queue)OVS-DPDKBig bag1.351.742.132.86Packet0.0580.0660.0920.202Mixed bag0.330.3930.5040.69Figure 6
System optimization test.Through the analysis of statistical calculation principles and third-party software, the experimental results of the functional test experiment are obtained, as shown in Table6. The data in the table show that there are varying degrees of delay in the interaction between information in embedded systems, and the more complex the interaction information, the longer the delay. After calculation, the average delay of the traditional interactive function is 5.4 min, and the average delay of the interactive function of the embedded system based on the distributed virtual environment is 1.4 min, which saves 4 min by comparison. The experimental results show that the designed embedded system has shorter interaction delay and stronger interaction.Table 6
System function test results and analysis.
Serial numberInteractive file typeSending timeTraditional interaction designThis paper introduces the system interaction designReceiving timeTime delay (min)Receiving timeTime delay (min)1Written words14 : 0014 : 03314 : 0112Data14 : 0514 : 08314 : 0613Written words14 : 1014 : 14414 : 1114Voice14 : 1514 : 251014 : 1725Voice14 : 3014 : 37714 : 322As can be seen from Table7 and Figure 7, the system kernel routines are largely related to the complexity of service energy consumption. This method helps to improve the accuracy of the energy analysis and optimization of the embedded operating system kernel and effectively supports the energy consumption evaluation and optimization design of the operating system and application software.Table 7
Analysis of the relationship between system service functions and energy consumption.
RoutineExecution timesEnergy consumptionAverage valueLowest valueHighest valueStandard deviationSys_getegid254.993.529.511.70Switch_to1101.241.081.780.16Sys_brk14712.214.4529.607.00Sys_open4471.6329.2445.9462.105Sys_read26644.748.10355.0040.89Tty_write29171.8831.46197.7832.31Do_timer8416.796.6232.054.70Figure 7
Analysis of the relationship between system service functions and energy consumption.


## 4.1. Performance Analysis of Embedded System

As shown in Table1, it is a parameter list of fog computing, cloud computing, and edge computing. Among them, 0–3 are cloud computing, 4-5 are fog computing, and 6 are edge computing parameters. The processing capacity of the system selected in this article is 800–5000 MIPS, the unit price is 3–6 CENTS, and the broadband settings are 1000 and 2000 MB.Table 1
List of parameters for fog computing, cloud computing, and edge computing.
Resource numberProcessing capacity (MIPS)Unit price (CENT/S)Broadband (MB)050006100012500510002250051000315004100041000420005100032000680031000It can be seen from Table2 and Figure 3 that, as the number of requests changes, the delay comparison of the three systems becomes more obvious. When the number of requests is 20, the latency of fog computing is 20 s, the latency of cloud computing IGA is 22 s, and the latency of cloud computing RRSA is 21 s, and the three are at the same level. However, when the number of requests reaches 200, the delay of fog calculation SLO is 121 s, while the delay of IGA reaches 168 s, and the delay of RRSA is 180 s, which are significantly higher than the delay of fog calculation. This shows that embedded systems based on fog computing have shorter time delays and perform better.Table 2
Delay comparison of embedded system under different number of requests.
Systems/Requests20406080100200Fog-SLO2035436077121Cloud-IGA2236456377168Cloud-RRSA2137496579180Figure 3
Delay comparison of the embedded system under different number of requests.It can be seen from Table3 and Figure 4 that, as the number of requests increases, the execution time of the three systems is rising, but the rate of increase is different. When the number of requests is 20, the execution time of fog calculation is 46 s, the execution time of cloud computing IGA is 47 s, and the execution time of RRSA is 48 s; when the number of requests is 200, the execution time of fog calculation is 200 s, and IGA is executed. The time is 246 s, and the execution time of RRSA is 247 s. It can be clearly seen that the execution time of IGA and RRSA exceeds the fog calculation, and the speed is significantly slower.Table 3
Comparison of system execution time under different number of requests.
Systems/requests20406080100200Fog-SLO4695146176180200Cloud-IGA47102149189192246Cloud-RRSA48103153190197247Figure 4
Comparison of system execution time under different number of requests.


## 4.2. Based on the Experimental Results of Sports Equipment Distributed Virtual Environment Embedded Systems

According to the province’s dynamic analysis report on economic development, selected sports equipment related data are shown in Table 4.It can be seen from Table4 and Figure 5 that, according to the current development trend of sports equipment, the sales of sports equipment will continue to rise in the future. In the case of treadmills, the annual demand is only 3 million units, and the sales are still increasing at an annual growth rate of 25.1%. The market growth is extremely good. The annual demand for basketball is also 1 million, and the annual market growth rate is 18.9%. If distributed virtual technology can be embedded in these sports equipment, it can better meet people's sports and relaxation needs. In the future, people's demand for embedded system sports equipment will show an exponential growth trend.Table 4
Sales of sports equipment.
Sports equipmentDemand (ten thousand)Output (ten thousand)Sales volume (ten thousand)Annual market growth rate (%)Treadmill30045040825.1Basketball10018011918.9Sport ware58129695.8Badminton1582132096.7Table Tennis1691871534.3Football761891025.9Figure 5
Sales of sports equipment.It can be seen from Table5 and Figure 6 that the system optimization test is compared by observing the hardware operation of the system. When dealing with different data packets, there is a big difference between tuning and an untuned system. After tuning and processing, the embedded system shows a better operating condition and a higher throughput rate.Table 5
System optimization test.
Throughput rateNo tuningCPU/memoryVirtio (multi-Queue)OVS-DPDKBig bag1.351.742.132.86Packet0.0580.0660.0920.202Mixed bag0.330.3930.5040.69Figure 6
System optimization test.Through the analysis of statistical calculation principles and third-party software, the experimental results of the functional test experiment are obtained, as shown in Table6. The data in the table show that there are varying degrees of delay in the interaction between information in embedded systems, and the more complex the interaction information, the longer the delay. After calculation, the average delay of the traditional interactive function is 5.4 min, and the average delay of the interactive function of the embedded system based on the distributed virtual environment is 1.4 min, which saves 4 min by comparison. The experimental results show that the designed embedded system has shorter interaction delay and stronger interaction.Table 6
System function test results and analysis.
Serial numberInteractive file typeSending timeTraditional interaction designThis paper introduces the system interaction designReceiving timeTime delay (min)Receiving timeTime delay (min)1Written words14 : 0014 : 03314 : 0112Data14 : 0514 : 08314 : 0613Written words14 : 1014 : 14414 : 1114Voice14 : 1514 : 251014 : 1725Voice14 : 3014 : 37714 : 322As can be seen from Table7 and Figure 7, the system kernel routines are largely related to the complexity of service energy consumption. This method helps to improve the accuracy of the energy analysis and optimization of the embedded operating system kernel and effectively supports the energy consumption evaluation and optimization design of the operating system and application software.Table 7
Analysis of the relationship between system service functions and energy consumption.
RoutineExecution timesEnergy consumptionAverage valueLowest valueHighest valueStandard deviationSys_getegid254.993.529.511.70Switch_to1101.241.081.780.16Sys_brk14712.214.4529.607.00Sys_open4471.6329.2445.9462.105Sys_read26644.748.10355.0040.89Tty_write29171.8831.46197.7832.31Do_timer8416.796.6232.054.70Figure 7
Analysis of the relationship between system service functions and energy consumption.


## 5. Conclusion

This article mainly studies the basketball sports equipment embedded system based on the distributed virtual environment, designs the sports equipment embedded system framework model, and divides the system software into six levels: system boot layer, operating system, drive layer, media library, application software layer, and user graphical interface. With high-end hardware and software facilities, sports equipment can better meet people's needs and improve the quality of exercise.The innovation of this article lies in the full application of distributed virtual reality technology, the design of the client embedded frame system, and the completion of the construction of immersive and interactive virtual fitness scenes under the formation of a specific fitness virtual environment. This paper makes full use of the combination of qualitative analysis and quantitative analysis and has been fully reflected in the analysis part.This article still has shortcomings. First, there are few combined applications of distributed virtual environments and basketball sports equipment, so there may be some practical problems in actual applications. Second, embedded systems based on distributed virtual environments are high-tech products. The price is relatively expensive, and it is difficult to provide services to the public, so it is necessary to continuously reduce costs to meet the consumption level of the public. The development and research of embedded systems for sports equipment based on distributed virtual environments is the general trend, and it can develop better and better in the future, which can not only bring more benefits to sports equipment service providers but also provide high quality to the public sports services.



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*Source: 5584125-2021-03-30.xml*