# Integrating the Big Data in Sports and Resource Interaction Using Artificial Neural Network

Authors: Tong Zhang
Journal: Mobile Information Systems
 (2022)

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## Abstract

Big data is the result of balancing computing power with the need for large, rapidly updated, and comprehensive datasets and is now widely used in urban planning, medicine, and other fields. With the advent of the era of big data, the sports industry must also adapt to this change. The era of big data brings new ideas to the development of sports resource interaction. At the same time, the demand for a high-efficiency and high-performance sports big data integration system is imminent. The interaction between college sports and community sports realizes the improvement of the practical ability of the two institutions and departments through the satisfaction of people’s own needs. And it affects people’s lifelong sports and health by improving external conditions to internal factors. It improves the quality of life and maintains social stability and peace, thereby ensuring the better development of the university and the community. At present, the speed of data integration in the sports big data integration system is relatively slow. In order to solve this problem, this study introduces BP neural network in the artificial neural network. It introduces the concept of the neural network and its related formulas in detail. By using the BP neural network classification algorithm for data classification integration, this study conducts training and performance testing of the network. It links the extraction, transformation, and loading of data. The experimental results show that when the number of databases is 2, 4, 6, and 8, the time taken to load data by the system in this study is 113 s, 87 s, 64 s, and 42 s, respectively. It can be seen that the system in this study has obvious advantages in data loading compared with the data integration system of the existing system data warehouse. The system data query designed in this study is more efficient and consumes less time.


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## Body

## 1. Introduction

As the big data age approaches, the content of big data becomes more familiar to humans. Sports is a special social and cultural activity created by the Chinese for thousands of years. Big data is an important opportunity for sports development and important data can be processed logically and efficiently from big data. It plays an important role in the development of coordination of school sports, social sports, and competitive sports. People play sports at home, school, or in the community. Sports information in families, schools, and communities will also be exchanged and communicated with each other. But family sports, school sports, and community sports have their own characteristics and main objects, and they are both related and different from each other.The university of the future should use its facilities and material resources to actively participate in the development of community sports and the creation of the sports industry. It meets the urgent needs of teachers, students, and citizens to improve their spare time and play sports. By exploring the interaction mechanism between college sports and community sports, it realizes the interaction of human, material, and cultural resources. It promotes the healthy development of Chinese community sports and college sports. It improves people’s physical and mental health. It improves the quality of life. It is an important pillar of the construction of socialist spiritual civilization.The innovations of this study are: (1) This article proposes to improve traditional learning algorithms with a combination of variable learning speed and acceleration. (2) This article uses a system for classifying data collected by sports big data integration systems and provides an effective comparison of network connectivity before and after algorithm upgrades.


## 2. Related Work

Regarding sports big data, relevant scientists have done the following research. By collecting and interpreting massive data sets, they provide a wealth of knowledge to guide decision-makers in all aspects of society. This is no exception in sports management. Watanabe addressed these issues by advancing the application of big data in sports management research and how to use big data to advance academic research in the sports industry. He had played a role in advancing the theory of sports analysis, generating new knowledge, and developing new inquiry, respectively. It lays the foundation for future research in this growing field in the field of sports management [1]. Big data, artificial intelligence, data analysis, machine learning, and neural networks are promising prospects in the current industry. It is followed by a rising star in the current technology field. Patel et al. researched the vast number of industries it covered. It provides an in-depth study of how the entire sports industry is affected in many ways. It is not only the performance on the field that is affected but also the commercial impact and the immersion of fans [2]. The rapid growth of analytical data and large databases affects almost every aspect of life. The sports industry did not survive this development. Morgulev et al. provided examples of three types of data-driven analysis performed in sports. It includes domain-level analysis focused on the behavior of players, coaches, and referees, analysis of management and policy makers’ decisions, and literary analysis of the use of sports data to address a variety of problems in the fields of economics and psychology.The following study was conducted by researchers responsible for the artificial nervous system. Alanis presented the results of using the Kalman filter training algorithm based on the widespread repetitive nervous system and its use in predicting electrical values. There are two cases: step forward andN forward. The stable implementation of the proposed artificial neural network was developed using an improved algorithm based on the Kalman filter. This demonstrates the benefits of the proposed forecasting system using one-step forward and N-step forward system data [3]. Isik and Inalli provided forecast data for heating system design. The information it receives from the Finnish Meteorological Institute has an artificial nervous system and an adaptive system based on ambiguous systems. The weather results are satisfactory. He also showed a map of the city’s annual solar potential and compared it to the MGM results. The results of the proposed method are consistent with the actual results. Decision makers can easily use these methods when creating applications [4]. Li et al. explored how neural networks can be used to manage protected neighborhoods connected to the Internet to reduce barriers. The neural network implements effective programming algorithms that are trained to disseminate over time. To increase efficiency and stability, he also introduced other methods, including the integration of the main input error signal and the connection of the mains voltage to the trained mains socket [5]. Through the investigation and analysis of the physical health data of Chinese adolescents, Ascione et al. proposed it as a part of the empirical research on the construction of the adolescent health big data management service system. The results of health intervention training can effectively improve the relational hypothesis of adolescent physical health. The results of the study show that adolescents’ physical health promotion theory can improve adolescents’ physical health through the intervention of students’ physical exercise. In terms of data processing, GBDT is more suitable when the training set is relatively large. With the increase in the sample size, the accuracy rate can reach 79.79% [6]. The above study conducted a detailed analysis of the big data sports program and the artificial nervous system. It is no wonder that these studies have made a significant contribution to the development of similar fields. It can teach many techniques and data analysis. However, there is little research in the field of artificial neural data in the field of artificial neuro systems and it is necessary to fully implement these algorithms to research in this field.


## 3. Sports Big Data Integration and Resource Interaction Methods

### 3.1. Data Integration

Data integration is the process of combining data from disparate data sources into a coherent dataset, whether logical or physical. The main task of data integration is to integrate interrelated, disparate, and heterogeneous data in a way that users can access transparently. General methods of data integration include federated database systems, middleware integration methods, and data warehouse methods. A federated database system transforms a set of views from disparate data sources into a common schema, allowing users to transparently access data from disparate data sources. Data schemas can be shared with other data sources to create a unified system [7]. The middleware integration approach provides users with a single interface to access data. From the user’s perspective, the integrated data source looks like a single entity. Middleware coordinates disparate data source systems and acts as a middleman. The data warehouse approach involves creating a data warehouse to store data. Basically, data replication is done by copying data from each data source to the same location, the data warehouse [8].The reasons for using the data warehouse method are as follows: (1) The data warehouse method already exists in a large number of business databases. It can realize the efficient integration of business data without affecting the original business system. It does not occupy local processing resources and can also store and integrate historical information. It also supports complex multidimensional queries. (2) The middleware integration method requires complex information filtering and integration processing. It will preempt local processing resources, is inefficient when frequently queried, and requires high costs. (3) The response time required by the federal database system to query is slightly longer, so frequent query is not suitable. In particular, this method is prone to problems such as lock contention and resource conflict [9].The data warehouse is to extract the data stored in different business systems into the data warehouse. The data in different business system data sources may have different names or physical meanings in the same fields due to different development systems, operating systems, development languages, and the habits of system developers. It is also a case of duplicate field names with different meanings. Therefore, it is very necessary to carry out data preprocessing according to a unified information standard. In this way, data consistency can be ensured, and system integration difficulties caused by data inconsistency can be avoided, which affects user decision-making [10]. (3) Relatively stable: The application of data warehouse mainly involves a lot of work of query, analysis, and mining. Relatively speaking, there are relatively few cases involving modification and deletion operations. Generally, only regular maintenance processing is required to extract new data, delete unnecessary data, and other operations, so it is relatively stable. (4) Time-varying: The data stored in the data warehouse is usually historical data. Timestamps are an important feature of data in a data warehouse. The mining and analysis of these data can predict the future development trend to a certain extent [11]. Figure 1 shows the system model of data integration.Figure 1
System model for data integration.Data integration is to integrate heterogeneous data logically or physically into a unified data platform [12]. It is convenient for users to access with a unified view and approach. It provides data integration, sharing, and fusion services. Heterogeneous data sources mainly have three characteristics: autonomy, distribution, and heterogeneity. Each data source is an independent entity with strong autonomy. To a certain extent, it can communicate directly and solely with other data sources and change its own data and structure without informing the integrated system.Data sources are generally distributed in different places. It is distributed in different places, and it relies on the network to exchange and share data [13]. Moreover, the distribution of data sources includes not only the difference in a physical location but also the distribution of data sources due to the difference in a logical location. Under normal circumstances, each data source system is independently developed by different developers in different periods of different development periods, and the development platforms and development languages used are different. All of them will cause difficulties in the unified management of data sources. Therefore, the heterogeneity of data is the core issue that needs to be considered in the process of data integration [14].The main components of the middleware model system are middleware and shells, and each data source is composed of shells. A wrapper is a component that uses a database to communicate with middleware. Middleware communicates with each data source through the framework. The user sends a request to the middleware, and the middleware divides the user’s request for each heterogeneous data source into sub-requests that can be processed by each data source. The wrapper encapsulates the data source, transforms the data model, and provides a single access mechanism [15]. Compared with the integrated database model, the advantage of the middleware model is that the unified data visualization logic allows users to view the integrated data source as a whole. The underlying details of the data are hidden and each data source can be accessed by the operating system. It also has to prevent differences in network protocols. Therefore, middleware can provide a relatively stable environment for high-level applications. The focus is on overall query optimization and performance. However, the middleware pattern is not a panacea. Middleware applications are less portable across different modes and are usually read-only. As shown in Figure 2, it is a middleware-based data integration model.Figure 2
Middleware-based data integration model.There are many reasons for the heterogeneity of data sources. The development period of each business system is different, the development platform is different, and the operating system is different. Whether the data is completely extracted from the original data source to the corresponding target database, the logical relationship of the data is different, the form of data storage is different, and the definition of data is different for different business systems. Managers and operation and maintenance personnel of each database have different names and physical meanings for specific fields of data. These are the reasons for the heterogeneity of data sources, resulting in difficulties in data integration.Data replication is the replication of data from independent data sources to other linked data sources to eliminate inconsistencies in heterogeneous data and coordinate the sharing and use of information. Data replication improves the performance of data integration systems by limiting the number of user keys for heterogeneous data sources and preventing query performance degradation caused by frequent access to heterogeneous data sources. So-called data warehouses are complex data repositories. Where data from different data sources is extracted, cleaned and transformed, and replicated in the data warehouse. The data warehouse itself is a database, which is very easy for users to access. It is also complex, time-sensitive, and resilient to change. Repositories store all the data needed to support decision-making, retrieval, and analysis of information. It also provides users with effective support for data analysis and implementation [16]. As shown in Figure 3, it is a data integration model based on the data warehouse.Figure 3
Data integration model based on the data warehouse.The earliest method of data integration is the method of schema integration. Other data integration methods are researched on the basis of schema integration methods. The federated database is a typical and widely used method in the schema integration method. It directly integrates the member databases, which can achieve the purpose of data sharing and exchange among the member databases. The operation of the system on each database is independent and not associated with other databases. In the federated database system, interfaces for mutual communication and access are added to all member databases. The federated database system can be a centralized database system or a distributed database system and other federated systems. In a federated database system, a federation engine needs to be installed and configured to communicate with external data sources. The function of the wrapper is to convert the user’s query request decomposed by the query analyzer into the query request that can be executed in each member database for execution. After each member database executes the request, it receives the returned result and processes it according to certain standards and rules. The query integrator synthesizes these query results and returns them to the application server.According to different modes and coupling methods, federated database systems can be divided into two forms: tightly coupled and loosely coupled. One or several unified global schemas need to be established in tightly coupled federated database systems. This unified global mode provides a unified access method, which has the advantage of being relatively static and highly integrated. However, adding data sources is more difficult. Building a global data schema is very complex, resulting in difficult system upgrades, poor scalability, and limited tightly coupled applications. On the other hand, loose coupling does not adopt the global mode and does not provide a unified interface. Although the integration is not as good as tight coupling, the dynamic performance advantage is obvious. Loose coupling does not require a highly integrated data, it is independent of an integrated system that connects data sources. Both modes have their own advantages, and how to choose depends on the specific scenario, such as the number of system components, user requirements, the size of the access volume, and the characteristics of the member database. The advantage of the federated database is that the system does not need to frequently extract incremental data, but directly sends requests to the member databases and then aggregates the structure, which has high real-time performance. However, the query efficiency of the federated database is low, and the resource consumption is obvious when the query request is too frequent.The middleware integration method is also a more common integration method using the global pattern. Compared with federated databases, middleware systems use a unified model to access heterogeneous databases. It integrates semi-structured or unstructured data in data sources, etc. Middleware is located in the middle of the application software and system platform, and it provides a unified data schema for applications to access data in heterogeneous data sources. Data access is also achieved through the data access common interface. The applications of each data source still complete their respective tasks. The main task of the middleware system is to provide an advanced retrieval service for users to access heterogeneous data.


### 3.2. Artificial Neural Network

A neural network is a network with a large number of interconnected computer units. Data processing in artificial neural networks occurs through communication between neurons. The storage of data and information presents itself as a decentralized physical connection that binds network components, while the study and identification of artificial neural networks are characterized by the extreme development of the weight of interconnected neurons [17]. In addition to the properties of network topography homogeneity, it is also an important feature of neural networks. Among the various neural enhancement networks, one of the most commonly used learning algorithms is the error exchange algorithm or BP algorithm. The basic idea of the BP network is that the output layer error is re-decentralized to gradually adjust system parameters and correct network error [18]. Figure 4 shows the functioning of the nervous system and blood pressure.Figure 4
BP neural network process.The whole learning process of the BP algorithm can be divided into two stages: the transfer phase of the training input and the transfer phase of the error sheet. The input data is first generated by the input layer, then the hidden nerve layer processes the data for analysis, and finally, the output layer generates the output data. In the process of direct communication, the weight of the connection between the layers of the BP nervous system does not change [19]. The weight of each nerve cell affects the state of the lower nerve that connects these neurons after contact. If the output does not meet the expected value, the BP neural network is not fully charged and enters the “reverse propagation” phase of the error scale. In the reverse propagation phase of the error signal, the error signal generated by the actual and ideal results is transmitted from the output layer back to the input layer of the network. When the error scale is distributed to each layer, the level of neurons in the first layer and the weight between the two existing layers are adjusted according to specific rules so that the result is close to the target result [20].A neural network is a network with a large number of interconnected computer units. In artificial neural networks, information is processed through communication between neurons and the storage of data, and information is expressed through physical connections distributed between network components. The study and identification of artificial neural networks are determined by the dynamic evolution of individual neural communication factors. In addition to the unit properties, an important feature of neural networks is the spatial network [21]. According to different methods of connection, neural networks can be divided into the following forms: (1) Advanced networks. Nerve cells are subdivided into a multilayer layer consisting of an intermediate layer (also called an indirect layer that can have multiple layers) and an output layer. The neurons in each layer receive only input from the neurons in the first layer. The following paragraphs do not comment on the previous paragraph. The input pattern gradually passes through each layer and eventually, the production layer is accepted as the output. (2) Pure feedback. This is the feedback from the output layer to the input layer. (3) Network connection rules. By combining neurons in one layer, subsequent inhibition or stimulation between neurons in the same layer can be achieved. (4) Interconnected networks (including full and partial connections). This type of network is a network that can connect two neurons [22].The learning process of the BP network consists of two parts: forward propagation and backward propagation. In the forward propagation, the input information is sent from the input layer to the output layer after being processed by the equipment of the hidden layer, and the state of the neurons in each layer only affects the state of the neurons in the next layer. If the desired output is not achieved at the output layer, it switches to backpropagation, and the error signal is returned along the original path of the neuron connections. During backpropagation, the connection weights between neurons in each layer are gradually changed. This process is repeated, and finally, the output error signal is within the tolerance limit of the system.BP neural network has the following basic characteristics: (1) It can learn and store relationships between a large number of inputs and outputs without prior knowledge of the mathematical equations describing these relationships. If the network can train a sufficient number of model pairs, it can perform a non-linear mapping from the input space to the output space. (2) Generalizability: if the network is fed data that was not observed during training, the network will be able to properly fit the input space to the output space. (3) Fault tolerance. Large errors in the input model, even a single error, have little effect on the input and output models of the network.Although the BP network has the above advantages and is widely used, it also has some disadvantages, namely (1) Due to the constant study speed, the integration speed of the network is very slow and the processing time is very slow. Learning is long. If the problem is complicated then the training time of the BP algorithm is very long. The main reason is that the tuition rate is too low, which can be improved using variable study or correction rates such as BP correction algorithm. (2) The BP algorithm can generate weights to a certain value, but there is no guarantee that this is a universal minimum error rate, as the slope can lead to a local minimum. This problem can be solved by other direct techniques such as BP emergency algorithm. (3) There are no theoretical guidelines for the choice of the number of layers and hidden layers in a network, which is usually determined by experience or repeated experiments. As a result, there are often multiple job vacancies on the Internet, which in turn increases the number of online jobs. (4) The learning and memory of the network are unstable. In other words, when adding training experiments, the trained neural network needs to be retrained, and the previous weights and thresholds are not remembered.To address these shortcomings of traditional BP algorithms, this article proposes to improve BP algorithms. It solves network training problems using variable speed learning methods and then tries to improve the connectivity of the algorithm so that the network does not fall to the lowest level locally during training. In practice, the value of e-learning communication significantly influences the integration and efficiency of algorithms. Its best value is usually related to a specific problem, so there is no reasonable study pace for each problem. In fact, despite certain problems, it is not always possible to find the right pace of learning. Since the algorithm is very sensitive to changes in study speed, if the study speed is set too low, the integration rate is very slow. If the study speed is too high, although the consolidation speed can be accelerated, it may cause a change in weight adjustment. Therefore, we need to change the pace of online learning to specific educational conditions.(1)Wm¯=12∑k=1Knkm¯−rkm¯2,where Wm¯ is the error mean squared error(2)invm¯=∑u=1lquvnum¯+ςv,nvm¯=11+e−invm¯,where u is the hidden layer nodes and  v is the output layer node.(3)quv=quv+μervm¯num¯,ςv=ςv+ϖervm¯,where quv is any weight and ςv is the arbitrary neuron threshold.(4)qvub+1=qvub+μbSb,where qvu is the initial output of the network and Sb is the negative gradient.(5)μb=2εμb−1,ε=sgnSbSb−1,where μ is the learning rate.(6)Δσr+1=μ∂W∂σ+εΔσr,where ε is the momentum coefficient and  Δσr+1 is the weighted adjustment.(7)Δσ≈−μ1−ε∂W∂σ,where −μ/1−ε is the scale factor.(8)W=12∗∑vnv−n^v2,where n^v is the actual output value and nv is the ideal output value.(9)quvr+1=quvr+μmur+mvr,where quvr+1 is the new weights after one correction and μ is the learning factor.(10)quvr+1=quvr+μnur∑u=1bsvr−nvr2,where b is the number of output neurons.(11)Gv=f∑u=1bσuvmu−xv,v=1,2,…,l,l is the number of hidden layer nodes and f is the hidden layer activation function.(12)φm=11+exp−xm,where x is the controllable slope.(13)Ik=∑v=1lGvσvk−yk,k=1,2,…,x,where G is the hidden layer output and σvk is the connection weight.(14)σuv=σuv+μGv1−Gvmu∑k=1xσvkek,σvk=σvk+μGvek,where e is the network prediction error, σuv is the network connection weight, and μ is the learning rate.(15)F=k∑u=1bxydnu−ou,where F is the individual fitness value.(16)fu=kFu,li=fu∑v=1Bfv,where Fu is the individual fitness function, k is the coefficient, and B is the number of individuals in the population.In this study, the improved BP neural network is used to classify sports data, and then the classified data are integrated respectively, as well as the distribution of population, land, funds, public resources, etc. among the sports departments.


## 3.1. Data Integration

Data integration is the process of combining data from disparate data sources into a coherent dataset, whether logical or physical. The main task of data integration is to integrate interrelated, disparate, and heterogeneous data in a way that users can access transparently. General methods of data integration include federated database systems, middleware integration methods, and data warehouse methods. A federated database system transforms a set of views from disparate data sources into a common schema, allowing users to transparently access data from disparate data sources. Data schemas can be shared with other data sources to create a unified system [7]. The middleware integration approach provides users with a single interface to access data. From the user’s perspective, the integrated data source looks like a single entity. Middleware coordinates disparate data source systems and acts as a middleman. The data warehouse approach involves creating a data warehouse to store data. Basically, data replication is done by copying data from each data source to the same location, the data warehouse [8].The reasons for using the data warehouse method are as follows: (1) The data warehouse method already exists in a large number of business databases. It can realize the efficient integration of business data without affecting the original business system. It does not occupy local processing resources and can also store and integrate historical information. It also supports complex multidimensional queries. (2) The middleware integration method requires complex information filtering and integration processing. It will preempt local processing resources, is inefficient when frequently queried, and requires high costs. (3) The response time required by the federal database system to query is slightly longer, so frequent query is not suitable. In particular, this method is prone to problems such as lock contention and resource conflict [9].The data warehouse is to extract the data stored in different business systems into the data warehouse. The data in different business system data sources may have different names or physical meanings in the same fields due to different development systems, operating systems, development languages, and the habits of system developers. It is also a case of duplicate field names with different meanings. Therefore, it is very necessary to carry out data preprocessing according to a unified information standard. In this way, data consistency can be ensured, and system integration difficulties caused by data inconsistency can be avoided, which affects user decision-making [10]. (3) Relatively stable: The application of data warehouse mainly involves a lot of work of query, analysis, and mining. Relatively speaking, there are relatively few cases involving modification and deletion operations. Generally, only regular maintenance processing is required to extract new data, delete unnecessary data, and other operations, so it is relatively stable. (4) Time-varying: The data stored in the data warehouse is usually historical data. Timestamps are an important feature of data in a data warehouse. The mining and analysis of these data can predict the future development trend to a certain extent [11]. Figure 1 shows the system model of data integration.Figure 1
System model for data integration.Data integration is to integrate heterogeneous data logically or physically into a unified data platform [12]. It is convenient for users to access with a unified view and approach. It provides data integration, sharing, and fusion services. Heterogeneous data sources mainly have three characteristics: autonomy, distribution, and heterogeneity. Each data source is an independent entity with strong autonomy. To a certain extent, it can communicate directly and solely with other data sources and change its own data and structure without informing the integrated system.Data sources are generally distributed in different places. It is distributed in different places, and it relies on the network to exchange and share data [13]. Moreover, the distribution of data sources includes not only the difference in a physical location but also the distribution of data sources due to the difference in a logical location. Under normal circumstances, each data source system is independently developed by different developers in different periods of different development periods, and the development platforms and development languages used are different. All of them will cause difficulties in the unified management of data sources. Therefore, the heterogeneity of data is the core issue that needs to be considered in the process of data integration [14].The main components of the middleware model system are middleware and shells, and each data source is composed of shells. A wrapper is a component that uses a database to communicate with middleware. Middleware communicates with each data source through the framework. The user sends a request to the middleware, and the middleware divides the user’s request for each heterogeneous data source into sub-requests that can be processed by each data source. The wrapper encapsulates the data source, transforms the data model, and provides a single access mechanism [15]. Compared with the integrated database model, the advantage of the middleware model is that the unified data visualization logic allows users to view the integrated data source as a whole. The underlying details of the data are hidden and each data source can be accessed by the operating system. It also has to prevent differences in network protocols. Therefore, middleware can provide a relatively stable environment for high-level applications. The focus is on overall query optimization and performance. However, the middleware pattern is not a panacea. Middleware applications are less portable across different modes and are usually read-only. As shown in Figure 2, it is a middleware-based data integration model.Figure 2
Middleware-based data integration model.There are many reasons for the heterogeneity of data sources. The development period of each business system is different, the development platform is different, and the operating system is different. Whether the data is completely extracted from the original data source to the corresponding target database, the logical relationship of the data is different, the form of data storage is different, and the definition of data is different for different business systems. Managers and operation and maintenance personnel of each database have different names and physical meanings for specific fields of data. These are the reasons for the heterogeneity of data sources, resulting in difficulties in data integration.Data replication is the replication of data from independent data sources to other linked data sources to eliminate inconsistencies in heterogeneous data and coordinate the sharing and use of information. Data replication improves the performance of data integration systems by limiting the number of user keys for heterogeneous data sources and preventing query performance degradation caused by frequent access to heterogeneous data sources. So-called data warehouses are complex data repositories. Where data from different data sources is extracted, cleaned and transformed, and replicated in the data warehouse. The data warehouse itself is a database, which is very easy for users to access. It is also complex, time-sensitive, and resilient to change. Repositories store all the data needed to support decision-making, retrieval, and analysis of information. It also provides users with effective support for data analysis and implementation [16]. As shown in Figure 3, it is a data integration model based on the data warehouse.Figure 3
Data integration model based on the data warehouse.The earliest method of data integration is the method of schema integration. Other data integration methods are researched on the basis of schema integration methods. The federated database is a typical and widely used method in the schema integration method. It directly integrates the member databases, which can achieve the purpose of data sharing and exchange among the member databases. The operation of the system on each database is independent and not associated with other databases. In the federated database system, interfaces for mutual communication and access are added to all member databases. The federated database system can be a centralized database system or a distributed database system and other federated systems. In a federated database system, a federation engine needs to be installed and configured to communicate with external data sources. The function of the wrapper is to convert the user’s query request decomposed by the query analyzer into the query request that can be executed in each member database for execution. After each member database executes the request, it receives the returned result and processes it according to certain standards and rules. The query integrator synthesizes these query results and returns them to the application server.According to different modes and coupling methods, federated database systems can be divided into two forms: tightly coupled and loosely coupled. One or several unified global schemas need to be established in tightly coupled federated database systems. This unified global mode provides a unified access method, which has the advantage of being relatively static and highly integrated. However, adding data sources is more difficult. Building a global data schema is very complex, resulting in difficult system upgrades, poor scalability, and limited tightly coupled applications. On the other hand, loose coupling does not adopt the global mode and does not provide a unified interface. Although the integration is not as good as tight coupling, the dynamic performance advantage is obvious. Loose coupling does not require a highly integrated data, it is independent of an integrated system that connects data sources. Both modes have their own advantages, and how to choose depends on the specific scenario, such as the number of system components, user requirements, the size of the access volume, and the characteristics of the member database. The advantage of the federated database is that the system does not need to frequently extract incremental data, but directly sends requests to the member databases and then aggregates the structure, which has high real-time performance. However, the query efficiency of the federated database is low, and the resource consumption is obvious when the query request is too frequent.The middleware integration method is also a more common integration method using the global pattern. Compared with federated databases, middleware systems use a unified model to access heterogeneous databases. It integrates semi-structured or unstructured data in data sources, etc. Middleware is located in the middle of the application software and system platform, and it provides a unified data schema for applications to access data in heterogeneous data sources. Data access is also achieved through the data access common interface. The applications of each data source still complete their respective tasks. The main task of the middleware system is to provide an advanced retrieval service for users to access heterogeneous data.


## 3.2. Artificial Neural Network

A neural network is a network with a large number of interconnected computer units. Data processing in artificial neural networks occurs through communication between neurons. The storage of data and information presents itself as a decentralized physical connection that binds network components, while the study and identification of artificial neural networks are characterized by the extreme development of the weight of interconnected neurons [17]. In addition to the properties of network topography homogeneity, it is also an important feature of neural networks. Among the various neural enhancement networks, one of the most commonly used learning algorithms is the error exchange algorithm or BP algorithm. The basic idea of the BP network is that the output layer error is re-decentralized to gradually adjust system parameters and correct network error [18]. Figure 4 shows the functioning of the nervous system and blood pressure.Figure 4
BP neural network process.The whole learning process of the BP algorithm can be divided into two stages: the transfer phase of the training input and the transfer phase of the error sheet. The input data is first generated by the input layer, then the hidden nerve layer processes the data for analysis, and finally, the output layer generates the output data. In the process of direct communication, the weight of the connection between the layers of the BP nervous system does not change [19]. The weight of each nerve cell affects the state of the lower nerve that connects these neurons after contact. If the output does not meet the expected value, the BP neural network is not fully charged and enters the “reverse propagation” phase of the error scale. In the reverse propagation phase of the error signal, the error signal generated by the actual and ideal results is transmitted from the output layer back to the input layer of the network. When the error scale is distributed to each layer, the level of neurons in the first layer and the weight between the two existing layers are adjusted according to specific rules so that the result is close to the target result [20].A neural network is a network with a large number of interconnected computer units. In artificial neural networks, information is processed through communication between neurons and the storage of data, and information is expressed through physical connections distributed between network components. The study and identification of artificial neural networks are determined by the dynamic evolution of individual neural communication factors. In addition to the unit properties, an important feature of neural networks is the spatial network [21]. According to different methods of connection, neural networks can be divided into the following forms: (1) Advanced networks. Nerve cells are subdivided into a multilayer layer consisting of an intermediate layer (also called an indirect layer that can have multiple layers) and an output layer. The neurons in each layer receive only input from the neurons in the first layer. The following paragraphs do not comment on the previous paragraph. The input pattern gradually passes through each layer and eventually, the production layer is accepted as the output. (2) Pure feedback. This is the feedback from the output layer to the input layer. (3) Network connection rules. By combining neurons in one layer, subsequent inhibition or stimulation between neurons in the same layer can be achieved. (4) Interconnected networks (including full and partial connections). This type of network is a network that can connect two neurons [22].The learning process of the BP network consists of two parts: forward propagation and backward propagation. In the forward propagation, the input information is sent from the input layer to the output layer after being processed by the equipment of the hidden layer, and the state of the neurons in each layer only affects the state of the neurons in the next layer. If the desired output is not achieved at the output layer, it switches to backpropagation, and the error signal is returned along the original path of the neuron connections. During backpropagation, the connection weights between neurons in each layer are gradually changed. This process is repeated, and finally, the output error signal is within the tolerance limit of the system.BP neural network has the following basic characteristics: (1) It can learn and store relationships between a large number of inputs and outputs without prior knowledge of the mathematical equations describing these relationships. If the network can train a sufficient number of model pairs, it can perform a non-linear mapping from the input space to the output space. (2) Generalizability: if the network is fed data that was not observed during training, the network will be able to properly fit the input space to the output space. (3) Fault tolerance. Large errors in the input model, even a single error, have little effect on the input and output models of the network.Although the BP network has the above advantages and is widely used, it also has some disadvantages, namely (1) Due to the constant study speed, the integration speed of the network is very slow and the processing time is very slow. Learning is long. If the problem is complicated then the training time of the BP algorithm is very long. The main reason is that the tuition rate is too low, which can be improved using variable study or correction rates such as BP correction algorithm. (2) The BP algorithm can generate weights to a certain value, but there is no guarantee that this is a universal minimum error rate, as the slope can lead to a local minimum. This problem can be solved by other direct techniques such as BP emergency algorithm. (3) There are no theoretical guidelines for the choice of the number of layers and hidden layers in a network, which is usually determined by experience or repeated experiments. As a result, there are often multiple job vacancies on the Internet, which in turn increases the number of online jobs. (4) The learning and memory of the network are unstable. In other words, when adding training experiments, the trained neural network needs to be retrained, and the previous weights and thresholds are not remembered.To address these shortcomings of traditional BP algorithms, this article proposes to improve BP algorithms. It solves network training problems using variable speed learning methods and then tries to improve the connectivity of the algorithm so that the network does not fall to the lowest level locally during training. In practice, the value of e-learning communication significantly influences the integration and efficiency of algorithms. Its best value is usually related to a specific problem, so there is no reasonable study pace for each problem. In fact, despite certain problems, it is not always possible to find the right pace of learning. Since the algorithm is very sensitive to changes in study speed, if the study speed is set too low, the integration rate is very slow. If the study speed is too high, although the consolidation speed can be accelerated, it may cause a change in weight adjustment. Therefore, we need to change the pace of online learning to specific educational conditions.(1)Wm¯=12∑k=1Knkm¯−rkm¯2,where Wm¯ is the error mean squared error(2)invm¯=∑u=1lquvnum¯+ςv,nvm¯=11+e−invm¯,where u is the hidden layer nodes and  v is the output layer node.(3)quv=quv+μervm¯num¯,ςv=ςv+ϖervm¯,where quv is any weight and ςv is the arbitrary neuron threshold.(4)qvub+1=qvub+μbSb,where qvu is the initial output of the network and Sb is the negative gradient.(5)μb=2εμb−1,ε=sgnSbSb−1,where μ is the learning rate.(6)Δσr+1=μ∂W∂σ+εΔσr,where ε is the momentum coefficient and  Δσr+1 is the weighted adjustment.(7)Δσ≈−μ1−ε∂W∂σ,where −μ/1−ε is the scale factor.(8)W=12∗∑vnv−n^v2,where n^v is the actual output value and nv is the ideal output value.(9)quvr+1=quvr+μmur+mvr,where quvr+1 is the new weights after one correction and μ is the learning factor.(10)quvr+1=quvr+μnur∑u=1bsvr−nvr2,where b is the number of output neurons.(11)Gv=f∑u=1bσuvmu−xv,v=1,2,…,l,l is the number of hidden layer nodes and f is the hidden layer activation function.(12)φm=11+exp−xm,where x is the controllable slope.(13)Ik=∑v=1lGvσvk−yk,k=1,2,…,x,where G is the hidden layer output and σvk is the connection weight.(14)σuv=σuv+μGv1−Gvmu∑k=1xσvkek,σvk=σvk+μGvek,where e is the network prediction error, σuv is the network connection weight, and μ is the learning rate.(15)F=k∑u=1bxydnu−ou,where F is the individual fitness value.(16)fu=kFu,li=fu∑v=1Bfv,where Fu is the individual fitness function, k is the coefficient, and B is the number of individuals in the population.In this study, the improved BP neural network is used to classify sports data, and then the classified data are integrated respectively, as well as the distribution of population, land, funds, public resources, etc. among the sports departments.


## 4. Sports Big Data Integration and Resource Interaction Experiment

The classification model based on the improved BP neural network includes three steps: structure of the BP neural network, training of the neural network BP, and classification of the neural network BP. Figure5 is the classification system of the nervous system flows.Figure 5
Neural network classification algorithm process.Prior to training, the experimental system also asks the user to determine the accuracy of the target network. If the trained neural network meets one of these two conditions, all training is considered complete. As shown in Table1, the speed of e-learning and the number of training hours were compared.Table 1
Network learning versus rate number of training sessions.
Number of network iterations trained1005001000Algorithm network accuracy0.41140.32210.1823Improved algorithm network accuracy0.45420.027210.1085Number of network iterations trained50001000020000Algorithm network accuracy0.07280.012320.00228Improved algorithm network accuracy0.05770.008910.00258It can be seen from the results that the proposed algorithm improves the integration of neural processes to some extent. In addition, both training results show that the neural network integrates faster at the beginning of the exercise. As training increases, the speed of integration decreases.In this article, the sorting rules are set to the correct sorting results if the error is less than 0.5 and the statistical sorting results are shown in Table2. The result is that the accuracy of the final classification is up to 98% or more.Table 2
Statistical classification results.
Experimental data setClassification typeTraining setTest setNumber of correct classificationsClassification accuracy (%)KJDKFirst classification results1000100098298.2Results after cross-validation1000100098798.7FHBFirst classification results20002000198699.3Results after cross-validation20002000197898.9In the case of different numbers of machines, the experiments of BP network training were done respectively, and the respective running times were recorded, as shown in Table3, which is a comparison table of cluster computing performance.Table 3
Cluster computing performance comparison table.
Number of nodesMethodCluster uptime (s)Stand-alone runtime (s)Acceleration ratio4Mean value method115635653.086Mean value method88535464.028Method by type82735614.36After completing the design of the classifier neural network model structure and parameters, it is necessary to apply the design values and sample sets for training. During training, running one round forward for all samples and modifying the weights in the reverse direction is called one training. Figure6 shows the single BP classification test results.Figure 6
Single BP classification test results.It can be seen that the average number of type 1 errors is 20.8, and the average number of type 2 errors is 29. From this, it can be calculated that the first type error rate is 0.1041, the second type error rate is 0.1542, and the average classification error rate of 10 groups of tests is 0.1282.On the basis of a single BP classification, a BP classification is regarded as a weak classification, and multiple weak classifications are integrated through an improved algorithm. This study sequentially trains a boosted classification consisting of 2 to 11 weak classifications of a single BP neural network, and the results are shown in Figure7.Figure 7
Relationship between the number of weak classifications and the error rate.Data acquisition, data conversion, and retrieval are very important aspects of data integration. Data retrieval, editing, and retrieval are the most important components of a database. In a large sports data model combined, reference tables and browsing tables store reference database data and metrics. Base tables, drop-down tables, and summary tables store business and actual data with varying accuracy. Base tables store business data at the finest granularity, and derived and summary tables store summarized business data. Figure8 shows the relationship among data extraction, transformation, loading, and mentioned three tables.Figure 8
Data extraction, transformation, and loading in relation to the three types of tables.Data integration systems based on the improved BP neural system designed in this article are compared to traditional data integration systems. Figure9 is the time used to retrieve the data.Figure 9
Time taken for data loading.The test results can clearly see that the system in this paper has obvious advantages in data loading compared with the data integration system of the existing system data warehouse. Moreover, the system in this study has good scalability, almost linear expansion, and the loading speed increases linearly when the number of nodes increases.In the case of the same hardware configuration, the system in this study uses four nodes to compare the impact of query complexity on performance with the existing system. As shown in Figure10, the query efficiency is compared. It can be seen from the figure that the system designed in this study has higher query efficiency. When the number of databases is different, the time required for data loading is less than that of the existing system.Figure 10
Query efficiency comparison.


## 5. Discussion

Through the results of the integration of sports big data, some suggestions for the interaction between college sports and community sports resources are put forward. The point of interest of university sports is to improve the training of students, and correspondingly drive the scientific research level and economic income of university sports. The point of interest in community sports is to gain more fitness opportunities and scientific fitness levels. The government’s interest lies in effectively organizing mass sports, forming a healthy sports culture environment, and promoting social development. The interests of society lie in obtaining opportunities for capital appreciation and a stable market. Economic method management is to make the interests of all parties gather and concentrate, and complement each other.The interactive form of college sports and community sports plays a role in three levels: material technology layer, organizational system layer, and value concept layer. In cooperation and interaction, college sports and community material and technology complement each other, improve the organizational system together, and promote each other’s values. In the interaction of competition, the level of material and technology should be improved through competition, the improvement of the organizational system should be improved through competition, and the mechanism of market competition should be appropriately introduced to promote the renewal of values. In weak interaction behavior, material technology is transmitted in one direction, the organizational system responds and adjusts to external changes, and values influence each other subtly.The interaction methods between college sports and community sports include strengthening group communication and guiding group goals; increasing group activities and cultivating sports interests; enhancing group feelings and norms and improving cohesion; improving the level of individual training and competitions to promote the size of sports groups; having iteractive exchanges; strengthening the construction of physical platforms to benefit the elderly; strengthening organizational construction and improve organizational capabilities; and using appropriate management strategies to improve management levels.


## 6. Conclusion

The interaction between science and technology and society is the continuous driving force for social development. The promotion of mass sports and public services in China requires the interaction of talents and resources. How the parties involved in social interaction integrate their actions through experiential mechanisms, how interactions are reinforced, how the characteristics of this interaction system, in turn, have an impact on the interaction parties’ understanding of relevant issues, and how to deal with and influence relevant issues. All of them require integrated analysis of sports big data to do so. This study introduces BP neural network to construct a data classification system. It accelerates the speed of sports big data integration. Moreover, this study puts forward some method suggestions for college sports and community sports. In this study, the classification error comparison method of different structures is used to determine the final network structure. It is not difficult to find this method, but a lot of comparative experiments are needed, so it is necessary to explore a simpler method to determine the topology of the network.



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*Source: 6762200-2022-08-23.xml*