# Construction of Big Data Technology Training Environment for Vocational Education Based on Edge Computing Technology

Authors: Libiao Cui
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the rapid growth of the BD (big data) industry, many universities are focusing their professional development and expansion efforts on cultivating talent in related industries as well as building and developing big data disciplines. The BD technical training platform of vocational education based on IPE (Integration of Production and Education) is built in this paper, and the agreement signing, process management, effectiveness evaluation, big data analysis, and statistics of the integration of production and education are realized using edge computing technology, providing a solution for the informatization development of the integration of production and education. The access sequence was discovered among some knowledge points, as well as the sequence of knowledge points frequently viewed by students, and the teaching platform adjusts the content of web pages based on these rules to better provide personalized services for users.


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## Body

## 1. Introduction

China is currently undergoing a significant industrial transformation and upgrading. The industrial structure is changing towards rationalization in order to improve international competitiveness, promote economic development, and improve people’s livelihoods. As a result, a large number of specialized technical talents are required by various industries to support the upgrading of the entire industry. All-round development talents are innovative talents [1]. It is necessary to cultivate, train, and enhance their innovative ability and awareness in a targeted manner, as well as to improve their overall innovative quality [2, 3]. With the rapid growth of the BD (big data) industry, many universities have turned their attention to cultivating talent in related industries as well as building and developing BD disciplines [4].In order to meet the needs of modern intelligent manufacturing enterprises for high-tech talents and to solve the problems of low experimental efficiency and no data in BD in the original training teaching [5, 6], it is urgent to build a training platform based on the combination of BD and virtual reality to cope with these problems and challenges [7]. In order to better collect and share educational BD, a vocational education BD technical training platform was built, the collection and sharing of educational BD on one platform were concentrated, and at the same time, online learning, education prediction, policy making, and other related functions were integrated into this platform; on the one hand, the construction of “smart education” was accelerated, and on the other hand, the transmission and sharing of educational resources were promoted, the utilization rate of educational resources was improved, and education was promoted to be more equitable.The goal of promoting wisdom education is to use advanced emerging technology to develop intelligent, effective, and precise education methods, as well as to adopt precise personnel training methods based on BD calculation results, laying a solid foundation for training high-quality technical personnel. BD also has revolutionary power in terms of university data collection, classroom teaching, development evaluation, and resource regulation [8, 9]. Furthermore, as the field of BD continues to develop, people from all walks of life are using BD facilities to process and analyze data. Teaching and practice based on the concepts, processing methods, and operations related to the IPE (Integration of Production and Education) are essential in order to adapt to the new situation of the BD era.The main contributions of this paper are as follows:(1)
Use edge computing technology to provide solutions for the development of industry-education integration informatization(2)
Analyze the access sequence between some knowledge points to find out students’ study habits for personalized service.


## 2. Related Work

According to reference [10], the effectiveness of industry-education integration is determined by the breadth, depth, and intensity of cooperation between education and industry at the joint point. The foothold of new engineering discipline construction, according to reference [11], is active docking and meeting the needs of industries and industries, and the main goal of new engineering discipline construction is to cultivate a group of excellent engineering scientific and technological talents with innovative and entrepreneurial ability, dynamic adaptability, and high quality. According to reference [12], the IPE should be more than just an educational system; it should also be an integral part of the economic and industrial systems. The IPE is the only way to improve the quality of vocational education in China’s critical period of industrial transformation and upgrading, according to reference [13], and it is necessary to figure out the crux of its existence and solve it. From the technical level of basic platform, network platform, information transformation of education and teaching resources, digital service system, and resource construction, reference [14] analyzes the current state of vocational education informatization and constructs the development direction of vocational education informatization.BD is a new concept of edge computing technology, and the changes it brings to the field of education cannot be ignored. Effectively integrating the superior resources of universities, enterprises, governments, and other parties to form a university fit can promote the upgrading of traditional industries, enhance the ability of scientific and technological innovation, and promote the transformation of scientific and technological achievements, which plays a very important role in building a perfect innovative talent training system. Reference [15] holds that BD will bring subversive changes to modern education, one is to realize educational feedback, the other is to realize personalized teaching, and the other is to improve learning efficiency. Reference [16] expounds the application status of BD in education in China from theory and practice. The realization of a multidimensional data analysis system for educational BD is one of the personalized data platform constructions mentioned in reference [17]. Reference [18] BD and other technologies are integrated into the training system framework of vocational education, and a vocational education wisdom management platform is built using “internet plus Vocational Education.” An outlier mining method based on frequent patterns is proposed in reference [19]. Frequent patterns, according to the algorithm, are general patterns with a high frequency of items. If an object in the data set has a small number of frequent patterns, it is more likely to be an outlier; otherwise, it is less likely to be an outlier. Reference [20] improved the distance-based outlier measurement method. The disadvantage of the distance-based method is that it has a high time complexity and is difficult to deal with the sparse problem of high-dimensional data; on the other hand, the mining results are dependent on parameter selection. Only global outliers can be found because the global threshold value is usually used as the main object of research and analysis, and the density characteristics of local areas are not fully considered.


## 3. Research Method

### 3.1. The Overall Design of BD Technology Training Platform for Vocational Education

IPE refers to making full use of the supply and demand relationship of industrial system and education system, combining teaching with production, realizing seamless connection between vocational schools, industries, and enterprises, cultivating high-quality modern professional talents who support the development of modern agriculture industry and service industry under the background of industrial and economic transformation and upgrading in China, and creating social production value.The two types of education are vocational and general education. It is not just schools that are affected. Students of vocational education, parents of students, teachers of vocational schools, employees of enterprises, technical instructors, and others are all connected in pairs [21]. As a result, the foundations of teaching research and the main body of teaching research in vocational education are schools and enterprises. Enterprises are crucial to the platform’s success. Enterprises provide venues for vocational education, raw materials for education reform direction, and a practical foundation for vocational education teaching and research. Teachers and businesses should collaborate to explore the vocational education teaching reform direction, cultivate new students with strong learning abilities and professional competitiveness, promote vocational education development, and use information data to support IPE teaching and research. The construction of BD application technology training platform should focus on the training teaching of BD core courses and technologies and combine theory with practice for practical teaching based on data and practical problems. The teaching philosophy is shown in Figure 1.Figure 1
Teaching idea.Based on the design training environment with the core technology of BD application as the main line, the platform integrates the most cutting-edge and practical BD application framework and related technologies in the industry. The management function of the platform should be simple and easy. The development and debugging environment of BD application technology should be tailored according to the scene of practical teaching, and teachers should be supported to customize relevant practical training contents, students should be supported to independently build the BD application environment, and various practical training methods such as project team and continuous integration should be supported to provide environmental support for college students’ innovation and entrepreneurship.The development of this platform draws on BD’s thinking and technology, as well as the networked hardware and software environment of vocational education-related roles, such as vocational colleges’ information systems and the Education Bureau’s data center. First and foremost, the basic data collection of vocational education resources and industry resources is ensured by establishing a dynamic vocational education data center and vocational education management communication platform. As a result, data collection, sharing, and circulation have emerged as major concerns. The vocational education information data platform uses BD collection technology to conduct data research and realize BD analysis and teaching and research analysis capabilities, allowing for the creation of a data analysis report and the digitization and visualization of enterprise needs. The industrial map was complemented with the business map [22]. Universities must collaborate with undergraduate colleges to build a complete ecological chain in fields like big data and artificial intelligence. A data-based five-chain integration system is constructed by defining the connotation definitions of industrial chain, specialty chain, talent chain, technology chain, and innovation chain, in order to resolve the contradiction between the goals and interests in the cooperation process between colleges and universities and to further promote cooperation between schools and enterprises to educate people, collaborative innovation, and achievement transformation.The functional architecture of vocational education BD technology training platform is shown in Figure2.Figure 2
Functional architecture of vocational education BD technology training platform.Based on the virtualization and networking of the experiment, the process of students’ operation and the results of the experiment are collected and analyzed in the cloud server, which can reduce the workload of teachers in supervising the experiment process and correcting the experiment report [23]. Based on the collected experimental data, statistical evaluation is carried out within the class, horizontal comparison is carried out between teaching classes, data comparison is provided between schools, and data support is comprehensively provided in the teaching management and quality supervision links, thus effectively promoting the teaching quality.In order to make the educational BD sharing platform develop for a long time, it was upgraded and maintained from many aspects and was given full play to its role in educational decision-making and improving the educational environment. On the one hand, the transformation and upgrading of the platform information database itself were strengthened, related technologies such as the Internet were improved, the integration of online and offline data resources was accelerated, and the scope and content of BD collection were expanded. On the other hand, expanding the application field of BD in education can expand the application range of BD in education to medical, financial, and other related fields and promote better cooperation between the education industry and other industries. At the same time, it can strengthen the research and development and application of BD-related technologies and promote the integration and sharing of various resources.After analyzing the data from students’ learning activities, teachers will be able to identify students’ weak links and deficiencies in the learning process, as well as provide more targeted guidance and learning suggestions to students to improve learning efficiency and level. After analyzing teachers’ teaching behavior, teachers’ strengths and weaknesses in the classroom were identified, and teachers were encouraged to improve their own teaching methods and work efficiency. Simultaneously, education-related data analysis can assist schools or educational authorities in better understanding the implementation of systems or policies, as well as the issues that arise during the implementation process, in order to improve the systems or measures and make policies that improve our educational environment.


### 3.2. Student Behavior Data Analysis

Individualized teaching is a kind of teaching mode to show individuality, which is centered on students. In the process of teaching, special attention should be paid to differences in personality, and personalized teaching strategies should be formulated, so as to improve learners’ initiative in learning and inspire them to discover and create themselves. It is a new teaching method of teaching students in accordance with their aptitude. To sum up, personalized teaching should meet the requirements of student-centered and individual needs of learners [24].The learning style, learning objectives, learning ability, progress, and incentive mechanism of each student is unique. Because each learner’s learning objectives are unique, he should be given the freedom to select the objectives he wishes to pursue. Without being constrained by the teacher, students can make their own learning progress based on their ability to learn new information. Because each student’s learning objectives and abilities are unique, a single incentive mechanism should not be used for all students; instead, different incentives should be used for different students. The following quadratic programming problems must be solved in order to discover this hyperplane:(1)minΦw=12w2s.tyiwT×xi+b−1≥0,i=1,2,⋯,n,where w is the normal vector, b is the bias term, and x is the feature. The Lagrange duality is used to solve the above quadratic programming problem.
(2)minLw,b,a=12w2−∑i=1lαiyiwT×xi+b−1.The relation ofw,α;b,α is
(3)maxWα=∑i=1lαi−12∑j=1lαiαjyiyjxiTxjs.t∑j=1lαiyi,αi≥0,i=1,2,⋯,l.The final optimal classification hyperplane is(4)fx=∑i=1lαi∗yixiTx+b,where αi∗ is the support vector point.If the target users are experienced, they can analyze the courses they want to take based on some basic information like their academic records and professional codes and then make recommendations. If the target scholar is a new user, it is necessary to create a questionnaire based on the learners’ registration, which requires them to fill in some information such as their professional codes and interesting knowledge contents, then match this information with learners in different clusters, find clusters that are similar to the target scholars’ preferences using the maximum similarity principle, and then perform the next recommendation service (Figure3).Figure 3
Recommended system flow chart.On the basis of serial shared decision tree, this section gives the problem description of parallel shared decision tree, and the definition and calculation process of split index and evaluation standard in parallel environment. PSDT (parallel shared decision tree) refers to a decision tree constructed by parallel strategy, which can classify data inD1 and D2.Given a nodev on PSDT, its node distribution similarity is
(5)DSNV=CDV1V×CDV2VCDV1V×CDV2V,where the numerator is the cross-product of vector CDV1V×CDV2V and the denominator is the product of modules of CDV1V×CDV2V vector.The data distribution similarity ofT is defined as
(6)DST=1m∑i=1m1n∑j=1mDSNVij,where Vij represents the nonroot node j stored in the computing node i, m is the number of computing nodes in the cluster, and n is the number of nonroot nodes of the tree T.In order to make PSDT have good data distribution similarity and classification accuracy at the same time, the quality evaluation standard and split index of PSDT are defined as follows:(7)SDTQT=minSAT,DST×avgSAT,DST.SDTQ considers both SA and DS and highlights the role of smaller elements in SA and DS. SDTQ can guarantee that a shared decision tree with a high SDTQ score must be a high-quality shared decision tree.Although the data record structure is not suitable for parallel calculation of splitting index, its storage cost is much less than that of attribute table structure, and the splitting process is simple. Therefore, replacing the attribute table structure with the data record structure as much as possible, especially in the node splitting stage, plays an important role in reducing the I/O amount of the algorithm. At the same time, in order to calculate the split index value of attributes in parallel, it is still inevitable to use attribute table structure. Therefore, this paper proposes a hybrid data structure, namely the HPSDT (hybrid parallel shared decision tree) algorithm.The HPSDT algorithm, like the PSDT algorithm, divides the node splitting process into two stages: calculating the splitting index and executing the splitting. Each stage corresponds to a Job, with Job1 being in charge of calculating the splitting index value and Job2 being in charge of splitting. As a result, the HPSDT algorithm divides each splitting process into two stages: calculating splitting index and node splitting stage, rather than the entire algorithm flow into two stages: root node splitting stage and cycle splitting stage. The HPSDT algorithm defines the distribution rules in the same way that PSDT algorithm does: Node and attribute tables with the same number of nodes and attributes are sent to the same Reduce. In comparison to the PSDT algorithm’s splitting process, the HPSDT algorithm’s splitting process is relatively simple, as it can be split directly according to the best splitting point without the use of a hash table.


## 3.1. The Overall Design of BD Technology Training Platform for Vocational Education

IPE refers to making full use of the supply and demand relationship of industrial system and education system, combining teaching with production, realizing seamless connection between vocational schools, industries, and enterprises, cultivating high-quality modern professional talents who support the development of modern agriculture industry and service industry under the background of industrial and economic transformation and upgrading in China, and creating social production value.The two types of education are vocational and general education. It is not just schools that are affected. Students of vocational education, parents of students, teachers of vocational schools, employees of enterprises, technical instructors, and others are all connected in pairs [21]. As a result, the foundations of teaching research and the main body of teaching research in vocational education are schools and enterprises. Enterprises are crucial to the platform’s success. Enterprises provide venues for vocational education, raw materials for education reform direction, and a practical foundation for vocational education teaching and research. Teachers and businesses should collaborate to explore the vocational education teaching reform direction, cultivate new students with strong learning abilities and professional competitiveness, promote vocational education development, and use information data to support IPE teaching and research. The construction of BD application technology training platform should focus on the training teaching of BD core courses and technologies and combine theory with practice for practical teaching based on data and practical problems. The teaching philosophy is shown in Figure 1.Figure 1
Teaching idea.Based on the design training environment with the core technology of BD application as the main line, the platform integrates the most cutting-edge and practical BD application framework and related technologies in the industry. The management function of the platform should be simple and easy. The development and debugging environment of BD application technology should be tailored according to the scene of practical teaching, and teachers should be supported to customize relevant practical training contents, students should be supported to independently build the BD application environment, and various practical training methods such as project team and continuous integration should be supported to provide environmental support for college students’ innovation and entrepreneurship.The development of this platform draws on BD’s thinking and technology, as well as the networked hardware and software environment of vocational education-related roles, such as vocational colleges’ information systems and the Education Bureau’s data center. First and foremost, the basic data collection of vocational education resources and industry resources is ensured by establishing a dynamic vocational education data center and vocational education management communication platform. As a result, data collection, sharing, and circulation have emerged as major concerns. The vocational education information data platform uses BD collection technology to conduct data research and realize BD analysis and teaching and research analysis capabilities, allowing for the creation of a data analysis report and the digitization and visualization of enterprise needs. The industrial map was complemented with the business map [22]. Universities must collaborate with undergraduate colleges to build a complete ecological chain in fields like big data and artificial intelligence. A data-based five-chain integration system is constructed by defining the connotation definitions of industrial chain, specialty chain, talent chain, technology chain, and innovation chain, in order to resolve the contradiction between the goals and interests in the cooperation process between colleges and universities and to further promote cooperation between schools and enterprises to educate people, collaborative innovation, and achievement transformation.The functional architecture of vocational education BD technology training platform is shown in Figure2.Figure 2
Functional architecture of vocational education BD technology training platform.Based on the virtualization and networking of the experiment, the process of students’ operation and the results of the experiment are collected and analyzed in the cloud server, which can reduce the workload of teachers in supervising the experiment process and correcting the experiment report [23]. Based on the collected experimental data, statistical evaluation is carried out within the class, horizontal comparison is carried out between teaching classes, data comparison is provided between schools, and data support is comprehensively provided in the teaching management and quality supervision links, thus effectively promoting the teaching quality.In order to make the educational BD sharing platform develop for a long time, it was upgraded and maintained from many aspects and was given full play to its role in educational decision-making and improving the educational environment. On the one hand, the transformation and upgrading of the platform information database itself were strengthened, related technologies such as the Internet were improved, the integration of online and offline data resources was accelerated, and the scope and content of BD collection were expanded. On the other hand, expanding the application field of BD in education can expand the application range of BD in education to medical, financial, and other related fields and promote better cooperation between the education industry and other industries. At the same time, it can strengthen the research and development and application of BD-related technologies and promote the integration and sharing of various resources.After analyzing the data from students’ learning activities, teachers will be able to identify students’ weak links and deficiencies in the learning process, as well as provide more targeted guidance and learning suggestions to students to improve learning efficiency and level. After analyzing teachers’ teaching behavior, teachers’ strengths and weaknesses in the classroom were identified, and teachers were encouraged to improve their own teaching methods and work efficiency. Simultaneously, education-related data analysis can assist schools or educational authorities in better understanding the implementation of systems or policies, as well as the issues that arise during the implementation process, in order to improve the systems or measures and make policies that improve our educational environment.


## 3.2. Student Behavior Data Analysis

Individualized teaching is a kind of teaching mode to show individuality, which is centered on students. In the process of teaching, special attention should be paid to differences in personality, and personalized teaching strategies should be formulated, so as to improve learners’ initiative in learning and inspire them to discover and create themselves. It is a new teaching method of teaching students in accordance with their aptitude. To sum up, personalized teaching should meet the requirements of student-centered and individual needs of learners [24].The learning style, learning objectives, learning ability, progress, and incentive mechanism of each student is unique. Because each learner’s learning objectives are unique, he should be given the freedom to select the objectives he wishes to pursue. Without being constrained by the teacher, students can make their own learning progress based on their ability to learn new information. Because each student’s learning objectives and abilities are unique, a single incentive mechanism should not be used for all students; instead, different incentives should be used for different students. The following quadratic programming problems must be solved in order to discover this hyperplane:(1)minΦw=12w2s.tyiwT×xi+b−1≥0,i=1,2,⋯,n,where w is the normal vector, b is the bias term, and x is the feature. The Lagrange duality is used to solve the above quadratic programming problem.
(2)minLw,b,a=12w2−∑i=1lαiyiwT×xi+b−1.The relation ofw,α;b,α is
(3)maxWα=∑i=1lαi−12∑j=1lαiαjyiyjxiTxjs.t∑j=1lαiyi,αi≥0,i=1,2,⋯,l.The final optimal classification hyperplane is(4)fx=∑i=1lαi∗yixiTx+b,where αi∗ is the support vector point.If the target users are experienced, they can analyze the courses they want to take based on some basic information like their academic records and professional codes and then make recommendations. If the target scholar is a new user, it is necessary to create a questionnaire based on the learners’ registration, which requires them to fill in some information such as their professional codes and interesting knowledge contents, then match this information with learners in different clusters, find clusters that are similar to the target scholars’ preferences using the maximum similarity principle, and then perform the next recommendation service (Figure3).Figure 3
Recommended system flow chart.On the basis of serial shared decision tree, this section gives the problem description of parallel shared decision tree, and the definition and calculation process of split index and evaluation standard in parallel environment. PSDT (parallel shared decision tree) refers to a decision tree constructed by parallel strategy, which can classify data inD1 and D2.Given a nodev on PSDT, its node distribution similarity is
(5)DSNV=CDV1V×CDV2VCDV1V×CDV2V,where the numerator is the cross-product of vector CDV1V×CDV2V and the denominator is the product of modules of CDV1V×CDV2V vector.The data distribution similarity ofT is defined as
(6)DST=1m∑i=1m1n∑j=1mDSNVij,where Vij represents the nonroot node j stored in the computing node i, m is the number of computing nodes in the cluster, and n is the number of nonroot nodes of the tree T.In order to make PSDT have good data distribution similarity and classification accuracy at the same time, the quality evaluation standard and split index of PSDT are defined as follows:(7)SDTQT=minSAT,DST×avgSAT,DST.SDTQ considers both SA and DS and highlights the role of smaller elements in SA and DS. SDTQ can guarantee that a shared decision tree with a high SDTQ score must be a high-quality shared decision tree.Although the data record structure is not suitable for parallel calculation of splitting index, its storage cost is much less than that of attribute table structure, and the splitting process is simple. Therefore, replacing the attribute table structure with the data record structure as much as possible, especially in the node splitting stage, plays an important role in reducing the I/O amount of the algorithm. At the same time, in order to calculate the split index value of attributes in parallel, it is still inevitable to use attribute table structure. Therefore, this paper proposes a hybrid data structure, namely the HPSDT (hybrid parallel shared decision tree) algorithm.The HPSDT algorithm, like the PSDT algorithm, divides the node splitting process into two stages: calculating the splitting index and executing the splitting. Each stage corresponds to a Job, with Job1 being in charge of calculating the splitting index value and Job2 being in charge of splitting. As a result, the HPSDT algorithm divides each splitting process into two stages: calculating splitting index and node splitting stage, rather than the entire algorithm flow into two stages: root node splitting stage and cycle splitting stage. The HPSDT algorithm defines the distribution rules in the same way that PSDT algorithm does: Node and attribute tables with the same number of nodes and attributes are sent to the same Reduce. In comparison to the PSDT algorithm’s splitting process, the HPSDT algorithm’s splitting process is relatively simple, as it can be split directly according to the best splitting point without the use of a hash table.


## 4. Results Analysis and Discussion

Innovation subjects closely focus on regional industrial layout, rely on enterprises, take products as the basis, and make concerted efforts of multiple subjects to form a network system of interactive connection between innovation supply and innovation demand. Under the background of information sharing, the training and delivery of talents by vocational education must keep pace with the pace of social and economic development. Therefore, it is of great educational and practical significance to build and fully apply the training platform based on the combination of BD and reality for the country to train high-quality laborers and technical talents to meet the job requirements of enterprises.When studying education, researchers should not only focus on whether the results of the model are good or bad but also have a deeper discussion, as shown in Figure4.Figure 4
Experimental results of different sequence lengths on accuracy and recall of student behavior model.When the length of the behavior sequence is increased from 1 to 20, the model’s performance improves and the accuracy exceeds 70%. This demonstrates that better prediction can be achieved if the student behavior model captures more objective features based on the existing sequence behavior features. After determining the basic framework of vocational education BD technology training platform construction, it is necessary to sort out the basic system of sharing platform construction, so that the basic layer, middle layer, and top layer can be linked together to form a whole, in order to make the platform construction more effective and reasonable. The foundation and key point of developing the BD technology training platform in vocational education is to improve data management. Data management is divided into two categories: data management and data manager management.As shown in Figure5, the experiment shows whether the feature dimensions of various training quantities will influence the choice of students’ behavior features in the evaluation of accuracy and recall rate methods.Figure 5
Experimental results of different feature dimensions on accuracy and recall of student behavior model.It is discovered that as the number of hidden units increases, the curves of the two indicators show a gentle trend. The more hidden layer feature dimensions are set, the better the students’ understanding will be. As a result, determining appropriate feature dimensions is beneficial in developing a student behavior model. Data has become the most important indicator of teaching improvement in education, particularly in school education. Typically, these numbers refer to test scores, as well as enrollment rates, attendance rates, dropout rates, and promotion rates, among other things. The creation of a vocational education information data platform can collect this information, match students’ abilities to enterprise needs, and investigate ways to cultivate students in education. Enterprise career data and student learning data were collected and analyzed to provide a reference for the IPE localization reform.In this section, the two algorithms are tested on Iris and Wine UCI data sets, and the clustering effects of DECluster (differential evolution algorithm) and IDECluster (improved differential evolution algorithm) are compared. In order to compare the convergence performance of DECluster and IDECluster, the convergence of this fitness function in Iris and Wine data sets with the number of iterations is shown in Figure6, respectively.Figure 6
Algorithm average comparison.From these clustering result graphs, it can be seen that theF-measure values of DECluster and IDECluster algorithms are higher than those of standard K-means methods on Iris and Wine data sets, which shows that these two algorithms have higher clustering accuracy.The data collected by the vocational BD technology training platform is all the data related to educational activities, with a wide range of data sources and different structures. Therefore, in the process of collection, data that may affect later analysis or irrelevant data may be collected, occupying storage space. It is necessary to strengthen the construction of BD technology training platform for vocational education at the level of laws or rules and regulations, make clear provisions on related issues, ensure that the collected objects have detailed right to know and data ownership, protect their privacy when using data, and at the same time, formulate corresponding laws, regulations, or policies to support the construction and development of educational BD platform, so as to enable more users to participate in and use the sharing platform, promote the platform construction, and ensure the reasonable, legal, and orderly operation of the platform. The IDECluster algorithm has good global optimization ability. The figure below shows that the improved algorithm is further compared with GA (genetic algorithm), TS (tabu search), SA (simulated annealing), and ACO (ant colony optimization), and the fitness function results are shown in Figures7 and 8.Figure 7
Comparison of fitness function results on Iris data set.Figure 8
Comparison of fitness function results on Wine data set.From the data in Figures7 and 8, it can be seen that compared with GA, TA, SA, and ACO algorithms, the IDECluster algorithm has better global optimization ability and achieves the best results in average, optimal, and minimum values of intraclass dispersion sum. This shows that the improved differential evolution clustering algorithm is effective, and the effect will be more obvious when it is applied to the course of clustering analysis.It gives curriculum developers, teachers, students, and managers a firm grasp on the curriculum system while also serving as a useful tool for curriculum management. As a result, data collection, sharing, and circulation have emerged as the most pressing concerns. The vocational education data platform uses BD collection technology to conduct data research and achieve BD analysis, as well as teaching and research analysis. This allows for the creation of a data analysis report and the digitization and visualization of business needs. The industrial and business maps were compared and contrasted. The employment standards of businesses can be derived from the industrial demand structure. This platform, which is based on “people,” is constantly “trimmed” and “communicated” and can be adjusted dynamically.In order to verify the effectiveness of the CPU-for-I/O strategy adopted by the HPSDT algorithm and compare it with PSDT algorithm, this experiment also runs the HPSDT algorithm on the Hadoop cluster built by three-blade servers, and the experimental results are shown in Figure9.Figure 9
Run time comparison.It can be seen from Figure9 that when the data set size is 90M, the HPSDT algorithm runs well. That is to say, the parallel HPSDT algorithm can handle larger data sets than the serial PSDT algorithm, that is, the HPSDT algorithm also has good scalability.The design and development of this platform serve the teaching and research of vocational education, and the security issues such as copyright, operation mode, business mode, and technology are often involved in the teaching and research process. Therefore, in design, besides following the rules of software development, confidentiality should also be considered, including the security of data resources, the privacy of user roles, and the security of preventing external forces from invading the system. Therefore, these problems should be considered in the choice of tools and technologies that can actually play a role in the teaching reform of vocational education and meet the actual needs of the current development of vocational education, have the corresponding technical team and postservice personnel, have the operating conditions, and can operate continuously.Parallel algorithms based on the MapReduce model can run in large-scale clusters, so the influence of cluster size on parallel algorithms is another focus of this experiment. In this experiment, the HPSDT algorithm is run on clusters with different numbers of nodes to observe the influence of the Hadoop cluster size on the algorithm. The experimental results are shown in Figure10.Figure 10
Cluster scale experiment.It can be seen from Figure10 that with the increasing number of computing nodes, the running time of the HPSDT algorithm gradually decreases. And with the increase of the data set, this trend is more obvious. It is worth noting that when the data set is small, adding more computing nodes does not result in a noticeable performance boost. This is due to the fact that small data sets have low resource requirements, a small number of computing nodes can achieve better parallelism, and more nodes provide limited acceleration while increasing communication costs, annual school-enterprise cooperation matters completion, annual evaluation level of school-enterprise cooperation, and so on. Enterprises are depicted as a result of their collaboration. As a result, the scope of educational BD acquisition should be clearly defined, and strict acquisition standards should be established, in order to be targeted, based on the function of the sharing platform and the needs and purposes of users. On the other hand, the means of obtaining BD in education must be clarified. BD can be obtained using a variety of data collection and data mining techniques in the third chapter. Simultaneously, an incentive mechanism must be established to encourage more schools and teachers to voluntarily share their own data, in order to continuously enrich and expand the sharing platform’s database.With the help of BD and related data processing technology, it is the ultimate goal of the quality evaluation of the IPE in higher vocational education to promote the organic combination of the massive data accumulated by higher vocational colleges and the talent demand data of society and enterprises/industries, so as to serve the talent cultivation in higher vocational education in a timely, efficient, and accurate manner.


## 5. Conclusion

The pertinence and effectiveness of the IPE work were improved by expanding the teaching and research function, innovating IPE teaching and research methods, and realizing IPE teaching and research networking as well as IPE management networking. IDECluster helps learners create personalized learning plans by recommending appropriate courses to target recommenders based on the learners’ interest in each course. BD technical training for vocational education based on IPE can meet IPE’s teaching and research needs, which helped China improve its vocational education quality and has significant development value.The author is still in the preliminary learning stage of data mining technology, so the designed model may have relatively simple functions. Because of the small amount of data, there is no reserved test data. In the next step, the author will make further research and exploration.



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*Source: 1060464-2022-02-24.xml*