# Research on Application Adaptability of Big Data and IoT in Discipline Construction of Mechanical Engineering Specialty in Universities

Authors: Chunxia Wang; Jian Xie
Journal: Security and Communication Networks
 (2021)

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## Abstract

With Industry 4.0 and another round of scientific and technological reform and development, industrial reform and development, the latest economic development, and the escalating complexity of the upcoming problems faced by human beings all over the world, there is an urgent need for cross-compounding between disciplines and disciplines, resulting in emerging engineering majors. In response to these situations, the article analyzes the characteristics, necessity, and main problems faced by the construction and talent cultivation of emerging engineering majors with multidisciplinary cross-composite and discusses the new initiatives of building emerging engineering majors with multidisciplinary cross-composite and innovative talent cultivation to encourage the cultivation of brave exploration and innovation, cultivate innovative talents with unique insights, and promote innovation and breakthroughs in economic, social, and scientific and technological development needs. Finally, the article takes mechanical and electronic engineering as an example to explore how the crossover and integration between several important aspects of the construction of emerging engineering majors are effectively carried out in order to provide certain reference and reference for the construction of related emerging engineering disciplines and majors.


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## Body

## 1. Introduction

Multidisciplinary intersection and integration refer to a comprehensive new discipline system formed by two or more disciplines through mutual penetration and integration. “Made in China 2025,” also known as “Industry 4.0,” is an important strategic plan for the development of the country from a large manufacturing country with a strong manufacturing country [1, 2]. Therefore, in this context, students' thinking quality, innovation ability, and the cultivation of the ability to solve practical problems in the workplace have raised higher requirements [3, 4], as shown in Figure 1.Figure 1
Mechanical and electrical engineering trends.The so-called composite talent is actually a person who has certain ability in all aspects and has outstanding ability in a specific aspect, which can also be understood as T-type talent as shown in Figure2. The major characteristics of complex talents in today's society are interdisciplinary, knowledge integration, and technology integration [5]. The construction of emerging engineering majors is a challenging, difficult, and university-wide systematic work, which needs to deeply explore the correlation between disciplines and the role of each other, to find the intersection and integration of disciplines and the fit point between disciplines and also to obey the needs of future scientific and technological development and economic and social development, to be able to cope with various uncertainties, and to form the amplification effect of intersection and integration between multiple disciplines.Figure 2
T-shaped talent diagram.Along with the progress of information technology and the advent of the era of big data, the process of discipline construction needs to deal with an increasingly wide range of data, while facing the increasing difficulties of data redundancy, excessive garbage data, data duplication entry, and lack of consistency in information exchange [6]. In addition, the conversion of data formats while maintaining data integrity is an important challenge for discipline construction when processing discipline construction data. Since the discipline construction data related to discipline construction is huge and complex and the discipline construction processing often involves more than a hundred parameters, it is difficult to measure them in the traditional way, so it is necessary to establish a unified and highly integrated information data processing system.According to the order of data processing, the discipline construction big data analysis system can be divided into 3 levels, which are data storage, data preprocessing, and building a dynamic discipline construction shared data analysis center. Among them, data storage can ensure the integrity of data retention, data processing can ensure the timeliness of data, and dynamic data analysis system can fully explore the value of data [7]. By combining the three into one, the value of big data can be more complete and relevant. The article analyzes the construction of the discipline construction data analysis system from the source data level, data storage level, data preprocessing level, and data analysis level for the big data from the discipline construction data center and external data sources. In the overall technical framework of the discipline construction data analysis system, it is mainly based on No SQL and Hadoop to preprocess and analyze the data, transform the traditional discipline construction data into full data, perform high-performance interactive analysis, and finally complete the analysis of the discipline construction big data [8].


## 2. Problems Faced by the Construction and Personnel Training of Mechanical Professions

At present, the current discipline majors in China's universities have not set special cross-disciplinary majors, which will have serious constraints on the future development and innovation of university students, and the obstacles set by the solidified discipline majors will affect students' enthusiasm to participate in cross-disciplinary or interdisciplinary topics. Since cross-discipline is the result of interpenetration and integration of many disciplines, the distance between disciplines and majors is large, and the knowledge background of students has obvious singularity and limitation, so colleges and universities cannot reasonably arrange the training courses for composite talents according to the goal of multidisciplinary cross-discipline talents training, and it is difficult to complete timely counseling and targeted training for students, which has largely restricted the cross-compounding among various disciplines and the extensive promotion of the construction of new engineering majors and the cultivation of innovative talents [9].In the process of construction and talent cultivation of emerging engineering majors with cross-disciplinary compound, in order to reduce the blindness of students in choosing majors, courses, and teaching time and teaching methods, corresponding management system and professional teachers in colleges and universities are needed to cooperate with them. At present, although some colleges and universities have introduced some related systems, due to the lack of policy support, the lack of specific measures and the lack of standardized management system, new ideas, new knowledge, and new methods in the interdisciplinary area are not easily accepted, which discourages the enthusiasm of teachers and students, hinders the creativity of students, and makes it difficult for the organization of teachers to get support and grow. This makes it difficult for the faculty to grow and develop and leads to many policies and systems that are merely formal, limiting the intersection of various disciplines [10, 11].The first priority is for universities to carry out discipline and professional construction is the interdisciplinary crossover and integration [12]. The development of science and technology gradually shows the changing trend of subdivision and crossover, and many key projects of national and local governments are in urgent need for collaboration between multiple disciplines, so the demand for complex innovative talents is gradually increasing, and the crossover between disciplines is conducive to the cultivation of complex and innovative talents. Therefore, the crossover and integration between disciplines can breed good opportunities for scientific research and innovation.We choose disciplinary crossover and integration to carry out cross-compounding between disciplines and disciplines for the construction of the emerging engineering majors and the cultivation of innovative talents. In addition, interdisciplinary is also the need to deepen education reform and improve the training quality of comprehensive talents.


## 3. Steps for Building a Data Analysis System for Academic Construction

### 3.1. NoSQL-Based Big Data Storage Level Management

Faced with the massive amount of discipline construction information data, NoSQL storage system can completely realize the storage and flexible management of discipline construction information in all aspects. There are three main storage methods in data storage, namely, NoSQL database, relational database, and HDFS distributed file system [13]. In the classification of storage forms, NoSQL stores massive data in the form of nonrelational and distributed data storage and stores graph data, Key-Value, and document-oriented data formats, thus realizing more flexible data scalability with high-speed read and write performance and superior query performance. Technical framework of academic construction data analysis system is shown in Figure 3.Figure 3
Technical framework of academic construction data analysis system.NoSQL data storage systems include both Master-Slave and P2P ring structures. Among them, Master-Slave has good controllability and simple design structure and often implements data distribution based on horizontal partitioning. By separating the functions between Master nodes and Slave nodes, the functional load of the nodes can be reduced, and the Master nodes can maintain and manage the Slave nodes. The disadvantage is that the node in the center of the Master node can easily become a bottleneck in the system, while the P2P ring structure system does not have a central node, so each node is equal and based on Hash data distribution, which has the advantages of good coordination and easy scalability. The above two architectures differ greatly, and each has certain functional limitations. Therefore, in the power system, it is necessary to combine the advantages of the distributed structure of P2P and the centralized structure of Master-Slave to form a corresponding data storage system. Common combinations are the combination of Master-Slave and chord or the combination of Content-Addressable Network to ensure that the data storage can be both global and local [14].


### 3.2. Hadoop-Based Discipline Construction Data Preprocessing

Hadoop is an open-source large-scale distributed computing framework that is reliable, efficient, and scalable and is therefore widely used in the field of big data processing [15]. Based on Hadoop and the existing grid discipline construction system, technicians can establish a new discipline construction big data preprocessing system model, combining Hadoop, HBase, and Hive in the discipline construction data preprocessing platform for data cleaning, integration, and attribution. We use noise processing to fill in the missing data and simplify the relationship of data attribute dimensions to complete the preprocessing of discipline construction data. This approach takes advantage of the characteristics of the Hadoop platform to add monitoring and control nodes for the tasks that need to be reprocessed, and each node corresponds to a task or a list of tasks that need to be reprocessed and starts the corresponding processing procedures and related rules for that task. The specific processing procedure is shown in Figure 4.Figure 4
Flowchart of big data preprocessing system for academic construction.


### 3.3. Hadoop-Based Analytics Cluster for Analyzing Discipline Construction Big Data

After preprocessing the discipline construction data based on Hadoop, we improve the shortcomings of the existing discipline construction analysis system, establish a discipline construction analysis system suitable for the current discipline construction needs, and analyze the discipline construction data [16]. The focus of the big data analysis system is on the supervision of the discipline construction management process and the analysis of relevant discipline construction indexes, such as project budget analysis, cost analysis, and risk analysis. In order to realize the effect of discipline construction analysis, firstly, we can investigate the current situation of discipline construction from the management side and analyze the difficult points of discipline construction management such as profit statement and management amount. Secondly, at the technical level, we can integrate the discipline construction management system by combining the advantages of a traditional database and new business intelligence. Based on the operation characteristics of discipline construction and discipline construction characteristics, the existing data is effectively stored and analyzed based on big data thinking, and the clustering algorithm is used to extract data features and explore data value. The specific discipline construction big data analysis process is shown in Figure 5.Figure 5
Hadoop-based big data analysis flowchart for discipline construction.


## 3.1. NoSQL-Based Big Data Storage Level Management

Faced with the massive amount of discipline construction information data, NoSQL storage system can completely realize the storage and flexible management of discipline construction information in all aspects. There are three main storage methods in data storage, namely, NoSQL database, relational database, and HDFS distributed file system [13]. In the classification of storage forms, NoSQL stores massive data in the form of nonrelational and distributed data storage and stores graph data, Key-Value, and document-oriented data formats, thus realizing more flexible data scalability with high-speed read and write performance and superior query performance. Technical framework of academic construction data analysis system is shown in Figure 3.Figure 3
Technical framework of academic construction data analysis system.NoSQL data storage systems include both Master-Slave and P2P ring structures. Among them, Master-Slave has good controllability and simple design structure and often implements data distribution based on horizontal partitioning. By separating the functions between Master nodes and Slave nodes, the functional load of the nodes can be reduced, and the Master nodes can maintain and manage the Slave nodes. The disadvantage is that the node in the center of the Master node can easily become a bottleneck in the system, while the P2P ring structure system does not have a central node, so each node is equal and based on Hash data distribution, which has the advantages of good coordination and easy scalability. The above two architectures differ greatly, and each has certain functional limitations. Therefore, in the power system, it is necessary to combine the advantages of the distributed structure of P2P and the centralized structure of Master-Slave to form a corresponding data storage system. Common combinations are the combination of Master-Slave and chord or the combination of Content-Addressable Network to ensure that the data storage can be both global and local [14].


## 3.2. Hadoop-Based Discipline Construction Data Preprocessing

Hadoop is an open-source large-scale distributed computing framework that is reliable, efficient, and scalable and is therefore widely used in the field of big data processing [15]. Based on Hadoop and the existing grid discipline construction system, technicians can establish a new discipline construction big data preprocessing system model, combining Hadoop, HBase, and Hive in the discipline construction data preprocessing platform for data cleaning, integration, and attribution. We use noise processing to fill in the missing data and simplify the relationship of data attribute dimensions to complete the preprocessing of discipline construction data. This approach takes advantage of the characteristics of the Hadoop platform to add monitoring and control nodes for the tasks that need to be reprocessed, and each node corresponds to a task or a list of tasks that need to be reprocessed and starts the corresponding processing procedures and related rules for that task. The specific processing procedure is shown in Figure 4.Figure 4
Flowchart of big data preprocessing system for academic construction.


## 3.3. Hadoop-Based Analytics Cluster for Analyzing Discipline Construction Big Data

After preprocessing the discipline construction data based on Hadoop, we improve the shortcomings of the existing discipline construction analysis system, establish a discipline construction analysis system suitable for the current discipline construction needs, and analyze the discipline construction data [16]. The focus of the big data analysis system is on the supervision of the discipline construction management process and the analysis of relevant discipline construction indexes, such as project budget analysis, cost analysis, and risk analysis. In order to realize the effect of discipline construction analysis, firstly, we can investigate the current situation of discipline construction from the management side and analyze the difficult points of discipline construction management such as profit statement and management amount. Secondly, at the technical level, we can integrate the discipline construction management system by combining the advantages of a traditional database and new business intelligence. Based on the operation characteristics of discipline construction and discipline construction characteristics, the existing data is effectively stored and analyzed based on big data thinking, and the clustering algorithm is used to extract data features and explore data value. The specific discipline construction big data analysis process is shown in Figure 5.Figure 5
Hadoop-based big data analysis flowchart for discipline construction.


## 4. Establishment of Evaluation Index System for Discipline Construction Data Analysis System

After constructing a big data analysis system for discipline construction, it is necessary to classify its functional requirements and select scientific evaluation indexes to rank the requirement levels, so as to facilitate the subsequent optimization of the system. The article selects the Self-Organizing Map (SOM) neural network algorithm [17] to classify the data samples in the system, and the specific process is as follows.


### 4.1. Network Initialization

SetSj is used to represent that there are j output neurons and the connections from input neurons to output neurons are set with smaller weights. When t = 0, the proximity neuron of j neurons is represented as Sj0; at time t, it is represented as Sjt, and as time goes on, Sj0 decreases.


### 4.2. Input Vector

Select the input values from the set and normalize the input values; the input vector is represented byX and the input is(1)X=x1,x2,x3,…,xnT.


### 4.3. Calculating Euclidean Distance

The Euclidean distance [18] is calculated for the input vectors and the weights between the individual neurons. The calculation is performed as follows:(2)dj=X−Wj=∑i=1mxit−wijt2,where wij denotes the weights between neuron i in the input layer and neuron j in the mapping layer. The neuron with the smallest Euclidean distance is labeled as the winning neuron j∗, and the set of adjacent neurons is output.


### 4.4. Correction Weights

Referring to equation (2), the output neuron and the weights in its vicinity are corrected.(3)Δwij=wijt+1−wijt=ηtxit−wijt,where η is the learning rate, is constant, and η ∈ [0, 1] and tends to 0 as time goes on.(4)ηt=1/tηt=0.21−t10000.


### 4.5. Calculated Output

OutputOk is expressed as(5)Ok=fmin|LX−Wj.The discipline construction management under the background of intelligence needs to establish a big data analysis system based on big data, dynamically evaluate the needs of different data, and timely adjust the weights according to the importance of the needs of different types of data, which is the key to optimize the data analysis system and improve the efficiency of discipline construction management.


## 4.1. Network Initialization

SetSj is used to represent that there are j output neurons and the connections from input neurons to output neurons are set with smaller weights. When t = 0, the proximity neuron of j neurons is represented as Sj0; at time t, it is represented as Sjt, and as time goes on, Sj0 decreases.


## 4.2. Input Vector

Select the input values from the set and normalize the input values; the input vector is represented byX and the input is(1)X=x1,x2,x3,…,xnT.


## 4.3. Calculating Euclidean Distance

The Euclidean distance [18] is calculated for the input vectors and the weights between the individual neurons. The calculation is performed as follows:(2)dj=X−Wj=∑i=1mxit−wijt2,where wij denotes the weights between neuron i in the input layer and neuron j in the mapping layer. The neuron with the smallest Euclidean distance is labeled as the winning neuron j∗, and the set of adjacent neurons is output.


## 4.4. Correction Weights

Referring to equation (2), the output neuron and the weights in its vicinity are corrected.(3)Δwij=wijt+1−wijt=ηtxit−wijt,where η is the learning rate, is constant, and η ∈ [0, 1] and tends to 0 as time goes on.(4)ηt=1/tηt=0.21−t10000.


## 4.5. Calculated Output

OutputOk is expressed as(5)Ok=fmin|LX−Wj.The discipline construction management under the background of intelligence needs to establish a big data analysis system based on big data, dynamically evaluate the needs of different data, and timely adjust the weights according to the importance of the needs of different types of data, which is the key to optimize the data analysis system and improve the efficiency of discipline construction management.


## 5. Initiatives for the Construction of Mechanical Professions and Personnel Training

In the internal management and the management system of many universities, a three-tier orientation structure is generally set up for faculties, departments, and teaching and research sections. The first problem caused by this three-level-oriented system is that the management is not efficient and the information is easily distorted. In order to change this three-level-oriented management model, teaching and research should be absorbed into the new system of discipline construction, and the old separation of teaching, research, and discipline construction should be broken, and the cross-fertilization of disciplines and the integration of teaching and research teams should be coordinated. Universities should set up a teaching organization system with the direction of discipline development, undertaking discipline project groups and professional course group construction as the basic units. The concept of each department should be gradually diluted, the curriculum and discipline direction should be strengthened, and two groups should be set up within the discipline, a curriculum group and a research group, so as to clarify the important position of interdisciplinary research in the work of the university and finally form an emerging engineering discipline with “five new” characteristics, namely, new structure, new quality, new concept, new system, and new mode (as shown in Figure6).Figure 6
“Five new features” of emerging engineering.We should explore and establish a new model of discipline team formation which is conducive to discipline crossover and integration and establish the operation mode, input mode, and assessment mode of “discipline leader + discipline innovation team + platform resources + actual project + assessment mechanism.” The traditional teaching idea of “knowledge-based core” is abandoned, and the teaching concept of “integration of teaching, learning and doing” is highlighted, not only focusing on the transmission of knowledge but also meeting the future development needs of students, focusing on the cultivation of students' innovation, practical ability, solidarity and cooperation, and entrepreneurial awareness [18–20]. We focus on the cultivation of students' innovation, practical ability, solidarity, and entrepreneurial consciousness. We will carry out deep cooperation with local enterprises. Take both universities and enterprises as the main body of mutual cooperation and build an emerging engineering specialty construction system and talent training system that can promote local economic construction and scientific and technological development. On the basis of the deep cooperation between universities and local enterprises, enterprises play the active role of school-enterprise industry-university-research cooperation and coestablish with schools the university students' groundsheet space and build students' innovation and entrepreneurship workshops through the Internet platform to assist and guide the university students in innovation and entrepreneurship.


## 6. Mechanical and Electronic Engineering Professional Development Initiative

Innovative Reform of the Teaching Mode of the Course in the Internet + Mode. In the context of the Internet and online learning platform era, the teacher in charge of the course needs to deeply integrate information technology with education and teaching, as well as online and offline education mode (as shown in Figure 7), to improve teaching quality with students as the center, and teaching reform needs to focus not only on how teachers teach and how students learn but also on the teaching and learning effects. To explore new teaching methods and innovative teaching model for the teaching characteristics of hydraulic and pneumatic transmission, we focus on cultivating students' innovative thinking and practical ability [21, 22]. The course adopts project, case-driven teaching mode, introducing the application background, function, realization, and requirements of the project and case; combining with the curriculum, introducing related fields and courses; analyzing the software used in the case, and finally introducing the overall working principle of the project and case and building a typical case hydraulic circuit. Project, case-oriented driven teaching method, as a method built on the basis of the constructivist teaching theory, emphasizes the cultivation of students' hands-on ability and innovation ability as the core. In the whole teaching process of hydraulic course, the hydraulic teacher acts as the organizer, guide, helper, and facilitator and gives full play to students' subjective initiative, enthusiasm, and innovation by using three major elements, such as theory, project case, and practical operation, so as to finally achieve the purpose of making students effectively apply and reconstruct the learned knowledge. Engineering application is the fundamental purpose of teaching hydraulic and pneumatic transmission theory, fully reflecting the characteristics of hydraulic and pneumatic transmission industry-university combination and engineering combination; in accordance with the application-oriented personnel training program and course syllabus training objectives, students have the ability to learn cross-disciplinary integration, build a comprehensive professional knowledge system, and master the necessary learning tools and software for the course [23]. The course practice teaching platform is an important part of the practical teaching link of talent cultivation in colleges and universities, and the construction of the course practice teaching platform aims to cultivate and enhance students' hands-on practical ability and operation skills. In order to ensure the quality of practical teaching links, we should update and use the experimental equipment and experimental platform in time, improve the practical teaching links such as virtual simulation experiment, hydraulic and pneumatic experiment, and electromechanical integration experiment, and implement modular hydraulic project teaching on the basis of building hydraulic test platform.Figure 7
Online and offline hybrid teaching model.In this new teaching model, students are the active constructors and assimilators of knowledge; teachers are the first organizers and instructors of school teaching; the content presented in the teaching materials is not the only way for teachers to impart knowledge but one of the objects for students to acquire knowledge. Compared with traditional teaching methods, “project and case-driven method” can stimulate and cultivate students' interest and desire to learn more [24]. Cultivate students’ innovative design of hydraulic and pneumatic transmission components and systems, so that students can have the ability to solve practical engineering problems. The construction of teaching materials is also an indispensable element of professional construction. In the selection of course materials, we encourage the use of national “12th Five-Year Plan” teaching materials, teaching materials for the 21st century, award-winning teaching materials at the provincial and ministerial level, and original foreign language teaching materials and other excellent teaching materials. At the same time, teachers are organized to write or participate in the preparation of high-quality application-oriented textbooks, such as national planning textbooks or high-quality textbooks for general higher education, so as to reflect new theories, new ideas, new technologies, and new methods in the field of industry into the textbooks and improve the novelty, practicality, and relevance of the textbooks and actively develop courseware and website resources with professional characteristics. Taking paper as the main textbook, the content of the textbook contains basic theory, basic methods, and system theory and introduces some of the latest and most practical hydraulic and pneumatic transmission technologies, and the preparation process carries out the principles of less and more precise, theory and practice, and application of learning, highlighting the teaching concept of “integration of teaching, learning, doing and thinking” (as shown in Figure 8), so that students can master the methods of hydraulic system analysis and design [24, 25]. Taking into account the theoretical knowledge of hydraulic and pneumatic transmission that students will involve and apply in their future work, in order to reflect the teaching goal of “learning for application,” we abandon cumbersome text narration and theoretical formula derivation on the premise that the basic content remains unchanged and clarify the objectives of the course through simple text narration and summary formula, which can improve students’ ability to integrate theory with practice. By means of school-enterprise cooperation, some typical project tasks from enterprises are included in conjunction with the curriculum standards, interspersing 2D code resources in the key principles, typical hydraulic components, and systems of the textbook, giving full play to the intuitive, three-dimensional and imaginative teaching aid function of 2D codes, and giving new characteristics such as movability, interactivity, and sharing to the traditional textbook, developing from the traditional flat and static book to a three-dimensional and dynamic book (as shown in Figure 8).Figure 8
Diagram of integration of teaching, learning, doing, and thinking.To cultivate students' practical operation ability as the training goal, reconstruct the structure of mechanical and electronic engineering, combine the established goals of talent training in colleges and universities, break the traditional and old-fashioned teaching mode, content, and curriculum system; the teaching content of the courses should directly correspond to the professional knowledge and skills required by the jobs and positions that students may engage in the future, discard the repetitive course content and obsolete ideas, and add the latest appropriate scientific research results and the most advanced professional knowledge and technology in the industry.


## 7. Case Study

Data analysis course is the core compulsory course for the majority of information and computing science in our university. Since the course was opened in 2006, the course team has formulated and revised a series of teaching documents such as theoretical syllabus, experimental syllabus, and teaching plan of data analysis course; continuously adjusted and improved the course system; and improved teaching methods and teaching means. We have introduced international advanced professional statistical analysis software SAS, SPSS, MATLAB, and Python to teach the practice and application of data analysis methods. Develop and produce teaching materials for theoretical and experimental course, and fully and appropriately use multimedia teaching [26].As shown in Figure9, the data analysis course has developed into a professional course with a complete content system and distinctive teaching characteristics. In terms of teaching content, this course organically integrates theoretical derivation and software application and combines basic principles and methods with practice [27]; in terms of teaching methods, according to the characteristics of information and computing science majors, combined with the international engineering education teaching concept, it emphasizes the instrumentality and applicability of the discipline and forms the teaching characteristic of “based on basic principles, highlighting applicability.” Teaching characteristics of the course are “based on basic principles and prominent application.” The data analysis course has built a course teaching resource library [24], a knowledge development resource library, a mathematical modeling competition resource library, an experimental teaching platform, and two rounds of lecture videos. Students can use the school's online teaching platform to independently consult relevant teaching resources, understand the nature, content, requirements, and teaching dynamics of the course, learn more extended knowledge, and realize online communication, discussion, and Q&A of course teaching, which broadens students' learning path, learning time, and space.Figure 9
Degree of use of discipline construction.According to the orientation of this mechanical specialty of our university, take the construction of provincial and national high-quality online open courses as a guide, implement quality education, teach according to the material, and determine the corresponding course content system structure [28, 29]; build high-quality course resources, focus on the training of students' ability to use computers to realize data processing and computer graphics, promote the modernization of teaching methods and teaching means, and improve the efficiency of both teaching and learning. Construction of high-quality network online course, on the basis of maintaining the existing characteristics and advantages, promotes the three-dimensional construction of this course and the overall optimization of the teaching model, enriches the course resources, and improves the quality of the course. Through the construction of online open course, we provide high-quality online course resources for teachers to carry out online and offline hybrid teaching and students to conduct independent learning, as shown in Figure 10.Figure 10
Teaching methods and teaching tools.Data analysis course mainly introduces the common statistical analysis methods of data processing and is a discipline with strong engineering applications [30]. This course is a characteristic and very active branch of mathematics discipline, which has a very important role in the training of students of information and computing science in our university and is an important professional foundation course for information and computing science majors. On the one hand, it is significantly different from other basic courses of the major, and its unique concepts and methods are very conducive to cultivating students' logical thinking and data processing ability, enabling them to master the basic ideas and methods of studying random phenomena and processing large amounts of statistical data; on the other hand, it is closely related to other disciplines and is the basis and interconnection link of many courses of information and computational science [31, 32]. It has an extremely important position and role in the professional curriculum. Therefore, the theoretical teaching concept is based on the basic integration of multiple directions, relying on research topics and emphasizing the training of students' comprehensive application ability, as well as focusing on scientific thinking methods, emphasizing the prominent role of important mathematical thinking methods and calculation tools, taking problem solving as the core, constructing a theoretical teaching mode of “target-driven, data analysis knowledge combined with practical application,” emphasizing the practical application background and application of course content according to the characteristics of close contact between course content and actual production and life. The course content is closely related to actual production and life, and the practical application background and scope of application are emphasized.


## 8. Conclusions

The new economy has put forward new requirements for talent cultivation. By studying the construction methods of emerging engineering majors, this thesis aims to explore how to effectively combine the talent demand in the construction links of emerging engineering majors, to carry out crossover and deep integration of related majors, and to cultivate new engineering and technology talents with high comprehensive quality to adapt to the development of industries. With Industry 4.0 and another round of scientific and technological reform and development, industrial reform and development, the latest economic development, and the escalating changes in the complexity of the upcoming problems faced by human beings worldwide, there is an urgent need for cross-compounding between disciplines, thus generating emerging engineering majors. In response to these situations, this paper analyzes the characteristics, necessity, and main problems faced by the construction and talent cultivation of emerging engineering majors with multidisciplinary cross-composite and discusses the new initiatives to build emerging engineering majors with multidisciplinary cross-composite and innovative talent cultivation, so as to encourage the cultivation of innovative talents that are brave to explore and innovate and have unique insights and to promote the innovation and breakthrough of economic, social, and scientific and technological development needs. This paper also explores the cross-fertilization of all major aspects of the construction of emerging engineering majors, taking mechanical and electronic engineering majors as an example. Through the research of this paper, we hope to provide some references for the construction of related new majors.



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