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Professional Construction and Talent Training Mode in the Era of Intelligent Internet of Things: Take Asset Appraisal Major as an Example
With the continuous development of Internet technology, mobile terminals are becoming more and more popular in people’s lives, and the unprecedented wave of informatization promotes the development of all aspects of people’s lives. The intelligent Internet of Things can not only bring people the information age but also allow learners to continuously learn and innovate online, breaking through the limitations of time and space. This paper aims to study how to make good use of the technology of intelligent Internet of Things to promote the development of professional construction and talent training mode in the era of intelligent Internet of Things. Based on the intelligent Internet of Things, this paper proposes data mining technology and an algorithm for association rules. The country should continue to innovate and research the mode of professional construction and personnel training. The experimental results of this paper show that, in 2015, there were about 106,000 asset appraisal practitioners and 34,000 Asset Appraisers in China. By 2019, there were about 121,000 asset appraisal practitioners and 41,000 Asset Appraisers in China. People and talents in the field of asset evaluation are in great need of intelligent IoT technology to improve the objectivity and accuracy of evaluation results, and the asset evaluation profession also needs intelligent IoT technology to innovate talent training models, and relevant personnel should continue to work hard on themselves to improve their own strength and continue to learn in combination with the intelligent Internet of Things.
As the trend of economic globalization becomes more and more obvious, the cooperation and exchanges between countries are getting closer and closer, and the division of labor is becoming more and more detailed. Traditional trade methods coexist with many new foreign trade methods. In the face of various new situations in the era of financial crisis, the traditional talent training model has become less and less suitable for the needs of the market and industry. How to deliver high-quality and compound talents to the society has become a new focus.
The Internet of Things refers to the connection of any object with the network through information sensing equipment and according to the agreed protocol, and the object exchanges and communicates information through the information transmission medium to realize intelligent identification, positioning, tracking, supervision, and other functions. Innovative education is carried out through the innovative talent training platform based on the intelligent Internet of Things. The use of the innovative talent training platform based on the intelligent Internet of Things will change the activities of learners, educators, and managers and improve the effect of education and teaching. It can provide new conditions and means for cultivating innovative talents and enrich the content of mobile learning research and application.
The innovations of this paper are as follows: (1) The theoretical knowledge of the intelligent Internet of Things and the talent training mode is introduced, and the intelligent Internet of Things is used to analyze how the interaction of the intelligent Internet of Things plays a role in the professional construction and talent training mode. (2) Data mining and association rules are expounded. Through experiments, it is found that the professional construction and personnel training mode based on the intelligent Internet of Things can promote the development of social economy.
2. Related Work
With the development of modern economy, the intelligent Internet of Things has also been widely used. Vijay S found that Internet of Things (IoT) devices have the ability to perform transmission control protocol communication on their own, which can handle part of the application logic. IoT, therefore, refers to a network of physical objects embedded with electronics, software, sensors, and connections to enable objects to exchange data with manufacturers, operators, and other connected devices. He expects the number of IoT devices to grow rapidly in the next few years. Although the scholar discovered the capabilities of IoT, he did not explain the importance of IoT . Deebak  found that the Internet of Things (IoT) leverages smart technologies to facilitate the design and development of smart city projects. It provides long-range communication between smart devices and application systems via low-power wide-area networks (LP-WAN) but fails to enable channelized access security. Therefore, he proposes a scheme for authentication and key management for smart IoT assistance systems. Although this scholar discovered that IoT has security problems and proposed a solution, he did not give a specific explanation of the solution . Tan et al.  found that, with the proliferation of 5G, sufficient interaction between Internet of Things (IoT) devices and cellular users will generate massive flows of cyber-physical information in real time. How to capture the insights behind this data in a smart city environment is receiving a lot of attention these days. They presented a highly functional metropolitan scene covered by multiple drones acting as caching edge computing nodes. Although the scholars introduced the large scene used, there is no experiment to prove the practicability of the scene . Liu et al.  found that wireless body sensing plays a crucial role in human-computer interaction environments, where human activities and even emotions can be recognized by computers. Each sensing technology has its own characteristics and advantages, making it suitable for specific application scenarios. For example, WiFi-based solutions can enable noninvasive human sensing, while RFID-based solutions can enable individual human sensing in multiperson scenarios. The scholars only roughly described the application of wireless human body sensing and did not describe it in detail . Razzaque et al.  discovered that the Internet of Things (IoT) envisions a future where digital and physical things can be connected through information and communication technologies to enable a range of applications and services. The nature of IoT, including hyperscale IoT, heterogeneity at the device and network level, and the large number of events that arise spontaneously from these things, will make developing diverse applications and services a very challenging task. Although they found the task to be very challenging, they did not introduce the challenges faced or give corresponding solutions . Lin et al.  found that edge computing has been proposed to be combined with IoT to enable computing service devices to be deployed at the edge of the network. It aims to improve the user’s experience. With the advantages of distributed architecture and closeness to end users, edge computing can provide faster response and higher quality of service for IoT applications. Although the scholars found that the combination of edge computing and the Internet of Things can enhance the user experience, they did not explain how to combine the two specifically . Dobkin  found few trials showing better treatments after stroke. Improvements in therapy have also not been as clinically robust as had been hoped. But, via the Internet, it is possible to remotely monitor the type, quantity, and quality of exercises in homes and communities and provide feedback to optimize training frequency, intensity, and progress at home. A suite of home-based rehabilitation Internet of Things (RIoT) devices could provide a theory-driven foundation for strengthening and fitness. RIoT may include wearable activity recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetition, velocity, accuracy, force, and spatiotemporal characteristics of movement. Although the scholar proposed a method to use the Internet of Things to improve the recovery rate of patients, the scholar did not have specific experiments to illustrate the feasibility of this method . Singh et al.  found that, to realize the broad vision of ubiquitous computing underpinned by the “Internet of Things” (IoT), application- and technology-based challenges must be broken. Working in IoT tends to be subsystems; it often focused on a specific technical problem or application area, before offloading data to the cloud. Therefore, this presents a problem of security risks. They focused on IoT security considerations from both the end user perspective and cloud provider perspective to identify security considerations that require further work. Although the scholars put forward the problem of security risks in the Internet of Things, they did not propose corresponding solutions .
3. Overview of the Data Mining Method Based on the Intelligent Internet of Things
3.1. Data Mining Technology Based on Intelligent Internet of Things
Data mining refers to the use of machine learning algorithms to obtain new knowledge from large amounts of data, which have the characteristics of large amounts of data and real data. The definition of data mining shows that, in practical applications, information with potential use value that people do not know in advance can be mined from a large amount of incomplete noise and fuzzy random data .
Data mining is more and more widely used in enterprises and institutions, and it has become a new economic asset. It is regarded as the minerals and oil of the new century, which has brought a new entrepreneurial direction, business model, and investment opportunities to the whole society. Data mining is the process by which researchers obtain valuable knowledge from large amounts of data, and it is defined as an important part of knowledge discovery. The implementation process of data mining is shown in Figure 1.
As shown in Figure 1, the realization process of data mining includes data cleaning, data integration, data selection, and data mining. The first four steps are the preprocessing stage of the data. Generally speaking, the implementation process of data mining is divided into several processes, such as determining the object of the mining task, data preprocessing, data mining, pattern evaluation, and knowledge expression .
3.1.1. Association Rule Algorithm
The confidence of the rule in the transaction set refers to the ratio of the item set including A and B to the item set including A, which is used to measure the credibility of the association rule . Association rules are not limited by the number of dependent variables and can find associations between data in large databases, so they are widely used. It is written down as follows:
Association rule mining is a very important research direction in the field of data mining. Through this technology, valuable data items can be mined from a large amount of data .
3.1.2. Perceptron Algorithm
The perceptron algorithm is an algorithm used to solve linearly separable problems. Its shortcoming is that it cannot solve nonlinearly separable problems. The following is an introduction to the perceptron algorithm . The perceptron is a binary classification algorithm, the input instance is a feature vector, and the instance category of the output instance has only two values of +1 and −1. Perceptron is a linear classification model, which is a kind of discriminant model. The structure diagram of the perceptron model is shown in Figure 2.
As shown in Figure 2, represents the value of the input sample on the i-th dimension, represents the weight connecting the perceptron and the input, , b represents the values calculated by the perceptron, and represents the threshold corresponding to the perceptron. Then the linear model represented by the perceptron model is as follows:
The training purpose of the perceptron model is to find the parameters, so that all categories can find a best hyperplane, so that all sample points are closest to the hyperplane . Consider the sample composition of the training set in the following formula:
In order to explain the algorithm simply, it is assumed that the algorithm is a binary classification problem, assuming that the positive class is +1 and the negative class is −1; then , assuming that the input through the perceptron model is , and the corresponding output is represented by . The correct classification can be expressed as follows:
Thus, the error is calculated by all the misclassified points in the model, and the error function is as follows:
The perceptron model has a good effect on linear classification, but the processing of simple nonlinear problems often fails to meet the requirements, and the algorithm cannot be iterative .
3.1.3. Gradient Descent
The gradient descent algorithm is a kind of convex optimization, and its core idea is as follows: the opposite direction of the gradient is the fastest direction in which the value of the convex function decreases. Through this idea, different values are generated for the continuous iteration of the function, which will eventually converge to a local optimal value . Consider its cost function as follows:
In applications, the loss function is usually associated with the optimization problem as a learning criterion. The cost function is the objective function used to find the optimal solution, which is also the role of the cost function. Among them, represents a piece of sample data in the training set, and n represents the total number of samples. Then it is easy to find the following formula:
The gradient descent algorithm learns like this until the algorithm converges or reaches the set number of iterations and finds the minimum value. Although the gradient descent algorithm is very good, there are still many problems . Gradient descent is widely used in machine learning, whether in linear regression or in logistic regression. Its main purpose is to find the minimum value of the objective function through iteration or to converge to the minimum value.
The stochastic gradient descent algorithm refers to randomly selecting a sample from the training dataset for learning each time; that is,
In the above formula, represents a randomly drawn sample. The batch gradient descent algorithm uses the entire training set each time, so these calculations are redundant because the same dataset is used each time. However, the disadvantage of the stochastic gradient descent algorithm is that each update of the simulation parameters does not follow the correct direction, so it may cause optimization fluctuations .
The AdaGrad algorithm uses a different learning rate for each different model parameter in each update process, and the gradient of the parameter of the objective function is ; that is,
The AdaGrad algorithm is to square the gradient of each iteration of each parameter and accumulate it and then divide the global learning rate by this number as a dynamic update of the learning rate. The main advantage of AdaGrad is that each parameter adapts to different learning rates. The disadvantage is that the sum of squares of the parameter gradient sequence must be calculated, and the learning efficiency continues to decline, eventually reaching a small value .
The Adam algorithm is also a method to determine gradient descent at different rates according to different parameters. The Adam algorithm obtains the advantages of the AdaGrad and RMSProp algorithms at the same time. Adam not only calculates the adaptive parameter learning rate based on the first-order moment mean like the RMSProp algorithm but also makes full use of the second-order moment mean of the gradient. Its calculation is shown as follows:where and are the weighted average and weighted biased variance of the gradient, respectively, and the bias correction for and is shown as follows:
Ultimately, Adam’s update formula is
The adaptive learning rates of Adam and AdaGrad are compared, and the effect is better .
3.1.4. Data Warehouse
A data warehouse is a strategic collection that provides all types of data support for decision-making processes at all levels of an enterprise. It is a single data store, created for analytical reporting and decision support purposes. The object to be classified is called a sample; for example, is a sample set. That is,
In the above formula, represents the data obtained by the i-th sample for the k-th indicator, and their average is calculated according to the following formula:
Then the standard deviation of these raw data is calculated as follows according to formula (14):
As a higher education institution, the central task is to cultivate high-level talents. Nowadays, the education informatization of various universities has made great progress . Although a large amount of educational data has been accumulated, it is rarely possible to analyze these data in detail with the research basis for training faculty members with data mining technology. The result of data mining and analysis in the process of talent training is that the daily management of talent training, the realization of database decision-making and management, and the construction and layout of database experts will be suitable for continuous tracking and evaluation of student training [22, 23].
3.2. Review of Talent Training Models in Foreign Universities
As a talent nation, American colleges and universities have always attached great importance to talent training. With the continuous development of the American economy and society, the curriculum system and talent innovation training of American colleges and universities have been in the process of reform and development. Their representative talent training models mainly include Harvard, MIT, and Columbia .(1)Harvard’s multitalent training model. Harvard University attaches great importance to cultivating students’ talents within and outside the discipline and sets up a diversified curriculum system. This will help guide students to establish a good self-concept, develop conscious study habits, absorb diverse knowledge, and give full play to their potential. This training mode can expand students’ vision of analyzing problems and provide students with a series of methods for understanding, analyzing, and solving problems in professional courses, and it can promote all-round development and innovation ability of human beings [25, 26].(2)MIT’s “dual system” talent training model. The “dual system” training mode not only means that students learn knowledge in school but also means that they also need to practice skills in enterprises. Among them, the training is in charge of vocational schools and enterprises. MIT attaches great importance to students’ hands-on ability.(3)Colombia’s extensive talent training model. The talent training model of Columbia University is characterized by compulsory education and interdisciplinary courses called “large-caliber training model based on general education.” Students can choose different levels of training packages according to their learning ability, learning status, and interest in learning. This talent training model promotes students’ innovative thinking mode, actively thinking about problems on the basis of general education, enabling students to have a wide range of knowledge and depth in and outside the field.
3.3. Professional Construction and Personnel Training of Asset Appraisal
The general purpose of asset appraisal or the basic purpose of asset appraisal is determined by the nature of asset appraisal and its basic functions. As a social intermediary activity for professionals to estimate and judge the value of assets at a specific time point and under specific conditions, the general purpose of asset evaluation can only be the fair value of assets at the time of evaluation. China’s asset appraisal industry has gradually developed in the process of the reform and opening up of the socialist market economy. As an independent professional market service industry, it has been recognized by the society and played an indispensable role. This article analyzes the number of appraisal practitioners and registered Asset Appraisers in China from 2015 to 2019, as shown in Table 1.
As shown in Table 1, China’s asset appraisal industry continues to grow, and the numbers of appraisal entrepreneurs and registered Asset Appraisers are also increasing, basically forming a talent team that meets the development needs of the appraisal industry. At the end of 2019, there were more than 100,000 Asset Appraisers and more than 40,000 registered Asset Appraisers in China. The asset appraisal talent training process is shown in Figure 3.
As shown in Figure 3, talent training in China’s asset appraisal industry has just started, and the training system is imperfect, resulting in uneven abilities, low overall level, and weak basic knowledge, making it difficult to adapt to the needs of the structure, appraisal industry, and social development. The problems that arise in evaluating the existing talent training system in the industry are shown in Figure 4.
As shown in Figure 4, the existing talent training system in China’s evaluation industry is not perfect, and there are some problems such as unclear training purposes, imperfect training methods, and unclear training objects. Therefore, the establishment of a new model must clarify the content and responsibilities of each component and adopt an effective adjustment mechanism that organically combines each component.
3.3.1. The Main Problems Existing in Talent Training in the Industry
Cultivating professionals with insufficient asset appraisal business limits the cultivation of high-end talents in the appraisal industry. It is not conducive to the establishment of a high-quality appraisal expert team and is not conducive to the healthy development of China’s asset appraisal industry. The low educational level of some asset appraisal industrialists is the main problem of the current appraisal team and the main obstacle in the process of talent training. The main obstacles in the talent training process are shown in Figure 5.
As shown in Figure 5, follow-up education is an educational activity for all members of society, especially adults, after school education and an important part of the lifelong learning system. Undoubtedly one of the best ways to achieve this is through follow-up education and training, and follow-up education should be standardized. As the initial intermediary service industry in China’s asset appraisal industry, in order to meet the needs of the development of the asset appraisal industry, it is necessary to continuously update knowledge and adjust the deviation from registered asset appraisal experts. One of the best ways to do this is through follow-up education and training.
3.3.2. Approaches to the Reform of the Talent Training Model
In order to enable students to integrate the learning of knowledge, attitudes, and skills, it is necessary to promote the combination of learning and work, implement cooperation between schools and enterprises, and implement the curriculum model of “open courses + courses + educational projects.” According to the professional characteristics, the educational model of practical training is constantly innovated. Scholars have put forward the talent training model of higher vocational education “teaching according to the position and integrating the curriculum certificate.” The integration of teaching by post and class certificate is shown in Figure 6.
As shown in Figure 6, taking the occupational standpoint as the starting point, the professional training process of various research projects is integrated. The job competency requirements are consistent with the purpose of professional training, and professional curriculum phases must be set. In order to achieve a high level of skills, a high-quality talent training model is opened.
(1) Clarify the Training Objectives and Accelerate the Improvement of the Professional Training System. According to the survey of talent training needs, Chinese enterprises currently have a low demand for students with secondary vocational education. Colleges must have a clear training purpose, mainly to cultivate practical international experts for enterprises. Specifically, it is to cultivate talents in the all-round development of morality, intelligence, and physique to meet the needs of enterprise development.
(2) Strengthen Practical Teaching and Establish an on-Campus Simulation Training Base. To cultivate applied asset evaluation talents, it is necessary to pay attention to the cultivation of students’ skills and quality, which is also in line with the teaching characteristics of secondary vocational schools. But, at present, the application forms of asset appraisal in various enterprises are various. Only relying on the simulation training platforms of the existing secondary vocational schools cannot effectively achieve the training effect, and it is far from enough, because these simulation training platforms are simple and incomplete. The simulation training platform is shown in Figure 7.
As shown in Figure 7, setting up a simulation company in a school can be combined with a simulation platform. By establishing a simulation company, it can improve students’ interest in learning and awareness of participating in the classroom, improve vocational skills and employment skills, enrich students’ cultural life after school, and cultivate students’ professional ethics. In addition, it can strengthen students’ social ability and organizational management ability and provide students with practical experience in society.
(3) Create a Workplace and Strengthen the Cultivation of Students’ Professional Quality. At present, the orientation of talent training in vocational schools needs to be further clarified, and the students trained lack skills and characteristics, which are still far from the requirements of enterprises. Graduates have to go through a long period of practice to adapt to their jobs, and the adaptation period is too long. At the same time, the content of students’ learning in school is very different from the actual requirements. Many graduates reflect that the knowledge they have learned in school cannot be used in actual positions, and the teaching content is seriously lagging behind. In addition, graduates have poor adaptability after transfer and are facing great reemployment pressure. All kinds of situations show that the current teaching mode and training method must be reformed, and, by reshaping the “student work site,” the training method is in line with the actual needs of enterprises and the development needs of students.
(4) Promote School-Enterprise Cooperation and Realize Seamless Connection of Talent Training. The “student work site” is close to the actual situation of the enterprise, creating an environment conducive to students’ skill learning and of course learning the strengths of the enterprise. The goal of talent development is to cultivate highly skilled personnel. Therefore, schools must create a good environment for students in terms of hardware equipment, curriculum, practical education, and so forth and increase students’ practical skills to improve their competitiveness.
4. Experiment and Analysis of Data Mining and Talent Training Mode
4.1. Performance Analysis of Adam Algorithm and AdaGrad Algorithm
This paper conducts experiments on the performance of Adam algorithm and AdaGrad algorithm. Since this experiment uses a multiclassification problem, the Softmax function is finally used as the basis for classification. In the process of designing the neural network, the number of neurons in each layer of 4 groups of experiments is designed as shown in Table 2.
As shown in Table 2, Softmax function, also known as normalized exponential function, is the generalization of the binary classification function sigmoid in multiclassification, and the purpose is to display the results of multiple classifications in the form of probability. Through optimization by different optimizers, the correct rate of classification results in the training process changes with the increase of the number of iteration steps as shown in Figure 8.
As shown in Figure 8, it can be seen from these figures that when the number of iteration steps is 10–100, the recognition accuracy rate of Adam algorithm reaches about 85%. The accuracy of the AdaGrad algorithm is about 75%. With the increase of the number of iterations, the correct rate of training continues to increase, and the increasing trend is basically caused by different optimization algorithms. It is found that the Adam algorithm is better than the AdaGrad algorithm in the training process.
When testing, the correct rate of the method using Adam gradient update is shown in Table 3.
As shown in Table 3, the correct rate of Adam algorithm increased from 50.67% at the beginning to 80.41% at the end, which increased by 29.74%. It is found that increasing the number of parameters makes the neural network model more prone to overfitting. If the number of parameters is too small, the model will be underfitted, and finally the Adam gradient update method is used.
4.2. Significance of the Talent Training Model in the Era of Intelligent Internet of Things
At present, with the rapid development of mobile Internet, the mobile phone market is booming. More and more information and data are transmitted and exchanged in society through these platforms. In such a development period with fierce competition, rapid social changes, and limited resources, as the source of national development, the importance of innovation is self-evident. With the development of the times and the advancement of technology, the training mode of innovative talents cannot be limited to the traditional teaching mode. Figure 9 shows the significance of the talent training model in the era of intelligent IoT.
As shown in Figure 9, the significance of the talent training model in the era of intelligent Internet of Things has the following points:(1)The use of the innovative talent training platform based on the mobile Internet will change the activities of learners, educators, and managers and improve the effect of education and teaching. The platform will enable learners to break through the limitations of time and space on the mobile learning platform. Through the mobile learning mode, the learning of innovative knowledge is completed, so as to effectively support the innovative activities of learners.(2)The use of innovative talent training platform based on mobile Internet will enrich the content of mobile learning research and application. Mobile learning, as a new learning mode that emerged in the 1990s, is in a stage of vigorous development with the progress of the times.(3)The innovative talent training platform based on the intelligent Internet of Things is learner-centered, which is convenient for learners to break the limitation of time and space and obtain learning resources. Its functions include the integration of system message sending, course learning, theoretical learning, and search and sharing of learning resources, which bring learners a more flexible and innovative learning experience.(4)The use of the innovative talent training platform based on the intelligent Internet of Things will provide new conditions and means for cultivating innovative talents. The research and development of innovative talent training mobile learning platform take advantage of the technological advantages of the mobile Internet. The high degree of temporal and spatial freedom of the mobile learning platform fills the gaps in the previous innovative talent training methods and provides teaching resources for innovative talent training.
In this era of rapid economic development, enterprises have higher and higher requirements for talents, so relevant personnel can only improve their own abilities through their own continuous progress to meet the talent needs of enterprises. Not only do they need to work hard but also relevant departments need to strengthen professional construction and the development of talent training models. The intelligent Internet of Things can bring unlimited knowledge to the relevant personnel and bring inspiration to the relevant departments to innovate the talent training model. This article elaborates on the training of asset evaluation talents. The method part of this paper is mainly based on the intelligent Internet of Things, and the association rules and perceptron algorithms in data mining technology are discussed in detail. Through the experimental analysis of this paper, it can be seen that, in order to strengthen professional construction and personnel training and improve professional ability, we must first further improve the review system of registered asset appraisers, improve the evaluation system, and improve the evaluation organization and management capabilities. This will play a role in selecting truly outstanding talents, and then a multilevel follow-up education and innovation system needs to be established and improved. Combined with the intelligent Internet of Things, it is very necessary to conduct research on professional construction and talent training mode.
This article does not cover data research. No data were used to support this study.
Conflicts of Interest
The author declares that there are no conflicts of interest.
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