Construction and Evaluation of Transformation Mode of the Achievements in Science and Technology in Colleges under the Background of Artificial Intelligence

Li, Zhibin; Xu, Yadan

doi:https://doi.org/10.1155/2022/9982871

Mathematical Problems in Engineering

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Abstract Introduction Materials and Methods Results and Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

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Robust Statistical Modeling and Machine Learning with Applications in Data Science

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Research Article | Open Access

Volume 2022 | Article ID 9982871 | https://doi.org/10.1155/2022/9982871

Construction and Evaluation of Transformation Mode of the Achievements in Science and Technology in Colleges under the Background of Artificial Intelligence

Zhibin Li¹and Yadan Xu¹

Academic Editor: Dost Muhammad Khan

Received11 Jan 2022

Revised03 Mar 2022

Accepted07 Mar 2022

Published21 Apr 2022

Abstract

The main objective of this work is to construct and evaluate the transformation mode of scientific and technological achievements in colleges and under the background of the wireless network for which an artificial intelligence backpropagation neural network (BPNN) model is utilized. Firstly, the work introduces the transformation mode of scientific and technological achievements. Secondly, the work establishes the evaluation system of the transformation mode of scientific and technological achievements in colleges and describes the steps of the fuzzy comprehensive evaluation (FCE) and analytic hierarchy process (AHP) in detail. Thirdly, it determines the weight value of the evaluation indexes of the transformation of scientific and technological achievements in colleges and conducts the consistency and fuzzy comprehensive evaluation of these evaluation indexes. Finally, to further verify the scientificity and practicality of the index system, an artificial intelligence BPNN model is implemented to further evaluate the system. It is evident from the results that FCE of the transformation level of scientific and technological achievements in colleges shows that the excellent credibility accounts for 32.73%, the good accounts for 53.24%, the medium accounts for 14.33%, and the poor is 0. The transformation of scientific and technological achievements in colleges is generally at the upper-medium level. The calculation of the weight of the indicators indicates that all the indicators established greatly impact the transformation mode of scientific and technological achievements in colleges. The results of the BPNN model show that the learning and prediction ability of the evaluation model has reached a high level, which verifies the scientificity and practicality of the evaluation index system. The exploration provides a reference basis for the construction and evaluation of the transformation model of scientific and technological achievements in colleges.

1. Introduction

The transformation from science and technology into productive forces forms a specific development model of the industrial chain, which has attracted the attention of various countries. The degree of the transformation gradually becomes an important symbol to measure a country’s economic development and progress [1]. With the rapid development of higher education, the scientific and technological work in colleges has gradually been improved from a few to the majority of colleges and universities. From the basic research, it gradually goes deep into applied research in various fields. Colleges have integrated many scientific and technological resources, such as researchers and projects, which have become powerful support for social and economic development [2]. The research on the development of science and technology in colleges not only promotes the socioeconomic and technological development of China but also improves the method of cultivating talents and their comprehensive quality [3].

With the increasing significance of cloud computing and mobile device interactions, the digital work environment is being perpetually transformed while forcing users to adapt to the emerging technologies and achievements in science and technology (AIS&T) are transformed into actual productivity [4]. In recent years, China has not only built many high-level research-oriented colleges aiming at training research talents but also made some achievements in the field of scientific research, many of which have been in line with international standards. However, according to statistics, 30% of AIS&T made by Chinese colleges are expected to be converted into productivity, while the actual conversion rate is only 15%. Therefore, how to increase the conversion rate of scientific and technological achievements has become one of the biggest focuses. Guan et al. [5] discussed the transformation mode of achievements in science and technology in colleges and universities in China. AIS&T is the mainstream of the national achievements in various transformation activities. However, the transformation of China’s achievements in science and technology is still in its infancy. After many years, China has given corresponding support, encouragement, guidance, and efforts to innovation and exploration based on a series of relevant policies issued by China. China has gained some experience in the transformation based on a series of relevant policies issued by China and achieved some economic and social benefits. Additionally, Zhang [6] sorted out the transformation technology of AIS&T and made a detailed analysis of the institutional factors restricting the transformation of AIS&T under the new circumstance. Some countermeasures and suggestions are given on constructing the transformation system of AIS&T from the aspects of exerting the market role, perfecting the right of transformation and disposal of AIS&T, improving the transformation efficiency of scientific and technological achievements, etc. Up to now, many colleges in China have established centers for the transformation of AIS&T, which shows that China attaches great importance to the transformation of AIS&T in colleges.

The main functions of colleges at present are scientific research, innovations, talent training, and serving society. These days, the economic competition has become more intense, which is the competition of talents in science and technology [7]. As the most important educational institute for China’s scientific and technological development, colleges have trained many talents in economic construction, which is essential in promoting the development of China’s national economy [8]. China’s colleges have developed rapidly recently. However, many problems have also been exposed, such as insufficient funds for science and technology and weak awareness of the personnel on the transformation of achievements. Although there are many achievements in science and technology every year, most of them still have not been transformed into actual productivity. The talents and scientific research work in colleges cannot effectively provide necessary services for the development of society and the economy, which affects the conversion rate of labour resources. Therefore, it is very necessary to construct a set of very suitable models for the transformation of scientific and technological achievements in colleges. Based on artificial intelligence, FCE and AHP are used to construct and evaluate the transformation mode of AIS&T in colleges. 10 provinces and cities with many colleges are selected for comparative analysis, providing power for the innovation of the transformation mode of AIS&T in colleges in the future.

2. Materials and Methods

2.1. Transformation Mode of AIS&T in Colleges

The transformation of AIS&T is a subsystem of social science and technology by using its resources for improving productivity in scientific research and technological development. It not only has many commonalities of ordinary scientific and technological achievements but also has many special properties [9]. Among them, the most obvious feature is the effective combination of university teaching and research, which is determined by the development law of higher education and its function. The transformation of AIS&T in colleges cannot be separated from the purpose of teaching and scientific research, whether it revolves around the teaching process and research or directly serves society.

The particularity of the transformation of AIS&T in colleges lies in the complementarity between its value and function. The task of colleges is not only to train talents but also to conduct scientific research, discover and solve problems, and improve efficiency. Colleges should create new knowledge, and AIS&T solved practical problems existing in society and serve today’s society, politics, economy, and culture. These factors determine that higher education is not the teaching isolated from college but must have these additional educational values. The transformation of AIS&T in colleges is a multitask and multiactivity work with many characteristics. The scientific and technological development project is fully supported by the government at all levels, which provides support for the progress of science and technology and economic construction and development. Undertaking high-tech development cooperation entrusted by factories, enterprises, and various projects is the traditional process of developing new products through technological innovation and providing services for the development of enterprises, which has become the inevitable requirement of higher education reform. The transformation activities of AIS&T in colleges have the characteristics of multilevel and multiforms, which make the management more complex, which is embodied in the flexibility and diversity of the management system. Management mechanism teaching, research, and transformation of AIS&T are different types of work in educational institutions. Colleges have a single management mode and single evaluation standard, which makes it difficult to effectively manage them. If colleges desire organizing and coordinating the scientific and technological development activities effectively, they must establish different types of transformation modes of scientific and technological achievements. Scientific research objects of the transformation of scientific and technological achievements are full-time and part-time personnel from diverse levels. In order to ensure the smooth progress of the transformation of scientific and technological achievements, colleges must handle the relationship with government departments, social organizations, and enterprises. The evaluation of investment benefit has various aspects, and that of the transformation efficiency of AIS&T is also the investor’s understanding of the transformation process of AIS&T. However, there are different types of investors in the transformation of AIS&T in colleges, and the investors include government, enterprises, social organizations, and individuals. Different investors have different expectations of input and demand. Meanwhile, there are also differences in the evaluation of output, but its expression forms are different due to the development of science and technology.

2.2. Fuzzy Comprehensive Evaluation (FCE) Method

The comprehensive evaluation method mainly comes from the previous fuzzy mathematics, which regards fuzzy objects and concepts reflecting fuzzy objects as fuzzy sets, constructs appropriate fuzzy membership functions, and then carries out a series of quantitative analyses on fuzzy objects [10, 11]. The Summarization of the characteristics of the FCE method mainly includes the following points:(1)The FCE method can carry out the multilevel evaluation, and this evaluation process can be repeated [12]. For the comprehensive evaluation results of the previous process, the previous results can be used as the input data basis of FCE in the subsequent evaluation process. Namely, for a relatively complicated evaluation object, previous results can be used for both single-level FCE and multilevel FCE.(2)Weight treatment of evaluation index: in the process of FCE, the weight coefficient vector of indicators appearing in the process is estimated by people, but it is a fuzzy vector, which is not produced with the process of FCE [13]. Because of the nature of the FCE method, the evaluation result of it is only a vector set. Therefore, the evaluation result is not a specific value, but a fuzzy vector set. Meanwhile, compared with the object to be evaluated, the evaluation result is unique [14].(3)The establishment of the evaluation hierarchy: usually, in the FCE method, a comment level domain is often set, and the meaning of each level set must be clear [15]. The evaluation process by the FCE method is as follows:

Parameter P is supposed to be the number of evaluation indexes. Then, the evaluation index U of the confused comprehensive evaluation object can be expressed as

The evaluation set V can be stated as

The fuzzy membership matrix R can be described as

In (3), u_i (i = 1, 2, …, p) stands for every factor of the evaluation object, = (r_i1, r_i2, …, r_im) refers to the fuzzy vector, and r_ij in row I and column j of matrix R represents the membership degree of the object to be evaluated to the hierarchical fuzzy subset.

The weight vector K of the evaluation factor is stated as

Among them, the element a_i (i = 1, 2, …, p) in the weight vector k is the subordinating degree of factors to the subset of fuzzy.

The evaluation result vector S is obtained by appropriately weighting the weight vector K and the membership matrix R of each evaluation object [16].

In equation (5), b₁ is obtained from the j^th column of R and K through operation, which means the membership degree of the object is to be evaluated to the fuzzy subset of the hierarchy as a whole.

Figure 1 lists the evaluation steps of the FCE method.

2.3. Analytic Hierarchy Process (AHP)

Analytical Hierarchy Process (AHP), first introduced by Saaty in 1980, is a systematic approach to solving complex and multilevel decision-making problems. Initially, the basic principle of AHP is to deal with complex problems of large systems and then to analyze and study these elements one by one, to classify the interactions among these elements according to the hierarchy [17]. Next, some relevant experts are needed to judge or score the influencing factors at each level and give the importance of the relative role of each factor in quantitative expression [18]. Then, based on the above steps, a related mathematical model is implemented, the calculation is made on the importance weights of various factors included in each level, and the weights are sorted. Finally, according to the results of the above sorting, the weight that has been determined is judged, so that the solution to the problem can be accurately determined. Among them, the characteristics of AHP are as follows:(1)Simple and easy to understand. It is necessary to input information when the decision is made, and this information is closely related to the decision-maker’s choice and judgment. Decision-makers can improve the scientific and quick effectiveness of decision-making [19]. The whole process of this decision-making can completely show the decision-maker’s deep understanding of the problem to be solved. This is a simple and practical decision-making method, so in many cases, when solving the problem, decision-makers can make decisions directly on the problems to be solved, without unnecessary processes.(2)Flexibility and practicality. AHP can both quantitatively and qualitatively analyze problems. This method makes full use of experience and decision-making and measures the nonquantitative factors, intangible factors, and tangible factors in decision-making by using the relative measurement method, and the decision-making process can also be analyzed [20]. AHP corrects the traditional impression that optimization methods can only deal with quantitative problems, which has been usually used in resource allocation, system analysis, and program evaluation.(3)Systematic. Decision-making methods can be divided into three types: one is to consider the problem as a whole, so that when it is necessary to make decisions on the problem, it can be studied on the basis of the research environment and the relationship among the various components of the system. Another is the so-called causal reasoning. In most cases, it is a simple decision to deal with problems; meanwhile, causality is a relatively simple method itself, which constitutes the thinking basis for judging and selecting problems in daily life. When solving complex problems, there will be the other effective method, that is, the decision thinking method, which is an effective method for solving complex problems. Because the system has an extensive hierarchy, this type of system can be extended to more complex systems [21, 22]. Figure 2 signifies the steps of AHP modelling.(4)Establishing hierarchical structure model: When the AHP is used to analyze problems, the first step is to stratify the problems, so that the established structure model can look more hierarchical and help to analyze problems well. By establishing this hierarchical model, the initially complicated problems will be layered and divided into a set containing many hierarchical factors [23]. The factors contained in the upper level will become the guidelines and then will affect the related factors in the later levels. Generally, these levels fall into three categories: the high level, the middle level, and the low level [24]. Figure 3 displays the specific structure.(5)Constructing judgment matrix: After the hierarchical structure model is implemented, it is necessary to compare the factors contained in each layer, specifically, according to the importance. The comparison should be made on each factor corresponding to each layer with the factors contained in the previous layer. Then the required judgment matrix should be constructed. The values corresponding to the elements in the established judgment matrix are the direct evaluation results of the relative importance of each factor in the problem to be solved [25]. Generally, 1-9 is used to indicate the degree of evaluation. And Figure 4 shows the specific meaning of each number. Two random indexes at the same level are represented by A_i and A_j. A_ij represents the comparison judgment value between A_i and A_i, and a_ji represents the comparison judgment value between A_j and A_i.(6)Single ranking and consistency test of hierarchical indicators: Suppose the consistency index to be C.I (Consistency Index); then:

In (6), stands for the largest eigenvalue and n means the order of the matrix.

Equation (7) is used for calculating the consistency ratio C.R (consistency ratio).

In equation (7), C.I represents the consistency index, and R.I (Random Index) is the consistency index corresponding to different orders of matrix “n”. The corresponding R.I values are found in Table 1 lists. Generally, when C.R is less than 0.1, the judgment matrix is considered to be a consistent matrix.(7)Overall ranking and consistency check of hierarchy indicators: It is assumed that consistency check has been carried out for each judgment matrix that compares the factors related to A_j in the previous criterion layer B, and it is also known that the consistency hierarchy indicators related to single ranking are C.I (j), (j = 1, 2, …, m), and the corresponding average randomly selected consistency indicators are R.I. Then the random consistency ratio of the final total ranking of the hierarchy is

When C.R is less than 0.1, it is considered that the overall ranking result of hierarchy has satisfactory consistency and the analysis result is acceptable.

2.4. Construction of Transformation Mode of AIS&T in Colleges Based on FCE Method

The evaluation system is implemented for the transformation of AIS&T in colleges, and this system is used to stimulate scientific research in colleges, combine AIS&T with market benefits, and take scientific research as a breakthrough to realize discipline development and enhance the comprehensive strength of disciplines. Through the implementation of the evaluation index system, colleges can enhance foreign exchanges and cooperation by relying on scientific and technological strength. A platform should be built for sharing information, technology, and capital between the government and enterprises to optimize scientific research on resource allocation and adjust the heavy work that is not conducive to the transformation so that colleges can get high-return investment in scientific research. Figure 5 shows the evaluation index system for the transformation of AIS&T in colleges.

2.5. The Fuzzy Evaluation Method Used to Evaluate the Transformation of AIS&T in Colleges

In order to further improve the level of the research in colleges and realize the transformation of scientific research achievements, a comprehensive evaluation is made on the transformation of AIS&T in colleges by using the FCE method. Due to the rapid development of this method, it is relatively flexible and practical, which has been favoured by many scholars globally. The scope of its application is gradually expanded, and it has the greatest advantage as it considers not only the complexity of the internal relations between objective things but also the fuzziness of the value system, according to the actual situation of the transformation of AIS&T in colleges and the specific classification of the evaluation system. Therefore, the evaluation system is divided into four grades, presented in Figure 6.

The above four evaluation grade elements are outstanding (V1), good (V2), moderate (V3), and poor (V4), which constitute the grade set V for evaluating the scientific research competitiveness of colleges.

3. Results and Discussion

3.1. Consistency Test of the Evaluation Indexes for Transformation of AIS&T in Colleges

According to the five index layers of the research (B1), applied research (B2), experimental development (B3), achievement transformation (B4), and achievement industrialization (B5), the consistency test is conducted. Figure 7 shows the calculated results.

Figure 7 reveals that the C.R values of index layers under B1-B5 are 0.061, 0.089, 0.047, 0.038, and 0.071, which are all smaller than 0.1. All index layers have passed the consistency test, so this weight can be adopted.

3.2. Weight and FCE of Evaluation Indexes for Transformation of AIS&T in Colleges

The evaluation index weights of the transformation of AIS&T in colleges are calculated, and the results are listed in Table 2.

Table 2 shows that the weight of academic value (C2) is the highest, with a value of 0.51, followed by the transformation rate of AIS&T in colleges (C20) with a value of 0.46, and the weight values of the three indicators, namely, the number of incubators in science parks (C13), the scale of industrialization of AIS&T (C21), and the actual income from the industrialization of AIS&T (C22), are all 0.36. This indicates that these indicators have a great influence on the transformation mode of AIS&T in colleges, and the weight value of patent transfer contract (C17) is the smallest, only 0.06, which shows that this indicator is relatively less important for the transformation mode of AIS&T in colleges.

The fuzzy evaluation matrix S is calculated through the above steps of FCE and normalized, and the final result is S = (0.3273 0.5324 0.1433 0). This develops that FCE on the transformation level of AIS&T in colleges includes 32.73% of excellent reliability, 53.24% of good reliability, 14.33% of medium reliability, and 0 of bad reliability. It is proved that the transformation of AIS&T in colleges is generally at the upper-middle level.

3.3. Evaluation Value of Transformation of AIS&T in Colleges in Each Province Yearly

The provinces and municipalities followed by the central government with a large number of colleges in China are taken as the research samples, namely, Beijing, Tianjin, Jiangsu, Zhejiang, Shaanxi, Sichuan, Anhui, Hunan, Shandong, and Henan provinces. 10 provinces and municipalities are numbered from 1 to 10 in turn. It is calculated that the transformation ability of AIS&T of colleges in 10 provinces and cities from 2018 to 2020 belongs to the subordinating degree of “excellent” mode. Figure 8 presents the calculation results.

Figure 8 reveals that the evaluation value of the transformation of AIS&T in Beijing (1) and Jiangsu (3) is higher than that in other provinces and cities, and the evaluation value of the transformation of university AIS&T in Beijing (1) is the highest, increasing from 0.9931 in 2018 to 0.9982 in 2020. The evaluation value of the transformation of AIS&T in colleges like Sichuan Province (6) and Anhui Province (7) obviously decreases with the increase of time.

4. Conclusion

Combining the FCE method with AHP, on the one hand, the accuracy of index weight calculation is improved; on the other hand, the calculated index weight is more scientific, and the most important point is that it provides an effective method for measuring the weight of the evaluation index accurately. Aiming at the evaluation index construction of the transformation, AHP is used to calculate the weight of each index, and the fuzzy evaluation method is also used to evaluate the transformation mode of AIS&T to form the evaluation system. The results show that the weight of the academic value (C2) is the highest, which is 0.51. These indexes have a great influence on the transformation mode of AIS&T in colleges and universities. FCE of the transformation level of AIS&T in colleges shows that the transformation of AIS&T in colleges is generally at the upper-middle level. A foundation is made for the evaluation of the transformation of AIS&T in colleges and a way provided to improve the transformation efficiency of AIS&T and promote the scientific and effective development of material productivity. However, there are still some shortcomings because of the limited time. There is no comparison between AIS&T and those in all provinces and cities in China, which may affect the research results to some extent.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

This work was supported by Basic and Applied Basic Research Project of Guangzhou Basic Research Plan in 2021, Research on Big Data Sharing Technology of Network Threat Intelligence Based on Ontology (Project no. 202102080243).

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Copyright © 2022 Zhibin Li and Yadan Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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