Dynamic Mechanism of Technology Innovation in Enterprise Based on Edge Cloud Computing Technology

doi:10.21203/rs.3.rs-2221206/v1

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Dynamic Mechanism of Technology Innovation in Enterprise Based on Edge Cloud Computing Technology

https://doi.org/10.21203/rs.3.rs-2221206/v1

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With the rapid expansion of artificial intelligence and information technology, improving the technological innovation ability of enterprises is an important means to enhance their competitiveness in the capital market. As a vital driving force leading economic development, enterprise technological innovation can further promote the improvement of production efficiency and change the mode of economic growth while promoting the liberalization of international trade. In addition, the neural network model in the field of deep learning (DL) has significant application value in guiding the flow of enterprise funds and innovation. Therefore, according to the Back Propagation neural network (BPNN) model in DL, the problems of enterprise technological innovation and industrial structure reform from the theoretical level are explored. And each constituent element of the enterprise's technological innovation and dynamic mechanism, and the interaction between the elements are deeply studied. At the same time, based on the neural network model, through the deployment and architecture of the edge computing server, the delay of network data transmission is reduced. Through the analysis of different business scenarios of edge cloud computing platforms, the service resources of the base station management platform are optimized, and the transmission speed of the network data of the enterprise communication data system is greatly improved. Since the incentive mechanism model involves government, market, industry, and school, the realization path of the incentive mechanism model is discussed based on these four factors to realize the effective operation of the incentive mechanism. The results manifest that the government and market factors can promote the technological innovation and reform of enterprises, while the industry and school factors have no significant effect on the innovation of enterprises. The research has practical application value for promoting the economic development and technological innovation reform of enterprises. Moreover, the research has a guiding significance for the application of edge cloud computing technology in enterprise technology innovation.

Edge cloud computing technology

Technological innovation

BP neural network

Industrial structure

Dynamic mechanism

With the continuous growth of economic globalization, technological innovation has become a decisive factor in the economic development of enterprises, regions, and even countries [1 ~ 3]. Since the 20th century, all countries and regions in the world have attached great importance to and strengthened technological innovation, which has penetrated into all fields of social life. At present, China's economy is in an important transition period. The central government emphasized that it is necessary to improve the ability of independent innovation, build an innovative country, and take the road of independent innovation with Chinese characteristics. Therefore, to achieve rapid and stable economic progress, the pace of technological innovation must be accelerated. Today, the scale of network computing data is so large that the network cannot support so many large traffic device connections. In addition, returning large amounts of data can cause system congestion and disconnected connections, making it more difficult for the system to support new delay-sensitive applications. Cloud computing is a new mode of storage and system computing. Through edge data processing close to the user network, some user data can be processed on the edge cloud platform without switching to the core network. This not only reduces the overall feedback pressure on the network but also reduces the client latency. Therefore, the implementation of the cloud edge platform can be regarded as the main solution to solve network transmission and computing pressure in the future.

However, when faced with the pressure of rising costs and intensified competition, most companies are still interested in low-cost expansion and competitive strategies in the game between technological innovation and conservatism, and companies with independent innovation are less involved [4]. After the emergence of new technologies, due to the objective existence of uncertain factors such as the marketization of technological innovation and cost balance, enterprises stagnate in this field and are only satisfied with imitating or introducing patents [5, 6].

To enhance market competitiveness and promote the rapid growth of the regional economy, technological innovation and economic system reform within the enterprise has become a momentous way to achieve it. As a vital driving force for leading economic development, enterprise technological innovation can further promote the improvement of production efficiency and the transformation of economic growth mode. Therefore, based on the Back Propagation neural network (BPNN) model in deep learning (DL), first, it analyzes the problems of enterprise technological innovation and industrial structure change from the theoretical level. Then, the model is established by the BPNN principle, and its constituent elements and dynamic mechanisms, as well as the interaction between the elements, are explored in detail. The research has practical application value for promoting economic development and the reform of enterprises’ technological innovation.

2.1 DL model and enterprise technological innovation

Regarding enterprise technological innovation and DL-related models, Wang et al. (2018) [7] conducted research on identity recognition technology by DL technology, and studied face recognition algorithms in the field of computer vision. The results show that common frameworks for face recognition include person feature extraction and matching. The combination of diverse feature extraction methods and matching strategies will produce various effects. However, if the extracted features are not discriminative, the final matching result will suffer. Zhao et al. (2019) [8] studied the application of DL in audio generation with the aim of reviewing and introducing advanced DL techniques and their effectiveness in speech/acoustic signal processing in a timely manner. A thorough study of various DL architectures was provided under the categories of discriminative and generative algorithms, including state-of-the-art generative adversarial networks (GAN) as ensemble models. It can provide valuable reference material for practitioners of acoustic signal generation. Cho et al. (2019) [9] investigated DL-inspired self-evolving computational material models and proposed information indexing and linking functions, which are crucial for injecting physical principles into machine learning (ML). The proposed framework showed that the fusion of information indices, link functions, evolutionary algorithms, and Bayesian update schemes can produce self-evolving computational material models. Gu et al. (2018) [10] analyzed the efficiency evaluation model of industrial enterprise technological innovation, and proposed an evaluation method based on data and judgment to rank the technological innovation capabilities and efficiency of Chinese enterprises of various sizes. The genetic algorithm is used as the solution method to solve the optimization model, and the simulation is carried out to verify the model and the algorithm. The findings denote that the proposed research framework can be applied to governments in many countries to better allocate resources for technological innovation and development.

In addition, many scholars have conducted research on the related applications of ML algorithms in enterprises. Lee et al. (2020) [11] explored the applications and challenges of ML algorithms. Three uses of ML in the enterprise are presented by discussing its categories. This research has vital reference value for reducing product service costs and speeding up business processes. Borowski (2021) [12] studied the innovation process of enterprise management in the energy and food industries, and analyzed the problems related to the production process in the learning organization through the survey and observation of these enterprises. The combination of production machines and digital technology enables the entire system, including employees, to efficiently communicate information related to each other at every stage of production. It has practical application value for the innovation and intelligent reform of enterprise organizational structure. Liu et al. (2021) [13] studied the innovation process and innovation driving force by the autocatalytic network based on the network resource fusion algorithm of ML. It indicated that the innovation network is an autocatalytic network, and the key to innovation lies in getting some key influencing factors in the core. Zhang et al. (2022) [14] constructed a computer model of enterprise green innovation by using a neural network (NN) algorithm. First, the elements and relationships of green technology innovation are analyzed and explained. Then, the theory of complex adaptive systems is introduced. The research results demonstrated that the proposed algorithm has important practical value for enterprise performance evaluation. Bao et al. (2022) [15] investigated the multi-agent knowledge integration process of enterprise management innovation from the perspective of NN, and analyzed the incentive relationship of knowledge transfer in the innovation process by the innovation mechanism of multi-agent structure. The research starts from the overall structure, on the basis of domestic and international knowledge flow management and control technology, knowledge exchange under social network, NN optimization algorithm, etc., the regional innovation network is analyzed from a new perspective, which has practical reference value for optimizing the connection between different subjects.

2.2 Research on the reform of enterprise organization structure and the innovation of dynamic mechanism

Many scholars have conducted relevant research on the organizational structure and innovation system of enterprises. Jia et al. (2020) [16] analyzed China's national innovation system. The results express that a top-down, government-oriented R&D system with a focus on large SOEs leaves little room for innovation policy by small and medium-sized enterprises (SMEs). Yanqiu et al. (2021) [17] studied the dynamic capability evolution model of the interaction between strategic orientation and organizational structure. From the perspective of process, it selects Haier Group as the research object based on contingency theory, and builds a model of interaction between strategic orientation and organizational structure. The findings indicate that the growth of enterprises will go through three different stages of scale expansion, technological innovation and sharing and opening, during which the environmental changes will prompt the focus of dynamic capabilities to change. Aboud et al. (2022) [18] investigated the characteristics and financial performance of China's split share structure reform, and examined the impact of China's recent share structure reform on the relationship between corporate ownership characteristics and corporate performance. It means that institutional reforms in China's stock market benefit investors by improving corporate financial performance. Yannan et al. (2018) [19] studied the game mechanism of corporate pollution control and trading policies, and it repositioned the economic benefits of enterprises and the social and environmental benefits of the government. This research can provide a reference for the reform of enterprise organizational structure.

Furthermore, for the risk assessment model of enterprise dynamics, Yuan et al. (2019) [20] explored the risk assessment model of enterprise comprehensive dynamics. By the enterprise hologram framework of big data language, it adopts the platform of dynamic fusion data as a tool and mechanism to describe the dynamic evolution of SMEs. It refers that the proposed risk assessment framework can provide a new solution for solving the long-standing credit risk assessment problem of SMEs. Chu et al. (2021) [21] proposed an evolutionary game-based policy simulation method to study the policy mechanism in air pollution control. The effectiveness of air pollution mechanisms involving multiple policy instruments is validated by conducting simulation studies. It displays that the proper coordination of these policy tools can solve the relationship between the government and enterprises, and has practical application value for realizing the innovation of the dynamic mechanism and the reform of the organizational structure of enterprises. Wang et al. (2021) [22] analyzed the dynamic customization model based on online communities, and proposed a dynamic customization model by mathematical online communities. According to a multi-objective optimization model, a trade-off challenge is formulated to optimize the balance between customer satisfaction and business profit. The proposed model can help researchers and practitioners to decide on the mode of cooperation with manufacturers. To sum up, enterprises can use the advantages of existing industrial space resources to carry out reform of the organizational structure and dynamic innovation, which can not only enhance the competitiveness of the enterprise itself, but also enhance the vitality and institutional innovation within the enterprise. It has significant advantages.

3.1 BPNN and enterprise technological innovation

The fully connected neural network is the simplest NN in DL [23, 24]. It can include multiple hidden layers, and each hidden layer can also include multiple neurons. It belongs to the feedforward neural network (FNN), the signal propagates in one direction, and there is no loop in the network. For the analysis of BPNN, it can be found that this is a multi-layer FNN, and its main characteristics are that the signal is forward-propagated, and the error is back-propagated. In the process of data transmission of BPNN, the first stage is the forward propagation of the signal, from the input layer to the hidden layer, and finally to the output layer. The second stage is the back propagation of the error, from the output layer to the hidden layer, and finally to the input layer, adjusting the weights and biases from the hidden layer to the output layer in turn, and from the input layer to the hidden layer. In BPNN, the calculation of the input vector X of the input layer, the output vector Y of the hidden layer and the output vector O of the output layer are expressed in equations (1) to (3) respectively:

$$\text{X}={\left[{x}_{1},{x}_{2},\dots ,{x}_{i},\dots ,{x}_{n}\right]}^{T}$$

$$\text{Y}={\left[{y}_{1},{y}_{2},{y}_{3},\dots ,{y}_{j},\dots ,{y}_{p}\right]}^{T}$$

$$\text{O}={\left[{o}_{1},{o}_{2},{o}_{3},\dots ,{o}_{k},\dots ,{o}_{q}\right]}^{T}$$

$n,p, \text{a}\text{n}\text{d} q$ represent the number of neurons in the input layer, hidden layer and output layer respectively. For the data ${O}_{k}$ of the output layer node, the calculation is as follows:

$${O}_{k}={f}_{2}\left(\sum _{j=1}^{p} {w}_{k}{Y}_{j}-{b}_{k}\right)$$

${w}_{k}$ means the output data of the hidden layer node, and ${Y}_{j}$ refers to the feedback error signal of the output layer unit. ${b}_{k}$ stands for the threshold of the network structure, and ${f}_{2}$ signifies the linear function of the upper layer input of the model.

The enterprise technological innovation method is studied using the DL algorithm. In the process of its reform, management is the human capital for the long-term development of an enterprise, and an important promoter to promote the blossom of new ideas and increase investment in technological innovation. In R&D activities, the government must not only understand market trends and consumer demands, but also monitor these activities, while bearing the consequences of R&D failures and the products entering the market. For the analysis of the structural network of enterprise technological innovation, the results are exhibited in Fig. 1:

3.2 Implementation of the dynamic mechanism model of enterprise technological innovation

The technological innovation capability of an enterprise is the foundation of its guarantee. At the same time, it is also the embodiment of enterprise technological innovation [25]. It includes innovative investment, innovative product productivity, and innovative product sales capabilities. The degree of support of enterprise technological innovation to promote the development of innovation activities directly affects the operation of its dynamic mechanism. In addition, market demand generates profits and drives the development of profitable markets. The government supports enterprises to increase investment in innovation, technology development, and modernization, and increase the production capacity of innovative products. With the successful sale of these products, the company has achieved excess profits. Profits intensify market competition, and market competition affects profit distribution.

Therefore, the operation mode of the dynamic mechanism of enterprise technological innovation can be summarized as, under the influence of external environmental factors, the external driving force (market demand driving force, market competition pressure, technology driving force, and government support force) directly or indirectly becomes its power source and supporting force around the profit-driving force of the enterprise. The innovation consciousness of enterprises can induce and expand this driving force. This kind of profit sensitivity can directly encourage enterprises to participate in technological innovation, or indirectly encourage scientific and technological personnel to participate in it through enterprise culture and internal incentive mechanisms. The technological innovation capability of the enterprise ensures the smooth progress of innovation activities. The latest technological innovation of the enterprise successfully feeds back its dynamic mechanism, stimulates new demands for innovation, and guides new activities. Figure 2 denotes the structure of the implemented innovation dynamic mechanism model:

3.3 Enterprise's innovation dynamic mechanism and technological reform

The structural analysis of the system is the basis of the research. As a system, the dynamic mechanism is composed of different subsystems in a certain form. It is characterized by the way that various elements are related to their own structural characteristics. The way enterprises participate in the reform of the incentive mechanism and technological innovation is not only the premise of the research, but also plays a significant guiding role in the whole research. The dynamic mechanism of enterprise shareholding itself is an extremely complex system and a complete whole. Hence, to analyze its internal operation law more comprehensively, it is divided into endogenous and exogenous dynamic systems. Among them, the endogenous dynamic system is an abstract concept on the basis of the interests of enterprises, and it is an organic combination that promotes enterprises to participate in the reform of organizational structure. As a for-profit organization, an enterprise must develop continuously under the influence of the market environment. It is an inevitable trend to generate demand in the process of domestic development. When a company's needs cannot be met through internal behavior, it seeks external help. Participating in the reform of the internal structure has become one of the means and channels to satisfy the demand. It can be seen that the driving force of the endogenous dynamic system comes from the practice of enterprise interest conversion. The essence of this process is the interaction and mutual promotion of many elements of the endogenous dynamic system. The analysis results of the influencing factors of the evaluation of enterprise innovation value are demonstrated in Fig. 3:

3.4 Analysis of edge cloud computing technology

In mobile networks, due to random changes in base station resource requirements, the relationship between the real world and the dynamic world tends to be complicated, and the deployment structure of edge cloud computing platforms will be constantly optimized. In order to solve the resource optimization problem of mobile computing platforms, a server implementation model based on uncertainty scheduling theory is implemented. As an overall optimization problem of mobile server resources with basic computing requirements, the real-time unification of network functions, storage resources, and users can be realized by using network devices. Edge cloud computing platforms are ubiquitous data reception centers that identify and process data by analyzing and interpreting it in the background. By implementing and coordinating solutions, incentives for technological innovation in enterprises can be effectively strengthened.

4.1 Enterprise's target market value and evaluation predicted value

The target market value and evaluation predicted value of the enterprise organizational structure are analyzed, and the results are illustrated in Figs. 4 and 5:

In Fig. 4, when the number of model iterations is 100, enterprise’s target market value of the traditional algorithm model is 7150. After optimization by the DL algorithm, the target market value can reach 8050. Compared with the original model, the market value has increased by 900. Moreover, with the increase in the number of model iterations, after 600 iterations of the system, the target market value of the enterprise using the traditional model is increased to 8750, while the DL algorithm-based value can be increased to 9750. It can be found that after using this algorithm to optimize the enterprise structure, the target market value of the same enterprise can be increased by up to 1000, indicating that the DL algorithm can fully tap the potential of the enterprise's target value.

Figure 5 refers that after 100 iterations of the model, the predicted average value of the traditional system is 6750, while the predicated value of the DL-based enterprise organizational structure system can reach 7750, which is greatly improved compared to the traditional algorithm. Besides, after 500 iterations of the model, the predicted values of the enterprise of both the traditional algorithm and the DL algorithm have reached their peaks. The predicted value of the traditional algorithm is 8500, while the predicted value of the organizational structure by the DL algorithm is 9750. Therefore, it means that the prediction performance of the model implemented by the DL algorithm for the organizational structure and performance innovation of the enterprise is better than that of the traditional algorithm.

4.2 Error analysis of the organizational structure and dynamic mechanism of enterprises

After sorting out the data of the enterprise's organizational structure innovation and the reform of dynamic mechanism, the error analysis data are displayed in Figs. 6 and 7.

Figure 6 indicates that the absolute error of the organizational structure of traditional enterprises decreases continuously with the increase of model epochs. When the number of epochs of the model is 0, the absolute error of the organizational structure of the enterprise is 6.2, and when the number of epochs reaches 3500, its absolute error has been reduced to 4.5. Moreover, the relative error of the organizational structure of the model increases as the number of epochs increases. When the epochs of the model are 0, the relative error of the organizational structure of the enterprise is 3.25, and after epochs reach 3500, its relative error has increased to 3.6. The results show that the growth of the number of epochs of the model is not obvious for the optimization of the organizational structure of the enterprise.

Figure 7 displays that in the reform of the dynamic mechanism of enterprise, the basic framework of enterprise technological innovation is built on the basis of the DL algorithm. Under the same number of epochs, the dynamic mechanism of the enterprise can achieve a better innovation effect. When the epochs of the model are 0, the absolute error of the dynamic mechanism of the enterprise is 6.1, and the relative error is 3.0. And when it reaches 3000, the absolute error is 4.1, and the relative error can be reduced to 2.3. Therefore, the model can improve the accuracy of the reform of the dynamic mechanism of the enterprise.

4.3 Analysis of model performance in technology innovation and organizational structure innovation

The performance of the proposed different models DL-based enterprise technological innovation and organizational structure innovation are compared. Figures 8 and 9 express the results.

Figure 8 shows that in the trend of the proportion change, when the proportion of technological innovation is 0, the dynamic mechanism coefficient of the traditional model is 0, while the enterprise’s dynamic mechanism coefficient of the improved model based on DL is 0.2. As the proportion of technological innovation increases, when the proportion reaches 1.0, the dynamic mechanism coefficient of the traditional model is 0.5, while that of the improved model by DL can reach 1.45.

Figure 9 implies that the variation trend of the innovation ratio of the organizational structure of the two models is different. When the innovation ratio of the model's organizational structure is 0, the dynamic mechanism coefficient of the traditional model in the system is 0.05, while the improved DL model is 0.18. In the case of an increase in the ratio of organizational structure innovation of enterprise, the dynamic mechanism coefficients of both two models are constantly increasing. When the innovation ratio of the model is 0.8, the dynamic mechanism coefficient of the traditional model is 0.4, while the DL algorithm model is 1.3. Consequently, comparing the parameter results of different models, it reveals that the use of the DL algorithm model can improve the performance level of the enterprise's organizational structure innovation.

With the change of the times and driven by the background of economic globalization, enterprise technological innovation has become a critical source of national economic growth. On account of the BPNN algorithm, the problems existing in the current enterprise technological innovation and industrial structure reform are analyzed from the theoretical level. Through the research and analysis of the dynamic mechanism of enterprise innovation, a reform model of the dynamic mechanism of enterprise innovation is constructed. The model is used to explore the target market value and the evaluation predicted value of the enterprise. Through the model analysis of mobile network and edge server technology, aiming at the resource optimization problem of mobile edge computing platform, the edge cloud computing technology and the server deployment model of uncertain programming theory are used to optimize, and the business management mode of enterprises is innovated. The results manifest that when the number of iterations of the model is 100, the target market value of the enterprise of the traditional algorithm model is 7150. After optimization by the DL algorithm, the target market value can reach 8050. Compared to the original model, the market value has increased by 900. The research has practical application value for promoting the development of enterprise technological innovation. However, some shortcomings are unavoidable. The main disadvantage is that the evaluation of enterprise value and technological innovation is complex. In the process of actual quantification and modeling, the comprehensiveness of enterprise technological innovation and structural reform needs to be further optimized. In the future, it is necessary to use this model to follow up and study the feedback mechanism and technological innovation simulation of enterprises.

Ethical Approval

Not Applicable.

Competing interests

The authors declare that there are no conflicts of interest.

Authors' contributions

ZC directed the study. SL performed the experiments and performed the analyses. ZC wrote the paper and agreed final submission.

Funding

This work was supported by Project of Hunan Provincial Social Science Achievement Evaluation Committee in 2020，research on dynamic mechanism and innovation mode of undertaking industrial transfer.( Project No. XSP20YBC117), and This work was supported by Project of Natural Science Foundation of Hunan Province in 2022，Research on the capacity and mode of undertaking industrial transfer with high quality in Shaoyang City.

Availability of data and materials

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

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No competing interests reported.

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Dynamic Mechanism of Technology Innovation in Enterprise Based on Edge Cloud Computing Technology

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Abstract

Figures

1. Introduction

2. Recent Related Research

2.1 DL model and enterprise technological innovation

2.2 Research on the reform of enterprise organization structure and the innovation of dynamic mechanism

3. Enterprise Mechanism Reform Model Based On Bpnn

3.1 BPNN and enterprise technological innovation

3.2 Implementation of the dynamic mechanism model of enterprise technological innovation

3.3 Enterprise's innovation dynamic mechanism and technological reform

3.4 Analysis of edge cloud computing technology

4. Results And Discussion

4.1 Enterprise's target market value and evaluation predicted value

4.2 Error analysis of the organizational structure and dynamic mechanism of enterprises

4.3 Analysis of model performance in technology innovation and organizational structure innovation

5. Conclusions

Declarations

References

Additional Declarations

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