# Analysis and Discussion of Digital Economy Management Data under the Background of Big Data

Authors: Jie Li; Dongphil Chun; Hengjun Luo; Yixuan Chang
Journal: Mobile Information Systems
 (2022)

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## Abstract

The aim of this study is to discuss how to effectively use big data in financial analysis and apply big data to various management decisions to effectively improve its efficiency. In order to study the application of big data in the field of digital economy, the study problem is data management that improves the needs of business model to a certain extent and responds to the pace of rapid development of science and technology and the transformation of business model in special times, so as to promote economic development. The methods used in this study are based on the information extracted from the software system log; this study introduces machine learning algorithms such as spatial convolution and fuzzy multicolumn convolution, deduces the interaction between visitors and software according to the system software access log, wavelet algorithm, and denoising algorithm, deduces the data value index of the secondary market, constructs the simulation software under MATLAB, and compares it with the previous system, by improving the prediction sensitivity of interaction and the prediction coupling of data value. The results of this study proved that the system improves the efficiency of data management.


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## Body

## 1. Introduction

In recent years, with the development of computer science and technology, big data has become a new type of technological reform. In the process of scientific and technological innovation, it has subverted the conventional technical mode and broken the dilemma that traditional technology is difficult to develop sustainably.When big data was used to describe network search in the early days, it needed to process or analyze a large number of data. It was a hot subject in academic and computer research at that time. In subsequent related data research, such as data warehouse, data security, data analysis, and data visualization, it has become the focus of various fields.The enterprise’s operation mode changes with the development of the times [1]. How to face difficulties in special times, the transformation of operation mode brings more vitality to the enterprise. Taking the retail industry in the epidemic situation in 2019 as an example, this paper describes the forced transformation of the retail industry, the use of big data technology to convert the previous offline sales to online sales, and profound changes that have taken place through the operation mode and consumption mode, so as to seek a breakthrough for enterprise development. Digital economy is an economic concept, a process in which human beings integrate resources directly or indirectly through big data to make them play a role, so as to promote economic development and to promote the transformation of old and new economic models, as well as major reforms in economic operation and daily life style, so as to improve the level of national economic development [2].Russia is a country with more land and fewer people and has land advantages [3]. That is why this paper analyzes global agricultural development data, proposes a digital agricultural system, establishes links between its elements and the environment, and develops a private method for implementing major changes to agricultural planning. With the increasing popularity of digital management, the R&D ability and cost control ability of enterprises have been improved. In the past, traditional management methods have a series of problems, such as lax quality information collection and management, inadequate investigation of quality problems, careless quality process management, low quality, efficiency, and information, and insufficient decision-making and implementation, which restrict the development of enterprises.With the popularization and application of big data, digital economy has become the driving force of global economic development, promoting the transformation of human economic form and reducing social transaction costs [4]. Starting from the international environment, this paper analyzes the development direction and benefits of some important international organizations in the field of digital economy, so as to analyze the current situation of China’s digital economy development and make progress from all aspects this paper demonstrates and puts forward some suggestions to promote the development of China’s digital economy. National economic transformation proposed the application of digital technology to enterprise economic activities [5]. The application of big data technology in digital economy has involved many financial fields.Big data is used extensively in all fields. By removing the limitations of the traditional model, businesses can innovate and expand their market reach while improving decision-making efficiency [6]. The study discusses how to effectively use big data in financial analysis and apply big data to various management decisions to effectively improve its efficiency. In order to study the application of big data in the field of digital economy, the data management improves the needs of business model to a certain extent and responds to the pace of rapid development of science and technology and the transformation of business model in special times, so as to promote economic development [7].


## 2. Structure and Evaluation Logic of Digital Economy Market

Many people do not know what digital mode is. It refers to the form of promoting the development of economic structure by using digital knowledge and information as the main body, high-tech information network as the carrier, and information and communication technology as the means to improve efficiency.According to the white paper, in 2017, China’s total digital economy was more than 28 trillion yuan, with a year-on-year nominal growth of nearly 20% and a GDP ratio of more than 30%. It shows that this economic model has become the driving force to promote China’s economic growth in recent years. In the long run, when the proportion of digital economy GDP will exceed 50%, China will fully enter the era of digital economy.If the digital sharing interactive system is regarded as an economic system, the natural person participants in the economic system, in addition to the data manager, only include two roles: visitors to the software platform software and traders in the big data asset secondary trading market [8]. Visitors obtain relevant data information through software and trade in the secondary market through resource integration, so as to achieve benefits. The whole architecture is to collect, store, extract, manage, and analyze large-scale data, so as to obtain relevant information efficiently and quickly [9].In Figure1, visitors extract information through the network platform or exchange information through the network platform, and the relevant information data are saved in the software background. The interaction volume and price can be fed back from the access log. After data analysis and processing, these data can provide reference value for the enterprise’s operation and provide services for its circulation and transaction in the subsequent secondary market, so as to realize digital data management.Figure 1
Structure of digital economy market.


### 2.1. Deduce the Interaction between Visitors and Software according to the Access Log

The evaluation mode of interaction between visitors and software is deduced according to the access log, as shown in formula (1):
(1)Xit=γT∙Ti+γS∙Sit+γP∙Pit+γG∙Git,where Xit is the weighted result of data trigger amount of user I at time t; Sit is the data submission amount of user I at time t (submit method); Pit is the amount of data logs of user I at time t (post method); Git is the data usage of user I at time t (get method); Ti is the other data trigger amount of user I at time t; γT, γS, γP, and γG are the weighting factors of the above four evaluation factors.In the actual economic treatment, the equation needs to be processed by linear integration after accumulation, as shown in formula (2):
(2)SX=∫mn∑i=1rXitdt,where SX is the access log evaluation factor; m and n are the time period threshold of integral function; and r is the number of users investigated by the integral function; the meanings of other mathematical symbols are the same as those above.According to the above principles, machine learning algorithms such as spatial convolution and fuzzy multicolumn convolution are introduced to push forward the timet axis for a certain period to form multiple interaction prediction values based on depth iterative regression from T1 to TN. The algorithm logic is shown in Figure 2:Figure 2
Logic diagram of data prediction algorithm.In Figure2, the basis function of the spatial convolution algorithm is shown in formula (3); the node function of FNN fuzzy neural network selects the sixth-order polynomial depth iterative regression basis function, as shown in formula (4); the node function of multicolumn fuzzy neural network selects the logarithmic depth iterative regression basis function, as shown in formula (5):
(3)y=∫−∞+∞gxqt−xdt,wheregx is the convoluted array function; qt−x is the convolution kernel function; t is the convolution pointer; x is the convolution control variable; and y is the convolution result.
(4)y=∑i=1n∑j=05Ajxij,(5)y=∑i=1nA∙logexi+B,where xi is the input value of the ith node of the previous neural network; y is the output value of this node; n is the number of nodes of the previous neural network; and A and B are the variables to be regressed, which refers to the variable to be regressed of the j-th order polynomial.In the figure, the user’s access log in a certain period is analyzed, and the machine learning algorithms such as spatial convolution and fuzzy multicolumn convolution are introduced to predict the interaction between the visitor and the software. This study mainly aims at the impact of data management on the economy and deduces the interaction volume and price trend of a product through the data obtained from the access log.


### 2.2. Deduce the Data Value Index of the Secondary Market according to the Interaction Volume Data

The interaction volume data is the total information production of the network platform. The correlation between the interaction volume data and the data value index of the secondary market is subtle. If they are intuitive and lack a direct logical relationship, but after denoising, it is concluded that the data signal-to-noise ratio is very low, the data value index of the secondary market can be deduced according to the interaction volume.In Figure3, the interactive data is exchanged by wavelet, the data is refined by translation, and the curve deviated from the original axis is pulled back, which can finally meet the analysis requirements by itself. After the difference noise reduction, the unavailable information is eliminated to obtain a credible correlation function.Figure 3
Logic of generation algorithm of transaction price estimation curve in secondary market.Daubechies (DBN) wavelet is adopted for wavelet transform, and its basis function is as follows ((6), (7), and (8)):
(6)py=∑k=0n−1Ckn−1+kyk,(7)C0ω=12∑k=02n−1hke−jkω=cos2ω2psin2ω2,(8)ψy=∫pypt−ydt,where n and k are the pointer variables; y is the controlling dependent variable; ω is the transformed periodic dependent variable; ψy is the final function of wavelet transform; py is the wavelet transform control function; C is the wavelet transform factor function; and e is the natural constant.The generation algorithm of random noise reduction function is shown in formula (9):
(9)∆Fx=ρA∙Rand∙SinρB∙Rand∙x+ρC∙Rand+ρD∙Rand,where Rand is the random numbers with interval on [0,1]; ρA, ρB, ρC, and ρD are the interval adjustment factor of random number; x is the independent variable; and ∆Fx is the dependent variable.The algorithm of noise reduction process is shown in formula (10):
(10)FSt=Xit−ψy+∆Fx,where the meaning of mathematical symbols is the same as that above.In the formula, the dependent variables include different data structures such ast, y and X; however, according to the conversion method of the previous formula, all dependent variables can be normalized and isomorphic, which is limited by space. Its processing method is not discussed here; through the comprehensive use of wavelet transform, random waveform superposition noise reduction, and cyclic learning, data analysis with high coupling degree can be realized under the condition of data relationship with low signal-to-noise ratio. Because the data prediction algorithm designed in Section 2.1 has fully expanded the independent variable function, the coupled dependent variable function can realize the follow-up expansion, so as to realize the data prediction analysis based on curve estimation algorithm.


## 2.1. Deduce the Interaction between Visitors and Software according to the Access Log

The evaluation mode of interaction between visitors and software is deduced according to the access log, as shown in formula (1):
(1)Xit=γT∙Ti+γS∙Sit+γP∙Pit+γG∙Git,where Xit is the weighted result of data trigger amount of user I at time t; Sit is the data submission amount of user I at time t (submit method); Pit is the amount of data logs of user I at time t (post method); Git is the data usage of user I at time t (get method); Ti is the other data trigger amount of user I at time t; γT, γS, γP, and γG are the weighting factors of the above four evaluation factors.In the actual economic treatment, the equation needs to be processed by linear integration after accumulation, as shown in formula (2):
(2)SX=∫mn∑i=1rXitdt,where SX is the access log evaluation factor; m and n are the time period threshold of integral function; and r is the number of users investigated by the integral function; the meanings of other mathematical symbols are the same as those above.According to the above principles, machine learning algorithms such as spatial convolution and fuzzy multicolumn convolution are introduced to push forward the timet axis for a certain period to form multiple interaction prediction values based on depth iterative regression from T1 to TN. The algorithm logic is shown in Figure 2:Figure 2
Logic diagram of data prediction algorithm.In Figure2, the basis function of the spatial convolution algorithm is shown in formula (3); the node function of FNN fuzzy neural network selects the sixth-order polynomial depth iterative regression basis function, as shown in formula (4); the node function of multicolumn fuzzy neural network selects the logarithmic depth iterative regression basis function, as shown in formula (5):
(3)y=∫−∞+∞gxqt−xdt,wheregx is the convoluted array function; qt−x is the convolution kernel function; t is the convolution pointer; x is the convolution control variable; and y is the convolution result.
(4)y=∑i=1n∑j=05Ajxij,(5)y=∑i=1nA∙logexi+B,where xi is the input value of the ith node of the previous neural network; y is the output value of this node; n is the number of nodes of the previous neural network; and A and B are the variables to be regressed, which refers to the variable to be regressed of the j-th order polynomial.In the figure, the user’s access log in a certain period is analyzed, and the machine learning algorithms such as spatial convolution and fuzzy multicolumn convolution are introduced to predict the interaction between the visitor and the software. This study mainly aims at the impact of data management on the economy and deduces the interaction volume and price trend of a product through the data obtained from the access log.


## 2.2. Deduce the Data Value Index of the Secondary Market according to the Interaction Volume Data

The interaction volume data is the total information production of the network platform. The correlation between the interaction volume data and the data value index of the secondary market is subtle. If they are intuitive and lack a direct logical relationship, but after denoising, it is concluded that the data signal-to-noise ratio is very low, the data value index of the secondary market can be deduced according to the interaction volume.In Figure3, the interactive data is exchanged by wavelet, the data is refined by translation, and the curve deviated from the original axis is pulled back, which can finally meet the analysis requirements by itself. After the difference noise reduction, the unavailable information is eliminated to obtain a credible correlation function.Figure 3
Logic of generation algorithm of transaction price estimation curve in secondary market.Daubechies (DBN) wavelet is adopted for wavelet transform, and its basis function is as follows ((6), (7), and (8)):
(6)py=∑k=0n−1Ckn−1+kyk,(7)C0ω=12∑k=02n−1hke−jkω=cos2ω2psin2ω2,(8)ψy=∫pypt−ydt,where n and k are the pointer variables; y is the controlling dependent variable; ω is the transformed periodic dependent variable; ψy is the final function of wavelet transform; py is the wavelet transform control function; C is the wavelet transform factor function; and e is the natural constant.The generation algorithm of random noise reduction function is shown in formula (9):
(9)∆Fx=ρA∙Rand∙SinρB∙Rand∙x+ρC∙Rand+ρD∙Rand,where Rand is the random numbers with interval on [0,1]; ρA, ρB, ρC, and ρD are the interval adjustment factor of random number; x is the independent variable; and ∆Fx is the dependent variable.The algorithm of noise reduction process is shown in formula (10):
(10)FSt=Xit−ψy+∆Fx,where the meaning of mathematical symbols is the same as that above.In the formula, the dependent variables include different data structures such ast, y and X; however, according to the conversion method of the previous formula, all dependent variables can be normalized and isomorphic, which is limited by space. Its processing method is not discussed here; through the comprehensive use of wavelet transform, random waveform superposition noise reduction, and cyclic learning, data analysis with high coupling degree can be realized under the condition of data relationship with low signal-to-noise ratio. Because the data prediction algorithm designed in Section 2.1 has fully expanded the independent variable function, the coupled dependent variable function can realize the follow-up expansion, so as to realize the data prediction analysis based on curve estimation algorithm.


## 3. Algorithm Effectiveness Evaluation

The simulation software is constructed under MATLAB, and the data prediction module of digital economic management information system is constructed based on the algorithm. The data prediction module of digital economic management information system supported by more mature nonlinear overall planning algorithm for complex systems in the technical market is selected with reference to the group, and the data performance of the two is compared.


### 3.1. Prediction Sensitivity to Interaction

Interaction refers to communication and interaction. It is built on an Internet platform to share resources, information or services, or exchange between the platform and users and users and users, so as to get more ideas and meet some needs among users. The amount of interaction refers to all production data on the network platform.Through the real-time interactive data of Guiyang big data asset exchange and the log data of five big data systems investigated, the software access log records all data information of the software. Through the analysis of the access log and the introduction of machine learning algorithms such as spatial convolution and fuzzy multi column convolution, the interaction between visitors and software can be predicted. The reference group selects the data prediction module of the digital economic management information system supported by the more mature nonlinear overall planning algorithm for complex systems in the technical market and observes its prediction sensitivity to the interaction volume of the access log on the software.In Table1, bivariate t-test was conducted for two different systems, and it was found that T<10.000 and P<0.01, with credible statistical difference; T value is the value in the output result of bivariate t verification. When T<10.000, it is considered that there is a statistical difference between the two columns of data, and the smaller the T value, the more significant the statistical difference is. When P<0.01, it is considered that the statistical result has a significant statistical difference, and the smaller the P value, the more significant the statistical meaning is.Table 1
Comparison of prediction sensitivity of data interaction volume of software platform (the data source is the real-time interactive data of Guiyang big data asset exchange).
SensitivitySubmit dataPost dataGet dataOther dataComprehensiveThe system96.32%88.76%92.41%85.42%90.74%Previous system90.21%84.43%85.38%81.87%85.47%T5.288.126.485.874.58P0.0090.0070.0080.0070.008In order to more intuitively reflect the prediction sensitivity of the upper system log data to the interaction volume, visualize the data in the table to get Figure4:Figure 4
Visual diagram of prediction sensitivity of data interaction volume of software platform.In Figure4, sensitivity refers to the proportion of all true positive data in all positive data. Although the difference between this scheme and the previous system algorithms is not great, the prediction sensitivity of the overall interaction volume is relatively satisfactory. Among them, the prediction sensitivity of the submit data reaches more than 96%. Today’s Turing test standard, neural network has more than 95% sensitivity; that is, its machine learning ability has fully met the needs of data management. These data can provide reference value for enterprises in operation and provide services for circulation and trading in the subsequent secondary market.


### 3.2. Prediction Coupling of Data Value

The data source is the real-time interactive data of Guiyang big data asset exchange and the log data of five big data systems investigated. The data cycle is from January 2019 to June 2021. In a three-and-a-half-year data cycle, analyze the data within a certain time limit, and explore the prediction coupling degree of software system log to data value.In Table2, bivariate t-test was conducted for different systems, and it was found that T<10.000 and P<0.01, with believable statistical difference; it shows that the system significantly improves the prediction coupling degree of software data asset value on the basis of the previous system. The data obtained from the software system log not only has high sensitivity to the prediction of interaction volume but also has a certain correlation to the coupling degree of software data asset value prediction, so as to improve the accuracy of software data asset value.Table 2
Comparison of predicted coupling degree of software data asset value (the data source is the real-time interactive data of Guiyang big data asset exchange).
Coupling degreeData inflection pointOpening valueClosing valuePeak valueValley valueComprehensiveThe system85.14%78.65%84.26%92.45%90.23%86.15%Previous system78.23%70.41%77.56%83.79%80.96%73.47%T4.755.126.295.084.175.24P0.0080.0070.0090.0080.0060.007In order to more intuitively reflect the prediction coupling degree of the upper system log data to the value of software data assets, visualize the data in the table to get Figure5:Figure 5
Visual diagram of prediction coupling degree of software data asset value.In Figure5, after the influencing factors in the data are removed through the wavelet exchange algorithm, the data more conducive to the research is obtained. Through the data, the software data asset value is predicted to obtain a higher coupling degree, which is conducive to the analysis of big data and improve the efficiency of data management.


## 3.1. Prediction Sensitivity to Interaction

Interaction refers to communication and interaction. It is built on an Internet platform to share resources, information or services, or exchange between the platform and users and users and users, so as to get more ideas and meet some needs among users. The amount of interaction refers to all production data on the network platform.Through the real-time interactive data of Guiyang big data asset exchange and the log data of five big data systems investigated, the software access log records all data information of the software. Through the analysis of the access log and the introduction of machine learning algorithms such as spatial convolution and fuzzy multi column convolution, the interaction between visitors and software can be predicted. The reference group selects the data prediction module of the digital economic management information system supported by the more mature nonlinear overall planning algorithm for complex systems in the technical market and observes its prediction sensitivity to the interaction volume of the access log on the software.In Table1, bivariate t-test was conducted for two different systems, and it was found that T<10.000 and P<0.01, with credible statistical difference; T value is the value in the output result of bivariate t verification. When T<10.000, it is considered that there is a statistical difference between the two columns of data, and the smaller the T value, the more significant the statistical difference is. When P<0.01, it is considered that the statistical result has a significant statistical difference, and the smaller the P value, the more significant the statistical meaning is.Table 1
Comparison of prediction sensitivity of data interaction volume of software platform (the data source is the real-time interactive data of Guiyang big data asset exchange).
SensitivitySubmit dataPost dataGet dataOther dataComprehensiveThe system96.32%88.76%92.41%85.42%90.74%Previous system90.21%84.43%85.38%81.87%85.47%T5.288.126.485.874.58P0.0090.0070.0080.0070.008In order to more intuitively reflect the prediction sensitivity of the upper system log data to the interaction volume, visualize the data in the table to get Figure4:Figure 4
Visual diagram of prediction sensitivity of data interaction volume of software platform.In Figure4, sensitivity refers to the proportion of all true positive data in all positive data. Although the difference between this scheme and the previous system algorithms is not great, the prediction sensitivity of the overall interaction volume is relatively satisfactory. Among them, the prediction sensitivity of the submit data reaches more than 96%. Today’s Turing test standard, neural network has more than 95% sensitivity; that is, its machine learning ability has fully met the needs of data management. These data can provide reference value for enterprises in operation and provide services for circulation and trading in the subsequent secondary market.


## 3.2. Prediction Coupling of Data Value

The data source is the real-time interactive data of Guiyang big data asset exchange and the log data of five big data systems investigated. The data cycle is from January 2019 to June 2021. In a three-and-a-half-year data cycle, analyze the data within a certain time limit, and explore the prediction coupling degree of software system log to data value.In Table2, bivariate t-test was conducted for different systems, and it was found that T<10.000 and P<0.01, with believable statistical difference; it shows that the system significantly improves the prediction coupling degree of software data asset value on the basis of the previous system. The data obtained from the software system log not only has high sensitivity to the prediction of interaction volume but also has a certain correlation to the coupling degree of software data asset value prediction, so as to improve the accuracy of software data asset value.Table 2
Comparison of predicted coupling degree of software data asset value (the data source is the real-time interactive data of Guiyang big data asset exchange).
Coupling degreeData inflection pointOpening valueClosing valuePeak valueValley valueComprehensiveThe system85.14%78.65%84.26%92.45%90.23%86.15%Previous system78.23%70.41%77.56%83.79%80.96%73.47%T4.755.126.295.084.175.24P0.0080.0070.0090.0080.0060.007In order to more intuitively reflect the prediction coupling degree of the upper system log data to the value of software data assets, visualize the data in the table to get Figure5:Figure 5
Visual diagram of prediction coupling degree of software data asset value.In Figure5, after the influencing factors in the data are removed through the wavelet exchange algorithm, the data more conducive to the research is obtained. Through the data, the software data asset value is predicted to obtain a higher coupling degree, which is conducive to the analysis of big data and improve the efficiency of data management.


## 4. Summary

Now, China’s scientific and technological level has entered a stage of rapid development. The changes of the times have led to the transformation of economic model. In recent years, the traditional economic model has gradually declined, and the changes of market environment and business mode have promoted the reform of economic system. The cross-border integration of Internet information data on major data industries and the innovation of application technology data have promoted the development of digital industry and produced relevant big data background [10]. It can systematically manage scattered data, facilitate people’s real life, and improve management efficiency. Starting from the demand relationship, demonstrate the data and digital infrastructure, introduce the characteristics of digital economy, and analyze the prospect of digital economy in the future from many aspects [11].This study contributes that the application of big data information management is changing the traditional life mode and business philosophy, realizing the integration and docking of digital information technology in many aspects, and promoting the development of big data information management into a new stage. In recent years, the management of big data has received unprecedented attention in various fields. Only by fully mining the value trend of big data management, predicting the feasibility of big data management, and formulating effective schemes for implementation can we really transform data management into the practice of digital economy [12].This study deduces the prediction sensitivity of software interaction volume through software access log, analyzes the economic management data through data extraction, and converts the data into information that can provide reference value for enterprises in operation, circulation, and trading services in subsequent secondary markets and the conversion of data information to knowledge energy and intelligent production capacity, so as to achieve the purpose of promoting economic development.The study of logarithmic digital economic management data still has some blind spots. Thus, it is necessary to improve digital management fundamentals, fully utilize the digital economy’s potential, and wait for scientific and technological advancements to further develop big data.



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*Source: 2848065-2022-03-30.xml*