# Construction of Economic Management Big Data Platform Based on Artificial Intelligence Algorithm Monitoring and Early Warning

Authors: Shuzhen Mao
Journal: Journal of Electrical and Computer Engineering
 (2022)

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## Abstract

The development of artificial intelligence and the emergence of big data have brought convenience to the development of various fields and also brought great influence to the economic field. There are many data sources for economic management, and the scale is huge, so how to manage these large-scale economic data has become an urgent problem to be solved. In addition, the issue of how to conduct security management on these large-scale data, protect the security of users’ accounts and property, and effectively monitor and prewarn economic market risks is also an urgent issue. This article aims to build an economic market risk monitoring and early warning platform through advanced science and technology such as artificial intelligence and big data to realize an intelligent risk control platform in the economic and financial fields, as well as a data-driven risk management model to create intelligent risk early warning and prevention and the response system to enhance the intelligent level of risk assessment, early warning, prevention, and disposal. Experiments show that the artificial intelligence algorithm monitoring and early warning economic management big data platform constructed in this article shows that its accuracy of economic risk prediction can reach more than 90%.


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## Body

## 1. Introduction

In recent years, the rapid development of information technology and communication networks and the vigorous development of the network economy have also brought greater difficulties to economic management. The network economy is that the laws of economic operation and some basic laws have changed; for example, online economic operation needs to be carried out according to the prescribed ways online and conform to the rules of online operation. But, because of the huge scale of economic data, the economic field cannot adjust its management system and management mode in time, making the economy face great management risks. In the process of transmission and communication on the Internet, the risk of inaccurate information being stolen or contaminated is also difficult to avoid [1]. In addition, the development of the network economy is also promoting the process of financial globalization and integration to the financial system. Therefore, with the development of the network economy, the risks faced by the economic field have also increased.The artificial intelligence algorithm economic monitoring and early warning system constructed in this article monitors the development of economic information data in the big data platform and at the same time predicts future economic development trends based on the development and changes of these economic data and predicts the development of financial markets. It is an important part of the economic field. Decision-makers provide a reference data to make correct decisions; use big data, cloud computing, artificial intelligence, blockchain, and other mobile Internet technologies to provide the driving force for the integration of technology and economy to promote the innovation and transformation of economic management models [2]; build a three-dimensional, socialized, and information-based monitoring and early warning system, which can detect the trend of economic crimes in time, curb the trend of high incidence of illegal fund-raising, and avoid the losses caused by illegal means to the economic field; and improve the risk assessment of the economic field. The accuracy of this can make risk assessment intelligent and automated and promote the smooth development and operation of the entire economic field.In order to grasp the development trend of the economic field, promote the stable development of the economy, and avoid possible risks in the economic field, many scholars have conducted research in this field. Among them, Pisareva, in view of the deployment of temporary processes in various socioeconomic systems and aggravating the crisis, proposed a formal method of setting to further improve the corresponding application tasks at all levels and in the field of socioeconomic development planning [3]. Lehrstuhl described the paradigm shift experienced in the field of macroeconomic research by adopting an evolutionary approach. The application of big data to economic problems can lead to new ways of thinking and research methods [4]. Lei et al. designed a new type of computer architecture, an accelerator based on an optical network chip (ONoC) to further accelerate the matching of citizens’ supply and demand in the sharing economy [5]. Holm and Ploug believed that the governance model including the possibility of making metachoices can achieve the best balance between personal interests and public interests [6]. Li et al. discussed the key gaps and opportunities in the economic field, referred to the existing literature on decision-making, scenario analysis, and scientific philosophy under uncertainty, and developed the basic concepts guiding the future application of big data to energy economic modeling [7]. However, although these studies have a good reference value for economic development, they need to manually find out the risk control in the economic field. There is no complete warning system, and there are few studies on economic risk prediction.This article has the following innovations: (1) Use big data technology to realize the integration of huge economic data information in the economic field, build a high-performance big data collection, storage, and analysis platform, and efficiently integrate various economic information data. Apply to risk control, reduce the overall risk of the online economy, and improve the ability to control economic risks. (2) Combine artificial intelligence, blockchain, and other mobile Internet technologies to design an economic management big data platform to realize the intelligent prediction of economic risks and the ability to control risks at the same time, improve the automation level of risk prediction and issue economic risk early warnings, and build artificial intelligence predictive warning system. (3) The newly constructed economic management big data platform based on artificial intelligence algorithm monitoring and warning has automatic learning function and automatic alarm device which is not available in the pain platform.


## 2. Construction Method of Economic Management Big Data Platform

### 2.1. Mobile Internet Technology

#### 2.1.1. Artificial Intelligence Algorithm Monitoring and Early Warning

A scientific technology used by artificial intelligence to expand, simulate, and extend human intelligence is to make computers more likely to approach human behavior and IQ [8]. The application of artificial intelligence technology is shown in Figure 1.Figure 1
Application of artificial intelligence.The fields of artificial intelligence applications are far more than those listed in Figure1. In the future, artificial intelligence technology will become more and more advanced, and the fields in which it can be used will become more and more extensive [9–11]. At present, the application of artificial intelligence in the economic field is becoming more and more extensive, such as the digital economy. Due to the emergence of the digital economy, it has brought us changes in the infrastructure in our lives and the development of emerging industries [12]. Of course, the development of the smart economy requires a complete set of smart forecasting systems to prevent economic risks and predict the development of future economic trends. Because the development of intelligent economy is affected by many uncertain factors, it is necessary for intelligent system to predict the development of intelligent economy in order to ensure the smooth operation of intelligent economy. For this reason, it is necessary to combine the data in the big data platform with corresponding algorithms, so that artificial intelligence can combine the corresponding algorithms to make predictions based on economic data. There are many algorithms used in artificial intelligence. In order to accurately predict various trends in economic management, this article uses a particle swarm algorithm that simulates the lost behavior of a flock of birds. This algorithm is used to reduce the error of predicting trends to ensure that the system is economical [13].What the artificial intelligence particle swarm optimization algorithm needs is to use the economic data in big data to calculate and then reduce the error step by step. We regard economic data as a particle, and the amount of economic data is constantly changing and fluctuating. The records of the stored data are different. Every time the economic data will be presented in a different location, we remember this location as(1)Citi=C1t1,C2t2,…,CNtii,i=1,2,3…N.In this formula,t is the time when the data is recorded, i represents the data label when the data is recorded, so that we can quickly find the data, and C represents the data in the server location.In the process of data update and iteration, the economic data of the optimal prediction of the data at thet-th time can be obtained, which we call the individual extreme value, which is recorded as(2)CiNti=Ci1t1,Ci2t2,…,CiNtii.When we analyze and integrate economic data, we need to analyze all economic data to be able to correctly grasp the risks that may exist in the process of economic development and other aspects of economic development. Therefore, there will be an overall extreme value. We denote this extreme value as(3)CiZti=Cin+1ti+1,Cin+2ti+2,…,CiZti+N⋅St.In the above equation,S is the weight that will be generated by the calculation of economic data in the whole. The speed of the i-th data in the t-th iteration update is(4)QitN=Q1t1,Q2t2,…,QitNi,i=1,2,3,…,N.Then the principle of the iterative update of the individual extreme value of thei-th data at the t-th time is as follows:(5)CiNti+1=Cit1+1,YCit2+1≥YCiNt,CiNti=Cit1,YCit≤YCiNt.In the above equation,Y(x) is the degree of adaptation function. All data is updated iteratively at time t, and all extreme values will also change according to the data update. The iterative update formula is as follows:(6)CiZti=GminYCiZti,x=CiZti,where G represents the value of the independent variable x in the function Y(x) and CiZti is the closest accurate actual economic data in the t-th iteration update, which we call the global optimal predicted economic data value.In artificial intelligence, we use particle swarm optimization algorithm to find the trend of economic development. At the same time, we can also use particle swarm optimization algorithm to predict problems and risks in economic data, reducing our search for local data in solving huge economic data. It can predict the possible risks of problems and guide and prevent them in time.(1). Blockchain technology. Before the large-scale economic data, the data information in our economic development needs to be absolutely confidential, so we need to choose an absolutely secure storage system to store this large-scale economic data information, and then add it to this storage server artificial intelligence password recognition algorithm [14]. The block storage mode in blockchain technology can ensure the security of these economic data. Block storage and central storage are shown in Figure 2.Figure 2
Block storage and central storage.As shown in Figure2, the block storage is compared with the central storage. It can be found that it is divided into block storage, and the information and data are not aggregated into a total storage server, so, in this way, it can be guaranteed that the data in a single storage server does not have the risk of data leakage from other storage servers. The economic data information in other storage servers will not be exposed to the risk. This will bring the storage security of economic data to a higher level. If it is economic data stored in a central storage server, it is possible that all economic information leakage will cause huge economic losses [15].Then, in the blockchain storage technology, we will use artificial intelligence technology to encrypt each blockchain. Through artificial intelligence technology, once information leaks, it will intelligently sound an alarm and immediately identify which part of the economic data leakage occurs to prevent greater economic losses [16]. Then the principle of adding a password to the blockchain is as follows.When we set the password, it takes timet and the number of passwords set is d. When we enter the password, the password is passed into the system. The artificial intelligence technology will intelligently identify the entered password. If the password is entered incorrectly, it will not be possible to view economic data information; the principle is as follows:(7)Code=fd∗rs.In the above equation,r is the ability of artificial intelligence to recognize whether it is a password input. Once it is found that this is not a password but other means, it will immediately give an intelligent alarm and prevent illegal intrusion from the outside world. The recognition principle is as follows.We assume that the number of passwords identified in artificial intelligence recognition is 4, and the password of one of the blockchains is set to 8888; then the first step of artificial intelligence recognition is to identify whether the number of passwords corresponds:(8)Code=f4∗rs=In4.If the number of passwords corresponds, the artificial intelligence will monitor whether the password is correct, and if it does not correspond, it will perform artificial intelligence to close the password input program so that the password cannot be entered. After entering the correct number of passwords, artificial intelligence will recognize whether the password is correct, if the following forms are recognized:(9)Code⟶8888=In8888⟶t∗vtrue.In this way, economic information data stored in one of our blockchains will be open for us to analyze and use. In order to ensure the security of the data in the data store, a password is set for each storage area [17, 18]. In this way, the risk of economic leakage can be better prevented.


### 2.2. The Monitoring and Early Warning of the Economy by Artificial Intelligence

The arrival of the information age not only brought convenience to economic development but also brought equal risks to the economic field [19]. For example, the financial risks brought by the development of our network economy will bring huge losses to the economic market, but the huge data information in the financial field cannot be detected manually, so it will greatly increase the occurrence of financial risks [20, 21]. Artificial intelligence is a computer technology that imitates the human brain. Therefore, with the help of artificial intelligence, we can quickly identify abnormal fluctuations in financial data and issue alarms in a timely manner, which also provides us with time to respond to financial risks [22]. The process of intelligently identifying economic risks is shown in Figure 3.Figure 3
Flow chart of intelligently identifying economic risks.As shown in Figure3, once the AI recognition system detects abnormal fluctuations in data or abnormal data, it will immediately issue an alarm. Before entering the recognition technology of artificial intelligence, economic data will undergo diversion analysis. The purpose of information diversion is to allow artificial intelligence to accurately identify economic data. The distribution of economic data is shown in Figure 4.Figure 4
Economic data distribution diagram.The data stored in the blockchain will be divided and summarized according to the number of block memories, and the data information stored in each block is different. Therefore, when the area is summarized, weighti will be generated. When there are W data streams, they will be summarized to the output layer M. The calculation formula is as follows:(10)M1=∑ixW1W2∗ϖ,Mn=∑ixWn−1Wn∗ϖ.In the above equation,x is the internal threshold of the system, and ϖ is the correlation coefficient matrix generated when processing the data stream, and its form is as follows:(11)ϖ=xni.Then the total amount of data recognized by intelligent artificial technology is calculated as follows:(12)H=M1+⋯Mn∗ϖ.In this way, entering the artificial intelligence risk identification system can be identified at a faster speed, and, in this identification process, the artificial intelligence algorithm monitoring and early warning system will predict the future economic development trend based on the indicators of economic development surprise. The economic early warning system was created following a crisis in the capitalist market, in order to avoid the negative consequences of economic shrinkage and to avoid greater losses due to failure to respond to the corresponding countermeasures when the economy is bad. Nowadays, with the development of technology and economy, the huge amount of economic data and information makes it difficult for humans to analyze, and the development of the digital economy also brings greater risks of business cycle fluctuations, so the use of artificial intelligence algorithms to monitor and early warning can promptly discover potential risks and then make corresponding countermeasures [23].In the artificial intelligence economic early warning system, in addition to predicting possible economic risks, we also need to provide early warning of the existing economic development boom, so that this big economic data platform can not only use artificial intelligence algorithm monitoring and early warning for risk prediction but also calculate the prosperity index of economic development, making it a way to detect economic risks and calculate the prosperity of economic development, so that we can better make corresponding countermeasures. Therefore, the current economic monitoring and early warning need to not only consider the economic development prosperity index but also monitor the abnormal fluctuations of economic data and potential risks in the economic field, so as to escort the economic development of our information age.


### 2.3. Construction of Big Data Monitoring and Early Warning Platform for Smart Economy

In order to allow the long-term and stable development of the economic market, we have rebuilt the economic monitoring and early warning system. This system combines artificial intelligence recognition technology and artificial intelligence algorithm monitoring and early warning. In combination with the big data platform to accommodate the large-scale economic information data, the newly built monitoring and early warning platform is shown in Figure5.Figure 5
Construction of monitoring and early warning platform.As we can see in Figure5, the newly constructed big data platform for economic management uses blockchain storage technology in the storage server for storing data, which can prevent the risk of economic information leakage in the first step of economic information storage. Of course, just in case, we still apply artificial intelligence monitoring and early warning to the blockchain storage server in the big data platform to ensure the security of economic data information [24]. In the artificial intelligence algorithm monitoring and early warning system, artificial intelligence algorithm technology is used to calculate the prosperity index of economic development, the prosperity index is an annual ranking based on factors such as wealth, economic growth, personal well-being, and quality of life, and artificial intelligence recognition technology is used to identify potential risks in the process of economic development, such as financial risks, management risks, fiscal risks, and industrial risks; artificial intelligence early warning technology can intelligently prevent and control some risks and send signals through certain alarm systems to let us know potential risks and make corresponding countermeasures.In order for artificial intelligence to more accurately predict the trend of economic development, we have built a new big data platform, the structure of which is shown in Figure6.Figure 6
The newly constructed big data platform.This big data platform contains all the economic data, which is complicated. Therefore, in the big data, it is necessary to have an internal data distribution system to divert and manage the economic big data. The economic data will not be unorganized, so data is split on this big data platform, and a series of identifications are performed on the artificial intelligence platform to promote the speed and accuracy of economic data analysis by the intelligent platform. Although artificial intelligence technology has brought profound changes to the production and life of human society, it will affect not only the industrial structure but also the consumption structure, and it will also have a positive effect on the steady development of the intelligent economy. However, the collection, storage, and use of data may cause information leakage, which may cause serious privacy issues. Therefore, we need to use artificial intelligence to predict the risks in the smart economy to reduce the economic information leakage in the information age [25]. We have integrated artificial intelligence, blockchain technology, and big data into the economic monitoring and early warning platform. In order to verify the feasibility of this platform in the economic field, we have carried out a series of experiment analysis.


## 2.1. Mobile Internet Technology

### 2.1.1. Artificial Intelligence Algorithm Monitoring and Early Warning

A scientific technology used by artificial intelligence to expand, simulate, and extend human intelligence is to make computers more likely to approach human behavior and IQ [8]. The application of artificial intelligence technology is shown in Figure 1.Figure 1
Application of artificial intelligence.The fields of artificial intelligence applications are far more than those listed in Figure1. In the future, artificial intelligence technology will become more and more advanced, and the fields in which it can be used will become more and more extensive [9–11]. At present, the application of artificial intelligence in the economic field is becoming more and more extensive, such as the digital economy. Due to the emergence of the digital economy, it has brought us changes in the infrastructure in our lives and the development of emerging industries [12]. Of course, the development of the smart economy requires a complete set of smart forecasting systems to prevent economic risks and predict the development of future economic trends. Because the development of intelligent economy is affected by many uncertain factors, it is necessary for intelligent system to predict the development of intelligent economy in order to ensure the smooth operation of intelligent economy. For this reason, it is necessary to combine the data in the big data platform with corresponding algorithms, so that artificial intelligence can combine the corresponding algorithms to make predictions based on economic data. There are many algorithms used in artificial intelligence. In order to accurately predict various trends in economic management, this article uses a particle swarm algorithm that simulates the lost behavior of a flock of birds. This algorithm is used to reduce the error of predicting trends to ensure that the system is economical [13].What the artificial intelligence particle swarm optimization algorithm needs is to use the economic data in big data to calculate and then reduce the error step by step. We regard economic data as a particle, and the amount of economic data is constantly changing and fluctuating. The records of the stored data are different. Every time the economic data will be presented in a different location, we remember this location as(1)Citi=C1t1,C2t2,…,CNtii,i=1,2,3…N.In this formula,t is the time when the data is recorded, i represents the data label when the data is recorded, so that we can quickly find the data, and C represents the data in the server location.In the process of data update and iteration, the economic data of the optimal prediction of the data at thet-th time can be obtained, which we call the individual extreme value, which is recorded as(2)CiNti=Ci1t1,Ci2t2,…,CiNtii.When we analyze and integrate economic data, we need to analyze all economic data to be able to correctly grasp the risks that may exist in the process of economic development and other aspects of economic development. Therefore, there will be an overall extreme value. We denote this extreme value as(3)CiZti=Cin+1ti+1,Cin+2ti+2,…,CiZti+N⋅St.In the above equation,S is the weight that will be generated by the calculation of economic data in the whole. The speed of the i-th data in the t-th iteration update is(4)QitN=Q1t1,Q2t2,…,QitNi,i=1,2,3,…,N.Then the principle of the iterative update of the individual extreme value of thei-th data at the t-th time is as follows:(5)CiNti+1=Cit1+1,YCit2+1≥YCiNt,CiNti=Cit1,YCit≤YCiNt.In the above equation,Y(x) is the degree of adaptation function. All data is updated iteratively at time t, and all extreme values will also change according to the data update. The iterative update formula is as follows:(6)CiZti=GminYCiZti,x=CiZti,where G represents the value of the independent variable x in the function Y(x) and CiZti is the closest accurate actual economic data in the t-th iteration update, which we call the global optimal predicted economic data value.In artificial intelligence, we use particle swarm optimization algorithm to find the trend of economic development. At the same time, we can also use particle swarm optimization algorithm to predict problems and risks in economic data, reducing our search for local data in solving huge economic data. It can predict the possible risks of problems and guide and prevent them in time.(1). Blockchain technology. Before the large-scale economic data, the data information in our economic development needs to be absolutely confidential, so we need to choose an absolutely secure storage system to store this large-scale economic data information, and then add it to this storage server artificial intelligence password recognition algorithm [14]. The block storage mode in blockchain technology can ensure the security of these economic data. Block storage and central storage are shown in Figure 2.Figure 2
Block storage and central storage.As shown in Figure2, the block storage is compared with the central storage. It can be found that it is divided into block storage, and the information and data are not aggregated into a total storage server, so, in this way, it can be guaranteed that the data in a single storage server does not have the risk of data leakage from other storage servers. The economic data information in other storage servers will not be exposed to the risk. This will bring the storage security of economic data to a higher level. If it is economic data stored in a central storage server, it is possible that all economic information leakage will cause huge economic losses [15].Then, in the blockchain storage technology, we will use artificial intelligence technology to encrypt each blockchain. Through artificial intelligence technology, once information leaks, it will intelligently sound an alarm and immediately identify which part of the economic data leakage occurs to prevent greater economic losses [16]. Then the principle of adding a password to the blockchain is as follows.When we set the password, it takes timet and the number of passwords set is d. When we enter the password, the password is passed into the system. The artificial intelligence technology will intelligently identify the entered password. If the password is entered incorrectly, it will not be possible to view economic data information; the principle is as follows:(7)Code=fd∗rs.In the above equation,r is the ability of artificial intelligence to recognize whether it is a password input. Once it is found that this is not a password but other means, it will immediately give an intelligent alarm and prevent illegal intrusion from the outside world. The recognition principle is as follows.We assume that the number of passwords identified in artificial intelligence recognition is 4, and the password of one of the blockchains is set to 8888; then the first step of artificial intelligence recognition is to identify whether the number of passwords corresponds:(8)Code=f4∗rs=In4.If the number of passwords corresponds, the artificial intelligence will monitor whether the password is correct, and if it does not correspond, it will perform artificial intelligence to close the password input program so that the password cannot be entered. After entering the correct number of passwords, artificial intelligence will recognize whether the password is correct, if the following forms are recognized:(9)Code⟶8888=In8888⟶t∗vtrue.In this way, economic information data stored in one of our blockchains will be open for us to analyze and use. In order to ensure the security of the data in the data store, a password is set for each storage area [17, 18]. In this way, the risk of economic leakage can be better prevented.


## 2.1.1. Artificial Intelligence Algorithm Monitoring and Early Warning

A scientific technology used by artificial intelligence to expand, simulate, and extend human intelligence is to make computers more likely to approach human behavior and IQ [8]. The application of artificial intelligence technology is shown in Figure 1.Figure 1
Application of artificial intelligence.The fields of artificial intelligence applications are far more than those listed in Figure1. In the future, artificial intelligence technology will become more and more advanced, and the fields in which it can be used will become more and more extensive [9–11]. At present, the application of artificial intelligence in the economic field is becoming more and more extensive, such as the digital economy. Due to the emergence of the digital economy, it has brought us changes in the infrastructure in our lives and the development of emerging industries [12]. Of course, the development of the smart economy requires a complete set of smart forecasting systems to prevent economic risks and predict the development of future economic trends. Because the development of intelligent economy is affected by many uncertain factors, it is necessary for intelligent system to predict the development of intelligent economy in order to ensure the smooth operation of intelligent economy. For this reason, it is necessary to combine the data in the big data platform with corresponding algorithms, so that artificial intelligence can combine the corresponding algorithms to make predictions based on economic data. There are many algorithms used in artificial intelligence. In order to accurately predict various trends in economic management, this article uses a particle swarm algorithm that simulates the lost behavior of a flock of birds. This algorithm is used to reduce the error of predicting trends to ensure that the system is economical [13].What the artificial intelligence particle swarm optimization algorithm needs is to use the economic data in big data to calculate and then reduce the error step by step. We regard economic data as a particle, and the amount of economic data is constantly changing and fluctuating. The records of the stored data are different. Every time the economic data will be presented in a different location, we remember this location as(1)Citi=C1t1,C2t2,…,CNtii,i=1,2,3…N.In this formula,t is the time when the data is recorded, i represents the data label when the data is recorded, so that we can quickly find the data, and C represents the data in the server location.In the process of data update and iteration, the economic data of the optimal prediction of the data at thet-th time can be obtained, which we call the individual extreme value, which is recorded as(2)CiNti=Ci1t1,Ci2t2,…,CiNtii.When we analyze and integrate economic data, we need to analyze all economic data to be able to correctly grasp the risks that may exist in the process of economic development and other aspects of economic development. Therefore, there will be an overall extreme value. We denote this extreme value as(3)CiZti=Cin+1ti+1,Cin+2ti+2,…,CiZti+N⋅St.In the above equation,S is the weight that will be generated by the calculation of economic data in the whole. The speed of the i-th data in the t-th iteration update is(4)QitN=Q1t1,Q2t2,…,QitNi,i=1,2,3,…,N.Then the principle of the iterative update of the individual extreme value of thei-th data at the t-th time is as follows:(5)CiNti+1=Cit1+1,YCit2+1≥YCiNt,CiNti=Cit1,YCit≤YCiNt.In the above equation,Y(x) is the degree of adaptation function. All data is updated iteratively at time t, and all extreme values will also change according to the data update. The iterative update formula is as follows:(6)CiZti=GminYCiZti,x=CiZti,where G represents the value of the independent variable x in the function Y(x) and CiZti is the closest accurate actual economic data in the t-th iteration update, which we call the global optimal predicted economic data value.In artificial intelligence, we use particle swarm optimization algorithm to find the trend of economic development. At the same time, we can also use particle swarm optimization algorithm to predict problems and risks in economic data, reducing our search for local data in solving huge economic data. It can predict the possible risks of problems and guide and prevent them in time.(1). Blockchain technology. Before the large-scale economic data, the data information in our economic development needs to be absolutely confidential, so we need to choose an absolutely secure storage system to store this large-scale economic data information, and then add it to this storage server artificial intelligence password recognition algorithm [14]. The block storage mode in blockchain technology can ensure the security of these economic data. Block storage and central storage are shown in Figure 2.Figure 2
Block storage and central storage.As shown in Figure2, the block storage is compared with the central storage. It can be found that it is divided into block storage, and the information and data are not aggregated into a total storage server, so, in this way, it can be guaranteed that the data in a single storage server does not have the risk of data leakage from other storage servers. The economic data information in other storage servers will not be exposed to the risk. This will bring the storage security of economic data to a higher level. If it is economic data stored in a central storage server, it is possible that all economic information leakage will cause huge economic losses [15].Then, in the blockchain storage technology, we will use artificial intelligence technology to encrypt each blockchain. Through artificial intelligence technology, once information leaks, it will intelligently sound an alarm and immediately identify which part of the economic data leakage occurs to prevent greater economic losses [16]. Then the principle of adding a password to the blockchain is as follows.When we set the password, it takes timet and the number of passwords set is d. When we enter the password, the password is passed into the system. The artificial intelligence technology will intelligently identify the entered password. If the password is entered incorrectly, it will not be possible to view economic data information; the principle is as follows:(7)Code=fd∗rs.In the above equation,r is the ability of artificial intelligence to recognize whether it is a password input. Once it is found that this is not a password but other means, it will immediately give an intelligent alarm and prevent illegal intrusion from the outside world. The recognition principle is as follows.We assume that the number of passwords identified in artificial intelligence recognition is 4, and the password of one of the blockchains is set to 8888; then the first step of artificial intelligence recognition is to identify whether the number of passwords corresponds:(8)Code=f4∗rs=In4.If the number of passwords corresponds, the artificial intelligence will monitor whether the password is correct, and if it does not correspond, it will perform artificial intelligence to close the password input program so that the password cannot be entered. After entering the correct number of passwords, artificial intelligence will recognize whether the password is correct, if the following forms are recognized:(9)Code⟶8888=In8888⟶t∗vtrue.In this way, economic information data stored in one of our blockchains will be open for us to analyze and use. In order to ensure the security of the data in the data store, a password is set for each storage area [17, 18]. In this way, the risk of economic leakage can be better prevented.


## 2.2. The Monitoring and Early Warning of the Economy by Artificial Intelligence

The arrival of the information age not only brought convenience to economic development but also brought equal risks to the economic field [19]. For example, the financial risks brought by the development of our network economy will bring huge losses to the economic market, but the huge data information in the financial field cannot be detected manually, so it will greatly increase the occurrence of financial risks [20, 21]. Artificial intelligence is a computer technology that imitates the human brain. Therefore, with the help of artificial intelligence, we can quickly identify abnormal fluctuations in financial data and issue alarms in a timely manner, which also provides us with time to respond to financial risks [22]. The process of intelligently identifying economic risks is shown in Figure 3.Figure 3
Flow chart of intelligently identifying economic risks.As shown in Figure3, once the AI recognition system detects abnormal fluctuations in data or abnormal data, it will immediately issue an alarm. Before entering the recognition technology of artificial intelligence, economic data will undergo diversion analysis. The purpose of information diversion is to allow artificial intelligence to accurately identify economic data. The distribution of economic data is shown in Figure 4.Figure 4
Economic data distribution diagram.The data stored in the blockchain will be divided and summarized according to the number of block memories, and the data information stored in each block is different. Therefore, when the area is summarized, weighti will be generated. When there are W data streams, they will be summarized to the output layer M. The calculation formula is as follows:(10)M1=∑ixW1W2∗ϖ,Mn=∑ixWn−1Wn∗ϖ.In the above equation,x is the internal threshold of the system, and ϖ is the correlation coefficient matrix generated when processing the data stream, and its form is as follows:(11)ϖ=xni.Then the total amount of data recognized by intelligent artificial technology is calculated as follows:(12)H=M1+⋯Mn∗ϖ.In this way, entering the artificial intelligence risk identification system can be identified at a faster speed, and, in this identification process, the artificial intelligence algorithm monitoring and early warning system will predict the future economic development trend based on the indicators of economic development surprise. The economic early warning system was created following a crisis in the capitalist market, in order to avoid the negative consequences of economic shrinkage and to avoid greater losses due to failure to respond to the corresponding countermeasures when the economy is bad. Nowadays, with the development of technology and economy, the huge amount of economic data and information makes it difficult for humans to analyze, and the development of the digital economy also brings greater risks of business cycle fluctuations, so the use of artificial intelligence algorithms to monitor and early warning can promptly discover potential risks and then make corresponding countermeasures [23].In the artificial intelligence economic early warning system, in addition to predicting possible economic risks, we also need to provide early warning of the existing economic development boom, so that this big economic data platform can not only use artificial intelligence algorithm monitoring and early warning for risk prediction but also calculate the prosperity index of economic development, making it a way to detect economic risks and calculate the prosperity of economic development, so that we can better make corresponding countermeasures. Therefore, the current economic monitoring and early warning need to not only consider the economic development prosperity index but also monitor the abnormal fluctuations of economic data and potential risks in the economic field, so as to escort the economic development of our information age.


## 2.3. Construction of Big Data Monitoring and Early Warning Platform for Smart Economy

In order to allow the long-term and stable development of the economic market, we have rebuilt the economic monitoring and early warning system. This system combines artificial intelligence recognition technology and artificial intelligence algorithm monitoring and early warning. In combination with the big data platform to accommodate the large-scale economic information data, the newly built monitoring and early warning platform is shown in Figure5.Figure 5
Construction of monitoring and early warning platform.As we can see in Figure5, the newly constructed big data platform for economic management uses blockchain storage technology in the storage server for storing data, which can prevent the risk of economic information leakage in the first step of economic information storage. Of course, just in case, we still apply artificial intelligence monitoring and early warning to the blockchain storage server in the big data platform to ensure the security of economic data information [24]. In the artificial intelligence algorithm monitoring and early warning system, artificial intelligence algorithm technology is used to calculate the prosperity index of economic development, the prosperity index is an annual ranking based on factors such as wealth, economic growth, personal well-being, and quality of life, and artificial intelligence recognition technology is used to identify potential risks in the process of economic development, such as financial risks, management risks, fiscal risks, and industrial risks; artificial intelligence early warning technology can intelligently prevent and control some risks and send signals through certain alarm systems to let us know potential risks and make corresponding countermeasures.In order for artificial intelligence to more accurately predict the trend of economic development, we have built a new big data platform, the structure of which is shown in Figure6.Figure 6
The newly constructed big data platform.This big data platform contains all the economic data, which is complicated. Therefore, in the big data, it is necessary to have an internal data distribution system to divert and manage the economic big data. The economic data will not be unorganized, so data is split on this big data platform, and a series of identifications are performed on the artificial intelligence platform to promote the speed and accuracy of economic data analysis by the intelligent platform. Although artificial intelligence technology has brought profound changes to the production and life of human society, it will affect not only the industrial structure but also the consumption structure, and it will also have a positive effect on the steady development of the intelligent economy. However, the collection, storage, and use of data may cause information leakage, which may cause serious privacy issues. Therefore, we need to use artificial intelligence to predict the risks in the smart economy to reduce the economic information leakage in the information age [25]. We have integrated artificial intelligence, blockchain technology, and big data into the economic monitoring and early warning platform. In order to verify the feasibility of this platform in the economic field, we have carried out a series of experiment analysis.


## 3. Experiments and Analysis of Smart Economy Big Data Monitoring and Early Warning Platform

### 3.1. Forecast of Potential Economic Risks

In this experiment, we will use a newly constructed platform to repredict the economic risk events of a listed company in the past 25 years. We first counted the various types of economic risks faced by the company’s economic field (including entities and networks) in the past 25 years, including the risks that the company avoided and undetected risks. The risk events are shown in Table1.Table 1
The company’s economic risk events.
Tangible economyCybereconomyTotalCorporate financial risk101626Corporate management risk42428Corporate industry risk6511Economic information leakage risk164056The above is the number of economic risk events that the company has experienced in 25 years. It is understood that, because of the huge scale of economic data, in the previous risk prediction system, only part of the split time was avoided, so some of the risks were recorded after the occurrence for future development of the company. However, due to the rapid development of the information technology era, the expansion of the company’s business scale, and the dramatic increase in the scale of economic data, the risk prediction system cannot take into account all economic data, so only part of the risk is avoided. So this time we use the newly constructed system to bring the economic data of the past 25 years into the newly constructed platform to monitor how many economic risk events can be detected by this platform and compare them with the risk events discovered by the past forecasting system. The result is shown in Figure7.Figure 7
Comparison chart of economic risk prediction.We can see in Figure7 that the company’s original early warning system has many deficiencies in the early warning of economic risks, and its accuracy of risk prediction is not high, so the company can avoid very few risks, which has caused a great deal to the company. The new risk early warning system studied in this article analyzes and recognizes the economic information data of the original 25 years. The detected risk events are close to the total number of risk events counted in the past 25 years. Its risk prediction accuracy rate can reach 98.21%.At the same time, we also separately counted the number of events predicted by the new monitoring and early warning platform for real economic risks and network economic risks. The statistical structure is shown in Table2.Table 2
The number of forecasted events for real economic risks and network economic risks.
Tangible economyCybereconomyTotalCorporate financial risk91524Corporate management risk32427Corporate industry risk5510Economic information leakage risk84048Total2584109Judging from the data in the table, it is more sensitive to the risk prediction of the network economy. The basic network economy risks have been predicted. Therefore, the newly constructed economic early warning system still has a high risk prediction accuracy, and, for every risk in the forecast, an alert will appear to remind us to take preventive measures to reduce the company’s economic losses.


### 3.2. Early Warning Experiment and Analysis of Economic Development Prosperity Index

The economic development prosperity index is a barometer of economic development. With 100 as the critical value, the value is between 0 and 200. The confidence index is higher than 100, indicating that the economy is in a prosperous state and the economy is developing in a good direction. The confidence index is lower than 100, indicating that it is in a downturn and the economic operation is developing in an unfavorable direction. This experiment needs to use the artificial intelligence particle swarm algorithm in the newly constructed platform to predict the company’s economic development boom early warning index for the next five years based on the company’s past economic development. Table3 shows the prosperous index of the company’s economic development in the past 20 years.Table 3
The company’s economic development index in the past 20 years.
YearIndexYearIndex200112020111222002110201210220031022013156200487201416720059220151462006992016134200710020171242008105201812020091072019892010118202086In order to verify the economic development prosperity index of the system in this paper, we use the prosperity index from 2001 to 2014 as the analysis sample in the big data platform. The company’s early warning prosperity index is compared with the data in Table3. The calculated early warning prosperity index is shown in Figure 8.Figure 8
Early warning prosperity index. (a) Prosperity index. (b) Particle swarm optimization process.
(a)(b)In Figure8, there is an optimization process of particle swarm algorithm, which is to reduce the calculation error of the early warning index, so that it can improve the accuracy of the early warning index [26]. We can see the comparison of the data in Figure 8, and we can find that basically the displayed prosperity index is greater than 100, which is similar to the development early warning index recorded in Table 3, so the company’s economy is developing steadily. However, in 2019, there is a big error with the data in Table 3. It is because 2019 is affected by the company’s internal factors. This is a factor that the platform cannot take into account, which leads to a big error.


### 3.3. Experiment Summary

Through experiment 1, we can see that the new intelligent platform is very sensitive to economic risk monitoring. Comparing its predicted data with actual data, basically all risk events can be accurately predicted, especially in the risk forecast of the network economy which can basically reach 99.2% of the forecast, while the accuracy of the real economy’s forecast is not very high. It may be because the information and data of the real economy need to be manually counted in the data platform. The data records made here may not be very accurate, so the sensitivity of this platform to the risk prediction of the real economy is not high; the second experiment is to provide early warning and prediction of the economic development prosperity index of this platform, and it can be found that this platform is very effective. The accuracy of the early warning of the economic development prosperity index is also very high. Through the verification of Experiment 1 and Experiment 2, the economic management big data platform built in this article for monitoring and early warning of artificial intelligence algorithms has high accuracy in predicting economic risks, and the prediction and calculation of the prosperity index of economic development are also very high, but, in the forecasting of the early warning prosperity index, the instantaneous emergent factors will also be inadequately considered, causing great errors.


## 3.1. Forecast of Potential Economic Risks

In this experiment, we will use a newly constructed platform to repredict the economic risk events of a listed company in the past 25 years. We first counted the various types of economic risks faced by the company’s economic field (including entities and networks) in the past 25 years, including the risks that the company avoided and undetected risks. The risk events are shown in Table1.Table 1
The company’s economic risk events.
Tangible economyCybereconomyTotalCorporate financial risk101626Corporate management risk42428Corporate industry risk6511Economic information leakage risk164056The above is the number of economic risk events that the company has experienced in 25 years. It is understood that, because of the huge scale of economic data, in the previous risk prediction system, only part of the split time was avoided, so some of the risks were recorded after the occurrence for future development of the company. However, due to the rapid development of the information technology era, the expansion of the company’s business scale, and the dramatic increase in the scale of economic data, the risk prediction system cannot take into account all economic data, so only part of the risk is avoided. So this time we use the newly constructed system to bring the economic data of the past 25 years into the newly constructed platform to monitor how many economic risk events can be detected by this platform and compare them with the risk events discovered by the past forecasting system. The result is shown in Figure7.Figure 7
Comparison chart of economic risk prediction.We can see in Figure7 that the company’s original early warning system has many deficiencies in the early warning of economic risks, and its accuracy of risk prediction is not high, so the company can avoid very few risks, which has caused a great deal to the company. The new risk early warning system studied in this article analyzes and recognizes the economic information data of the original 25 years. The detected risk events are close to the total number of risk events counted in the past 25 years. Its risk prediction accuracy rate can reach 98.21%.At the same time, we also separately counted the number of events predicted by the new monitoring and early warning platform for real economic risks and network economic risks. The statistical structure is shown in Table2.Table 2
The number of forecasted events for real economic risks and network economic risks.
Tangible economyCybereconomyTotalCorporate financial risk91524Corporate management risk32427Corporate industry risk5510Economic information leakage risk84048Total2584109Judging from the data in the table, it is more sensitive to the risk prediction of the network economy. The basic network economy risks have been predicted. Therefore, the newly constructed economic early warning system still has a high risk prediction accuracy, and, for every risk in the forecast, an alert will appear to remind us to take preventive measures to reduce the company’s economic losses.


## 3.2. Early Warning Experiment and Analysis of Economic Development Prosperity Index

The economic development prosperity index is a barometer of economic development. With 100 as the critical value, the value is between 0 and 200. The confidence index is higher than 100, indicating that the economy is in a prosperous state and the economy is developing in a good direction. The confidence index is lower than 100, indicating that it is in a downturn and the economic operation is developing in an unfavorable direction. This experiment needs to use the artificial intelligence particle swarm algorithm in the newly constructed platform to predict the company’s economic development boom early warning index for the next five years based on the company’s past economic development. Table3 shows the prosperous index of the company’s economic development in the past 20 years.Table 3
The company’s economic development index in the past 20 years.
YearIndexYearIndex200112020111222002110201210220031022013156200487201416720059220151462006992016134200710020171242008105201812020091072019892010118202086In order to verify the economic development prosperity index of the system in this paper, we use the prosperity index from 2001 to 2014 as the analysis sample in the big data platform. The company’s early warning prosperity index is compared with the data in Table3. The calculated early warning prosperity index is shown in Figure 8.Figure 8
Early warning prosperity index. (a) Prosperity index. (b) Particle swarm optimization process.
(a)(b)In Figure8, there is an optimization process of particle swarm algorithm, which is to reduce the calculation error of the early warning index, so that it can improve the accuracy of the early warning index [26]. We can see the comparison of the data in Figure 8, and we can find that basically the displayed prosperity index is greater than 100, which is similar to the development early warning index recorded in Table 3, so the company’s economy is developing steadily. However, in 2019, there is a big error with the data in Table 3. It is because 2019 is affected by the company’s internal factors. This is a factor that the platform cannot take into account, which leads to a big error.


## 3.3. Experiment Summary

Through experiment 1, we can see that the new intelligent platform is very sensitive to economic risk monitoring. Comparing its predicted data with actual data, basically all risk events can be accurately predicted, especially in the risk forecast of the network economy which can basically reach 99.2% of the forecast, while the accuracy of the real economy’s forecast is not very high. It may be because the information and data of the real economy need to be manually counted in the data platform. The data records made here may not be very accurate, so the sensitivity of this platform to the risk prediction of the real economy is not high; the second experiment is to provide early warning and prediction of the economic development prosperity index of this platform, and it can be found that this platform is very effective. The accuracy of the early warning of the economic development prosperity index is also very high. Through the verification of Experiment 1 and Experiment 2, the economic management big data platform built in this article for monitoring and early warning of artificial intelligence algorithms has high accuracy in predicting economic risks, and the prediction and calculation of the prosperity index of economic development are also very high, but, in the forecasting of the early warning prosperity index, the instantaneous emergent factors will also be inadequately considered, causing great errors.


## 4. Discussion

This article first explains the artificial intelligence algorithm and establishes a theoretical basis for the later platform construction. Different types of risks in the economic field have a huge impact on economic development. Especially in the era of advanced information technology and rapid development of the network economy, the process of global economic integration is accelerating. Once economic risks appear, they will have a series of economic losses; that is, the world economy nowadays affects the whole body. Therefore, a more intelligent monitoring and early warning system is needed to monitor economic development. In addition, the development of the information age has led to a dramatic increase in the scale of economic data, so it is necessary to analyze and protect the security of these data with the help of advanced science and technology such as big data and blockchain.This article discusses the construction of an economic management big data platform with artificial intelligence algorithm monitoring and early warning. The previous economic early warning system has been improved to make it more suitable for the era of the prevailing network economy. In addition, the economic risk early warning system and the forecasting functions of economic development trends have been integrated on this platform, hoping to simplify the economic field’s economic data. The repeatability of the analysis simplifies the work of economic data analysis. Improving the work efficiency in the economic field is to share the big data platform for risk prediction and economic development trends in the economic field to realize the corresponding function of the function. The newly constructed economic management big data platform relies on the blockchain storage technology, which can largely ensure the security of economic data and escort the development of economy.This article verifies through experiments that the artificial intelligence algorithm monitoring and early warning economic management big data platform constructed in this article has high accuracy for economic risk prediction in the economic field and is especially suitable for the current era of network economy and the risks to the network economy. Forecasting is more sensitive than the risk prediction of the real economy, so I think this economic management big data platform needs to be improved. The risk prediction of the real economy can reach the same accuracy as the accuracy of the risk prediction of the network economy, accurately ensuring the smooth operation of the real economy. This article also has high accuracy for the economic development prospects’ prosperity index. Therefore, although the economic management big data platform constructed in this article is lacking in the prediction of the real economy, it has high accuracy in other aspects. At the same time, the blockchain technology in the platform can ensure the security of economic data information and improve the security protection index of economic information, which is very suitable for the current era of network economy.


## 5. Conclusions

This article describes the mobile Internet technology and explains how to ensure the security of economic data and information with artificial intelligence algorithms and blockchain technology. There are many factors that affect economic development, so the artificial intelligence algorithm monitoring and early warning economic management big data platform that this article studies combines big data and blockchain technology to strengthen the security of economic data storage to reduce the risk of information leakage. Then artificial intelligence early warning technology and recognition technology are used to identify the abnormal fluctuations of economic data in the cycle, and algorithm technology is used to calculate the economic development prosperity index, hoping to predict the economic development trend. This paper conducts experiments on this platform and it is found that the research in this paper is successful. The use of artificial intelligence to monitor and early-warn the various influencing factors in the economic field can find the potential factors affecting economic development with the greatest probability and make early response measures. However, the economic management big data platform constructed in this article is still slightly insufficient in the forecasting function of the real economy and needs to be improved. In this paper, although artificial intelligence algorithm warning technology is used in the construction of the big data platform for economic management, uncertain factors are still not taken into account. It is hoped that this aspect can be overcome in future research. Is the economic management of the big data platform more advanced?.



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*Source: 1206405-2022-04-14.xml*