# Complexity of the Analysis of Financial Cloud Based on Fuzzy Theory in the Wisdom of Sustainable Urban Development

Authors: Yi Chen; Dong Yao; Yinying Duan
Journal: Complexity
 (2021)

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## Abstract

With the rapid growth of our country’s economy, fuzzy theory has become more and more widely used in the financial field. This research mainly explores the application of financial cloud based on fuzzy theory in the sustainable development of smart cities. This research will apply the related knowledge of fuzzy theory, combine the traditional information system risk assessment thought and cloud computing system risk assessment, and propose a series of risk assessment models for the cloud computing system. First, design a smart city model to analyze the potential security issues of the financial cloud computing system. The security level model of cloud computing establishes an index system for evaluation objects based on the level of the security model and uses expert evaluation methods to build models for all levels of the risk profile through the analysis-level process and establish a fuzzy relationship model for each evaluation object value. We objectively evaluate the smart city model based on the designed financial cloud platform. Then, specific statistical analysis is performed on the fuzzy relationship model corresponding to the weight value of the evaluation object, the calculation result is finally obtained, and the risk assessment report of the financial cloud computing system is provided. For every 2% increase in trade dependence, the informatization level of smart cities will drop by about 0.03% on average. The findings have long strengthened the overall coordinated development of the financial system, optimized the financial structure, improved development efficiency, promoted close integration of science and technology and finance, and played the role of government leader it was to fulfill. It shows that we need to maximize it and improve the informatization when building smart cities. The level of development is very important for accelerating urban construction.


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## Body

## 1. Introduction

With the development of economy and the progress of science and technology, more and more information is spread on the Internet and sent through the Internet. People have entered the era of information explosion and sank in the ocean of information. The problem people face is no longer too little information, but too much information. To find necessary information accurately and quickly from a large amount of information and to predict people’s future actions through a large amount of information became a social participant. This is an urgent problem for enterprises to solve. People have entered the era of big data. The development of technologies such as data mining and cloud computing helps people solve these problems. These Internet solutions to information-related issues actually correspond to related information optimization models.The essence of the construction of smart cities and the new urbanization is based on the theme of sustainable environmental protection, and people’s living environment is the core of the construction. Smart cities are not only the direction of new urbanization, but also an important starting point for new urbanization. The construction of smart cities with the theme of green, environmental protection and energy saving is an important carrier for the development of new urbanization. Smart cities will promote the concrete construction of new urbanization, make urban infrastructure more perfect, promote the modern development of industry and agriculture, strengthen the connection of human activities through information means, improve service quality, and create a more refined urban management model. Smart cities solve the urban and rural problems encountered in the development of new urbanization. This can be seen as a supplement to the development of new urbanization at the macrolevel.Cloud computing is rapidly becoming the new norm in the financial industry. Misra believes that the level of virtualization makes mature cloud solutions very convenient in terms of quantity or quality. He studied the realization process of cloud services in financial services, intermediaries, and banking through a participant-based stakeholder model. Through a hypothetical comprehensive case study of a bank, the drivers of adoption are discussed in detail. Use a technique based on participant dependency to analyze modeling requirements before changes and draw a roadmap and rationale. By using the stakeholder model, the dependence and relationship between different stakeholders are studied. In addition, it discusses how decision makers in the financial services industry evaluate, integrate, and eventually migrate to the new architecture. The stakeholder model he proposed lacks specific experimental verification [1]. The growing demand for financial companies to mitigate the losses caused by cyber incidents is driving the rapid development of cybersecurity insurance (CI). Gai believes that the CI implementation covers all aspects of a cyber incident. He studied the implementation of CI based on cloud service products and proposed a security network event analysis framework using big data, called cost-conscious hierarchical network event analysis (CA-HCIA). He uses Monte Carlo simulation to extract event features based on the training data set. The main algorithms in CA-HCIA include Monte Carlo network feature extraction (MC2FE) and optimal cost balance (OCA) algorithms. His research process lacks experimental comparison [2]. The combination of cloud systems and multimedia big data is a novel method for financial service organizations (FSI) to effectively diversify services. Li believes that when the service media channel changes, service availability usually conflicts with security constraints. He proposed a novel method for obtaining secure financial services based on multimedia big data in cloud computing using semantic-based access control (SBAC) technology. This model is designed to protect access security between various media through multiple cloud platforms. The main algorithms supporting this model include ontology-based access recognition (OBAR) algorithm and semantic information matching (SIM) algorithm. The basic idea of his research is not clear enough [3]. Recently, encrypted cloud computing has become an interesting research area. Kirlar believes that, due to the activities between service providers and service requesters, it is very important to share and coordinate computing resources. He uses game theory methods derived from networks, servers, operating systems, and storage devices to mathematically associate the field with game theory. He proposed a new and efficient encryption system using XTR (Effective and Compact Subgroup Tracking Representation), which has semantic security. The encryption system he proposed is not very secure [4].The evaluation index system is established by factor analysis, and the weight of factors affecting financial indicators is determined by the analysis hierarchy process. The current market comparison method focuses on the appraiser’s subjective judgment when determining the price factors, but the analysis level process can reasonably judge the weight of the factors that affect the multistage and multipurpose financial indicators through two judgment matrices. Fuzzy theory is used to comprehensively evaluate each item and then determine the evaluation model of specific factors of the item. Compared with the current market comparison method, it shows the collective wisdom in determining comparative transaction examples and eliminates the shortcomings of simple average in final pricing. The application and operation steps of the fuzzy evaluation model in the actual evaluation are explained through empirical analysis, and it is clear that the application of the model is scientific and objective.


## 2. Sustainable Development of Smart Cities

### 2.1. Fuzzy Theory

#### 2.1.1. Overview of Fuzzy Theory

Fuzzy theory has excellent generalization ability and is especially suitable for dealing with complex problems. And it is widely used in pattern recognition, automatic control information processing, and other fields. Fuzzy theory is a parallel network with powerful computing power, similar to the parallel structure of multisensor information fusion. Fuzzy theory has inherent advantages in the description and processing of uncertain events and incorrect knowledge. Reducing the fuzzy rule set according to the degree of support of the rule can reduce the influence of noise data and deviation value to a certain extent. The incompleteness of noise data and rule set affects the classification accuracy of the algorithm. If the sample data is large, the following formula can be used to calculate the support of fuzzy rules to simplify the calculation process [5]:(1)RSrx=∑j=1X¯∏j=1NfsupBik∈BiμBjkxi,j∑i=1X∏j=1NfsupBjk∈BjμBjkxi,j.Takexi as the sample of the training data set RSrx and X¯ as the subset of X [6].On the basis of the above definition, for those samples whose corresponding rules cannot be found in the rule set, recent rules are used to classify such samples. The nearest rule is essentially an approximation of the rule corresponding to the sample to be classified. This definition is based on the unique nature of the classification problem. That is, the sample and its nearest neighbors often belong to the same class. According to the above definition, the latest rules are used to classify samples that cannot find corresponding rules in the rule set. The most recent rule is essentially an approximation of the rule corresponding to the classification sample and is based on the inherent characteristics of the classification problem. In other words, most of the samples and recent ones belong to the same category [7].(2)Ifi,fj=∫∫pfi,fjlogpfi,fjpfipfjdfidfj.Here,pfi and pfj are the probability distributions of feature fi or fj. Among clustering algorithms based on objective functions, FCM clustering algorithm is the most perfect theoretical result and the most widely used algorithm. FCM’s clustering algorithm divides the data set X into C categories. The clustering center and composition model are V=xjand U=vi, respectively, which indicates the degree of membership of the X sample to the i-th cluster center. The best objective function of the FCM algorithm is as follows [8]:(3)minimizeJmU,V=∑i=1c∑j=1nμijmxj−vi2.Here,JmU,V is the Euclidean distance of the cluster centers. μ is called a weighted index, which may affect the clustering effect of FCM. Generally speaking, time series multistep (long-term) forecasting refers to the forecast of multiple data points in the future. With the deepening of people’s research problems and actual needs, multistep forecasting is more valuable in theory and application than single-step forecasting. At the same time, most time series models take the improvement of forecast accuracy as the only goal. However, in practical problems, not only does the model need to predict correctly, but the model data is also transparent, which can explain the forecast results [9].


#### 2.1.2. Fuzzy Time Series

We will define the universeT asUmin−U1,Umax+U2 and it can be divided into different language intervals; the membership functions of these language intervals are(4)Hl=gHlr1r1+gHlr2r2+…+gHlrnrn.Among them,U represents the financial industry amplitude of historical data and gHi represents the language interval corresponding to the language value. All fuzzy logic relations consist of two consecutive fuzzy sets.By constructing a weighting matrix and then performing renormalization, it is defined as the following formula:(5)qis=q1∑j=1mqj,q2∑j=1mqj,…,qm∑j=1mqj.Among them,qi represents the weight corresponding to the fuzzy sequence at time i, the denominator represents the sum of the weights corresponding to the fuzzy sequence, and the equation on the left is the renormalization weighting matrix.Finally, we get the fuzzy matrix and weighting matrix through the above steps:(6)Hs=Kdfs−1×ϖis−1.The formula obtained by the adaptive expectation model is as follows:(7)Fs=ds−1+g×Fs−ds−1.We get the final prediction result through the formula and then revise the financial market price amplitude forecast based on the fuzzy time series:(8)G−Fs=ds−1+βNN−1∑i=1N∑j=1nDijGis−qjs−1,where the left side of the equation represents the final predicted value of the index.(9)R=∑i=1nactuals−gs2n.Formula (9) expresses the evaluation of the prediction performance of the fuzzy time series. There is no interaction between the fuzzy plates, and the connection matrix is asymmetric. The application of fuzzy theory in the financial cloud can improve the accuracy of its analysis.


### 2.2. Smart City

Smart cities are regarded as the final version of future cities. Take wisdom as the main feature of urban development and promote the sustainable development of modern cities. Its root lies in the development led by human wisdom. A longitudinal timeline analysis of the historical development of smart cities shows that it can be divided into 3 different stages, namely, digital city, networked city, and smart city. Because the second half of development is based on the previous stage, they cannot be separated from each other. A smart city is an innovative design that integrates informatization, industrialization, and urbanization and realizes all cloud sharing and interconnection. In other words, the city’s data and information utilization rate is higher. All these prospects need to introduce the concepts of sustainable innovation and ecological development on the premise of technical support [10].With the development of the times, the construction of smart cities must become the main route of industrialization, but in this area of research, information architecture and big data implementations support technologies in other areas of later times. Smart cities need to pay attention to all kinds of application solutions in the early stages. It is a visual design stage of cutting-edge data reconstruction and information analysis. To build a smart city, both of these points are indispensable [11].


### 2.3. Financial Cloud

#### 2.3.1. Overview of Financial Cloud

Financial cloud computing is a combination of computer technology and financial innovation technology, which is widely used in my country. Experts and scholars in various fields have begun research on the construction of cloud computing infrastructure and the effective use of resources. The ITINN diagram of the framework process of financial cloud computing is shown in Figure1:Figure 1
ITINN diagram of financial cloud computing framework process.Using ITINN, people can access financial services to solve various problems. However, one person can play many roles on the Internet. This phenomenon has brought serious problems to ITIN’s network security, greatly worsening risks such as operational risks, market selection risks, and network and information security risks. ITIN responds to risks by establishing a highly reliable, reasonable, and effective risk assessment model. The expected improvement target model of each financial cloud computing user is as follows [12]:(10)ETu=a1∗ln1+TeTu−maxxK10+K1v11+TeTu−maxxRI/K10+K1.Among them,ETu is the expected value. In order to achieve widespread adoption of cloud computing, risk management is very important. The user must be aware of the risks associated with the application and the data transfer process in order to be able to study the appropriate mechanism. However, due to the inherent characteristics of cloud computing and the dependence of cloud computing service providers on risk control, the risk management of cloud computing is different from the risk management of previous computing environments. Through the risk management framework, users can decide on cloud migration. Especially through this framework, users can identify risks based on the relative importance of transition goals and use semiquantitative methods to analyze financial market risks. In this way, users can make correct cloud migration decisions according to a specific transition plan [13].


#### 2.3.2. Entropy Weight-Financial Cloud Evaluation Model

This paper will use the forward cloud generator and the X-condition cloud generator to model the evaluation grade cloud. According to the evaluation grade standard, the three parameters (Rx, Rn, and Ge) of the cloud model are used to represent the cloud of each grade of smart city finance. Specific steps are as follows:The entropy method is used to calculate the weight of each indicator of the evaluation system, and the weight vector is(11)ϑ=ϑ1,ϑ2,…,ϑn.Determine the cloud characteristic value according to the evaluation grade classification standard and the corresponding grade of each evaluation index:(12)Rx,Rn,Ge.The three characteristic value parametersRx,Rn,Ge of each grade cloud are determined by the upper and lower boundary values of each evaluation index corresponding to its grade, which can be calculated by the corresponding characteristic value parameter value formula. There is data xij, where i is the evaluation index, j is the evaluation level corresponding to data x, and the qualitative language concept of index i corresponding to its level j in the data xij can be expressed by a cloud model, and a cloud model of i∗j can be obtained.Since the median value of each level is the qualitative concept that best represents the level, the expected value is expressed as(13)Rxij=xij1+xij22.As the boundary of each level,xij belongs to both the upper level and the next level. Therefore, the boundary value of the upper and lower levels of membership is equal, so that(14)exp−xij1−xij228Rnij2≈0.5.Finally, get the entropy value as(15)Rnij=xij1−xij22.35.Superentropy is generally obtained based on entropy and experience and mainly reflects the thickness of the cloud.According to the characteristic parameter values of the three clouds that have been calculated and the actual evaluation index data after screening, theX-condition cloud generator algorithm is used to obtain the membership degree of each index corresponding to each level, and the membership of each index obtained in degree constitutes the membership degree matrix T=ϕijm×n, and then the membership degree of the actual index data belonging to each level of cloud is(16)ϕ0=exp−x0−Rx22Rnn2.Among them, Rnn is a normal random distribution with Rn as the expectation and Ge as the standard deviation. The specific formula is(17)Rnn=randn1∗Ge+Gn.Fuzzy transform the obtained weight vector and the membership matrix of each evaluation object:(18)Y=U⊗I.Among themY=y1,y2,…,yl.yl represents the degree to which each evaluation object belongs to a certain level, and the level of the object to be evaluated can be obtained, that is, the level with the largest degree of membership.


### 2.4. Sustainable Development

Improvements in these areas can also increase the motivation for new urbanization and sustainable development. The popularization and utilization of smart applications have brought innovation and accumulation of knowledge and have become the central driving force of urban development. The accumulation of this knowledge has a great impact on the protection and optimization of the urban environment, the development of high-tech industries, and the promotion of urban culture and technological vitality. Build a comprehensive urban-rural information platform, strengthen urban-rural interaction, grasp rich information on employment and life services, accelerate the urbanization of immigrant workers, and promote the development of rural e-commerce. Based on the integration of urban and rural comprehensive development ideas, individual management systems realize resource sharing and utilization and sustainable development, so that conversion can also change the original regional division plan [14].


## 2.1. Fuzzy Theory

### 2.1.1. Overview of Fuzzy Theory

Fuzzy theory has excellent generalization ability and is especially suitable for dealing with complex problems. And it is widely used in pattern recognition, automatic control information processing, and other fields. Fuzzy theory is a parallel network with powerful computing power, similar to the parallel structure of multisensor information fusion. Fuzzy theory has inherent advantages in the description and processing of uncertain events and incorrect knowledge. Reducing the fuzzy rule set according to the degree of support of the rule can reduce the influence of noise data and deviation value to a certain extent. The incompleteness of noise data and rule set affects the classification accuracy of the algorithm. If the sample data is large, the following formula can be used to calculate the support of fuzzy rules to simplify the calculation process [5]:(1)RSrx=∑j=1X¯∏j=1NfsupBik∈BiμBjkxi,j∑i=1X∏j=1NfsupBjk∈BjμBjkxi,j.Takexi as the sample of the training data set RSrx and X¯ as the subset of X [6].On the basis of the above definition, for those samples whose corresponding rules cannot be found in the rule set, recent rules are used to classify such samples. The nearest rule is essentially an approximation of the rule corresponding to the sample to be classified. This definition is based on the unique nature of the classification problem. That is, the sample and its nearest neighbors often belong to the same class. According to the above definition, the latest rules are used to classify samples that cannot find corresponding rules in the rule set. The most recent rule is essentially an approximation of the rule corresponding to the classification sample and is based on the inherent characteristics of the classification problem. In other words, most of the samples and recent ones belong to the same category [7].(2)Ifi,fj=∫∫pfi,fjlogpfi,fjpfipfjdfidfj.Here,pfi and pfj are the probability distributions of feature fi or fj. Among clustering algorithms based on objective functions, FCM clustering algorithm is the most perfect theoretical result and the most widely used algorithm. FCM’s clustering algorithm divides the data set X into C categories. The clustering center and composition model are V=xjand U=vi, respectively, which indicates the degree of membership of the X sample to the i-th cluster center. The best objective function of the FCM algorithm is as follows [8]:(3)minimizeJmU,V=∑i=1c∑j=1nμijmxj−vi2.Here,JmU,V is the Euclidean distance of the cluster centers. μ is called a weighted index, which may affect the clustering effect of FCM. Generally speaking, time series multistep (long-term) forecasting refers to the forecast of multiple data points in the future. With the deepening of people’s research problems and actual needs, multistep forecasting is more valuable in theory and application than single-step forecasting. At the same time, most time series models take the improvement of forecast accuracy as the only goal. However, in practical problems, not only does the model need to predict correctly, but the model data is also transparent, which can explain the forecast results [9].


### 2.1.2. Fuzzy Time Series

We will define the universeT asUmin−U1,Umax+U2 and it can be divided into different language intervals; the membership functions of these language intervals are(4)Hl=gHlr1r1+gHlr2r2+…+gHlrnrn.Among them,U represents the financial industry amplitude of historical data and gHi represents the language interval corresponding to the language value. All fuzzy logic relations consist of two consecutive fuzzy sets.By constructing a weighting matrix and then performing renormalization, it is defined as the following formula:(5)qis=q1∑j=1mqj,q2∑j=1mqj,…,qm∑j=1mqj.Among them,qi represents the weight corresponding to the fuzzy sequence at time i, the denominator represents the sum of the weights corresponding to the fuzzy sequence, and the equation on the left is the renormalization weighting matrix.Finally, we get the fuzzy matrix and weighting matrix through the above steps:(6)Hs=Kdfs−1×ϖis−1.The formula obtained by the adaptive expectation model is as follows:(7)Fs=ds−1+g×Fs−ds−1.We get the final prediction result through the formula and then revise the financial market price amplitude forecast based on the fuzzy time series:(8)G−Fs=ds−1+βNN−1∑i=1N∑j=1nDijGis−qjs−1,where the left side of the equation represents the final predicted value of the index.(9)R=∑i=1nactuals−gs2n.Formula (9) expresses the evaluation of the prediction performance of the fuzzy time series. There is no interaction between the fuzzy plates, and the connection matrix is asymmetric. The application of fuzzy theory in the financial cloud can improve the accuracy of its analysis.


## 2.1.1. Overview of Fuzzy Theory

Fuzzy theory has excellent generalization ability and is especially suitable for dealing with complex problems. And it is widely used in pattern recognition, automatic control information processing, and other fields. Fuzzy theory is a parallel network with powerful computing power, similar to the parallel structure of multisensor information fusion. Fuzzy theory has inherent advantages in the description and processing of uncertain events and incorrect knowledge. Reducing the fuzzy rule set according to the degree of support of the rule can reduce the influence of noise data and deviation value to a certain extent. The incompleteness of noise data and rule set affects the classification accuracy of the algorithm. If the sample data is large, the following formula can be used to calculate the support of fuzzy rules to simplify the calculation process [5]:(1)RSrx=∑j=1X¯∏j=1NfsupBik∈BiμBjkxi,j∑i=1X∏j=1NfsupBjk∈BjμBjkxi,j.Takexi as the sample of the training data set RSrx and X¯ as the subset of X [6].On the basis of the above definition, for those samples whose corresponding rules cannot be found in the rule set, recent rules are used to classify such samples. The nearest rule is essentially an approximation of the rule corresponding to the sample to be classified. This definition is based on the unique nature of the classification problem. That is, the sample and its nearest neighbors often belong to the same class. According to the above definition, the latest rules are used to classify samples that cannot find corresponding rules in the rule set. The most recent rule is essentially an approximation of the rule corresponding to the classification sample and is based on the inherent characteristics of the classification problem. In other words, most of the samples and recent ones belong to the same category [7].(2)Ifi,fj=∫∫pfi,fjlogpfi,fjpfipfjdfidfj.Here,pfi and pfj are the probability distributions of feature fi or fj. Among clustering algorithms based on objective functions, FCM clustering algorithm is the most perfect theoretical result and the most widely used algorithm. FCM’s clustering algorithm divides the data set X into C categories. The clustering center and composition model are V=xjand U=vi, respectively, which indicates the degree of membership of the X sample to the i-th cluster center. The best objective function of the FCM algorithm is as follows [8]:(3)minimizeJmU,V=∑i=1c∑j=1nμijmxj−vi2.Here,JmU,V is the Euclidean distance of the cluster centers. μ is called a weighted index, which may affect the clustering effect of FCM. Generally speaking, time series multistep (long-term) forecasting refers to the forecast of multiple data points in the future. With the deepening of people’s research problems and actual needs, multistep forecasting is more valuable in theory and application than single-step forecasting. At the same time, most time series models take the improvement of forecast accuracy as the only goal. However, in practical problems, not only does the model need to predict correctly, but the model data is also transparent, which can explain the forecast results [9].


## 2.1.2. Fuzzy Time Series

We will define the universeT asUmin−U1,Umax+U2 and it can be divided into different language intervals; the membership functions of these language intervals are(4)Hl=gHlr1r1+gHlr2r2+…+gHlrnrn.Among them,U represents the financial industry amplitude of historical data and gHi represents the language interval corresponding to the language value. All fuzzy logic relations consist of two consecutive fuzzy sets.By constructing a weighting matrix and then performing renormalization, it is defined as the following formula:(5)qis=q1∑j=1mqj,q2∑j=1mqj,…,qm∑j=1mqj.Among them,qi represents the weight corresponding to the fuzzy sequence at time i, the denominator represents the sum of the weights corresponding to the fuzzy sequence, and the equation on the left is the renormalization weighting matrix.Finally, we get the fuzzy matrix and weighting matrix through the above steps:(6)Hs=Kdfs−1×ϖis−1.The formula obtained by the adaptive expectation model is as follows:(7)Fs=ds−1+g×Fs−ds−1.We get the final prediction result through the formula and then revise the financial market price amplitude forecast based on the fuzzy time series:(8)G−Fs=ds−1+βNN−1∑i=1N∑j=1nDijGis−qjs−1,where the left side of the equation represents the final predicted value of the index.(9)R=∑i=1nactuals−gs2n.Formula (9) expresses the evaluation of the prediction performance of the fuzzy time series. There is no interaction between the fuzzy plates, and the connection matrix is asymmetric. The application of fuzzy theory in the financial cloud can improve the accuracy of its analysis.


## 2.2. Smart City

Smart cities are regarded as the final version of future cities. Take wisdom as the main feature of urban development and promote the sustainable development of modern cities. Its root lies in the development led by human wisdom. A longitudinal timeline analysis of the historical development of smart cities shows that it can be divided into 3 different stages, namely, digital city, networked city, and smart city. Because the second half of development is based on the previous stage, they cannot be separated from each other. A smart city is an innovative design that integrates informatization, industrialization, and urbanization and realizes all cloud sharing and interconnection. In other words, the city’s data and information utilization rate is higher. All these prospects need to introduce the concepts of sustainable innovation and ecological development on the premise of technical support [10].With the development of the times, the construction of smart cities must become the main route of industrialization, but in this area of research, information architecture and big data implementations support technologies in other areas of later times. Smart cities need to pay attention to all kinds of application solutions in the early stages. It is a visual design stage of cutting-edge data reconstruction and information analysis. To build a smart city, both of these points are indispensable [11].


## 2.3. Financial Cloud

### 2.3.1. Overview of Financial Cloud

Financial cloud computing is a combination of computer technology and financial innovation technology, which is widely used in my country. Experts and scholars in various fields have begun research on the construction of cloud computing infrastructure and the effective use of resources. The ITINN diagram of the framework process of financial cloud computing is shown in Figure1:Figure 1
ITINN diagram of financial cloud computing framework process.Using ITINN, people can access financial services to solve various problems. However, one person can play many roles on the Internet. This phenomenon has brought serious problems to ITIN’s network security, greatly worsening risks such as operational risks, market selection risks, and network and information security risks. ITIN responds to risks by establishing a highly reliable, reasonable, and effective risk assessment model. The expected improvement target model of each financial cloud computing user is as follows [12]:(10)ETu=a1∗ln1+TeTu−maxxK10+K1v11+TeTu−maxxRI/K10+K1.Among them,ETu is the expected value. In order to achieve widespread adoption of cloud computing, risk management is very important. The user must be aware of the risks associated with the application and the data transfer process in order to be able to study the appropriate mechanism. However, due to the inherent characteristics of cloud computing and the dependence of cloud computing service providers on risk control, the risk management of cloud computing is different from the risk management of previous computing environments. Through the risk management framework, users can decide on cloud migration. Especially through this framework, users can identify risks based on the relative importance of transition goals and use semiquantitative methods to analyze financial market risks. In this way, users can make correct cloud migration decisions according to a specific transition plan [13].


### 2.3.2. Entropy Weight-Financial Cloud Evaluation Model

This paper will use the forward cloud generator and the X-condition cloud generator to model the evaluation grade cloud. According to the evaluation grade standard, the three parameters (Rx, Rn, and Ge) of the cloud model are used to represent the cloud of each grade of smart city finance. Specific steps are as follows:The entropy method is used to calculate the weight of each indicator of the evaluation system, and the weight vector is(11)ϑ=ϑ1,ϑ2,…,ϑn.Determine the cloud characteristic value according to the evaluation grade classification standard and the corresponding grade of each evaluation index:(12)Rx,Rn,Ge.The three characteristic value parametersRx,Rn,Ge of each grade cloud are determined by the upper and lower boundary values of each evaluation index corresponding to its grade, which can be calculated by the corresponding characteristic value parameter value formula. There is data xij, where i is the evaluation index, j is the evaluation level corresponding to data x, and the qualitative language concept of index i corresponding to its level j in the data xij can be expressed by a cloud model, and a cloud model of i∗j can be obtained.Since the median value of each level is the qualitative concept that best represents the level, the expected value is expressed as(13)Rxij=xij1+xij22.As the boundary of each level,xij belongs to both the upper level and the next level. Therefore, the boundary value of the upper and lower levels of membership is equal, so that(14)exp−xij1−xij228Rnij2≈0.5.Finally, get the entropy value as(15)Rnij=xij1−xij22.35.Superentropy is generally obtained based on entropy and experience and mainly reflects the thickness of the cloud.According to the characteristic parameter values of the three clouds that have been calculated and the actual evaluation index data after screening, theX-condition cloud generator algorithm is used to obtain the membership degree of each index corresponding to each level, and the membership of each index obtained in degree constitutes the membership degree matrix T=ϕijm×n, and then the membership degree of the actual index data belonging to each level of cloud is(16)ϕ0=exp−x0−Rx22Rnn2.Among them, Rnn is a normal random distribution with Rn as the expectation and Ge as the standard deviation. The specific formula is(17)Rnn=randn1∗Ge+Gn.Fuzzy transform the obtained weight vector and the membership matrix of each evaluation object:(18)Y=U⊗I.Among themY=y1,y2,…,yl.yl represents the degree to which each evaluation object belongs to a certain level, and the level of the object to be evaluated can be obtained, that is, the level with the largest degree of membership.


## 2.3.1. Overview of Financial Cloud

Financial cloud computing is a combination of computer technology and financial innovation technology, which is widely used in my country. Experts and scholars in various fields have begun research on the construction of cloud computing infrastructure and the effective use of resources. The ITINN diagram of the framework process of financial cloud computing is shown in Figure1:Figure 1
ITINN diagram of financial cloud computing framework process.Using ITINN, people can access financial services to solve various problems. However, one person can play many roles on the Internet. This phenomenon has brought serious problems to ITIN’s network security, greatly worsening risks such as operational risks, market selection risks, and network and information security risks. ITIN responds to risks by establishing a highly reliable, reasonable, and effective risk assessment model. The expected improvement target model of each financial cloud computing user is as follows [12]:(10)ETu=a1∗ln1+TeTu−maxxK10+K1v11+TeTu−maxxRI/K10+K1.Among them,ETu is the expected value. In order to achieve widespread adoption of cloud computing, risk management is very important. The user must be aware of the risks associated with the application and the data transfer process in order to be able to study the appropriate mechanism. However, due to the inherent characteristics of cloud computing and the dependence of cloud computing service providers on risk control, the risk management of cloud computing is different from the risk management of previous computing environments. Through the risk management framework, users can decide on cloud migration. Especially through this framework, users can identify risks based on the relative importance of transition goals and use semiquantitative methods to analyze financial market risks. In this way, users can make correct cloud migration decisions according to a specific transition plan [13].


## 2.3.2. Entropy Weight-Financial Cloud Evaluation Model

This paper will use the forward cloud generator and the X-condition cloud generator to model the evaluation grade cloud. According to the evaluation grade standard, the three parameters (Rx, Rn, and Ge) of the cloud model are used to represent the cloud of each grade of smart city finance. Specific steps are as follows:The entropy method is used to calculate the weight of each indicator of the evaluation system, and the weight vector is(11)ϑ=ϑ1,ϑ2,…,ϑn.Determine the cloud characteristic value according to the evaluation grade classification standard and the corresponding grade of each evaluation index:(12)Rx,Rn,Ge.The three characteristic value parametersRx,Rn,Ge of each grade cloud are determined by the upper and lower boundary values of each evaluation index corresponding to its grade, which can be calculated by the corresponding characteristic value parameter value formula. There is data xij, where i is the evaluation index, j is the evaluation level corresponding to data x, and the qualitative language concept of index i corresponding to its level j in the data xij can be expressed by a cloud model, and a cloud model of i∗j can be obtained.Since the median value of each level is the qualitative concept that best represents the level, the expected value is expressed as(13)Rxij=xij1+xij22.As the boundary of each level,xij belongs to both the upper level and the next level. Therefore, the boundary value of the upper and lower levels of membership is equal, so that(14)exp−xij1−xij228Rnij2≈0.5.Finally, get the entropy value as(15)Rnij=xij1−xij22.35.Superentropy is generally obtained based on entropy and experience and mainly reflects the thickness of the cloud.According to the characteristic parameter values of the three clouds that have been calculated and the actual evaluation index data after screening, theX-condition cloud generator algorithm is used to obtain the membership degree of each index corresponding to each level, and the membership of each index obtained in degree constitutes the membership degree matrix T=ϕijm×n, and then the membership degree of the actual index data belonging to each level of cloud is(16)ϕ0=exp−x0−Rx22Rnn2.Among them, Rnn is a normal random distribution with Rn as the expectation and Ge as the standard deviation. The specific formula is(17)Rnn=randn1∗Ge+Gn.Fuzzy transform the obtained weight vector and the membership matrix of each evaluation object:(18)Y=U⊗I.Among themY=y1,y2,…,yl.yl represents the degree to which each evaluation object belongs to a certain level, and the level of the object to be evaluated can be obtained, that is, the level with the largest degree of membership.


## 2.4. Sustainable Development

Improvements in these areas can also increase the motivation for new urbanization and sustainable development. The popularization and utilization of smart applications have brought innovation and accumulation of knowledge and have become the central driving force of urban development. The accumulation of this knowledge has a great impact on the protection and optimization of the urban environment, the development of high-tech industries, and the promotion of urban culture and technological vitality. Build a comprehensive urban-rural information platform, strengthen urban-rural interaction, grasp rich information on employment and life services, accelerate the urbanization of immigrant workers, and promote the development of rural e-commerce. Based on the integration of urban and rural comprehensive development ideas, individual management systems realize resource sharing and utilization and sustainable development, so that conversion can also change the original regional division plan [14].


## 3. Inquiry Experiments on the Sustainable Development of Smart Cities

### 3.1. Research Object

The group’s total investment in smart city projects is 60 million yuan (including financing from third parties), mainly provided by bank loans and corporate bond issuance. In addition, for projects funded by group F, third-party investment institutions will raise funds through project equity financing and mezzanine finance. The financing structure of smart city infrastructure construction projects is shown in Table1. The holding structure of the company group is 60%, the government is 20%, and the third-party investment is 20%. For details, see Table 1. Project financing time service is the best principle for construction costs. According to the project investment progress, the investment in recent years is through bank loans and bond issuance and other methods for financing [15].Table 1
Financing structure of smart city infrastructure construction projects.
FinancingAmountTermsInterest rateOther costsGuarantee/mortgageThe total amount dueBank loan2 600046.440Government guarantee22240Corporate bonds2000077.40Guarantee feesGuarantee company30400Equity financing26000320–240Guarantee company0Mezzanine financing4000The termInterest rate0Guarantee company4200–6000


### 3.2. Risk-Taking Construction

According to the complexity of the PFI project plan at the initial stage, the government and company departments conduct detailed analysis and investigations during the project preparation stage and develop their own plans for risk sharing. Next, the government allocates the risks according to the preallocated risks, and the risks borne by the government and corporate departments alone determine the composition of these shared risks. After that, the government and company departments conduct resource assessment and model building on the results of the first sharing stage, and then both parties formulate a systematic risk management plan to evaluate and set prices for the risks they bear. Finally, the two parties reached a risk expectation agreement and signed the contract. If there are doubts about the risk allocated to either party, the risk allocation mechanism will be redetermined and a new risk allocation will be made [16].


### 3.3. Construction of Financial Cloud Platform

Based on the experience in the design and application of cloud computing data centers, temporary data centers and new data centers built by smart cities will be designed and implemented internationally, including the decoration engineering and security design and implementation of computer rooms, as well as system integration and operation and maintenance services such as network, storage, server, cloud management platform, and technologies for intelligent information construction and subsequent development, providing professional technical support [17].


### 3.4. Model Test

This study combined exploratory factor analysis and confirmatory factor analysis to test the model. SPSS and Amos were selected for data processing. Use principal component analysis to extract factors, filter data, and propose indicator hypotheses. Through analysis, the hypothesis of the previous stage is verified, and the construction equation measurement model of the evaluation index system is finally determined for extraction factor analysis [18].


### 3.5. Establishing a Fuzzy Relationship Model

In the process of information system risk assessment, due to the discrete and discontinuous distribution of risk factors, it is impossible to quantitatively and accurately explain multiple factors. Therefore, it is necessary to use fuzzy theory to process fuzzy information and effectively quantify fuzzy information. This research aims at the financial innovation of national commercial banks, so the data is comparable in principle. In this study, the expert evaluation method was used to analyze the data and establish a membership model. The method is equivalent to data collection to a certain extent to avoid subjectivity [19].In the process of risk assessment using fuzzy theory, in order to qualitatively deal with the evaluation object, it is necessary to rely on the opinions of experts in related fields. To effectively use expert advice on the Internet, natural language must be digitized and replaced with a series of numbers. In order to achieve this goal, fuzzy calculations are used to convert the opinions of experts into fuzzy theoretical problems to solve. Regarding this fuzzy problem, expert evaluation methods are often used to establish fuzzy relationship models. The analysis results of the final layer are evaluated by 40 experts, and the evaluation results of 6 stages are obtained, and the fuzzy relationship model is finally determined [20].


### 3.6. Data Evaluation

The measurement unit of some indicators varies from time to time, and statistical data needs to be processed uniformly. The amount of data itself cannot be directly used in the evaluation index system. It is necessary to analyze the relationship between the evaluation indexes scientifically and objectively and to standardize the data. This research uses Z-Score technology to standardize the constructed data. In order to avoid the fitting of data in exploratory factor analysis and confirmatory factor analysis, the sorted data is divided into two parts. 200 data pieces were used for search factor analysis, and 200 data pieces were used for detection factor analysis [21].


## 3.1. Research Object

The group’s total investment in smart city projects is 60 million yuan (including financing from third parties), mainly provided by bank loans and corporate bond issuance. In addition, for projects funded by group F, third-party investment institutions will raise funds through project equity financing and mezzanine finance. The financing structure of smart city infrastructure construction projects is shown in Table1. The holding structure of the company group is 60%, the government is 20%, and the third-party investment is 20%. For details, see Table 1. Project financing time service is the best principle for construction costs. According to the project investment progress, the investment in recent years is through bank loans and bond issuance and other methods for financing [15].Table 1
Financing structure of smart city infrastructure construction projects.
FinancingAmountTermsInterest rateOther costsGuarantee/mortgageThe total amount dueBank loan2 600046.440Government guarantee22240Corporate bonds2000077.40Guarantee feesGuarantee company30400Equity financing26000320–240Guarantee company0Mezzanine financing4000The termInterest rate0Guarantee company4200–6000


## 3.2. Risk-Taking Construction

According to the complexity of the PFI project plan at the initial stage, the government and company departments conduct detailed analysis and investigations during the project preparation stage and develop their own plans for risk sharing. Next, the government allocates the risks according to the preallocated risks, and the risks borne by the government and corporate departments alone determine the composition of these shared risks. After that, the government and company departments conduct resource assessment and model building on the results of the first sharing stage, and then both parties formulate a systematic risk management plan to evaluate and set prices for the risks they bear. Finally, the two parties reached a risk expectation agreement and signed the contract. If there are doubts about the risk allocated to either party, the risk allocation mechanism will be redetermined and a new risk allocation will be made [16].


## 3.3. Construction of Financial Cloud Platform

Based on the experience in the design and application of cloud computing data centers, temporary data centers and new data centers built by smart cities will be designed and implemented internationally, including the decoration engineering and security design and implementation of computer rooms, as well as system integration and operation and maintenance services such as network, storage, server, cloud management platform, and technologies for intelligent information construction and subsequent development, providing professional technical support [17].


## 3.4. Model Test

This study combined exploratory factor analysis and confirmatory factor analysis to test the model. SPSS and Amos were selected for data processing. Use principal component analysis to extract factors, filter data, and propose indicator hypotheses. Through analysis, the hypothesis of the previous stage is verified, and the construction equation measurement model of the evaluation index system is finally determined for extraction factor analysis [18].


## 3.5. Establishing a Fuzzy Relationship Model

In the process of information system risk assessment, due to the discrete and discontinuous distribution of risk factors, it is impossible to quantitatively and accurately explain multiple factors. Therefore, it is necessary to use fuzzy theory to process fuzzy information and effectively quantify fuzzy information. This research aims at the financial innovation of national commercial banks, so the data is comparable in principle. In this study, the expert evaluation method was used to analyze the data and establish a membership model. The method is equivalent to data collection to a certain extent to avoid subjectivity [19].In the process of risk assessment using fuzzy theory, in order to qualitatively deal with the evaluation object, it is necessary to rely on the opinions of experts in related fields. To effectively use expert advice on the Internet, natural language must be digitized and replaced with a series of numbers. In order to achieve this goal, fuzzy calculations are used to convert the opinions of experts into fuzzy theoretical problems to solve. Regarding this fuzzy problem, expert evaluation methods are often used to establish fuzzy relationship models. The analysis results of the final layer are evaluated by 40 experts, and the evaluation results of 6 stages are obtained, and the fuzzy relationship model is finally determined [20].


## 3.6. Data Evaluation

The measurement unit of some indicators varies from time to time, and statistical data needs to be processed uniformly. The amount of data itself cannot be directly used in the evaluation index system. It is necessary to analyze the relationship between the evaluation indexes scientifically and objectively and to standardize the data. This research uses Z-Score technology to standardize the constructed data. In order to avoid the fitting of data in exploratory factor analysis and confirmatory factor analysis, the sorted data is divided into two parts. 200 data pieces were used for search factor analysis, and 200 data pieces were used for detection factor analysis [21].


## 4. Sustainable Development of Smart Cities

### 4.1. Exploratory Factor Analysis

The results of KMO test and Bartlett sphericity test show that the KMO value is 0.870, which means that there is no significant difference in the degree of correlation between the variables, which can be considered very suitable for factor analysis. The Bartlett value is 3013.022 and the associated probability is less than 0.01, which means that the correlation coefficient matrix is not an identity matrix, so it is considered suitable for factor analysis. Exploratory factor analysis refers to a factor analysis method that performs dimensionality reduction analysis on multiple factors to find common factors. In this study, SPSS software was used to perform exploratory factor analysis on the data, which made the operation simple and easy to understand. The principal component analysis method is selected to perform factor analysis on the twenty-six-indicator sample data in the past five years. The first analysis result shows that the KMO measurement value is less than 0.5, and the Bartlett sphere test significance coefficient is not less than 0.01, so the indicators need to be deleted. When deleting, it should be noted that the number of deleted indicators is less than 3 dimensions, and the total load is small. The index that crosses other indexes has larger load. It should be deleted when the meaning of the indicator is different from other indicators in the same dimension. After many calculations and analyses, the length of the optical cable line, the year-end mobile phone user rate, the proportion of e-commerce transaction activities, the income from the letter business, the harmless treatment rate of domestic waste, the centralized treatment rate of sewage treatment plants, and general industrial solids were deleted in sequence. The comprehensive utilization rate of waste, the green coverage rate of built-up areas, the proportion of administrative villages that have Internet broadband services, and the per capita GDP are ten indicators, leaving 17 indicators. The analysis results are shown in Table2. Observed from the table, the KMO value is 0.674, which is greater than 0.5, the approximate chi-square of Bartlett’s sphere test is 739.470, and the Sig is 0.000, which is less than 0.01. Therefore, the index system formed by these 17 indicators is very suitable for factor analysis [22–24].Table 2
KMO and Bartlett’s test.
KMO and Bartlett’s testThe Kaiser-Meyer-OIkin metric of sampling adequacy0.674Bartlett’s sphericity test approximates chi-square739.470df136Sig0.000The estimated values of various parameters each month are shown in Table3. According to the estimated parameter values, we find that the company’s own publicity resources are very rich in several portal websites in our country, which leads to a high value of a; that is, the company has an excellent publicity effect through the characteristics of its own portal websites. The β value is equivalent to the Internet user innovation diffusion coefficient (0.0001), which is in line with the diffusion law of Internet services. The y value satisfies the limit of 0.17 ≤ X ≤ 0.66, but it is slightly low. This is mainly because mobile cloud computing is in its infancy in our country, the number of accumulated users is not enough, the industry mechanism is not perfect, and the effect of word-of-mouth communication is not significant. Among them, we determine the social impact target, the perceived cost target, the perceived risk target, and the performance expectation improvement target to have weights of 0.35, 0.2, 0.05, and 0.4, respectively. Among them, most Internet companies pay insufficient attention to user information leakage. Therefore, the weight coefficient of the perceived risk target is lower than that of several other targets. According to the calculation results, when the company’s subsidiaries make investment decisions for new products, 12% of the funds should be used for the promotion of new products to enhance users’ social impact goals. 67.2% of the funds are used for technological innovation of the data processing capabilities of new products to improve user performance expectations and reduce user perceived costs. The remaining 20.8% of the funds are used for technological innovation of the security protection capabilities of new products and a series of measures such as preventing user information leakage to reduce the perceived risks of users using new products. After validating the models, cloud providers make investment decisions based on the multipurpose model provided in this article. If there is no change in the total investment of the cloud provider, by changing the value of the major investment, users can achieve user performance expectations, social impact, tangible costs, and vision. The four objectives of risk are maximized and optimized to improve personal QoE and create maximum value for cloud providers and users [23].Table 3
Various parameter values estimated every month.
Month1234567891012Q(T) (10,000 people)60220274.62422933674406.020.329.467.2A(T) (10,000 yuan)32.22202006020060603002006060S(T) (10,000 people)77.26466.762.276.472.677.26.39.222.229.9Table4 is the evaluation index system of the sustainable development level of smart cities. This paper uses SPSS software to analyze the correlation of the original indicators, verify and eliminate some indicators, and establish a smart city sustainable development level indicator system. Import the standardized data into the SPSS19.0 software, and obtain the correlation coefficient and significance level matrix between the indicators through correlation analysis, and conduct a comparative analysis of related indicators in the following text.Table 4
Evaluation index system of sustainable development level of smart city.
Target layerSubtarget levelFirst-level indexSecondary indicatorsIndexSmart citySupport levelBasic information facilitiesInfrastructure investment accounts for the proportion of social fixed asset investmentX1Internet broadband penetration rateX2Basic network construction levelX3Construction and sharing of basic information resourcesX4The proportion of science and technology expenditures in local fiscal expendituresX5Technological innovation drivenThe proportion of scientific research funding in GDPX6City cloud platform application situationX7City innovation and entrepreneurship levelX8Government online service levelX9Policy supportSocialized people’s livelihood service levelX10Data open service levelX11Citizen information promotion trainingX12Development planning statusX13Social levelThe level of urbanizationUrbanization rateX14People’s livelihoodPublic library collectionX15Number of college studentsX16Economic levelMarket sizeTotal investment in fixed assetsX17GDP growth rateX18Emerging industry development levelInformation service practitionersX19Citizen’s network levelX20The added value of the tertiary industry as a proportion of GDPX21According to the final 13 index variables, the evaluation indexes are arranged in order of size and graded. In this paper, by referring to relevant documents and combining with the development status of smart cities in our country, all indicators are divided into 5 levels to evaluate the development level of smart cities. Level I indicates that the level of smart city development is high, and level II indicates that the level of smart city development is relatively high. High, level III indicates that the level of smart city development is medium, level IV indicates that the level of smart city development is weak, and level V indicates that the level of smart city development is weak.The specific evaluation index levels are divided as follows: The positive index takes indexX1 (the proportion of infrastructure investment in social fixed asset investment) as an example, the first-level interval is [5.4003, 5.9844], the second-level interval is [4.2319, 5.4003), and the third-level interval is [3.0635, 4.2319), the interval of level IV is [1.8952, 3.0635), the interval of level V is [1.3110, 1.8952), the negative index is X13 (energy consumption per unit of GDP) as an example, and the interval of level I is [0.160, 0.351], the second-level interval is [0.351, 0.733), the third-level interval is [0.733, 1.115), the fourth-level interval is [1.115, 1.497), and the fifth-level interval is [1.497, 1.688]. The results of the standard classification of all indicators are shown in Figure 2.Figure 2
Characteristic value of the financial cloud model of smart city development level.


### 4.2. Revised Analysis of the Index System

To 10 appraisal experts, the smart city model constructed the 2 levels of 9 relative importance judgment value scoring tables for the first round of scoring. Due to the differences in the knowledge background of the experts and the angle of understanding of the problem, the first round of score statistics is used to propose the evaluation items with more controversial scores. Experts give their opinions, discuss thoroughly, and basically reach an agreement before proceeding to the second round. Round importance verdicts are assigned, the scores of most experts in the second assignment result are combined to create a verdict matrix for each relative importance, and computer data processing and integrity testing are performed. Through the descriptive statistical analysis of the survey data, 39 factors that have little effect on the price of the city’s financial market were removed from the 53 factors. Through factor analysis of the remaining 25 factors, the status of the indicator system has changed. The factor level is revised from the original 22 factors to 8 items, and the factor level is revised from the original 72 index factors to 28 items. The revised result of the index system is shown in Figure3. You can see that the factors of eigenvalues greater than 2 there are 8, respectively, 20.828, 2.088, 2.080, 2.688, 2.898, 2.828, 2.288, and 2.282, the eight principal component factors of variance contribution rate were 88.2%, 8.688%, 8.8%, 6.909%, 6.680%, 8.892%, 8.229%, and 8.882%, and the cumulative variance contribution rate is 92.8%, containing 92.8% of the original index information, thus ideal factor extraction results. The 8 common factors extracted can explain 92.8% of the variance. Therefore, it can be seen that the model has good structural validity. From Figure 3, we can see that r has the characteristic of peak and tail, which does not satisfy the normal distribution. Since all ARMA models imply the assumption that the time series is stationary, then we must perform a unit root test on r to judge the stationarity of r. The ADF test is used to test whether r is stable. The test results show that the ADF test statistics (absolute value) are all significantly greater than the critical value at the 2%, 8%, and 20% significance levels, and the null hypothesis containing unit roots is rejected; that is, if there is a unit root, the rate of return sequence is stationary [25–27].Figure 3
The revised results of the index system.There are many application scenarios of visual design in smart cities, and its breadth and breadth involve all walks of life, but it is defined as the field of medical visualization. The fundamental principles of visual design and the level of interactive psychological thinking do have similarities. More important is the cognitive and interactive psychological thinking of users and audiences. In this more serious application scenario, no matter the interface display of the mobile or fixed end, it is necessary to present a lightweight, simple, and direct design, because of the visual design. The main idea is to solve the problem. Through 30 experts to evaluate the factors of the last layer, the evaluation results of model safety are shown in Figure4. Among the 30 experts, 20 regarded it as safe, 5 regarded it as relatively safe, and 5 regarded it as unsafe. Then the membership degree of the operation model antisabotage measures at the safety level is 2/3. The degree of membership of the safer level is 1/6. The membership degree of the general level of security is 1/6. The degree of membership of the more dangerous level is 0. The degree of membership of the dangerous level is 0. The average daily rate of return of the company is positive. Among them, the average rate of return of mezzanine financing is the highest, reaching 0.068%. The return performance is the best, followed by corporate bonds and bank financing. The difference between the maximum and minimum values of equity financing is the smallest, and the standard deviation is also the smallest, only 0.864%, indicating that equity financing has the smallest stock market volatility and the most stable stock market compared to other representative countries; the standard deviation corresponding to other earnings is the largest, indicating that, compared with other representative countries, the stock market is the most volatile. The maximum and minimum values of other profit yields are close to the daily limit and down limit, respectively, indicating that the volatility is quite astonishing under the negative and positive impact of the market. Then use the J-B test to test the normality of the rate of return, which also includes the kurtosis and skewness of the rate of return. The J-B test result shows that the rate of return significantly rejects the assumption of normal distribution. The skewness values of the yields are all less than 0, except for bank loans; the skewness of other yields are all negative; that is, the distribution is a negative skew distribution, which means their kurtosis is more inclined to the right; their kurtosis values are far. It is much greater than 3, so the yield has a lean kurtosis shape. Financial time series usually have the characteristics of sharp peaks and thick tails, showing the dependence of tail risks. Therefore, the research of tail correlation is very important, which provides us with a preliminary understanding of the model of return [28, 29].Figure 4
Evaluation results of model safety.


### 4.3. Membership Function Parameter Setting

Table5 shows the parameters of each membership function after training. The shape of the function before and after training has changed, which is due to the changes in the parameters of the characterizing function of each fuzzy set. These are all in order to enable the function after training to more accurately describe the complex function relationship within the training sample. Therefore, it is proved that the entire training process is similar to an optimization process. Through continuous iterative attempts, the process of describing the training samples more comprehensively and completely within the acceptable error range is optimized. This process is mainly through optimization and adjustment of the parameters of the membership function, and it is not achieved by adjusting the membership function type.Table 5
Membership function parameters after training.
Membership functionParameters (mf1)Parameters (mf2)Parameters (mf3)Enter 1(0.243, 2.018, 0.0598)(0.373, 1.978, 0.629)(0.358, 1.978, 0.939)Enter 2(0.231, 2.12, 0.078)(0.12, 2.005, 0.581)(0.294, 2.006, 0.955)Enter 3(0.525, 1.992, 0.193)(0.395, 2.018, 0.691)(0.036, 2.007, 1.035)Enter 4(0.311, 2.014, 0.068)(0.278, 1.998, 0.459)(0.295, 2.014, 0.892)Enter 5(0.334, 2.002, 0.07)(0.198, 2.013, 0.467)(0.376, 1.998, 0.889)As shown in Figure5, they are the average error value and the fluctuation error value of the 9 membership functions. From the figure above, the average error of the trapezoidal membership function is 9.5, which is the largest, and the average error of the triangular membership function is the next 9th, combined with Gaussian, π-type, sigmoidal, differential sigmoidal, product-type sigmoidal, and bell-type six types of membership. The average error of the degree function is relatively close to about 5, and the average error of the Gaussian membership function is about 1.5 minimum. It is proved that, from the point of view of average error, Gaussian membership function is the most suitable function type for urban sustainable performance evaluation. At the same time, the average error of the triangle and trapezoid of the two linear membership functions is significantly higher than the average error of the other functions.Figure 5
Comparison of average error and fluctuation error of different membership functions.From the point of view of the fluctuation error value, the fluctuation error of the trapezoidal membership function is the largest at 22, and the fluctuation error of the triangular membership function is the second highest. The five types of membership are combined Gauss,π, sigmoidal, differential sigmoidal, and product sigmoidal. The average error of the function is relatively close to about 15, the fluctuation error of the bell-shaped membership function is 9, and the fluctuation error of the Gaussian membership function is about 6 minimum. It proves that, from the perspective of fluctuation error, the Gaussian membership function is still the most suitable function type for the evaluation of urban sustainable performance. At the same time, the fluctuation error of the triangle and trapezoid of the two linear membership functions is significantly higher than the fluctuation error of other nonlinear membership functions, which proves that, from the perspective of fluctuation error, the linear membership function’s evaluation error in evaluating the sustainable performance of the city is obviously larger than the evaluation error of the nonlinear membership function. This conclusion also proves that the sustainability evaluation of smart cities is a complex, multiobjective giant system, and there are complex nonlinear relationships between variables themselves.In order to reduce the error, the original data of the evaluation index system is first normalized and divided according to a certain ratio. 10 evaluation objects are randomly selected as the training set, and the remaining 25 are used as the test set. The evaluation, training set, and test set feedback results are shown in Figure6. It can be seen from Figure 6 that the training results are better, and the correlation coefficient is 0.9682.Figure 6
Comparison between the result of the fuzzy theory test knot and the real value.


### 4.4. Fuzzy Theory Model Test Results

As shown in Figure7, the 8 membership functions have certain training errors in 7 systems, plus the bell-shaped membership function of the previous chapter, the largest belongs to the trapezoidal membership in FTM7, the error is 8.87%, the smallest belongs to the Gaussian membership function and has an error of 0.09% in FTM1, and the errors of FTM2 and FTM7 are significantly higher than those of the other five FTM models. This is because FTM2 is a secondary system and has a certain cumulative error. Because the output after FTM3 and FTM4 training is the input of FTM2. FTM7 is a three-level system with greater cumulative error. The output after training of FTM1, FTM2, FTM5, and FTM6 is the input of FTM7. However, the training errors of these systems are within the acceptable range, so they are all effective FTM models.Figure 7
7 FTM model training errors.As shown in Figure8, different membership functions use 185 test samples and the corresponding fuzzy calculation values are more or less different, plus the training error of the bell-shaped membership function, the smallest error is Gaussian membership, the “Chongqing City” error of the degree function is 0.01%, and the largest is “Tongchuan City” error of 8.36%, and both are within the acceptable range (<0.1). It is proved that these nine membership functions are acceptable on the fuzzy model. After verifying the effectiveness of different membership functions in FTM1, the remaining 6 FTM systems can also be trained according to the same steps. The following table shows the training errors of the remaining 8 membership functions in 7 FTM systems.Figure 8
Fuzzy theory model test results.As shown in Figure9, it is the accuracy test of Fuzzy Theory Model (FTM) in different cities. It can be seen that there will be certain errors. The largest error is “Fuzhou City” in FTM2, with an error of 6.68%, and the smallest error is “Beijing” in FTM1; the specific error is 0.02%. Prove that these 7 fuzzy theoretical models after training can be used to evaluate the sustainable performance of smart cities.Figure 9
Accuracy test of fuzzy model.


### 4.5. Corporate Value Assessment Results

The traditional methods of determining membership degree mainly include expert experience method, fuzzy statistics method, and binary comparison ranking method. Most of these methods are based on subjective value or empirical formula to obtain membership degree, and there are more subjective factors in it. According to the principle of the reverse membership cloud generator, the membership function obtained by statistical analysis of the data effectively avoids the influence of subjective factors and makes the result more objective and true. However, this method requires a large number of sample points. In this article, we use a method of scoring based on objective statistical data, based on a horizontal comparative analysis of commercial banks’ financial innovation and data availability, in a cloud model at each evaluation index of each evaluation object, intending to perform an evaluation. The impact of this has solved the problem of horizontal comparison data shortage, making the evaluation result closer to objective reality. Figure10 shows the financing progress of smart city infrastructure construction projects. The regression coefficient of Ftr is around -0.04, and both pass the 1% significance test, which shows that, for every 2% increase in foreign trade dependence, the level of smart city informatization will drop by about 0.03% on average. If the company can successfully complete the software development of the electronic medical record system in accordance with the project progress, change the sales strategy, and accelerate the recovery of sales accounts, the future profit growth space can be expected. After adding the interaction terms of financial development and technological innovation variables, the signs of the variables have not changed much, but the significance has improved. The signs of the three interaction terms are significantly positive, which fully shows that the combination of financial development and technological innovation is helpful to promote the development of smart city informatization, and the degree of financial development promotes the construction and development of smart city informatization which is affected by the level of technological innovation. The regression coefficients of DLR∗LnP, FDS∗LnP, and GS∗LnP are 0.029, 0.015, and 0.011, respectively. Although the coefficients are small, they all show a significant positive correlation at the 1% level. The improvement of financial development efficiency is combined with technology. The development of innovation has the most obvious role in promoting the informatization of smart cities, while the expansion of financial scale and the development of the stock market play a relatively small role through technological innovation, and there is a lot of room for improvement [30].Figure 10
Financing progress of smart city infrastructure construction projects.Combine different needs, with the help of fundamental design principles and methods, and then complete a set of practical design application methods. The final value of credit risk assessment is shown in Figure11. It can be seen from the final value of credit risk assessment and the trend chart that the risk assessment method based on fuzzy theory can compare the credit risk status of multiple companies in multiple time periods, and the trend of change can be judged at a glance. The horizontal comparison between enterprises has obtained more intuitive results. The evaluation results obtained by the fuzzy comprehensive evaluation method and the results obtained by the fuzzy catastrophe theory are simultaneously reflected in the broken line chart 4. From the risk fluctuations, we can see that although the results obtained by the comprehensive evaluation method can also reflect changes in risks, when emergencies such as melamine occurred in 2007 and 2008, the evaluation results did not well reflect the risk status caused by such mutations, and the evaluation results in 2007 did not change much from that in 2006, indicating that the method is not sensitive enough to predict sudden risks and has a certain delay. This result is also due to the fact that the fuzzy comprehensive evaluation method is a method to comprehensively judge the overall risk. The occurrence of an emergency will often only affect a certain factor in the evaluation system. If the weight of the factor is small, it will not have a very significant impact on the overall result. In the face of emergencies, the supply chain financial risk assessment model based on the catastrophe theory model basically agrees with the actual situation in the supply chain financial credit risk assessment results, which has certain objective rationality and has certain advantages compared with other models. In addition, the regression coefficient of Gov fluctuates around 0.1 and most of them are significant, indicating that, in view of the large investment in the initial stage of smart city construction, the slow output, and income, the effective support provided by the government in terms of capital and talent policies can play an irreplaceable role in the construction of smart cities leading and guiding role [31, 32].Figure 11
Final value of credit risk assessment.


## 4.1. Exploratory Factor Analysis

The results of KMO test and Bartlett sphericity test show that the KMO value is 0.870, which means that there is no significant difference in the degree of correlation between the variables, which can be considered very suitable for factor analysis. The Bartlett value is 3013.022 and the associated probability is less than 0.01, which means that the correlation coefficient matrix is not an identity matrix, so it is considered suitable for factor analysis. Exploratory factor analysis refers to a factor analysis method that performs dimensionality reduction analysis on multiple factors to find common factors. In this study, SPSS software was used to perform exploratory factor analysis on the data, which made the operation simple and easy to understand. The principal component analysis method is selected to perform factor analysis on the twenty-six-indicator sample data in the past five years. The first analysis result shows that the KMO measurement value is less than 0.5, and the Bartlett sphere test significance coefficient is not less than 0.01, so the indicators need to be deleted. When deleting, it should be noted that the number of deleted indicators is less than 3 dimensions, and the total load is small. The index that crosses other indexes has larger load. It should be deleted when the meaning of the indicator is different from other indicators in the same dimension. After many calculations and analyses, the length of the optical cable line, the year-end mobile phone user rate, the proportion of e-commerce transaction activities, the income from the letter business, the harmless treatment rate of domestic waste, the centralized treatment rate of sewage treatment plants, and general industrial solids were deleted in sequence. The comprehensive utilization rate of waste, the green coverage rate of built-up areas, the proportion of administrative villages that have Internet broadband services, and the per capita GDP are ten indicators, leaving 17 indicators. The analysis results are shown in Table2. Observed from the table, the KMO value is 0.674, which is greater than 0.5, the approximate chi-square of Bartlett’s sphere test is 739.470, and the Sig is 0.000, which is less than 0.01. Therefore, the index system formed by these 17 indicators is very suitable for factor analysis [22–24].Table 2
KMO and Bartlett’s test.
KMO and Bartlett’s testThe Kaiser-Meyer-OIkin metric of sampling adequacy0.674Bartlett’s sphericity test approximates chi-square739.470df136Sig0.000The estimated values of various parameters each month are shown in Table3. According to the estimated parameter values, we find that the company’s own publicity resources are very rich in several portal websites in our country, which leads to a high value of a; that is, the company has an excellent publicity effect through the characteristics of its own portal websites. The β value is equivalent to the Internet user innovation diffusion coefficient (0.0001), which is in line with the diffusion law of Internet services. The y value satisfies the limit of 0.17 ≤ X ≤ 0.66, but it is slightly low. This is mainly because mobile cloud computing is in its infancy in our country, the number of accumulated users is not enough, the industry mechanism is not perfect, and the effect of word-of-mouth communication is not significant. Among them, we determine the social impact target, the perceived cost target, the perceived risk target, and the performance expectation improvement target to have weights of 0.35, 0.2, 0.05, and 0.4, respectively. Among them, most Internet companies pay insufficient attention to user information leakage. Therefore, the weight coefficient of the perceived risk target is lower than that of several other targets. According to the calculation results, when the company’s subsidiaries make investment decisions for new products, 12% of the funds should be used for the promotion of new products to enhance users’ social impact goals. 67.2% of the funds are used for technological innovation of the data processing capabilities of new products to improve user performance expectations and reduce user perceived costs. The remaining 20.8% of the funds are used for technological innovation of the security protection capabilities of new products and a series of measures such as preventing user information leakage to reduce the perceived risks of users using new products. After validating the models, cloud providers make investment decisions based on the multipurpose model provided in this article. If there is no change in the total investment of the cloud provider, by changing the value of the major investment, users can achieve user performance expectations, social impact, tangible costs, and vision. The four objectives of risk are maximized and optimized to improve personal QoE and create maximum value for cloud providers and users [23].Table 3
Various parameter values estimated every month.
Month1234567891012Q(T) (10,000 people)60220274.62422933674406.020.329.467.2A(T) (10,000 yuan)32.22202006020060603002006060S(T) (10,000 people)77.26466.762.276.472.677.26.39.222.229.9Table4 is the evaluation index system of the sustainable development level of smart cities. This paper uses SPSS software to analyze the correlation of the original indicators, verify and eliminate some indicators, and establish a smart city sustainable development level indicator system. Import the standardized data into the SPSS19.0 software, and obtain the correlation coefficient and significance level matrix between the indicators through correlation analysis, and conduct a comparative analysis of related indicators in the following text.Table 4
Evaluation index system of sustainable development level of smart city.
Target layerSubtarget levelFirst-level indexSecondary indicatorsIndexSmart citySupport levelBasic information facilitiesInfrastructure investment accounts for the proportion of social fixed asset investmentX1Internet broadband penetration rateX2Basic network construction levelX3Construction and sharing of basic information resourcesX4The proportion of science and technology expenditures in local fiscal expendituresX5Technological innovation drivenThe proportion of scientific research funding in GDPX6City cloud platform application situationX7City innovation and entrepreneurship levelX8Government online service levelX9Policy supportSocialized people’s livelihood service levelX10Data open service levelX11Citizen information promotion trainingX12Development planning statusX13Social levelThe level of urbanizationUrbanization rateX14People’s livelihoodPublic library collectionX15Number of college studentsX16Economic levelMarket sizeTotal investment in fixed assetsX17GDP growth rateX18Emerging industry development levelInformation service practitionersX19Citizen’s network levelX20The added value of the tertiary industry as a proportion of GDPX21According to the final 13 index variables, the evaluation indexes are arranged in order of size and graded. In this paper, by referring to relevant documents and combining with the development status of smart cities in our country, all indicators are divided into 5 levels to evaluate the development level of smart cities. Level I indicates that the level of smart city development is high, and level II indicates that the level of smart city development is relatively high. High, level III indicates that the level of smart city development is medium, level IV indicates that the level of smart city development is weak, and level V indicates that the level of smart city development is weak.The specific evaluation index levels are divided as follows: The positive index takes indexX1 (the proportion of infrastructure investment in social fixed asset investment) as an example, the first-level interval is [5.4003, 5.9844], the second-level interval is [4.2319, 5.4003), and the third-level interval is [3.0635, 4.2319), the interval of level IV is [1.8952, 3.0635), the interval of level V is [1.3110, 1.8952), the negative index is X13 (energy consumption per unit of GDP) as an example, and the interval of level I is [0.160, 0.351], the second-level interval is [0.351, 0.733), the third-level interval is [0.733, 1.115), the fourth-level interval is [1.115, 1.497), and the fifth-level interval is [1.497, 1.688]. The results of the standard classification of all indicators are shown in Figure 2.Figure 2
Characteristic value of the financial cloud model of smart city development level.


## 4.2. Revised Analysis of the Index System

To 10 appraisal experts, the smart city model constructed the 2 levels of 9 relative importance judgment value scoring tables for the first round of scoring. Due to the differences in the knowledge background of the experts and the angle of understanding of the problem, the first round of score statistics is used to propose the evaluation items with more controversial scores. Experts give their opinions, discuss thoroughly, and basically reach an agreement before proceeding to the second round. Round importance verdicts are assigned, the scores of most experts in the second assignment result are combined to create a verdict matrix for each relative importance, and computer data processing and integrity testing are performed. Through the descriptive statistical analysis of the survey data, 39 factors that have little effect on the price of the city’s financial market were removed from the 53 factors. Through factor analysis of the remaining 25 factors, the status of the indicator system has changed. The factor level is revised from the original 22 factors to 8 items, and the factor level is revised from the original 72 index factors to 28 items. The revised result of the index system is shown in Figure3. You can see that the factors of eigenvalues greater than 2 there are 8, respectively, 20.828, 2.088, 2.080, 2.688, 2.898, 2.828, 2.288, and 2.282, the eight principal component factors of variance contribution rate were 88.2%, 8.688%, 8.8%, 6.909%, 6.680%, 8.892%, 8.229%, and 8.882%, and the cumulative variance contribution rate is 92.8%, containing 92.8% of the original index information, thus ideal factor extraction results. The 8 common factors extracted can explain 92.8% of the variance. Therefore, it can be seen that the model has good structural validity. From Figure 3, we can see that r has the characteristic of peak and tail, which does not satisfy the normal distribution. Since all ARMA models imply the assumption that the time series is stationary, then we must perform a unit root test on r to judge the stationarity of r. The ADF test is used to test whether r is stable. The test results show that the ADF test statistics (absolute value) are all significantly greater than the critical value at the 2%, 8%, and 20% significance levels, and the null hypothesis containing unit roots is rejected; that is, if there is a unit root, the rate of return sequence is stationary [25–27].Figure 3
The revised results of the index system.There are many application scenarios of visual design in smart cities, and its breadth and breadth involve all walks of life, but it is defined as the field of medical visualization. The fundamental principles of visual design and the level of interactive psychological thinking do have similarities. More important is the cognitive and interactive psychological thinking of users and audiences. In this more serious application scenario, no matter the interface display of the mobile or fixed end, it is necessary to present a lightweight, simple, and direct design, because of the visual design. The main idea is to solve the problem. Through 30 experts to evaluate the factors of the last layer, the evaluation results of model safety are shown in Figure4. Among the 30 experts, 20 regarded it as safe, 5 regarded it as relatively safe, and 5 regarded it as unsafe. Then the membership degree of the operation model antisabotage measures at the safety level is 2/3. The degree of membership of the safer level is 1/6. The membership degree of the general level of security is 1/6. The degree of membership of the more dangerous level is 0. The degree of membership of the dangerous level is 0. The average daily rate of return of the company is positive. Among them, the average rate of return of mezzanine financing is the highest, reaching 0.068%. The return performance is the best, followed by corporate bonds and bank financing. The difference between the maximum and minimum values of equity financing is the smallest, and the standard deviation is also the smallest, only 0.864%, indicating that equity financing has the smallest stock market volatility and the most stable stock market compared to other representative countries; the standard deviation corresponding to other earnings is the largest, indicating that, compared with other representative countries, the stock market is the most volatile. The maximum and minimum values of other profit yields are close to the daily limit and down limit, respectively, indicating that the volatility is quite astonishing under the negative and positive impact of the market. Then use the J-B test to test the normality of the rate of return, which also includes the kurtosis and skewness of the rate of return. The J-B test result shows that the rate of return significantly rejects the assumption of normal distribution. The skewness values of the yields are all less than 0, except for bank loans; the skewness of other yields are all negative; that is, the distribution is a negative skew distribution, which means their kurtosis is more inclined to the right; their kurtosis values are far. It is much greater than 3, so the yield has a lean kurtosis shape. Financial time series usually have the characteristics of sharp peaks and thick tails, showing the dependence of tail risks. Therefore, the research of tail correlation is very important, which provides us with a preliminary understanding of the model of return [28, 29].Figure 4
Evaluation results of model safety.


## 4.3. Membership Function Parameter Setting

Table5 shows the parameters of each membership function after training. The shape of the function before and after training has changed, which is due to the changes in the parameters of the characterizing function of each fuzzy set. These are all in order to enable the function after training to more accurately describe the complex function relationship within the training sample. Therefore, it is proved that the entire training process is similar to an optimization process. Through continuous iterative attempts, the process of describing the training samples more comprehensively and completely within the acceptable error range is optimized. This process is mainly through optimization and adjustment of the parameters of the membership function, and it is not achieved by adjusting the membership function type.Table 5
Membership function parameters after training.
Membership functionParameters (mf1)Parameters (mf2)Parameters (mf3)Enter 1(0.243, 2.018, 0.0598)(0.373, 1.978, 0.629)(0.358, 1.978, 0.939)Enter 2(0.231, 2.12, 0.078)(0.12, 2.005, 0.581)(0.294, 2.006, 0.955)Enter 3(0.525, 1.992, 0.193)(0.395, 2.018, 0.691)(0.036, 2.007, 1.035)Enter 4(0.311, 2.014, 0.068)(0.278, 1.998, 0.459)(0.295, 2.014, 0.892)Enter 5(0.334, 2.002, 0.07)(0.198, 2.013, 0.467)(0.376, 1.998, 0.889)As shown in Figure5, they are the average error value and the fluctuation error value of the 9 membership functions. From the figure above, the average error of the trapezoidal membership function is 9.5, which is the largest, and the average error of the triangular membership function is the next 9th, combined with Gaussian, π-type, sigmoidal, differential sigmoidal, product-type sigmoidal, and bell-type six types of membership. The average error of the degree function is relatively close to about 5, and the average error of the Gaussian membership function is about 1.5 minimum. It is proved that, from the point of view of average error, Gaussian membership function is the most suitable function type for urban sustainable performance evaluation. At the same time, the average error of the triangle and trapezoid of the two linear membership functions is significantly higher than the average error of the other functions.Figure 5
Comparison of average error and fluctuation error of different membership functions.From the point of view of the fluctuation error value, the fluctuation error of the trapezoidal membership function is the largest at 22, and the fluctuation error of the triangular membership function is the second highest. The five types of membership are combined Gauss,π, sigmoidal, differential sigmoidal, and product sigmoidal. The average error of the function is relatively close to about 15, the fluctuation error of the bell-shaped membership function is 9, and the fluctuation error of the Gaussian membership function is about 6 minimum. It proves that, from the perspective of fluctuation error, the Gaussian membership function is still the most suitable function type for the evaluation of urban sustainable performance. At the same time, the fluctuation error of the triangle and trapezoid of the two linear membership functions is significantly higher than the fluctuation error of other nonlinear membership functions, which proves that, from the perspective of fluctuation error, the linear membership function’s evaluation error in evaluating the sustainable performance of the city is obviously larger than the evaluation error of the nonlinear membership function. This conclusion also proves that the sustainability evaluation of smart cities is a complex, multiobjective giant system, and there are complex nonlinear relationships between variables themselves.In order to reduce the error, the original data of the evaluation index system is first normalized and divided according to a certain ratio. 10 evaluation objects are randomly selected as the training set, and the remaining 25 are used as the test set. The evaluation, training set, and test set feedback results are shown in Figure6. It can be seen from Figure 6 that the training results are better, and the correlation coefficient is 0.9682.Figure 6
Comparison between the result of the fuzzy theory test knot and the real value.


## 4.4. Fuzzy Theory Model Test Results

As shown in Figure7, the 8 membership functions have certain training errors in 7 systems, plus the bell-shaped membership function of the previous chapter, the largest belongs to the trapezoidal membership in FTM7, the error is 8.87%, the smallest belongs to the Gaussian membership function and has an error of 0.09% in FTM1, and the errors of FTM2 and FTM7 are significantly higher than those of the other five FTM models. This is because FTM2 is a secondary system and has a certain cumulative error. Because the output after FTM3 and FTM4 training is the input of FTM2. FTM7 is a three-level system with greater cumulative error. The output after training of FTM1, FTM2, FTM5, and FTM6 is the input of FTM7. However, the training errors of these systems are within the acceptable range, so they are all effective FTM models.Figure 7
7 FTM model training errors.As shown in Figure8, different membership functions use 185 test samples and the corresponding fuzzy calculation values are more or less different, plus the training error of the bell-shaped membership function, the smallest error is Gaussian membership, the “Chongqing City” error of the degree function is 0.01%, and the largest is “Tongchuan City” error of 8.36%, and both are within the acceptable range (<0.1). It is proved that these nine membership functions are acceptable on the fuzzy model. After verifying the effectiveness of different membership functions in FTM1, the remaining 6 FTM systems can also be trained according to the same steps. The following table shows the training errors of the remaining 8 membership functions in 7 FTM systems.Figure 8
Fuzzy theory model test results.As shown in Figure9, it is the accuracy test of Fuzzy Theory Model (FTM) in different cities. It can be seen that there will be certain errors. The largest error is “Fuzhou City” in FTM2, with an error of 6.68%, and the smallest error is “Beijing” in FTM1; the specific error is 0.02%. Prove that these 7 fuzzy theoretical models after training can be used to evaluate the sustainable performance of smart cities.Figure 9
Accuracy test of fuzzy model.


## 4.5. Corporate Value Assessment Results

The traditional methods of determining membership degree mainly include expert experience method, fuzzy statistics method, and binary comparison ranking method. Most of these methods are based on subjective value or empirical formula to obtain membership degree, and there are more subjective factors in it. According to the principle of the reverse membership cloud generator, the membership function obtained by statistical analysis of the data effectively avoids the influence of subjective factors and makes the result more objective and true. However, this method requires a large number of sample points. In this article, we use a method of scoring based on objective statistical data, based on a horizontal comparative analysis of commercial banks’ financial innovation and data availability, in a cloud model at each evaluation index of each evaluation object, intending to perform an evaluation. The impact of this has solved the problem of horizontal comparison data shortage, making the evaluation result closer to objective reality. Figure10 shows the financing progress of smart city infrastructure construction projects. The regression coefficient of Ftr is around -0.04, and both pass the 1% significance test, which shows that, for every 2% increase in foreign trade dependence, the level of smart city informatization will drop by about 0.03% on average. If the company can successfully complete the software development of the electronic medical record system in accordance with the project progress, change the sales strategy, and accelerate the recovery of sales accounts, the future profit growth space can be expected. After adding the interaction terms of financial development and technological innovation variables, the signs of the variables have not changed much, but the significance has improved. The signs of the three interaction terms are significantly positive, which fully shows that the combination of financial development and technological innovation is helpful to promote the development of smart city informatization, and the degree of financial development promotes the construction and development of smart city informatization which is affected by the level of technological innovation. The regression coefficients of DLR∗LnP, FDS∗LnP, and GS∗LnP are 0.029, 0.015, and 0.011, respectively. Although the coefficients are small, they all show a significant positive correlation at the 1% level. The improvement of financial development efficiency is combined with technology. The development of innovation has the most obvious role in promoting the informatization of smart cities, while the expansion of financial scale and the development of the stock market play a relatively small role through technological innovation, and there is a lot of room for improvement [30].Figure 10
Financing progress of smart city infrastructure construction projects.Combine different needs, with the help of fundamental design principles and methods, and then complete a set of practical design application methods. The final value of credit risk assessment is shown in Figure11. It can be seen from the final value of credit risk assessment and the trend chart that the risk assessment method based on fuzzy theory can compare the credit risk status of multiple companies in multiple time periods, and the trend of change can be judged at a glance. The horizontal comparison between enterprises has obtained more intuitive results. The evaluation results obtained by the fuzzy comprehensive evaluation method and the results obtained by the fuzzy catastrophe theory are simultaneously reflected in the broken line chart 4. From the risk fluctuations, we can see that although the results obtained by the comprehensive evaluation method can also reflect changes in risks, when emergencies such as melamine occurred in 2007 and 2008, the evaluation results did not well reflect the risk status caused by such mutations, and the evaluation results in 2007 did not change much from that in 2006, indicating that the method is not sensitive enough to predict sudden risks and has a certain delay. This result is also due to the fact that the fuzzy comprehensive evaluation method is a method to comprehensively judge the overall risk. The occurrence of an emergency will often only affect a certain factor in the evaluation system. If the weight of the factor is small, it will not have a very significant impact on the overall result. In the face of emergencies, the supply chain financial risk assessment model based on the catastrophe theory model basically agrees with the actual situation in the supply chain financial credit risk assessment results, which has certain objective rationality and has certain advantages compared with other models. In addition, the regression coefficient of Gov fluctuates around 0.1 and most of them are significant, indicating that, in view of the large investment in the initial stage of smart city construction, the slow output, and income, the effective support provided by the government in terms of capital and talent policies can play an irreplaceable role in the construction of smart cities leading and guiding role [31, 32].Figure 11
Final value of credit risk assessment.


## 5. Conclusion

In order to help optimize the industrial structure and promote the transformation of economic development models, through smart city research, it has contributed to the improvement of urban competitiveness. As the future development trend of various countries, the construction of smart cities requires a variety of information technologies to improve the existing urban environment. The realization of a smart city depends on the level of technology mastered by the country. Standing at the forefront of science and technology is to control the market and control the power. Intelligent control and processing of smart cities can be timely, reliable, and effective and can improve city management capabilities. Urban management models need to be synchronized with people’s standard of living. In today’s society, people’s living standards are much better than they used to be. Prior to using the concepts of information technology and smart city construction, it is not possible to realize a city using traditional methods. The construction of an optimized management system is conducive to the city’s response to various unexpected disasters.With the development of global integration, modern fiscal structures are mainly presented in the form of networks. Because financial markets are interconnected and affect each other, individual financial markets are not only restricted by themselves, but also affected by changes in external financial markets. The supporting role of venture capital cannot be compared with other forms of investment, and it is mainly supported by venture capital companies. Turning high-tech achievements into productivity is the rapid development of venture capital, which has become a new industry in terms of capital scale and production, and has played a great role in promoting the development of the national economy.The existing Internet finance model we are studying is an important part of the financial ecological field centered on financial enterprises. These parts play various functions and jointly promote the overall improvement and evolution of the financial ecological field through mutual adjustment. As long as you join a financial ecosystem, you can quickly and simply complete a variety of financial management life, purchase demand and other products, provide personalized Web services, master the characteristics of consumption and financing, correctly discover needs, and reduce our burden in the era of information explosion. Investigation and rational use of information greatly promote our lives.



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*Source: 3444437-2021-07-17.xml*