# Models in the Construction of Accounting Informatization Transformation Based on Digital Twin

Authors: Jilu Liu
Journal: Security and Communication Networks
 (2022)

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## Abstract

The construction of accounting informatization is an effective tool for enterprises to transform from focusing on financial accounting to focusing on group management and decision support. Digital twins make full use of physical models, sensor updates and other data, integrate interdisciplinary and multi-scale modeling processes, and perform complete mapping in virtual space, which can reflect the process of the entire life cycle of the corresponding physical equipment. Based on the technology of digital twin, this paper solved the related problems in the transformation and construction of accounting informatization, and proposed a clustering analysis algorithm. CompanyA was taken as the experimental object, companies B and C were taken as the experimental control group, combined with data analysis, among the accountants of company A, 179 people have a supportive attitude towards the transformation of accounting information, 16 people have a neutral attitude, and 5 people have an opposing attitude, accounting for 89.5%, 8% and 2.5% respectively. However, among companies B and C, 196 and 199 people hold a favorable attitude, accounting for 98% and 99.5% respectively. The experimental results showed that the acceptance of the accounting informatization transformation is still relatively high among the employees of the enterprise.


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## Body

## 1. Introduction

In recent years, with the rapid development of the world economy, the requirements for corporate financial management have become more stringent. Compared with the subsequent financial management model, the transformation of the financial management model is required, and the model for accounting personnel to carry out informatization work is coming soon. This paper enriched the related research on the construction of accounting informatization based on digital twin. Judging from the existing literature, most of the research on digital twins and accounting informatization transformation and construction are carried out separately, and the content of combining the two is still relatively small at present. In the analysis of this paper, the digital twin and accounting informatization transformation were combined to make the research more convincing by analyzing specific cases.Enterprises realize the transformation of accounting, from traditional accounting work or stand-alone accounting software to digital twin accounting service products, which greatly improves the efficiency of accounting work. The innovation of this paper is to analyze the informatization transformation of accounting in combination with the clustering algorithm under the digital twin technology. To a certain extent, this can enable enterprises to innovate in financial management. Such innovation will also allow enterprises to stand out in the fierce market competition and improve their market competitiveness.


## 2. Related Work

With the development of the economy, the requirements of enterprises for accounting are gradually increasing, and the demand for accounting informatization is also more urgent. Scholars from all walks of life have also carried out corresponding research in this field. Liu and Bai believed that the debate on whether different organizational structures of enterprises would lead to differences in the disclosure of accounting information by enterprises was becoming more and more intense, which provided a corresponding basis for the government to establish environmental accounting information disclosure standards [1]. Huynh proposed to discuss business accounting information in the research model, and the research results showed that the acceptance of the accounting information system by the enterprise indicates the influence of organizational culture on the quality of enterprise accounting information [2]. Al-Hattami proposed the structural equation model of PLS. The usage of the model was predicted by information quality, and this model is used to study the transformation and construction of accounting informatization [3]. Syrtseva and Cheban believed that accounting compliance system was a theory to ensure the development and operation of enterprise accounting information quality, then the methodological basis of the research was general science and special cognitive methods [4]. Alasbahi and Ishwara believed that if the accounting information contained in the financial report has a set of characteristics, then the financial report would be considered to be of quality [5]. However, it is more important to pay attention to the transformation of accounting informatization, which is not considered in the above research.In recent years, with the rapid development of information technology, the attention to digital information has become more and more frequent. Gvishiani et al. believed that digital information was a scientific discipline, its origin and development in theory and practice, and outlined the main characteristics and methods of big data processing and analysis [6]. Ownby et al. argued that theoretical approaches to archaeology focus on “big data”, that is, the generation of large and diverse datasets that reflect advances in scientific method and data science [7]. Periola et al. proposed the scope of heterogeneous edge computing to enable low-latency transmission of growing underwater big data, and this experiment showed that using underwater computing entities can improve network performance and associated capital costs [8]. Ramos et al. proposed the knowledge of seasonal climate prediction, and the environment of centralized data storage [9]. Fattahi et al. considered dimensionality reduction as a preprocessing step in machine learning to remove unwanted features and improve learning accuracy [10]. However, the relevant researches only study the accuracy and efficiency of digital twins, and there are few studies that combine accounting information with digital twins.


## 3. Method of Accounting Informatization Transformation Based on Digital Twin Technology

### 3.1. Transformation of Accounting Informatization

Accounting informatization is a new technology provided by social science and technology reform for enterprise financial management. Enterprise accounting should conform to social science and technology reform and make necessary measures [11]. This is an important channel for business leaders to obtain the latest information on the company’s financial situation in the online environment. It helps to improve the competitiveness of enterprises and solve the problems in the process of accounting computerization, thereby improving the decision-making ability of enterprise accounting management and the management level of the company and this is conducive to promoting the improvement of professional skills of accountants [12]. As shown in Figure 1, it is a related diagram of enterprise accounting informatization.Figure 1
Enterprise accounting informatization.The transformation of accounting informatization refers to the integration of information technology and enterprise accounting, so that the modern accounting information system can obtain the response and control of accounting, and the accounting management and decision support are combined into one, so that the enterprise can achieve the goal of value-added [13, 14]. The purpose of accounting informatization transformation is to transform the traditional financial management mode into a systematic and digital financial management mode [15]. As shown in Figure 2, it is a comparison chart of the office scenes of accountants before and after the transformation of enterprise accounting informatization.Figure 2
Comparison of office scenes of accountants before and after the transformation of enterprise accounting informatization.As can be seen from Figure2, before the transformation, accountants need to calculate and record relevant financial information through a large amount of paper, which has a large workload and a high risk of errors [16]. After the transformation, accountants have switched from traditional manual bookkeeping to computerized work, which means that corporate accounting staff only need to handle financial work on the computer, which greatly improves work efficiency and reduces the risk of errors [17].


### 3.2. Digital Twin

Digital twin, also known as digital mapping, is an inanimate digital copy, which realizes the seamless connection of information between the physical entity and the digital copy through the information bridge established between the physical world and the virtual world [18]. With the continuous development of digital twins, a large number of experiments using digital twin conceptual models to research products have emerged in enterprise development. Researchers can analyze and optimize experimental objects by creating virtual models that are the same as the real world [19]. As shown in Figure 3, the model demonstration using digital twin technology.Figure 3
Model demonstration using digital twin technology.With the integrated development and practical application of digital information technology and intelligent manufacturing, digital twins have received high attention [20–22]. In order to better apply digital twins in various fields, scholars have proposed a five-dimensional structural conceptual model of digital twins, including five parts: physical entity scene, virtual scene, twin data, service and connection [23]. As shown in Figure 4, it is a five-dimensional structural model of the digital twin.Figure 4
Digital twin five-dimensional structural model.XBT is used as a general framework to study the relationship, evolution, etc. between physical forms and twin forms, and the model of XBT is formed in the form of formula (1):(1)XBTQE,VE,Ss,BB,CY.Among them,XBT represents the twin form of the information platform. QE represents the scene in physical form, VE represents the virtual scene, Ss represents the service, BB represents the twin data, and CY represents the connection between the components. On the basis of this model, a digital twin that meets the technical requirements of digital twin can be constructed.The digital twin relies on the support of a large number of static models and logical models with dynamic properties, so that the world coordinates of the twin in the virtual space produce three main transformations: translation, scaling and rotation. Using homogeneous coordinate notation, translate any vectorm,n,o in the scene by tm,tn,to distances from each coordinate in space.(2)100tm010tn001to0001mno1=m+tmn+tno+to1.If the scaling coefficient iskm,kn,ko, when the three scaling coefficients k are equal, the scaling is agreed; if the three scaling coefficients k are not equal, it is non-uniform scaling. Non-uniform scaling will change the relevant angle and size ratio of 3D modeling, resulting in a reduction in the realism of the model in the virtual scene, so non-uniform scaling is not selected in most cases. To scale any vector m,n,o in space by a factor of km,kn,ko, its expression is:(3)km0000kn0000ko00001mno1=kmmknnkoo1.Using the rotation matrix around the three coordinate axes ofM,N,O, a matrix that is rotated by any angle along any coordinate axis is obtained. The matrix of the rotation angle of the object around the M,N,O-axis, the expressions as:(4)Vmθ=10000cosθ−sinθ00sinθcosθ00001.Vmθ=10000cosθ−sinθ00sinθcosθ00001,Vmθ=10000cosθ−sinθ00sinθcosθ00001,cosθ+Vm21−cosαVmVn1−cosα−VosinαVmVo1−cosα+Vnsinα0VmVn1−cosα+Vosinαcosθ+Vn21−cosαVoVn1−cosα−Vmsinα0VmVn1−cosα−VosinαVoVn1−cosα+Vmsinαcosθ+Vo21−cosα00001.


#### 3.2.1. OptimizeK-Means Clustering Based on the Cuckoo Algorithm

Missing value processing is mainly a method of filling or discarding entries with missing data in the data [24]. Commonly used missing data imputation methods mainly include clustering imputation method, artificial neural network imputation method and so on. This paper mainly analyzes the clustering interpolation method. The clustering algorithm mainly classifies the data according to the similarity of the data samples, and then divides the categories [25]. As shown in Figure 5, it is the clustering algorithm model.Figure 5
Clustering algorithm model.Because the model display effect of theL-means clustering algorithm is very likely to be selected by the cluster center in the initial state, it needs to be selected carefully. Once the selection is wrong, it may lead to the emergence of local optimal problems [26]. According to the relevant documents of the query, the cuckoo algorithm can better balance the capabilities of local search and global search, and it is not easy to appear local optimal features and lack of global optimal features.Suppose the data set isA, the data sample is y, the data sample dimension is x, and the number of clusters is k, define the data set A=m1,m2,…,my, define the data sample mi=mi1,mi2,…,mij,i=1,2,3,…,y,j1,2,3,…,x, define the cluster center r=r1,r2,…,ry, and define the cluster result R=R1,R2,…,Ry. The process of clustering is:Set the number of clustersK, the maximum number of iterations maxnum, and the fitness function gm as shown in formula (5):(5)gm=∑i=1yodistmi,ro2.Selectk samples arbitrarily from A as the initial value, and then calculate the updated value of gm:(6)Aij+1=Aij+φ⋅Iκ,Levyκ∼sκ,κ∈1,3.Among them,φ represents the control amount, usually φ=1, and Levyκ represents the random search path, Iκ is the distribution subject to Levy, and Levy represents the random step size.The updated values are divided into clusters again, and the value ofgm corresponding to the calculation result is calculated by formula (5) and compared with the current updated numerical results, and the optimal data is obtained:(7)ro=∑xi∈Rimiyo,o=1,2,…,k.


#### 3.2.2. Missing Data Filling Algorithm ofK-Means Clustering after Optimization

Fill in the missing data of the optimizedK-means clustering, and fill in the real data during the operation. The specific process is shown in Figure 6.Figure 6
Operation flow of missing data filling algorithm.Denote the incomplete data setA, y data samples, and each sample includes x attributes, denoted as Ai=mi1,mi2,…,mix. If the incomplete dataset is randomly divided into q groups, the filling expression for the missing values of the samples in the group is:(8)mip=Qy∑i=1Q/ymip.The differenceΔt between the two filling values before and after is used as the recursive termination condition, and the calculation expression of Δt is:(9)Δt=1xy∑i=1y∑j=1xmijnew−mijlast.WhenΔt is less than the set threshold or the algorithm reaches the maximum number of iterations, the recursion is terminated, and the complete data set after filling is output.Define the sample setB, define A as the sample point, and define C as the sample point of the cluster. The known Mahalanobis distance, Chebyshev distance, Minkowski distance, etc. can be used to calculate the expression for the distance between any two samples, Minkowski distance and Mahalanobis distance are as shown in formulas (10) and (11):The Minkowski distance is:(10)distA,C=∑i=1yai−bip1/p.The Mahalanobis distance is:(11)distA,C=Ai−CiT∑−1Ai−Ci.The Mahalanobis distance is not related to the measurement unit of the original data of the sample points, so the Mahalanobis distance is selected as the calculation for the distance between the sample points. Calculate the reachable distance of the local cluster sample points according to the Mahalanobis distance, that is, calculate the reachable distance between theδ-th cluster point of point G and G, and the specific calculation is shown in formula (12):(12)distkG,P=MAXdistkG,distkG,P.Then, the local reachability density is calculated. The local reachability density refers to the average reachable distance from all pointsP in the neighborhood of point G to point G. The smaller the distance, the greater the local reachability density; the greater the distance, the smaller the local reachability density.Calculate the sum of the close distances ofk, and the k-nearest neighbor distance sum of G with respect to B is represented by dG,Bk:(13)dG,Bk=∑P∈YkGdistkG,P.Then calculate the local reachability density of pointG, denoted by, which refers to the reciprocal of the close distance sum of point G and the data points of its neighbors, as shown in formula (14):(14)ldkG=YkGdG,Bk,Finally,lofGk is obtained by calculating the average of the ratio of the local reachable density of point YkG in the neighborhood of point G to the local reachable density of point G, as shown in formula (15):(15)lofGk=1YkG∑P∈YkGldkG,IflofGk is close to 1, it means that the local reachability density of point G is similar to the local reachability density of its neighboring points. If lofGk is less than 1, it means that the local reachable density of point G is greater than the density of its neighboring points, and it is judged that point G is a dense point. If lofGk is greater than 1, it means that the local reachable density of point G is smaller than the density of its neighboring points, and it is judged that point G is an abnormal point.


## 3.1. Transformation of Accounting Informatization

Accounting informatization is a new technology provided by social science and technology reform for enterprise financial management. Enterprise accounting should conform to social science and technology reform and make necessary measures [11]. This is an important channel for business leaders to obtain the latest information on the company’s financial situation in the online environment. It helps to improve the competitiveness of enterprises and solve the problems in the process of accounting computerization, thereby improving the decision-making ability of enterprise accounting management and the management level of the company and this is conducive to promoting the improvement of professional skills of accountants [12]. As shown in Figure 1, it is a related diagram of enterprise accounting informatization.Figure 1
Enterprise accounting informatization.The transformation of accounting informatization refers to the integration of information technology and enterprise accounting, so that the modern accounting information system can obtain the response and control of accounting, and the accounting management and decision support are combined into one, so that the enterprise can achieve the goal of value-added [13, 14]. The purpose of accounting informatization transformation is to transform the traditional financial management mode into a systematic and digital financial management mode [15]. As shown in Figure 2, it is a comparison chart of the office scenes of accountants before and after the transformation of enterprise accounting informatization.Figure 2
Comparison of office scenes of accountants before and after the transformation of enterprise accounting informatization.As can be seen from Figure2, before the transformation, accountants need to calculate and record relevant financial information through a large amount of paper, which has a large workload and a high risk of errors [16]. After the transformation, accountants have switched from traditional manual bookkeeping to computerized work, which means that corporate accounting staff only need to handle financial work on the computer, which greatly improves work efficiency and reduces the risk of errors [17].


## 3.2. Digital Twin

Digital twin, also known as digital mapping, is an inanimate digital copy, which realizes the seamless connection of information between the physical entity and the digital copy through the information bridge established between the physical world and the virtual world [18]. With the continuous development of digital twins, a large number of experiments using digital twin conceptual models to research products have emerged in enterprise development. Researchers can analyze and optimize experimental objects by creating virtual models that are the same as the real world [19]. As shown in Figure 3, the model demonstration using digital twin technology.Figure 3
Model demonstration using digital twin technology.With the integrated development and practical application of digital information technology and intelligent manufacturing, digital twins have received high attention [20–22]. In order to better apply digital twins in various fields, scholars have proposed a five-dimensional structural conceptual model of digital twins, including five parts: physical entity scene, virtual scene, twin data, service and connection [23]. As shown in Figure 4, it is a five-dimensional structural model of the digital twin.Figure 4
Digital twin five-dimensional structural model.XBT is used as a general framework to study the relationship, evolution, etc. between physical forms and twin forms, and the model of XBT is formed in the form of formula (1):(1)XBTQE,VE,Ss,BB,CY.Among them,XBT represents the twin form of the information platform. QE represents the scene in physical form, VE represents the virtual scene, Ss represents the service, BB represents the twin data, and CY represents the connection between the components. On the basis of this model, a digital twin that meets the technical requirements of digital twin can be constructed.The digital twin relies on the support of a large number of static models and logical models with dynamic properties, so that the world coordinates of the twin in the virtual space produce three main transformations: translation, scaling and rotation. Using homogeneous coordinate notation, translate any vectorm,n,o in the scene by tm,tn,to distances from each coordinate in space.(2)100tm010tn001to0001mno1=m+tmn+tno+to1.If the scaling coefficient iskm,kn,ko, when the three scaling coefficients k are equal, the scaling is agreed; if the three scaling coefficients k are not equal, it is non-uniform scaling. Non-uniform scaling will change the relevant angle and size ratio of 3D modeling, resulting in a reduction in the realism of the model in the virtual scene, so non-uniform scaling is not selected in most cases. To scale any vector m,n,o in space by a factor of km,kn,ko, its expression is:(3)km0000kn0000ko00001mno1=kmmknnkoo1.Using the rotation matrix around the three coordinate axes ofM,N,O, a matrix that is rotated by any angle along any coordinate axis is obtained. The matrix of the rotation angle of the object around the M,N,O-axis, the expressions as:(4)Vmθ=10000cosθ−sinθ00sinθcosθ00001.Vmθ=10000cosθ−sinθ00sinθcosθ00001,Vmθ=10000cosθ−sinθ00sinθcosθ00001,cosθ+Vm21−cosαVmVn1−cosα−VosinαVmVo1−cosα+Vnsinα0VmVn1−cosα+Vosinαcosθ+Vn21−cosαVoVn1−cosα−Vmsinα0VmVn1−cosα−VosinαVoVn1−cosα+Vmsinαcosθ+Vo21−cosα00001.


### 3.2.1. OptimizeK-Means Clustering Based on the Cuckoo Algorithm

Missing value processing is mainly a method of filling or discarding entries with missing data in the data [24]. Commonly used missing data imputation methods mainly include clustering imputation method, artificial neural network imputation method and so on. This paper mainly analyzes the clustering interpolation method. The clustering algorithm mainly classifies the data according to the similarity of the data samples, and then divides the categories [25]. As shown in Figure 5, it is the clustering algorithm model.Figure 5
Clustering algorithm model.Because the model display effect of theL-means clustering algorithm is very likely to be selected by the cluster center in the initial state, it needs to be selected carefully. Once the selection is wrong, it may lead to the emergence of local optimal problems [26]. According to the relevant documents of the query, the cuckoo algorithm can better balance the capabilities of local search and global search, and it is not easy to appear local optimal features and lack of global optimal features.Suppose the data set isA, the data sample is y, the data sample dimension is x, and the number of clusters is k, define the data set A=m1,m2,…,my, define the data sample mi=mi1,mi2,…,mij,i=1,2,3,…,y,j1,2,3,…,x, define the cluster center r=r1,r2,…,ry, and define the cluster result R=R1,R2,…,Ry. The process of clustering is:Set the number of clustersK, the maximum number of iterations maxnum, and the fitness function gm as shown in formula (5):(5)gm=∑i=1yodistmi,ro2.Selectk samples arbitrarily from A as the initial value, and then calculate the updated value of gm:(6)Aij+1=Aij+φ⋅Iκ,Levyκ∼sκ,κ∈1,3.Among them,φ represents the control amount, usually φ=1, and Levyκ represents the random search path, Iκ is the distribution subject to Levy, and Levy represents the random step size.The updated values are divided into clusters again, and the value ofgm corresponding to the calculation result is calculated by formula (5) and compared with the current updated numerical results, and the optimal data is obtained:(7)ro=∑xi∈Rimiyo,o=1,2,…,k.


### 3.2.2. Missing Data Filling Algorithm ofK-Means Clustering after Optimization

Fill in the missing data of the optimizedK-means clustering, and fill in the real data during the operation. The specific process is shown in Figure 6.Figure 6
Operation flow of missing data filling algorithm.Denote the incomplete data setA, y data samples, and each sample includes x attributes, denoted as Ai=mi1,mi2,…,mix. If the incomplete dataset is randomly divided into q groups, the filling expression for the missing values of the samples in the group is:(8)mip=Qy∑i=1Q/ymip.The differenceΔt between the two filling values before and after is used as the recursive termination condition, and the calculation expression of Δt is:(9)Δt=1xy∑i=1y∑j=1xmijnew−mijlast.WhenΔt is less than the set threshold or the algorithm reaches the maximum number of iterations, the recursion is terminated, and the complete data set after filling is output.Define the sample setB, define A as the sample point, and define C as the sample point of the cluster. The known Mahalanobis distance, Chebyshev distance, Minkowski distance, etc. can be used to calculate the expression for the distance between any two samples, Minkowski distance and Mahalanobis distance are as shown in formulas (10) and (11):The Minkowski distance is:(10)distA,C=∑i=1yai−bip1/p.The Mahalanobis distance is:(11)distA,C=Ai−CiT∑−1Ai−Ci.The Mahalanobis distance is not related to the measurement unit of the original data of the sample points, so the Mahalanobis distance is selected as the calculation for the distance between the sample points. Calculate the reachable distance of the local cluster sample points according to the Mahalanobis distance, that is, calculate the reachable distance between theδ-th cluster point of point G and G, and the specific calculation is shown in formula (12):(12)distkG,P=MAXdistkG,distkG,P.Then, the local reachability density is calculated. The local reachability density refers to the average reachable distance from all pointsP in the neighborhood of point G to point G. The smaller the distance, the greater the local reachability density; the greater the distance, the smaller the local reachability density.Calculate the sum of the close distances ofk, and the k-nearest neighbor distance sum of G with respect to B is represented by dG,Bk:(13)dG,Bk=∑P∈YkGdistkG,P.Then calculate the local reachability density of pointG, denoted by, which refers to the reciprocal of the close distance sum of point G and the data points of its neighbors, as shown in formula (14):(14)ldkG=YkGdG,Bk,Finally,lofGk is obtained by calculating the average of the ratio of the local reachable density of point YkG in the neighborhood of point G to the local reachable density of point G, as shown in formula (15):(15)lofGk=1YkG∑P∈YkGldkG,IflofGk is close to 1, it means that the local reachability density of point G is similar to the local reachability density of its neighboring points. If lofGk is less than 1, it means that the local reachable density of point G is greater than the density of its neighboring points, and it is judged that point G is a dense point. If lofGk is greater than 1, it means that the local reachable density of point G is smaller than the density of its neighboring points, and it is judged that point G is an abnormal point.


## 3.2.1. OptimizeK-Means Clustering Based on the Cuckoo Algorithm

Missing value processing is mainly a method of filling or discarding entries with missing data in the data [24]. Commonly used missing data imputation methods mainly include clustering imputation method, artificial neural network imputation method and so on. This paper mainly analyzes the clustering interpolation method. The clustering algorithm mainly classifies the data according to the similarity of the data samples, and then divides the categories [25]. As shown in Figure 5, it is the clustering algorithm model.Figure 5
Clustering algorithm model.Because the model display effect of theL-means clustering algorithm is very likely to be selected by the cluster center in the initial state, it needs to be selected carefully. Once the selection is wrong, it may lead to the emergence of local optimal problems [26]. According to the relevant documents of the query, the cuckoo algorithm can better balance the capabilities of local search and global search, and it is not easy to appear local optimal features and lack of global optimal features.Suppose the data set isA, the data sample is y, the data sample dimension is x, and the number of clusters is k, define the data set A=m1,m2,…,my, define the data sample mi=mi1,mi2,…,mij,i=1,2,3,…,y,j1,2,3,…,x, define the cluster center r=r1,r2,…,ry, and define the cluster result R=R1,R2,…,Ry. The process of clustering is:Set the number of clustersK, the maximum number of iterations maxnum, and the fitness function gm as shown in formula (5):(5)gm=∑i=1yodistmi,ro2.Selectk samples arbitrarily from A as the initial value, and then calculate the updated value of gm:(6)Aij+1=Aij+φ⋅Iκ,Levyκ∼sκ,κ∈1,3.Among them,φ represents the control amount, usually φ=1, and Levyκ represents the random search path, Iκ is the distribution subject to Levy, and Levy represents the random step size.The updated values are divided into clusters again, and the value ofgm corresponding to the calculation result is calculated by formula (5) and compared with the current updated numerical results, and the optimal data is obtained:(7)ro=∑xi∈Rimiyo,o=1,2,…,k.


## 3.2.2. Missing Data Filling Algorithm ofK-Means Clustering after Optimization

Fill in the missing data of the optimizedK-means clustering, and fill in the real data during the operation. The specific process is shown in Figure 6.Figure 6
Operation flow of missing data filling algorithm.Denote the incomplete data setA, y data samples, and each sample includes x attributes, denoted as Ai=mi1,mi2,…,mix. If the incomplete dataset is randomly divided into q groups, the filling expression for the missing values of the samples in the group is:(8)mip=Qy∑i=1Q/ymip.The differenceΔt between the two filling values before and after is used as the recursive termination condition, and the calculation expression of Δt is:(9)Δt=1xy∑i=1y∑j=1xmijnew−mijlast.WhenΔt is less than the set threshold or the algorithm reaches the maximum number of iterations, the recursion is terminated, and the complete data set after filling is output.Define the sample setB, define A as the sample point, and define C as the sample point of the cluster. The known Mahalanobis distance, Chebyshev distance, Minkowski distance, etc. can be used to calculate the expression for the distance between any two samples, Minkowski distance and Mahalanobis distance are as shown in formulas (10) and (11):The Minkowski distance is:(10)distA,C=∑i=1yai−bip1/p.The Mahalanobis distance is:(11)distA,C=Ai−CiT∑−1Ai−Ci.The Mahalanobis distance is not related to the measurement unit of the original data of the sample points, so the Mahalanobis distance is selected as the calculation for the distance between the sample points. Calculate the reachable distance of the local cluster sample points according to the Mahalanobis distance, that is, calculate the reachable distance between theδ-th cluster point of point G and G, and the specific calculation is shown in formula (12):(12)distkG,P=MAXdistkG,distkG,P.Then, the local reachability density is calculated. The local reachability density refers to the average reachable distance from all pointsP in the neighborhood of point G to point G. The smaller the distance, the greater the local reachability density; the greater the distance, the smaller the local reachability density.Calculate the sum of the close distances ofk, and the k-nearest neighbor distance sum of G with respect to B is represented by dG,Bk:(13)dG,Bk=∑P∈YkGdistkG,P.Then calculate the local reachability density of pointG, denoted by, which refers to the reciprocal of the close distance sum of point G and the data points of its neighbors, as shown in formula (14):(14)ldkG=YkGdG,Bk,Finally,lofGk is obtained by calculating the average of the ratio of the local reachable density of point YkG in the neighborhood of point G to the local reachable density of point G, as shown in formula (15):(15)lofGk=1YkG∑P∈YkGldkG,IflofGk is close to 1, it means that the local reachability density of point G is similar to the local reachability density of its neighboring points. If lofGk is less than 1, it means that the local reachable density of point G is greater than the density of its neighboring points, and it is judged that point G is a dense point. If lofGk is greater than 1, it means that the local reachable density of point G is smaller than the density of its neighboring points, and it is judged that point G is an abnormal point.


## 4. Experiments on the Transformation of Accounting Informatization

### 4.1. Experiment Design of Accounting Informatization Transformation

To further understand the significance of accounting informatization transformation to the future development of enterprises, the existing research results of accounting informatization transformation are organized. The research objects of this paper were from three group companies, all of which have carried out the transformation and construction of the accounting information of the enterprises. This paper took the accounting practitioners of companyA as the research object, and the accounting practitioners of companies B and C as the experimental control group to conduct a questionnaire survey. A total of 600 questionnaires were distributed, and each company issued 200 copies. 6 questions were raised, mainly including whether to accept the transformation of enterprise accounting information, whether the work efficiency has changed after the transformation, the accountants’ views on the transformation, the professional ability of the accountants, how the career prospects will change after the accounting information transformation, and the working years of the accountants.


### 4.2. Results of Accounting Informatization Transformation

Due to the relatively low entry threshold in the accounting industry, many accountants have entered the industry late, or some staff have switched jobs to engage in accounting work, so their understanding of computerized accounting is not as much as that of professionals. Therefore, some accountants cannot accept the transformation of accounting information. This paper studied this issue, and the main contents are as:


#### 4.2.1. Whether to Accept Yhe Transformation of Corporate Accounting Information

Through the analysis of the data in Table1, the results were obtained: Among the attitudes of the accountants of company A towards the transformation of accounting information, 179 people expressed support, 16 people were neutral, and 5 people were opposed, accounting for 89.5%, 8% and 2.5% respectively. Although 89.5% of people support the transformation of accounting information by enterprises, which is a relatively large proportion, in the control group, 196 and 199 people from company B and company C hold a favorable attitude, accounting for 98% and 99.5% respectively. The transformation of corporate accounting information is also a process for employees to accept new things. The employees’ acceptance of new things is still relatively low, while compared with the two companies in the control group.Table 1
Attitudes of accounting practitioners in three companies to the transformation of corporate accounting information.
Support attitudeNeutral attitudeOppositionA179165B19631C19910


#### 4.2.2. Whether the Work Efficiency Has Changed after the Transformation

From Table2, it can be seen that after the company information transformation, 125 people think that the work efficiency has improved, 12 people think that the work efficiency has not changed, but 63 people think that the work efficiency has decreased, which accounted for as high as 31.5%. In the control group, among the staff of company B, 173 people feel that their work efficiency has increased, accounting for about 86.5%, and 5 people think that their work efficiency has decreased, accounting for 2.5%. Among C enterprises, 177 people have increased their work efficiency after the transformation, accounting for about 88.5%, while 3 people think that their work efficiency has decreased, accounting for 1.5%. After the transformation of accounting information, generally speaking, the work efficiency of staff will be improved, because the transformation of enterprise accounting information is to improve the work efficiency of accountants and save working time. The main reason for the decline in work efficiency is that the existing professional capabilities of employees are not enough to support the work content after the transformation, and work efficiency will decline to a certain extent.Table 2
Changes in the work efficiency of accounting practitioners in the three companies after the transformation.
Increased work efficiencyWork efficiency remains unchangedReduced work efficiencyA1251263B173225C177203


#### 4.2.3. Accountants’ Views on Transformation

It can be seen from Figure7 that 96 accountants of Company A believe that the transformation of the company’s accounting information requires higher professional skills for accountants, accounting for about 48%. In the control group, company B has 109 people in this part, accounting for about 54.5%, and company C has 89 people who hold this idea, accounting for 44.5%. Holding such an idea is because after the transformation of accounting information, it is necessary to learn all professional knowledge related to accounting when dealing with traditional hierarchical accounting entries and other processes. Therefore, under this new working mode, all processes such as accounting, auditing, and reporting are all in one system. As long as the professional level is sufficient, one person can complete the entire task, so that the professional requirements for accountants have also increased. Some people believe that after the transformation, the probability of work mistakes can be reduced, which is certain. Because the traditional manual bookkeeping is converted into computerized accounting, and the entire accounting book set is imported into the system, the system will automatically remind when an error occurs and the next work process cannot be carried out. This reduces the error rate of accounting work to a certain extent. Of course, a small number of people believe that the transformation of corporate accounting information is a normal process for companies. In company A, 15 people hold this attitude, and in the control group, 5 people and 5 people respectively believe that accounting information transformation is the work process of the company.Figure 7
Accountants’ perceptions of transformation at three companies.


#### 4.2.4. Professional Competence of Accountants

The stronger the professional ability of accountants, the more solid their professional basic knowledge, and the faster they accept the transformation of accounting information. The professional ability of accounting can be seen from the grade certificate obtained by the accountant. As shown in Table3, Company A has 112 junior accountants, 78 intermediate accountants and 10 senior accountants, accounting for 56%, 39% and 5% respectively. On the whole, the structure of accountants in Company A is general, and the proportion of high-level accountants is relatively small. In the data of the control group, Company B has 96 junior accountants, 81 intermediate accountants, and 23 senior accountants, accounting for 48%, 40.5% and 11.5%, respectively. The personnel structure of company C is similar to that of company B, which meant that the accounting personnel of the control group have stronger professional ability than those of company A. Company A should encourage employees to actively learn professional knowledge and improve professional skills, and can also give certain incentives to improve the enthusiasm of employees to learn professional skills.Table 3
Survey on the professional grades of accountants in the three companies.
Junior accountantIntermediate accountantSenior accountantA1127810B968123C899120


#### 4.2.5. How Will the Career Prospects Change after the Transformation of Accounting Information (Multiple Choice)

As can be seen from Table4, the impact of accounting information transformation on accountants is still great. According to the survey results, it can be seen that the accountants of the three companies have roughly the same views on this issue, and more than 150 people choose each option. Most people believe that after the transformation, the professional skills of accounting will be more demanding, and the threshold for accounting practitioners will also be raised. Therefore, not all people can enter the industry, and they still need to have high working ability and rich professional knowledge. Of course, the market’s demand for accounting will also decrease, among them, there are 198 accountants in company A who have such an idea. The accountants of the company believe that the future career prospects are more difficult, and the competitiveness among accountants is more intense. Because of the popularization of accounting computerization, corporate accountants can have multiple positions, and one person can hold multiple positions, and most of the accounting work is handed over to one staff member.Table 4
How will the career prospects change after the transformation of accounting information (multiple choices).
Professional requirements are highRaising barriers to entryReduced need for accountingA178190198B158196199C174178193


#### 4.2.6. Working Years of Accountants

It can be seen from Figure8 that among the working years of accountants, the number of accountants with less than 5 years accounted for the largest proportion, followed by those with 5–10 years, and finally the proportion of those with more than 10 years. In company A, 127 people have worked for less than 5 years, accounting for 63.5%, and the ratio exceeds half; 56 people have worked for 5–10 years, accounting for 28%; and 17 people with more than 10 years of work experience, accounting for only 8.5%. From this part of the data, it can be seen that there are very few accountants with more experience in company A. Looking at the data of the control group, there are 98 employees in company B who have worked for less than 5 years, accounting for 49%; 76 employees with 5–10 years of experience, and 26 senior accountants with more than 10 years of experience, the proportions are 38% and 13% respectively. The staffing ratio of Company C is similar to that of Company B, which indicated that the staff of the two companies in the control group have longer working experience. In general, the accountants of Company A are slightly lower than the accountants of Company B and Company C in terms of professional skills and years of employment. A company should strengthen the training of professional skills of accountants, vigorously recruit experienced accountants, and strengthen the management of company staff.Figure 8
Working years of accountants.


### 4.3. Results of Improving Accounting Informatization Transformation

Through the statistical analysis of the above charts, we can improve the plan for the transformation of enterprise accounting informatization based on digital twin technology: there are many factors that affect the transformation of enterprise accounting informatization, and different research methods have different research focuses. The transformation of enterprise accounting informatization is an important subject studied by many experts and scholars. In the article, although the digital twin technology and clustering algorithm were used to raise 6 related questions to the survey objects and draw corresponding conclusions, it still needs to be improved in the experimental process. First, the sample size of this study is limited. It is recommended to increase the sample size to ensure the accuracy of the experimental results. Second, this article only compared and analyzed the three companies, and did not expand the survey objects, such as accounting students. There has not been much research on digital twin technology in the module of enterprise transformation, and further research needs to be done on the transformation of digital twin technology in enterprise accounting information technology.


## 4.1. Experiment Design of Accounting Informatization Transformation

To further understand the significance of accounting informatization transformation to the future development of enterprises, the existing research results of accounting informatization transformation are organized. The research objects of this paper were from three group companies, all of which have carried out the transformation and construction of the accounting information of the enterprises. This paper took the accounting practitioners of companyA as the research object, and the accounting practitioners of companies B and C as the experimental control group to conduct a questionnaire survey. A total of 600 questionnaires were distributed, and each company issued 200 copies. 6 questions were raised, mainly including whether to accept the transformation of enterprise accounting information, whether the work efficiency has changed after the transformation, the accountants’ views on the transformation, the professional ability of the accountants, how the career prospects will change after the accounting information transformation, and the working years of the accountants.


## 4.2. Results of Accounting Informatization Transformation

Due to the relatively low entry threshold in the accounting industry, many accountants have entered the industry late, or some staff have switched jobs to engage in accounting work, so their understanding of computerized accounting is not as much as that of professionals. Therefore, some accountants cannot accept the transformation of accounting information. This paper studied this issue, and the main contents are as:


### 4.2.1. Whether to Accept Yhe Transformation of Corporate Accounting Information

Through the analysis of the data in Table1, the results were obtained: Among the attitudes of the accountants of company A towards the transformation of accounting information, 179 people expressed support, 16 people were neutral, and 5 people were opposed, accounting for 89.5%, 8% and 2.5% respectively. Although 89.5% of people support the transformation of accounting information by enterprises, which is a relatively large proportion, in the control group, 196 and 199 people from company B and company C hold a favorable attitude, accounting for 98% and 99.5% respectively. The transformation of corporate accounting information is also a process for employees to accept new things. The employees’ acceptance of new things is still relatively low, while compared with the two companies in the control group.Table 1
Attitudes of accounting practitioners in three companies to the transformation of corporate accounting information.
Support attitudeNeutral attitudeOppositionA179165B19631C19910


### 4.2.2. Whether the Work Efficiency Has Changed after the Transformation

From Table2, it can be seen that after the company information transformation, 125 people think that the work efficiency has improved, 12 people think that the work efficiency has not changed, but 63 people think that the work efficiency has decreased, which accounted for as high as 31.5%. In the control group, among the staff of company B, 173 people feel that their work efficiency has increased, accounting for about 86.5%, and 5 people think that their work efficiency has decreased, accounting for 2.5%. Among C enterprises, 177 people have increased their work efficiency after the transformation, accounting for about 88.5%, while 3 people think that their work efficiency has decreased, accounting for 1.5%. After the transformation of accounting information, generally speaking, the work efficiency of staff will be improved, because the transformation of enterprise accounting information is to improve the work efficiency of accountants and save working time. The main reason for the decline in work efficiency is that the existing professional capabilities of employees are not enough to support the work content after the transformation, and work efficiency will decline to a certain extent.Table 2
Changes in the work efficiency of accounting practitioners in the three companies after the transformation.
Increased work efficiencyWork efficiency remains unchangedReduced work efficiencyA1251263B173225C177203


### 4.2.3. Accountants’ Views on Transformation

It can be seen from Figure7 that 96 accountants of Company A believe that the transformation of the company’s accounting information requires higher professional skills for accountants, accounting for about 48%. In the control group, company B has 109 people in this part, accounting for about 54.5%, and company C has 89 people who hold this idea, accounting for 44.5%. Holding such an idea is because after the transformation of accounting information, it is necessary to learn all professional knowledge related to accounting when dealing with traditional hierarchical accounting entries and other processes. Therefore, under this new working mode, all processes such as accounting, auditing, and reporting are all in one system. As long as the professional level is sufficient, one person can complete the entire task, so that the professional requirements for accountants have also increased. Some people believe that after the transformation, the probability of work mistakes can be reduced, which is certain. Because the traditional manual bookkeeping is converted into computerized accounting, and the entire accounting book set is imported into the system, the system will automatically remind when an error occurs and the next work process cannot be carried out. This reduces the error rate of accounting work to a certain extent. Of course, a small number of people believe that the transformation of corporate accounting information is a normal process for companies. In company A, 15 people hold this attitude, and in the control group, 5 people and 5 people respectively believe that accounting information transformation is the work process of the company.Figure 7
Accountants’ perceptions of transformation at three companies.


### 4.2.4. Professional Competence of Accountants

The stronger the professional ability of accountants, the more solid their professional basic knowledge, and the faster they accept the transformation of accounting information. The professional ability of accounting can be seen from the grade certificate obtained by the accountant. As shown in Table3, Company A has 112 junior accountants, 78 intermediate accountants and 10 senior accountants, accounting for 56%, 39% and 5% respectively. On the whole, the structure of accountants in Company A is general, and the proportion of high-level accountants is relatively small. In the data of the control group, Company B has 96 junior accountants, 81 intermediate accountants, and 23 senior accountants, accounting for 48%, 40.5% and 11.5%, respectively. The personnel structure of company C is similar to that of company B, which meant that the accounting personnel of the control group have stronger professional ability than those of company A. Company A should encourage employees to actively learn professional knowledge and improve professional skills, and can also give certain incentives to improve the enthusiasm of employees to learn professional skills.Table 3
Survey on the professional grades of accountants in the three companies.
Junior accountantIntermediate accountantSenior accountantA1127810B968123C899120


### 4.2.5. How Will the Career Prospects Change after the Transformation of Accounting Information (Multiple Choice)

As can be seen from Table4, the impact of accounting information transformation on accountants is still great. According to the survey results, it can be seen that the accountants of the three companies have roughly the same views on this issue, and more than 150 people choose each option. Most people believe that after the transformation, the professional skills of accounting will be more demanding, and the threshold for accounting practitioners will also be raised. Therefore, not all people can enter the industry, and they still need to have high working ability and rich professional knowledge. Of course, the market’s demand for accounting will also decrease, among them, there are 198 accountants in company A who have such an idea. The accountants of the company believe that the future career prospects are more difficult, and the competitiveness among accountants is more intense. Because of the popularization of accounting computerization, corporate accountants can have multiple positions, and one person can hold multiple positions, and most of the accounting work is handed over to one staff member.Table 4
How will the career prospects change after the transformation of accounting information (multiple choices).
Professional requirements are highRaising barriers to entryReduced need for accountingA178190198B158196199C174178193


### 4.2.6. Working Years of Accountants

It can be seen from Figure8 that among the working years of accountants, the number of accountants with less than 5 years accounted for the largest proportion, followed by those with 5–10 years, and finally the proportion of those with more than 10 years. In company A, 127 people have worked for less than 5 years, accounting for 63.5%, and the ratio exceeds half; 56 people have worked for 5–10 years, accounting for 28%; and 17 people with more than 10 years of work experience, accounting for only 8.5%. From this part of the data, it can be seen that there are very few accountants with more experience in company A. Looking at the data of the control group, there are 98 employees in company B who have worked for less than 5 years, accounting for 49%; 76 employees with 5–10 years of experience, and 26 senior accountants with more than 10 years of experience, the proportions are 38% and 13% respectively. The staffing ratio of Company C is similar to that of Company B, which indicated that the staff of the two companies in the control group have longer working experience. In general, the accountants of Company A are slightly lower than the accountants of Company B and Company C in terms of professional skills and years of employment. A company should strengthen the training of professional skills of accountants, vigorously recruit experienced accountants, and strengthen the management of company staff.Figure 8
Working years of accountants.


## 4.2.1. Whether to Accept Yhe Transformation of Corporate Accounting Information

Through the analysis of the data in Table1, the results were obtained: Among the attitudes of the accountants of company A towards the transformation of accounting information, 179 people expressed support, 16 people were neutral, and 5 people were opposed, accounting for 89.5%, 8% and 2.5% respectively. Although 89.5% of people support the transformation of accounting information by enterprises, which is a relatively large proportion, in the control group, 196 and 199 people from company B and company C hold a favorable attitude, accounting for 98% and 99.5% respectively. The transformation of corporate accounting information is also a process for employees to accept new things. The employees’ acceptance of new things is still relatively low, while compared with the two companies in the control group.Table 1
Attitudes of accounting practitioners in three companies to the transformation of corporate accounting information.
Support attitudeNeutral attitudeOppositionA179165B19631C19910


## 4.2.2. Whether the Work Efficiency Has Changed after the Transformation

From Table2, it can be seen that after the company information transformation, 125 people think that the work efficiency has improved, 12 people think that the work efficiency has not changed, but 63 people think that the work efficiency has decreased, which accounted for as high as 31.5%. In the control group, among the staff of company B, 173 people feel that their work efficiency has increased, accounting for about 86.5%, and 5 people think that their work efficiency has decreased, accounting for 2.5%. Among C enterprises, 177 people have increased their work efficiency after the transformation, accounting for about 88.5%, while 3 people think that their work efficiency has decreased, accounting for 1.5%. After the transformation of accounting information, generally speaking, the work efficiency of staff will be improved, because the transformation of enterprise accounting information is to improve the work efficiency of accountants and save working time. The main reason for the decline in work efficiency is that the existing professional capabilities of employees are not enough to support the work content after the transformation, and work efficiency will decline to a certain extent.Table 2
Changes in the work efficiency of accounting practitioners in the three companies after the transformation.
Increased work efficiencyWork efficiency remains unchangedReduced work efficiencyA1251263B173225C177203


## 4.2.3. Accountants’ Views on Transformation

It can be seen from Figure7 that 96 accountants of Company A believe that the transformation of the company’s accounting information requires higher professional skills for accountants, accounting for about 48%. In the control group, company B has 109 people in this part, accounting for about 54.5%, and company C has 89 people who hold this idea, accounting for 44.5%. Holding such an idea is because after the transformation of accounting information, it is necessary to learn all professional knowledge related to accounting when dealing with traditional hierarchical accounting entries and other processes. Therefore, under this new working mode, all processes such as accounting, auditing, and reporting are all in one system. As long as the professional level is sufficient, one person can complete the entire task, so that the professional requirements for accountants have also increased. Some people believe that after the transformation, the probability of work mistakes can be reduced, which is certain. Because the traditional manual bookkeeping is converted into computerized accounting, and the entire accounting book set is imported into the system, the system will automatically remind when an error occurs and the next work process cannot be carried out. This reduces the error rate of accounting work to a certain extent. Of course, a small number of people believe that the transformation of corporate accounting information is a normal process for companies. In company A, 15 people hold this attitude, and in the control group, 5 people and 5 people respectively believe that accounting information transformation is the work process of the company.Figure 7
Accountants’ perceptions of transformation at three companies.


## 4.2.4. Professional Competence of Accountants

The stronger the professional ability of accountants, the more solid their professional basic knowledge, and the faster they accept the transformation of accounting information. The professional ability of accounting can be seen from the grade certificate obtained by the accountant. As shown in Table3, Company A has 112 junior accountants, 78 intermediate accountants and 10 senior accountants, accounting for 56%, 39% and 5% respectively. On the whole, the structure of accountants in Company A is general, and the proportion of high-level accountants is relatively small. In the data of the control group, Company B has 96 junior accountants, 81 intermediate accountants, and 23 senior accountants, accounting for 48%, 40.5% and 11.5%, respectively. The personnel structure of company C is similar to that of company B, which meant that the accounting personnel of the control group have stronger professional ability than those of company A. Company A should encourage employees to actively learn professional knowledge and improve professional skills, and can also give certain incentives to improve the enthusiasm of employees to learn professional skills.Table 3
Survey on the professional grades of accountants in the three companies.
Junior accountantIntermediate accountantSenior accountantA1127810B968123C899120


## 4.2.5. How Will the Career Prospects Change after the Transformation of Accounting Information (Multiple Choice)

As can be seen from Table4, the impact of accounting information transformation on accountants is still great. According to the survey results, it can be seen that the accountants of the three companies have roughly the same views on this issue, and more than 150 people choose each option. Most people believe that after the transformation, the professional skills of accounting will be more demanding, and the threshold for accounting practitioners will also be raised. Therefore, not all people can enter the industry, and they still need to have high working ability and rich professional knowledge. Of course, the market’s demand for accounting will also decrease, among them, there are 198 accountants in company A who have such an idea. The accountants of the company believe that the future career prospects are more difficult, and the competitiveness among accountants is more intense. Because of the popularization of accounting computerization, corporate accountants can have multiple positions, and one person can hold multiple positions, and most of the accounting work is handed over to one staff member.Table 4
How will the career prospects change after the transformation of accounting information (multiple choices).
Professional requirements are highRaising barriers to entryReduced need for accountingA178190198B158196199C174178193


## 4.2.6. Working Years of Accountants

It can be seen from Figure8 that among the working years of accountants, the number of accountants with less than 5 years accounted for the largest proportion, followed by those with 5–10 years, and finally the proportion of those with more than 10 years. In company A, 127 people have worked for less than 5 years, accounting for 63.5%, and the ratio exceeds half; 56 people have worked for 5–10 years, accounting for 28%; and 17 people with more than 10 years of work experience, accounting for only 8.5%. From this part of the data, it can be seen that there are very few accountants with more experience in company A. Looking at the data of the control group, there are 98 employees in company B who have worked for less than 5 years, accounting for 49%; 76 employees with 5–10 years of experience, and 26 senior accountants with more than 10 years of experience, the proportions are 38% and 13% respectively. The staffing ratio of Company C is similar to that of Company B, which indicated that the staff of the two companies in the control group have longer working experience. In general, the accountants of Company A are slightly lower than the accountants of Company B and Company C in terms of professional skills and years of employment. A company should strengthen the training of professional skills of accountants, vigorously recruit experienced accountants, and strengthen the management of company staff.Figure 8
Working years of accountants.


## 4.3. Results of Improving Accounting Informatization Transformation

Through the statistical analysis of the above charts, we can improve the plan for the transformation of enterprise accounting informatization based on digital twin technology: there are many factors that affect the transformation of enterprise accounting informatization, and different research methods have different research focuses. The transformation of enterprise accounting informatization is an important subject studied by many experts and scholars. In the article, although the digital twin technology and clustering algorithm were used to raise 6 related questions to the survey objects and draw corresponding conclusions, it still needs to be improved in the experimental process. First, the sample size of this study is limited. It is recommended to increase the sample size to ensure the accuracy of the experimental results. Second, this article only compared and analyzed the three companies, and did not expand the survey objects, such as accounting students. There has not been much research on digital twin technology in the module of enterprise transformation, and further research needs to be done on the transformation of digital twin technology in enterprise accounting information technology.


## 5. Discussion

Due to the rapid development of the economy, the implementation of the economic law is also more stringent, and the requirements for accounting positions in all walks of life are getting higher and higher, so the requirements for the transformation of accounting informatization are urgent. This paper integrated the opinions of corporate accountants on the transformation of accounting informatization and the employment prospects of accountants, and used digital twin technology to conduct a questionnaire survey on accounting practitioners in Company A. The main reasons why accountants reject accounting informatization are as: (1) The professional knowledge of accountants themselves is not comprehensive, and they do not fully grasp the information technology. (2) After the transformation of accounting informatization, the demand for accountants has decreased, and it is difficult for accountants to find employment. (3) Accounting information technology requires accounting professionals to have solid professional knowledge and practical ability, and the professionals need to obtain more certificates. Therefore, the transformation of accounting informatization has advantages and disadvantages.This paper was devoted to researching the application of various algorithm models based on digital twin technology, and applying it to the research of enterprise accounting informatization transformation. This is not only an extension of the research scope of digital twin technology, but also a new attempt to research the transformation of enterprise accounting informatization. The results of the case study in this paper showed that digital twin technology has a positive effect on the research on the transformation and construction of accounting informatization in enterprises. The research in this paper has a certain reference value for the future accounting informatization transformation, and it is also a new expansion of the research field of digital twin technology.


## 6. Conclusions

This paper took digital twin technology as the main research method, took the accountants of companyA as the research object, selected relevant data, conducted experimental research, and drew the conclusions: enterprises should strengthen the transformation process of accounting informatization, which is conducive to the selection of excellent accountants; accountants need to improve their professional skills to cope with the current changing employment environment; enterprises need to encourage accountants to obtain corresponding certificates, improve their skills, and provide more professional services for enterprises; the society should provide corresponding help to accountants who are unemployed in the process of accounting informatization transformation, so as to solve the problem of difficult employment of accountants. From the three aspects of individuals, enterprises and society, it provides certain help for accounting staff and solves corresponding problems for accounting staff, which is conducive to the stable development of society and enterprises, and is more conducive to the transformation and construction of accounting informatization. Through the case study of this paper, the effectiveness of digital twin technology in the construction of accounting informatization transformation was illustrated.



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*Source: 3186768-2022-10-07.xml*