Enterprise digital development risk early warning method based on multi-view analysis

Abstract

The invention belongs to the technical field of enterprise digital development, and particularly relates to an enterprise digital development risk early warning method based on multi-view analysis. Comprising the following steps: S1, constructing a risk factor set; s2, constructing a risk quantitative evaluation model based on EW-AHP; s3, a risk evolution early warning model based on DEMATEL-ISM is constructed; according to the invention, the enterprise digital development risk early warning system based on multi-view analysis is constructed, so that comprehensive and systematic assessment of multi-dimensional risk factors such as technology, operation, management and law in the enterprise digital process is realized. Risk quantitative evaluation is performed by introducing an EW-AHP model, so that the evaluation result is more objective and scientific. A DEMATEL-ISM model is integrated, interaction and influence mechanisms among risk factors are deeply analyzed, and clear risk evolution paths and early warning indexes are provided for enterprises. The uncertainty in the digital transformation process is effectively reduced, the anti-risk capability and market competitiveness of enterprises are improved, and smooth promotion and sustainable development of digital transformation of the enterprises are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise digital development, and specifically relates to an enterprise digital development risk early warning method based on multi-perspective analysis. Background Art

[0002] With the rapid development of information technology and the accelerated advancement of the global digitalization process, enterprise digitalization has become a key path to enhance enterprise competitiveness and achieve long-term sustainable development. More and more companies are deepening their understanding of digitalization and fully realizing the huge potential of digital means in optimizing internal operating processes, improving production efficiency, and innovating service models. By introducing digital technologies, enterprises have not only greatly improved operational efficiency, but also successfully achieved innovation in service models, thus standing out with unique advantages in the fiercely competitive business environment and becoming leaders in the industry. The practical results of enterprise digitalization have undoubtedly pointed out the direction for the future development of the industry.

[0003] However, enterprises are faced with complex and changeable risk factors in the process of digital development. These risk factors not only involve technical aspects, such as data security and system stability, but also cover multiple dimensions such as enterprise operations, management, and law. Traditional enterprise risk warning methods often focus on a single perspective or a single field. For example, risk warning from a technical perspective mainly focuses on technical failures, system crashes, and other issues, but ignores potential risks in enterprise operations and management; while risk warning from an operational and management perspective may ignore the uncertainty brought about by technological updates and iterations. In addition, the risk factors in the process of enterprise digital development are characterized by relevance, complexity, and dynamism. Different risk factors may be interrelated and influence each other, and with changes in the external environment and adjustments to the internal operations of the enterprise, risk factors will continue to evolve and develop. Therefore, risk warning methods from a single perspective or a single field can no longer meet the actual needs of enterprise digital development. Summary of the invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a risk warning method for enterprise digital development based on multi-perspective analysis, so as to comprehensively consider multiple dimensions such as technology, operation, management, and law, comprehensively evaluate the potential risks in the digital development of enterprises, improve the security and stability of the digital development of enterprises, provide scientific basis and decision-making support for enterprise decision makers, and help enterprises achieve digital transformation and sustainable development.

[0005] The object of the present invention is achieved by: a method for early warning of enterprise digital development risks based on multi-perspective analysis, comprising the following steps:

[0006] S1. Construct risk factor set;

[0007] S2. Construct a risk quantitative assessment model based on EW-AHP;

[0008] S3. Construct a risk evolution early warning model based on DEMATEL-ISM.

[0009] Furthermore, the step S1 constructs a risk factor set, including constructing a cognitive risk factor set, a practical risk factor set and an effectiveness risk factor set; the cognitive risk factor set includes market cognitive level risks, technical cognitive level risks and legal cognitive level risks; the practical risk factor set includes data cloud conversion risks, financial risks and supply chain risks; the effectiveness risk factor set includes process automation risks, talent development risks and organizational change risks.

[0010] Furthermore, the step S2 of constructing a risk quantification assessment model based on EW-AHP includes:

[0011] S21. Analyze typical business scenarios and build risk quantitative assessment indicators;

[0012] S22, determine the index weight based on the EW method; determine the characteristic vector based on the hierarchical analysis method; calculate the comprehensive weight of the evaluation index;

[0013] S23. Determine the factor membership, construct the risk assessment fuzzy relationship, and determine the risk level.

[0014] Furthermore, the step S21 of performing typical business scenario analysis and constructing risk quantification assessment indicators includes:

[0015] Identify typical business scenarios during the digital transformation of enterprises, and clarify the development risks through typical business scenarios. Typical business scenarios during the operation of enterprises include technology updates, enterprise development, legal disputes and enterprise operations. Construct risk quantitative assessment indicators for the digital transformation of enterprises based on typical business scenarios. The first-level indicators include technology update indicators, enterprise development indicators, legal affairs processing indicators and enterprise operation indicators. Each first-level indicator contains multiple second-level indicators.

[0016] The indicator factor set in the risk quantitative assessment indicator system is as follows:

[0017] First-level index factor: C = {C1, C2, C3, C4};

[0018] Secondary index factor: C1 = {C 11 ,C 12 ,C 13 ,C 14 ,C 15},C2={C 21 ,C 22 ,C 23 ,C 24,C 25}, C3={C 31 ,C 32 ,C 33 ,C 34},C4={C 41 ,C 42 ,C 43}.

[0019] Furthermore, the step S22 determines the indicator weight based on the EW method, determines the characteristic vector based on the hierarchical analysis method, and calculates the comprehensive weight of the evaluation indicator, including:

[0020] (1) Determine indicator weights based on the EW method

[0021] According to the definition of information entropy, the entropy value H of j evaluation indicators and i evaluation factors is obtained j , the formula is as follows,

[0022]

[0023]

[0024] Among them, n is the number of scoring experts, λ ij represents the weight of the i-th expert on the j-th indicator, Y ij represents the score of the i-th expert on the j-th indicator, i = 1, 2, ..., n;

[0025] The indicator weight calculation formula is as follows:

[0026]

[0027] Among them, ω j represents the entropy weight, ω j The sum is 1, m represents the number of indicators, j = 1, 2, ..., m;

[0028] (2) Determine the feature vector based on the analytic hierarchy process

[0029] Targeting C, C ij The table shows the relative importance of the i-th indicator and the j-th indicator. The larger the ratio, the greater the relative importance. The constructed standard takes a scale of 1-9. The judgment matrix C is as follows:

[0030]

[0031] The square root method is used to calculate the eigenvectors and eigenroots of the judgment matrix. The calculation process is as follows;

[0032] ① For each element C in the judgment matrix C ij After normalization, we get the matrix B = (b ij )n×n ,in,

[0033]

[0034] ②The normalized element b ij By taking the mth root of the row product, we get the weight vector W = (W1, W2, ..., W m );

[0035]

[0036] ③Normalize the weight vector W to obtain the feature vector R, that is

[0037]

[0038] ④ Calculate the maximum eigenvalue of the weight vector R, that is, obtain the maximum eigenvalue μ of the judgment matrix A max ;

[0039]

[0040] In order to test the logical rationality of the judgment matrix during construction and reduce interference from human factors, the consistency index CI is used to judge the consistency of the matrix; the consistency ratio CR is also introduced to judge whether the matrix is ​​consistent. When the consistency ratio calculation result is less than 0.1, the judgment matrix can be considered consistent, otherwise the judgment matrix needs to be reconstructed until it is consistent. Among them, the average random consistency index value RI is used to calculate the consistency ratio, and m is the number of dimensions, that is, the order of the judgment matrix. ;

[0041]

[0042]

[0043] (3) Calculate the comprehensive weight of evaluation indicators

[0044] The comprehensive weight of the evaluation index is calculated using the characteristic vector R determined by the hierarchical analysis method and the index weight ω determined by the entropy weight method. The formula is as follows:

[0045] V ij =αω ij +(1-α)R ij (11)

[0046] Among them, α is the weight coefficient, and the specific value is determined according to the tendency of the evaluation method, 0<α<1.

[0047] Furthermore, the step S23 determines the factor membership, constructs the risk assessment fuzzy relationship, and determines the risk level including:

[0048] The enterprise digital transformation risk indicators are scored one by one, and the risk levels are divided into 5 levels: low risk, relatively low risk, general, relatively high risk, and high risk. The number of scorers at each security level is divided by the total number of scorers as the membership value of the evaluation factor to the security level. The membership matrix G of each factor is expressed as

[0049]

[0050] Comprehensive weight V = (V i1 ,V i2 ,V i3 ,…,V im ) Comprehensively evaluate the secondary evaluation indicators and obtain the evaluation vector U of the secondary evaluation indicator layer.

[0051] U=V×G (13)

[0052] The safety score is determined by the weighted evaluation principle. The median of each safety level is taken to form E = (95, 85, 75, 65, 30). The risk score of the digital transformation enterprise is calculated, and the risk level D is determined by the score range.

[0053] D=E×U T (14).

[0055] Furthermore, the step S3 of constructing a risk evolution warning model based on DEMATEL-ISM includes:

[0056] S31. Conduct key factor analysis under multi-perspective characteristics, establish a key factor set under multi-perspective characteristics, and determine the risk warning indicators for enterprise digital transformation; establish a direct impact matrix of enterprise digital development risks and standardize the direct impact matrix;

[0057] S32. Calculate the comprehensive impact matrix of the factors affecting the digital development risk of an enterprise; calculate the influence, influence, centrality and cause of each factor affecting the digital development risk of an enterprise; calculate the overall relationship matrix of the factors affecting the digital development risk of an enterprise; determine the reachable matrix of the factors affecting the digital development project risk of an enterprise;

[0058] S33. Determine the reachable set, predecessor set and intersection of the risk influencing factors of enterprise digital development; divide the levels of the risk influencing factors of enterprise digital development; and establish a multi-level hierarchical structure model of the risk influencing factors of enterprise digital development.

[0059] Furthermore, the step S31 includes:

[0060] The key factors that lead to risks in the digital development of enterprises are analyzed from the perspectives of technology, operation management, legality and compliance, and market. The key factors from the technical perspective include data migration, technical compatibility, network security, technical cost, and technical dependence; the key factors from the operation management perspective include operational efficiency, process change, organizational culture, talent training, and financial management; the key factors from the legality and compliance perspective include intellectual property rights, data ownership, contractual legal risks, and third-party risks; the key factors from the market perspective include market positioning, adaptability to market changes, and customer data; the selection of enterprise digital transformation risk warning indicators is mainly based on the key factors under the above multi-perspective characteristics, including technical indicators, operation management indicators, legal indicators, and market indicators;

[0061] According to the relationship between different influencing factors of enterprise digital development risk, a ij Refers to F i =(i=1,2,…,) and F j =(j=1,2,…,), and the correlation degrees 0, 1, 2, and 3 are used to represent the factors F respectively. i Factor F j The degree of influence, i.e. a ij =0, 1, 2, 3 represent no impact, weak impact, medium impact, and strong impact respectively; the specific value of the impact degree is determined by the expert scoring method, and the direct impact matrix A of the risk influencing factors of enterprise digital development is obtained;

[0062]

[0063] Normalize the direct impact matrix of the factors affecting the digital development risk of an enterprise. Sum the elements of each row in the direct impact matrix and find the maximum value. According to formula (16), the normalized direct impact B can be obtained.

[0064]

[0065] in, Represents the maximum value of the row sum.

[0066] Furthermore, the step S32 includes

[0067] By accumulating direct and indirect impacts, the comprehensive impact matrix C is finally determined.

[0068]

[0069] Among them, E is the unit matrix, because b ij ∈[0,1), when k→∞, B k →0, then

[0070] C=B(EB)-1 (18)

[0071] Note the influencing factor F i =(i=1,2,…,n) the influence is f, the influence is e; the centrality is M, the cause is N, then the calculation formulas for the influence, influence, centrality and cause of the influencing factor are as follows:

[0072]

[0073]

[0074] M i =f i +e i (twenty one)

[0075] N i =f i -e i (twenty two)

[0076] When calculating the overall relationship matrix D of the risk influencing factors of enterprise digital development, it is only necessary to add its unit matrix on the basis of C. The calculation formula is as follows:

[0077] D=C+E (23)

[0078] Where E is the identity matrix;

[0079] The reachability matrix K = [k ij ] n×n , which indicates whether there is a path between any two vertices in a directed graph consisting of n points. Each point represents an influencing factor. If the influencing factor F i and influencing factors F j If there is a path between them, then element k ij =1, otherwise k ij =0, k ij The calculation formula is as follows,

[0080]

[0081] Among them, λ is the threshold.

[0082] Furthermore, the step S33 includes

[0083] Reachable set R i It refers to the set of influencing factors corresponding to the columns of the i-th row of the reachable matrix K, which represents the influencing factor F i Can reach the set composed of other influencing factors; the preceding set S iIt refers to the set of influencing factors corresponding to the row with influencing factor 1 in the i-th column of the reachable matrix K, which represents all influencing factors F that can be reached. i The set of influencing factors is as follows:

[0084]

[0085] According to formula (25), if there is R i =R i ∩S i (i=1,2,…,n), then the intersection of the reachable set and the predecessor set is a non-empty set, and F i The top-level influencing factors are removed, and the influencing factors of the i-th row and i-th column of the reachable matrix K are removed. All F i The set of S is recorded as the set of top-level influencing factors; if there is S i =R i ∩S i (i=1,2,…,n), then F i As the lowest level influencing factor, the influencing factors of the i-th row and i-th column of the reachable matrix K are also crossed out. All F i The set of is recorded as the set of the lowest level influencing factors; the reachable matrix after the above processing is called the contraction point reachable matrix, and the contraction point reachable matrix is ​​used in the same way to obtain the second highest level and the second lowest level influencing factor sets, and so on until all the influencing factors of the reachable matrix K are crossed out, and the hierarchical order of each factor is recorded;

[0086] The core work of establishing a multi-level hierarchical structure model is hierarchical division, that is, determining the position of each influencing factor in the hierarchical structure, and drawing a hierarchical structure model of the factors affecting the digital development of an enterprise based on the hierarchical division of the factors affecting the risk of digital development of an enterprise.

[0087] Beneficial effects of the present invention: The present invention provides an enterprise digital development risk warning method based on multi-perspective analysis, which realizes a comprehensive and systematic evaluation of multi-dimensional risk factors such as technology, operation, management, and law in the process of enterprise digitalization by constructing an enterprise digital development risk warning system based on multi-perspective analysis. This technical solution not only improves the accuracy and timeliness of risk warnings, but also introduces the EW-AHP model to conduct risk quantitative assessment, making the assessment results more objective and scientific. In addition, the present invention also integrates the DEMATEL-ISM model, deeply analyzes the interaction and influence mechanism between risk factors, and provides enterprises with a clear risk evolution path and early warning indicators. By implementing the technical solution of the present invention, enterprises can identify and respond to potential risks in advance, effectively reduce the uncertainty in the digital transformation process, enhance the risk resistance and market competitiveness of enterprises, and ensure the smooth progress and sustainable development of enterprise digital transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0089] Figure 1 A flowchart of the enterprise digital development risk early warning method based on multi-perspective analysis; DETAILED DESCRIPTION

[0090] The present invention will be further described below in conjunction with the accompanying drawings.

[0091] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0092] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0093] like Figure 1 As shown, a method for early warning of enterprise digital development risks based on multi-perspective analysis of the present invention comprises the following steps:

[0094] S1. Construct risk factor set;

[0095] S2. Construct a risk quantitative assessment model based on EW-AHP;

[0096] S3. Construct a risk evolution early warning model based on DEMATEL-ISM.

[0097] Furthermore, the step S1 constructs a risk factor set including constructing a cognitive risk factor set, a practical risk factor set and an effectiveness risk factor set, as shown in Table 1; the cognitive risk factor set includes market cognitive level risks, technical cognitive level risks and legal cognitive level risks; the practical risk factor set includes data cloud conversion risks, financial risks and supply chain risks; the effectiveness risk factor set includes process automation risks, talent development risks and organizational change risks.

[0098] Table 1 Construction of risk factor set

[0099]

[0100] In the process of digital development, enterprises will be affected by different factors at the cognitive, practical and effectiveness levels, and the risks involved need to be clarified by enterprises so that they can be better prevented.

[0101] The cognitive risk factor set refers to the inability to accurately assess and respond to related risks due to cognitive biases or deficiencies in the market environment, technological development, laws and regulations, competitors, etc. during the digital development of an enterprise.

[0102] (1) Market awareness

[0103] At the market awareness level, there are external risks such as environmental change risk and market volatility risk. Environmental changes generally refer to policy environment, natural environment, etc. The digital development of enterprises will face problems such as changes in government policy orientation and reduction of government subsidies due to the emergence of new technologies and new rules. Since digital development is mainly on the data and network ends, enterprises are relatively less affected by changes in the natural environment. Because digital development requires enterprises to invest in human resources, funds and other resources, external market fluctuations may increase corporate costs and reduce product competitiveness, which in turn affects the development of digitalization.

[0104] (2) Technical cognition level

[0105] At the technical cognition level, enterprises lack sufficient knowledge of the development trend of digital technology, their own R&D capabilities or the feasibility of technology applications, which affects product innovation and competitiveness, including data ownership risks and network security risks. Unclear definition of data ownership leads to problems in digital governance, data concentration, and user personal data protection, which will bring potential risks to enterprise development. Therefore, data ownership, that is, risks in the ownership, use, protection and transaction of data, will have an important impact on the digital development of enterprises and need to be clarified by enterprises. In the network environment, due to information security threats caused by various reasons, these risks may damage the confidentiality, integrity and availability of information systems. In the process of digital development of enterprises, they will be affected by various external and internal network security issues, such as viruses, phishing emails, forged URLs, etc., which lead to damage to enterprise data, leakage of enterprise information, etc., or loss of internal data information due to personnel negligence, software vulnerabilities, etc.

[0106] (3) Legal cognition

[0107] In terms of legal awareness, enterprises need to ensure that their operations and data processing activities comply with laws and regulations during the digitalization process, which involves data protection, network security, intellectual property rights, etc. In addition, the transparency of the data processing process and data processing results will also generate the risk of violations caused by information leakage or failure to disclose the corresponding data.

[0108] The set of practical risk factors refers to the risks that enterprises may encounter in the implementation of their digital strategies and daily operations, mainly including data cloud conversion risks, financial risks, supply chain risks, etc.

[0109] Enterprises may face a series of risks when migrating their businesses to cloud computing environments. These risks may involve data security, system stability, cost control, compliance, service availability, etc. In the process of digital development, enterprises may encounter financial problems such as reduced economic benefits, broken capital chains, and damaged credit due to various factors. In the process of digital transformation, enterprises may face risks in the supply chain management process such as supply chain disruptions caused by changes in suppliers.

[0110] The efficiency risk factor set refers to the risk of low efficiency, poor benefits or failure to achieve goals due to internal management, operating procedures, employee behavior and other factors during the operation of the enterprise. It mainly includes process automation risks, talent training risks and organizational change risks.

[0111] Digital development will promote the automation of enterprise processes. Although it brings benefits such as efficiency improvement and cost savings to enterprises, it may also be accompanied by some risks during the implementation process. The digital development of enterprises will inevitably prompt enterprises to carry out reform activities such as structural adjustment, process optimization, and cultural reshaping. However, in the process of organizational change, problems such as employee resistance, lack of leadership, cultural conflicts, resource allocation, and customer loss will inevitably arise within the enterprise. These risks will hinder the process of digital development of enterprises. Digital development will bring new technologies, new projects, and new structures, which will require targeted training of professional talents. In the process of talent training, there may be problems with training results, communication problems, and adaptability problems, which will hinder the digitalization process of enterprises.

[0112] Furthermore, the step S2 of constructing a risk quantification assessment model based on EW-AHP includes:

[0113] S21. Analyze typical business scenarios and build risk quantitative assessment indicators;

[0114] S22, determine the index weight based on the EW method; determine the characteristic vector based on the hierarchical analysis method; calculate the comprehensive weight of the evaluation index;

[0115] S23. Determine the factor membership, construct the risk assessment fuzzy relationship, and determine the risk level.

[0116] Furthermore, the step S21 of performing typical business scenario analysis and constructing risk quantification assessment indicators includes:

[0117] Identify typical business scenarios during the digital transformation of enterprises, and clarify the development risks through typical business scenarios. Typical business scenarios during the operation of enterprises include technology updates, enterprise development, legal disputes and enterprise operations. Construct risk quantitative assessment indicators for the digital transformation of enterprises based on typical business scenarios. The first-level indicators include technology update indicators, enterprise development indicators, legal affairs processing indicators and enterprise operation indicators. Each first-level indicator contains multiple second-level indicators.

[0118] The digitalization process of an enterprise is not a transformation in a single direction, but a joint development from multiple aspects such as operation management, technology, etc. In view of the various risk factors in the digital development of the above-mentioned enterprises, this paper clarifies the typical business scenarios of enterprises in the process of digital transformation, and clarifies the development risks through typical business scenarios. The enterprises mainly have the following typical business scenarios in the operation process.

[0119] (1) Technology Update

[0120] In the process of digital transformation of enterprises, it is necessary to digitally upgrade existing technical equipment. There are various risks in this scenario. Data loss and data corruption are prone to occur during the process of organizing, transferring and uploading local data to the cloud, resulting in the damage of historical customer and product information of the enterprise, reducing production efficiency and affecting the digital transformation of the enterprise. In addition, the process of enterprise software upgrade and data transfer is vulnerable to network attacks, Trojan viruses, etc., and targeted supervision of such issues is required.

[0121] (2) Enterprise development

[0122] The digital transformation of an enterprise will inevitably affect the strategic development direction and organizational framework of the enterprise. The strategic development of an enterprise involves in-depth insights into market trends, competitive environment, technological changes, laws and regulations, organizational capabilities, resource allocation, and other aspects to identify potential threats and opportunities, thereby helping the enterprise to formulate effective risk response strategies and flexibly adjust strategic plans to ensure that the enterprise can continuously and stably achieve its development goals and maintain its competitive advantage. The organizational framework of an enterprise needs to be based on the perspectives of both senior managers and grassroots employees. For the former, the speed of enterprise software and hardware updates and the optimization of business processes increase the risk of learning and usage costs for senior managers, and may also generate new departments and managers, resulting in changes in the power structure; for the latter, changes in business processes increase learning costs, which may lead to problems such as increased time and increased error rates, and changes in the organizational structure will also affect grassroots employees.

[0123] (3) Legal disputes

[0124] Enterprises need to have dedicated departments or employees to deal with legal disputes. With the advancement of digital transformation, legal disputes of new forms and contents will arise. Therefore, the original departments or employees face certain risks when dealing with such legal disputes related to digitalization, data ownership and other issues.

[0125] (4) Business operations

[0126] In terms of business operations, enterprises need to consider external markets and internal management. In view of the market environment, enterprises need to take into account both upstream suppliers and downstream customer groups. In the process of digital transformation, they need to maintain good relationships with suppliers and find suppliers that meet the needs of digital development to avoid the risk of production interruption caused by supplier changes after the upgrade is completed. In addition, enterprises also need to maintain downstream customers, ensure market share during the transformation and upgrading process, and ensure their competitiveness in the industry. Internal financial management will bring challenges to the digital transformation of enterprises. In normal operation, problems such as cash flow and corporate debt can be kept stable by relying on financial management. However, in the process of digital transformation and upgrading of enterprises, improper management of this business scenario may lead to debt risks, capital chain disruption risks and other events.

[0127] Table 2 Risk quantitative assessment indicator system

[0128]

[0129] The indicator factor set in the risk quantitative assessment indicator system is as follows:

[0130] First-level index factor: C = {C1, C2, C3, C4};

[0131] Secondary index factor: C1 = {C 11 ,C 12 ,C 13 ,C 14 ,C 15},C2={C 21 ,C 22 ,C 23 ,C 24 ,C 25}, C3={C 31 ,C 32 ,C 33 ,C 34},C4={C 41 ,C 42 ,C 43}.

[0132] Furthermore, the step S22 determines the indicator weight based on the EW method, determines the characteristic vector based on the hierarchical analysis method, and calculates the comprehensive weight of the evaluation indicator, including:

[0133] (1) Determine indicator weights based on the EW method

[0134] Entropy is a concept derived from the second law of thermodynamics to describe the degree of energy uniformity. It was introduced into information theory by Shannon to describe the degree of information disorder. The smaller its value, the smaller the system disorder. Therefore, information entropy can be used to evaluate the order of system information. After the evaluation object is given in the evaluation index system, the differences between different indicator factors are also different, and the validity of information is also different. The smaller the information entropy of the indicator factor, the more information the indicator factor reflects, the greater its role in the evaluation process, and the higher its weight. The entropy weight method can eliminate the subjective behavior of calculating the weights of each indicator. It is an objective assignment method, and the results obtained can better reflect objective facts.

[0135] According to the definition of information entropy, the entropy value H of j evaluation indicators and i evaluation factors is obtained j , the formula is as follows,

[0136]

[0137]

[0138] Among them, n is the number of scoring experts, λ ij represents the weight of the i-th expert on the j-th indicator, Y ij represents the score of the i-th expert on the j-th indicator, i = 1, 2, ..., n;

[0139] The indicator weight calculation formula is as follows:

[0140]

[0141] Among them, ω j represents the entropy weight, ω j The sum is 1, m represents the number of indicators, j = 1, 2, ..., m;

[0142] (2) Determine the feature vector based on the analytic hierarchy process

[0143] The AHP combines qualitative and quantitative evaluations and is systematic and hierarchical. The AHP is simple and clear, and has a wide range of applications. It can be used in situations where information is imperfect or where subjective management makes judgments. The AHP better demonstrates the relative importance of different levels. Using the AHP to analyze expert opinions in enterprise safety evaluation can prevent misjudgments caused by subjective opinions and lack of experience of experts.

[0144] Targeting C, C ij The table shows the relative importance of the i-th indicator and the j-th indicator. The larger the ratio, the greater the relative importance. The constructed standard takes a scale of 1-9. The judgment matrix C is as follows:

[0145]

[0146] By calculating the maximum eigenvalue of the judgment matrix and normalizing it, we can get the weights corresponding to different indicators. The methods for calculating the maximum eigenvalue of the weight include square root method, iteration method, and product method. The following uses the square root method to calculate the eigenvector and eigenroot of the judgment matrix. The calculation process is as follows;

[0147] ① For each element C in the judgment matrix C ij After normalization, we get the matrix B = (b ij ) n×n ,in,

[0148]

[0149] ②The normalized element b ij By taking the mth root of the row product, we get the weight vector W = (W1, W2, ..., W m );

[0150]

[0151] ③Normalize the weight vector W to obtain the feature vector R, that is

[0152]

[0153] ④ Calculate the maximum eigenvalue of the weight vector R, that is, obtain the maximum eigenvalue μ of the judgment matrix A max ;

[0154]

[0155] In order to test the logical rationality of the judgment matrix during construction and reduce interference from human factors, the consistency index CI is used to judge the consistency of the matrix; the consistency ratio CR is also introduced to judge whether the matrix is ​​consistent. When the consistency ratio calculation result is less than 0.1, the judgment matrix can be considered consistent, otherwise the judgment matrix needs to be reconstructed until it is consistent. Among them, the average random consistency index value RI is used to calculate the consistency ratio, and m is the number of dimensions, that is, the order of the judgment matrix. ;

[0156]

[0157]

[0158] (3) Calculate the comprehensive weight of evaluation indicators

[0159] The comprehensive weight of the evaluation index is calculated using the characteristic vector R determined by the hierarchical analysis method and the index weight ω determined by the entropy weight method. The formula is as follows:

[0160] V ij =αω ij +(1-α)R ij (11)

[0161] Among them, α is the weight coefficient, and the specific value is determined according to the tendency of the evaluation method, 0<α<1.

[0162] Furthermore, the step S23 determines the factor membership, constructs the risk assessment fuzzy relationship, and determines the risk level including:

[0163] The enterprise digital transformation risk indicators are scored one by one, and the risk levels are divided into 5 levels: low risk, relatively low risk, general, relatively high risk, and high risk. The number of scorers at each security level is divided by the total number of scorers as the membership value of the evaluation factor to the security level. The membership matrix G of each factor is expressed as

[0164]

[0165] Comprehensive weight V = (V i1 ,V i2 ,V i3 ,…,V im ) Comprehensively evaluate the secondary evaluation indicators and obtain the evaluation vector U of the secondary evaluation indicator layer.

[0166] U=V×G (13)

[0167] The safety score is determined by the weighted evaluation principle. The median of each safety level is taken to form E = (95, 85, 75, 65, 30). The risk score of the digital transformation enterprise is calculated, and the risk level D is determined by the score range.

[0168] D=E×U T (14).

[0170] Furthermore, the step S3 of constructing a risk evolution warning model based on DEMATEL-ISM includes:

[0171] S31. Conduct key factor analysis under multi-perspective characteristics, establish a key factor set under multi-perspective characteristics, and determine the risk warning indicators for enterprise digital transformation; establish a direct impact matrix of enterprise digital development risks and standardize the direct impact matrix;

[0172] S32. Calculate the comprehensive impact matrix of the factors affecting the digital development risk of an enterprise; calculate the influence, influence, centrality and cause of each factor affecting the digital development risk of an enterprise; calculate the overall relationship matrix of the factors affecting the digital development risk of an enterprise; determine the reachable matrix of the factors affecting the digital development project risk of an enterprise;

[0173] S33. Determine the reachable set, predecessor set and intersection of the risk influencing factors of enterprise digital development; divide the levels of the risk influencing factors of enterprise digital development; and establish a multi-level hierarchical structure model of the risk influencing factors of enterprise digital development.

[0174] Furthermore, the step S31 includes:

[0175] The digital development of an enterprise is a long and complex process, so enterprises need to observe potential risks from different perspectives and issue early warnings in a timely manner. The present invention mainly analyzes the key factors that lead to risks in the digital development of an enterprise from the perspectives of technology, operation management, legality and compliance, and market. The key factors from the technical perspective include data migration, technical compatibility, network security, technical cost, and technical dependence, as shown in Table 3; the key factors from the operation management perspective include operational efficiency, process change, organizational culture, talent training, and financial management; the key factors from the legality and compliance perspective include intellectual property rights, data ownership, contractual legal risks, and third-party risks; the key factors from the market perspective include market positioning, adaptability to market changes, and customer data; the selection of risk warning indicators for enterprise digital transformation is mainly based on the key factors under the above-mentioned multi-perspective characteristics, including technical indicators, operation management indicators, legal indicators, and market indicators, as shown in Table 4;

[0176] Table 3 Key factor set under multi-view features

[0177]

[0178] Table 4 Early warning indicators for enterprise digital development risks

[0179]

[0180]

[0181] According to the relationship between different influencing factors of enterprise digital development risk, a ij Refers to F i =(i=1,2,…,) and F j =(j=1,2,…,), and the correlation degrees 0, 1, 2, and 3 are used to represent the factors F respectively. i Factor F j The degree of influence, i.e. a ij =0, 1, 2, 3 represent no impact, weak impact, medium impact, and strong impact respectively; the specific value of the impact degree is determined by the expert scoring method, and the direct impact matrix A of the risk influencing factors of enterprise digital development is obtained;

[0182]

[0183] Normalize the direct impact matrix of the factors affecting the digital development risk of an enterprise. Sum the elements of each row in the direct impact matrix and find the maximum value. According to formula (16), the normalized direct impact B can be obtained.

[0184]

[0185] in, Represents the maximum value of the row sum.

[0186] Furthermore, the step S32 includes

[0187] In order to further consider the indirect impact relationship between causal factors and the ripple effect caused by the change of one factor, the comprehensive impact matrix C can be finally determined by accumulating the direct impact and indirect impact.

[0188]

[0189] Among them, E is the unit matrix, because b ij ∈[0,1), when k→∞, B k →0, then

[0190] C=B(EB) -1 (18)

[0191] The influence degree is the comprehensive influence value of each row element on all other elements in the comprehensive influence matrix C, which is obtained by adding the values ​​of each row element of C; the influence degree is the comprehensive influence value of each column corresponding element on all other elements, which is obtained by adding the values ​​of each column element of C; the centrality of the influencing factor can be obtained by adding the influence degree and the influence degree of the influencing factor. The centrality indicates the importance of the influencing factor in risk control. The larger the centrality, the greater the correlation between the influencing factor and other factors, and the higher the importance of the influencing factor; the influencing factor F is recorded as i =(i=1,2,…,n) the influence is f, the influence is e; the centrality is M, the cause is N, then the calculation formulas for the influence, influence, centrality and cause of the influencing factor are as follows:

[0192]

[0193]

[0194] M i =f i +e i (twenty one)

[0195] N i =f i -e i (twenty two)

[0196] Since the comprehensive impact matrix D only considers the impact of each factor on other factors, but not the impact on itself, the impact of each factor on itself is just on the lower right diagonal of the matrix, which can be represented by the unit matrix E. Therefore, when calculating the overall relationship matrix D of the risk influencing factors of enterprise digital development, it is only necessary to add its unit matrix on the basis of C. The calculation formula is as follows:

[0197] D=C+E (23)

[0198] Where E is the identity matrix;

[0199] The reachability matrix K = [k ij ] n×n , which indicates whether there is a path between any two vertices in a directed graph consisting of n points. Each point represents an influencing factor. If the influencing factor F i and influencing factors F j If there is a path between them, then element k ij =1, otherwise k ij =0, k ij The calculation formula is as follows,

[0200]

[0201] Among them, λ is the threshold. The purpose of setting the threshold is to screen the influence relationship between indicators with a smaller degree of influence. Reasonable setting of the threshold can help simplify the hierarchical structure of the influence relationship between factors, and at the same time, it can also retain and reflect the main influence relationship of each factor on other factors to the greatest extent, so it will not affect the judgment of the decision. Since the size of the threshold can directly affect the structure of the reachable matrix level and the division of the hierarchy between the influencing factors, the setting of the threshold needs to be cautious. There are three common methods:

[0202] (a) Setting the threshold using the empirical method. This can be determined based on the research of experts and predecessors in this field. For systems with a small number of influencing factors, it is usually not necessary to simplify the system structure and the threshold can be set to zero. Since there are many factors affecting the risk of digital development of enterprises, the relationships are relatively complex, and there are few similar methods for research, it is impossible to obtain an empirical threshold.

[0203] (b) Setting thresholds by metrological inspection method. This method is similar to the operation of multiple value verification, which generally requires a large amount of preliminary data research, and the data obtained from the research is relatively complete and the conditions are relatively harsh.

[0204] (c) Mean value method. In recent years, some researchers have begun to use the mean value of the comprehensive influence matrix as the threshold of the reachable matrix. For example, when studying the influencing factors of the cost of steel structure prefabricated buildings, this method was used as the threshold of the overall relationship matrix, and a relatively ideal model result was obtained. Some researchers will make some deformations based on the mean value method, such as adding the standard deviation of the sample, and then use the summed result as the threshold. This method usually requires a smaller deviation and is an optimization adjustment of the mean value method under specific circumstances. The mean value method can be applied to the establishment of the relationship model when there are many factors, while avoiding a large amount of preliminary data collection work and improving the operability of the method.

[0205] Furthermore, the step S33 includes

[0206] Reachable set R i It refers to the set of influencing factors corresponding to the columns of the i-th row of the reachable matrix K, which represents the influencing factor F i Can reach the set composed of other influencing factors; the preceding set S i It refers to the set of influencing factors corresponding to the row with influencing factor 1 in the i-th column of the reachable matrix K, which represents all influencing factors F that can be reached. i The set of influencing factors is as follows:

[0207]

[0208] According to formula (25), if there is R i =R i ∩S i (i=1,2,…,n), then the intersection of the reachable set and the predecessor set is a non-empty set, and F i The top-level influencing factors are removed, and the influencing factors of the i-th row and i-th column of the reachable matrix K are removed. All F i The set of S is recorded as the set of top-level influencing factors; if there is S i =R i ∩S i (i=1,2,…,n), then F i As the lowest level influencing factor, the influencing factors of the i-th row and i-th column of the reachable matrix K are also crossed out. All F i The set of is recorded as the set of the lowest level influencing factors; the reachable matrix after the above processing is called the contraction point reachable matrix, and the contraction point reachable matrix is ​​used in the same way to obtain the second highest level and the second lowest level influencing factor sets, and so on until all the influencing factors of the reachable matrix K are crossed out, and the hierarchical order of each factor is recorded;

[0209] The core work of establishing a multi-level hierarchical structure model is hierarchical division, that is, determining the position of each influencing factor in the hierarchical structure, and drawing a hierarchical structure model of the factors affecting the digital development of an enterprise based on the hierarchical division of the factors affecting the risk of digital development of an enterprise.

[0210] In summary, the enterprise digital development risk warning method based on multi-perspective analysis of the present invention realizes a comprehensive and systematic evaluation of multi-dimensional risk factors such as technology, operation, management, and law in the process of enterprise digitalization by constructing an enterprise digital development risk warning system based on multi-perspective analysis. This technical solution not only improves the accuracy and timeliness of risk warning, but also introduces the EW-AHP model to conduct risk quantitative assessment, making the assessment results more objective and scientific. In addition, the present invention also integrates the DEMATEL-ISM model, deeply analyzes the interaction and influence mechanism between risk factors, and provides enterprises with a clear risk evolution path and early warning indicators. By implementing the technical solution of the present invention, enterprises can identify and respond to potential risks in advance, effectively reduce the uncertainty in the digital transformation process, enhance the risk resistance and market competitiveness of enterprises, and ensure the smooth advancement and sustainable development of enterprise digital transformation.

[0211] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0212] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A risk warning method for enterprise digital development based on multi-perspective analysis, characterized in that: The following steps are involved: S1. Construct risk factor set; S2. Construct a risk quantitative assessment model based on EW-AHP; S3. Construct a risk evolution early warning model based on DEMATEL-ISM.

2. The enterprise digital development risk early warning method based on multi-perspective analysis as claimed in claim 1, characterized in that: The step S1 of constructing a risk factor set includes constructing a cognitive risk factor set, a practical risk factor set and an efficacy risk factor set; The set of cognitive risk factors includes market cognitive level risk, technical cognitive level risk and legal cognitive level risk; The set of practical risk factors includes data cloud conversion risk, financial risk and supply chain risk; the set of performance risk factors includes process automation risk, talent development risk and organizational change risk.

3. The enterprise digital development risk early warning method based on multi-perspective analysis as claimed in claim 1, characterized in that: The step S2 of constructing a risk quantification assessment model based on EW-AHP includes: S21. Analyze typical business scenarios and build risk quantitative assessment indicators; S22, determine the index weight based on the EW method; determine the characteristic vector based on the hierarchical analysis method; calculate the comprehensive weight of the evaluation index; S23. Determine the factor membership, construct the risk assessment fuzzy relationship, and determine the risk level.

4. The enterprise digital development risk early warning method based on multi-perspective analysis as claimed in claim 1 is characterized in that: The step S21 of analyzing typical business scenarios and constructing risk quantification assessment indicators includes: Identify typical business scenarios during the digital transformation of enterprises, and clarify the development risks through typical business scenarios. Typical business scenarios during the operation of enterprises include technology updates, enterprise development, legal disputes and enterprise operations. Construct risk quantitative assessment indicators for the digital transformation of enterprises based on typical business scenarios. The first-level indicators include technology update indicators, enterprise development indicators, legal affairs processing indicators and enterprise operation indicators. Each first-level indicator contains multiple second-level indicators. The indicator factor set in the risk quantitative assessment indicator system is as follows: First-level index factor: C = {C1, C2, C3, C4}; Secondary index factor: C1 = {C 11 ,C 12 ,C 13 ,C 14 ,C 15 },C2={C 21 ,C 22 ,C 23 ,C 24 ,C 25 }, C3={C 31 ,C 32 ,C 33 ,C 34 },C4={C 41 ,C 42 ,C 43 }.

5. The enterprise digital development risk early warning method based on multi-perspective analysis as claimed in claim 1, characterized in that: The step S22 determines the index weight based on the EW method, determines the characteristic vector based on the hierarchical analysis method, and calculates the comprehensive weight of the evaluation index, including: (1) Determine indicator weights based on the EW method According to the definition of information entropy, the entropy value H of j evaluation indicators and i evaluation factors is obtained j , the formula is as follows, Among them, n is the number of scoring experts, λ ij represents the weight of the i-th expert on the j-th indicator, Y ij represents the score of the i-th expert on the j-th indicator, i = 1, 2, ..., n; The indicator weight calculation formula is as follows: Among them, ω j represents the entropy weight, ω j The sum is 1, m represents the number of indicators, j = 1, 2, ..., m; (2) Determine the feature vector based on the analytic hierarchy process Targeting C, C ij The table shows the relative importance of the i-th indicator and the j-th indicator. The larger the ratio, the greater the relative importance. The constructed standard takes a scale of 1-9. The judgment matrix C is as follows: The square root method is used to calculate the eigenvectors and eigenroots of the judgment matrix. The calculation process is as follows; 1① For each element C in the judgment matrix C ij After normalization, we get the matrix B = (b ij ) n×n , in, ②The normalized element b ij Take the mth root of the row product and get the weight vector In=(In1,In2,…,In m ); ③Normalize the weight vector W to obtain the feature vector R, that is ④ Calculate the maximum eigenvalue of the weight vector R, that is, obtain the maximum eigenvalue μ of the judgment matrix A max ; In order to test the logical rationality of the judgment matrix during construction and reduce interference from human factors, the consistency index CI is used to judge the consistency of the matrix; the consistency ratio CR is also introduced to judge whether the matrix is ​​consistent. When the consistency ratio calculation result is less than 0.1, the judgment matrix can be considered consistent, otherwise the judgment matrix needs to be reconstructed until it is consistent. Among them, the average random consistency index value RI is used to calculate the consistency ratio, and m is the number of dimensions, that is, the order of the judgment matrix. ; (3) Calculate the comprehensive weight of evaluation indicators The comprehensive weight of the evaluation index is calculated using the characteristic vector R determined by the hierarchical analysis method and the index weight ω determined by the entropy weight method. The formula is as follows: V ij =oh ij +(1-α)R ij (11) Among them, α is the weight coefficient, and the specific value is determined according to the tendency of the evaluation method, 0<α<1.

6. The enterprise digital development risk early warning method based on multi-perspective analysis as claimed in claim 1, characterized in that: The step S23 determines the factor membership, constructs the risk assessment fuzzy relationship, and determines the risk level, including: The enterprise digital transformation risk indicators are scored one by one, and the risk levels are divided into 5 levels: low risk, relatively low risk, general, relatively high risk, and high risk. The number of scorers at each security level is divided by the total number of scorers as the membership value of the evaluation factor to the security level. The membership matrix G of each factor is expressed as Comprehensive weight V = (V i1 ,V i2 ,V i3 ,…,V im ) Comprehensively evaluate the secondary evaluation indicators and obtain the evaluation vector U of the secondary evaluation indicator layer. U=V×G (13) The safety score is determined by the weighted evaluation principle. The median of each safety level is taken to form E = (95, 85, 75, 65, 30). The risk score of the digital transformation enterprise is calculated, and the risk level D is determined by the score range. D=E×U T (14)。 7. The enterprise digital development risk early warning method based on multi-perspective analysis as claimed in claim 1 is characterized in that: The step S3 of constructing a risk evolution warning model based on DEMATEL-ISM includes: S31. Conduct key factor analysis under multi-perspective characteristics, establish a key factor set under multi-perspective characteristics, and determine the risk warning indicators for enterprise digital transformation; establish a direct impact matrix of enterprise digital development risks and standardize the direct impact matrix; S32. Calculate the comprehensive impact matrix of the factors affecting the digital development risk of an enterprise; calculate the influence, influence, centrality and cause of each factor affecting the digital development risk of an enterprise; calculate the overall relationship matrix of the factors affecting the digital development risk of an enterprise; determine the reachable matrix of the factors affecting the digital development project risk of an enterprise; S33. Determine the reachable set, predecessor set and intersection of the risk influencing factors of enterprise digital development; divide the levels of the risk influencing factors of enterprise digital development; and establish a multi-level hierarchical structure model of the risk influencing factors of enterprise digital development.

8. The enterprise digital development risk early warning method based on multi-perspective analysis as claimed in claim 1, characterized in that: The step S31 comprises: The key factors that lead to risks in the digital development of enterprises are analyzed from the perspectives of technology, operation management, legality and compliance, and market. The key factors from the technical perspective include data migration, technical compatibility, network security, technical cost, and technical dependence; the key factors from the operation management perspective include operational efficiency, process change, organizational culture, talent training, and financial management; the key factors from the legality and compliance perspective include intellectual property rights, data ownership, contractual legal risks, and third-party risks; the key factors from the market perspective include market positioning, adaptability to market changes, and customer data; the selection of enterprise digital transformation risk warning indicators is mainly based on the key factors under the above multi-perspective characteristics, including technical indicators, operation management indicators, legal indicators, and market indicators; According to the relationship between different influencing factors of enterprise digital development risk, a ij Refers to F i =(i=1,2,…,) and F j =(j=1,2,…,), and the correlation degrees 0, 1, 2, and 3 are used to represent the factors F respectively. i Factor F j The degree of influence, i.e. ij =0, 1, 2, 3 represent no impact, weak impact, medium impact, and strong impact respectively; the specific value of the impact degree is determined by the expert scoring method, and the direct impact matrix A of the risk influencing factors of enterprise digital development is obtained; Normalize the direct impact matrix of the factors affecting the digital development risk of an enterprise. Sum the elements of each row in the direct impact matrix and find the maximum value. According to formula (16), the normalized direct impact B can be obtained. in, Represents the maximum value of the row sum.

9. The enterprise digital development risk early warning method based on multi-perspective analysis as claimed in claim 1, characterized in that: The step S32 includes By accumulating direct and indirect impacts, the comprehensive impact matrix C is finally determined. Among them, E is the unit matrix, because b ij ∈[0,1), when k→∞, B k →0, then C=B(E-B) -1 (18) Note the influencing factor F i =(i=1,2,…,n) the influence is f, the influence is e; the centrality is M, the cause is N, then the calculation formulas for the influence, influence, centrality and cause of the influencing factor are as follows: M i =f i +e i (21) N i =f i -e i (22) When calculating the overall relationship matrix D of the risk influencing factors of enterprise digital development, it is only necessary to add its unit matrix on the basis of C. The calculation formula is as follows: D=C+E (23) Where E is the identity matrix; The reachability matrix K = [k ij ] n×n , which indicates whether there is a path between any two vertices in a directed graph consisting of n points. Each point represents an influencing factor. If the influencing factor F i and influencing factors F j If there is a path between them, then element k ij =1, otherwise k ij =0, k ij The calculation formula is as follows, Among them, λ is the threshold.

10. The enterprise digital development risk early warning method based on multi-perspective analysis as claimed in claim 1, characterized in that: The step S33 includes Reachable set R i It refers to the set of influencing factors corresponding to the columns of the i-th row of the reachable matrix K, which represents the influencing factor F i Can reach the set composed of other influencing factors; the preceding set S i It refers to the set of influencing factors corresponding to the row with influencing factor 1 in the i-th column of the reachable matrix K, which represents all influencing factors F that can be reached. i The set of influencing factors is as follows: According to formula (25), if there is R i =R i ∩S i (i=1,2,…,n), then the intersection of the reachable set and the predecessor set is a non-empty set, and F i The top-level influencing factors are removed, and the influencing factors of the i-th row and i-th column of the reachable matrix K are removed. All F i The set of S is recorded as the set of top-level influencing factors; if there is S i =R i ∩S i (i=1,2,…,n), then F i As the lowest level influencing factor, the influencing factors of the i-th row and i-th column of the reachable matrix K are also crossed out. All F i The set of is recorded as the set of the lowest level influencing factors; the reachable matrix after the above processing is called the contraction point reachable matrix, and the contraction point reachable matrix is ​​used in the same way to obtain the second highest level and the second lowest level influencing factor sets, and so on until all the influencing factors of the reachable matrix K are crossed out, and the hierarchical order of each factor is recorded; The core work of establishing a multi-level hierarchical structure model is hierarchical division, that is, determining the position of each influencing factor in the hierarchical structure, and drawing a hierarchical structure model of the factors affecting the digital development of an enterprise based on the hierarchical division of the factors affecting the risk of digital development of an enterprise.

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