Enterprise digital transformation evaluation method and terminal

Abstract

According to the enterprise digital transformation evaluation method and terminal provided by the invention, a multi-dimensional evaluation index system is established based on a balance score card method; obtaining enterprise historical data from an enterprise database, and screening evaluation indexes in the tree data structure of the multi-dimensional evaluation index system through information contribution rate calculation to form a to-be-evaluated index system; for each evaluation index in the to-be-evaluated index system, generating a hierarchical structure model comprising a target layer, a criterion layer and a scheme layer, and calculating an index weight by using an analytic hierarchy process; determining an enterprise digital transformation evaluation grade based on a fuzzy comprehensive evaluation method according to enterprise historical data; according to the method, a balance score card, an analytic hierarchy process and a fuzzy comprehensive evaluation method are combined, and the digital transformation of a power grid enterprise is comprehensively evaluated by constructing a multi-dimensional evaluation index system; the defects of a traditional analytic hierarchy process are overcome, and the adaptability and accuracy of evaluation are improved by means of a fuzzy comprehensive evaluation method.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise digital transformation evaluation, and in particular to an enterprise digital transformation evaluation method and terminal. Background Art

[0002] With the digital transformation of the global economy, many companies are facing the challenge of transforming from traditional operating models to digital and intelligent ones.

[0003] Traditional methods for evaluating the effectiveness of digital transformation often focus on financial indicators, which makes it difficult to fully reflect the multi-dimensional impact of digital transformation. Financial performance is the main indicator for evaluating a company's operating capabilities, but relying solely on financial indicators cannot fully measure the impact of digital transformation on the company's overall performance.

[0004] Therefore, when enterprises undergo digital transformation, they need to establish a more comprehensive evaluation system to provide more accurate evaluation results. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and terminal for evaluating the digital transformation of an enterprise, so as to achieve a more accurate evaluation of the digital transformation of an enterprise.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for evaluating enterprise digital transformation, comprising the steps of:

[0008] S1. Establish a multi-dimensional evaluation index system based on the balanced scorecard method;

[0009] The multidimensional evaluation index system stores data in a tree data structure, wherein the tree data structure includes at least two data nodes, and the data nodes are used to store sub-node data of a preset category;

[0010] S2, obtaining enterprise historical data from the enterprise database, and screening the evaluation indicators in the tree data structure of the multidimensional evaluation indicator system by calculating the information contribution rate to form an indicator system to be evaluated;

[0011] S3, for each evaluation index in the index system to be evaluated, generate a hierarchical model including a target layer, a criterion layer, and a scheme layer, and calculate the index weight using a hierarchical analysis method;

[0012] S4. Determine the enterprise's digital transformation evaluation level based on the historical data of the enterprise and the fuzzy comprehensive evaluation method.

[0013] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0014] An enterprise digital transformation evaluation terminal includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0015] S1. Establish a multi-dimensional evaluation index system based on the balanced scorecard method;

[0016] The multi-dimensional evaluation index system stores data in a tree data structure, wherein the tree data structure includes at least two data nodes, and the data nodes are used to store sub-node data of a preset category;

[0017] S2, obtaining enterprise historical data from the enterprise database, and screening the evaluation indicators in the tree data structure of the multidimensional evaluation indicator system by calculating the information contribution rate to form an indicator system to be evaluated;

[0018] S3, for each evaluation index in the index system to be evaluated, generate a hierarchical model including a target layer, a criterion layer, and a scheme layer, and calculate the index weight using a hierarchical analysis method;

[0019] S4. Determine the enterprise's digital transformation evaluation level based on the historical data of the enterprise and the fuzzy comprehensive evaluation method.

[0020] The beneficial effects of the present invention are as follows: an enterprise digital transformation evaluation method and terminal of the present invention, combined with a balanced scorecard, a hierarchical analysis method and a fuzzy comprehensive evaluation method, conducts a comprehensive evaluation of the digital transformation of power grid enterprises by constructing a multi-dimensional evaluation index system; it can be closely combined with the strategic goals of power grid enterprises to ensure that the evaluation results are consistent with the strategic development direction of the enterprise. At the same time, the method overcomes the shortcomings of the traditional hierarchical analysis method and uses the fuzzy comprehensive evaluation method to improve the adaptability and accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of a method for evaluating enterprise digital transformation according to an embodiment of the present invention;

[0022] Figure 2 This is a structural diagram of an enterprise digital transformation evaluation terminal according to an embodiment of the present invention;

[0023] Figure 3 This is an example diagram of the financial dimension of a method for evaluating enterprise digital transformation according to an embodiment of the present invention;

[0024] Figure 4 This is an example diagram of customer latitude of an enterprise digital transformation evaluation method according to an embodiment of the present invention;

[0025] Figure 5 This is an example diagram of the internal process latitude of an enterprise digital transformation evaluation method according to an embodiment of the present invention;

[0026] Figure 6 This is an example diagram of the learning and growth dimensions of an enterprise digital transformation evaluation method according to an embodiment of the present invention;

[0027] Figure 7 This is a structural example diagram of a hierarchical model of an enterprise digital transformation evaluation method according to an embodiment of the present invention;

[0028] Description of labels:

[0029] 1. An enterprise digital transformation evaluation terminal; 2. Processor; 3. Memory. DETAILED DESCRIPTION

[0030] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0031] Please refer to Figure 1 as well as Figure 2 , a method for evaluating enterprise digital transformation, comprising the steps of:

[0032] S1. Establish a multi-dimensional evaluation index system based on the balanced scorecard method;

[0033] The multi-dimensional evaluation index system stores data in a tree data structure, wherein the tree data structure includes at least two data nodes, and the data nodes are used to store sub-node data of a preset category;

[0034] S2, obtaining enterprise historical data from the enterprise database, and screening the evaluation indicators in the tree data structure of the multidimensional evaluation indicator system by calculating the information contribution rate to form an indicator system to be evaluated;

[0035] S3, for each evaluation index in the index system to be evaluated, generate a hierarchical model including a target layer, a criterion layer, and a scheme layer, and calculate the index weight using a hierarchical analysis method;

[0036] S4. Determine the enterprise's digital transformation evaluation level based on the historical data of the enterprise and the fuzzy comprehensive evaluation method.

[0037] From the above description, it can be seen that the beneficial effects of the present invention are: an enterprise digital transformation evaluation method of the present invention combines the balanced scorecard, the hierarchical analysis method and the fuzzy comprehensive evaluation method, and conducts a comprehensive evaluation of the digital transformation of power grid enterprises by constructing a multi-dimensional evaluation index system; it can be closely combined with the strategic goals of the power grid enterprise to ensure that the evaluation results are consistent with the strategic development direction of the enterprise. At the same time, this method overcomes the shortcomings of the traditional hierarchical analysis method, and uses the fuzzy comprehensive evaluation method to improve the adaptability and accuracy of the evaluation.

[0038] Further, step S2 comprises the steps of:

[0039] S21, calculate the correlation coefficient matrix X of the m initial indicators in the multidimensional evaluation indicator system T The eigenvalue λ of X j (j=1,2,3...m):

[0040] X T X-λ j E m |=0;

[0041] Among them, X represents the indicator data matrix after Z standardization;

[0042] The initial indicators include qualitative data and quantitative data, among which the quantitative data include asset-liability ratio, return on net assets, digital profit contribution rate, digital revenue contribution rate, interest coverage ratio, total asset turnover rate, operating income, period expense ratio, net operating cash flow, client digital coverage rate, customer service ticket response timeliness rate, customer problem escalation rate, customer data resource ownership, online service rate, data consistency rate, data completeness rate, digital sharing platform coverage rate, load forecast accuracy rate, business online processing rate, digital radiation rate of the industry chain, digital infrastructure coverage rate, digital capital investment, digital research investment, digital employee retention rate and data assetization degree;

[0043] X T represents the transposed matrix of matrix X, E m represents the m-order identity matrix;

[0044] S22. Determine the key factors to be retained:

[0045] Variance contribution rateω j The cumulative variance contribution rate of the larger p factors Ω p satisfy:

[0046]

[0047] Then keep these p key factors;

[0048] Among them, M0 represents the critical value of the cumulative variance contribution rate of the p factors after screening that can explain most of the information of the original indicator set;

[0049] S23, calculate the factor loading matrix A = (a ij ) m×p :

[0050]

[0051] Among them, ξ j(j=1,2,3...p) is the jth largest eigenvalue λ in the correlation coefficient matrix j The corresponding orthogonalized eigenvectors;

[0052] Calculate the initial index X i Information contribution rate I i :

[0053]

[0054] S24. Calculate the sum of the information contribution rates of the s initial indicators with relatively large information contribution rates, and obtain the cumulative information contribution rate R s :

[0055]

[0056] Among them, I mi It is the information contribution rate of the i-th largest one after all the m initial indicators are sorted from largest to smallest;

[0057] S25. According to the cumulative information contribution rate R s To filter indicators:

[0058] If present:

[0059] R s-1 <R0≤R s ;

[0060] Then retain the s initial indicators with larger information contribution rate;

[0061] Among them, R0 represents the critical value of the cumulative information contribution rate;

[0062] S26. Calculate the morbidity index CI for the retained initial indicators s calculate:

[0063]

[0064] in, are the maximum and minimum eigenvalues ​​of the correlation coefficient matrix of the s initial indicators;

[0065] If the pathological index is less than the preset pathological threshold, there is no need to remove the initial index to form an index system to be evaluated, otherwise, the process proceeds to step S27;

[0066] S27, among the remaining initial indicators, the initial indicator with the smallest information contribution rate among the two initial indicators with the largest absolute value of the Pearson correlation coefficient is eliminated, and the process returns to step S26.

[0067] From the above description, it can be seen that the indicators under each of the four dimensions of the balanced scorecard are screened through the above method to form an indicator system to be evaluated.

[0068] Further, step S3 comprises the steps of:

[0069] S31, generating a hierarchical structure model including a target layer, a criterion layer, and a solution layer according to various evaluation indicators in the indicator system to be evaluated;

[0070] S32, obtaining a comparative evaluation of the relative importance of every two indicators in the evaluation indicator system to be evaluated, and constructing a comparative judgment matrix A n×n :

[0071]

[0072] S33, merging the comparison judgment matrices from different sources to generate a unique integrated matrix

[0073]

[0074] S34, normalize the geometric mean of each row of the unique integrated matrix to obtain the weight vector W of each evaluation index i (i=1,2,3..,n):

[0075]

[0076] From the above description, it can be seen that based on the above steps, the calculation of indicator weights is achieved through the hierarchical analysis method.

[0077] Furthermore, step S3 further includes the steps of:

[0078] S34, performing a consistency check according to the unique integrated matrix and the weight vectors of the respective evaluation indicators, and proceeding to the next step only after the consistency check passes.

[0079] From the above description, it can be seen that the matrix is ​​checked for consistency to ensure its validity.

[0080] Further, step S4 comprises the steps of:

[0081] S41, establishing an evaluation index set and a weight set according to the hierarchical structure model and the weight vector of the evaluation index;

[0082] S42, setting a comment set and assigning values ​​to the evaluation levels to establish a fuzzy judgment vector;

[0083] S43, obtaining the index evaluation based on the comment set from multiple sources, quantifying the evaluation index to obtain the membership F of the i-th evaluation index to the j-th evaluation index, the membership F represents the proportion of the source that gives the j-th evaluation to the i-th evaluation index in the total sources, and thereby establishing the fuzzy relationship matrix F ij :

[0084]

[0085] S44, the weight vector W i Perform fuzzy operation with the fuzzy matrix Fij to obtain the row vector U i :

[0086]

[0087] And determine the fuzzy evaluation results of the indicator layer:

[0088]

[0089] S45. Calculate comprehensive score Y ij :

[0090]

[0091] The enterprise’s digital transformation evaluation level is determined based on the comprehensive score.

[0092] From the above description, it can be seen that based on the fuzzy comprehensive evaluation method, the specific level of digital transformation of the evaluation object can be obtained according to the level division.

[0093] Please refer to Figure 2 , an enterprise digital transformation evaluation terminal, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0094] S1. Establish a multi-dimensional evaluation index system based on the balanced scorecard method;

[0095] The multi-dimensional evaluation index system stores data in a tree data structure, wherein the tree data structure includes at least two data nodes, and the data nodes are used to store sub-node data of a preset category;

[0096] S2, obtaining enterprise historical data from the enterprise database, and screening the evaluation indicators in the tree data structure of the multidimensional evaluation indicator system by calculating the information contribution rate to form an indicator system to be evaluated;

[0097] S3, for each evaluation index in the index system to be evaluated, generate a hierarchical model including a target layer, a criterion layer, and a scheme layer, and calculate the index weight using a hierarchical analysis method;

[0098] S4. Determine the enterprise's digital transformation evaluation level based on the historical data of the enterprise and the fuzzy comprehensive evaluation method.

[0099] From the above description, it can be seen that the beneficial effects of the present invention are: an enterprise digital transformation evaluation terminal of the present invention, combining the balanced scorecard, hierarchical analysis method and fuzzy comprehensive evaluation method, constructs a multi-dimensional evaluation index system to comprehensively evaluate the digital transformation of power grid enterprises; it can be closely combined with the strategic goals of power grid enterprises to ensure that the evaluation results are consistent with the strategic development direction of the enterprise. At the same time, this method overcomes the shortcomings of the traditional hierarchical analysis method, and uses the fuzzy comprehensive evaluation method to improve the adaptability and accuracy of the evaluation.

[0100] Further, step S2 comprises the steps of:

[0101] S21, calculate the correlation coefficient matrix X of the m initial indicators in the multidimensional evaluation indicator system T The eigenvalue λ of X j (j=1,2,3...m):

[0102] X T X-λ j E m |=0;

[0103] Among them, X represents the indicator data matrix after Z standardization;

[0104] The initial indicators include qualitative data and quantitative data, among which the quantitative data include asset-liability ratio, return on net assets, digital profit contribution rate, digital revenue contribution rate, interest coverage ratio, total asset turnover rate, operating income, period expense ratio, net operating cash flow, client digital coverage rate, customer service ticket response timeliness rate, customer problem escalation rate, customer data resource ownership, online service rate, data consistency rate, data completeness rate, digital sharing platform coverage rate, load forecast accuracy rate, business online processing rate, digital radiation rate of the industry chain, digital infrastructure coverage rate, digital capital investment, digital research investment, digital employee retention rate and data assetization degree;

[0105] X T represents the transposed matrix of matrix X, E m represents the m-order identity matrix;

[0106] S22. Determine the key factors to be retained:

[0107] Variance contribution rateω jThe cumulative variance contribution rate of the larger p factors Ω p satisfy:

[0108]

[0109] Then keep these p key factors;

[0110] Among them, M0 represents the critical value of the cumulative variance contribution rate of the p factors after screening that can explain most of the information of the original indicator set;

[0111] S23, calculate the factor loading matrix A = (a ij ) m×p :

[0112]

[0113] Among them, ξ j (j=1,2,3...p) is the jth largest eigenvalue λ in the correlation coefficient matrix j The corresponding orthogonalized eigenvectors;

[0114] Calculate the initial index X i Information contribution rate I i :

[0115]

[0116] S24. Calculate the sum of the information contribution rates of the s initial indicators with relatively large information contribution rates, and obtain the cumulative information contribution rate R s :

[0117]

[0118] Among them, I mi It is the information contribution rate of the i-th largest one after all the m initial indicators are sorted from largest to smallest;

[0119] S25. According to the cumulative information contribution rate R s To filter indicators:

[0120] If present:

[0121] R s-1 <R0≤R s ;

[0122] Then retain the s initial indicators with larger information contribution rate;

[0123] Among them, R0 represents the critical value of the cumulative information contribution rate;

[0124] S26. Calculate the morbidity index CI for the retained initial indicators s calculate:

[0125]

[0126] in, are the maximum and minimum eigenvalues ​​of the correlation coefficient matrix of the s initial indicators;

[0127] If the pathological index is less than the preset pathological threshold, there is no need to remove the initial index to form an index system to be evaluated, otherwise, the process proceeds to step S27;

[0128] S27, among the remaining initial indicators, the initial indicator with the smallest information contribution rate among the two initial indicators with the largest absolute value of the Pearson correlation coefficient is eliminated, and the process returns to step S26.

[0129] From the above description, it can be seen that the indicators under each of the four dimensions of the balanced scorecard are screened through the above method to form an indicator system to be evaluated.

[0130] Further, step S3 comprises the steps of:

[0131] S31, generating a hierarchical structure model including a target layer, a criterion layer, and a solution layer according to various evaluation indicators in the indicator system to be evaluated;

[0132] S32, obtaining a comparative evaluation of the relative importance of every two indicators in the evaluation indicator system to be evaluated, and constructing a comparative judgment matrix A n×n :

[0133]

[0134] S33, merging the comparison judgment matrices from different sources to generate a unique integrated matrix

[0135]

[0136] S34, normalize the geometric mean of each row of the unique integrated matrix to obtain the weight vector W of each evaluation index i (i=1,2,3..,n):

[0137]

[0138] From the above description, it can be seen that based on the above steps, the calculation of indicator weights is achieved through the hierarchical analysis method.

[0139] Furthermore, step S3 further includes the steps of:

[0140] S34, performing a consistency check according to the unique integrated matrix and the weight vectors of the respective evaluation indicators, and proceeding to the next step only after the consistency check passes.

[0141] From the above description, it can be seen that the matrix is ​​checked for consistency to ensure its validity.

[0142] Further, step S4 comprises the steps of:

[0143] S41, establishing an evaluation index set and a weight set according to the hierarchical structure model and the weight vector of the evaluation index;

[0144] S42, setting a comment set and assigning values ​​to the evaluation levels to establish a fuzzy judgment vector;

[0145] S43, obtaining the index evaluation based on the comment set from multiple sources, quantifying the evaluation index to obtain the membership F of the i-th evaluation index to the j-th evaluation index, the membership F represents the proportion of the source that gives the j-th evaluation to the i-th evaluation index in the total sources, and thereby establishing the fuzzy relationship matrix F ij :

[0146]

[0147] S44, the weight vector W i Perform fuzzy operation with the fuzzy matrix Fij to obtain the row vector U i :

[0148]

[0149] And determine the fuzzy evaluation results of the indicator layer:

[0150]

[0151] S45. Calculate comprehensive score Y ij :

[0152]

[0153] The enterprise’s digital transformation evaluation level is determined based on the comprehensive score.

[0154] From the above description, it can be seen that based on the fuzzy comprehensive evaluation method, the specific level of digital transformation of the evaluation object can be obtained according to the level division.

[0155] The present invention provides an enterprise digital transformation evaluation method and terminal, which are suitable for evaluating the digital transformation of an enterprise to promote the enterprise's decision-making and resource allocation during the digital transformation process.

[0156] Please refer to Figure 1 as well as Figures 3 to 7 , Embodiment 1 of the present invention is:

[0157] A method for evaluating enterprise digital transformation, comprising the steps of:

[0158] S1. Establish a multi-dimensional evaluation index system based on the balanced scorecard method;

[0159] Please refer to Figures 3 to 6 The multidimensional evaluation index system stores data in a tree data structure, and the tree data structure includes four dimensional nodes: finance, customer, internal process, and learning and growth. The financial dimension node includes multiple sub-nodes reflecting financial health and profitability, the customer dimension node includes multiple sub-nodes reflecting the relationship between the enterprise and the customer, the internal process dimension node includes multiple sub-nodes reflecting the efficiency of the internal process, and the learning and growth dimension node includes multiple sub-nodes reflecting learning and development capabilities.

[0160] In this embodiment, the indicator system formulation principles are used as a guide, and the balanced scorecard method is used to formulate the indicator range and indicator attributes under each dimension, so as to preliminarily determine the evaluation indicator system. The indicator system formulation principles refer to the five principles consisting of: scientificity, comprehensiveness, effectiveness, development, and the combination of qualitative and quantitative;

[0161] Among them, scientificity means that the construction of the indicator system should be reasonable, and all indicators should be authentic and effective;

[0162] Comprehensiveness means that the construction of the indicator system should follow the order from goal to criterion and then to plan, and obtain evaluation indicators layer by layer, and each layer of indicators should be practical and have sufficient explanatory and generalization capabilities;

[0163] Validity means that in order to ensure that the weights and total weights of indicators at each level can be obtained smoothly through the hierarchical analysis method, the construction of indicators should be accessible and verifiable, so as to reduce the errors in the analysis results.

[0164] Development means that when constructing digital transformation evaluation indicators for enterprises, the stage of digital transformation of the enterprise should be considered, and the possible changes of the enterprise in the future should be considered based on the current stage. The construction of the indicator system should reflect the above-mentioned possible changes;

[0165] The combination of qualitative and quantitative means that since qualitative analysis lacks the support of objective data and is highly subjective, it is difficult to obtain quantitative results, while quantitative analysis can reveal data characteristics and obtain accurate quantitative results. Therefore, in order to cope with the situation where some indicators in the indicator system are suitable for quantification and some indicators are difficult to quantify, the principle of combining qualitative and quantitative methods should be adopted.

[0166] The balanced scorecard method is a strategic management tool whose core idea is to achieve comprehensive evaluation and management of organizational performance by converting the organization's strategic goals into a series of measurable indicators. This method breaks through the traditional performance evaluation model based mainly on financial indicators, and emphasizes examining organizational performance from multiple dimensions, including finance, customers, internal processes, and learning and growth.

[0167] Among them, the financial dimension focuses on the financial performance of the organization, such as revenue growth, cost control, profit margin, asset utilization, etc. These indicators reflect the financial health and profitability of the enterprise;

[0168] The customer dimension involves the relationship between the organization and its customers, including customer satisfaction, customer loyalty, market share, customer acquisition cost, etc. These indicators help organizations understand customer needs and improve customer value;

[0169] The internal process dimension evaluates the efficiency and effectiveness of the organization's internal processes, such as production cycle, product quality, service response time, etc. These indicators help organizations optimize processes and improve operational efficiency;

[0170] The learning and growth dimension focuses on the organization's learning and development capabilities, including employee training, knowledge management, technological innovation, cultural construction, etc. These indicators help the organization to continuously improve and adapt to changes.

[0171] The indicator system preliminarily determined on the basis of determining the indicator scope and indicator attributes of each dimension under the balanced scorecard refers to the indicator system determined after comprehensively considering the particularities of the organizational process, business process, etc. of the power grid enterprise, and analyzing the digital characteristics and value creation path of the power grid enterprise. The indicator system contains 10 secondary indicators under each dimension, as shown in Table 1 below:

[0172] Table 1

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180] S2, obtaining enterprise historical data from the enterprise database, and screening the evaluation indicators in the tree data structure of the multidimensional evaluation indicator system by calculating the information contribution rate to form an indicator system to be evaluated;

[0181] Step S2 comprises the steps of:

[0182] S21, calculate the correlation coefficient matrix X of the m initial indicators in the multidimensional evaluation indicator system T The eigenvalue λ of X j (j=1,2,3...m):

[0183] X T X-λ j E m |=0;

[0184] Among them, X represents the indicator data matrix after Z standardization;

[0185] The initial indicators include qualitative data and quantitative data, among which the quantitative data include asset-liability ratio, return on net assets, digital profit contribution rate, digital revenue contribution rate, interest coverage ratio, total asset turnover rate, operating income, period expense ratio, net operating cash flow, client digital coverage rate, customer service ticket response timeliness rate, customer problem escalation rate, customer data resource ownership, online service rate, data consistency rate, data completeness rate, digital sharing platform coverage rate, load forecast accuracy rate, business online processing rate, digital radiation rate of the industry chain, digital infrastructure coverage rate, digital capital investment, digital research investment, digital employee retention rate and data assetization degree;

[0186] X T represents the transposed matrix of matrix X, E m represents the m-order identity matrix;

[0187] S22. Determine the key factors to be retained:

[0188] Variance contribution rateω j The cumulative variance contribution rate of the larger p factors Ω p satisfy:

[0189]

[0190] Then keep these p key factors.

[0191] In this embodiment, according to existing research, it is more reasonable for M0 to be 70%.

[0192] Among them, M0 represents the critical value of the cumulative variance contribution rate of the p factors after screening that can explain most of the information of the original indicator set. According to existing theories, it is usually taken as 70%;

[0193] S23, calculate the factor loading matrix A = (a ij ) m×p :

[0194]

[0195] Among them, ξ j (j=1,2,3...p) is the jth largest eigenvalue λ in the correlation coefficient matrix j The corresponding orthogonalized eigenvectors;

[0196] Calculate the initial index X i Information contribution rate I i :

[0197]

[0198] S24. Calculate the sum of the information contribution rates of the s initial indicators with relatively large information contribution rates, and obtain the cumulative information contribution rate R s :

[0199]

[0200] Among them, I mi It is the i-th largest information contribution rate after all the m initial indicators are sorted from large to small.

[0201] Cumulative information contribution rate R s It refers to the ratio of the sum of the information contribution rates of s indicators with relatively large information contribution rates to the sum of the information contribution rates of all m original indicators. It reflects the relative proportion of the information of all m original indicators that are cumulatively explained by the s indicators with large information contribution rates. Obviously, the cumulative information contribution rate R s The larger it is, the more fully these s indicators with larger information contribution rates explain the information of the original indicator set, and the more suitable these s indicators are to replace the original indicator set as evaluation indicators.

[0202] S25. According to the cumulative information contribution rate R s To filter indicators:

[0203] If present:

[0204] R s-1 <R0≤R s ;

[0205] Then retain the s initial indicators with larger information contribution rate;

[0206] Among them, R0 represents the critical value of the cumulative information contribution rate, which is usually taken as 0.7 according to existing theories.

[0207] This formula shows that the indicator that needs to be retained is the cumulative information contribution rate R s When the cumulative information contribution rate is just greater than the critical point R0, the s indicator with the larger information contribution rate uses the existing factor analysis theory to retain the 70% standard of the key factor and takes R0 as 0.7

[0208] S26. Calculate the morbidity index CI for the retained initial indicators s calculate:

[0209]

[0210] in, are the maximum and minimum eigenvalues ​​of the correlation coefficient matrix of the s initial indicators, respectively;

[0211] If the pathological index is less than the preset pathological threshold, there is no need to eliminate the initial indicators to form an indicator system to be evaluated, otherwise the process goes to step S27.

[0212] If the pathological index of the remaining initial indicators is not greater than 10, it means that the overall information overlap level of the remaining initial indicators is not high, and there is no need to eliminate the information overlapping indicators; otherwise, it means that the overall information overlap level of the remaining initial indicators is high, and it is necessary to further eliminate some information overlapping indicators to reduce the overall information overlap level between the indicators.

[0213] S27, among the remaining initial indicators, the initial indicator with the smallest information contribution rate among the two initial indicators with the largest absolute value of the Pearson correlation coefficient is eliminated, and the process returns to step S26.

[0214] In this embodiment, the initial indicators with relatively small information contribution rates are eliminated from the two initial indicators with the largest absolute values ​​of the Pearson correlation coefficients among the remaining initial indicators. If the information contribution rates of the two initial indicators are the same, a qualitative analysis is performed based on the actual meaning of the initial indicators. For the remaining initial indicators, the above process is repeated according to step S26 and step S27, and the cycle is repeated until the pathological index CI of the remaining k indicators is finally k When it is no more than 10, stop eliminating indicators with overlapping information, and use the above method to screen indicators under each dimension of the balanced scorecard to form an indicator system to be evaluated.

[0215] S3, for each evaluation index in the index system to be evaluated, generate a hierarchical model including a target layer, a criterion layer, and a scheme layer, and calculate the index weight using a hierarchical analysis method;

[0216] Step S3 includes the steps of:

[0217] S31. Generate a hierarchical structure model including a target layer, a criterion layer, and a solution layer according to various evaluation indicators in the indicator system to be evaluated.

[0218] In this embodiment, by calculating the indicator information contribution rate to screen the indicators under each dimension of the balanced scorecard, a hierarchical structure model including a target layer, a criterion layer, and a solution layer is formed. The structure example of the structure model can be referred to Figure 7 .

[0219] S32, obtaining a comparative evaluation of the relative importance of every two indicators in the evaluation indicator system to be evaluated, and constructing a comparative judgment matrix A n×n :

[0220]

[0221] In this embodiment, the relative importance of each layer of factors is compared and judged, and the reference scale for quantifying the qualitative factors is:

[0222]

[0223] By having experts judge and score the relative importance of each of the two selected indicators according to the standards in the above table, we can obtain the n-order comparative judgment matrix A. n×n .

[0224] S33, merging the comparison judgment matrices from different sources to generate a unique integrated matrix

[0225]

[0226] In this embodiment, the expert matrix is ​​merged, and the scoring matrix formed by m experts (m = 1, 2, .. k) is multiplied bit by bit using the geometric mean method, and then the matrix is ​​raised to the mth power to obtain a unique integrated matrix

[0227] S34, normalize the geometric mean of each row of the unique integrated matrix to obtain the weight vector W of each evaluation index i (i=1,2,3..,n):

[0228]

[0229] In this embodiment, the weight vector of each factor is obtained by normalizing the geometric mean of each row of the merged expert matrix.

[0230] Step S3 also includes the steps of:

[0231] S34, performing a consistency check according to the unique integrated matrix and the weight vectors of the respective evaluation indicators, and proceeding to the next step only after the consistency check passes.

[0232] In this embodiment, the consistency check formula is:

[0233] CR = CI / RI;

[0234]

[0235] In the formula, λ max is the maximum characteristic root of the judgment matrix, n is the matrix order, λ max The calculation formula is as follows:

[0236]

[0237] in is the judgment integration matrix, W is the weight vector, For the matrix The i-th component of ;

[0238] The numerical judgment of RI related to the matrix order is as follows:

[0239]

[0240] If CR>0.1, it means that the consistency check of the judgment matrix has not passed, and the matrix needs to be corrected twice until it passes the test; if CR<0.1, it means that the consistency of the judgment matrix is ​​acceptable.

[0241] S4. Determine the enterprise digital transformation evaluation level based on the historical data of the enterprise and the fuzzy comprehensive evaluation method;

[0242] Step S4 comprises the steps of:

[0243] S41, establishing an evaluation index set and a weight set according to the hierarchical structure model and the weight vector of the evaluation index;

[0244] S42. Set a comment set and assign values ​​to the evaluation levels to establish a fuzzy judgment vector.

[0245] In this embodiment, a comment set is set and a value is assigned to each level, and V = (v1, v2..., vi). There are four evaluation levels, namely leading level [90, 100], standard level [80, 90], leap level [60, 80], and initial level [0, 60). The fuzzy judgment vector V = (95, 85, 70, 30).

[0246] S43, obtaining the index evaluation based on the comment set from multiple sources, quantifying the evaluation index to obtain the membership F of the i-th evaluation index to the j-th evaluation index, the membership F represents the proportion of the source that gives the j-th evaluation to the i-th evaluation index in the total sources, and thereby establishing the fuzzy relationship matrix Fij :

[0247]

[0248] In this embodiment, multiple evaluators are invited to evaluate the evaluation indicators according to the comment set. After quantifying the indicators, the membership degree of the ith factor to the jth evaluation is obtained. The membership degree F represents the proportion of the number of people who gave the jth evaluation to the ith factor in the total number of people participating in the evaluation, thereby establishing a fuzzy relationship matrix.

[0249] S44, the weight vector W i Perform fuzzy operation with the fuzzy matrix Fij to obtain the row vector U i :

[0250]

[0251] And determine the fuzzy evaluation results of the indicator layer:

[0252]

[0253] S45. Calculate comprehensive score Y ij :

[0254]

[0255] The enterprise’s digital transformation evaluation level is determined based on the comprehensive score.

[0256] In this embodiment, the comprehensive score Y ij for:

[0257]

[0258] Finally, based on the grade classification, the specific grade of digital transformation of the evaluation object can be obtained.

[0259] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0260] An enterprise digital transformation evaluation terminal 1 includes a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps of an enterprise digital transformation evaluation method in the first embodiment are implemented.

[0261] In summary, the enterprise digital transformation evaluation method and terminal provided by the present invention combine the balanced scorecard, hierarchical analysis method and fuzzy comprehensive evaluation method, and conduct a comprehensive evaluation of the digital transformation of power grid enterprises by constructing a multi-dimensional evaluation index system; it can be closely combined with the strategic goals of power grid enterprises to ensure that the evaluation results are consistent with the strategic development direction of the enterprise. At the same time, this method overcomes the shortcomings of the traditional hierarchical analysis method, and uses the fuzzy comprehensive evaluation method to improve the adaptability and accuracy of the evaluation.

[0262] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for evaluating enterprise digital transformation, characterized in that: Includes steps: S1. Establish a multi-dimensional evaluation index system based on the balanced scorecard method; The multidimensional evaluation index system stores data in a tree data structure, wherein the tree data structure includes at least two data nodes, and the data nodes are used to store sub-node data of a preset category; S2, obtaining enterprise historical data from the enterprise database, and screening the evaluation indicators in the tree data structure of the multidimensional evaluation indicator system by calculating the information contribution rate to form an indicator system to be evaluated; S3, for each evaluation index in the index system to be evaluated, generate a hierarchical model including a target layer, a criterion layer, and a scheme layer, and calculate the index weight using a hierarchical analysis method; S4. Determine the enterprise's digital transformation evaluation level based on the historical data of the enterprise and the fuzzy comprehensive evaluation method.

2. According to the enterprise digital transformation evaluation method of claim 1, it is characterized in that: Step S2 comprises the steps of: S21, calculate the correlation coefficient matrix X of the m initial indicators in the multidimensional evaluation indicator system T The eigenvalue λ of X j (j=1,2,3...m): X T X-L j E m |=0; Among them, X represents the indicator data matrix after Z standardization; The initial indicators include qualitative data and quantitative data, among which the quantitative data include asset-liability ratio, return on net assets, digital profit contribution rate, digital revenue contribution rate, interest coverage ratio, total asset turnover rate, operating income, period expense ratio, net operating cash flow, client digital coverage rate, customer service ticket response timeliness rate, customer problem escalation rate, customer data resource ownership, online service rate, data consistency rate, data completeness rate, digital sharing platform coverage rate, load forecast accuracy rate, business online processing rate, digital radiation rate of the industry chain, digital infrastructure coverage rate, digital capital investment, digital research investment, digital employee retention rate and data assetization degree; X T represents the transposed matrix of matrix X, E m represents the m-order identity matrix; S22. Determine the key factors to be retained: Variance contribution rateω j The cumulative variance contribution rate of the larger p factors Ω p satisfy: Then keep these p key factors; Among them, M0 represents the critical value of the cumulative variance contribution rate; S23, calculate the factor loading matrix A = (a ij ) m×p : Among them, ξ j (j=1,2,3...p) is the jth largest eigenvalue λ in the correlation coefficient matrix j The corresponding orthogonalized eigenvectors; Calculate the initial index X i Information contribution rate I i : S24. Calculate the sum of the information contribution rates of the s initial indicators with relatively large information contribution rates, and obtain the cumulative information contribution rate R s : Among them, I mi It is the information contribution rate of the i-th largest one after all the m initial indicators are sorted from largest to smallest; S25. Based on the cumulative information contribution rate R s To filter indicators: If present: R s-1 <R0≤R s ; Then retain the s initial indicators with larger information contribution rate; Among them, R0 represents the critical value of the cumulative information contribution rate; S26. Calculate the morbidity index CI for the retained initial indicators s calculate: in, are the maximum and minimum eigenvalues ​​of the correlation coefficient matrix of the s initial indicators, respectively; If the pathological index is less than the preset pathological threshold, there is no need to remove the initial index to form an index system to be evaluated, otherwise, the process proceeds to step S27; S27, among the remaining initial indicators, the initial indicator with the smallest information contribution rate among the two initial indicators with the largest absolute value of the Pearson correlation coefficient is eliminated, and the process returns to step S26.

3. According to the enterprise digital transformation evaluation method of claim 1, it is characterized in that: Step S3 includes the steps of: S31, generating a hierarchical structure model including a target layer, a criterion layer, and a solution layer according to various evaluation indicators in the indicator system to be evaluated; S32, obtaining a comparative evaluation of the relative importance of every two indicators in the evaluation indicator system to be evaluated, and constructing a comparative judgment matrix A n×n : S33, merging the comparison judgment matrices from different sources to generate a unique integrated matrix S34, normalize the geometric mean of each row of the unique integrated matrix to obtain the weight vector W of each evaluation index i (i=1,2,3..,n):

4. The enterprise digital transformation evaluation method according to claim 3 is characterized in that: Step S3 also includes the steps of: S34, performing a consistency check based on the unique integrated matrix and the weight vectors of each evaluation index, and proceeding to the next step only after the consistency check passes.

5. The enterprise digital transformation evaluation method according to claim 1 is characterized in that: Step S4 comprises the steps of: S41, establishing an evaluation index set and a weight set according to the hierarchical structure model and the weight vector of the evaluation index; S42, setting a comment set and assigning values ​​to the evaluation levels to establish a fuzzy judgment vector; S43, obtaining the index evaluation based on the comment set from multiple sources, quantifying the evaluation index to obtain the membership F of the i-th evaluation index to the j-th evaluation index, the membership F represents the proportion of the source that gives the j-th evaluation to the i-th evaluation index in the total sources, and thereby establishing the fuzzy relationship matrix F ij : S44, the weight vector W i Perform fuzzy operation with the fuzzy matrix Fij to obtain the row vector U i : And determine the fuzzy evaluation results of the indicator layer: S45. Calculate comprehensive score Y ij : The enterprise’s digital transformation evaluation level is determined based on the comprehensive score.

6. An enterprise digital transformation evaluation terminal, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: S1. Establish a multi-dimensional evaluation index system based on the balanced scorecard method; The multidimensional evaluation index system stores data in a tree data structure, wherein the tree data structure includes at least two data nodes, and the data nodes are used to store sub-node data of a preset category; S2, obtaining enterprise historical data from the enterprise database, and screening the evaluation indicators in the tree data structure of the multidimensional evaluation indicator system by calculating the information contribution rate to form an indicator system to be evaluated; S3, for each evaluation index in the index system to be evaluated, generate a hierarchical model including a target layer, a criterion layer, and a scheme layer, and calculate the index weight using a hierarchical analysis method; S4. Determine the enterprise's digital transformation evaluation level based on the historical data of the enterprise and the fuzzy comprehensive evaluation method.

7. The enterprise digital transformation evaluation terminal according to claim 6, characterized in that: Step S2 comprises the steps of: S21, calculate the correlation coefficient matrix X of the m initial indicators in the multidimensional evaluation indicator system T The eigenvalue λ of X j (j=1,2,3...m): X T X-L j E m |=0; Among them, X represents the indicator data matrix after Z standardization; The initial indicators include qualitative data and quantitative data, among which the quantitative data include asset-liability ratio, return on net assets, digital profit contribution rate, digital revenue contribution rate, interest coverage ratio, total asset turnover rate, operating income, period expense ratio, net operating cash flow, client digital coverage rate, customer service ticket response timeliness rate, customer problem escalation rate, customer data resource ownership, online service rate, data consistency rate, data completeness rate, digital sharing platform coverage rate, load forecast accuracy rate, business online processing rate, digital radiation rate of the industry chain, digital infrastructure coverage rate, digital capital investment, digital research investment, digital employee retention rate and data assetization degree; X T represents the transposed matrix of matrix X, E m represents the m-order identity matrix; S22. Determine the key factors to be retained: Variance contribution rateω j The cumulative variance contribution rate of the larger p factors Ω p satisfy: Then keep these p key factors; Among them, M0 represents the critical value of the cumulative variance contribution rate; S23, calculate the factor loading matrix A = (a ij ) m×p : Among them, ξ j (j=1,2,3...p) is the jth largest eigenvalue λ in the correlation coefficient matrix j The corresponding orthogonalized eigenvectors; Calculate the initial index X i Information contribution rate I i : S24. Calculate the sum of the information contribution rates of the s initial indicators with relatively large information contribution rates, and obtain the cumulative information contribution rate R s : Among them, I mi It is the information contribution rate of the i-th largest one after all the m initial indicators are sorted from largest to smallest; S25. Based on the cumulative information contribution rate R s To filter indicators: If present: R s-1 <R0≤R s ; Then retain the s initial indicators with larger information contribution rate; Among them, R0 represents the critical value of the cumulative information contribution rate; S26. Calculate the morbidity index CI for the retained initial indicators s calculate: in, are the maximum and minimum eigenvalues ​​of the correlation coefficient matrix of the s initial indicators, respectively; If the pathological index is less than the preset pathological threshold, there is no need to remove the initial index to form an index system to be evaluated, otherwise, the process proceeds to step S27; S27, among the remaining initial indicators, the initial indicator with the smallest information contribution rate among the two initial indicators with the largest absolute value of the Pearson correlation coefficient is eliminated, and the process returns to step S26.

8. The enterprise digital transformation evaluation terminal according to claim 6, characterized in that: Step S3 includes the steps of: S31, generating a hierarchical structure model including a target layer, a criterion layer, and a solution layer according to various evaluation indicators in the indicator system to be evaluated; S32, obtaining a comparative evaluation of the relative importance of every two indicators in the evaluation indicator system to be evaluated, and constructing a comparative judgment matrix A n×n : S33, merging the comparison judgment matrices from different sources to generate a unique integrated matrix S34, normalize the geometric mean of each row of the unique integrated matrix to obtain the weight vector W of each evaluation index i (i=1,2,3..,n):

9. The enterprise digital transformation evaluation terminal according to claim 8, characterized in that: Step S3 also includes the steps of: S34, performing a consistency check based on the unique integrated matrix and the weight vectors of each evaluation index, and proceeding to the next step only after the consistency check passes.

10. The enterprise digital transformation evaluation terminal according to claim 6, characterized in that: Step S4 comprises the steps of: S41, establishing an evaluation index set and a weight set according to the hierarchical structure model and the weight vector of the evaluation index; S42, setting a comment set and assigning values ​​to the evaluation levels to establish a fuzzy judgment vector; S43, obtaining the index evaluation based on the comment set from multiple sources, quantifying the evaluation index to obtain the membership F of the i-th evaluation index to the j-th evaluation index, the membership F represents the proportion of the source that gives the j-th evaluation to the i-th evaluation index in the total sources, and thereby establishing the fuzzy relationship matrix F ij : S44, the weight vector W i Perform fuzzy operation with the fuzzy matrix Fij to obtain the row vector U i : And determine the fuzzy evaluation results of the indicator layer: S45. Calculate comprehensive score Y ij : The enterprise’s digital transformation evaluation level is determined based on the comprehensive score.

Images