Urban rail transit enterprise digital transformation capability maturity evaluation method and system

Abstract

The invention provides an urban rail transit enterprise digital transformation capability maturity evaluation method and system, and the method comprises the steps: constructing a hierarchical evaluation system, designing expert questionnaires based on the hierarchical evaluation system, and enabling the expert questionnaires to comprise an index correlation questionnaire used for determining the index weight and a five-level comment questionnaire used for evaluating the index level; collecting first survey data of the index correlation questionnaire, calculating a weight vector of each level of index, outputting a weight complementary matrix, collecting second survey data of the five-level comment questionnaire, calculating a membership degree of each level of index, and outputting a membership degree matrix; a weight complementary matrix and the membership matrix are combined to calculate a comprehensive score of each level of index, the maturity level of enterprise digital transformation is determined according to the comprehensive score, an evaluation result is output, relative importance among the indexes is accurately captured, and scientificity and accuracy of an evaluation system are ensured; the objectivity of comment scoring is enhanced, and powerful support is provided for optimizing a transformation strategy.

Description

Technical Field

[0001] This invention relates to the field of transportation system management technology, and in particular to a method and system for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises. Background Technology

[0002] As my country's urban rail transit shifts from a phase of rapid construction to one of high-quality development, and against the backdrop of smart urban rail development, the urban rail industry is actively seeking transformation and upgrading, moving from informatization to digitalization. Traditional urban rail transit companies calculate the digital capability index corresponding to the evaluated object based on the values ​​of each evaluation indicator, the target value of each indicator at the final digital maturity stage, and the weight of each indicator, ultimately determining the company's specific digital maturity stage. However, in adopting this method, simply using the natural number average method to allocate weights within each dimension, treating each dimension as having equal weight, is suitable for evaluating independent and easily quantifiable indicators. For indicators with varying degrees of importance and mutual influence within a set of dimensions, this method cannot accurately reflect the differences in importance between dimensions and their mutual impact. Furthermore, for subjective indicators that are difficult to quantify, such as the degree of management change, strategic clarity, and execution, this method is insufficient for accurate evaluation because subjective evaluations are often influenced by the evaluator's personal experience, viewpoints, and preferences, leading to inconsistent evaluation logic that is difficult to control. Summary of the Invention

[0003] This invention provides a method and system for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises. It addresses the shortcomings of traditional enterprise digital capability evaluation methods, which use equal weighting and simple quantification, failing to accurately assess the differences in importance and mutual influence among indicators, and making it difficult to effectively quantify subjective indicators, resulting in evaluation results that lack specificity and accuracy.

[0004] This invention provides a method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises, comprising: A hierarchical evaluation system is constructed, which includes indicators at multiple different levels; Based on the hierarchical evaluation system, an expert questionnaire was designed, which includes an indicator relevance questionnaire for determining indicator weights and a five-level comment questionnaire for evaluating indicator levels. Collect the first survey data of the index correlation questionnaire, calculate the weight vector of each level of index based on the first survey data, and output the weight complementarity matrix; and collect the second survey data of the five-level comment questionnaire, calculate the membership degree of each level of index based on the second survey data, and output the membership degree matrix. The comprehensive score of each level of indicator is calculated by combining the complementary weight matrix and the membership matrix. The maturity level of the enterprise's digital transformation is determined based on the comprehensive score, and the evaluation result is output.

[0005] According to the method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises provided by the present invention, the step of designing the index relevance questionnaire based on the hierarchical evaluation system includes: Analyze the logical relationships between the indicators at each level in the hierarchical evaluation system; The questionnaire was designed using a complementary matrix structure based on the logical relationships between the indicators at each level.

[0006] According to the method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises provided by the present invention, the hierarchical evaluation system includes primary indicators, secondary indicators, and tertiary indicators. The five-level evaluation questionnaire designed based on the hierarchical evaluation system includes: Differentiated comments are designed for each level 3 indicator, including quantitative and qualitative comments. For quantitative indicator comments, a five-level evaluation standard with specific interval values ​​is designed; for qualitative indicator comments, a five-level evaluation standard based on behavioral characteristic descriptions is designed.

[0007] According to the method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises provided by this invention, the calculation of the weight vector for each level of indicators includes: A 1-9 scale method is used to design an evaluation scale for the relative importance of indicators, and a complementary weight matrix is ​​constructed based on the evaluation scale. Calculate the eigenvectors of the complementary weight matrix to obtain the initial index weights; A consistency check is performed on the indicator weights, and the initial indicator weights are corrected based on the consistency check to obtain the weight vectors of indicators at each level.

[0008] According to the method for evaluating the digital transformation capability maturity of urban rail transit enterprises provided by the present invention, the consistency check of the indicator weights includes: Calculate the largest eigenvalue of the weighted complementary matrix; The consistency of the index weights is checked based on the largest eigenvalue of the complementary weight matrix.

[0009] According to the method for evaluating the digital transformation capability maturity of urban rail transit enterprises provided by the present invention, the consistency check of the indicator weights further includes: A random consistency index is introduced, and the random consistency of the indicator weights is tested based on the random consistency index.

[0010] According to the method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises provided by the present invention, the calculation of the membership degree of each level indicator based on the second survey data includes: The second survey data of the five-level rating questionnaire was converted into a numerical matrix, where rows represent evaluation indicators and columns represent the five rating levels L1 to L5. The frequency of evaluations at each level is normalized to form an initial membership degree distribution; The standard values ​​of each indicator at level L5 are selected as the optimal reference sequence, and the correlation between the actual evaluation value of each level 3 indicator and the optimal reference sequence is calculated. The initial membership distribution is modified based on the degree of association to generate the final membership matrix.

[0011] According to the method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises provided by the present invention, the step of calculating the comprehensive score of each level of indicators by combining the complementary weight matrix and the membership matrix includes: The weighted score of each tertiary indicator is obtained by performing matrix multiplication on the weight coefficient of each indicator and its corresponding membership vector. The weighted scores of each tertiary indicator are summed, and the scores of the tertiary indicators are aggregated according to their respective secondary indicators to calculate the scores of the secondary indicators. The scores of the secondary indicators are aggregated according to their respective primary indicators to obtain the scores of the primary indicators; The scores of all primary indicators are weighted and summed to obtain a comprehensive score of the enterprise's digital transformation capabilities.

[0012] According to the method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises provided by the present invention, the primary indicators include at least one of digital transformation strategy, digital processes and governance, digital technology application, and talent and organizational development. The secondary indicators include at least one of the following: leadership awareness of digital transformation, positioning of enterprise digital organization, top-level design and planning, digitalization of business processes, integrity of digital governance, platform support capabilities, and digital team building and collaboration. The three-level indicators include at least one of the following: leadership awareness, digital transformation leadership style, IT expenditure as a percentage of revenue, data architecture integrity, cloud platform engine capabilities, and the development of a digital professional team.

[0013] This invention also provides a digital transformation capability maturity evaluation system for urban rail transit enterprises, comprising: A construction module is used to construct a hierarchical evaluation system, which includes indicators at multiple different levels. The design module is used to design an expert questionnaire based on the hierarchical evaluation system. The expert questionnaire includes an indicator relevance questionnaire for determining indicator weights and a five-level comment questionnaire for evaluating indicator levels. The calculation module is used to collect the first survey data of the index correlation questionnaire, calculate the weight vector of each level of index based on the first survey data, output the weight complementarity matrix, and collect the second survey data of the five-level comment questionnaire, calculate the membership degree of each level of index based on the second survey data, and output the membership degree matrix. The output module is used to calculate the comprehensive score of each level of indicators by combining the weight complement matrix and the membership matrix, determine the maturity level of the enterprise's digital transformation based on the comprehensive score, and output the evaluation result.

[0014] This invention provides a method and system for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises. The method involves constructing a hierarchical evaluation system, which includes indicators at multiple different levels. Based on this system, an expert questionnaire is designed, comprising an indicator relevance questionnaire for determining indicator weights and a five-level evaluation questionnaire for assessing indicator levels. The method collects first survey data from the indicator relevance questionnaire, calculates the weight vectors of each level of indicators based on the first survey data, and outputs a weight complementarity matrix. Additionally, it collects second survey data from the five-level evaluation questionnaire, calculates the membership degree of each level of indicators based on the second survey data, and outputs the membership degree. The evaluation system employs a matrix approach. By combining the complementary weight matrix and the membership matrix, a comprehensive score is calculated for each level of indicators. Based on this comprehensive score, the maturity level of the enterprise's digital transformation is determined, and the evaluation results are output. A matrix-based questionnaire is designed to accurately capture the relative importance of each indicator and calculate weight coefficients, ensuring the scientific rigor and accuracy of the evaluation system. A questionnaire with specific indicators is designed to enhance the objectivity of the evaluation scores. By combining the membership matrix with weights, the scores of all indicators are calculated to obtain a comprehensive evaluation result of the urban rail transit enterprise's digital transformation capabilities. This provides a strong support for enterprises to comprehensively examine their performance in key areas of digital transformation and optimize their transformation strategies. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is one of the flowcharts illustrating the method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises provided in this embodiment of the invention; Figure 2 This is the second flowchart of the method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the functional structure of the digital transformation capability maturity evaluation system for urban rail transit enterprises provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0018] Figure 1 A flowchart of the method for evaluating the digital transformation capability maturity of urban rail transit enterprises provided in this embodiment of the invention is shown below. Figure 1 As shown in the embodiment of the present invention, the method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises includes: Step 101: Construct a hierarchical evaluation system, which includes indicators at multiple different levels; Step 102: Design an expert questionnaire based on the hierarchical evaluation system. The expert questionnaire includes an indicator correlation questionnaire for determining indicator weights and a five-level comment questionnaire for evaluating indicator levels. Step 103: Collect the first survey data of the index correlation questionnaire, calculate the weight vector of each level index based on the first survey data, output the weight complementarity matrix, and collect the second survey data of the five-level comment questionnaire, calculate the membership degree of each level index based on the second survey data, and output the membership degree matrix. In this embodiment of the invention, the questionnaire data (in Excel, CSV, or other formats) of each expert is cleaned, formatted, and numerically converted before being imported into a computer program. Within the program, the three-level indicators under the same level are constructed into an N×M matrix structure. The values ​​in the matrix are normalized to generate a mutually exclusive matrix to represent the influence relationships between the indicators.

[0019] Step 104: Calculate the comprehensive score of each level of indicators by combining the complementary weight matrix and the membership matrix, determine the maturity level of the enterprise's digital transformation based on the comprehensive score, and output the evaluation result.

[0020] In this embodiment of the invention, the hierarchical evaluation system takes into account the attributes and characteristics of the urban rail transit industry. In the comprehensive evaluation, especially the evaluation questionnaire, it adopts a non-traditional evaluation method such as "good, medium, poor" instead of a general evaluation method. Instead, it combines the meaning of each indicator and designs different evaluation items in a more targeted manner, which is more conducive to objectively reflecting the company's digital capabilities or level in each indicator.

[0021] Traditional urban rail transit companies calculate the digital capability index corresponding to the evaluated object based on the value of each evaluation indicator, the target value of each evaluation indicator in the final digital maturity stage, and the weight of each evaluation indicator, and finally determine the specific digital maturity stage of the enterprise. However, in the process of adopting the above method, if the weight is simply allocated by averaging natural numbers within each dimension, treating each dimension as having the same weight, this method is suitable for evaluating independent and easily quantifiable indicators. For indicators with different levels of importance and mutual influence among a set of dimensions, this method cannot accurately reflect the differences in importance between dimensions and their mutual influence. In addition, for subjective indicators that are difficult to quantify, such as the degree of management change, strategic clarity, and execution, this method is difficult to evaluate accurately because subjective evaluation is often influenced by factors such as the evaluator's personal experience and opinion preferences, resulting in inconsistent evaluation logic and difficulty in control.

[0022] The present invention provides a method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises. This method constructs a hierarchical evaluation system, which includes indicators at multiple different levels. Based on this system, an expert questionnaire is designed, comprising an indicator relevance questionnaire for determining indicator weights and a five-level evaluation questionnaire for assessing indicator levels. First survey data from the indicator relevance questionnaire is collected; weight vectors for each level of indicators are calculated based on the first survey data, and a complementary weight matrix is ​​output. Second survey data from the five-level evaluation questionnaire is collected; membership degrees for each level of indicators are calculated based on the second survey data, and membership degrees are output. The evaluation system employs a matrix approach. By combining the complementary weight matrix and the membership matrix, a comprehensive score is calculated for each level of indicators. Based on this comprehensive score, the maturity level of the enterprise's digital transformation is determined, and the evaluation results are output. A matrix-based questionnaire is designed to accurately capture the relative importance of each indicator and calculate weight coefficients, ensuring the scientific rigor and accuracy of the evaluation system. A questionnaire with specific indicators is designed to enhance the objectivity of the evaluation scores. By combining the membership matrix with weights, the scores of all indicators are calculated to obtain a comprehensive evaluation result of the urban rail transit enterprise's digital transformation capabilities. This provides a strong support for enterprises to comprehensively examine their performance in key areas of digital transformation and optimize their transformation strategies.

[0023] Based on any of the above embodiments, the hierarchical evaluation system includes primary indicators, secondary indicators, and tertiary indicators. The primary indicators include, but are not limited to, digital transformation strategy, digital processes and governance, digital technology application, and talent and organizational development. The secondary indicators include, but are not limited to, leadership awareness of digital transformation, positioning of the enterprise's digital organization, top-level design and planning, digitalization of business processes, integrity of digital governance, platform support capabilities, and digital team building and collaboration. The three-level indicators include, but are not limited to, leadership awareness, digital transformation leadership style, IT expenditure as a percentage of revenue, data architecture integrity, cloud platform engine capabilities, and the development of a digital professional team.

[0024] In this embodiment of the invention, the construction of a digital professional team includes, but is not limited to, data sharing and application level, data security management and control measures, data-driven decision-making capabilities, cloud platform engine capabilities, big data service capabilities, agile development and delivery capabilities, digital customer experience index, online coverage of passenger users, mobile business coverage, coverage of typical new technologies, digital professional team construction, digital employee training, digital talent evaluation system, and digital KPI assessment system. The hierarchical evaluation system is shown in Table 1.

[0025] Table 1 Hierarchical Evaluation System

[0026] In this embodiment of the invention, the digital transformation strategy encompasses multiple aspects. First, it includes leadership awareness regarding digital transformation, encompassing the management's emphasis on and determination regarding the transformation vision, as well as widespread awareness and a positive attitude throughout the organization from top to bottom. Second, it involves the hierarchical positioning of the information technology or digital transformation organization within the enterprise, and its core position and important role in driving the transformation process. Third, it ensures that the enterprise's top-level design for digital transformation is fully released, effectively implemented, and comprehensively covered. Finally, from a financial perspective, it reflects the reasonable ratio of IT expenditure to revenue, the arrangement of investment intensity and the assessment of return on investment, as well as a multi-level project funding guarantee mechanism to ensure the smooth progress of the transformation process and the maximization of resource utilization.

[0027] Digital Processes and Governance: On the one hand, the digital capabilities of business processes are reflected by the level of digitalization of internal processes and the overall performance of business collaboration during digital transformation. On the other hand, the completeness of digital governance is comprehensively evaluated from multiple dimensions, such as the integrity of data architecture, the scope and application level of data sharing, the effectiveness of data security control measures, and the ability to make data-driven decisions.

[0028] Digital Technology Applications: In terms of platform capabilities, the application of digital technologies in urban rail transit encompasses cloud platforms, big data services, and agile development and delivery capabilities for overall business digitalization. Building upon this digital foundation, it further covers digital customer experience, online user coverage of the urban rail transit app, mobile coverage of internal business processes, and the coverage of advanced technologies such as artificial intelligence, the Internet of Things, and digital twins across various business areas.

[0029] Talent and Organizational Development: The focus is on monitoring the development of digital professional teams, including their talent structure, professional skills, teamwork capabilities, and the effectiveness of cross-departmental collaboration. Simultaneously, attention should be paid to the methods and effectiveness of digital employee development and training, whether a clear digital talent development strategy has been formulated, and whether a comprehensive digital talent evaluation system has been established. Furthermore, it is necessary to assess whether effective incentive mechanisms have been implemented and whether an innovation-oriented digital KPI assessment system has been established.

[0030] Based on any of the above embodiments, the design of the indicator relevance questionnaire based on the hierarchical evaluation system includes: Analyze the logical relationships between the indicators at each level in the hierarchical evaluation system; The questionnaire was designed using a complementary matrix structure based on the logical relationships between the indicators at each level.

[0031] In this embodiment of the invention, the questionnaire is designed according to the analytic hierarchy process (AHP). The importance of each set of indicators is compared pairwise within the same hierarchical dimension, and the measurement scale is divided into 9 levels. The values ​​9, 7, 5, 3, and 1 correspond to absolutely important, very important, relatively important, slightly important, and equally important, respectively. Values ​​2, 4, 6, and 8 fall between adjacent judgments, as shown in Table 2. Table 2 Scale Selection Rules

[0032] Questionnaire design: Based on the scaling, an expert questionnaire was designed for pairwise factor comparisons. Other levels of questionnaires followed the same format. Questionnaire completion is shown in Table 3. Table 3. Sample Survey Questionnaire

[0033] The questionnaire results were organized, and the questionnaire Y or check-in results were converted into corresponding scale values. The questionnaire return and scale conversion are shown in Table 4. Table 4 Example of Indicator Judgment Matrix

[0034] This invention's embodiments are based on expert evaluation in the form of questionnaires. This method is characterized by small data volume, significant weighting of expert scores, and rudimentary features. After evaluating the suitability of Recurrent Neural Networks (RNNs) for large samples and high-dimensional matrix scenarios such as Principal Component Analysis (PCA), and considering the strong constraints that consistency discrimination matrices place on data structure, Fuzzy Hierarchical Analysis (FAHP) is adopted as a more suitable tool. Specifically, it organizes the data into a low-rank matrix form combining parent and child subsets to better adapt to the characteristics of subjective evaluation. Furthermore, consistency checks can be used to examine and correct the consistency of the expert evaluation's intent.

[0035] The elements in the mutual exclusion matrix must satisfy the following properties: ; .

[0036] The design of the five-level evaluation questionnaire based on the hierarchical evaluation system includes: Differentiated comments are designed for each level 3 indicator, including quantitative and qualitative comments. For quantitative indicator comments, a five-level evaluation standard with specific interval values ​​is designed; for qualitative indicator comments, a five-level evaluation standard based on behavioral characteristic descriptions is designed.

[0037] In this embodiment of the invention, the design of the indicator evaluation questionnaire is closely integrated with the digitalization characteristics of the urban rail transit industry. To improve the objectivity and accuracy of the questionnaire, a hierarchical and specific design approach is adopted. Specifically, for each tertiary indicator, different five-level progressive questions are set, corresponding to five stages: initial stage (L1), start-up stage (L2), accelerated development stage (L3), rapid growth stage (L4), and mature and stable stage (L5). The calculation results of the tertiary indicators can be summed and aggregated into the first and second levels, thereby achieving a comprehensive analysis of the entire evaluation system, as shown in Table 5. Table 5. Examples of Comprehensive Indicator Comments

[0038] The computer program reads the questionnaire dataset file of the indicator system, converts the string into an array, identifies outliers and fills in missing values, and then converts it into a matrix object format of floating-point values ​​to prepare for the next step of matrix operations by the computer program, as shown in Table 6: Table 6. Matrix Data Structure Diagram

[0039] Based on any of the above embodiments, the calculation of the weight vector for each level of indicator includes: Step 201: Design an evaluation scale for the relative importance of indicators using the 1-9 scale method, and construct a weighted complementary matrix based on the evaluation scale; Step 202: Calculate the eigenvectors of the complementary weight matrix to obtain the initial index weights; Step 203: Perform a consistency check on the indicator weights, and correct the initial indicator weights based on the consistency check to obtain the weight vectors of indicators at each level.

[0040] Calculate the weight vector of each set of indicators at different levels. .

[0041] Based on any of the above embodiments, the consistency check of the indicator weights includes: Step 301: Calculate the largest eigenvalue of the complementary weight matrix; Step 302: Perform a consistency check on the index weights based on the largest eigenvalue of the complementary weight matrix.

[0042] In this embodiment of the invention, the consistency check of the indicator weights further includes: A random consistency index is introduced, and the random consistency of the indicator weights is tested based on the random consistency index.

[0043] The embodiment of this invention uses the sum-product method to obtain the largest eigenvalue: The specific steps for the consistency check are as follows: A single-level consistency test is performed to determine the reasonableness of the obtained indicator weight values. The consistency of each level of the judgment matrix provided in the questionnaire is tested. A CI close to 0 indicates satisfactory consistency. In a single-level random consistency test, a random consistency index (RI) is introduced to measure the magnitude of the consistency index (CI). The RI value can be obtained from a table. The random consistency rate (CR) is calculated as CI / RI, and a CR < 0.1 indicates that the matrix has achieved good consistency.

[0044] The overall level consistency test is obtained by multiplying the consistency index CI_i of each single level with its corresponding weight W_i and then summing the results.

[0045] The overall stratified random consistency test is obtained by multiplying the RI values ​​of each stratum's single ranking by their corresponding weights and then summing the results. For individual questionnaires that affected the consistency judgment, we communicated with experts and made corrections accordingly.

[0046] Based on any of the above embodiments, the calculation of the membership degree of each level indicator based on the second survey data includes: Step 401: Convert the second survey data of the five-level comment questionnaire into a numerical matrix, where rows represent evaluation indicators and columns represent the five comment levels L1 to L5. Step 402: Normalize the evaluation frequency of each level to form an initial membership degree distribution; Step 403: Select the standard value of each indicator at level L5 as the optimal reference sequence, and calculate the degree of correlation between the actual evaluation value of each level 3 indicator and the optimal reference sequence; Step 404: Correct the initial membership degree distribution according to the degree of association to generate the final membership degree matrix.

[0047] Based on any of the above embodiments, the step of calculating the comprehensive score of each level of indicators by combining the complementary weight matrix and the membership matrix includes: The weighted score of each tertiary indicator is obtained by performing matrix multiplication on the weight coefficient of each indicator and its corresponding membership vector. The weighted scores of each tertiary indicator are summed, and the scores of the tertiary indicators are aggregated according to their respective secondary indicators to calculate the scores of the secondary indicators. The scores of the secondary indicators are aggregated according to their respective primary indicators to obtain the scores of the primary indicators; The scores of all primary indicators are weighted and summed to obtain a comprehensive score of the enterprise's digital transformation capabilities.

[0048] like Figure 2 As shown in the embodiment of the present invention, the method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises specifically includes: (1) Construct an indicator system to deeply understand the internal and external environmental characteristics of digital transformation of urban rail transit enterprises, identify key factors, and design a three-level indicator structure and specific indicators; (2) Design questionnaires based on indicators, select scales, and distribute and collect questionnaires online or offline; (3) Classify the questionnaires, identify and process abnormal data, and form a dataset that is easy for computer programs to read and load; (4) The discrimination matrix is ​​constructed by reading the file dataset through the calculation program, the matrix values ​​are normalized and dimensionless, and a mutual exclusion matrix is ​​generated; (5) Calculate the average eigenvalue based on each mutual exclusion matrix, and calculate the weight of each third-level index by substituting back into the equation; (6) Conduct a consistency test, calculate the consistency index CI and the random consistency index RI. For matrices that fail the test, the experts who filled out the questionnaire need to re-evaluate and correct their original data to make them meet the requirements of logic and consistency. (7) The membership matrix is ​​calculated by grey relational analysis, multiplied by the weights to obtain the scores, and then summarized into the first-level indicators; In this embodiment of the invention, the indicator comment questionnaire is processed and transformed into a comment dataset E matrix.

[0049] The dimensionless processing of the comment dataset E matrix was performed, and the membership degree was calculated.

[0050] The membership degree calculation uses the grey relational algorithm to obtain the correlation coefficient between the i-th scheme and the j-th index using the optimal index set x* and the E matrix. .

[0051] The optimal value is obtained by finding the sum of squared deviations. The resolution coefficient is obtained by substituting it into the equation. The membership matrix R is obtained by calculating using the formula.

[0052] (8) Combination The weight vector is multiplied by the membership matrix R to obtain the comprehensive evaluation score for each tertiary indicator. By using the mean method or summation method of upward aggregation, the comprehensive scores of the primary and secondary indicators can be calculated layer by layer.

[0053] (9) Select the maximum value item from L1 to L5 based on the comprehensive score, and use it as the overall maturity indicator of the current digital transformation. At the same time, it can also obtain the specific maturity indicators of different levels L1 to L5, thereby determining the stage of digital transformation and further supporting the improvement direction and transformation strategy optimization of urban rail transit enterprises.

[0054] The method for evaluating the maturity of digital transformation capabilities of urban rail transit enterprises provided in this invention includes the following steps: In the indicator system design stage, a hierarchical indicator system is constructed based on the key factors of digital transformation of urban rail transit enterprises; in the questionnaire design stage, a logically rigorous and easy-to-understand questionnaire template is designed based on the constructed indicator system; in the questionnaire dataset processing stage, questionnaires are collected and organized, and abnormal and missing values ​​are handled, and numerical transformations are performed to form a dataset that can be loaded into the calculation program; the normalized matrix weight calculation is performed by a computer program automatically reading questionnaire data, transforming the data, and generating a normalized matrix to prepare for subsequent analysis; in the weight calculation stage, the program performs precise calculations on each discrimination matrix to obtain the weight parameters of each indicator; in the consistency verification stage, logical errors in the discrimination matrices of all questionnaires are identified and verified, and cases that do not meet the consistency requirements are optimized; in the membership degree calculation stage, the membership matrix is ​​obtained through grey relational analysis; and in the comprehensive score calculation stage, the comprehensive score and level of the maturity of digital transformation capabilities of urban rail transit enterprises are obtained by combining the weights and membership degrees of each indicator. The entire process is logically rigorous, and the indicators and questionnaires are designed in a hierarchical and specific manner to minimize subjective interference and ensure the accuracy and reliability of the evaluation results.

[0055] The following describes the digital transformation capability maturity evaluation system for urban rail transit enterprises provided by this invention. The digital transformation capability maturity evaluation system for urban rail transit enterprises described below can be referred to in correspondence with the digital transformation capability maturity evaluation method for urban rail transit enterprises described above.

[0056] Figure 3 The functional structure diagram of the digital transformation capability maturity evaluation system for urban rail transit enterprises provided in this embodiment of the invention is as follows: Figure 3 As shown in the embodiment of the present invention, the digital transformation capability maturity evaluation system for urban rail transit enterprises includes: Module 301 is used to construct a hierarchical evaluation system, which includes indicators at multiple different levels. Design module 302 is used to design an expert questionnaire based on the hierarchical evaluation system. The expert questionnaire includes an indicator correlation questionnaire for determining indicator weights and a five-level comment questionnaire for evaluating indicator levels. The calculation module 303 is used to collect the first survey data of the index correlation questionnaire, calculate the weight vector of each level index based on the first survey data, output the weight complementarity matrix, and collect the second survey data of the five-level comment questionnaire, calculate the membership degree of each level index based on the second survey data, and output the membership degree matrix. Output module 304 is used to calculate the comprehensive score of each level of indicators by combining the weight complement matrix and the membership matrix, determine the maturity level of the enterprise's digital transformation based on the comprehensive score, and output the evaluation result.

[0057] The digital transformation capability maturity evaluation system for urban rail transit enterprises provided in this invention constructs a hierarchical evaluation system, which includes indicators at multiple different levels. Based on this hierarchical evaluation system, an expert questionnaire is designed, comprising an indicator relevance questionnaire for determining indicator weights and a five-level comment questionnaire for evaluating indicator levels. First survey data from the indicator relevance questionnaire is collected; weight vectors for each level of indicators are calculated based on the first survey data, and a weight complementarity matrix is ​​output. Second survey data from the five-level comment questionnaire is collected; membership degrees for each level of indicators are calculated based on the second survey data, and membership degrees are output. The evaluation system employs a matrix approach. By combining the complementary weight matrix and the membership matrix, a comprehensive score is calculated for each level of indicators. Based on this comprehensive score, the maturity level of the enterprise's digital transformation is determined, and the evaluation results are output. A matrix-based questionnaire is designed to accurately capture the relative importance of each indicator and calculate weight coefficients, ensuring the scientific rigor and accuracy of the evaluation system. A questionnaire with specific indicators is designed to enhance the objectivity of the evaluation scores. By combining the membership matrix with weights, the scores of all indicators are calculated to obtain a comprehensive evaluation result of the urban rail transit enterprise's digital transformation capabilities. This provides a strong support for enterprises to comprehensively examine their performance in key areas of digital transformation and optimize their transformation strategies.

[0058] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0059] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the digital transformation capability maturity of an urban rail transit enterprise, characterized in that, The method comprises the following steps: constructing a hierarchical evaluation system, wherein the hierarchical evaluation system comprises multiple indicators at different levels; designing an expert questionnaire based on the hierarchical evaluation system, wherein the expert questionnaire comprises an indicator correlation questionnaire for determining the weight of an indicator and a five-level comment questionnaire for evaluating the level of an indicator; collecting first survey data of the indicator correlation questionnaire, calculating a weight vector of each level indicator according to the first survey data, outputting a weight complementary matrix, collecting second survey data of the five-level comment questionnaire, calculating the membership degree of each level indicator according to the second survey data, and outputting a membership degree matrix; combining the weight complementary matrix and the membership degree matrix to calculate the comprehensive score of each level indicator, determining the maturity level of enterprise digital transformation according to the comprehensive score, and outputting the evaluation result.

2. The urban rail transit enterprise digital transformation capability maturity evaluation method according to claim 1, characterized in that, The method of designing the indicator correlation questionnaire based on the hierarchical evaluation system comprises the following steps: analyzing the logical correlation between indicators at different levels in the hierarchical evaluation system; designing the questionnaire in a complementary matrix structure according to the logical correlation between indicators at different levels. 3.The method according to claim 1, characterized in that, The hierarchical evaluation system comprises primary indicators, secondary indicators and tertiary indicators, and the method of designing the five-level comment questionnaire based on the hierarchical evaluation system comprises the following steps: designing differentiated comment items for each tertiary indicator, wherein the differentiated comment items comprise quantitative indicator comment items and qualitative indicator comment items; designing five-level evaluation standards for specific interval values for quantitative indicator comment items, and designing five-level evaluation standards based on behavior characteristic descriptions for qualitative indicator comment items.

4. The urban rail transit enterprise digital transformation capability maturity evaluation method according to claim 1, characterized in that, The method of calculating the weight vector of each level indicator comprises the following steps: designing an evaluation scale for the relative importance between indicators by using a 1-9 scale method, constructing a weight complementary matrix based on the evaluation scale; calculating the eigenvector of the weight complementary matrix to obtain an initial indicator weight; performing consistency check on the indicator weight, modifying the initial indicator weight according to the consistency check, and obtaining the weight vector of each level indicator.

5. The urban rail transit enterprise digital transformation capability maturity evaluation method according to claim 4, characterized in that, The method of performing consistency check on the indicator weight comprises the following steps: calculating the maximum eigenvalue of the weight complementary matrix; performing consistency check on the indicator weight based on the maximum eigenvalue of the weight complementary matrix.

6. The urban rail transit enterprise digital transformation capability maturity evaluation method according to claim 5, characterized in that, The method of performing consistency check on the indicator weight further comprises the following steps: introducing a random consistency index, and performing random consistency check on the indicator weight based on the random consistency index.

7. The urban rail transit enterprise digital transformation capability maturity evaluation method according to claim 1, characterized in that, The method of calculating the membership degree of each level indicator according to the second survey data comprises the following steps: converting the second survey data of the five-level comment questionnaire into a numerical matrix, wherein the rows represent the evaluation indicators and the columns represent the L1-L5 five-level comment grades; normalizing the evaluation frequency of each grade to form an initial membership degree distribution; selecting the standard value of each indicator at the L5 grade as an optimal reference sequence, calculating the correlation degree between the actual evaluation value of each tertiary indicator and the optimal reference sequence; modifying the initial membership degree distribution according to the correlation degree to generate a final membership degree matrix. 8.The method according to claim 3, characterized in that, The method of calculating the comprehensive score of each level indicator by combining the weight complementary matrix and the membership degree matrix comprises the following steps: The weight coefficient of each third-level index is multiplied by the corresponding membership vector to obtain a weighted score value of the index; The weighted score values of each third-level index are summed up, and the third-level index scores are classified and aggregated according to the corresponding second-level indexes to calculate the second-level index scores; The second-level index scores are classified and aggregated according to the corresponding first-level indexes to obtain the first-level index scores; The first-level index scores are summed up to obtain a comprehensive score of the enterprise digital transformation capability. 9.The method of claim 8, wherein, The first-level indexes include at least one of digital transformation strategy, digital process and governance, digital technology application, and talent and organization construction; The second-level indexes include at least one of digital transformation leadership awareness, enterprise digital agency positioning, top-level design planning, business process digitization, digital governance completeness, platform support capability, and digital team construction and collaboration; The third-level indexes include at least one of leadership awareness, digital transformation leadership style, IT expenditure to revenue ratio, data architecture integrity, cloud platform engine capability, and digital professional team construction.

10. A digital transformation capability maturity evaluation system for urban rail transit enterprises, characterized in that, Comprise: A construction module for constructing a hierarchical evaluation system, wherein the hierarchical evaluation system comprises indexes of different levels; A design module for designing an expert questionnaire based on the hierarchical evaluation system, wherein the expert questionnaire comprises an index correlation questionnaire for determining index weight and a five-level comment questionnaire for evaluating index level; A calculation module for collecting first investigation data of the index correlation questionnaire, calculating weight vectors of indexes of each level according to the first investigation data, outputting a weight complementary matrix, collecting second investigation data of the five-level comment questionnaire, calculating membership degrees of indexes of each level according to the second investigation data, and outputting a membership matrix; An output module for calculating comprehensive scores of indexes of each level in combination with the weight complementary matrix and the membership matrix, determining a maturity level of enterprise digital transformation according to the comprehensive scores, and outputting an evaluation result.

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