Digital transformation capability development evaluation method and system for national and provincial trunk line construction

Abstract

The invention relates to the technical field of national and provincial trunk line construction opening, in particular to a national and provincial trunk line construction digital transformation capability development evaluation method and system, and the method comprises the steps: setting an index system which represents the national and provincial trunk line digital transformation development level and comprises a first-level index, a second-level index and a third-level index; based on the characteristics of highway engineering construction, determining the index weight among the three-level indexes by combining a Delphi method and an analytic hierarchy process; performing consistency verification on the index weight to obtain a key influence factor weight matrix of the three-level index; calculating each secondary index value according to the key influence factor weight matrix; combining the weight of the second-level index with the second-level index value, and calculating a digital transformation development index; a Delphi method and an analytic hierarchy process are combined, so that subjective deviation can be effectively reduced, the accuracy and objectivity of an evaluation process are improved, the reasonability of index weights is ensured, and the scientificity of an evaluation result is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of open technology for national and provincial trunk line construction, and in particular to a method and system for evaluating the development of digital transformation capabilities for national and provincial trunk line construction. Background Art

[0002] With the rapid development of information technology and the rise of digital transformation, digital transportation and the digital economy have become important trends in current social development. Digital transformation has brought tremendous changes and opportunities to various fields. As a key component of this transformation, highway infrastructure construction also faces new challenges and development opportunities.

[0003] Amidst rapid development, traditional infrastructure construction, often relying on manual operations and record-keeping, has struggled to meet rapidly growing demand. It also faces the following challenges: 1) low efficiency, a need to refine the digital landscape framework, and enhanced digital collaboration; 2) inconsistent quality and potential safety issues associated with traditional construction methods; and 3) the need to further strengthen data security, big data analysis capabilities, and long-term data storage standards. While the infrastructure construction industry lags behind other sectors in digital transformation, it possesses enormous potential. Faced with current challenges and demands, it urgently needs to embark on digital transformation, providing valuable insights for its peers nationwide.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a method and system for evaluating the development of digital transformation capabilities for national and provincial trunk line construction, thereby effectively solving the problems in the background technology.

[0006] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction, comprising the following steps:

[0007] S10: Establish an indicator system to represent the development level of digital transformation of national and provincial trunk lines, including first-level indicators, second-level indicators and third-level indicators;

[0008] S20: Based on the characteristics of highway engineering construction, the Delphi method and the analytic hierarchy process are combined to determine the index weights between the three-level indicators;

[0009] S30: performing consistency verification on the index weights to obtain a weight matrix of key influencing factors of the three-level indicators;

[0010] S40: Calculating the values ​​of each secondary indicator according to the key influencing factor weight matrix;

[0011] S50: Combining the weight of the secondary indicator with the secondary indicator value to calculate the digital transformation development index.

[0012] Furthermore, in step S10, the first-level indicators and the second-level indicators specifically include:

[0013] The first-level indicator is the level of digital transformation and development of national and provincial trunk lines;

[0014] The secondary indicators include digital management index, digital design index, digital safety index, digital construction index, industrialized construction index, digital quality index, structural health index, green development index, and digital synergy index.

[0015] Furthermore, in step S20, the index weights between the three-level indicators are determined, and the model includes:

[0016]

[0017] Where W n ′ is the nth element of the feature vector, and W is the normalized weight vector.

[0018] Furthermore, in step S30, consistency verification is performed on the index weights, and the steps include:

[0019] S31: Calculate consistency index model;

[0020] S32: Find the corresponding average random consistency index RI;

[0021] S33: Calculate the random consistency ratio CR.

[0022] Furthermore, in step S31, calculating the consistency index model includes:

[0023]

[0024] Where λ max is the maximum eigenvalue of matrix A; n is the number of rows of matrix A; when CI = 0, it means that the matrix is ​​a completely consistent matrix, and the larger the CI, the more inconsistent the matrix.

[0025] Furthermore, in step S33, the random consistency ratio CR is calculated, and the model includes:

[0026]

[0027] In the formula, CI is the consistency index, RI is the average random consistency index;

[0028] When CR < 0.1, the consistency of the judgment matrix is ​​considered acceptable, otherwise the judgment matrix needs to be modified to make its consistency acceptable.

[0029] Furthermore, in step S30, according to the Delphi method principle, the importance level of the secondary indicators is divided into 9 levels, where level 1 is the lowest importance, and the importance increases gradually upwards, and level 9 is the highest importance.

[0030] Furthermore, in step S40, an indicator evaluation study is conducted on the capability level of each business direction of the secondary indicator.

[0031] Furthermore, in step S50, calculating the digital transformation development index model includes:

[0032] DTDI=0.22A+0.184B+0.148C+0.136D+0.063E+0.088F+0.06G+0.059H+0.042I;

[0033] In the formula, A stands for digital management, B stands for digital design, C stands for digital quality, D stands for digital monitoring, E stands for digital construction, F stands for digital construction, G stands for digital safety, H stands for digital dual carbon, and I stands for digital collaboration.

[0034] The present invention also includes a national and provincial trunk line construction digital transformation capability development evaluation system, using the above method, including:

[0035] The indicator setting unit is used to set the indicator system representing the development level of digital transformation of national and provincial trunk lines, including first-level indicators, second-level indicators and third-level indicators;

[0036] An index weight unit is used to determine the index weights between the three-level indicators based on the characteristics of highway engineering construction by combining the Delphi method and the analytic hierarchy process;

[0037] A verification unit, configured to verify the consistency of the index weights to obtain a weight matrix of key influencing factors of the three-level indicators;

[0038] A secondary index value calculation unit, configured to calculate the value of each secondary index according to the key influencing factor weight matrix;

[0039] The result calculation unit is used to combine the weight of the secondary indicator with the secondary indicator value to calculate the digital transformation development index.

[0040] The beneficial effects of the present invention are:

[0041] The combination of the Delphi method and the analytic hierarchy process can effectively reduce subjective bias and improve the accuracy and objectivity of the evaluation process. The Delphi method ensures the rationality of indicator weights by summarizing expert opinions, while the AHP method uses a systematic mathematical model to analyze and calculate weights, enhancing the scientific nature of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 A flowchart of the evaluation method for the development of digital transformation capabilities for national and provincial trunk lines;

[0044] Figure 2 A process flow chart for the development and evaluation method of digital transformation capabilities for national and provincial trunk lines;

[0045] Figure 3 It is a schematic diagram of the first-level indicators, second-level indicators and third-level indicators;

[0046] Figure 4 This is a statistical chart of expert survey results;

[0047] Figure 5 A structural diagram of the digital transformation capability development evaluation system for national and provincial trunk lines. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0049] like Figures 1 to 4 A method for evaluating the development of digital transformation capabilities for national and provincial trunk line construction is shown in the figure, including the following steps:

[0050] S10: Establish an indicator system to represent the development level of digital transformation of national and provincial trunk lines, including first-level indicators, second-level indicators, and third-level indicators, making the evaluation more comprehensive and detailed;

[0051] S20: Based on the inherent characteristics of highway engineering construction, the Delphi method and the analytic hierarchy process (AHP) are combined to determine the index weights between the three-level indicators. The combination of the Delphi method and the analytic hierarchy process (AHP) can effectively reduce subjective bias and improve the accuracy and objectivity of the evaluation process.

[0052] S30: Conduct consistency verification on the index weights to obtain the weight matrix of key influencing factors of the three-level indicators. This consistency verification avoids inconsistencies caused by subjective judgment and ensures the reliability of the evaluation results.

[0053] S40: Calculate the value of each secondary indicator according to the weight matrix of key influencing factors;

[0054] S50: The weights of the secondary indicators are combined with the secondary indicator values ​​to calculate the Digital Transformation Development Index (DTDI).

[0055] Through a standardized indicator system and scientific methods, the evaluation efficiency is significantly improved and the dependence on human resources is reduced; through detailed indicator decomposition, the weak links in highway construction are identified, thereby achieving targeted improvements; through quantitative indicators to evaluate various aspects of project management, it can effectively help national and provincial trunk road management departments to evaluate the digital steering capabilities of national and provincial trunk roads.

[0056] Combining the Delphi method with the Analytic Hierarchy Process (AHP) can effectively reduce subjective bias and improve the accuracy and objectivity of the evaluation process. The Delphi method ensures the rationality of indicator weights by summarizing expert opinions, while the AHP method uses a systematic mathematical model to analyze and calculate weights, enhancing the scientific nature of the evaluation results.

[0057] The present invention has constructed a method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction. By systematically evaluating the digital transformation capabilities of national and provincial trunk line construction, the digital transformation capabilities of national and provincial trunk line construction are expressed by calculating nine important factors: digital management, digital design, digital quality, digital monitoring, digital construction, digital construction, digital security, digital dual carbon, and digital collaboration. The processing flow of the method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction provided by the embodiment of the present invention is as follows: Figure 2 shown.

[0058] In this embodiment, if Figure 3 As shown, in step S10, the primary indicators and secondary indicators specifically include:

[0059] The first-level indicator is the level of digital transformation and development of national and provincial trunk lines;

[0060] The secondary indicators include Digital Management Index (DMI), Digital Design Index (DDI), Digital Safety Index (DSI), Digital Construction Index (DCI), Industrialized Construction Index (FCI), Digital Quality Index (DQI), Structural Health Index (SHI), Green Development Index (EPI), and Digital Synergy Index (DII).

[0061] The three-level indicators include:

[0062] The Digital Management Index (DMI) includes: project management, internal control digital processes, digitalization of organizational management systems, digital talent, and supplier database;

[0063] The Digital Design Index (DDI) includes: project BIM library, design node management, design drawing management, standardized design drawing library, design carbon quota library, post-design evaluation, and digital scientific research results transformation;

[0064] The Digital Safety Index (DSI) includes: emergency management, video deployment management, safety inspection management, safety activity management, safety funding management, safety warning management, and safety education management;

[0065] The Digital Construction Index (DCI) includes: digital construction of pavement, digital construction of bridges, and digital construction of tunnels;

[0066] The Industrialized Construction Index (FCI) includes: industrialized construction ratio, base construction status, production problem handling, quality problem analysis, and the promotion ratio of smart construction sites and smart communities;

[0067] The Digital Quality Index (DQI) includes: quality testing, early warning management, and quality traceability;

[0068] Structural Health Index (SHI) includes: physical mechanical status, construction posture, and damage warning;

[0069] The Green Development Index (EPI) includes: green real-time monitoring, green cost analysis, grid management, carbon emission management, and construction exemptions;

[0070] The Digital Interaction Index (DII) includes digital assets, traffic perception management, smart maintenance, future scenario display, and digital delivery.

[0071] By setting first-, second-, and third-level indicators, the plan achieves a comprehensive assessment of the digital transformation development level of national and provincial trunk lines; the indicator system covers multiple key areas such as digital management, design, safety, construction, industrialized construction, quality, structural health, green development, and collaboration.

[0072] In step S20, a judgment matrix of three-level indicators is constructed by the Delphi method, including:

[0073] In a certain hierarchical structure, each element and the next layer of elements it controls constitute a sub-region. The relative importance of each element in the sub-region is judged by numerical values ​​using the expert survey method (Delphi). Suppose there are n single evaluation indicators a1, a2…, a n , by evaluating the relationship between these n single evaluation indicators one by one, we can get the indicator judgment matrix as shown in the formula.

[0074]

[0075] A is an n×n matrix. ij Is a single evaluation indicator a i with a j The scale value corresponding to the degree of importance after comparison. The definition of judgment matrix scale is shown in Table 1.

[0076] Table 1 Judgment matrix scale definition

[0077]

[0078] The comparison of the importance of two targets is always done under certain criteria. Because people can only intuitively judge the difference of 9 levels at most, and the difference is more subtle, people cannot intuitively distinguish, so 9 levels are used. Generally speaking, the judgment matrix is ​​constructed by domain experts based on their intuition. For n criteria, the judgment matrix A = (a ij ) n×n , obviously a ij Satisfy the model:

[0079] a ij >0, a ii =1;

[0080] total The second judgment shows that A is a reciprocal matrix with all diagonal elements being 1. Such an n-order matrix can be represented as an upper triangular or lower triangular matrix. However, the element a of A is ij Usually not transitive, i.e. a ij ·a jk ≠a ik This is caused by the complexity of things and the limitations of human cognition. ij ·a jk =a ik If it holds, then A is called a consistency matrix. From judging the matrix A to deriving the order of elements by importance under certain criteria, the consistency of the matrix A plays a vital role.

[0081] As a priority of the above embodiment, in step S20, the index weights between the three-level indicators are determined, and the model includes:

[0082] AW′=λ′ max ;

[0083] Where W′ is the maximum eigenvalue λ of matrix A max The corresponding eigenvector;

[0084] Models that represent the relative importance of various indicators include:

[0085] W′=(W1′, W2′,…,W n ′);

[0086] By solving the formula, we can get λ max And the corresponding eigenvector W′ is obtained. The normalized model of W′ is as follows:

[0087]

[0088] Where W n ′ is the nth element of the feature vector, and W is the normalized weight vector.

[0089] W′ is the maximum eigenvalue λ of the matrix A max The corresponding eigenvector;

[0090] The W obtained after normalization is the weight vector of each evaluation index;

[0091] The weights and index values ​​calculated by mathematical models provide a quantitative basis for decision-making, reducing the uncertainty and arbitrariness in the decision-making process.

[0092] The judgment matrix obtained from the expert questionnaire may not be a consistent matrix. Judgment matrices often contain contradictions such as "A is more important than B, B is more important than C, and C is more important than A," or other more complex contradictions. First, perform a consistency check on the judgment matrix. Only matrices that pass the consistency check are valuable.

[0093] In this embodiment, in step S30, consistency verification of the index weights is performed, and the steps include:

[0094] S31: Calculate consistency index model;

[0095] S32: Find the corresponding average random consistency index RI; see Table 2:

[0096] Table 2 Average random consistency index

[0097]

[0098] S33: Calculate the random consistency ratio CR.

[0099] The calculation of consistency index (CI) and random consistency ratio (CR) provides a quantitative evaluation method for the consistency of the judgment matrix, making the evaluation process systematic and standardized and avoiding arbitrariness in the evaluation; by calculating consistency index (CI) and random consistency ratio (CR), inconsistencies in the judgment matrix can be detected and corrected to ensure the credibility of the evaluation results.

[0100] In step S31, calculating the consistency index model includes:

[0101]

[0102] Where λ max is the maximum eigenvalue of matrix A. According to the Perron theorem of positive matrix, λ max exists and is unique; n is the number of rows in matrix A; when CI = 0, it means that the matrix is ​​a completely consistent matrix, and the larger the CI, the less consistent the matrix is.

[0103] As a priority of the above embodiment, in step S33, the random consistency ratio CR is calculated, and the model includes:

[0104]

[0105] In the formula, CI is the consistency index, RI is the average random consistency index;

[0106] When CR < 0.1, the consistency of the judgment matrix is ​​considered acceptable. Otherwise, the judgment matrix needs to be revised to make its consistency acceptable. By revising the judgment matrix, the inconsistency caused by subjective judgment can be corrected, ensuring the validity of the evaluation results, avoiding erroneous judgments or inaccurate evaluation results caused by matrix inconsistency, and thus improving the reliability of the final evaluation.

[0107] In this embodiment, in step S30, according to the Delphi method principle, the importance of the secondary indicators is divided into 9 levels. The importance is divided into 9 levels, with level 1 being the lowest importance and the importance increasing gradually upwards, and level 9 being the highest importance. According to the different application scenarios of the secondary indicators, the importance levels of the indicators are preliminarily divided. Through research, the correlation matrix of the nine major influencing factors and related indicators is as follows:

[0108] Table 3 Correlation between key influencing factors and indicators

[0109]

[0110] After initially confirming the relevance of indicators, the Delphi method was used to evaluate the relevance of indicators. The criticality of digital control indicators was obtained according to the Delphi method, and the criticality of indicators was analyzed.

[0111] Table 4 Statistics of expert survey results

[0112]

[0113]

[0114] According to the Delphi method, digital construction has the highest recognition among key factors influencing digital transformation, followed by digital design and digital management. Digital dual carbon has the lowest recognition within the industry, followed by digital collaboration and digital construction.

[0115] At the same time, the survey results on the synergistic relationship between key influencing factors and other indicators show that digital construction, digital quality, and digital safety have the highest correlation with other indicators, and the average scores are all above 8, which is highly consistent with the initial score of this project.

[0116] Through the expert questionnaire 4 and Figure 4 Expert survey results were statistically analyzed to verify the indicator correlation evaluation. The data model relationships for key indicators were then calculated. Based on the correlation evaluation matrix for related indicators, the importance of correlation was ranked, with a 1-7 ranking based on the correlation values. Using the AHP calculation rules, the weighted indicators for key influencing indicators of digital management and control were obtained as follows:

[0117] Table 5 Correlation between key influencing factors and indicators

[0118]

[0119] According to the indicator index, the AHP hierarchical analysis results are calculated.

[0120] Table 6 Results of the key indicator hierarchical analysis

[0121]

[0122]

[0123] Based on the results of AHP, a consistency test was conducted. The corresponding RI value was found to be 1.45 from the RI table, and the CR value could be calculated from CI and RI.

[0124] Table 7 Consistency test results of precast bridge components

[0125]

[0126] Through expert survey and analytic hierarchy process, we calculated the weights of key influencing factors of digital transformation and obtained the following results:

[0127] Table 8 Overall indicator weights

[0128]

[0129] As a priority of the above embodiment, in step S40, after studying the third-level indicator development evaluation to obtain the second-level indicator evaluation standard, in order to more scientifically and reasonably evaluate the effectiveness of digital management and control construction, an indicator evaluation study is conducted on the capability level of each business direction of the second-level indicator, thereby improving the overall digital transformation business capability indicator standard system;

[0130] Secondary indicators include digital management, digital design, digital quality, digital monitoring, digital fabrication, digital construction, digital safety, digital dual carbon, and digital collaboration. Digital management capabilities are reflected in the application of digital technologies and tools to manage and monitor national and provincial trunk line construction projects. Key components include project management, digital internal control processes, digital organizational management systems, digital talent, and a supplier database. Digital design capabilities are reflected in the process of utilizing digital tools and technologies to design national and provincial trunk line construction plans, aiming to improve design accuracy, visualization, and collaborative efficiency, thereby providing optimized design solutions for national and provincial trunk line construction. Key components include the project BIM library, design node management, design drawing management, standardized design drawing library, design carbon quota library, post-design evaluation, and the transformation of digital scientific research results. Digital safety capabilities are reflected in ensuring the safety of people and assets during the construction of national and provincial trunk lines. Key components include emergency management, video deployment management, safety inspection management, safety activity management, safety funding management, safety warning management, and safety education management. Digital construction capabilities encompass the actual construction of national and provincial trunk roads using automated construction equipment and control technologies. Their goal is to improve construction efficiency, reduce errors and waste, and ensure quality and safety during the construction process. Key components include digital pavement construction, digital bridge construction, and digital tunnel construction. Digital construction capabilities address the production needs of prefabricated components for transportation infrastructure and reflect the overall level of back-end production and manufacturing. Key components include the proportion of industrialized construction of key bridges and structures, base construction status, production problem resolution, the proportion of smart construction sites and smart communities promoted, and quality issue analysis. Digital quality capabilities encompass the use of IoT testing equipment and standards to inspect and verify the physical quality of national and provincial trunk road projects. Their goal is to provide accurate and reliable project quality data, promptly identify and resolve issues, and ensure that quality standards for national and provincial trunk road construction are met. Key components include project quality testing, early warning management, and quality traceability. Digital monitoring capabilities encompass real-time monitoring and data collection of national and provincial trunk road construction through digital means. This provides timely access to project status and performance data, providing a scientific basis for decision-making and ensuring the safe and sustainable operation of national and provincial trunk roads. Key elements include the physical mechanical state of the project, construction posture, and damage warnings. Digital dual-carbon capabilities encompass the impact of carbon emissions and energy consumption throughout the construction process of national and provincial trunk lines. By reducing carbon emissions and conserving energy, they promote low-carbon development and environmental sustainability in national and provincial trunk line construction. Key elements include real-time green monitoring, green cost analysis, grid management, carbon emission management, and construction exemptions.Digital collaboration capabilities are demonstrated by leveraging digital platforms and tools to enable collaborative work and information sharing among all stakeholders throughout the project lifecycle. These capabilities encompass collaboration among design teams, construction teams, supervisors, industry supervisors, regulatory bodies, local authorities, and operational management units. Key components include digital assets, traffic perception management, intelligent maintenance, future scenario demonstrations, and digital delivery.

[0131] By analyzing the key factors influencing each business capability within digital management and control, we clarified the evaluation dimensions and directions for business capability indicators. Focusing on these overall indicator dimensions, we categorized the data sources for business capability indicators, organized data acquisition methods, and analyzed the representation dimensions. A three-level indicator analysis checklist was established.

[0132] Table 9 Analysis of digital management and control business capability indicators

[0133]

[0134]

[0135] Based on the analysis of the overall indicators in the previous article, it can be seen that in the process of national and provincial trunk line construction, efficient and accurate evaluation of various business capabilities requires a specific quantitative evaluation method. Therefore, it is planned to study the contribution of the key influencing factors of the nine business capability indexes and determine the weight calculation coefficients and methods for each index. Based on the above, the "Delphi method + hierarchical analysis method" numerical calculation method is continued to be used to calculate the weight coefficients for the secondary indicators and the corresponding tertiary indicators, as follows:

[0136] (1) Digital Management Index (DMI)

[0137] Using the Delphi method to construct a judgment matrix, we compared the key influencing factors of digital management, including project management (A), internal control digital process (B), organizational management system digitalization (C), digital talent (D), and supplier base (F). The comparison was divided into nine levels. Through extensive expert research and comparison, we concluded the following digital management index weight judgment matrix:

[0138]

[0139] It performs consistency test on the judgment matrix and verifies that CI meets the consistency requirements. And the maximum eigenvalue λ of matrix A max The corresponding eigenvector calculation and normalization process are used to obtain the weight matrix of each key influencing factor of the digital management index as shown below:

[0140] W=(W1, W2, ..., W5)=(0.2, 0.1, 0.2, 0.25, 0.25);

[0141] (2) Digital Design Index (DDI)

[0142] The Delphi method was used to construct a judgment matrix, and the key influencing factors of digital design, including BIM library (A), design node management (B), design drawing management (C), standardized design library (D), design carbon quota library (E), post-design evaluation (F), and digital scientific research results transformation (G), were compared pairwise. The comparison levels were divided into 9 levels. Through extensive expert research and comparison, the digital design index weight judgment matrix was obtained as follows:

[0143]

[0144] It performs consistency test on the judgment matrix and verifies that CI meets the consistency requirements. And the maximum eigenvalue λ of matrix A max The corresponding eigenvector calculation and normalization process are used to obtain the weight matrix of each key influencing factor of the digital design index as shown below:

[0145] W=(W1, W2, ..., W7)=(0.12, 0.15, 0.08, 0.21, 0.13, 0.18, 0.13);

[0146] (3) Digital Security Index (Safeguarding) DSI

[0147] Using the Delphi method to construct a judgment matrix, we compared key digital security factors—emergency management (A), video deployment management (B), security inspection management (C), security activity management (D), security funding management (E), security warning management (F), and security education management (G)—two by two. The comparisons were divided into nine levels. Through extensive expert research and comparison, we arrived at the following digital security index weight judgment matrix:

[0148]

[0149] It performs consistency test on the judgment matrix and verifies that CI meets the consistency requirements. And the maximum eigenvalue λ of matrix A max The corresponding eigenvector calculation and normalization process are used to obtain the weight matrix of each key influencing factor of the digital security index as shown below:

[0150] W=(W1, W2, ..., W7)=(0.15, 0.21, 0.11, 0.17, 0.07, 0.12, 0.17);

[0151] (4) Digital Construction Index (DCI)

[0152] The Delphi method was used to construct a judgment matrix, and the key influencing factors of digital construction (pavement digital construction A), bridge digital construction B, and tunnel digital construction C) were compared pairwise. The comparison levels were divided into 9 levels. Through extensive expert research and comparison, the digital construction index weight judgment matrix was obtained as follows:

[0153]

[0154] It performs consistency test on the judgment matrix and verifies that CI meets the consistency requirements. And the maximum eigenvalue λ of matrix A max The corresponding eigenvector calculation and normalization process are used to obtain the weight matrix of each key influencing factor of the digital construction index as shown below:

[0155] W=(W1, W2, W3)=(0.35, 0.25, 0.4);

[0156] (5) Digital Construction Index (Industrialized Construction) FCI

[0157] Using the Delphi method to construct a judgment matrix, we compared key factors influencing digital construction, including the proportion of industrialized construction (A), base construction status (B), production problem handling (C), quality problem analysis (D), and the proportion of smart construction sites and smart communities (E). The comparison was divided into nine levels. Through extensive expert research and comparison, we concluded the following judgment matrix for the digital construction index weight:

[0158]

[0159] It performs consistency test on the judgment matrix and verifies that CI meets the consistency requirements. And the maximum eigenvalue λ of matrix A max The corresponding eigenvector calculation and normalization process are used to obtain the weight matrix of each key influencing factor of the digital construction index as shown below:

[0160] W=(W1, W2, ..., W5)=(0.15, 0.23, 0.11, 0.42, 0.09);

[0161] (6) Digital Quality Index (DQI)

[0162] The Delphi method was used to construct a judgment matrix, and the key factors affecting digital quality (project quality detection A, early warning management B, and quality traceability C) were compared pairwise. The comparison levels were divided into 9 levels. Through extensive expert research and comparison, the digital quality index weight judgment matrix was obtained as follows:

[0163]

[0164] It performs consistency test on the judgment matrix and verifies that CI meets the consistency requirements. And the maximum eigenvalue λ of matrix A max The corresponding eigenvector calculation and normalization process are used to obtain the weight matrix of each key influencing factor of the digital quality index as shown below:

[0165] W=(W1, W2, W3)=(0.35, 0.23, 0.42);

[0166] (7) Digital Monitoring (Structural Health) Index SHI

[0167] The judgment matrix was constructed using the Delphi method, and the key influencing factors of digital monitoring, namely, physical mechanical state A, construction posture B, and damage warning C, were compared pairwise. The comparison levels were divided into 9 levels. Through extensive expert research and comparison, the digital monitoring index weight judgment matrix was obtained as follows:

[0168]

[0169] It performs consistency test on the judgment matrix and verifies that CI meets the consistency requirements. And the maximum eigenvalue λ of matrix A max The corresponding eigenvector calculation and normalization process are used to obtain the weight matrix of each key influencing factor of the digital monitoring index as shown below:

[0170] W=(W1, W2, W3)=(0.33, 0.28, 0.39);

[0171] (8) Digital Twin Carbon Index (EPI)

[0172] A judgment matrix was constructed using the Delphi method, and pairwise comparisons were conducted on the key influencing factors of digital dual carbon: green real-time monitoring A, green cost analysis B, grid management C, carbon emission management D, and construction exemption E. The comparison levels were divided into 9 levels. Through extensive expert research and comparison, the digital dual carbon index weight judgment matrix was obtained as follows:

[0173]

[0174] It performs consistency test on the judgment matrix and verifies that CI meets the consistency requirements. And the maximum eigenvalue λ of matrix A max The corresponding eigenvector calculation and normalization process are used to obtain the weight matrix of each key influencing factor of the digital dual carbon index as shown below:

[0175] W=(W1, W2, W3, W4, W5)=(0.14, 0.18, 0.22, 0.19, 0.27);

[0176] (9) Digital Synergy Index (DII)

[0177] Using the Delphi method to construct a judgment matrix, we compared the key influencing factors of digital collaboration (digital assets A), traffic perception management B, smart maintenance C, future scenario display D, and digital delivery E) in pairs, with the comparison levels divided into 9 levels. Through extensive expert research and comparison, we concluded the digital collaboration index weight judgment matrix as follows:

[0178]

[0179] It performs consistency test on the judgment matrix and verifies that CI meets the consistency requirements. And the maximum eigenvalue λ of matrix A max The corresponding eigenvector calculation and normalization process are used to obtain the weight matrix of each key influencing factor of the digital synergy index as shown below:

[0180] W=(W1, W2, W3, W4, W5)=(0.19, 0.25, 0.13, 0.17, 0.26);

[0181] As a priority of the above embodiment, in step S50, calculating the digital transformation development index (DTDI) model includes:

[0182] DTDI=0.22A+0.184B+0.148C+0.136D+0.063E+0.088F+0.06G+0.059H+0.042I;

[0183] In the formula, A stands for digital management, B stands for digital design, C stands for digital quality, D stands for digital monitoring, E stands for digital construction, F stands for digital construction, G stands for digital safety, H stands for digital dual carbon, and I stands for digital collaboration.

[0184] (1) Digital Management Index (DMI)

[0185] DMI=0.2A1+0.1B1+0.2C1+0.25D1+0.25E1;

[0186] In the formula, A1 represents project management, B1 represents internal control digital process, C1 represents digitalization of organizational management system, D1 represents digital talent, and E1 represents supplier database.

[0187] (2) Digital Design Index (DDI)

[0188] DDI=0.12A2+0.15B2+0.08C2+0.21D2+0.13E2+0.18F2+0.13G2;

[0189] In the formula, A2 is the project BIM library, B2 is the design node management, C2 is the design drawing management, D2 is the standardized design drawing library, E2 is the design carbon quota library, F2 is the post-design evaluation, and G2 is the transformation of digital scientific research results.

[0190] (3) Digital Security Index (DSI)

[0191] DSI=0.15A3+0.21B3+0.11C3+0.17D3+0.07E3+0.12F3+0.17G3;

[0192] In the formula, A3 is emergency management, B3 is video deployment management, C3 is safety inspection management, D3 is safety activity management, E3 is safety funding management, F3 is safety warning management, and G3 is safety education management.

[0193] (4) Digital Construction Index (DCI)

[0194] DCI=0.35A4+0.25B4+0.4C4;

[0195] Where A4 represents the digital construction of pavement, B4 represents the digital construction of bridges, and C4 represents the digital construction of tunnels.

[0196] (5) Industrialized Construction Index (FCI)

[0197] FCI=0.15A5+0.23B5+0.11C5+0.42D5+0.09E5;

[0198] In the formula, A5 is the proportion of industrialized construction, B5 is the base construction situation, C5 is the handling of production problems, D5 is the analysis of quality problems, and E5 is the promotion ratio of smart construction sites and smart communities.

[0199] (6) Digital Quality Index (DQI)

[0200] DQI=0.35A6+0.23B6+0.42C6;

[0201] In the formula, A6 represents project quality inspection, B6 represents early warning management, and C6 represents quality traceability.

[0202] (7) Structural Health Index (SHI)

[0203] SHI=0.33A7+0.28B7+0.39C7;

[0204] Where A7 is the entity mechanical state, B7 is the construction posture, and C7 is the damage warning.

[0205] (8) Green Development Index (EPI)

[0206] EPI=0.14A8+0.18B8+0.22C8+0.19D8+0.27E8;

[0207] In the formula, A8 represents green real-time monitoring, B8 represents green cost analysis, C8 represents grid management, D8 represents carbon emission management, and E8 represents construction exemption.

[0208] (9) Digital Interaction Index (DII)

[0209] DII=0.19A9+0.25B9+0.13C9+0.17D9+0.26E9;

[0210] In the formula, A9 represents digital assets, B9 represents traffic perception management, C9 represents smart maintenance, D9 represents future scenario display, and E9 represents digital delivery.

[0211] In summary, the method of the present invention can be used to quantitatively evaluate the development level of digital transformation of different national and provincial trunk lines, which is of great significance for guiding the development level of digital transformation of national and provincial trunk lines at the macro level in various places.

[0212] Based on the nine digital business capabilities, the business direction capability level evaluation indicators are determined, and an evaluation system is constructed from an all-round evaluation of the digital transformation capabilities of national and provincial trunk lines.

[0213] The present invention also includes a national and provincial trunk line construction digital transformation capability development evaluation system, as a priority of the above embodiment, such as Figure 5 As shown, using the method as described above, including:

[0214] The indicator setting unit is used to set the indicator system representing the development level of digital transformation of national and provincial trunk lines, including first-level indicators, second-level indicators and third-level indicators;

[0215] The index weight unit is used to determine the index weights between the three-level indicators based on the characteristics of highway engineering construction by combining the Delphi method and the hierarchical analysis method;

[0216] The verification unit is used to verify the consistency of the index weights and obtain the weight matrix of key influencing factors of the three-level indicators;

[0217] A secondary indicator value calculation unit is used to calculate the value of each secondary indicator according to the key influencing factor weight matrix;

[0218] The result calculation unit is used to combine the weight of the secondary indicator with the secondary indicator value to calculate the digital transformation development index (DTDI).

[0219] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0220] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the development of digital transformation capabilities for national and provincial trunk line construction, characterized by: The steps include: S10: Establish an indicator system to represent the development level of digital transformation of national and provincial trunk lines, including first-level indicators, second-level indicators and third-level indicators; S20: Based on the characteristics of highway engineering construction, the Delphi method and the analytic hierarchy process are combined to determine the index weights between the three-level indicators; S30: performing consistency verification on the index weights to obtain a weight matrix of key influencing factors of the three-level indicators; S40: Calculating the values ​​of each secondary indicator according to the key influencing factor weight matrix; S50: Combining the weight of the secondary indicator with the secondary indicator value to calculate the digital transformation development index.

2. The method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction according to claim 1 is characterized in that: In step S10, the first-level indicators and the second-level indicators specifically include: The first-level indicator is the level of digital transformation and development of national and provincial trunk lines; The secondary indicators include digital management index, digital design index, digital safety index, digital construction index, industrialized construction index, digital quality index, structural health index, green development index, and digital synergy index.

3. The method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction according to claim 1 is characterized in that: In step S20, the index weights between the three-level indicators are determined, and the model includes: Where W n ′ is the nth element of the feature vector, and W is the normalized weight vector.

4. The method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction according to claim 1 is characterized in that: In step S30, consistency verification is performed on the index weights, including: S31: Calculate consistency index model; S32: Find the corresponding average random consistency index RI; S33: Calculate the random consistency ratio CR.

5. The method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction according to claim 4 is characterized in that: In step S31, calculating the consistency index model includes: Where λ max is the maximum eigenvalue of matrix A; n is the number of rows of matrix A; when CI = 0, it means that the matrix is ​​a completely consistent matrix, and the larger the CI, the more inconsistent the matrix.

6. The method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction according to claim 4 is characterized in that: In step S33, the random consistency ratio CR is calculated, and the model includes: In the formula, CI is the consistency index, RI is the average random consistency index; When CR < 0.1, the consistency of the judgment matrix is ​​considered acceptable, otherwise the judgment matrix needs to be modified to make its consistency acceptable.

7. The method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction according to claim 1 is characterized in that: In step S30, according to the Delphi method principle, the importance level of the secondary indicators is divided into 9 levels, where level 1 is the lowest importance, and the importance increases gradually upwards, and level 9 is the highest importance.

8. The method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction according to claim 1 is characterized in that: In step S40, an indicator evaluation study is conducted on the capability level of each business direction of the secondary indicator.

9. The method for evaluating the development of digital transformation capabilities of national and provincial trunk line construction according to claim 1 is characterized in that: In step S50, the calculation of the digital transformation development index (DTDI) model includes: DTDI=0.22A+0.184B+0.148C+0.136D+0.063E+0.088F+0.06G+ 0.059H+0.042I; In the formula, A stands for digital management, B stands for digital design, C stands for digital quality, D stands for digital monitoring, E stands for digital construction, F stands for digital construction, G stands for digital safety, H stands for digital dual carbon, and I stands for digital collaboration.

10. A national and provincial trunk line construction digital transformation capability development evaluation system, characterized by: Use of the method according to any one of claims 1 to 9, comprising: The indicator setting unit is used to set the indicator system representing the development level of digital transformation of national and provincial trunk lines, including first-level indicators, second-level indicators and third-level indicators; An index weight unit is used to determine the index weights between the three-level indicators based on the characteristics of highway engineering construction by combining the Delphi method and the analytic hierarchy process; A verification unit, configured to verify the consistency of the index weights to obtain a weight matrix of key influencing factors of the three-level indicators; A secondary index value calculation unit, configured to calculate the value of each secondary index according to the key influencing factor weight matrix; The result calculation unit is used to combine the weight of the secondary indicator with the secondary indicator value to calculate the digital transformation development index.

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