Railway data value evaluation method and device based on analytic hierarchy process

Abstract

The invention provides a railway data value evaluation method and device based on an analytic hierarchy process, and relates to the technical field of railway data processing, and the method comprises the steps: determining a railway data value evaluation system; according to the initial score corresponding to each evaluation index under the evaluation elements, constructing a first judgment matrix corresponding to the evaluation elements; the first judgment matrix comprises a plurality of first elements, and each first element represents a relative importance degree between an evaluation index corresponding to a column where the first element is located and an evaluation index corresponding to a row where the first element is located; if the consistency verification of the first judgment matrix is passed, adjusting the first judgment matrix according to the maximum characteristic value of the first judgment matrix to obtain a second judgment matrix; if the consistency verification of the second judgment matrix is passed, determining an index weight corresponding to each evaluation index under the evaluation elements according to each second element in the second judgment matrix; the data value of the railway data is calculated according to the index weight and the index score corresponding to the to-be-evaluated railway data, and the evaluation reliability is high.

Description

Technical Field

[0001] The present invention relates to the technical field of railway data processing, and in particular to a railway data value assessment method and device based on hierarchical analysis method. Background Art

[0003] In the prior art, the traditional method for evaluating the value of railway data is the cost method. The traditional cost method evaluates the value of assets by estimating the cost of rebuilding assets and deducting depreciation. However, most of the research on the value evaluation of railway data using this method remains at the theoretical level and lacks practicality, resulting in poor reliability of the results of railway data value evaluation. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a railway data value assessment method and device based on hierarchical analysis method, which improves the reliability of railway data value assessment.

[0005] In a first aspect, the present invention provides a railway data value assessment method based on hierarchical analysis method, the method comprising the following steps: Determine a railway data value assessment system; the railway data value assessment system includes at least one assessment element and an assessment indicator under each assessment element; For any evaluation factor, a first judgment matrix corresponding to the evaluation factor is constructed according to the initial scores corresponding to each evaluation indicator under the evaluation factor; the first judgment matrix includes a plurality of first elements, each of which is used to represent the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; When it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; When it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, determining the indicator weight corresponding to each of the evaluation indicators under the evaluation factor according to each second element in the second judgment matrix; According to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

[0006] According to a railway data value assessment method based on hierarchical analysis method provided by the present invention, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix, including: Determine, according to the maximum eigenvalue of the first judgment matrix, the partial derivative value corresponding to each of the first elements in the first judgment matrix; Determining a third element affecting the consistency of the first judgment matrix according to the partial derivative values ​​corresponding to each of the first elements; According to the partial derivative value corresponding to the third element, the third element is adjusted to obtain an adjusted third element; A second judgment matrix is ​​obtained according to the adjusted third element and the first judgment matrix.

[0007] According to a railway data value assessment method based on hierarchical analysis method provided by the present invention, the indicator weight corresponding to each evaluation indicator under the evaluation factor is determined according to each second element in the second judgment matrix, including: For any of the evaluation indicators in the second judgment matrix, sum the second elements on the column where the evaluation indicator is located to obtain a column sum; Determine a normalized second judgment matrix according to each of the first elements and the column sum; According to each fourth element in the normalized second judgment matrix, the indicator weight corresponding to each evaluation indicator is determined.

[0008] According to a railway data value evaluation method based on hierarchical analysis method provided by the present invention, the data value corresponding to the railway data in the system to be evaluated is calculated according to the indicator weight corresponding to each evaluation indicator under each evaluation factor and the score corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated, including: Calculating a value adjustment coefficient according to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated; Determine the replacement cost corresponding to the railway data in the railway system to be evaluated according to the research and development cost and operation and maintenance cost corresponding to the railway data in the railway system to be evaluated and the replacement cost composition ratio corresponding to the railway data in the railway system to be evaluated; The value adjustment coefficient is added by one to obtain an addition result, and the addition result is multiplied by the replacement cost to obtain the data value corresponding to the railway data in the system to be evaluated.

[0009] According to a railway data value assessment method based on hierarchical analysis method provided by the present invention, when it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain the second judgment matrix, and the method further includes: Performing a consistency check on the first judgment matrix to obtain a check result; the check result is not completely consistent or completely consistent; When it is determined that the consistency check result of the first judgment matrix is ​​completely consistent, the indicator weight corresponding to each of the evaluation indicators under the evaluation factor is determined according to each of the first elements in the first judgment matrix.

[0010] According to a railway data value assessment method based on hierarchical analysis method provided by the present invention, the consistency check of the first judgment matrix to obtain the check result includes: Sort the first elements of each row in the first judgment matrix from small to large to obtain a sorting result of each row; If there is a situation where the sorting result of the first elements of any of the rows in the first judgment matrix is ​​inconsistent with the sorting results of the first elements of other rows in the first judgment matrix except the row, determining the verification result as not completely consistent; If the sorting result of the first elements of any of the rows in the first judgment matrix is ​​consistent with the sorting results of the first elements of other rows in the first judgment matrix except the row, the verification result is determined to be completely consistent.

[0011] In a second aspect, the present invention further provides a railway data value assessment device based on hierarchical analysis method, the device comprising the following modules: A system construction module is used to determine a railway data value evaluation system; the railway data value evaluation system includes at least one evaluation element and evaluation indicators under each evaluation element; An evaluation module, for constructing, for any evaluation factor, a first judgment matrix corresponding to the evaluation factor according to the initial scores corresponding to each evaluation indicator under the evaluation factor; the first judgment matrix includes a plurality of first elements, each of which is used to represent the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; When it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; When it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, determining the indicator weight corresponding to each of the evaluation indicators under the evaluation factor according to each second element in the second judgment matrix; According to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

[0012] In a third aspect, the present invention further provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a railway data value assessment method based on the hierarchical analysis method as described above is implemented.

[0013] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described railway data value assessment methods based on the analytic hierarchy process.

[0014] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned railway data value assessment methods based on the hierarchical analysis method.

[0015] The railway data value evaluation method and device based on the hierarchical analysis method provided by the present invention first determine a railway data value evaluation system, wherein the railway data value evaluation system includes at least one evaluation element and evaluation indicators under each evaluation element; then, for any evaluation element, according to the initial score corresponding to each evaluation indicator under the evaluation element, a first judgment matrix corresponding to the evaluation element is constructed, wherein the first judgment matrix includes a plurality of first elements, each first element is used to characterize the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; when it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; when it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, according to each second element in the second judgment matrix, the indicator weight corresponding to each evaluation indicator under the evaluation element is determined; and then, according to the indicator weight corresponding to each evaluation indicator under each evaluation element and the score corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

[0016] The present invention first establishes a data value assessment system and provides a relatively comprehensive data element value research framework. In addition, a first judgment matrix is ​​first constructed according to the initial scores corresponding to each evaluation indicator under the evaluation element, that is, the initial weights of the indicators. Then, the consistency judgment process is improved to quickly discover inconsistencies in expert judgments, and the matrix is ​​subjected to sensitivity analysis. When it is determined that the consistency check of the first judgment matrix fails, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix. When the second judgment matrix passes the consistency check, the indicator weights are calculated according to the second judgment matrix that passes the consistency check, thereby realizing data value assessment and improving the reliability of railway data value assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is one of the flow charts of the railway data value assessment method based on the hierarchical analysis method provided by the present invention.

[0019] Figure 2 It is a dimensional schematic diagram of the railway data value assessment system provided by the present invention.

[0020] Figure 3 This is the second flow chart of the railway data value assessment method based on the hierarchical analysis method provided by the present invention.

[0021] Figure 4 This is the third flow chart of the railway data value assessment method based on the hierarchical analysis method provided by the present invention.

[0022] Figure 5 It is a structural schematic diagram of a railway data value assessment device based on the hierarchical analysis method provided by the present invention.

[0023] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first" and "second" are generally a class, and the number of objects is not limited. For example, the first node can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally represents that the front and back associated objects are in an "or" relationship.

[0026] Combine the following Figure 1-Figure 6 The present invention describes a railway data value assessment method and device based on hierarchical analysis method.

[0027] Figure 1 This is one of the flow charts of the railway data value assessment method based on the hierarchical analysis method provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101: Determine a railway data value evaluation system; the railway data value evaluation system includes at least one evaluation element and evaluation indicators under each evaluation element; Specifically, it should be noted that the execution subject of this embodiment is an electronic device, which is used to implement value assessment of railway data and improve the reliability of value assessment results.

[0028] First, a railway data value evaluation system may be determined, wherein the railway data value evaluation system includes at least one evaluation element and evaluation indicators under each evaluation element. Figure 2 is a dimensional schematic diagram of the railway data value assessment system provided by the present invention, such as Figure 2 As shown, the value assessment system includes the following: According to the system characteristics of the system to be evaluated, the three-level data attribute indicators under the three secondary indicators (quality factors, cost factors, and application factors) are determined. The secondary indicator quality factors are subdivided into accuracy, consistency, completeness, standardization, timeliness, and accessibility; the secondary indicator cost factors are subdivided into initial expenses, direct costs, and indirect costs; the secondary indicator application factors are subdivided into applicable scope, application scenarios, and application risks. The definition and specific calculation method of each third-level data attribute indicator are determined to form a data value evaluation indicator system.

[0029] Step 102: for any evaluation factor, a first judgment matrix corresponding to the evaluation factor is constructed according to the initial scores corresponding to each evaluation indicator under the evaluation factor; the first judgment matrix includes a plurality of first elements, each first element being used to represent the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; Specifically, based on the constructed value assessment system, the indicator weights of each indicator are further calculated to facilitate the subsequent calculation of the final data value in combination with the indicator scores.

[0030] For any secondary indicator (quality factor, cost factor, application factor), construct the first judgment matrix corresponding to the evaluation factor.

[0031] For example, the Delphi method is first used to perform an initial weighted score on the relative importance of the three-level indicators under the quality factor, cost factor and application factor. Invite multiple experts to score the relative importance of the three-level indicators of the data assets to be valued based on the data value indicator evaluation system, with a score range of 0 to 10 points. The higher the expert's score, the higher the importance of the indicator dimension of the asset to be valued.

[0032] Furthermore, a first judgment matrix can be constructed according to the initial weight scores of the indicators, wherein the first judgment matrix includes multiple first elements, each first element being used to characterize the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located.

[0033] Furthermore, the first judgment matrix can be checked for consistency based on the hierarchical analysis method. The purpose of checking the consistency of the first judgment matrix is ​​as follows: 1. Ensure the logical consistency of judgments: prevent logical contradictions, such as A is more important than B, B is more important than C, but C is considered more important than A.

[0034] 2. Improve the scientificity and rationality of decision-making: By checking the consistency of the judgment matrix, ensure that the calculated weights are reliable and avoid decision-making errors caused by the inconsistency of the judgment matrix.

[0035] 3. Evaluate the rationality of expert scoring: In AHP, the determination of weights often relies on the subjective judgment of experts, and consistency testing can evaluate whether these judgments are reasonable.

[0036] Step 103: When it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix.

[0037] Specifically, a consistency check may be performed on the first judgment matrix. It is understandable that the consistency check result may be completely consistent or not completely consistent.

[0038] In the present invention, when it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, the first judgment matrix can be adjusted according to the maximum eigenvalue of the first judgment matrix, and the second judgment matrix can be constructed according to the adjusted elements. That is, the corresponding importance score is modified. For example, the first element with the greatest impact on the inconsistency is first found, and then the first element with the greatest impact on the inconsistency is adjusted. After that, the result can be fed back to the expert, and the expert makes appropriate adjustments to the judgment matrix, thereby fundamentally solving the inconsistency problem of the judgment matrix and improving the consistency of the first judgment matrix A.

[0039] Step 104: when it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, determine the indicator weight corresponding to each evaluation indicator under the evaluation factor according to each second element in the second judgment matrix; Specifically, after the adjusted second judgment matrix passes the consistency check, the indicator weight corresponding to each evaluation indicator under the evaluation factor is further determined according to each second element in the second judgment matrix. For example, the weight of each factor is calculated using the eigenvalue method.

[0040] Step 105: Calculate the data value corresponding to the railway data in the system to be evaluated according to the indicator weight corresponding to each evaluation indicator under each evaluation factor and the score corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated.

[0041] Specifically, after calculating the indicator weight corresponding to each evaluation indicator, the data value corresponding to the railway data in the railway system to be evaluated can be further calculated by combining the scores corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated.

[0042] It should be noted that, based on the comprehensive evaluation method, it is also necessary to confirm the weights of the three secondary indicators in this step. The method of determining the weights is the same as above, and the final value of the system is calculated based on the scoring results and weights.

[0043] The method provided in this embodiment first determines a railway data value evaluation system, wherein the railway data value evaluation system includes at least one evaluation element and evaluation indicators under each evaluation element; then, for any evaluation element, according to the initial score corresponding to each evaluation indicator under the evaluation element, a first judgment matrix corresponding to the evaluation element is constructed, wherein the first judgment matrix includes multiple first elements, and each first element is used to characterize the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; when it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; when it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, the indicator weight corresponding to each evaluation indicator under the evaluation element is determined according to each second element in the second judgment matrix; and then, according to the indicator weight corresponding to each evaluation indicator under each evaluation element and the score corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

[0044] The present invention first establishes a data value assessment system and provides a relatively comprehensive data element value research framework. In addition, a first judgment matrix is ​​first constructed according to the initial scores corresponding to each evaluation indicator under the evaluation element, that is, the initial weights of the indicators. Then, the consistency judgment process is improved to quickly discover inconsistencies in expert judgments, and the matrix is ​​subjected to sensitivity analysis. When it is determined that the consistency check of the first judgment matrix fails, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix. When the second judgment matrix passes the consistency check, the indicator weights are calculated according to the second judgment matrix that passes the consistency check, thereby realizing data value assessment and improving the reliability of railway data value assessment.

[0045] According to a railway data value assessment method based on hierarchical analysis method provided by the present invention, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix, including: Determine the partial derivative values ​​corresponding to each first element in the first judgment matrix according to the maximum eigenvalue of the first judgment matrix; Determine, according to the partial derivative values ​​corresponding to the first elements, a third element affecting the consistency of the first judgment matrix; According to the partial derivative value corresponding to the third element, the third element is adjusted to obtain an adjusted third element; A second judgment matrix is ​​obtained according to the adjusted third element and the first judgment matrix.

[0046] Specifically, in some embodiments, the specific implementation process of step 102 of adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain the second judgment matrix is ​​implemented by the following steps: According to the maximum eigenvalue of the first judgment matrix, the partial derivative values ​​corresponding to the first elements in the first judgment matrix are determined.

[0047] Among them, calculating the maximum eigenvalue of the judgment matrix usually involves the following steps: 1. Obtain the first judgment matrix: The first judgment matrix A is a square matrix that represents the relative importance of each evaluation index. If the first judgment matrix is ​​of order n, it has n rows and n columns.

[0048] 2. Eigenvalue calculation: The eigenvalue is obtained by solving the characteristic equation, which is obtained by transforming the judgment matrix A as follows: det(A−λI)=0

[0049] Where det represents the determinant, λ represents the eigenvalue, and I is the unit matrix of the same order as A.

[0050] 3. Solve the characteristic equation: Solving the above equation will give all the eigenvalues ​​of the matrix A. Since the matrix A is a positive reciprocal matrix, its largest eigenvalue should be close to the matrix order n.

[0051] 4. Determine the maximum eigenvalue: From the solved eigenvalues, select the largest eigenvalue, denoted as λ max , which is the maximum eigenvalue we need.

[0052] It should be noted that when the first judgment matrix A is completely consistent, λ max= n; and when A cannot be completely consistent, λ max >n. So improving consistency is to make λ max Minimize as much as possible.

[0053] Furthermore, according to the maximum eigenvalue of the first judgment matrix, the partial derivative values ​​corresponding to the first elements in the first judgment matrix are determined, and according to the partial derivative values ​​corresponding to the first elements, the third element affecting the consistency of the first judgment matrix is ​​determined. According to the partial derivative value corresponding to the third element, the third element is adjusted to obtain the adjusted third element.

[0054] Exemplarily, the process of finding the third element that affects the consistency of the first judgment matrix and adjusting the third element is as follows: If you remember , and Respectively represent the first judgment matrix A and the transposed matrix A of the first judgment matrix TIt is not difficult to verify that the eigenvectors corresponding to the main eigenvalues ​​of have the same eigenvalues. Then:

[0055] A: The first judgment matrix, that is, the initial judgment matrix provided by the experts; : The maximum eigenvalue of the first judgment matrix A; a ij : The element in the i-th row and j-th column of the original judgment matrix; :Calculation of partial derivatives; n: the number of rows and columns of the matrix; u: eigenvector corresponding to the principal eigenvalue of the first judgment matrix A; A T : First, determine the transposed matrix of matrix A, whose row and column indexes are exactly the reverse of the column and row indexes of the original matrix A; v: Matrix A T The eigenvector corresponding to the main eigenvalue of ; u i : The elements contained in the eigenvector corresponding to the main eigenvalue of the first judgment matrix A; v i : Matrix A T The elements contained in the eigenvector corresponding to the main eigenvalue of .

[0056] There are two purposes for doing this: First, to find a small number of elements that have a greater impact on consistency so that the number of elements to be corrected is as small as possible. The bigger, the The greater the impact on consistency, the more experts are recommended to correct such elements; second, to obtain information about elements that are too large or too small, when <0, in order to reduce λ max The value of a needs to be increased ij Otherwise, adjust it downward to guide experts to make appropriate adjustments to the matrix.

[0057] Furthermore, the second judgment matrix can be obtained according to the adjusted third element and the first judgment matrix, that is, a modified judgment matrix can be obtained, so as to facilitate the subsequent calculation of indicator weights based on the second judgment matrix.

[0058] The method provided in this embodiment first determines the partial derivative corresponding to each first element in the first judgment matrix according to the maximum eigenvalue of the first judgment matrix; then, according to the partial derivative corresponding to each first element, determines the third element that affects the consistency of the first judgment matrix; then, according to the partial derivative corresponding to the third element, adjusts the third element to obtain the adjusted third element, and obtains the second judgment matrix according to the adjusted third element and the first judgment matrix. In the present invention, the inconsistency in the initial weight score of the expert judgment can be quickly discovered, and then the sensitivity analysis of the first judgment matrix is ​​performed, and the results are fed back to the expert, and the expert makes appropriate adjustments to the judgment matrix to obtain the second judgment matrix, which can fundamentally solve the inconsistency problem of the judgment matrix.

[0059] According to a railway data value evaluation method based on hierarchical analysis method provided by the present invention, according to each second element in the second judgment matrix, the indicator weight corresponding to each evaluation indicator under the evaluation factor is determined, including: For any evaluation index in the second judgment matrix, sum the second elements on the column where the evaluation index is located to obtain a column sum; Determine a normalized second judgment matrix according to each first element and the column sum; According to each fourth element in the normalized second judgment matrix, the indicator weight corresponding to each evaluation indicator is determined.

[0060] Specifically, in some embodiments, the specific implementation process of step 103 of determining the indicator weight corresponding to each evaluation indicator under the evaluation factor according to each second element in the second judgment matrix includes the following steps: First, for any evaluation indicator in the second judgment matrix, the second elements on the column where the evaluation indicator is located are summed to obtain the column sum; then, the normalized second judgment matrix is ​​determined according to the first elements and the column sum; further, the indicator weight corresponding to each evaluation indicator is determined according to the fourth elements in the normalized second judgment matrix.

[0061] Exemplarily, the process of calculating the indicator weight corresponding to each evaluation indicator is as follows: (a) Calculate the column sum: Add the elements of each column of the judgment matrix to obtain the column sum.

[0062]

[0063] (b) Normalized judgment matrix: Divide each element in the judgment matrix by the column sum of the column in which the element is located to obtain the normalized judgment matrix.

[0064]

[0065] (c) Calculate the weight vector: average each row to obtain the indicator weight vector.

[0066]

[0067] Among them: a ij is the element in the i-th row and j-th column of the original judgment matrix; is the sum of the elements in the jth column; is the element in the i-th row and j-th column of the normalized judgment matrix; is the jth element in the weight vector.

[0068] The method provided in this embodiment sums the second elements on the column where the evaluation indicator is located according to any evaluation indicator in the second judgment matrix that passes the consistency check to obtain the column sum; then, the normalized second judgment matrix is ​​determined according to each first element and the column sum; further, the indicator weight corresponding to each evaluation indicator is determined according to each fourth element in the normalized second judgment matrix. The second judgment matrix in the present invention is a judgment matrix determined based on the improved hierarchical analysis method, and the accuracy of determining the indicator weight corresponding to each evaluation indicator based on the second judgment matrix is ​​relatively high.

[0069] According to a railway data value evaluation method based on hierarchical analysis method provided by the present invention, according to the indicator weight corresponding to each evaluation indicator under each evaluation factor and the score corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated, including: Calculate the value adjustment coefficient based on the indicator weight corresponding to each evaluation indicator under each evaluation factor and the score corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated; Determine the replacement cost of the railway data in the railway system to be evaluated based on the research and development cost, operation and maintenance cost, and replacement cost composition ratio of the railway data in the railway system to be evaluated; Add one to the value adjustment coefficient to obtain the added result, and multiply the added result by the replacement cost to obtain the data value corresponding to the railway data in the system to be evaluated.

[0070] Specifically, in some embodiments, the specific implementation process of calculating the data value corresponding to the railway data in the system to be evaluated in step 105 includes the following steps: First, the value adjustment coefficient is calculated based on the indicator weights corresponding to each evaluation indicator under each evaluation factor and the scores corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated. Among them, the value adjustment coefficient can be regarded as a coefficient for correcting the value of basic data such as replacement cost. Based on the value adjustment coefficient, the final data value calculation can be more accurate and reliable.

[0071] For example, the value adjustment coefficient U is calculated using the following formula:

[0072] Among them, U represents the value adjustment coefficient, , , They represent the weighting coefficients of the evaluation factors, quality factors, cost factors, and application factors, that is, the weight vector, , , They respectively represent the indicator scores of the quality factor, cost factor and application factor of the evaluation elements in the railway data of the system to be evaluated.

[0073] Furthermore, the replacement cost corresponding to the railway data in the railway system to be evaluated can be determined based on the R&D cost, operation and maintenance cost corresponding to the railway data in the railway system to be evaluated, and the replacement cost composition ratio corresponding to the railway data in the railway system to be evaluated. Among them, the replacement cost can be understood as the initial data value corresponding to the railway data, and the replacement cost C can be converted according to the price replacement coefficient, which consists of the R&D cost and operation and maintenance cost of the data or system, and the ratio of the two is configured according to the characteristics of the data or system.

[0074] Furthermore, after calculating the replacement cost and value adjustment coefficient corresponding to the railway data, the final value of the railway data can be further calculated based on the replacement cost and the value adjustment coefficient. For example, the value adjustment coefficient is added by one to obtain an addition result, and the addition result is multiplied by the replacement cost to obtain the data value corresponding to the railway data in the system to be evaluated.

[0075] For example, the data value corresponding to the railway data in the system to be evaluated is calculated using the following formula:

[0076] in, represents the data asset value of the railway data in the system to be evaluated, C represents the replacement cost, Represents the value adjustment factor.

[0077] The replacement cost is calculated as follows: C = γ (ɑ1 C 1+ ɑ2 C2)

[0078] Among them, C1 represents R&D cost, C2 represents operation and maintenance cost, ɑ1 represents the proportion of R&D cost in total cost, ɑ2 represents the proportion of operation and maintenance cost in total cost, and the sum of ɑ1 and ɑ2 is 1. γ is the target price reset coefficient.

[0079] The method provided in this embodiment first calculates the value adjustment coefficient according to the indicator weight corresponding to each evaluation indicator under each evaluation factor and the score corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated; then, the replacement cost corresponding to the railway data in the railway system to be evaluated is determined according to the research and development cost, operation and maintenance cost corresponding to the railway data in the railway system to be evaluated, and the replacement cost composition ratio corresponding to the railway data in the railway system to be evaluated; then, the value adjustment coefficient is added by one to obtain the addition result, and the addition result is multiplied by the replacement cost to obtain the data value corresponding to the railway data in the system to be evaluated. In the present invention, the value adjustment coefficient is calculated according to the indicator score and the indicator weight, so as to calculate the maximum value of the railway data of the system to be evaluated in combination with the replacement cost, and the reliability of the final result is high.

[0080] According to a railway data value assessment method based on hierarchical analysis method provided by the present invention, when it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain the second judgment matrix, and the method further includes: Performing a consistency check on the first judgment matrix to obtain a check result; the check result is not completely consistent or completely consistent; When it is determined that the consistency check result of the first judgment matrix is ​​completely consistent, the indicator weight corresponding to each evaluation indicator under the evaluation factor is determined according to each first element in the first judgment matrix.

[0081] Specifically, in some embodiments, when it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, step 103 adjusts the first judgment matrix according to the maximum eigenvalue of the first judgment matrix, and also includes performing a consistency check on the first judgment matrix before obtaining the second judgment matrix.

[0082] First, a consistency check is performed on the first judgment matrix to obtain a check result, wherein the check result is not completely consistent or completely consistent.

[0083] The purpose of consistency check of judgment matrix is ​​to ensure the logical consistency of judgment matrix constructed in Analytic Hierarchy Process (AHP). AHP decomposes complex decision-making problems into multiple factors and sub-factors by establishing a hierarchical model, and determines the relative importance (weight) of each factor by comparing these factors one by one. In AHP, consistency check is a very important step. This process mainly determines whether the matrix meets the consistency requirements by calculating the consistency ratio (CR) of the consistency index. Specifically, if the judgment matrix is ​​inconsistent, it may lead to deviation in the decision result. Therefore, the matrix needs to be adjusted to improve its consistency, so as to ensure the accuracy and reliability of the calculation results.

[0084] Exemplarily, the step of judging the consistency of the matrix includes, for example: 1. Calculate the consistency index CI (Consistency Index).

[0085] 2. Find the corresponding average random consistency index RI (Random Index).

[0086] 3. Calculate the consistency ratio CR and determine whether it is less than 0.1. If so, the consistency is considered acceptable; otherwise, the judgment matrix needs to be modified.

[0087] Through the consistency check process, possible logical errors in the judgment matrix can be identified to ensure the scientificity and rationality of the decision-making process.

[0088] Further, when it is determined that the consistency check result of the first judgment matrix is ​​completely consistent, the indicator weight corresponding to each evaluation indicator under the evaluation factor can be determined according to each first element in the first judgment matrix. It should be noted that the process of calculating the indicator weight corresponding to each evaluation indicator under the evaluation factor by the first judgment matrix is ​​similar to the process of determining the indicator weight corresponding to each evaluation indicator under the evaluation factor according to the second judgment matrix, which will not be repeated here.

[0089] The method provided in this embodiment first performs a consistency check on the first judgment matrix to obtain a check result, wherein the check result is not completely consistent or completely consistent. When it is determined that the consistency check result of the first judgment matrix is ​​completely consistent, the indicator weight corresponding to each evaluation indicator under the evaluation factor is determined according to each first element in the first judgment matrix. The indicator weight calculated based on this is more accurate, which is convenient for improving the reliability of subsequent calculation of data value.

[0090] According to a railway data value assessment method based on hierarchical analysis method provided by the present invention, a consistency check is performed on the first judgment matrix to obtain a check result, including: Sort the first elements of each row in the first judgment matrix from small to large to obtain the sorting results of each row; If the sorting result of the first elements of any row in the first judgment matrix is ​​inconsistent with the sorting result of the first elements of other rows except the row in the first judgment matrix, the verification result is determined to be not completely consistent; If the sorting result of the first elements of any row in the first judgment matrix is ​​consistent with the sorting results of the first elements of other rows except the row in the first judgment matrix, the verification result is determined to be completely consistent.

[0091] Specifically, the implementation process of performing consistency check on the first judgment matrix in this embodiment includes the following steps: First, the first elements of each row in the first judgment matrix are sorted from small to large to obtain the sorting results of each row. That is, the first judgment matrix A is first subjected to a qualitative consistency check, that is, the first elements of each row of A are sorted from small to large, that is, the weights determined for the row are in descending order, and then the sorting results of each row are compared.

[0092] Further, it is determined whether the sorting result of the first elements of any row in the first judgment matrix is ​​inconsistent with the sorting result of the first elements of other rows in the first judgment matrix except the row. ik >a ij with a lk lj (i l, k j) Whether they appear at the same time. If they appear at the same time, it is considered that indicator X k With indicator X j The important relationship between them cannot be determined, which is one of the fundamental reasons for the inconsistency of calculation results.

[0093] Based on the above judgment results, the following consistency check results are determined: If the sorting result of the first elements of any row in the first judgment matrix is ​​inconsistent with the sorting result of the first elements of other rows in the first judgment matrix except row . ik >a ij with a lk lj (i l, k j) If both of them occur at the same time, the verification result is determined to be not completely consistent; ​​If the sorting result of the first elements of any row in the first judgment matrix is ​​consistent with the sorting result of the first elements of other rows in the first judgment matrix except row . ik >a ij with a lk lj (i l, k j) If they do not appear at the same time, it is considered that matrix A can basically pass the qualitative test, and the verification result is determined to be completely consistent. After that, the weight value of each indicator can be directly calculated using the traditional method.

[0094] The consistency verification method provided in this embodiment firstly sorts the first elements of each row in the first judgment matrix from small to large to obtain the sorting results of each row; if the sorting results of the first elements of any row in the first judgment matrix are inconsistent with the sorting results of the first elements of other rows except the row in the first judgment matrix, the verification result is determined to be incompletely consistent; if the sorting results of the first elements of any row in the first judgment matrix are consistent with the sorting results of the first elements of other rows except the row in the first judgment matrix, the verification result is determined to be completely consistent.

[0095] Compared with the traditional consistency check method (the traditional hierarchical analysis method performs consistency check on the judgment matrix to ensure , this test has great inaccuracy), the improvement is that the inconsistency in expert judgment can be quickly discovered, and then the sensitivity analysis of the matrix can be performed, and the results can be fed back to the experts, who will make appropriate adjustments to the judgment matrix, thereby fundamentally solving the inconsistency problem of the judgment matrix.

[0096] For example, Figure 3 This is the second flow chart of the railway data value assessment method based on the hierarchical analysis method provided by the present invention, such as Figure 3 As shown, the method includes: Step 301: Obtain the initial judgment matrix A of the expert.

[0097] Step 302: Determine a ik >a ij with a lk lj Whether (i≠l, k≠j) appear at the same time.

[0098] Step 303: Calculate λ max and r ij .

[0099] Step 304: Calculate λ max R ij The partial derivative of . ​​

[0100] Step 305: Find the a with the largest absolute value of partial derivative ij .

[0101] If a ij If the absolute value of the partial derivative of is greater than 0, execute step 306; Step 306: Lower a ij The value of .

[0102] If a ij If the absolute value of the partial derivative of is less than 0, execute step 307; Step 307: Increase a ij The value of .

[0103] Step 308: Obtain a new judgment matrix A'.

[0104] Step 309: Calculate the column sum of the judgment matrix.

[0105] Step 310: Normalize the judgment matrix.

[0106] Step 311: Calculate the indicator weight vector.

[0107] Figure 4 This is the third flow chart of the railway data value assessment method based on the hierarchical analysis method provided by the present invention, such as Figure 4 As shown, the method includes: Step 401: configure the parameters of the data value assessment method.

[0108] Step 402: Determine the composition of the secondary and tertiary data attribute indicators contained in the evaluation factors.

[0109] Step 403: Use the Delphi method to score the relative importance of the secondary and tertiary indicators.

[0110] Step 404: Use the improved analytic hierarchy process to perform a score consistency test.

[0111] If the result of step 404 is yes, then step 405 is executed; If the execution result of step 404 is no, step 403 is continued after adjusting the score.

[0112] Step 405: Calculate the index weights.

[0113] Step 406: Score the indicators of the system data to be evaluated, and calculate the scores of the previous level evaluation factors based on the indicator scores and indicator weights.

[0114] Step 407: Select an evaluation method based on the characteristics of the data to be evaluated, and perform calculations after supplementing the method content to obtain the data value.

[0115] The railway data value assessment device based on the hierarchical analysis method provided by the present invention is described below. The railway data value assessment device based on the hierarchical analysis method described below and the railway data value assessment method based on the hierarchical analysis method described above can be referred to each other.

[0116] Figure 5 Schematic diagram of the structure of the railway data value assessment device based on the hierarchical analysis method provided by the present invention, such as Figure 5 As shown, the railway data value assessment device 500 based on hierarchical analysis method includes the following modules: The system construction module 510 is used to determine the railway data value evaluation system; the railway data value evaluation system includes at least one evaluation element and evaluation indicators under each evaluation element; An evaluation module 520 is used to construct a first judgment matrix corresponding to any evaluation factor according to the initial scores corresponding to each evaluation indicator under the evaluation factor; the first judgment matrix includes a plurality of first elements, each of which is used to represent the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; When it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; When it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, determining the indicator weight corresponding to each of the evaluation indicators under the evaluation factor according to each second element in the second judgment matrix; According to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

[0117] The device provided in this embodiment, first, the system construction module 510 is used to determine the railway data value evaluation system, wherein the railway data value evaluation system includes at least one evaluation element and evaluation indicators under each evaluation element; then, the evaluation module 520 is used to construct a first judgment matrix corresponding to the evaluation element according to the initial score corresponding to each evaluation indicator under the evaluation element for any evaluation element, wherein the first judgment matrix includes multiple first elements, and each first element is used to characterize the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; when it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; when it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, the indicator weight corresponding to each evaluation indicator under the evaluation element is determined according to each second element in the second judgment matrix; and then, according to the indicator weight corresponding to each evaluation indicator under each evaluation element and the score corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

[0118] The present invention first establishes a data value assessment system and provides a relatively comprehensive data element value research framework. In addition, a first judgment matrix is ​​first constructed according to the initial scores corresponding to each evaluation indicator under the evaluation element, that is, the initial weights of the indicators. Then, the consistency judgment process is improved to quickly discover inconsistencies in expert judgments, and the matrix is ​​subjected to sensitivity analysis. When it is determined that the consistency check of the first judgment matrix fails, the first judgment matrix is ​​adjusted according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix. When the second judgment matrix passes the consistency check, the indicator weights are calculated according to the second judgment matrix that passes the consistency check, thereby realizing data value assessment and improving the reliability of railway data value assessment.

[0119] According to the railway data value evaluation device 500 based on the hierarchical analysis method provided by the present invention, the evaluation module 520 is specifically used for: Determine, according to the maximum eigenvalue of the first judgment matrix, the partial derivative value corresponding to each of the first elements in the first judgment matrix; Determining a third element affecting the consistency of the first judgment matrix according to the partial derivative values ​​corresponding to each of the first elements; According to the partial derivative value corresponding to the third element, the third element is adjusted to obtain an adjusted third element; A second judgment matrix is ​​obtained according to the adjusted third element and the first judgment matrix.

[0120] According to the railway data value assessment device 500 based on the hierarchical analysis method provided by the present invention, the assessment module 520 is further used for: For any of the evaluation indicators in the second judgment matrix, sum the second elements on the column where the evaluation indicator is located to obtain a column sum; Determine a normalized second judgment matrix according to each of the first elements and the column sum; According to each fourth element in the normalized second judgment matrix, the indicator weight corresponding to each evaluation indicator is determined.

[0121] According to the railway data value assessment device 500 based on the hierarchical analysis method provided by the present invention, the assessment module 520 is further used for: Calculating a value adjustment coefficient according to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated; Determine the replacement cost corresponding to the railway data in the railway system to be evaluated according to the research and development cost and operation and maintenance cost corresponding to the railway data in the railway system to be evaluated and the replacement cost composition ratio corresponding to the railway data in the railway system to be evaluated; The value adjustment coefficient is added by one to obtain an addition result, and the addition result is multiplied by the replacement cost to obtain the data value corresponding to the railway data in the system to be evaluated.

[0122] According to the railway data value assessment device 500 based on the hierarchical analysis method provided by the present invention, the assessment module 520 is further used for: Performing a consistency check on the first judgment matrix to obtain a check result; the check result is not completely consistent or completely consistent; When it is determined that the consistency check result of the first judgment matrix is ​​completely consistent, the indicator weight corresponding to each of the evaluation indicators under the evaluation factor is determined according to each of the first elements in the first judgment matrix.

[0123] According to the railway data value assessment device 500 based on the hierarchical analysis method provided by the present invention, the assessment module 520 is further used for: Sort the first elements of each row in the first judgment matrix from small to large to obtain a sorting result of each row; If there is a situation where the sorting result of the first elements of any of the rows in the first judgment matrix is ​​inconsistent with the sorting results of the first elements of other rows in the first judgment matrix except the row, determining the verification result as not completely consistent; If the sorting result of the first elements of any of the rows in the first judgment matrix is ​​consistent with the sorting results of the first elements of other rows in the first judgment matrix except the row, the verification result is determined to be completely consistent.

[0124] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communications interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the railway data value assessment method based on the hierarchical analysis method, and the method includes: Determine a railway data value assessment system; the railway data value assessment system includes at least one assessment element and an assessment indicator under each assessment element; For any evaluation factor, a first judgment matrix corresponding to the evaluation factor is constructed according to the initial scores corresponding to each evaluation indicator under the evaluation factor; the first judgment matrix includes a plurality of first elements, each of which is used to represent the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; When it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; When it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, determining the indicator weight corresponding to each of the evaluation indicators under the evaluation factor according to each second element in the second judgment matrix; According to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

[0125] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0126] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the railway data value assessment method based on the hierarchical analysis method provided by the above methods, the method includes: Determine a railway data value assessment system; the railway data value assessment system includes at least one assessment element and an assessment indicator under each assessment element; For any evaluation factor, a first judgment matrix corresponding to the evaluation factor is constructed according to the initial scores corresponding to each evaluation indicator under the evaluation factor; the first judgment matrix includes a plurality of first elements, each of which is used to represent the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; When it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; When it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, determining the indicator weight corresponding to each of the evaluation indicators under the evaluation factor according to each second element in the second judgment matrix; According to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

[0127] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the railway data value assessment method based on the hierarchical analysis method provided by the above methods, the method comprising: Determine a railway data value assessment system; the railway data value assessment system includes at least one assessment element and an assessment indicator under each assessment element; For any evaluation factor, a first judgment matrix corresponding to the evaluation factor is constructed according to the initial scores corresponding to each evaluation indicator under the evaluation factor; the first judgment matrix includes a plurality of first elements, each of which is used to represent the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; When it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; When it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, determining the indicator weight corresponding to each of the evaluation indicators under the evaluation factor according to each second element in the second judgment matrix; According to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

[0128] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0129] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A railway data value assessment method based on hierarchical analysis method, characterized in that: include: Determine the railway data value assessment system; The railway data value assessment system includes at least one assessment element and assessment indicators under each assessment element; For any evaluation factor, a first judgment matrix corresponding to the evaluation factor is constructed according to the initial scores corresponding to each evaluation indicator under the evaluation factor; the first judgment matrix includes a plurality of first elements, each of which is used to represent the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; When it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; When it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, determining the indicator weight corresponding to each of the evaluation indicators under the evaluation factor according to each second element in the second judgment matrix; According to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

2. The railway data value assessment method based on hierarchical analysis method according to claim 1 is characterized in that: The step of adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix includes: Determine, according to the maximum eigenvalue of the first judgment matrix, the partial derivative value corresponding to each of the first elements in the first judgment matrix; Determining a third element affecting the consistency of the first judgment matrix according to the partial derivative values ​​corresponding to each of the first elements; According to the partial derivative value corresponding to the third element, the third element is adjusted to obtain an adjusted third element; A second judgment matrix is ​​obtained according to the adjusted third element and the first judgment matrix.

3. The railway data value assessment method based on hierarchical analysis method according to claim 1 is characterized in that: The step of determining the indicator weight corresponding to each evaluation indicator under the evaluation factor according to each second element in the second judgment matrix includes: For any of the evaluation indicators in the second judgment matrix, sum the second elements on the column where the evaluation indicator is located to obtain a column sum; Determine a normalized second judgment matrix according to each of the first elements and the column sum; According to each fourth element in the normalized second judgment matrix, the indicator weight corresponding to each evaluation indicator is determined.

4. The railway data value assessment method based on hierarchical analysis method according to claim 1 is characterized in that: The calculating the data value corresponding to the railway data in the system to be evaluated according to the indicator weight corresponding to each evaluation indicator under each evaluation factor and the score corresponding to each evaluation indicator corresponding to the railway data in the railway system to be evaluated comprises: Calculating a value adjustment coefficient according to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated; Determine the replacement cost corresponding to the railway data in the railway system to be evaluated according to the research and development cost and operation and maintenance cost corresponding to the railway data in the railway system to be evaluated and the replacement cost composition ratio corresponding to the railway data in the railway system to be evaluated; The value adjustment coefficient is added by one to obtain an addition result, and the addition result is multiplied by the replacement cost to obtain the data value corresponding to the railway data in the system to be evaluated.

5. The railway data value assessment method based on hierarchical analysis method according to claim 1 is characterized in that: When it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, before adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain the second judgment matrix, the method further includes: Performing a consistency check on the first judgment matrix to obtain a check result; the check result includes incomplete consistency or complete consistency; When it is determined that the consistency check result of the first judgment matrix is ​​completely consistent, the indicator weight corresponding to each of the evaluation indicators under the evaluation factor is determined according to each of the first elements in the first judgment matrix.

6. The railway data value assessment method based on hierarchical analysis method according to claim 5 is characterized in that: The performing consistency check on the first judgment matrix to obtain a check result includes: Sort the first elements of each row in the first judgment matrix from small to large to obtain a sorting result of each row; If there is a situation where the sorting result of the first elements of any of the rows in the first judgment matrix is ​​inconsistent with the sorting results of the first elements of other rows in the first judgment matrix except the row, determining the verification result as not completely consistent; If the sorting result of the first elements of any of the rows in the first judgment matrix is ​​consistent with the sorting results of the first elements of other rows in the first judgment matrix except the row, the verification result is determined to be completely consistent.

7. A railway data value assessment device based on hierarchical analysis method, characterized in that: include: System building module, used to determine the railway data value assessment system; The railway data value assessment system includes at least one assessment element and assessment indicators under each assessment element; An evaluation module, for constructing, for any evaluation factor, a first judgment matrix corresponding to the evaluation factor according to the initial scores corresponding to each evaluation indicator under the evaluation factor; the first judgment matrix includes a plurality of first elements, each of which is used to represent the relative importance between the evaluation indicator corresponding to the column where the first element is located and the evaluation indicator corresponding to the row where the first element is located; When it is determined that the consistency check result of the first judgment matrix is ​​not completely consistent, adjusting the first judgment matrix according to the maximum eigenvalue of the first judgment matrix to obtain a second judgment matrix; When it is determined that the consistency check result of the second judgment matrix is ​​completely consistent, determining the indicator weight corresponding to each of the evaluation indicators under the evaluation factor according to each second element in the second judgment matrix; According to the indicator weight corresponding to each of the evaluation indicators under each of the evaluation factors and the score corresponding to each of the evaluation indicators corresponding to the railway data in the railway system to be evaluated, the data value corresponding to the railway data in the system to be evaluated is calculated.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the railway data value assessment method based on the hierarchical analysis method as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the railway data value assessment method based on the hierarchical analysis method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the railway data value assessment method based on the hierarchical analysis method as described in any one of claims 1 to 6 is implemented.

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