Public data asset value evaluation method

Abstract

The invention discloses a public data asset value evaluation method. The method comprises the following steps: step 1, calculating the cost TC of a public data asset to be evaluated; 2, constructing a public data capitalization index evaluation system; step 3, according to the public data capitalization index evaluation system, establishing an influence factor set, constructing a fuzzy evaluation matrix, and calculating to obtain a dynamic adjustment coefficient; and step 4, calculating an evaluation result of the public data asset value to be evaluated according to the data asset cost expenditure in the step 1 and the dynamic adjustment coefficient in the step 3. The method provided by the invention is established based on a value compensation principle, has few uncertain parameters, can adapt to public data asset value evaluation requirements of a multi-element scene, and is not limited to a specific scene in the vertical field.

Description

Technical Field

[0001] The invention relates to an asset evaluation method, a public data asset value evaluation method. Background Art

[0002] This section merely provides background information related to the present disclosure and is not necessarily prior art.

[0003] In promoting the construction of smart cities, it is necessary to process massive amounts of public data. The collection, storage, and processing of massive amounts of data have brought burdens. How to truly give full play to the role of data as a production factor and promote data assetization and authorized operation has become the focus of the current industry. In the process of data assetization, data asset value assessment plays an indispensable role in the deep utilization, efficient circulation, and fair trading of data, but at the same time it faces bottlenecks such as unclear concept definition, vague value formation mechanism, and limited applicability of assessment models. Therefore, how to clearly define data assets and their ownership, clarify the value formation mechanism of data assets, how to optimize the data asset value assessment model, and improve the data asset value assessment system have become the research focus in the field of data asset theory research.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] Purpose of the invention: The technical problem to be solved by the present invention is to provide a method for evaluating the value of public data assets in view of the shortcomings of the prior art.

[0006] In order to solve the above technical problems, the present invention discloses a method for evaluating the value of public data assets, comprising the following steps:

[0007] Step 1: Calculate the cost expenditure TC of the public data assets to be evaluated;

[0008] Step 2: Build a public data assetization indicator evaluation system;

[0009] Step 3: According to the public data assetization index evaluation system, establish a set of influencing factors, construct a fuzzy evaluation matrix, and calculate the dynamic adjustment coefficient;

[0010] Step 4: Calculate the evaluation result of the public data asset value to be evaluated based on the data asset cost expenditure in step 1 and the dynamic adjustment coefficient in step 3.

[0011] Furthermore, the specific method for calculating the cost expenditure TC of the public data asset to be evaluated in step 1 is as follows:

[0012]

[0013] Among them, TC k Represents the cost expenditure of the kth type, and there are m types.

[0014] Furthermore, the public data assetization indicator evaluation system described in step 2 includes:

[0015] Step 2-1, construct an evaluation index system for public data assets, which specifically includes: primary index, secondary index and tertiary index. The above indexes are basically constructed into a tree structure;

[0016] Step 2-2, construct a judgment matrix based on the above evaluation index system;

[0017] Step 2-3, solve the eigenvector of the judgment matrix as the indicator weight.

[0018] Furthermore, the public data assetization indicator evaluation system described in step 2 specifically includes:

[0019] The first-level indicators include: data governance capabilities, supporting safeguards and data service quality; among them,

[0020] The data governance capability includes the following secondary indicators: infrastructure and data resources; the infrastructure includes the following tertiary indicators: computing power and storage and network communication; the data resources include the following tertiary indicators: standardization, accuracy, consistency, timeliness, accessibility and metadata management;

[0021] The supporting safeguard measures include the following secondary indicators: policies and regulations, security and data standards. The policies and regulations include the following third-level indicators: data registration and confirmation system, data evaluation and pricing system and data asset income distribution system; the security includes the following third-level indicators: confidentiality, integrity and availability; the data standards include the following third-level indicators: uniformity of data terminology and the intensity of standard implementation;

[0022] The data service quality includes the following secondary indicators: data circulation and data application. The data circulation includes the following tertiary indicators: transaction circulation heat; the data application includes the following tertiary indicators: application innovation, application effectiveness, response speed and user satisfaction.

[0023] Furthermore, the construction of the judgment matrix described in step 2-2 includes:

[0024] Using the Saaty scaling method, the importance of each third-level indicator in the evaluation index system constructed in step 2-1 is compared pairwise to obtain the judgment matrix A.

[0025] Furthermore, the judgment matrix A described in step 2-2 is expressed as follows:

[0026]

[0027] Among them, a i , j It represents the scores of the i-th third-level indicator and the j-th third-level indicator according to the Saaty scaling method, and n is the number of third-level indicators.

[0028] Furthermore, the step 2-3 of solving the characteristic vector for the judgment matrix includes:

[0029] Step 2-3-1, normalize the judgment matrix A by column to obtain a matrix A′, where each element in the matrix A′ is expressed as:

[0030]

[0031] Among them, a i ' j represents the element in the i-th row and j-th column of the matrix A′, a ij represents the element in the i-th row and j-th column of the judgment matrix A, and n is the number of three-level indicators;

[0032] Step 2-3-2, first add the matrix A′ by rows, and then normalize by columns to get the eigenvector W:

[0033] W=[w1,w2,…,w n ] T

[0034] Among them, w n It represents the nth element in the feature vector W, that is, the weight of the nth third-level indicator.

[0035] Furthermore, the calculation of the dynamic adjustment coefficient described in step 3 includes:

[0036] Step 3-1, define the evaluation level, and divide the scoring result scores described in step 2-2 into five levels, namely S1, S2, S3, S4 and S5;

[0037] Step 3-2, establish the influencing factor set B, as follows:

[0038] B={B1,B2,…,B l}

[0039] Among them, B l Represents the indicator under the lth first-level indicator;

[0040] Step 3-3, score and evaluate the influencing factor set B, construct the fuzzy evaluation matrix F, and perform matrix multiplication calculation on it and the eigenvector W, as follows:

[0041] W′=F×W=[w1′,w2′,...,w n ′] T

[0042] Among them, w n ′ is the nth element in the intermediate result matrix W′;

[0043] Step 3-4, calculate the sum of all element values ​​in the intermediate result matrix W′, denoted as W score , calculate the dynamic adjustment coefficient k, as follows:

[0044]

[0045] Among them, P1, P2, P3, P4 and P5 represent the corresponding probability of occurrence of S1, S2, S3, S4 and S5 respectively.

[0046] Furthermore, the fuzzy evaluation matrix F described in step 3-3 is expressed as follows:

[0047] F=f1,f2,…,f n

[0048] Among them, f n It represents the score of the nth third-level indicator, and its value range is {S1, S2, S3, S4, S5}.

[0049] Furthermore, the evaluation result of calculating the value of the public data asset in step 4 includes:

[0050] P=TC×(1+k)

[0051] Wherein, P is the evaluation result.

[0052] Beneficial effects:

[0053] The method proposed in the present invention is built on the principle of value compensation, has fewer uncertainty parameters, and can adapt to the public data asset value assessment needs in multiple scenarios, and is not limited to specific scenarios in vertical fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0055] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0056] This paper optimizes the cost method based on the value compensation principle and proposes a method for evaluating the value of public data assets by combining the characteristics of data elements. Figure 1 As shown, the following steps are included:

[0057] Step 1: Calculate the data asset cost expenditure TC, including development cost TC1, operation and maintenance cost TC2, management cost TC3, etc., namely:

[0058]

[0059] Step 2: Construct a public data assetization index evaluation system table, establish a weight quantification model based on the scaling method, and calculate the weight of the influencing factors of each index; the public data assetization index evaluation system is shown in Table 1:

[0060] Table 1. Public data assetization indicator evaluation system

[0061]

[0062]

[0063] Based on the above table, a weight quantification model is established, and the scoring is based on the Saaty scaling method (reference: Bai Yu. Research on Enterprise Data Asset Value Assessment Model [D]. Shanxi University of Finance and Economics, 2023. DOI: 10.27283 / d.cnki.gsxcc.2023.000514.), as shown in Table 2. The average value is taken to establish the judgment matrix A, and the eigenvector of the matrix A is solved to obtain the weight of each factor at the level of the three-level title. The specific method of solving the indicator weight according to the judgment matrix is ​​as follows:

[0064] Table 2 Saaty scaling table

[0065]

[0066] Step 2-1: Based on the Saaty scaling method, compare the importance of the three-level indicators two by two to form a judgment matrix A, which can be expressed as:

[0067]

[0068] Step 2-1: Normalize the matrix A by column to get the matrix A′. Each element in the matrix is ​​expressed as:

[0069]

[0070] In this formula, the value of n is the number of third-level indicators.

[0071] Step 2-2: First add the matrix A′ by rows, then normalize by columns to get the eigenvector W = [w1,w2,...,w21 ] T .

[0072] Step 3: According to the public data assetization indicator evaluation system table, establish the influencing factor set, construct the fuzzy evaluation matrix, and finally calculate the dynamic adjustment coefficient. The specific steps are as follows:

[0073] Step 3-1: Define the evaluation scores. When designing the questionnaire, divide the evaluation levels into four levels: strong, relatively strong, medium, relatively weak and weak, corresponding to evaluation scores of 100, 85, 70, 55 and 40 points respectively.

[0074] Step 3-2: Establish the influencing factor set B. The main influencing factor set B of the public data asset value = {C, D, E}, where C represents the indicator set under data governance capabilities, D represents the indicator set under supporting safeguards, and E represents the indicator set under data service quality.

[0075] Step 3-3: Score and evaluate all the three-level indicators of set B, and construct the fuzzy evaluation matrix F. The elements in F are the scores of each three-level indicator, and the value range is {100, 85, 70, 55, 40}. And perform matrix multiplication with the eigenvector W, that is, W′=F×W=[w1′,w2′,...,w2′1] T

[0076] Step 3-4: Calculate the sum of all element values ​​in W′, denoted as W score , the calculation of the dynamic adjustment coefficient k is shown as follows:

[0077]

[0078] In this formula, P1, P2, P3, P4, and P5 represent the probability of occurrence of the corresponding scores respectively.

[0079] Step 4: Based on the data asset cost expenditure in step 1 and the dynamic adjustment coefficient in step 3, the public data value valuation P is calculated according to the following formula:

[0080] P=TC×(1+k)

[0081] Example:

[0082] According to the above technical solution, a specific implementation process is further described below:

[0083] Step 1: Calculate the cost expenditure TC of a data asset, including development cost TC1 = 1 million yuan, operation and maintenance cost TC2 = 500,000 yuan, management cost TC2 = 500,000 yuan, etc., that is:

[0084]

[0085] Step 2: Construct a public data assetization index evaluation system table, establish a weight quantification model based on the scaling method, and calculate the weight of the influencing factors of each index; the public data assetization index evaluation system table is shown in Table 3:

[0086] Table 3 Public data assetization indicator evaluation system

[0087]

[0088]

[0089] Based on the above table, a weight quantification model is established. The scoring is based on the Saaty scaling method. The average value is taken to establish the judgment matrix A. The eigenvector of the matrix A is solved to obtain the weight of each factor in the three-level title at that level. The specific method of solving the indicator weight according to the judgment matrix is ​​as follows:

[0090] Step 2-1: Normalize the matrix A by column to get the matrix A′. Each element in the matrix is ​​expressed as:

[0091]

[0092] In this formula, the value of n is the number of third-level indicators.

[0093] Step 2-2: Add the matrix A′ by rows and then normalize by columns to get the eigenvector

[0094] Step 3: According to the public data assetization indicator evaluation system table, establish the influencing factor set, construct the fuzzy evaluation matrix, and finally calculate the dynamic adjustment coefficient. The specific steps are as follows:

[0095] Step 3-1: Define the evaluation scores. When designing the questionnaire, divide the evaluation levels into four levels: strong, relatively strong, medium, relatively weak and weak, corresponding to evaluation scores of 100, 85, 70, 55 and 40 points respectively.

[0096] Step 3-2: Establish the influencing factor set B. The main influencing factor set B of the public data asset value = {C, D, E}, where C represents the indicator set under data governance capabilities, D represents the indicator set under supporting safeguards, and E represents the indicator set under data service quality.

[0097] Step 3-3: Score and evaluate set B, construct the fuzzy evaluation matrix F, and perform matrix multiplication on it and the eigenvector W, that is, W′=F×W=[w1′,w2′,...,w2′1] T

[0098] Step 3-4: Calculate the sum of all element values ​​in W′, denoted as W score, the calculation of the dynamic adjustment coefficient k is shown as follows:

[0099]

[0100] In this formula, P1, P2, P3, P4, and P5 represent the probability of occurrence of the corresponding scores respectively.

[0101] Step 4: Based on the data asset cost expenditure in step 1 and the dynamic adjustment coefficient in step 3, the public data value valuation P is calculated according to the following formula:

[0102] P = TC × (1 + k) = 200 × (1 + 0.245) = 2.499 million yuan

[0103] In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit, wherein the computer storage medium can store a computer program, and when the computer program is executed by the data processing unit, the invention content of a method for evaluating the value of public data assets provided by the present invention and some or all of the steps in each embodiment can be executed. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0104] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present invention can be implemented by means of computer programs and their corresponding general hardware platforms. Based on such an understanding, the technical solutions in the embodiments of the present invention can be essentially or partly contributed to the prior art in the form of computer programs, i.e., software products, which can be stored in a storage medium and include several instructions for enabling a device including a data processing unit (which can be a personal computer, a server, a single-chip microcomputer, an MCU or a network device, etc.) to execute the methods described in various embodiments of the present invention or certain parts of the embodiments.

[0105] The present invention provides a method and idea for public data asset value assessment. There are many methods and approaches to implement the technical solution. The above is only a preferred implementation of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A method for evaluating the value of public data assets, characterized in that: The following steps are involved: Step 1: Calculate the cost expenditure TC of the public data assets to be evaluated; Step 2: Build a public data assetization indicator evaluation system; Step 3: According to the public data assetization index evaluation system, establish a set of influencing factors, construct a fuzzy evaluation matrix, and calculate the dynamic adjustment coefficient; Step 4: Calculate the evaluation result of the public data asset value to be evaluated based on the data asset cost expenditure in step 1 and the dynamic adjustment coefficient in step 3.

2. A method for evaluating the value of public data assets according to claim 1, characterized in that: The specific method for calculating the cost expenditure TC of the public data assets to be evaluated as described in step 1 is as follows: Among them, TC k Represents the cost expenditure of the kth type, and there are m types.

3. A method for evaluating the value of public data assets according to claim 2, characterized in that: The public data assetization indicator evaluation system described in step 2 includes: Step 2-1, construct an evaluation index system for public data assets, which specifically includes: primary index, secondary index and tertiary index. The above indexes are basically constructed into a tree structure; Step 2-2, construct a judgment matrix based on the above evaluation index system; Step 2-3, solve the eigenvector of the judgment matrix as the indicator weight.

4. A method for evaluating the value of public data assets according to claim 3, characterized in that: The public data assetization indicator evaluation system described in step 2 specifically includes: The first-level indicators include: data governance capabilities, supporting safeguards and data service quality; among them, The data governance capability includes the following secondary indicators: infrastructure and data resources; the infrastructure includes the following tertiary indicators: computing power and storage and network communication; the data resources include the following tertiary indicators: standardization, accuracy, consistency, timeliness, accessibility and metadata management; The supporting safeguard measures include the following secondary indicators: policies and regulations, security and data standards. The policies and regulations include the following third-level indicators: data registration and confirmation system, data evaluation and pricing system and data asset income distribution system; the security includes the following third-level indicators: confidentiality, integrity and availability; the data standards include the following third-level indicators: uniformity of data terminology and the intensity of standard implementation; The data service quality includes the following secondary indicators: data circulation and data application. The data circulation includes the following tertiary indicators: transaction circulation heat; the data application includes the following tertiary indicators: application innovation, application effectiveness, response speed and user satisfaction.

5. A method for evaluating the value of public data assets according to claim 3, characterized in that: The construction of the judgment matrix described in step 2-2 includes: Using the Saaty scaling method, the importance of each third-level indicator in the evaluation index system constructed in step 2-1 is compared pairwise to obtain the judgment matrix A.

6. A method for evaluating the value of public data assets according to claim 5, characterized in that: The judgment matrix A described in step 2-2 is expressed as follows: Among them, a i , j It represents the scores of the i-th third-level indicator and the j-th third-level indicator according to the Saaty scaling method, and n is the number of third-level indicators.

7. A method for evaluating the value of public data assets according to claim 6, characterized in that: Solving the eigenvector of the judgment matrix described in step 2-3 includes: Step 2-3-1, normalize the judgment matrix A by column to obtain matrix A ′ , matrix A ′ Each element in is represented as: Among them, a i ′ j Represents the matrix A ′ The element in the i-th row and j-th column, a uj represents the element in the i-th row and j-th column of the judgment matrix A, and n is the number of three-level indicators; Step 2-3-2, for matrix A ′ First add by row, then normalize by column to get the eigenvector W: In=[in1,in2,…,in n ] T Among them, w n It represents the nth element in the feature vector W, that is, the weight of the nth third-level indicator.

8. A method for evaluating the value of public data assets according to claim 7, characterized in that: The calculation of the dynamic adjustment factor described in step 3 includes: Step 3-1, define the evaluation level, and divide the scoring result scores described in step 2-2 into five levels, namely S1, S2, S3, S4 and S5; Step 3-2, establish the influencing factor set B, as follows: B={B1,B2,…,B l } Among them, B l Represents the indicator under the lth first-level indicator; Step 3-3, score and evaluate the influencing factor set B, construct the fuzzy evaluation matrix F, and perform matrix multiplication calculation on it and the eigenvector W, as follows: IN ′ =F×W=[w1 ′ ,w2 ′ ,...,In n ′ ] T Among them, w n ′ is the intermediate result matrix W ′ The nth element in ; Step 3-4, calculate the intermediate result matrix W ′ The sum of all element values ​​in is denoted by W score , calculate the dynamic adjustment coefficient k, as follows: Among them, P1, P2, P3, P4 and P5 represent the corresponding probability of occurrence of S1, S2, S3, S4 and S5 respectively.

9. A method for evaluating the value of public data assets according to claim 8, characterized in that: The fuzzy evaluation matrix F described in step 3-3 is expressed as follows: F=f1,f2,…,f n Among them, f n It represents the score of the nth third-level indicator, and its value range is {S1, S2, S3, S4, S5}.

10. A method for evaluating the value of public data assets according to claim 9, characterized in that: The evaluation result of calculating the value of the public data asset in step 4 includes: P=TC×(1+k) Wherein, P is the evaluation result.

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