Data asset value evaluation method based on fuzzy analytic hierarchy process
Through the combination of fuzzy hierarchical analysis and the income method, the problem of inaccurate data asset value assessment in the existing technology is solved, and more accurate value assessment is achieved in different application scenarios, which improves the authenticity and rationality of the assessment.
Patent Information
- Application Number
- CN202410457846.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-04-16
- Publication Date
- 2025-03-04
AI Technical Summary
Existing data asset value evaluation methods, such as cost method, income method and market method, have problems such as insufficient accuracy, high subjectivity, and dependence on mature market environment, making it difficult to accurately evaluate the value of data assets in different application scenarios.
Fuzzy hierarchical analysis (FAHP) combined with the income method is used to evaluate the value of data assets in hierarchical terms, and the fuzzy evaluation method and hierarchical analysis method are used to determine the value of data assets in different application scenarios, and multi-level evaluation indicators and scenario factors are set to calculate the value manifestation of data assets in different application scenarios.
This method can more accurately evaluate the value of data assets in different application scenarios, considering the impact of multi-layer influence indicators and application scenarios, and the evaluation results are more realistic and reasonable, solving the problems of difficulty in evaluating and pricing in the circulation and transaction process.
Smart Images
Figure CN119558917A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing technology, and in particular to a data asset value assessment method based on fuzzy hierarchical analysis, which is applicable to data assets, value assessment, data asset evaluation indicators, fuzzy evaluation methods, fuzzy hierarchical analysis, and income method assessment. Background Art
[0002] In order to accelerate the construction of the data infrastructure system, support the exploration of diversified pricing models and price formation mechanisms that are in line with the characteristics of data elements, promote the paid use of public data for digital development according to government guidance prices, and the independent pricing of corporate and personal information data markets. All regions are actively promoting the construction of data trading venues and optimizing the trading environment. Data value assessment is a key link in the construction of the data element market and promoting the circulation of data elements. The original data asset value assessment methods mainly include cost method, income method and market method. The cost method estimates the value of data assets through historical costs or simple replacement costs. Due to the instability of the historical costs of data assets and the large differences in input costs in different application scenarios, it is difficult to determine the relevant scarcity coefficients, renewal rates and other factors. Therefore, the use of the cost method to evaluate the value of data assets is not accurate and comprehensive enough; the income method is a method of converting the expected future income from the use of data assets into cash. Using the income method to estimate the value of data assets requires predicting income-related indicators such as income amount, data asset cash flow, income period, and discount rate. However, predictions are more subjective and arbitrary, which have a greater impact on the final evaluation results; the market method determines the value of data assets based on the laws of market development and changes and similar or similar value changes in the market. The market method requires a mature and active market environment to determine the value of Kobe data assets and relevant value adjustment coefficients. Under the current market environment, the limitations of using the market method to evaluate data assets are relatively large. Summary of the invention
[0003] In view of the above problems, this paper proposes to use fuzzy hierarchical analysis (FAHP) + income method to evaluate the value of data assets in different data application scenarios. Fuzzy hierarchical analysis is a method for multi-dimensional and hierarchical evaluation of data asset value in different application scenarios based on data assets. The value of data assets in different application scenarios is determined by fuzzy evaluation method, hierarchical analysis method, scoring method and income method. Using fuzzy hierarchical analysis method, data asset evaluation indicators are divided into two layers. According to the pre-established data asset evaluation scale table, the evaluation indicators are compared with each other. According to the importance, the comparison judgment matrix of the impact of the secondary evaluation indicators of data assets on the expected income in a specific application scenario is constructed. The weight vector of the first-level evaluation indicator is calculated by the scenario factor and the secondary evaluation indicator using the hierarchical analysis method, and the expected income value of the data asset in this scenario is further calculated using the specific scoring rules and the income method. Since the indicator weights of the data asset value index of the same data asset in different application scenarios are different, the present invention uses the improved hierarchical analysis method, adds the scenario factor and the expected income method to evaluate the data asset. The invention can be used to measure the value of the same data asset in different application scenarios. According to the application scenario used by the data asset, the evaluation index that affects the value of the data asset in the scenario is deeply analyzed, and the multi-level evaluation index in the application scenario is set. By establishing a multi-level index hierarchy, the hierarchical analysis and fuzzy evaluation method are used to determine the scores of the evaluation indexes at each level in different application scenarios, and the weight vectors of the indicators at each level of the data asset are calculated. Finally, the income method is used to calculate the assessed value of the data asset. Using this method to evaluate the value of data assets is closer to scenario-based applications, comprehensively considers the analysis and calculation of various index factors that affect the value of data assets, deeply analyzes the impact of various indicators on future expected returns, and uses fuzzy hierarchical analysis + income method to calculate the expected value of data assets. The evaluation results are more realistic and the evaluation process is more reasonable, which solves the problem of difficult evaluation and pricing of data assets in the circulation and trading process.
[0004] A data asset value assessment method based on fuzzy hierarchical analysis includes the following steps: S1. Determine the hierarchical structure related to data asset value indicators The value assessment system of data assets is determined as two levels of indicators: the first-level indicators are: data quality Q, data capacity C, data application management A, and data risk R; the second-level indicators corresponding to the data capacity dimension include data scale, data type, data density, data magnitude, data structure, data legal attributes, data security, data usability, data source reliability, and data ethical constraints; S2. Construct a data asset index comparison scale The data asset indicator comparison scale table refers to the reference value of the importance of different indicators of the same level of data assets when they are compared with each other in a certain application scenario S. Taking a certain indicator of the same level as the criterion, other indicators of the same level are compared in turn, and the comparison scale a is set to express the relative importance of the i-th indicator and other indicators of the same level. The value of is 1-9 and its reciprocal; S3. Calculate the weight vector of indicators at all levels of data assets According to the relative importance of the evaluation indicators, the data asset evaluation indicator scale table is used to compare the same level of data asset indicators in a specific scenario in pairs, and a first-level indicator comparison judgment matrix is constructed. , the comparison and judgment matrix of the secondary indicators quality Q, application A, capacity C, and risk R , , , , and calculate the weight vectors of the first-level and second-level indicators; (1) Calculate the weight vector of the first-level index of the data The first step is to construct a first-level indicator comparison judgment matrix , The value of is a specific scene The importance of quality, application, capacity and risk indicators are compared by scoring, and the comparison result is the value and its reciprocal in the comparison scale table; (1) The second step is to combine specific application scenario factors , normalize and calculate the comparison judgment matrix corresponding to the first-level index (2) Calculate the normalized matrix of the column vector of the first-level index comparison judgment matrix: , (3) The fuzzy consistency vector is obtained by summing up the normalized matrix of the first-level index of data assets by row (4) have to , after normalization, the weight of the first-level indicator of data value assessment is obtained (5) The weight vector of the first-level indicator of data value is calculated as ; (2) Calculate the secondary indicator weight vectors of the quality dimension, capacity dimension, application dimension, and risk dimension of data asset value assessment The first step is to construct a comparison matrix of secondary evaluation indicators for data asset value assessment; Comparative judgment matrix based on data quality dimensions , The value of is a specific scene The importance of the secondary evaluation indicators of the quality dimension is compared by scoring. The comparison results are the values and their reciprocals in the scale table, 2, 4, 6, 8 in the scale table and their reciprocals, and the comparison between integrity i and consistency j is , then the consistency j is compared with the integrity i and the value is , which is characterized by: ; ; (i=1,2,……n,j=1,2,……m), so the data quality comparison judgment matrix for: (6) The same calculation method is used to calculate the comparison judgment matrix of the data capacity dimension. , Comparative judgment matrix of data application dimensions And the comparative judgment matrix of data risk dimensions They are: (7) (8) (9) The second step is to calculate the normalized matrix corresponding to the secondary indicators of data secondary production evaluation Combined with specific application scenario factors , using the comparison judgment matrix column normalization algorithm to calculate the weight vector of the secondary indicators of the data asset quality dimension , (10) Therefore, the secondary indicator comparison and judgment matrix of the data asset quality dimension is normalized by column as follows: , (11) Similarly, The normalized matrix of data asset application dimension is: , (12) The normalized matrix of data asset capacity dimension is: , (13) The normalized matrix of data asset risk dimension is: , (14) The third step is to sum the influencing factors of the normalized matrix of data asset quality dimension by row. ; (15) have to , After normalizing the influencing factors of the data asset quality dimension, the secondary indicator weight of the data asset quality dimension is obtained. (m=4) (16) Therefore, the weight vector of the secondary indicators in the data asset quality dimension is: (m=4) (17) Similarly, the weight vectors of the secondary indicators of data asset application dimension, data asset capacity dimension, and data asset risk dimension can be calculated. (m=4) (18) (m=4) (19) (m=4) (20) S4. Estimation of the index scores for the impact of each secondary index of data assets on expected returns For a specific application scenario, a certain number of data records to be evaluated are randomly selected, the secondary indicator scores of the evaluation data are calculated, and finally the secondary indicator evaluation vector is obtained; Therefore, the score vector of the impact of the secondary indicators of the data asset quality dimension on the expected return is: (twenty one) Similarly, the fuzzy evaluation vector of the impact of the secondary indicators of data application, capacity, and risk dimensions on expected returns is: , , ; S5. Calculate the score of the first-level index of data assets for expected returns Multiply the weight vector of the secondary indicators of data asset quality, application, capacity, and risk dimensions by the corresponding expected return impact evaluation vector to construct the evaluation score of the primary indicator of data assets on expected returns. The evaluation score of the quality dimension is: , (twenty two) Similarly, the evaluation scores of the first-level indicators of data asset application, capacity, and risk dimensions on expected returns can be calculated. , , , the evaluation vector of the expected return of the first-level index of data assets is: (twenty three) S6. Calculate the fuzzy evaluation vector of the expected return of data assets.
[0005] Multiply the weight vector of the first-level index of data assets by the fuzzy evaluation matrix of the first-level index for expected returns to obtain the fuzzy evaluation vector of the first-level index of data assets for expected returns (twenty four) S7. Calculate the value of data assets using the income approach (25) , indicating the data asset evaluation value , represents the expected return of data assets in year t , represents the fuzzy evaluation value of the expected return of the j-th data asset primary indicator , represents the update frequency of data assets in year t n, represents the expected income period of the data asset i represents the discount rate of data assets.
[0006] Beneficial effects of the invention: 1. Determine the weight of data asset evaluation indicators based on the fuzzy hierarchical analysis method of scenarios. The existing technology only uses a single static evaluation method. This invention uses a multi-level fuzzy evaluation method to determine the impact of each dimension of data assets on its value, and sets different scenario impact values for different application scenarios, so as to estimate the data asset value indicators in a more in-depth and detailed manner.
[0007] 2. Calculate the expected income value of data assets based on the income method. Apply the income method and the above fuzzy evaluation index values to construct a fuzzy hierarchical income method data asset value assessment model. Compared with the existing single income method for calculating the value of data assets, the fuzzy hierarchical income method adds weight factors for multi-layer influencing indicators of data assets, influencing factors of application scenarios, etc., to further accurately evaluate the future income of data assets. BRIEF DESCRIPTION OF THE DRAWINGS
[0009] Figure 1 This is a flowchart of data asset value assessment based on fuzzy hierarchical analysis; Figure 2 This is a hierarchical structure diagram of the data asset indicators to be evaluated; Figure 3 A flowchart for evaluating the value of data assets for a certain enterprise; Figure 4 It is an indicator system for data asset evaluation of a certain enterprise. DETAILED DESCRIPTION
[0010] The fuzzy hierarchical analysis data asset assessment method is to set the application scenario of the data asset and determine the hierarchical structure of the data asset evaluation indicators under the application scenario, including the first-level evaluation indicators of data quality, data application, data capacity, and data risk. Each first-level evaluation indicator corresponds to multiple second-level evaluation indicators. The second-level indicators of the data quality dimension include data integrity, correctness, consistency, repeatability, etc.; the second-level indicators of the data application dimension include data scale, data type, data density, etc.; the second-level indicators of the data capacity dimension include data scale, data type, data density, etc.; the second-level indicators of the data risk dimension include data security, data usability, and data source reliability.
[0011] Construct a data asset comparison scale table, determine the comparison and judgment matrix of the secondary evaluation indicators of data assets in different application scenarios based on the scale table, and calculate the weight vector of the secondary indicators of data assets through normalization. Use the hierarchical analysis method to calculate the weight vector of the primary indicators based on the weight vector of the secondary indicators, construct a fuzzy evaluation matrix of data assets for expected returns, and then calculate the fuzzy evaluation vector of data asset indicators for expected returns. Finally, use the income method to calculate the data asset evaluation value. The specific implementation steps are as follows: Figure 1 shown.
[0012] 1. Determine the hierarchy of data asset value indicators.
[0013] The value assessment system of data assets is determined as two-level indicators: first-level indicators: data quality (Q), data capacity (C), data application management (A), and data risk (R).
[0014] The secondary indicators corresponding to the data quality dimension include data integrity, correctness, consistency, repeatability, etc.; the secondary indicators corresponding to the data application management dimension include data scarcity, data timeliness, data multidimensionality, data mining, data economy, etc.; the secondary indicators corresponding to the data capacity dimension include data scale, data type, data density, data magnitude, data structure, etc.; the secondary indicators corresponding to the data risk dimension include data legal attributes, data security, data usability, data source reliability, data ethical constraints, etc. Figure 2 shown.
[0015] 2. Constructing a data asset indicator comparison scale The data asset indicator comparison scale table refers to the reference value of the importance of different indicators of the same level of data assets when they are compared in a certain application scenario (s). Taking a certain indicator of the same level as the criterion, compare other indicators of the same level in turn, and set the comparison scale a to express the relative importance (or superiority) of the i-th indicator and other indicators of the same level. The value of is 1-9 and its reciprocal, as shown in Table 1.
[0016] Table 1 Comparison scale of data asset indicators 3 Calculate the weight vector of indicators at all levels of data assets According to the relative importance of the evaluation indicators, the data asset evaluation indicator scale table is used to compare the same level of data asset indicators in a specific scenario in pairs, and the first-level indicator comparison judgment matrix V=( ), the comparison judgment matrix Q of the secondary indicators quality (Q), application (A), capacity (C), and risk (R) dimensions is ( )、A=( )、C=( )、R=( ), and calculate the weight vectors of the first-level and second-level indicators.
[0017] (1) Calculate the weight vector of the first-level index of the data The first step is to construct a first-level indicator comparison judgment matrix V=( ), The value of is a specific scene The importance of quality, application, capacity and risk indicators are compared by scoring, and the comparison results are the values and their reciprocals in the comparison scale table (Table 1).
[0018] Table 2 First-level indicator comparison judgment matrix = (1) The second step is to combine specific application scenario factors , normalize and calculate the comparison judgment matrix corresponding to the first-level index ,(i,j=1,2,3,4) (2) Calculate the normalized matrix of the column vector of the first-level index comparison judgment matrix: , (3) The fuzzy consistency vector is obtained by summing up the normalized matrix of the first-level index of data assets by row (4) have to ,After normalization, we get the weights of the first-level indicators (quality, capacity, application, and risk) of data value assessment ( ) (5) The weight vector of the first-level indicator of data value is calculated as .
[0019] (2) Calculate the secondary indicator weight vectors of the quality dimension, capacity dimension, application dimension, and risk dimension of data asset value assessment The first step is to construct a comparison matrix of secondary evaluation indicators for data asset value assessment.
[0020] Comparative judgment matrix based on data quality dimensions For example, The value of is a specific scene The following table 1 compares the importance of the secondary evaluation indicators of the quality dimension through scoring. The comparison results are the values and their reciprocals in the scale table (Table 1). It can also be 2, 4, 6, 8 and their reciprocals in the scale table, such as the comparison between integrity i and consistency j is , then the consistency j is compared with the integrity i and the value is , which is characterized by: ; ; (i=1,2,……n,j=1,2,……m), so the data quality comparison judgment matrix for: (6) The same calculation method is used to calculate the comparison judgment matrix of the data capacity dimension. , Comparative judgment matrix of data application dimensions And the comparative judgment matrix of data risk dimensions They are: (7) (8) (9) The second step is to calculate the normalized matrix corresponding to the secondary indicators of data secondary production evaluation Combined with specific application scenario factors , using the comparison judgment matrix column normalization algorithm to calculate the weight vector of the secondary indicators of the data asset quality dimension , (10) Therefore, the secondary indicator comparison and judgment matrix of the data asset quality dimension is normalized by column as follows: , (11) Similarly, The normalized matrix of data asset application dimension is: , (12) The normalized matrix of data asset capacity dimension is: , (13) The normalized matrix of data asset risk dimension is: , (14) The third step is to sum the influencing factors of the normalized matrix of data asset quality dimension by row. ,i=1,2,3,…m; (15) have to , After normalizing the influencing factors of the data asset quality dimension, the secondary indicator weight of the data asset quality dimension is obtained. (0< <1) i=1,2...,m (m=4) (16) Therefore, the weight vector of the secondary indicators in the data asset quality dimension is: (m=4) (17) Similarly, the weight vectors of the secondary indicators of data asset application dimension, data asset capacity dimension, and data asset risk dimension can be calculated. = (m=4) (18) (m=4) (19) = (m=4) (20) 3. Estimating the score of the index for the impact of each secondary index of data assets on expected returns For a specific application scenario, a certain number of data records to be evaluated are randomly selected, and the secondary indicator scores of the evaluation data are calculated according to the quality, application, and risk assessment tables 3, 4, 5, and 6, and finally the secondary indicator evaluation vector is obtained.
[0021] Table 3 Data quality assessment table Indicator scoring criteria <![CDATA[Integrity (P q1 )]]> (Number of records meeting the conditions in the extracted data / Total number of extracted data) × 100% <![CDATA[Consistency (P q2 )]]> (Number of inconsistent records in the extracted data / Total number of extracted data) × 100% <![CDATA[Repeatability (P q3 )]]> (Number of duplicate records in the extracted data / Total number of extracted data) × 100% <![CDATA[Accuracy (P q4 )]]> (Number of correct records in the extracted data / Total number of extracted data) × 100% Therefore, the score vector of the impact of the secondary indicators of the data asset quality dimension on the expected return is: (twenty one) Similarly, the fuzzy evaluation vector of the impact of the secondary indicators of data application, capacity, and risk dimensions on expected returns is: , , .
[0022] Table 4 Data application evaluation table Indicator scoring criteria <![CDATA[Scarcity (Pa 1 )]]> (Number of supply richness records in the extracted data / Total number of extracted data) × 100% <![CDATA[Timeliness (Pa 2 ).]]> (Number of records collected within the effective time of the extracted data / Total number of extracted data) × 100% <![CDATA[Multi-dimensionality (Pa 3 )]]> (Number of dimension data records in the extracted data / Total number of extracted data) × 100% <![CDATA[Economy (Pa 4 )]]> (Number of records with economic value in the extracted data in this scenario / Total number of extracted data) × 100% Table 5 Data capacity evaluation table Indicator scoring criteria <![CDATA[Data scale (Pc 1 )]]> (Size of extracted data table fields / total size of the field) × 100% <![CDATA[Data type (Pc 2 )]]> (Number of data types in the extracted data / Total number of data types) × 100% <![CDATA[Data density (Pc 3 )]]> (Number of duplicate records in the extracted data / Total number of extracted data) × 100% <![CDATA[Data magnitude (Pc 4 )]]> (Amount of extracted data / Total amount of extracted data in this field) × 100% Table 6 Data risk assessment table Indicator scoring criteria <![CDATA[Legal attribute (Pq 1 )]]> (Number of illegal records in the extracted data that meet this scenario / Total number of extracted data) × 100% <![CDATA[Security attribute (Pq 2 )]]> (Number of sensitive data records in the extracted data / Total number of extracted data) × 100% <![CDATA[Source reliability (Pq 3 )]]> (Number of data records with unreliable sources in the extracted data / Total number of extracted data) × 100% <![CDATA[Ease of use (Pq 4 )]]> (Number of data records with high difficulty in use among the extracted data / Total number of extracted data) × 100% 4. Calculate the score of the first-level index of data assets for expected returns Multiply the weight vector of the secondary indicators of data asset quality, application, capacity, and risk dimensions by the corresponding expected return impact evaluation vector to construct the evaluation score of the primary indicator of data assets on expected returns. The evaluation score of the quality dimension is: , (twenty two) Similarly, the evaluation scores of the first-level indicators of data asset application, capacity, and risk dimensions on expected returns can be calculated. , , , the evaluation vector of the expected return of the first-level index of data assets is: (twenty three) 5. Calculate the fuzzy evaluation vector of the expected return of data assets.
[0023] Multiply the weight vector of the first-level index of data assets by the fuzzy evaluation matrix of the first-level index for expected returns to obtain the fuzzy evaluation vector of the first-level index of data assets for expected returns (twenty four) 6. Calculate the value of data assets using the income approach (25) , indicating the data asset evaluation value , represents the expected return of data assets in year t , represents the fuzzy evaluation value of the expected return of the j-th data asset primary indicator , represents the update frequency of data assets in year t n, represents the expected income period of the data asset i, represents the data asset discount rate In the process of opening up new areas, enterprises need to evaluate the value of the accumulated production and operation data. This invention is used to estimate the value of the enterprise's future benefits from the enterprise's data. According to the data intrinsic value evaluation method, the four dimensions of enterprise data quality, capacity, application, and risk are set as the first-level indicators for the enterprise data asset evaluation, and the corresponding second-level indicator system is evaluated. The evaluation process is as follows: Figure 3 As shown: The first step is to establish an evaluation system. Determine the four primary indicators of quality, application, capacity, and risk to establish an evaluation system, such as Figure 4 shown.
[0024] The second step is to determine the primary indicator comparison judgment matrix. The primary indicator comparison judgment matrix is constructed in this scenario according to the scale table developed by the present invention.
[0025] And calculate the first-level indicator weight vector =[0.6, 0.3, 0.5, -0.4], The third step is to determine the secondary indicator weight vector. Construct a secondary indicator comparison and judgment matrix, and use the normalization algorithm to calculate the secondary indicator weight vectors of quality, application, capacity, and risk dimensions.
[0026] Secondary indicator comparison judgment matrix: Secondary indicator weight vector: The fourth step is to determine the impact score of the secondary indicator on the expected return. For a specific application scenario, a certain number of data records to be evaluated are randomly selected, and the secondary indicator scores of the evaluation data are calculated according to the quality, application, and risk assessment tables 3, 4, 5, and 6, and finally the secondary indicator evaluation vector is obtained.
[0027] Step 5: Determine the score of the secondary indicator on the expected return. Based on the secondary indicator score and the secondary indicator weight vector, calculate the score of the secondary indicator's impact on the expected return in this application scenario.
[0028] Quality Dimensions: = , application dimension: = , Capacity Dimension: , risk dimension: Step 6: Determine the fuzzy evaluation vector of the first-level indicator for the expected return Multiply the weight vector of the first-level index of data assets by the fuzzy evaluation matrix of the first-level index for expected returns to obtain the fuzzy evaluation vector of the first-level index of data assets for expected returns = × Step 7: Determine the value of the asset. Calculate the value of data assets using the income approach , indicating the data asset evaluation value , represents the expected return of data assets in year t , represents the fuzzy evaluation value of the expected return of the j-th data asset primary indicator , represents the update frequency of data assets in year t, n, represents the expected income period of the data asset, i represents the discount rate of data assets.
[0029] The fuzzy hierarchical data asset value assessment method is a method used to assess and quantify the value of enterprise data assets. This method is based on the multi-level assessment influencing factors, scenario factors, and benefit factors of data assets. It uses fuzzy hierarchical analysis, scoring methods, and data asset benefit methods to assess and analyze data assets to improve assessment efficiency and accuracy, and ensure that the assessment process is reliable, credible, and verifiable. It provides credible, reliable, and available solutions for enterprise data asset inventory, quality improvement, and value discovery, helping organizations to inventory, value, and account for data assets, achieve increased capital, increased production, and increased credit, and help increase the value and transaction of data elements.
[0030] Glossary FAHP (Fuzzy analytic hierarchy process): Fuzzy analytic hierarchy process is a system analysis method combining qualitative and quantitative methods proposed by Professor TL Saaty of American operations research in the 1970s. This method is for quantitative evaluation indicators.
[0031] Income approach (income capitalization approach): The income approach is a method of converting the expected future income from the use of data assets into current income. Using the income approach to estimate the value of data assets requires predicting income-related indicators such as income amount, data asset cash flow, income period, and discount rate.
Claims
1. A data asset value assessment method based on fuzzy hierarchical analysis, characterized in that The steps include: S1. Determine the hierarchical structure related to data asset value indicators The value assessment system of data assets is determined as two levels of indicators: the first-level indicators are: data quality Q, data capacity C, data application management A, and data risk R; the second-level indicators corresponding to the data capacity dimension include data scale, data type, data density, data magnitude, data structure, data legal attributes, data security, data usability, data source reliability, and data ethical constraints; S2. Construct a data asset index comparison scale The data asset indicator comparison scale table refers to the reference value of the importance of different indicators of the same level of data assets when they are compared with each other in a certain application scenario S. Taking a certain indicator of the same level as the criterion, other indicators of the same level are compared in turn, and the comparison scale a is set to express the relative importance of the i-th indicator and other indicators of the same level. The value of is 1-9 and its reciprocal; S3. Calculate the weight vector of indicators at all levels of data assets According to the relative importance of the evaluation indicators, the data asset evaluation indicator scale table is used to compare the same level of data asset indicators in a specific scenario in pairs, and a first-level indicator comparison judgment matrix is constructed. , the comparison and judgment matrix of the secondary indicators quality Q, application A, capacity C, and risk R , , , , and calculate the weight vectors of the first-level and second-level indicators; (1) Calculate the weight vector of the first-level index of the data The first step is to construct a first-level indicator comparison judgment matrix , The value of is a specific scene The importance of quality, application, capacity and risk indicators are compared by scoring, and the comparison result is the value and its reciprocal in the comparison scale table; （1） The second step is to combine specific application scenario factors , normalize and calculate the comparison judgment matrix corresponding to the first-level index （2） Calculate the normalized matrix of the column vector of the first-level index comparison judgment matrix: , （3） The fuzzy consistency vector is obtained by summing up the normalized matrix of the first-level index of data assets by row （4） have to , after normalization, the weight of the first-level indicator of data value assessment is obtained （5） The weight vector of the first-level indicator of data value is calculated as ; (2) Calculate the secondary indicator weight vectors of the quality dimension, capacity dimension, application dimension, and risk dimension of data asset value assessment The first step is to construct a comparison matrix of secondary evaluation indicators for data asset value assessment; Comparative judgment matrix based on data quality dimensions , The value of is a specific scene The importance of the secondary evaluation indicators of the quality dimension is compared by scoring, and the comparison between integrity i and consistency j is , then the consistency j is compared with the integrity i and the value is , which is characterized by: ; ; (i=1,2,……n,j=1,2,……m), so the data quality comparison judgment matrix for: （6） The same calculation method is used to calculate the comparison judgment matrix of the data capacity dimension. , Comparative judgment matrix of data application dimensions And the comparative judgment matrix of data risk dimensions They are: （7） （8） （9） The second step is to calculate the normalized matrix corresponding to the secondary indicators of data secondary production evaluation Combined with specific application scenario factors , using the comparison judgment matrix column normalization algorithm to calculate the weight vector of the secondary indicators of the data asset quality dimension ， （10） Therefore, the secondary indicator comparison and judgment matrix of the data asset quality dimension is normalized by column as follows: , （11） Similarly, The normalized matrix of data asset application dimension is: , (12) The normalized matrix of data asset capacity dimension is: , (13) The normalized matrix of data asset risk dimension is: , (14) The third step is to sum the influencing factors of the normalized matrix of data asset quality dimension by row. ,i=1,2,3,…m; （15） have to , After normalizing the influencing factors of the data asset quality dimension, the secondary indicator weight of the data asset quality dimension is obtained. i=1,2...,m (m=4) (16) Therefore, the weight vector of the secondary indicators in the data asset quality dimension is: (m=4) （17） Similarly, the weight vectors of the secondary indicators of data asset application dimension, data asset capacity dimension, and data asset risk dimension can be calculated. (m=4) （18） (m=4) （19） (m=4) （20） S4. Estimation of the index scores for the impact of each secondary index of data assets on expected returns For a specific application scenario, a certain number of data records to be evaluated are randomly selected, the secondary indicator scores of the evaluation data are calculated, and finally the secondary indicator evaluation vector is obtained; Therefore, the score vector of the impact of the secondary indicators of the data asset quality dimension on the expected return is: （21） Similarly, the fuzzy evaluation vector of the impact of the secondary indicators of data application, capacity, and risk dimensions on expected returns is: , , ; S5. Calculate the score of the first-level index of data assets for expected returns Multiply the weight vector of the secondary indicators of data asset quality, application, capacity, and risk dimensions by the corresponding expected return impact evaluation vector to construct the evaluation score of the primary indicator of data assets on expected returns. The evaluation score of the quality dimension is: ， （22） Similarly, the evaluation scores of the first-level indicators of data asset application, capacity, and risk dimensions on expected returns can be calculated. , , , the evaluation vector of the expected return of the first-level index of data assets is: （23） S6. Calculate the fuzzy evaluation vector of expected returns of data assets Multiply the weight vector of the first-level index of data assets by the fuzzy evaluation matrix of the first-level index for expected returns to obtain the fuzzy evaluation vector of the first-level index of data assets for expected returns （24） S7. Calculate the value of data assets using the income approach （25） , indicating the data asset evaluation value , represents the expected return of data assets in year t , represents the fuzzy evaluation value of the expected return of the j-th data asset primary indicator , represents the update frequency of data assets in year t n, represents the expected income period of the data asset i represents the discount rate of data assets.