Data asset value evaluation method and system, management method, terminal and medium

Abstract

The invention belongs to the field of data asset management, and particularly discloses a data asset value evaluation method and system, a management method, a terminal and a medium, and the method comprises the steps: evaluating the data asset quality based on a data quality evaluation index, and obtaining a data asset quality evaluation result; correcting the reset cost method model by using the data asset quality evaluation result, and calculating the lowest price of the data assets by using the corrected reset cost method model; correcting the physical option method model by using the data asset quality evaluation result, and calculating the highest price of the data assets by using the physical option method model; the data asset value evaluation result is obtained. According to the method, the data asset value evaluation is corrected based on the data asset quality evaluation result, the influence of the application scene on the data asset value evaluation is considered, the data asset value is accurately quantified, and the accuracy of the data asset value evaluation is improved.

Description

Technical Field

[0001] The present invention belongs to the field of data asset management, and particularly relates to a method and system for evaluating the value of data assets, a management method, a terminal, and a medium. Background Art

[0002] With the rapid development of the digital age, data has become a crucial asset for enterprises and organizations. The evaluation of the value of data assets plays a key role in activities such as data transactions, enterprise investment decisions, and financial statement preparation. Accurate evaluation results of data asset value are beneficial to improving the effectiveness of data asset monetization.

[0003] Currently, the main methods for evaluating the value of data assets include the cost method, the market method, and the income method. For example, the prior art discloses a method for pricing data assets, which determines the cost price of data assets using multiple pricing reference factors and determines the pricing of data assets based on the cost results and market prices. The pricing reference factors include various costs such as data acquisition costs, data preprocessing costs, and data R & D costs. However, considering the uneven quality of data, low-quality data may lead to decision-making errors, while high-quality data can create greater value for enterprises, and the social and economic benefits generated by different application scenarios are different. The existing evaluation methods do not consider the quality differences of data assets and the impact of application scenarios on the value of data assets, resulting in inaccurate evaluation results and unable to accurately reflect the true value of data assets. Summary of the Invention

[0004] To solve the above problems, the present invention provides a method and system for evaluating the value of data assets, a management method, a terminal, and a medium, which correct the evaluation of the value of data assets based on the data asset quality evaluation results, and at the same time consider the impact of application scenarios on the evaluation of the value of data assets, accurately quantify the value of data assets, and improve the accuracy of the evaluation of the value of data assets.

[0005] In a first aspect, the technical solution of the present invention provides a method for evaluating the value of data assets, including the following steps: Evaluate the quality of data assets based on data quality evaluation indicators to obtain the data asset quality evaluation results; Construct a replacement cost method model, correct the replacement cost method model using the data asset quality evaluation results, and calculate the minimum price of the data assets using the corrected replacement cost method model; Construct a real options method model considering application scenarios, correct the real options method model using the data asset quality evaluation results, and calculate the maximum price of the data assets using the real options method model; Take the minimum price and the maximum price of the data assets as the data asset price range, and this data asset price range is the evaluation result of the value of the data assets.

[0006] In an alternative embodiment, a replacement cost method model is constructed, specifically including the replacement cost method model represented by the following formula: (1) In the formula, is the lowest price of the data asset calculated by formula (1), is the replacement cost, is the expected profit rate, is the depreciation rate.

[0007] In an alternative embodiment, the replacement cost method model is corrected using the data asset quality evaluation results, and the corrected replacement cost method model is used to calculate the lowest price of the data asset, specifically including: Considering the functional depreciation rate of the data asset, the replacement cost method model is corrected using the data asset quality evaluation results to obtain the corrected replacement cost method model, expressed as (2) In the formula, is the lowest price of the data asset calculated by formula (2), is the data asset quality evaluation result; The replacement cost and expected profit rate of the data asset are obtained, and the corrected replacement cost method model is used to calculate the lowest price of the data asset.

[0008] In an alternative embodiment, a real options method model considering the application scenario is constructed, specifically including the real options method model considering the application scenario represented by the following formula: (3) In the formula, is the option price of the data asset calculated by formula (3), is the present value of the expected income, , respectively represent , values of the cumulative normal distribution function of, is the risk-free rate of return, is the service life stipulated in the contract; is the replacement cost; wherein, (4) (5) (6) (7) In the formula, is the income life, , is the The expected return of the data asset for each income year is the return of the th application scenario in the th income year is the number of application scenarios in the th income year; is the income discount rate is the price volatility.

[0009] In an alternative embodiment, the real options method model is corrected using the data asset quality evaluation result, and the maximum price of the data asset is calculated using the real options method model, specifically including: The real options method model is corrected using the data asset quality evaluation result to obtain the corrected real options method model, denoted as (8) wherein is the maximum price of the data asset calculated by formula (8), is the data asset quality evaluation result; Determine that the income period is the usage period stipulated in the contract; Obtain the replacement cost, risk-free rate of return, usage period stipulated in the contract, number of application scenarios for each income year, income for each application scenario for each income year, and price volatility of the data asset, and calculate the maximum price of the data asset using the corrected real options method model.

[0010] In an alternative embodiment, the data asset quality is evaluated based on data quality evaluation indicators to obtain a data asset quality evaluation result, specifically including: Determine data quality evaluation indicators, including normativity, integrity, accuracy, consistency, timeliness, and accessibility; Multiple experts assign scores to the importance of each data quality evaluation indicator; For each data quality evaluation indicator, calculate the arithmetic mean of the importance assignment scores given by all experts to obtain the weight of the data quality evaluation indicator; Calculate the quality of the data asset according to the calculation method of each data quality evaluation indicator; Weightedly sum the qualities corresponding to each data quality evaluation indicator of the data asset to obtain the data asset quality evaluation result.

[0011] In a second aspect, the technical solution of the present invention provides a data asset value evaluation system, including a quality evaluation module for evaluating the data asset quality based on data quality evaluation indicators to obtain a data asset quality evaluation result; The minimum price calculation module is used to construct a replacement cost method model, correct the replacement cost method model using the data asset quality evaluation results, and calculate the minimum price of the data asset using the corrected replacement cost method model; The maximum price calculation module is used to construct a real options method model considering the application scenario, correct the real options method model using the data asset quality evaluation results, and calculate the maximum price of the data asset using the real options method model; The value evaluation result determination module is used to take the minimum price and the maximum price of the data asset as the data asset price range, and this data asset price range is the data asset value evaluation result.

[0012] Thirdly, the technical solution of the present invention provides a data asset management method, including the following steps: Govern the target data resources to form a data asset catalog; Register the data asset on the registration service platform to obtain an asset certificate; Use the method described in any one of the above to evaluate the value of the data asset; Convert the digital asset value evaluation result.

[0013] Fourthly, the technical solution of the present invention provides a terminal, including: A memory for storing a data asset value evaluation program; A processor for implementing the steps of the data asset value evaluation method described in any one of the above when executing the data asset value evaluation program.

[0014] Fifthly, the technical solution of the present invention provides a computer-readable storage medium, on which a data asset value evaluation program is stored, and when the data asset value evaluation program is executed by a processor, the steps of the data asset value evaluation method described in any one of the above are implemented.

[0015] A method and system for evaluating the value of data assets, a management method, a terminal, and a medium provided by the present invention have the following beneficial effects compared with the prior art: First, evaluate the quality of data assets based on data quality evaluation indicators, and correct and reset the cost method model and the real option method model with the quality evaluation results. The real option method model takes into account the application scenario. Then, calculate the digital asset price with the corrected model to obtain the value evaluation result of the data asset. This method fully considers the impact of data quality on the asset value and can more accurately reflect the true value of the data asset compared with the traditional evaluation method. Moreover, use the reset cost method model to calculate the lowest price and the real option method model to calculate the highest price, and obtain a price range as the data asset evaluation result. Determine a price range through the results of the two methods to further improve the accuracy of the data asset value evaluation and avoid the limitations of single price evaluation. In addition, the real option method model takes into account the application scenario, which can fully reflect the value difference of data assets in different application scenarios, making the evaluation result more in line with the actual business application and helping enterprises to more accurately explore the value of data assets in different scenarios. Based on the more accurate data asset value evaluation result, it is helpful for the value conversion of data assets and improves the effectiveness of data asset value conversion. BRIEF DESCRIPTION OF THE DRAWINGS In order to more clearly illustrate the technical solutions of the present invention, the drawings required for description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a schematic flowchart of a data asset management method provided by an embodiment of the present invention.

[0017] Figure 2 It is a schematic flowchart of a data asset value evaluation method provided by an embodiment of the present invention.

[0018] Figure 3 It is a schematic flowchart of data asset quality evaluation.

[0019] Figure 4 It is a schematic flowchart of data asset value evaluation.

[0020] Figure 5 It is a schematic diagram of data asset application scenarios and economic benefits.

[0021] Figure 6 It is a schematic block diagram of the structure of a data asset value evaluation system provided by an embodiment of the present invention.

[0022] Figure 7 It is a schematic diagram of the structure of a terminal provided by an embodiment of the present invention. Detailed implementation manners

[0023] To make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the specific embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention. The terms used in the description of the present invention in this specification are only for the purpose of describing specific embodiments, and are not intended to limit the present invention.

[0025] Current data has become a crucial asset for enterprises and organizations. However, enterprise data asset management focuses on the processing, integration, and correlation analysis of data assets and cannot perform effective value conversion. To achieve the value conversion of data assets and ensure the effectiveness of data asset value conversion, this embodiment provides a data asset management method.

[0026] Figure 1 The following is a schematic flowchart of a data asset management method provided by an embodiment of the present invention, which specifically includes the following steps.

[0027] S1. Govern the target data resources to form a data asset catalog.

[0028] This step realizes data governance. First, the required data resources are governed, including collecting, storing, processing, and analyzing the data resources, and finally forming a data asset catalog. In an alternative implementation, master data (business core data) management, data security management, data quality management, data sharing management, data compliance management, and metadata (data about data, providing data attribute information) management of data resources are included.

[0029] In the data collection part, first, clarify the collection objectives, communicate with each business department to understand its business requirements and data usage scenarios, and determine the data types, scope, and time period to be collected. Then, select the collection channels, including choosing appropriate collection channels according to the data types and sources. Exemplarily, internal data can be obtained from the enterprise's business systems, databases, log files, etc.; external data can be collected through web crawlers, data cooperation, public data platforms, etc. At the same time, formulate a collection plan, plan the collection frequency, such as real-time collection, scheduled collection (daily, weekly, etc.). Also, determine the technical solutions for data collection, including the tools and interfaces used. For example, use ETL (Extract, Transform, Load) tools to extract data from the database, or obtain external data through API interfaces. Finally, execute data collection, start the data collection work according to the established plan and technical solutions. During the collection process, monitor the collection progress and data quality in real time, and promptly handle abnormal situations that occur during the collection process, such as network interruptions, data format errors, etc.

[0030] In the data storage part, first, select the storage architecture. According to the data scale, data types (structured, semi-structured, unstructured), and usage requirements, choose an appropriate storage architecture. Relational databases are suitable for the storage and management of structured data, such as MySQL, Oracle; non-relational databases are more suitable for semi-structured and unstructured data, such as MongoDB for document storage and HBase for distributed storage of large amounts of data. For the storage of large amounts of data, consider building a data warehouse or data lake architecture. Then, conduct data storage design, design a reasonable data table structure and storage layout. For relational databases, follow the database design paradigm to ensure data integrity and consistency; for non-relational databases, conduct reasonable schema design according to their characteristics and business requirements. At the same time, consider data partitioning, indexing, and backup strategies to improve data read and write performance and security. Finally, import the collected data into the corresponding storage system according to the designed storage structure and format. During the data entry process, perform data cleaning and preliminary verification to remove duplicate data, error data, and incomplete data.

[0031] The data processing part can include data cleaning, data transformation, and data integration. The data cleaning process identifies and corrects noise, missing values, and outliers in the data. The data transformation process converts the data into a format suitable for analysis and application. If the data comes from multiple data sources, data integration is required to integrate the data from different sources into a unified data view.

[0032] The data analysis part includes exploratory data analysis (EDA) of data. Using data visualization tools and statistical analysis methods, a preliminary exploration of the processed data is carried out. Also, in accordance with business requirements and analysis objectives, in-depth data analysis is performed according to the selected analysis methods (such as clustering analysis) and models.

[0033] Sort out the data that has been collected, stored, processed, and analyzed, and clarify key information such as the source, content, format, storage location, usage frequency, and business value of the data. At the same time, according to the data classification criteria and business logic, design the structure of the data asset catalog. It can be classified and organized according to dimensions such as data themes (such as customer data, product data, transaction data), data types (structured data, unstructured data), or business areas (sales, finance, production), etc., to ensure that the catalog structure is clear, easy to search, and manage. Enter the sorted data asset information into the data asset catalog management system according to the defined catalog structure, which can include detailed information such as the name, description, person in charge, data format, update frequency, etc. of the data asset, and at the same time establish the association relationship between data assets for easy data retrieval and use.

[0034] S2. Register the data asset on the registration service platform to obtain an asset certificate.

[0035] This step realizes the registration and confirmation of data assets. Register and log in on the selected registration service platform, enter the data asset information to submit a registration application. After the registration application is approved, the platform will generate a data asset certificate. The asset certificate usually contains the basic information of the data asset, such as name, registration number, registration date, registration entity, etc., as well as the electronic signature of the platform.

[0036] S3. Conduct a value assessment of the data asset.

[0037] This step realizes the value assessment of the data asset, including two parts: data asset quality evaluation and data asset value assessment. The results of the data asset quality evaluation are applied to the data asset value assessment process to improve the accuracy of the data value assessment. This part will be described in detail in the subsequent embodiments and will not be elaborated here.

[0038] S4. Convert the value assessment results of the digital asset.

[0039] This step realizes value conversion based on the value assessment results of the data asset. The value conversion of digital assets includes being included in the financial statements, trading, and development and utilization. Being included in the financial statements enables the data asset to be reflected in the financial statements and enhances the enterprise's asset value; the trading link determines a reasonable price based on accurate value assessment.

[0040] The data asset management method of this embodiment includes multiple key links such as data governance, registration and confirmation of rights, value assessment, and value transformation. Compared with the prior art that focuses on the processing, integration, and correlation analysis of data assets, this embodiment extends to use the confirmed data assets for value assessment, provides a data basis for the value transformation of data assets, and realizes the value transformation of data assets using the value assessment results.

[0041] Figure 2 It is a schematic flowchart of a data asset value assessment method provided by an embodiment of the present invention. Among them, Figure 2 The execution subject can be a data asset value assessment system. The data asset value assessment method provided by the embodiment of the present invention is executed by a computer device. Correspondingly, the data asset value assessment system runs in the computer device. According to different requirements, the order of the steps in this flowchart can be changed, and some can be omitted.

[0042] As Figure 2 shown, the method includes the following steps.

[0043] SS1, Evaluate the quality of data assets based on data quality evaluation indicators to obtain a data asset quality evaluation result.

[0044] Based on a pre-set data quality evaluation index system, comprehensively evaluate the quality of data assets. These indicators can cover multiple dimensions such as the accuracy, integrity, consistency, and timeliness of data. Through the quantitative analysis of the performance of data assets in each dimension, a data asset quality evaluation result is finally obtained, providing an important basis for subsequent model construction and price calculation.

[0045] SS2, Construct a replacement cost method model, correct the replacement cost method model using the data asset quality evaluation result, and calculate the minimum price of the data asset using the corrected replacement cost method model.

[0046] Construct a replacement cost method model, which evaluates the value based on the cost required to reconstruct or purchase the same or similar data assets as the evaluated data assets. Use the data asset quality evaluation result obtained in the first step to correct the model and correct the value deviation caused by data quality differences. After correction, use this model to calculate the minimum price of the data asset, setting a lower limit for the data asset price from the cost perspective.

[0047] SS3, Construct a real options method model considering the application scenario, correct the real options method model using the data asset quality evaluation result, and calculate the maximum price of the data asset using the real options method model.

[0048] Construct a real option method model considering the application scenario. The real option method takes into account the potential value of data assets in different application scenarios to reflect the flexibility and uncertainty of the value of data assets. Similarly, correct the model with the data asset quality evaluation results to more accurately reflect the impact of data quality on value. Use the corrected model to calculate the maximum price of the data asset and set an upper limit for the data asset price from the perspective of application value.

[0049] SS4. Take the minimum price and the maximum price of the data asset as the data asset price range, and this data asset price range is the data asset value evaluation result.

[0050] Combine the minimum price calculated in the second step and the maximum price calculated in the third step to form a data asset price range. This range comprehensively considers the cost and potential application value of the data asset. As the final result of the data asset value evaluation, it provides a reasonable price reference range for both the data asset owner and the user, which helps in making decisions regarding the value monetization of data assets.

[0051] The data asset value evaluation method provided in this embodiment first evaluates the quality of the data asset based on data quality evaluation indicators, and corrects the replacement cost method model and the real option method model with the quality evaluation results. The real option method model considers the application scenario, and then calculates the price using the corrected model to obtain the data asset value evaluation result. This method fully considers the impact of data quality on asset value. Compared with traditional evaluation methods, it can more accurately reflect the true value of data assets. Moreover, it uses the replacement cost method model to calculate the minimum price and the real option method model to calculate the maximum price, and obtains a price range as the data asset evaluation result. By determining a price range through the results of the two methods, it further improves the accuracy of data asset value evaluation and avoids the limitations of single price evaluation. In addition, the real option method model considers the application scenario, which can fully reflect the value differences of data assets in different application scenarios, making the evaluation result more in line with actual business applications and helping enterprises to more accurately explore the value of data assets in different scenarios. Based on a more accurate data asset value evaluation result, it helps in the value conversion of data assets and improves the effectiveness of data asset value conversion.

[0052] In some alternative implementation manners, as Figure 3 shown, step SS1 for evaluating the quality of the data asset includes determining data quality evaluation indicators, then quantifying each evaluation indicator, calculating the weight of each evaluation indicator, and finally obtaining the evaluation result through weighted evaluation. Specifically, it includes the following steps.

[0053] SS1.1. Determine and quantify the data quality evaluation indicators.

[0054] In these alternative embodiments, the evaluation metrics include normativity, integrity, accuracy, consistency, timeliness, and accessibility. These are the first-level metrics, and each first-level metric can further include multiple second-level metrics. Table 1 shows the data quality evaluation metrics.

[0055] Table 1: Data Quality Evaluation Metrics

[0056] SS1.2, Multiple experts assign scores to indicate the importance of each data quality evaluation metric.

[0057] In these alternative embodiments, the Analytic Hierarchy Process (AHP) is used to determine the influence weights of each evaluation metric on data quality. First, multiple experts assign scores to indicate the importance of each hierarchical metric.

[0058] SS1.3, For each data quality evaluation metric, calculate the arithmetic mean of the importance scores assigned by all experts to obtain the weight of the data quality evaluation metric.

[0059] Since the opinions of each expert on the metric weights have equal influence, to ensure the fairness and accuracy of the results, the final weight value is the arithmetic mean of all expert opinions, denoted as .

[0060] SS1.4, Calculate the quality of the data asset according to the calculation method of each data quality evaluation metric.

[0061] Calculate the quality scores of each evaluation metric according to the calculation method in Table 1 based on the actual situation of the data asset, denoted as .

[0062] SS1.5, Weighted sum the qualities corresponding to each data quality evaluation metric of the data asset to obtain the data asset quality evaluation result.

[0063] The data asset quality evaluation result is obtained by weighted average ， 。

[0064] It should be noted that in these alternative embodiments, each first-level indicator has multiple second-level indicators. For each first-level indicator, multiple experts score the importance of each of its second-level indicators, and the arithmetic mean of all experts' scores is taken as the weight of the second-level indicator. Then, the quality of each second-level indicator is calculated according to the actual situation, and the weighted sum of the quality scores of all second-level indicators is taken as the quality evaluation result of the corresponding first-level indicator.

[0065] In some alternative embodiments, such as Figure 4 shown, the value assessment of data assets includes three parts: calculating the minimum price of data assets, calculating the maximum price of data assets, and obtaining the price range.

[0066] In these alternative embodiments, taking into account the interests of both the data asset owner and the user, the input cost of the owner is used as the minimum price, and the maximum benefit of the user is used as the maximum price. The replacement cost method is respectively used to calculate the minimum price and the real option method is used to calculate the maximum price , and based on the data asset quality evaluation result for correction, and at the same time considering the impact of different application scenarios on the value of enterprise data assets.

[0067] The first part calculates the minimum price of data assets. First, determine that the minimum price calculation method is the replacement cost method and construct a replacement cost method model. Specifically, the replacement cost method model is represented by the following formula: (1) In the formula, is the minimum price of data assets calculated by formula (1), is the replacement cost, is the expected profit rate, is the depreciation rate.

[0068] The replacement cost includes the equipment and labor costs for collecting, storing, and processing data, as well as the upfront R & D costs. The expected profit rate needs to be determined according to the specific application scenario. Different owners have different expected profit rates for enterprise data assets in different scenarios.

[0069] It should be noted that the higher the quality of data assets, the higher their utilization value and the lower their depreciation rate. In these alternative implementation methods, only the functional depreciation of enterprise data assets is considered. The characteristics of data assets determine the factors affecting their data quality and assign corresponding weights to each factor. Currently, most research uses indicators such as the integrity, accuracy, security, and industry development trend of data assets that affect data quality as indicators for the functional depreciation of data assets. Different weights are assigned to each indicator to represent the degree of influence on the functional depreciation of data assets, and the functional depreciation rate of data assets is calculated by weighted averaging the scores of each indicator. Based on this, in these alternative implementation methods, considering the functional depreciation rate of data assets, the replacement cost method model is corrected using the data asset quality evaluation results to obtain the corrected replacement cost method model, which is expressed as, (2) In the formula, is the minimum price of the data asset calculated by formula (2), is the data asset quality evaluation result.

[0070] In the actual calculation process, the replacement cost and expected profit rate of the data asset are obtained, and the corrected replacement cost method model, that is, formula (2), is used to calculate the minimum price of the data asset. The second part calculates the maximum price of the data asset. First, the calculation method for the maximum price is determined as the real options method considering the application scenario, and a real options method model considering the application scenario is constructed, which is specifically represented by the following formula for the real options method model considering the application scenario: (3) In the formula, is the option price of the data asset calculated by formula (3), is the present value of the expected return, , respectively represent , the cumulative normal distribution function values of, is the risk-free rate of return, is the contractually agreed usage life; is the replacement cost. In a specific embodiment, the risk-free rate of return uses the yield of the same-period national debt.

[0071] In formula (3), (4) (5) (6) (7) is the The expected income of data assets per year, application scenarios of data assets and economic benefits are as follows: Figure 5 As shown in the figure, the value of data assets varies with different application scenarios. In these optional implementations, data assets are priced based on the different economic benefits generated by specific application scenarios. The incremental benefit method is used to calculate the expected benefits of enterprise data assets by comparing the difference in social and economic benefits generated before and after the application of enterprise data assets. , as calculated in formula (5), For the Year of income The benefits of each application scenario, For the Number of application scenarios per revenue year, is the income period, . The risk accumulation method is used to determine the discount rate, that is, , It is the price volatility, which is determined by technology risk, market risk, capital risk and management risk. The market volatility of similar intangible assets in the same period can be used.

[0072] In these optional implementations, the real options model is corrected using the data asset quality evaluation results. Specifically, the corrected real options model is expressed as: (8) In the formula, The highest price of data assets calculated by formula (8) is The data asset quality evaluation results.

[0073] In the actual calculation process, the first step is to determine the income period as the useful life agreed in the contract, that is, Then, we obtain the replacement cost, risk-free rate of return, contractually agreed useful life, number of application scenarios in each revenue year, revenue of each application scenario in each revenue year, and price volatility of the data asset, and use the corrected real option model to calculate the maximum price of the data asset, that is, use formula (3) to formula (8) to calculate the maximum price of the data asset.

[0074] Finally, the data asset value assessment interval is obtained as follows: .

[0075] An embodiment of a data asset value assessment method is described in detail above. Based on the data asset value assessment method described in the above embodiment, an embodiment of the present invention also provides a data asset value assessment system corresponding to the method.

[0076] Figure 6A schematic block diagram of the structure of a data asset value evaluation system provided by an embodiment of the present invention. In this embodiment, the data asset value evaluation system 600 can be divided into multiple functional modules according to the functions it performs, such as Figure 6 as shown. The functional modules may include: a quality evaluation module 610, a minimum price calculation module 620, a maximum price calculation module 630, and a value evaluation result determination module 640. The module referred to in the present invention means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory.

[0077] The quality evaluation module 610 is used to evaluate the quality of data assets based on data quality evaluation indicators to obtain a data asset quality evaluation result.

[0078] The minimum price calculation module 620 is used to construct a replacement cost method model, correct the replacement cost method model using the data asset quality evaluation result, and calculate the minimum price of the data asset using the corrected replacement cost method model.

[0079] The maximum price calculation module 630 is used to construct a real options method model considering the application scenario, correct the real options method model using the data asset quality evaluation result, and calculate the maximum price of the data asset using the real options method model.

[0080] The value evaluation result determination module 640 is used to take the minimum price and the maximum price of the data asset as the data asset price range, and this data asset price range is the data asset value evaluation result.

[0081] The data asset value evaluation system in this embodiment is used to implement the aforementioned data asset value evaluation method. Therefore, the specific implementation manners in this system can be seen in the embodiment part of the data asset value evaluation method in the previous text. Therefore, its specific implementation manners can be referred to the descriptions of the corresponding various part embodiments, and will not be elaborated here.

[0082] In addition, since the data asset value evaluation system in this embodiment is used to implement the aforementioned data asset value evaluation method, its function corresponds to the function of the above method, and will not be elaborated here.

[0083] Figure 7 A schematic diagram of the structure of a terminal 700 provided by an embodiment of the present invention, including: a processor 710, a memory 720, and a communication unit 730. When the processor 710 is used to implement the data asset value evaluation program stored in the memory 720, the following steps are implemented: Evaluate the quality of data assets based on data quality evaluation indicators to obtain a data asset quality evaluation result; Build a replacement cost method model, correct the replacement cost method model using the data asset quality evaluation results, and calculate the minimum price of the data asset using the corrected replacement cost method model; Build a real option method model considering the application scenario, correct the real option method model using the data asset quality evaluation results, and calculate the maximum price of the data asset using the real option method model; Take the minimum price and the maximum price of the data asset as the data asset price range, and this data asset price range is the data asset value evaluation result.

[0084] The present invention also provides a computer storage medium. The storage medium mentioned here can be a magnetic disk, an optical disk, a read-only memory (abbreviation: ROM), a random access memory (abbreviation: RAM), etc.

[0085] The computer storage medium stores a data asset value evaluation program. When the data asset value evaluation program is executed by a processor, the following steps are implemented: Evaluate the data asset quality based on the data quality evaluation indicators to obtain the data asset quality evaluation results; Build a replacement cost method model, correct the replacement cost method model using the data asset quality evaluation results, and calculate the minimum price of the data asset using the corrected replacement cost method model; Build a real option method model considering the application scenario, correct the real option method model using the data asset quality evaluation results, and calculate the maximum price of the data asset using the real option method model; Take the minimum price and the maximum price of the data asset as the data asset price range, and this data asset price range is the data asset value evaluation result.

[0086] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data asset value assessment method, characterized in that: The following steps are involved: Evaluate the quality of data assets based on data quality evaluation indicators to obtain data asset quality evaluation results; Construct a replacement cost model, use the data asset quality evaluation results to correct the replacement cost model, and use the corrected replacement cost model to calculate the minimum price of data assets; Construct a real options model that takes into account application scenarios, use the data asset quality evaluation results to correct the real options model, and use the real options model to calculate the maximum price of data assets; The lowest price and the highest price of the data asset are taken as the data asset price range, and the data asset price range is the result of the data asset value assessment.

2. The data asset value assessment method according to claim 1 is characterized in that: Constructing a replacement cost method model, specifically including expressing the replacement cost method model by the following formula: （1） In the formula, The lowest price of data assets calculated by formula (1) is is the replacement cost, is the expected profit rate, is the depreciation rate.

3. The data asset value assessment method according to claim 2 is characterized in that: The replacement cost model is corrected using the data asset quality evaluation results, and the corrected replacement cost model is used to calculate the minimum price of data assets, including: Considering the functional depreciation rate of data assets, the replacement cost method model is corrected using the data asset quality evaluation results to obtain the corrected replacement cost method model, which is expressed as: （2） In the formula, The lowest price of data assets calculated by formula (2) is The results of data asset quality evaluation; Obtain the replacement cost and expected profit margin of the data asset, and use the corrected replacement cost model to calculate the minimum price of the data asset.

4. The data asset value assessment method according to claim 1, characterized in that: A real options model considering application scenarios is constructed, specifically including the following formula representing the real options model considering application scenarios: （3） In the formula, The data asset option price calculated by formula (3) is is the present value of expected returns, , Respectively , The cumulative normal distribution function value of is the risk-free rate of return, The useful life is as agreed in the contract; is the replacement cost; in, (4) （5） （6） （7） In the formula, is the income period, , For the The expected return of data assets in a revenue year, For the Year of income The benefits of each application scenario, For the Number of application scenarios per revenue year; is the earnings discount rate, , is the price volatility.

5. The data asset value assessment method according to claim 4 is characterized in that: The real options model is corrected using the data asset quality evaluation results, and the real options model is used to calculate the maximum price of data assets, including: The real options model is corrected using the data asset quality evaluation results to obtain the corrected real options model, which is expressed as: （8） In the formula, The highest price of data assets calculated by formula (8) is The results of data asset quality evaluation; The income period is determined to be the useful life agreed in the contract; Obtain the replacement cost, risk-free rate of return, contractually agreed useful life, number of application scenarios in each revenue year, revenue of each application scenario in each revenue year, and price volatility of the data asset, and use the corrected real options model to calculate the maximum price of the data asset.

6. The data asset value assessment method according to any one of claims 1 to 5, characterized in that: Evaluate the data asset quality based on the data quality evaluation indicators to obtain the data asset quality evaluation results, including: Determine and quantify data quality evaluation indicators, including standardization, completeness, accuracy, consistency, timeliness, and accessibility; Multiple experts assign values ​​and scores to the importance of each data quality evaluation indicator; For each data quality evaluation indicator, the arithmetic mean of the importance scores given by all experts is calculated to obtain the weight of the data quality evaluation indicator; Calculate the quality of data assets according to the calculation methods of various data quality evaluation indicators; The quality corresponding to each data quality evaluation indicator of the data asset is weighted and summed to obtain the data asset quality evaluation result.

7. A data asset value assessment system, characterized in that: include, The quality evaluation module is used to evaluate the quality of data assets based on data quality evaluation indicators and obtain data asset quality evaluation results; A minimum price calculation module is used to construct a replacement cost method model, use the data asset quality evaluation results to correct the replacement cost method model, and use the corrected replacement cost method model to calculate the minimum price of data assets; The maximum price calculation module is used to build a real option model that takes into account the application scenario, use the data asset quality evaluation results to correct the real option model, and use the real option model to calculate the maximum price of the data asset; The value assessment result determination module is used to use the lowest price and the highest price of the data asset as the data asset price range, and the data asset price range is the data asset value assessment result.

8. A data asset management method, characterized in that: The following steps are involved: Govern the target data resources to form a data asset catalog; Register data assets on the registration service platform and obtain asset certificates; Use the method described in any one of claims 1 to 6 to evaluate the value of data assets; Convert the results of digital asset valuation into value.

9. A terminal, characterized in that: include: A memory device for storing a data asset value assessment program; A processor, used to implement the steps of the data asset value assessment method as described in any one of claims 1 to 6 when executing the data asset value assessment program.

10. A computer-readable storage medium, characterized in that: The readable storage medium stores a data asset value assessment program, which, when executed by a processor, implements the steps of the data asset value assessment method according to any one of claims 1 to 6.

Images