CN114897322A - Data asset value evaluation system and method - Google Patents

Data asset value evaluation system and method Download PDF

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CN114897322A
CN114897322A CN202210432735.6A CN202210432735A CN114897322A CN 114897322 A CN114897322 A CN 114897322A CN 202210432735 A CN202210432735 A CN 202210432735A CN 114897322 A CN114897322 A CN 114897322A
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scarcity
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周辉
王雷
王兰虎
闫文光
刘绍宇
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Shuanxin Beijing Technology Co ltd
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Abstract

The application provides a data asset value evaluation system and a method, comprising the following steps: the catalog module is used for acquiring a data set and generating a data catalog according to the data set; the calculation module is used for searching the historical transaction price of the data based on the data catalog and calculating the scarcity degree, the demand degree and the data quantity of the data; and the evaluation module is used for judging the occupation attribute of the data, calling a price formula according to the occupation attribute, and inputting the historical transaction price, the scarcity degree and the demand degree to calculate the asset value. According to the method and the device, the rarity and the demand of the data assets are scored, the value interval of the data assets is calculated, and the accuracy of data asset value evaluation is improved.

Description

Data asset value evaluation system and method
Technical Field
The application requests to protect a data evaluation technology, and particularly relates to a data asset value evaluation system. The application also relates to a data asset value assessment method.
Background
Data assets are data that have ownership, unique value, non-materiality, and variable value, and therefore, the data asset value is difficult to objectively evaluate.
At present, the value of the data asset is usually determined by negotiation of two transaction parties, and the true value of the data is difficult to reflect due to the determination mode of the value of the data asset, so that great loss is easily brought to the transaction parties. Therefore, it is important to accurately determine the data asset value interval. However, the existing data asset value assessment is influenced by human factors too much, and the assessment is not accurate.
Disclosure of Invention
In order to solve the technical problems mentioned in the background art, the present application provides a data asset value evaluation system. The application also relates to a data asset value assessment method.
The application provides a data asset value evaluation system, including:
the catalog module is used for acquiring a data set and generating a data catalog according to the data set;
the calculation module is used for searching the historical transaction price of the data based on the data catalog and calculating the scarcity degree, the demand degree and the data quantity of the data;
and the evaluation module is used for judging the occupation attribute of the data, calling a price formula according to the occupation attribute, and inputting the historical transaction price, the scarcity degree and the demand degree to calculate the asset value.
Optionally, the calculation module includes:
the first calling unit is used for calling a first data processing model, inputting a group of data to be detected in the first data processing model and obtaining a processing result;
the missing calculation unit is used for unpacking the data to be detected into a plurality of independent data and inputting the data to be detected which lacks one independent data into the first data processing model to obtain a comparison result;
and the first comparison unit is used for obtaining the scarcity score of the independent data based on the similarity of the processing result and the comparison result.
Optionally, the range of the scarcity score is 0-1, and the smaller the scarcity score is, the more scarcity the scarcity score is.
Optionally, the computing module further includes:
the second calling unit is used for calling a second data processing model, inputting the data to be tested and the client data into the second data processing model and obtaining a comparison result of the data to be tested and the client data;
and the second comparison unit is used for scoring the data to be detected according to the comparison result.
Optionally, the data set includes a plurality of data to be measured.
The application also provides a data asset value assessment method, which comprises the following steps:
acquiring a data set, and generating a data catalog according to the data set;
searching historical transaction prices of the data based on the data catalog, and calculating the scarcity, the demand and the data quantity of the data;
and judging the occupation attribute of the data, calling a price formula according to the occupation attribute, and inputting the historical transaction price, the scarcity degree and the demand degree to calculate the asset value.
Optionally, the scarcity calculation includes:
calling a first data processing model, inputting a group of data to be detected in the first data processing model, and obtaining a processing result;
the data to be detected is unpacked into a plurality of independent data, the data to be detected which lacks one independent data is input into the first data processing model, and a comparison result is obtained;
and obtaining the scarcity score of the independent data based on the similarity of the processing result and the comparison result.
Optionally, the range of the scarcity score is 0-1, and the smaller the scarcity score is, the more scarcity the scarcity score is.
Optionally, the calculating the demand includes:
calling a second data processing model, inputting data to be tested and customer data into the second data processing model, and obtaining a comparison result of the data to be tested and the customer data;
and scoring the data to be detected according to the comparison result.
Optionally, the data set includes a plurality of data to be measured.
Compared with the prior art, the application has the advantages that:
the application provides a data asset value evaluation system, including: the catalog module is used for acquiring a data set and generating a data catalog according to the data set; the calculation module is used for searching the historical transaction price of the data based on the data catalog and calculating the scarcity degree, the demand degree and the data quantity of the data; and the evaluation module is used for judging the occupation attribute of the data, calling a price formula according to the occupation attribute, and inputting the historical transaction price, the scarcity degree and the demand degree to calculate the asset value. According to the method and the device, the rarity and the demand of the data assets are scored, the value interval of the data assets is calculated, and the accuracy of data asset value evaluation is improved.
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FIG. 1 is a schematic diagram of a data asset worth assessment system of the present application.
Fig. 2 is a flowchart of the scarcity calculation in the present application.
FIG. 3 is a flow chart of a data asset worth assessment in the present application.
Detailed Description
The following is an example of a specific implementation process provided for explaining the technical solutions to be protected in the present application in detail, but the present application may also be implemented in other ways than those described herein, and a person skilled in the art may implement the present application by using different technical means under the guidance of the idea of the present application, so that the present application is not limited by the following specific embodiments.
The application provides a data asset value evaluation system, including: the catalog module is used for acquiring a data set and generating a data catalog according to the data set; the calculation module is used for searching the historical transaction price of the data based on the data directory and calculating the scarcity degree, the demand degree and the data quantity of the data; and the evaluation module is used for judging the occupation attribute of the data, calling a price formula according to the occupation attribute, and inputting the historical transaction price, the scarcity degree and the demand degree to calculate the asset value. According to the method and the device, the rarity and the demand of the data assets are scored, the value interval of the data assets is calculated, and the accuracy of data asset value evaluation is improved.
FIG. 1 is a schematic diagram of a data asset worth assessment system of the present application.
Referring to fig. 1, a catalog module 101 is configured to obtain a data set and generate a data catalog according to the data set.
A data set refers to a collection of data assets that includes one or more categories of data. In the dataset, data is stored in a tree structured process, for example: a data set is provided with a plurality of primary class data folders, each primary class data folder is divided into a plurality of secondary class data folders, and a final data storage folder is known. The data folder is a final storage location of data, and has a plurality of upper data folders, the data folder includes: a file folder.
Scanning the existing data set, generating a directory according to the tree structure of the data, wherein the directory comprises each data and a superior folder of the data, and establishing the inclusion relationship between the superior folder and the inferior folder and the identity attribute marked with the data.
The data has identity data, the data in the same folder may have different identity data, and the data in different folders may have the same identity attribute, which represents the source of the data, i.e., the data has the same source.
The data folder may have one or more pieces of data therein, the one or more pieces of data having the same or different identity attributes.
And the calculating module 102 is used for searching the historical transaction price of the data based on the data catalogue, and calculating the scarcity degree, the demand degree and the data quantity of the data.
The data catalog represents the category of the data, and the historical transaction price of the data is inquired according to the category of the data. Specifically, the calculation module is provided with a historical price storage unit, the historical price storage unit records the data types and prices of data transactions of the past time, and then the historical transaction prices of the data are extracted according to the data types in the data directory.
One preferred method is to extract data for the same identity attribute based on the identity attribute tagged in the data catalog and to extract the historical transaction price for the data asset based on the identity attribute. Of course, at this time, the data stored in the historical price storage unit should also be stored separately according to the identity attribute.
Fig. 2 is a flowchart of the scarcity calculation in the present application.
Referring to fig. 2, the computing module 102 includes a first invoking unit 201, a missing computing unit 202, and a first comparing unit 203, and the data asset sequentially passes through the first invoking unit 201, the missing computing unit 202, and the first comparing unit 203 to obtain the scarcity of the data asset.
The first invoking unit 201 is configured to invoke a first data processing model, input a set of data to be tested in the first data processing model, and obtain a processing result.
The first data processing model comprises a plurality of data processing models which are stored in a model storage module, and the data processing models are called from the model storage module when the rarity degree of the data is calculated.
The first data processing model refers to an AI model applied to the data asset, and comprises a prediction model, an analysis module and the like. Specifically, the first data processing model need not be a pre-trained model with accurate results.
The data in the data set is classified according to the identity to form a plurality of data to be tested, and the data to be tested is formed by a plurality of independent data.
And inputting the data to be detected into the first data processing model, and acquiring a processing result obtained by the first data model according to the data to be detected.
The missing calculation unit 202 is configured to decapsulate the to-be-detected data into a plurality of independent data, and input the to-be-detected data lacking one independent data into the first data processing model to obtain a comparison result.
The data to be detected is formed by combining a plurality of independent data with the same identity attribute, in the step, the independent data are divided, and then a missing bit is used for forming a plurality of missing data to be detected. Specifically, the missing data to be detected has N, where N is the data amount in the data to be detected.
And respectively inputting the data to be detected with data missing required to be subjected to the scarcity calculation into the first data processing model to obtain a comparison result, wherein the comparison result is a processing result corresponding to the data to be detected missing one independent data.
A first comparison unit 203, configured to obtain a scarcity score of the independent data based on the similarity between the processing result and the comparison result.
And after the processing result and the comparison result are obtained, scoring is carried out according to the recognition degrees of the processing result and the comparison result, wherein the similarity is the ratio of the values of the processing result and the comparison result.
The calculation module 102 is provided with a similarity-rareness conversion module, and the conversion module determines rareness according to the similarity through a series of calculations.
Specifically, the similarity may be expressed as
Figure BDA0003608779940000051
A is the value of the processing result, B is the value of the comparison result, and in this case, when A is larger, B is smaller, the
Figure BDA0003608779940000052
The larger the value of (A), the moreThat is, the larger the difference, the smaller the scarcity.
At the moment, the division intervals of the similarity are set, and the scarcity is mapped according to the division intervals to obtain a scarcity comparison table. Specifically, the scarcity degree is set to be 0-1, wherein the smaller the number is, the more scarcity is proved.
When the scarcity degree calculation is completed, the demand degree calculation is further required, where the demand degree needs to be determined according to whether the user needs to perform the determination, specifically, the calculating module 102 further includes:
and the second calling unit is used for calling a second data processing model, inputting the data to be tested and the client data into the second data processing model and obtaining a comparison result of the data to be tested and the client data.
And the second data processing model is used for comparing the user demand data with the data to be detected to obtain the similarity, and determining the demand degree according to the similarity. The second prediction model is a pre-trained prediction model and is used for judging data similarity.
And the second comparison unit is used for scoring the data to be detected according to the comparison result. Wherein, the comparison result is similarity, and when the similarity is larger, the demand degree is larger, and when the similarity is smaller, the demand degree is smaller. And setting a plurality of demand intervals according to the similarity to map, and obtaining the demand of the data to be tested. The numerical value of the demand degree is 0-1, and the larger the numerical value is, the larger the demand degree is.
And the evaluation module 103 is used for judging the occupation attribute of the data, calling a price formula according to the occupation attribute, and inputting the historical transaction price, the scarcity degree and the demand degree to calculate the asset value.
The occupancy attributes include: the data assets can be provided independently by exclusive, nonexclusive, exclusive and non-exclusive, wherein exclusive or exclusive refers to that the ownership of the data assets is limited and only one person exists, and non-exclusive or non-exclusive refers to that the ownership of the data assets belongs to a plurality of persons and entities having the data assets can provide the data assets independently.
First, the occupancy attribute of the data set is determined, and the value of the data asset is calculated as follows:
Figure BDA0003608779940000061
wherein J is the projected value of the data asset and L is s Is the last year price in the historical trading prices, L p Is the historical trading price average before the last year price, Q is the desirability value, and M is the scarcity value.
Based on the above formula, the value of the data asset can be preliminarily calculated, but the volatility in the historical transaction price needs to be considered, and the interval expansion is performed on the value, which is specifically as follows:
Figure BDA0003608779940000071
Figure BDA0003608779940000072
based on the calculation, the J epsilon (J) is finally obtained min ，J max )。
Wherein, the a 1 、a 2 、a n Representing each historical trading price, said n representing the number of said historical trading prices, said i representing the ith a.
And after the calculation of the price interval is finished, multiplying the price interval by the data volume to obtain a final price interval.
The application also provides a data asset value evaluation method, which finally obtains the price interval through processing and calculating data,
FIG. 3 is a flow chart of a data asset worth assessment in the present application.
Referring to fig. 3, S301 obtains a data set, and generates a data directory according to the data set.
A data set refers to a collection of data assets that includes one or more categories of data. In the dataset, data is stored in a tree structured process, for example: a data set is provided with a plurality of primary class data folders, each primary class data folder is divided into a plurality of secondary class data folders, and a final data storage folder is known. The data folder is a final storage location of data, and has a plurality of upper data folders, and the data folder includes: a file folder.
Scanning the existing data set, generating a directory according to the tree structure of the data, wherein the directory comprises each data and a superior folder of the data, and establishing the inclusion relationship between the superior folder and the inferior folder and the identity attribute marked with the data.
The data has identity data, the data in the same folder may have different identity data, and the data in different folders may have the same identity attribute, which represents the source of the data, i.e., the data has the same source.
The data storage folder may have one or more pieces of data, and the one or more pieces of data have the same or different identity attributes.
Referring to fig. 3, in S302, historical transaction prices of data are searched based on the data directory, and scarcity, demand and data quantity of the data are calculated;
the data catalog represents the category of the data, and the historical transaction price of the data is inquired according to the category of the data. Specifically, the calculation module is provided with a historical price storage unit, the historical price storage unit records the data types and prices of data transactions of the past time, and then the historical transaction prices of the data are extracted according to the data types in the data directory.
One preferred method is to extract data for the same identity attribute based on the identity attribute tagged in the data catalog and to extract the historical transaction price for the data asset based on the identity attribute. Of course, the data stored in the historical price storage unit at this time should also be stored separately according to the identity attribute.
Fig. 2 is a flowchart of the scarcity calculation in the present application.
Referring to fig. 2, the computing module 102 includes a first invoking unit 201, a missing computing unit 202, and a first comparing unit 203, and the data asset sequentially passes through the first invoking unit 201, the missing computing unit 202, and the first comparing unit 203 to obtain the scarcity of the data asset.
The first invoking unit 201 is configured to invoke a first data processing model, input a set of data to be tested in the first data processing model, and obtain a processing result.
The first data processing model comprises a plurality of data processing models which are stored in a model storage module, and the data processing models are called from the model storage module when the rarity degree of the data is calculated.
The first data processing model refers to an AI model applied to the data asset, and includes a prediction model, an analysis module, and the like. Specifically, the first data processing model need not be a pre-trained model with accurate results.
The data in the data set is classified according to the identity to form a plurality of data to be tested, and the data to be tested is formed by a plurality of independent data.
And inputting the data to be detected into the first data processing model, and acquiring a processing result obtained by the first data model according to the data to be detected.
The missing calculation unit 202 is configured to decapsulate the to-be-detected data into a plurality of independent data, and input the to-be-detected data lacking one independent data into the first data processing model to obtain a comparison result.
The data to be detected is formed by combining a plurality of independent data with the same identity attribute, in the step, the independent data are divided, and then a plurality of missing data to be detected are formed by missing one bit. Specifically, the missing data to be detected has N, where N is the data amount in the data to be detected.
And respectively inputting the data to be detected with data missing required to be subjected to the scarcity calculation into the first data processing model to obtain a comparison result, wherein the comparison result is a processing result corresponding to the data to be detected missing one independent data.
A first comparison unit 203, configured to obtain a scarcity score of the independent data based on the similarity between the processing result and the comparison result.
And after the processing result and the comparison result are obtained, scoring is carried out according to the recognition degrees of the processing result and the comparison result, wherein the similarity is the ratio of the values of the processing result and the comparison result.
The calculation module 102 is provided with a similarity-rareness conversion module, and the conversion module determines rareness according to the similarity through a series of calculations.
Specifically, the similarity may be expressed as
Figure BDA0003608779940000091
A is the value of the processing result, B is the value of the comparison result, and in this case, when A is larger, B is smaller, the
Figure BDA0003608779940000092
The larger the value of (a), i.e. the larger the difference, the smaller the scarcity.
At the moment, the division intervals of the similarity are set, and the scarcity is mapped according to the division intervals to obtain a scarcity comparison table. Specifically, the scarcity degree is set to be 0-1, wherein the smaller the number is, the more scarcity is proved.
When the scarcity degree calculation is completed, the demand degree calculation is further required, where the demand degree needs to be determined according to whether the user needs to perform the determination, specifically, the calculating module 102 further includes:
and the second calling unit is used for calling a second data processing model, inputting the data to be tested and the client data into the second data processing model and obtaining a comparison result of the data to be tested and the client data.
And the second data processing model is used for comparing the user demand data with the data to be detected to obtain the similarity, and determining the demand degree according to the similarity. The second prediction model is a pre-trained prediction model and is used for judging data similarity.
And the second comparison unit is used for scoring the data to be detected according to the comparison result. Wherein, the comparison result is similarity, and when the similarity is larger, the demand degree is larger, and when the similarity is smaller, the demand degree is smaller. And setting a plurality of demand intervals according to the similarity to map, and obtaining the demand of the data to be tested. The numerical value of the demand degree is 0-1, and the larger the numerical value is, the larger the demand degree is.
Referring to fig. 3, in step S303, the occupation attribute of the data is determined, a price formula is called according to the occupation attribute, and the historical transaction price, the scarcity degree, and the demand degree are input to calculate the asset value.
The occupancy attributes include: the data assets can be provided independently by exclusive, nonexclusive, exclusive and non-exclusive, wherein exclusive or exclusive refers to that the ownership of the data assets is limited and only one person exists, and non-exclusive or non-exclusive refers to that the ownership of the data assets belongs to a plurality of persons and entities having the data assets can provide the data assets independently.
First, the occupancy attribute of the data set is determined, and the value of the data asset is calculated as follows:
Figure BDA0003608779940000101
wherein J is the projected value of the data asset and L is s Is the last year price in the historical trading prices, L p Is the historical trading price average before the last year price, Q is the desirability value, and M is the rareness value.
Based on the above formula, the value of the data asset can be preliminarily calculated, but the volatility in the historical transaction price needs to be considered, and the interval expansion is performed on the value, which is specifically as follows:
Figure BDA0003608779940000102
Figure BDA0003608779940000103
based on the calculation, the J epsilon (J) is finally obtained min ，J max )。
Wherein, the a 1 、a 2 、a n Representing each historical trading price, said n representing the number of said historical trading prices, said i representing the ith a.
And after the calculation of the price interval is finished, multiplying the price interval by the data volume to obtain a final price interval.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.

Claims (10)

1. A data asset value assessment system, comprising:
the catalog module is used for acquiring a data set and generating a data catalog according to the data set;
the calculation module is used for searching the historical transaction price of the data based on the data catalog and calculating the scarcity degree, the demand degree and the data quantity of the data;
and the evaluation module is used for judging the occupation attribute of the data, calling a price formula according to the occupation attribute, and inputting the historical transaction price, the scarcity degree and the demand degree to calculate the asset value.
2. The data asset worth assessment system according to claim 1, wherein said computing module comprises:
the first calling unit is used for calling a first data processing model, inputting a group of data to be detected in the first data processing model and obtaining a processing result;
the missing calculation unit is used for unpacking the data to be detected into a plurality of independent data and inputting the data to be detected which lacks one independent data into the first data processing model to obtain a comparison result;
and the first comparison unit is used for obtaining the scarcity score of the independent data based on the similarity of the processing result and the comparison result.
3. The data asset value assessment system of claim 2, wherein said scarcity score ranges from 0 to 1, and wherein the smaller the scarcity score the more scarce.
4. The data asset worth assessment system according to claim 1, wherein said computing module further comprises:
the second calling unit is used for calling a second data processing model, inputting the data to be tested and the client data into the second data processing model and obtaining a comparison result of the data to be tested and the client data;
and the second comparison unit is used for scoring the data to be detected according to the comparison result.
5. The data asset value assessment system according to claim 1, wherein said data set comprises a plurality of data under test.
6. A data asset value assessment method, comprising:
acquiring a data set, and generating a data catalog according to the data set;
searching historical transaction prices of the data based on the data catalog, and calculating the scarcity, the demand and the data quantity of the data;
and judging the occupation attribute of the data, calling a price formula according to the occupation attribute, and inputting the historical transaction price, the scarcity degree and the demand degree to calculate the asset value.
7. The data asset worth assessment method according to claim 6, wherein said scarcity calculation comprises:
calling a first data processing model, inputting a group of data to be detected in the first data processing model, and obtaining a processing result;
unpacking the data to be detected into a plurality of independent data, and inputting the data to be detected which lacks one independent data into the first data processing model to obtain a comparison result;
and obtaining the scarcity score of the independent data based on the similarity of the processing result and the comparison result.
8. The data asset value assessment method of claim 7, wherein said scarcity score ranges from 0 to 1, and wherein the smaller the scarcity score, the more scarcity.
9. The data asset worth assessment method according to claim 6, wherein said desirability calculation comprises:
calling a second data processing model, inputting data to be tested and customer data into the second data processing model, and obtaining a comparison result of the data to be tested and the customer data;
and scoring the data to be detected according to the comparison result.
10. The data asset value assessment method of claim 6, wherein said data set comprises a plurality of data under test.
CN202210432735.6A 2022-04-21 2022-04-21 Data asset value evaluation system and method Pending CN114897322A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115689596A (en) * 2022-08-27 2023-02-03 北京华宜信科技有限公司 Non-customized data asset valuation method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115689596A (en) * 2022-08-27 2023-02-03 北京华宜信科技有限公司 Non-customized data asset valuation method
CN115689596B (en) * 2022-08-27 2023-07-07 北京华宜信科技有限公司 Non-customized data asset valuation method

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