Method and device for evaluating maturity of data assets

Abstract

The invention discloses a data asset maturity assessment method and device, and the method comprises the steps: obtaining data assets of a specific application, the data assets comprising one or more data sets, and each data set comprising a data matrix; obtaining multi-dimensional features of each data set and a spatial complete set of each feature, wherein the multi-dimensional features comprise any one or more of the following: a right path, an application region, an application industry and an application scene; determining each evaluation index of the data set according to the multi-dimensional features; determining the weight of the evaluation index; and calculating the maturity of the data set according to each evaluation index of the data set and the weight of the evaluation index. By using the scheme of the invention, the evaluation of the digital assets can be accurately and effectively realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data asset evaluation, and in particular to a method and device for evaluating the maturity of data assets. Background Art

[0002] Data assets refer to data resources that are legally owned or controlled, can be measured, and bring economic and social value to an organization. In the digital economy, data asset evaluation is crucial to a company's strategic decision-making and value creation.

[0003] Data asset maturity is considered from the basic attributes, business elements, management elements, and value elements of data asset information elements in the national standard "Requirements for Data Asset Management of Information Technology Services" GBT 40685, and diagnoses the maturity of data assets in the process of development from data resources to data assets. Accurate assessment of data asset maturity can provide effective information support for data management and help enterprises improve their data management level.

[0004] In the existing technology, there is currently no effective solution for how to accurately assess the maturity of digital assets. Summary of the invention

[0005] The present invention provides a method and device for evaluating the maturity of data assets, so as to accurately and effectively realize the evaluation of digital assets.

[0006] To this end, the present invention provides the following technical solutions: A method for evaluating the maturity of data assets, the method comprising: Acquire data assets for a specific application, wherein the data assets include one or more data sets, each data set including a data matrix; Obtaining multidimensional features of each data set and a spatial full set of each feature, wherein the multidimensional features include any one or more of the following: right path, application region, application industry, and application scenario; Determine each evaluation index of the data set according to the multidimensional features; Determining the weights of the evaluation indicators; The maturity of the data set is calculated based on the evaluation indicators and their weights.

[0007] Optionally, the evaluation indicators include any one or more of the following: integrity indicator, record indicator, data rights path indicator, application area indicator, application industry indicator, and application scenario indicator.

[0008] Optionally, determining each evaluation index of the data set according to the multidimensional feature includes: Sure Dataset Integrity indicators as follows: For any data , set the completeness score ,but: = , Represents a null value, For the dataset the number of rows, For the dataset The number of columns; Determine the dataset Data recording indicators It is expressed as follows: Set data record score ; in, It is a dataset The number of rows, and the data set , , The following conditions must be met at the same time: (1) , , Mutually exclusive; (2) ; (3) min( ); (4) min( ); but: ; Determine the dataset Data rights path indicators It is expressed as follows: Set Data Rights Path Score ; in, represents the complete set of data rights path space; then: ; Determine the dataset Application area indicators It is expressed as follows: Applying geospatial information to any data , , For the dataset Data application area space, set application area score ; Among them, regional space , , The following conditions must be met at the same time: (1) , , Mutually exclusive; (2) ; but: ; Determine the dataset Application industry indicators It is expressed as follows: For any application industry , , For the dataset Application industry, set application industry score , For the complete set of data application industry space; then: = ,in, , ; Determine the dataset The application scenario indicators are as follows: For any application scenario , , , For the dataset Application scenarios, set application scenario scores , is the complete set of data application scenario space, then: = , is the total number of application scene spaces.

[0009] Optionally, determining the weight of the evaluation indicator includes: Constructing a judgment matrix, wherein the judgment matrix is ​​used to represent the relative importance scores of the evaluation indicators after pairwise comparison and judgment; Comparing and judging the evaluation indicators in pairs based on the judgment matrix to obtain the relative importance of the evaluation indicators; Performing hierarchical single sorting according to the relative importance and the set judgment matrix scale to determine the weight of the evaluation index; the hierarchical single sorting refers to the sorting weight of the relative importance of a factor at the same level to a factor at the previous level; The judgment matrix is ​​checked for consistency and adjusted to ensure that the judgment matrix meets the consistency requirement.

[0010] Optionally, constructing a judgment matrix includes: Determine the hierarchical structure of indicators; A judgment matrix is ​​constructed based on the hierarchical structure of the indicators.

[0011] Optionally, the setting of the judgment matrix scale includes: setting the judgment matrix scale to 9 levels according to the relative importance of two evaluation indicators.

[0012] Optionally, performing hierarchical single sorting according to the relative importance and a set judgment matrix scale to determine the weight of the evaluation index includes: According to the relative importance and the set judgment matrix scale, hierarchical single sorting is performed to obtain the characteristic roots and eigenvectors of each judgment matrix; The feature vector is normalized, and the normalized feature vector is used as the ranking weight of the evaluation index at this level for the evaluation index to which it belongs.

[0013] Optionally, the performing consistency check on the judgment matrix includes: Calculate the consistency index of the judgment matrix , is the characteristic root of the judgment matrix, is the order; According to the consistency index The consistency of the judgment matrix is ​​determined.

[0014] A data asset maturity assessment device, the device comprising: A data acquisition module, used to acquire data assets of a specific application, wherein the data assets include one or more data sets; A feature acquisition module, used to acquire multi-dimensional features of each data set and a spatial full set of each feature, wherein the multi-dimensional features include any one or more of the following: right path, application region, application industry, and application scenario; An evaluation index determination module, used to determine each evaluation index of the data set according to the multidimensional features; A weight determination module, used to determine the weight of the evaluation index; The calculation module is used to calculate the maturity of the data set according to the evaluation indicators and weights of the data set.

[0015] Optionally, the weight determination module includes: A judgment matrix construction unit, used to establish a judgment matrix, wherein the judgment matrix is ​​used to represent the relative importance scores of the evaluation indicators after pairwise comparison and judgment; A judgment unit, used for comparing and judging the evaluation indicators in pairs based on the judgment matrix to obtain the relative importance of the evaluation indicators; A hierarchical analysis unit is used to perform hierarchical single sorting according to the relative importance and the set judgment matrix scale to determine the weight of the evaluation index; the hierarchical single sorting refers to the sorting weight of the relative importance of the same level factor to a factor of the previous level factor; The checking unit is used to check and adjust the consistency of the judgment matrix so that the judgment matrix meets the consistency requirement.

[0016] A computer-readable storage medium stores a computer program, which executes the steps of the data asset maturity assessment method when executed by a processor.

[0017] The data asset maturity assessment method and device provided by the present invention obtains the multidimensional features of each data set and the spatial full set of each feature based on the basic attributes, business elements, management elements, and value elements of data assets, selects multiple assessment indicators, determines the weight of each assessment indicator, and then calculates the maturity of the corresponding data set based on each assessment indicator and its weight. Using the scheme of the present invention, the maturity of data assets can be assessed from multiple angles and aspects, the accuracy of the assessment results can be improved, and effective information support can be provided for data management, helping enterprises to improve their data management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 It is a flow chart of a method for evaluating the maturity of data assets according to an embodiment of the present invention; Figure 2 is a flow chart for determining the weight of an evaluation index in an embodiment of the present invention; Figure 3 It is a structural schematic diagram of a data asset maturity evaluation device according to an embodiment of the present invention; Figure 4 It is a structural diagram of a weight determination module in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0021] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0022] Selecting integrity and number of records in data quality as measurement indicators of basic data attributes can reflect the essential information of data in general terms; the rights attribute focuses on the clarity of property rights, and selecting the rights path as a measurement indicator can truly reflect the ownership information of data assets; whether data resources have application scenarios is an important manifestation of the value that data resources can bring to the organization, and the application region, application industry, and scenario type of data, selecting these three indicators can fully display the application of data assets. Based on this, the present invention provides a method and device for evaluating the maturity of data assets, which selects multiple core evaluation indicators from four aspects: basic attributes of data assets, business elements, management elements, and value elements, to achieve accurate and effective evaluation of data assets.

[0023] like Figure 1 FIG. 1 is a flow chart of a method for evaluating the maturity of data assets according to an embodiment of the present invention, comprising the following steps: In step 101 , data assets of a specific application are obtained, where the data assets include one or more data sets.

[0024] It should be noted that the data volume, data type, data value range, etc. corresponding to different data sets may be different. The specific form of the data set may be a table or other format. For example, a data set includes multiple attribute parameters and corresponding multiple sets of values ​​for each attribute parameter. The attribute parameters include, for example, the student's student ID, age, grade, etc.

[0025] The data assets may include multiple or multiple data sets. For example, the data assets used in a certain application scenario include: k data sets, and all data sets of this data asset are represented as , the i-th data set in S Can be expressed as a matrix, , , represents a positive real number, then the data set It can be expressed as:

[0026] In the embodiment of the present invention, the maturity of each data set in the data assets may be determined.

[0027] In step 102, the multidimensional features of each data set and the spatial full set of each feature are obtained, wherein the multidimensional features include any one or more of the following: right path, application region, application industry, and application scenario. The rights path mentioned above refers to the path for obtaining data property rights, such as acquisition through transactions, legal authorization, independent production, or uncertainty; The application region refers to the area where the data can be applied; The application industry refers to the industry to which the data can be applied, such as industrial data, telecommunications data, financial data, etc. It can also be regarded as the industry type to which the data belongs; The application scenarios refer to the types of scenarios in which data can be applied, such as operation and maintenance scenarios, development scenarios, business scenarios, etc.

[0028] Since the data in different data sets have different characteristics, in a specific embodiment, different features may be selected according to the data characteristics of each data set.

[0029] Using the above dataset Take it as an example, the above four features can be obtained, as follows: Dataset The rights path is ; Dataset The application area is , Indicates the total number of application regions; Dataset The application industries are , Indicates the total number of application industries; Dataset The application scenario is , Indicates the total number of application scenarios.

[0030] in: The complete set of data rights path space is , is the total number of rights path spaces; The complete set of data application area space is , The total number of geographical spaces used; The complete collection of data application industry space is , is the total number of application industry spaces; The complete set of data application scenario space is , is the total number of application scene spaces.

[0031] In step 103, each evaluation index of the data set is determined according to the multi-dimensional features.

[0032] The evaluation indicators may include but are not limited to any one or more of the following: integrity indicators, record indicators, data rights path indicators, application area indicators, application industry indicators, application scenario indicators, etc.

[0033] Assume data set for The data matrix, is the number of rows, The number of columns is as follows. The above evaluation indicators are described in detail below.

[0034] (1) Determine the data set Integrity indicators as follows: For any data , set the completeness score ,but: = , Represents a null value.

[0035] (2) Determine the data set Data recording indicators It is expressed as follows: Set data record score ; in, It is a dataset The number of rows, and the data set , , The following conditions must be met at the same time: (1) , , Mutually exclusive; (2) ; (3) min( ); (4) min( ); but: .

[0036] (3) Determine the data set Data rights path indicators It is expressed as follows: Set Data Rights Path Score ; in, represents the complete set of data rights path space; then: .

[0037] (4) Determine the data set Application area indicators It is expressed as follows: Applying geospatial information to any data , , For the dataset Data application area space, set application area score ; Among them, regional space , , The following conditions must be met at the same time: (1) , , Mutually exclusive; (2) ; but: .

[0038] (5) Determine the data set Application industry indicators It is expressed as follows: For any application industry , , For the dataset Application industry, set application industry score ; in, For the complete set of data application industry space; then: = ; in, , .

[0039] (6) Determine the data set The application scenario indicators are as follows: For any application scenario , , , For the dataset Application scenarios, set application scenario scores ; in, is the complete set of data application scenario space, then: = ; in, is the total number of application scene spaces.

[0040] In step 104, the weight of the evaluation index is determined.

[0041] In some embodiments, the analytic hierarchy process can be used to determine the weight of each evaluation index. The basic idea of ​​the analytic hierarchy process is to transform the overall judgment of the weights of multiple elements (i.e., evaluation indicators) that constitute a complex problem into a "two-by-two comparison" of these elements, and then transform them into a ranking judgment of the overall weights of these elements, and finally determine the weight of each element.

[0042] like Figure 2 FIG. 1 is a flow chart of determining the weight of an evaluation index in an embodiment of the present invention, comprising the following steps: Step 201, constructing a judgment matrix.

[0043] The judgment matrix is ​​used to represent the relative importance scores of the evaluation indicators after pairwise comparison and judgment.

[0044] It should be noted that pairwise comparisons between evaluation indicators can be carried out through survey interviews and expert consultation methods.

[0045] Specifically, a hierarchical structure of indicators may be determined, and a judgment matrix may be established based on the hierarchical structure of indicators.

[0046] Step 202: Compare and judge the evaluation indicators in pairs based on the judgment matrix to obtain the relative importance of the evaluation indicators.

[0047] Specifically, the relative importance of each evaluation indicator at each level in the evaluation system is judged in the form of numerical values.

[0048] For example, the relative importance of different evaluation indicators can be expressed using the structure shown in Table 1 below: Table 1

[0049] Among them, the judgment matrix Relative to the evaluation index In terms of evaluation indicators and The relative importance of for The importance of for The importance of each other is the reciprocal of each other.

[0050] It should be noted that any judgment matrix should satisfy .

[0051] Step 203: Perform hierarchical single sorting according to the relative importance and the set judgment matrix scale to determine the weight of the evaluation index.

[0052] For example, the scale of the judgment matrix and its meaning are shown in Table 2 below: Table 2

[0053] The hierarchical single ranking refers to the ranking weight of the relative importance of a factor at the same level to a factor at the previous level. According to the judgment matrix, the weight of the importance order of the evaluation indicators at the previous level (i.e., the level where the judgment matrix is ​​located) associated with the judgment matrix is ​​calculated. Specifically, it can be summarized as the problem of calculating the characteristics and eigenvectors of the judgment matrix, that is, , the calculation satisfies The maximum eigenvalue of the judgment matrix and the eigenvector , and normalize the eigenvector, and the eigenvector obtained after normalization As the evaluation index of this level (that is, the level where the judgment matrix is ​​located) For its membership evaluation index The sorting weight of .

[0054] Due to the influence of some subjective and objective factors, it is difficult for the judgment matrix to be strictly consistent. Therefore, when obtaining the maximum characteristic root of the judgment matrix Finally, the consistency of the judgment matrix needs to be tested.

[0055] In the embodiment of the present invention, in order to check the consistency of the judgment matrix, the consistency index CI can be calculated, for example, defined as ,in, Indicates the order, i.e. the consistency level.

[0056] When CI=0, the judgment matrix is ​​completely consistent. The larger the value, the larger the CI, and the poorer the consistency of the judgment matrix.

[0057] Furthermore, in order to check whether the judgment matrix has satisfactory consistency, CI can also be compared with the average random consistency index RI. The value of the average random consistency index RI is shown in Table 3: Table 3

[0058] If the judgment matrix CR=CI / RI<0.10, the judgment matrix has satisfactory consistency, otherwise the judgment matrix needs to be adjusted. The specific adjustment method may be to supplement the judgment matrix through survey interview method, expert consultation method, etc.

[0059] Step 204: Perform consistency check and adjustment on the judgment matrix to make the judgment matrix meet the consistency requirement.

[0060] After the judgment matrix meets the consistency requirements, the weight of each evaluation indicator can be determined.

[0061] It should be noted that the embodiment of the present invention is not limited to the above-mentioned analytic hierarchy process to determine the weight of each evaluation index, and other methods may also be used, such as expert scoring method, priority diagram method, geometric mean and arithmetic mean.

[0062] Continue to take photos Figure 1 In step 105, the maturity of the data set is calculated based on the evaluation indicators and their weights of the data set.

[0063] For the dataset in data asset S , its maturity The weighted calculation can be performed according to each evaluation indicator and its weight, namely: ; in, to is the weight corresponding to the corresponding evaluation index, which is determined by calculating the eigenvector of the judgment matrix as described above, and satisfies the following constraints: .

[0064] It should be noted that if the data assets include multiple data sets, the maturity of each data set can be calculated according to the above method.

[0065] Accordingly, the maturity of the data asset can be qualitatively evaluated based on the maturity of the data sets it contains, or quantitatively evaluated, for example, by calculating the average maturity of all data sets, and using this value to reflect the overall maturity of the data asset.

[0066] Accordingly, an embodiment of the present invention also provides a data asset maturity evaluation device, such as Figure 3 Shown is a structural schematic diagram of the device.

[0067] The data asset maturity evaluation device 300 includes the following modules: A data acquisition module 301 is used to acquire data assets of a specific application, where the data assets include one or more data sets; The feature acquisition module 302 is used to acquire the multi-dimensional features of each data set and the spatial full set of each feature, wherein the multi-dimensional features include any one or more of the following: right path, application region, application industry, and application scenario; the detailed description of each feature can be found in the description of the embodiment of the method of the present invention, which will not be repeated here; An evaluation index determination module 303 is used to determine each evaluation index of the data set according to the multidimensional features; in some embodiments, the evaluation index may include but is not limited to any one or more of the following: integrity index, record index, data right path index, application region index, application industry index, application scenario index, etc.; A weight determination module 304 is used to determine the weight of the evaluation index; The calculation module 305 is used to calculate the maturity of the data set according to the evaluation indicators of the data set and their weights, such as obtaining the maturity of the data set through weighted calculation.

[0068] A specific structure of the weight determination module 304 is as follows: Figure 4 As shown, the following units may be included: A judgment matrix construction unit 341 is used to establish a judgment matrix, wherein the judgment matrix is ​​used to represent the relative importance scores of the evaluation indicators after pairwise comparison and judgment; A judgment unit 342 is used to compare and judge the evaluation indicators in pairs based on the judgment matrix to obtain the relative importance of the evaluation indicators; A hierarchical analysis unit 343 is used to perform hierarchical single sorting according to the relative importance and the set judgment matrix scale; the hierarchical single sorting refers to the sorting weight of the relative importance of a factor at the same level to a factor at the previous level; The checking unit 344 is used to check and adjust the consistency of the judgment matrix so that the judgment matrix meets the consistency requirement.

[0069] The detailed description of the above modules and units can be referred to the relevant description in the data asset maturity assessment method of the present invention, which will not be repeated here.

[0070] The data asset maturity assessment method and device provided by the present invention obtains the multidimensional features of each data set and the spatial full set of each feature based on the basic attributes, business elements, management elements, and value elements of data assets, selects multiple assessment indicators, determines the weight of each assessment indicator, and then calculates the maturity of the corresponding data set based on each assessment indicator and its weight. Using the scheme of the present invention, the maturity of data assets can be assessed from multiple angles and aspects, the accuracy of the assessment results can be improved, and effective information support can be provided for data management, helping enterprises to improve their data management level.

[0071] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0072] The present invention also provides a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and the computer program can be executed when it is run. Figure 1 or Figure 2 The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. The storage medium may also include a non-volatile memory (non-volatile) or a non-transitory memory, etc.

[0073] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a server or a data provider to another website site, a computer, a server or a data provider by wired or wireless means.

[0074] The embodiments of the present invention are described in detail above. The present invention is described in detail using specific implementation methods herein. The description of the above embodiments is only used to help understand the method and system of the present invention. It is only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should belong to the scope of protection of the present invention, and the content of this specification should not be understood as limiting the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the maturity of data assets, characterized in that: The method comprises: Acquire data assets for a specific application, wherein the data assets include one or more data sets, each data set including a data matrix; Obtaining multidimensional features of each data set and a spatial full set of each feature, wherein the multidimensional features include any one or more of the following: right path, application region, application industry, and application scenario; Determine each evaluation index of the data set according to the multidimensional features; Determining the weights of the evaluation indicators; The maturity of the data set is calculated based on the evaluation indicators and their weights.

2. The method for evaluating the maturity of data assets according to claim 1, characterized in that: The evaluation indicators include any one or more of the following: integrity indicators, record indicators, data rights path indicators, application area indicators, application industry indicators, and application scenario indicators.

3. The method for evaluating the maturity of data assets according to claim 2, characterized in that: Determining the evaluation indicators of the data set according to the multidimensional features includes: Sure Dataset Integrity indicators as follows: For any data , set the completeness score ,but: = , Represents a null value, For the dataset the number of rows, For the dataset The number of columns; Determine the dataset Data recording indicators It is expressed as follows: Set data record score ; in, It is a dataset The number of rows, and the data set , , The following conditions must be met at the same time: (1) , , Mutually exclusive; （2） ； (3) min( ); (4) min( ); but: ; Determine the dataset Data rights path indicators It is expressed as follows: Set Data Rights Path Score ; in, represents the complete set of data rights path space; then: ; Determine the dataset Application area indicators It is expressed as follows: Applying geospatial information to any data , , For the dataset Data application area space, set application area score ; Among them, regional space , , The following conditions must be met at the same time: (1) , , Mutually exclusive; （2） ； but: ; Determine the dataset Application industry indicators It is expressed as follows: For any application industry , , For the dataset Application industry, set application industry score , For the complete set of data application industry space; then: = ,in, , ; Determine the dataset The application scenario indicators are as follows: For any application scenario , , , For the dataset Application scenarios, set application scenario scores , is the complete set of data application scenario space, then: = , is the total number of application scene spaces.

4. The method for evaluating the maturity of data assets according to claim 1, characterized in that: Determining the weight of the evaluation index includes: Constructing a judgment matrix, wherein the judgment matrix is ​​used to represent the relative importance scores of the evaluation indicators after pairwise comparison and judgment; Based on the judgment matrix, the evaluation indicators are compared and judged in pairs to obtain the relative importance of the evaluation indicators; Performing hierarchical single sorting according to the relative importance and the set judgment matrix scale to determine the weight of the evaluation index; the hierarchical single sorting refers to the sorting weight of the relative importance of a factor at the same level to a factor at the previous level; The judgment matrix is ​​checked for consistency and adjusted to ensure that the judgment matrix meets the consistency requirement.

5. The method for evaluating the maturity of data assets according to claim 4, characterized in that: The construction of the judgment matrix comprises: Determine the hierarchical structure of indicators; A judgment matrix is ​​constructed based on the hierarchical structure of the indicators.

6. The method for evaluating the maturity of data assets according to claim 4, characterized in that: The set judgment matrix scale includes: According to the relative importance of the two evaluation indicators, the judgment matrix scale is set to 9 levels.

7. The method for evaluating the maturity of data assets according to claim 4, characterized in that: The step of performing hierarchical single sorting according to the relative importance and the set judgment matrix scale to determine the weight of the evaluation index includes: According to the relative importance and the set judgment matrix scale, hierarchical single sorting is performed to obtain the characteristic roots and eigenvectors of each judgment matrix; The feature vector is normalized, and the normalized feature vector is used as the ranking weight of the evaluation index at this level for the evaluation index to which it belongs.

8. The method for evaluating the maturity of data assets according to claim 7, characterized in that: The consistency check of the judgment matrix comprises: Calculate the consistency index of the judgment matrix , is the characteristic root of the judgment matrix, is the order; According to the consistency index The consistency of the judgment matrix is ​​determined.

9. A data asset maturity assessment device, characterized in that: The device comprises: A data acquisition module, used to acquire data assets of a specific application, wherein the data assets include one or more data sets; A feature acquisition module, used to acquire multi-dimensional features of each data set and a spatial full set of each feature, wherein the multi-dimensional features include any one or more of the following: right path, application region, application industry, and application scenario; An evaluation index determination module, used to determine each evaluation index of the data set according to the multidimensional features; A weight determination module, used to determine the weight of the evaluation index; The calculation module is used to calculate the maturity of the data set according to the evaluation indicators and weights of the data set.

10. The data asset maturity evaluation device according to claim 9, characterized in that: The weight determination module comprises: A judgment matrix construction unit, used to establish a judgment matrix, wherein the judgment matrix is ​​used to represent the relative importance scores of the evaluation indicators after pairwise comparison and judgment; A judgment unit, used for comparing and judging the evaluation indicators in pairs based on the judgment matrix to obtain the relative importance of the evaluation indicators; A hierarchical analysis unit is used to perform hierarchical single sorting according to the relative importance and the set judgment matrix scale to determine the weight of the evaluation index; the hierarchical single sorting refers to the sorting weight of the relative importance of the same level factor to a factor of the previous level factor; The checking unit is used to check and adjust the consistency of the judgment matrix so that the judgment matrix meets the consistency requirement.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating the maturity of data assets described in any one of claims 1 to 8 are executed.

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