Data asset assessment method and device

Abstract

The invention provides a data asset assessment method, which comprises the following steps: determining data asset reference values, the number of which is a first numerical value; the comprehensive weight of each data asset reference value is determined based on a multi-dimensional evaluation model and an evaluation index of a second numerical dimension, and the multi-dimensional evaluation model is obtained based on the evaluation index of the second numerical dimension; and obtaining a data asset evaluation value according to the data asset reference value and the corresponding comprehensive weight. According to the method provided by the embodiment of the invention, the real value of the data assets can be reflected more comprehensively.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data asset evaluation method, device, electronic device and computer-readable medium. Background Art

[0002] Data assets refer to the collection of various types of data held by the government or enterprises to support their business operations. For the government, its core value is reflected in improving government management and service efficiency, optimizing resource allocation, strengthening economic monitoring and analysis, and enhancing market supervision capabilities. For enterprises, the value of data assets is reflected in optimizing decision-making, improving operational efficiency, reducing costs, promoting innovation, and improving the overall competitiveness of enterprises. From a technical perspective, data assets are defined as various types of information stored in electronic form, which have various forms, including but not limited to text documents, image materials, video materials, etc. From the perspective of business management, data assets focus on data resources that are crucial in the operation and management of government and enterprises, such as real-time information on the supply chain, market dynamics data, customer behavior analysis, etc. These data are an indispensable basis for making strategic decisions.

[0003] With the rapid development of information technology, data has become an important asset for all kinds of organizations. How to scientifically and comprehensively evaluate the value of data assets has become a difficult problem that needs to be solved urgently. Traditional data asset evaluation methods often focus on a single dimension, such as the cost method, the income method, or the market method, which is difficult to fully reflect the true value of data assets. Summary of the invention

[0004] The present application provides a data asset evaluation method, which can more comprehensively reflect the true value of data assets. The method includes the following steps:

[0005] Determine a data asset reference value, wherein the amount of the data asset reference value is a first value;

[0006] Determining a comprehensive weight of each of the data asset reference values ​​based on a multi-dimensional evaluation model and an evaluation index of a second numerical dimension, wherein the multi-dimensional evaluation model is obtained based on the evaluation index of the second numerical dimension;

[0007] The data asset valuation is obtained based on the data asset reference value and its corresponding comprehensive weight.

[0008] In some embodiments, the step of obtaining the evaluation index of the second numerical dimension based on the multi-dimensional evaluation model includes:

[0009] Determining an evaluation index of a second numerical dimension;

[0010] Acquire a first comparison judgment matrix based on the evaluation index;

[0011] The characteristic vector of the first comparison judgment matrix is ​​obtained according to the first comparison judgment matrix, and the characteristic vector is used as the multi-dimensional evaluation model.

[0012] In some embodiments, the step of obtaining the comparison judgment matrix of the evaluation index includes:

[0013] Constructing an initial comparison judgment matrix based on the evaluation index;

[0014] Performing a consistency check on the initial comparison judgment matrix to obtain a consistency ratio;

[0015] When the consistency ratio is less than a predetermined threshold, a first comparison judgment matrix of the evaluation index is determined.

[0016] In some embodiments, before the step of determining the comprehensive weight of each of the data asset reference values, the step further includes:

[0017] Constructing a second comparison and judgment matrix for each of the evaluation indicators;

[0018] Obtaining the maximum eigenvalue of each second comparison judgment matrix;

[0019] According to the maximum eigenvalue, obtaining an eigenvector corresponding to the maximum eigenvalue;

[0020] A eigenvector matrix is ​​obtained according to the eigenvectors.

[0021] In some embodiments, the step of determining the comprehensive weight of each of the data asset reference values ​​based on the multi-dimensional evaluation model and the evaluation index of the second numerical dimension includes:

[0022] The comprehensive weight of each of the data asset reference values ​​is determined according to the eigenvectors of the first comparison judgment matrix and the eigenvector matrix.

[0023] In some embodiments, the step of determining the comprehensive weight of each of the data asset reference values ​​according to the eigenvectors of the first comparison judgment matrix and the eigenvector matrix includes:

[0024] The product of the eigenvector of the first comparison judgment matrix and the eigenvector matrix is ​​calculated to obtain a comprehensive weight of the first number of numerical values.

[0025] In some embodiments, the step of obtaining the data asset evaluation value according to the data asset reference value and its corresponding comprehensive weight includes:

[0026] The data asset valuation is obtained by taking a weighted average of the data asset reference values ​​and their corresponding comprehensive weights.

[0027] In some embodiments, the present application further proposes a data asset evaluation device, including:

[0028] A reference module, configured to determine a data asset reference value, wherein the number of the data asset reference value is a first value;

[0029] A weight determination module, used to determine the comprehensive weight of each of the data asset reference values ​​based on a multi-dimensional evaluation model and an evaluation index of a second numerical dimension, wherein the multi-dimensional evaluation model is obtained based on the evaluation index of the second numerical dimension;

[0030] The evaluation module is used to obtain the data asset evaluation value based on the data asset reference value and its corresponding comprehensive weight.

[0031] In some embodiments, an electronic device is also proposed, including a memory and a processor, wherein the memory stores computer executable instructions, and the processor implements the method of the above embodiment when running the computer executable instructions on the memory.

[0032] In some embodiments, a computer-readable storage medium is further proposed, on which a computer program is stored. When the computer program is executed by a processor, the method of the above embodiment is implemented.

[0033] The data asset evaluation method of the above embodiment determines the comprehensive weight of the data asset reference value based on a multi-dimensional evaluation model and multi-dimensional evaluation indicators, and obtains the data asset evaluation value based on the data asset reference value and its corresponding comprehensive weight, thereby avoiding the one-sidedness of a single data asset evaluation method and making the data asset evaluation process more reasonable. At the same time, the method of the above embodiment is more concise and effective, making the data asset evaluation more comprehensive and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments.

[0035] Figure 1 is a flowchart of a data asset evaluation method in some embodiments;

[0036] Figure 2 A flowchart of a step of a data asset evaluation method in some other embodiments;

[0037] Figure 3 A flowchart of a step of a data asset evaluation method in some other embodiments;

[0038] Figure 4 A flowchart of a step of a data asset evaluation method in some other embodiments;

[0039] Figure 5 is a structural block diagram of a data asset evaluation device in some embodiments;

[0040] Figure 6 It is a schematic diagram of the structure of an electronic device in some embodiments. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0043] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0044] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0045] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0046] In this application, a data asset evaluation method is proposed, such as Figure 1 As shown, the method includes:

[0047] Step 101, determine a data asset reference value, the quantity of the data asset reference value is a first value.

[0048] In this embodiment, m data asset evaluation methods are selected (or other similar data assets are referred to), and data asset reference values ​​P1, P2, ..., P m In this example, P is the reference value (price) of the data asset. Supplier 1 is priced at RMB 1,500, Supplier 2 is priced at RMB 1,800, and Supplier 3 is priced at RMB 600. These are reference values ​​obtained by each supplier based on a comprehensive consideration of the scale, quality, and cost of their data assets.

[0049] Step 102, based on the multi-dimensional evaluation model and the evaluation index of the second numerical dimension, determine the comprehensive weight of each data asset reference value, wherein the multi-dimensional evaluation model is obtained based on the evaluation index of the second numerical dimension.

[0050] In this embodiment, the evaluation indicators of the second numerical dimension include but are not limited to the following five dimensions, including data asset scale, data asset call information, data quality, data cost and data life cycle. The multi-dimensional evaluation model is obtained through the above multi-dimensional evaluation indicators to determine the comprehensive weight corresponding to each data asset reference value.

[0051] The explanation of the evaluation indicators of the five dimensions is as follows: Data asset scale includes the total amount of data assets to be evaluated, that is, the number, type, coverage, etc. of data. The larger the data asset scale, the greater the value of the data to be evaluated. Data asset call information refers to the information on the data asset being called, including the number of times the data asset is called (daily call number, monthly call number, total call number, etc.), the number of calling users (total number of users, number of active users, etc.), the number of calling business categories, etc. This indicator indirectly evaluates the value of data assets through the user's use behavior of data assets. The more times the call is made, the larger the number of calling users, and the more business categories are called, the greater the value of the data asset. Data quality is an indicator for analyzing and evaluating the accuracy, timeliness, comparability, consistency, applicability, availability, and economy of data assets, and comprehensively evaluates the quality of data assets. The higher the quality of data assets, the greater their value. Data costs mainly include the cost of producing (or acquiring) this batch of data, labor costs, time costs, etc., as well as the costs required for the later storage and maintenance of this batch of data assets. Data life cycle refers to the validity period of data assets. The value of data assets will gradually decay over time until they lose their economic value.

[0052] In this embodiment, the first numerical values ​​may be the same or different and may be set according to user needs, and are not specifically limited here.

[0053] Step 103, obtaining the data asset evaluation value according to the data asset reference value and its corresponding comprehensive weight.

[0054] In this embodiment, the reference value of the data asset and its corresponding comprehensive weight are weighted averaged to obtain the evaluation value of the data asset.

[0055] The data asset evaluation method of the above embodiment determines the comprehensive weight of the data asset reference value based on a multi-dimensional evaluation model and multi-dimensional evaluation indicators, and obtains the data asset evaluation value based on the data asset reference value and its corresponding comprehensive weight, thereby avoiding the one-sidedness of a single data asset evaluation method and making the data asset evaluation process more reasonable. At the same time, the method of the above embodiment is more concise and effective, making the data asset evaluation more comprehensive and efficient.

[0056] In some embodiments, Figure 2 As shown, before step 102, the step of obtaining the multi-dimensional evaluation model based on the evaluation index of the second numerical dimension includes:

[0057] Step 201, determining the evaluation index of the second numerical dimension.

[0058] In this embodiment, the evaluation indicators of the second numerical dimension include but are not limited to the following five dimensions, including data asset scale, data asset call information, data quality, data cost and data life cycle.

[0059] Step 202: Obtain a first comparison and judgment matrix based on the evaluation index.

[0060] In this embodiment, there are n evaluation indicators, X=(x i，j ) n×n Represented as the constructed comparative judgment matrix. The elements in the matrix where x ij Indicates the importance level of the i-th element to the j-th element. In this example, i, j = 1, 2, 3, 4, 5.

[0061] In this embodiment, the comparison judgment matrix may be filled in with the scores given by industry experts. The comparison judgment matrix uses 1-9 to represent the importance, and may also use numerical values ​​as long as they can reflect the quantitative relationship.

[0062] in:

[0063] (1) 1 means that the two factors have equal importance.

[0064] (2)3 means that one of the two factors is slightly more important than the other.

[0065] (3) 5 means that one of the two factors is significantly more important than the other.

[0066] (4)7 indicates that one of the two factors is more important than the other.

[0067] (5) 9 means that one of the two factors is extremely more important than the other.

[0068] (6) 2468 means the importance level is between the above two levels.

[0069] During the construction of the comparison and judgment matrix at each level, industry experts will judge the relative importance of the influencing factors, x ij It indicates the importance level of the i-th factor to the j-th factor. The larger the number, the higher the relative importance.

[0070] Step 203: Obtain the eigenvector of the first comparison judgment matrix according to the first comparison judgment matrix, and use the eigenvector as the multi-dimensional evaluation model. In this embodiment, the maximum eigenvalue λ of the first comparison judgment matrix X is obtained. max ; Find the eigenvector W corresponding to the maximum eigenvalue max ; Normalize the feature vector to get and will As the multidimensional evaluation model.

[0071] According to the first comparison judgment matrix of the above embodiment, a multi-dimensional evaluation model based on multiple evaluation indicators is obtained.

[0072] In some embodiments, Figure 3 As shown, the step of obtaining a first comparison judgment matrix based on the evaluation index includes:

[0073] Step 301: construct an initial comparison judgment matrix based on evaluation indicators.

[0074] Step 302: Perform a consistency check on the initial comparison judgment matrix to obtain a consistency ratio.

[0075] In this example, the initial comparison judgment matrix needs to be checked for consistency, and iterative adjustments are performed if the conditions are not met.

[0076] First, the consistency test is performed on the initial comparison judgment matrix, and the consistency index CI is calculated. λ max is the maximum eigenvalue of the comparison judgment matrix.

[0077] Secondly, the consistency ratio CR is calculated, where RI is the average random consistency index, which can be obtained by looking up a table, as shown in the following table. The table shows the generally recognized empirical values. In this example, n=5, corresponding to RI=1.12.

[0078] n 3 4 5 6 7 8 9 RI 0.52 0.89 1.12 1.26 1.36 1.41 1.46

[0079] Step 303: When the consistency ratio is less than a predetermined threshold, determine a first comparison judgment matrix of the evaluation index.

[0080] If the calculated consistency ratio CR is greater than 0.1, the constructed comparison judgment matrix is ​​considered unacceptable, and the process returns to step 3 to correct the item with the highest degree of deviation in the comparison judgment matrix, and then repeats steps 301 and 302 until CR is less than 0.1. After CR is less than 0.1, the result is considered acceptable, and the comparison judgment matrix of the evaluation index is determined.

[0081] In some embodiments, Figure 4 As shown, before the step of determining the comprehensive weight of each of the data asset reference values, the method further includes:

[0082] Step 401: construct a second comparison and judgment matrix for each of the evaluation indicators.

[0083] Refer to the above steps 201 to 202 to construct the initial price comparison judgment matrix for each evaluation indicator. In this embodiment, for the second value n evaluation indicators, the judgment matrix is ​​constructed for the first value m data asset evaluation methods, that is, n m-order judgment matrices Y1, Y2, ..., Y n . Where Y=(y) m×m .

[0084] Similarly, it is necessary to judge the matrices Y1, Y2, ..., Y n Perform consistency check, for the specific process refer to step 302 to step 303 of the above embodiment, if the conditions are not met, perform iterative adjustment.

[0085] Step 402: Obtain the maximum eigenvalue of each second comparison judgment matrix.

[0086] In this embodiment, the eigenvalue method is used to calculate the comprehensive weight of the index. First, each second comparison judgment matrix Y1, Y2, ..., Y n The maximum eigenvalue λ max .

[0087] Step 403: Obtain an eigenvector corresponding to the maximum eigenvalue according to the maximum eigenvalue.

[0088] Get the comparison judgment matrix Y1, Y2, ..., Y n The eigenvector λ corresponding to the maximum eigenvalue i，i =1~n.

[0089] Step 404, obtaining an eigenvector matrix according to the eigenvector.

[0090] According to the eigenvector λ i，i=1~n, forming a normalized weight vector matrix:

[0091]

[0092] In some embodiments, the step of determining the comprehensive weight of each of the data asset reference values ​​based on the multi-dimensional evaluation model and the evaluation index of the second numerical dimension includes:

[0093] The comprehensive weight of each of the data asset reference values ​​is determined according to the eigenvectors of the first comparison judgment matrix and the eigenvector matrix.

[0094] In this embodiment, the product of the eigenvector of the first comparison judgment matrix and the eigenvector matrix is ​​calculated to obtain the comprehensive weight of the first number of numerical values.

[0095] That is, the eigenvector of the first comparison judgment matrix The product of V obtained in step 404 is

[0096] The above-mentioned embodiments achieve a comprehensive assessment of the value of data assets by comprehensively considering multiple dimensions such as call information of data assets, differences in business indicators, customer feedback, and information data.

[0097] In some embodiments, a data asset valuation value is obtained based on a data asset reference value and its corresponding comprehensive weight.

[0098] In this example, the operational weighted average method can be used to obtain the valuation P of the assessed asset, that is, P = P1F1 + P2F2 +, ..., + P m F m The following is an example of a complete data asset evaluation process to introduce the digital asset evaluation method of this application.

[0099] A certain company is an intellectual property data provider. In the process of pricing its products, it uses the method proposed in the embodiment of the present application to evaluate the value of its data assets.

[0100] First, construct the first comparison judgment matrix X as follows:

[0101] Table 1 Comparison judgment matrix X

[0102] index Asset size Call Information quality cost life cycle Asset size 1 2 3 4 5 Call Information 1 / 2 1 3 5 4 quality 1 / 3 1 / 3 1 3 5 cost 1 / 4 1 / 5 1 / 3 1 3 life cycle 1 / 5 1 / 4 1 / 5 1 / 3 1

[0103] The eigenvalues ​​and eigenvectors of the first comparison judgment matrix in Table 1 are obtained by using the method described in the embodiment of the present application and are shown in Table 2 below.

[0104] Table 2 Index analysis results

[0105] index Eigenvector Weight value % Maximum eigenvalue CI value Asset size 0.7348 39.22 5.341 0.08525 Call Information 0.5711 30.47 - - quality 0.3168 16.91 - - cost 0.1575 8.41 - - life cycle 0.0934 4.99 - -

[0106] By looking up the RI value table, it can be found that when n=5, the RI value is 1.12. After calculation, the consistency test result can be obtained, as shown in Table 3 below.

[0107] Table 3 Consistency test table

[0108] Maximum eigenvalue CI RI CR result 5.341 0.08525 1.12 0.0761 Satisfy the test results

[0109] Furthermore, the second comparison judgment matrix of the five indicators is constructed as follows:

[0110] Comparative judgment matrix of data asset scale

[0111] Comparative judgment matrix of data asset call information

[0112] Comparative judgment matrix of data quality

[0113] Comparative judgment matrix of data cost

[0114] Comparative judgment matrix of data life cycle

[0115] The relevant indicators of Y1, Y2, ..., Y5 are shown in Table 4.

[0116] Table 4

[0117]

[0118] The normalized weight vector corresponding to the maximum eigenvalue constitutes the matrix V as follows:

[0119]

[0120] The comprehensive weight vector:

[0121] The products of three other intellectual property data suppliers are selected as reference prices and reference values ​​for data assets, as shown in Table 5 below.

[0122] Finally, the final valuation of the data asset can be obtained based on the comprehensive weight vector obtained in the above process and the value of the reference asset.

[0123] P=P1F1+P2F2+P3F3

[0124] =0.4671*1500+0.4247*1800+0.1082*600

[0125] =1530

[0126] Table 5 Competitive Asset Price List

[0127]

[0128] In some embodiments, the present application also proposes a data asset evaluation device, such as Figure 5 As shown, including:

[0129] A reference module 501 is used to determine a data asset reference value, wherein the number of the data asset reference value is a first value;

[0130] A weight determination module 502, configured to determine a comprehensive weight of each of the data asset reference values ​​based on a multi-dimensional evaluation model and an evaluation index of a second numerical dimension, wherein the multi-dimensional evaluation model is obtained based on the evaluation index of the second numerical dimension;

[0131] The evaluation module 503 is used to obtain the data asset evaluation value according to the data asset reference value and its corresponding comprehensive weight.

[0132] The functions or operation steps implemented by the above modules are similar to the above methods. Please refer to the corresponding parts for details and will not be described in detail here.

[0133] Figure 6 This is a schematic diagram of the structure of an electronic device provided in another embodiment of the present application. The electronic device 4000 includes a processor 41, and may also include an input device 42, an output device 43, and a memory 44. The input device 42, the output device 43, the memory 44, and the processor 41 are interconnected via a bus.

[0134] The memory includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), which is used for related instructions and data.

[0135] The input device is used to input data and / or signals, and the output device is used to output data and / or signals. The output device and the input device can be independent devices or an integrated device.

[0136] The processor may include one or more processors, for example, one or more central processing units (CPUs). When the processor is a CPU, the CPU may be a single-core CPU or a multi-core CPU. The processor may also include one or more special-purpose processors, which may include GPUs, FPGAs, etc., for accelerated processing. The memory is used to store program code and data of the network device. The processor is used to call the program code and data in the memory and execute the steps in the above method embodiment. For details, please refer to the description in the method embodiment, which will not be repeated here.

[0137] Understandably, Figure 5 Only a simplified design of the electronic device is shown. In practical applications, the electronic device may also include other necessary components, including but not limited to any number of input / output devices, processors, controllers, memories, etc., and all electronic devices that can implement the embodiments of the present application are within the protection scope of the present application.

[0138] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0139] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the division of the unit is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling, direct coupling, or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0140] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can 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. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media integrations. The available medium may be a read-only memory (ROM), or a random access memory (RAM), or a magnetic medium, such as a floppy disk, a hard disk, a tape, a disk, or an optical medium, such as a digital versatile disc (DVD), or a semiconductor medium, such as a solid state disk (SSD), etc.

[0142] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A data asset evaluation method, characterized in that: The following steps are involved: Determining a data asset reference value, wherein the number of the data asset reference value is a first value; Determining a comprehensive weight of each of the data asset reference values ​​based on a multi-dimensional evaluation model and an evaluation index of a second numerical dimension, wherein the multi-dimensional evaluation model is obtained based on the evaluation index of the second numerical dimension; The data asset valuation is obtained based on the data asset reference value and its corresponding comprehensive weight.

2. The method according to claim 1, characterized in that The step of obtaining the evaluation index of the multi-dimensional evaluation model based on the second numerical dimension includes: Determining an evaluation index of a second numerical dimension; Acquire a first comparison judgment matrix based on the evaluation index; The characteristic vector of the first comparison judgment matrix is ​​obtained according to the first comparison judgment matrix, and the characteristic vector is used as the multi-dimensional evaluation model.

3. The method according to claim 2, characterized in that The step of obtaining the first comparison judgment matrix of the evaluation index comprises: Constructing an initial comparison judgment matrix based on the evaluation index; Performing a consistency check on the initial comparison judgment matrix to obtain a consistency ratio; When the consistency ratio is less than a predetermined threshold, a first comparison judgment matrix of the evaluation index is determined.

4. The method according to claim 2, characterized in that: Before the step of determining the comprehensive weight of each of the data asset reference values, it also includes: Constructing a second comparison and judgment matrix for each of the evaluation indicators; Obtaining the maximum eigenvalue of each second comparison judgment matrix; According to the maximum eigenvalue, obtaining an eigenvector corresponding to the maximum eigenvalue; A eigenvector matrix is ​​obtained according to the eigenvectors.

5. The method according to claim 4, characterized in that The step of determining the comprehensive weight of each data asset reference value based on the multi-dimensional evaluation model and the evaluation index of the second numerical dimension comprises: The comprehensive weight of each of the data asset reference values ​​is determined according to the eigenvectors of the first comparison judgment matrix and the eigenvector matrix.

6. The method according to claim 5, characterized in that The step of determining the comprehensive weight of each of the data asset reference values ​​according to the eigenvector of the first comparison judgment matrix and the eigenvector matrix comprises: The product of the eigenvector of the first comparison judgment matrix and the eigenvector matrix is ​​calculated to obtain a comprehensive weight of the first number of numerical values.

7. The method according to claim 1, characterized in that According to the data asset reference value and its corresponding comprehensive weight, the steps to obtain the data asset valuation include: The data asset valuation is obtained by taking a weighted average of the data asset reference values ​​and their corresponding comprehensive weights.

8. A data asset evaluation device, characterized in that: include: A reference module, configured to determine a data asset reference value, wherein the number of the data asset reference value is a first value; A weight determination module, used to determine the comprehensive weight of each of the data asset reference values ​​based on a multi-dimensional evaluation model and an evaluation index of a second numerical dimension, wherein the multi-dimensional evaluation model is obtained based on the evaluation index of the second numerical dimension; The evaluation module is used to obtain the data asset evaluation value based on the data asset reference value and its corresponding comprehensive weight.

9. An electronic device, comprising a memory and a processor, wherein the memory stores computer executable instructions, and the processor implements the method according to claims 1-7 when executing the computer executable instructions on the memory.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method according to claims 1-7 is implemented.

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