Data asset value evaluation method and system

Abstract

The invention relates to a data asset value evaluation method and system, and the method comprises the steps: constructing a value data growth model, and obtaining equipment data through the calculation of the value data growth model; dividing the equipment data into a plurality of data sets according to data types through a Pareto optimal solution; filtering the data in the plurality of data sets through a ridge regression fitting method to obtain data assets; constructing an asset discounting model, and calculating a first income-overhead ratio of the data assets through the asset discounting model; and determining an optimal income-to-overhead ratio according to the second income-to-overhead ratio of the target asset and the first income-to-overhead ratio. According to the method, the flexibility, the accuracy and the objectivity of income-overhead ratio calculation are improved, and the actual value of data assets can be reflected more truly.

Description

Technical Field

[0001] The present invention belongs to the field of data asset stock evaluation, and in particular, relates to a data asset value evaluation method and system. Background Art

[0002] With the development of the digital economy, data asset transactions are becoming increasingly frequent. Unified evaluation standards are crucial to ensuring the fairness, justice and transparency of the market. By clarifying the construction of the evaluation model, the determination of parameters, and the calculation method of the data asset sharing rate, it provides a reference example for evaluation institutions and enterprises in the industry, promotes the comparability and credibility of the evaluation results, and promotes the healthy development of the data asset value evaluation industry.

[0003] Traditional data asset valuation methods, such as market method, cost method and income method, have a low conversion rate of valuable data into data assets, so there are many limitations when applied to data asset valuation. Summary of the invention

[0004] In view of the above deficiencies in the prior art, the purpose of the invention is to provide a data asset value assessment method and system.

[0005] A first aspect of the present invention provides a data asset value assessment method, comprising:

[0006] S1: constructing a value data growth model, and obtaining device data through calculation using the value data growth model;

[0007] S2: dividing the device data into multiple data sets according to data types through a Pareto optimal solution;

[0008] S3: Filter the data in multiple data sets through ridge regression fitting method to obtain data assets;

[0009] S4: constructing an asset discount model, and calculating a first income-expense ratio of the data asset by using the asset discount model;

[0010] S5: Determine the optimal revenue-to-expense ratio based on the second revenue-to-expense ratio of the target asset and the first revenue-to-expense ratio to complete the data asset value assessment.

[0011] According to a data asset value assessment method provided by the present invention, step S1 further comprises:

[0012] S11: Collect log data;

[0013] S12: construct a basic model, and continue to estimate the parameters in the basic model through the log data to obtain a value data growth model;

[0014] S13: Obtaining equipment data through calculation using the value data growth model.

[0015] According to a data asset value assessment method provided by the present invention, the expression of the basic model in step S12 is:

[0016]

[0017] Among them, t is the current calculation time, N t is the number of devices in the network at time t, m is the first device growth constant to be estimated, p is the second device growth constant to be estimated, and q is the third device growth constant to be estimated.

[0018] According to a data asset value assessment method provided by the present invention, the data types in step S2 include: data integrity type, data standardization type, data accuracy type, data timeliness type, data richness type, and data coverage type.

[0019] According to a data asset value assessment method provided by the present invention, the expression of the ridge regression fitting method in step S3 is:

[0020] ||Xθ-y|| 2 +‖Γθ|| 2 ;

[0021] Among them, X is the input data, θ is the fitting hyperparameter, Y is the output prediction result, and Γ is the weight constant.

[0022] According to a data asset value assessment method provided by the present invention, the expression of the first income-cost ratio in step S4 is:

[0023]

[0024] Where S is the first benefit-cost ratio obtained by calculation, I(G v ) is the income of data assets, C(G v ) is the cost of data assets;

[0025]

[0026] Among them, α is the weight factor, I is the data index value in the data asset, is the computing capacity requirement value of the i-th data in the data asset, j is the data index value in the associated asset, is the computing power requirement value of the jth data in the associated asset, is the actual proportion of the j-th data in the associated assets after being mapped.

[0027] According to a data asset value assessment method provided by the present invention, step S5 specifically includes:

[0028] When the first revenue-to-expense ratio is greater than the second revenue-to-expense ratio, taking the first revenue-to-expense ratio as the optimal revenue-to-expense ratio;

[0029] When the second revenue-to-expense ratio is greater than the first revenue-to-expense ratio, taking the second revenue-to-expense ratio as the optimal revenue-to-expense ratio;

[0030] When the first revenue-to-cost ratio is equal to the second revenue-to-cost ratio, the first revenue-to-cost ratio is used as the optimal revenue-to-cost ratio.

[0031] The second aspect of the present invention also provides a data asset value assessment system, comprising:

[0032] Growth calculation module: used to build a value data growth model, and obtain device data through calculation of the value data growth model;

[0033] A partitioning module: used for partitioning the device data into a plurality of data sets according to data types by using a Pareto optimal solution;

[0034] Filtering module: used to filter data in multiple data sets through ridge regression fitting method to obtain data assets;

[0035] Calculation module: used to construct an asset discount model, and calculate the first income-cost ratio of the data asset through the asset discount model;

[0036] Optimization module: used to determine the optimal revenue-to-expense ratio based on the second revenue-to-expense ratio of the target asset and the first revenue-to-expense ratio to complete the data asset value assessment.

[0037] A third aspect of the present invention provides a data asset value assessment device, comprising:

[0038] A memory and at least one processor, wherein instructions are stored in the memory;

[0039] At least one of the processors calls the instructions in the memory to enable the data asset value assessment device to execute the data asset value assessment method as described in any one of the above items.

[0040] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, and the instructions, when executed by a processor, implement a data asset value assessment method as described in any one of the above items.

[0041] The beneficial effects of the present invention are as follows:

[0042] The present invention provides a data asset value assessment method, system, device and storage medium. By constructing a value data growth model, using the Pareto optimal solution to divide the data set, and the ridge regression fitting method to filter the data, a systematic data asset value assessment method is proposed, which can more accurately identify and assess the value of data assets, thereby helping to more effectively manage and utilize data resources. When dividing data sets, the present invention takes into account multiple data types such as data integrity, standardization, accuracy, timeliness, richness and coverage. This comprehensive consideration method helps to ensure the accuracy and comprehensiveness of data asset value assessment and avoids missing any key factors that may affect the value of data. In addition, by constructing an asset discount model, the present invention can calculate the first income-to-expense ratio of data assets while taking into account the income and expenses of data assets. In addition, by introducing parameters such as weight factors, data index values, and computing power requirements, the calculation of the income-to-expense ratio is made more flexible and accurate, and can more truly reflect the actual value of data assets. Finally, after comparing the first income-to-expense ratio with the second income-to-expense ratio of the target asset, the present invention can determine the optimal income-to-expense ratio, thereby completing the data asset value assessment. The comparison and determination process helps to ensure the objectivity and fairness of the assessment results and provide a valuable reference basis. Through the method of the present invention, the value of data assets can be better understood, thereby formulating more effective data management and utilization strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are only used to illustrate specific embodiments and are not considered to limit the present invention. In the entire drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0044] Figure 1 A schematic diagram of a data asset value assessment method provided by an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of the structure of a data asset value assessment system provided in an embodiment of the present invention.

[0046] Reference numerals:

[0047] 100. Growth calculation module; 200. Division module; 300. Filtering module; 400. Calculation module; 500. Optimization module. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0049] Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention.

[0050] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of methods and systems consistent with some aspects of the present invention as detailed in the appended claims.

[0052] In order to better understand the present invention, the terms appearing in the embodiments of the present invention are explained in detail below.

[0053] Data assets refer to data resources that are owned or controlled by specific entities and can bring benefits. There is no unified standard for the definition of data assets, but they usually include the following characteristics: data assets are a type of resource that records data in a physical or electronic form, such as digital information, text information, image information, etc.; data assets are a type of asset that is legally owned or controlled by a specific entity and can bring economic benefits in the future; data assets are a factor, and like capital and technology, they are an important factor in production and business activities.

[0054] Pareto optimal solution: refers to a solution that cannot improve on one objective without being inferior to other objectives under given resource conditions. In other words, the Pareto optimal solution is a solution that cannot be further improved in a multi-objective optimization problem, that is, without any objective getting worse, it is also impossible to make the situation or objectives of others better.

[0055] Combine the following Figure 1 to Figure 2 Embodiments of the present invention are described.

[0056] like Figure 1 As shown, the first aspect of the present invention provides a data asset value assessment method, comprising:

[0057] S1: constructing a value data growth model, and obtaining device data through calculation using the value data growth model.

[0058] Step S1 is intended to serve as a preprocessing link for converting valuable data into data assets. Specifically, first, various types of device data stored in logs within the network are collected, and then a growth model is deployed on the server. The selected valuable data growth model is determined by analyzing the log data type, and the parameters in the growth model are estimated based on the time and quantity relationship of the data to obtain a complete growth model that matches the group of data with the data assets, thereby completing the preprocessing link for converting the valuable data analyzed in this analysis into data assets.

[0059] Wherein, step S1 further comprises:

[0060] S11: Collect log data.

[0061] S12: Construct a basic model, and continue to estimate the parameters in the basic model through the log data to obtain a value data growth model.

[0062] The expression of the basic model in step S12 is:

[0063]

[0064] Among them, t is the current calculation time, N tis the number of devices in the network at time t, m is the first device growth constant to be estimated, p is the second device growth constant to be estimated, and q is the third device growth constant to be estimated.

[0065] S13: Obtaining equipment data through calculation using the value data growth model.

[0066] In steps S11 to S13, the selected value data growth model is first determined by analyzing the log data. The parameters in the growth model are estimated based on the time and quantity relationship of the data. The expression of the growth model is as above. By fitting the data, the values ​​of the three constants can be determined to obtain a complete growth model that matches the set of data and the data assets.

[0067] S2: Divide the device data into multiple data sets according to data types through the Pareto optimal solution.

[0068] Among them, the data types in step S2 include: data integrity type, data standardization type, data accuracy type, data timeliness type, data richness type, and data coverage type.

[0069] Furthermore, data integrity mainly reflects the availability of data records. When a large number of data are missing in data assets, the data assets may lose their value. Therefore, whether it is original data or data assets, the integrity of the data should be verified as much as possible. Unnecessary data missing should not occur in any link, which will lead to incomplete data assets and affect the value of data assets.

[0070] Data standardization is one of the prerequisites for the effective application of data assets. Data standardization means that data records comply with specifications and are logically reasonable. Compliance with specifications mainly evaluates the consistency of data in format or type, ensuring that there is no dirty data in the format or type of data assets. Logical rationality mainly evaluates whether the values ​​of data items conform to a certain logic, and the inherent logical relationship between data items also needs to be reasonable.

[0071] Data accuracy is due to the variety of data sources. It is very important to ensure the reliability and accuracy of data. The first prerequisite for ensuring data accuracy is to ensure the reliability of the data source. Only when the source is reliable, it is meaningful to verify whether there are errors or anomalies in the data record information. Generally speaking, the distribution of data items conforms to the law of normal distribution. Some significantly large or significantly small abnormal data can be easily discovered, but it is more difficult to check for insignificant abnormal data, which may require the help of complex data analysis and relevant business scenario knowledge.

[0072] Data timeliness mainly evaluates the extent to which the value of data assets decays over time. In the era of big data, data is updated very quickly, and the validity period of data becomes relatively short. For example, user consumption data changes every day, and the ranking data of recommended products to users will also change accordingly. The reference value of previous user consumption data will gradually decay, and even earlier data will be eliminated. Generally speaking, setting a reasonable time decay factor for data in different time periods can evaluate the timeliness of data.

[0073] Data richness mainly evaluates the richness of valuable information items in data assets. The more data items that can be effectively utilized in a data asset, the greater its value. In addition, the value of different data items may be different. The correlation and distinctiveness between data items are also aspects that need to be considered. Data items with redundant information need to be subjected to information loss.

[0074] Data coverage is relatively easy to evaluate, and it mainly evaluates how much sample data the data asset can cover. The higher the data coverage, the higher its utilization rate and the greater its value.

[0075] The specific Pareto optimal solution, or the expression of the objective function of the Pareto classification model is:

[0076] min f(x)=(f1(x),f2(x),...,f k (x)),x∈Ω;

[0077] Among them, Ω is the decision space, k is the dimension of the target space mapped to the decision space, f is the mapping function, and x is the input data. The above objective function generally needs to include all the optimization objectives of the optimization problem, and the decision space needs to cover the entire variable space.

[0078] The specific process of step S2 is to design corresponding evaluation indicators and methods for each data type. For data integrity, calculate the missing rate of data; for data standardization, check the consistency of data format and type; for data accuracy, compare the consistency of data source and data record; for data timeliness, set the time decay factor to evaluate the change of data value over time; for data richness, count the number and relevance of valuable information items; for data coverage, calculate the proportion of samples covered by data.

[0079] Subsequently, based on the Pareto optimal solution, in the context of data asset value assessment, the goal is to find the optimal way to divide the data set so that the data in each set performs best in the corresponding type. The specific process is to sort all data items under each data type and arrange them according to the evaluation indicators; according to the Pareto principle, that is, 80% of the value often comes from 20% of the input, select a part of the data items with the best performance in each data type. These selected data items will form the head data set of the corresponding type, and the remaining data items will form the tail data set; then, according to the results of the above sorting and selection, divide the device data into multiple data sets, each set corresponds to a data type and contains the best performing data items under that type.

[0080] S3: Filter the data in multiple data sets through the ridge regression fitting method to obtain data assets.

[0081] Wherein, the expression of the ridge regression fitting method in step S3 is:

[0082] ||Xθ-y|| 2 +||Γθ|| 2 ;

[0083] Among them, X is the input data, θ is the fitting hyperparameter, y is the output prediction result, and Γ is the weight constant.

[0084] The purpose of step S3 is to use the ridge regression fitting algorithm to filter out the data in each set that are not involved in the value assessment, and finally convert the data that meets all 6 indicators into data assets.

[0085] S4: construct an asset discount model, and calculate the first income-expense ratio of the data asset through the asset discount model.

[0086] The expression of the first benefit-cost ratio in step S4 is:

[0087]

[0088] Where S is the first benefit-cost ratio obtained by calculation, I(G v ) is the income of data assets, C(G v ) is the cost of data assets;

[0089]

[0090] Among them, α is the weight factor, i is the data index value in the data asset, is the computing capacity requirement value of the i-th data in the data asset, j is the data index value in the associated asset, is the computing power requirement value of the jth data in the associated asset, is the actual proportion of the j-th data in the associated assets after being mapped.

[0091] Furthermore, in step S4, an asset discount model is first constructed to calculate the income-expense ratio of data assets and associated assets, thereby obtaining the estimated data asset discount rate. From the perspective of data assets, the income is determined by the resource requirements of the data, specifically including the data resource requirements and the associated data resource requirements, and the data cost itself is defined as the sum of the actual data resources allocated to the data. After determining the definition of the data's own income and the associated data cost, the income-expense ratio can be defined as the ratio of the two, so the asset discount model as shown in the above formula can be obtained.

[0092] S5: Determine the optimal revenue-to-expense ratio based on the second revenue-to-expense ratio of the target asset and the first revenue-to-expense ratio to complete the data asset value assessment.

[0093] Wherein, step S5 specifically includes:

[0094] When the first revenue-to-expense ratio is greater than the second revenue-to-expense ratio, taking the first revenue-to-expense ratio as the optimal revenue-to-expense ratio;

[0095] When the second revenue-to-expense ratio is greater than the first revenue-to-expense ratio, taking the second revenue-to-expense ratio as the optimal revenue-to-expense ratio;

[0096] When the first revenue-to-cost ratio is equal to the second revenue-to-cost ratio, the first revenue-to-cost ratio is used as the optimal revenue-to-cost ratio.

[0097] After steps S1 to S4, the first benefit-to-cost ratio can be obtained. The purpose of step S5 is to combine the mapping cost calculated for the target asset with the mapping benefit determined in step S4 to determine the benefit-to-cost ratio. If the mapping cost calculated for the target asset is lower than the benefit-to-cost ratio in step S4, the mapping cost calculated for the target asset is used as the optimal benefit-to-cost ratio. Otherwise, the benefit-to-cost ratio calculated in step S4 is used as the optimal benefit-to-cost ratio.

[0098] like Figure 2 As shown, the second aspect of the present invention further provides a data asset value assessment system, comprising:

[0099] Growth calculation module 100: used to construct a value data growth model, and obtain device data through calculation of the value data growth model;

[0100] A partitioning module 200 is used to partition the device data into multiple data sets according to data types by using a Pareto optimal solution;

[0101] Filtering module 300: used to filter data in multiple data sets by using ridge regression fitting method to obtain data assets;

[0102] Calculation module 400: used to construct an asset discount model, and calculate the first income-cost ratio of the data asset through the asset discount model;

[0103] Optimization module 500: used to determine the optimal revenue-to-cost ratio according to the second revenue-to-cost ratio of the target asset and the first revenue-to-cost ratio to complete the data asset value assessment.

[0104] The present invention also provides a data asset value assessment device, comprising:

[0105] A memory and at least one processor, wherein instructions are stored in the memory;

[0106] At least one of the processors calls the instructions in the memory to enable the data asset value assessment device to execute the data asset value assessment method as described in any one of the above items.

[0107] The present invention also provides a computer-readable storage medium having instructions stored thereon, and when the instructions are executed by a processor, the data asset value assessment method as described in any one of the above items is implemented.

[0108] Furthermore, a data asset value assessment device provided by the present invention may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU), for example, one or more processors and memories, one or more storage media for storing applications or data, such as one or more massive storage devices, wherein the memories and storage media may be short-term storage or persistent storage, and the program stored in the storage medium may include one or more modules, each module may include a series of instruction operations in the data asset value assessment device, and further, the processor may be configured to communicate with the storage medium and execute a series of instruction operations in the storage medium on the data asset value assessment device.

[0109] It may also include one or more power supplies, one or more wired or wireless network interfaces, one or more input and output interfaces, and one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that the data asset value assessment device structure provided by the present invention does not constitute a limitation on the device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0110] The present invention provides a data asset value assessment method, system, device and storage medium. The method collects various types of device data stored in logs in a network, and then deploys a growth model on a server. According to the time and quantity relationship of the data, the parameters in the growth model are estimated to obtain a complete growth model that matches the group of data with the data assets, thereby completing the link of converting valuable data into data assets. Secondly, the pre-processed data is converted into 6 data sets using the Pareto optimal solution through six value assessment indicators. Then, the ridge regression fitting algorithm is used to filter out the data in each set as data participating in the value assessment. Finally, the data that meets all 6 indicators is converted into data assets. Then, an asset discount model is constructed to calculate the income-expense ratio of data assets and related assets, thereby obtaining the estimated data asset discount rate. Finally, the optimal income-expense ratio can be determined, thereby completing the data asset value assessment. The present invention helps to ensure the objectivity and fairness of the assessment results, and provides a valuable reference basis for asset operators, so as to better understand the value of data assets and formulate more effective data management and utilization strategies.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the above embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily thought of by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. A data asset value assessment method, characterized in that: include: S1: constructing a value data growth model, and obtaining device data through calculation using the value data growth model; S2: dividing the device data into multiple data sets according to data types through a Pareto optimal solution; S3: Filter the data in multiple data sets through ridge regression fitting method to obtain data assets; S4: constructing an asset discount model, and calculating a first income-expense ratio of the data asset by using the asset discount model; S5: Determine the optimal revenue-to-expense ratio based on the second revenue-to-expense ratio of the target asset and the first revenue-to-expense ratio to complete the data asset value assessment.

2. A data asset value assessment method according to claim 1, characterized in that: Step S1 further comprises: S11: Collect log data; S12: construct a basic model, and continue to estimate the parameters in the basic model through the log data to obtain a value data growth model; S13: Obtaining equipment data through calculation using the value data growth model.

3. A data asset value assessment method according to claim 2, characterized in that: The expression of the basic model in step S12 is: Among them, t is the current calculation time, N t is the number of devices in the network at time t, m is the first device growth constant to be estimated, p is the second device growth constant to be estimated, and q is the third device growth constant to be estimated.

4. A data asset value assessment method according to claim 1, characterized in that: The data types in step S2 include: data integrity type, data standardization type, data accuracy type, data timeliness type, data richness type, and data coverage type.

5. A data asset value assessment method according to claim 1, characterized in that: The expression of the ridge regression fitting method in step S3 is: ‖Xθ-y‖ 2 +‖Γθ‖ 2 ; Among them, X is the input data, θ is the fitting hyperparameter, Y is the output prediction result, and Γ is the weight constant.

6. A data asset value assessment method according to claim 1, characterized in that: The expression of the first benefit-cost ratio in step S4 is: Where S is the first benefit-cost ratio obtained by calculation, I(G v ) is the income of data assets, C(G v ) is the cost of data assets; Among them, α is the weight factor, i is the data index value in the data asset, is the computing capacity requirement value of the i-th data in the data asset, j is the data index value in the associated asset, is the computing power requirement value of the jth data in the associated asset, is the actual proportion of the j-th data in the associated assets after being mapped.

7. A data asset value assessment method according to claim 1, characterized in that: Step S5 specifically includes: When the first revenue-to-expense ratio is greater than the second revenue-to-expense ratio, taking the first revenue-to-expense ratio as the optimal revenue-to-expense ratio; When the second revenue-to-expense ratio is greater than the first revenue-to-expense ratio, taking the second revenue-to-expense ratio as the optimal revenue-to-expense ratio; When the first revenue-to-cost ratio is equal to the second revenue-to-cost ratio, the first revenue-to-cost ratio is used as the optimal revenue-to-cost ratio.

8. A data asset value assessment system, characterized in that: include: Growth calculation module: used to build a value data growth model, and obtain device data through calculation of the value data growth model; A partitioning module: used for partitioning the device data into a plurality of data sets according to data types by using a Pareto optimal solution; Filtering module: used to filter data in multiple data sets through ridge regression fitting method to obtain data assets; Calculation module: used to construct an asset discount model, and calculate the first income-cost ratio of the data asset through the asset discount model; Optimization module: used to determine the optimal revenue-to-expense ratio based on the second revenue-to-expense ratio of the target asset and the first revenue-to-expense ratio to complete the data asset value assessment.

9. A data asset value assessment device, characterized in that: include: A memory and at least one processor, wherein instructions are stored in the memory; At least one of the processors calls the instructions in the memory to enable the data asset value assessment device to execute the data asset value assessment method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed by the processor, the data asset value assessment method according to any one of claims 1 to 7 is implemented.

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