Data resource pricing method and device, electronic equipment and storage medium
By combining data quality evaluation and utility functions with a profit model, the market adaptability problem of data value determination is solved, and the accuracy of data resource pricing and profit maximization are achieved.
Patent Information
- Application Number
- CN202510865454.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-25
- Publication Date
- 2025-10-28
AI Technical Summary
In the data factor market, how to accurately determine the value of data to adapt to the changing needs of the market, especially the pricing challenges caused by the uncertainty of data value and the dynamic changes in market supply and demand.
By using data quality evaluation functions and data resource utility functions, combined with the relationship between profit, expected revenue, and cost, the value of data resources is determined, thereby maximizing profits.
It improves the accuracy of data resource pricing, enables flexible adaptation to market changes, reflects user purchasing intentions, and maximizes profits.
Smart Images
Figure CN120851918A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and in particular to a data resource pricing method, apparatus, electronic device, and storage medium. Background Technology
[0002] Against the backdrop of the rapid development of the data factor market, higher demands are placed on the efficient allocation, fair trading, and free flow of data resources. Therefore, it is necessary to price data resources.
[0003] The challenge of pricing data resources lies in the uncertainty of data value and the dynamic changes in market supply and demand. Therefore, how to accurately determine the value of data in line with market changes is a technical problem that needs to be solved. Summary of the Invention
[0004] This application provides a data resource pricing method, apparatus, electronic device, and storage medium, which helps improve the accuracy of data resource pricing in response to market changes.
[0005] To address the aforementioned problems, firstly, embodiments of this application provide a data resource pricing method, including:
[0006] Based on the data quality evaluation function, the data quality evaluation value of the data resource to be priced is determined according to its attributes.
[0007] Based on the data resource utility function, the data resource utility value of the data resource to be priced is determined according to the data quality evaluation value;
[0008] Based on the correlation between data value and data resource utility value, the value of the data resource to be priced is determined according to the data resource utility value. The correlation is determined based on the relationship between profit, expected revenue and cost to maximize the profit.
[0009] Optionally, the attributes include accuracy, completeness, and redundancy;
[0010] The process of determining the data quality evaluation value of the data resource to be priced based on the data quality evaluation function and according to the attributes of the data resource to be priced includes:
[0011] Based on the first weight of accuracy, the second weight of completeness, and the third weight of redundancy, the accuracy, completeness, and redundancy are weighted and summed to obtain the data quality evaluation value of the data resource to be priced.
[0012] Optionally, the data resource utility function is a monotonically increasing function with respect to data quality, the data resource utility value is always positive, and the data resource utility function is a convex function.
[0013] Optionally, the data resource utility function is represented as follows:
[0014]
[0015] Where α1 and α2 are constants, α1>0, α2<0, q represents data quality, and X(q) represents data resource utility value.
[0016] Optionally, α1 and α2 are determined by linear regression based on the user's survey value of the utility of the data resource samples in the data resource sample set and the data quality evaluation value of the data resource samples.
[0017] Optionally, the relationship between the data value and the data resource utility value is expressed as follows:
[0018]
[0019] Where p represents the data value, p w X(q) represents the maximum acceptable price for the user, X(q) represents the utility value of the data resource, and β is a constant greater than 0.
[0020] Optionally, the cost is related to data quality, but not to data value;
[0021] The expected revenue is a function of data quality and data value.
[0022] Secondly, embodiments of this application provide a data resource pricing device, comprising:
[0023] The data quality determination module is used to determine the data quality evaluation value of the data resource to be priced based on the attributes of the data resource to be priced, according to the data quality evaluation function.
[0024] The data utility determination module is used to determine the data resource utility value of the data resource to be priced based on the data resource utility function and the data quality evaluation value.
[0025] The data value determination module is used to determine the value of the data resource to be priced based on the correlation between data value and data resource utility value, wherein the correlation is determined based on the relationship between profit, expected revenue and cost to maximize the profit.
[0026] Optionally, the attributes include accuracy, completeness, and redundancy;
[0027] The data quality determination module is specifically used for:
[0028] Based on the first weight of accuracy, the second weight of completeness, and the third weight of redundancy, the accuracy, completeness, and redundancy are weighted and summed to obtain the data quality evaluation value of the data resource to be priced.
[0029] Optionally, the data resource utility function is a monotonically increasing function with respect to data quality, the data resource utility value is always positive, and the data resource utility function is a convex function.
[0030] Optionally, the data resource utility function is represented as follows:
[0031]
[0032] Where α1 and α2 are constants, α1>0, α2<0, q represents data quality, and X(q) represents data resource utility value.
[0033] Optionally, α1 and α2 are determined by linear regression based on the user's survey value of the utility of the data resource samples in the data resource sample set and the data quality evaluation value of the data resource samples.
[0034] Optionally, the relationship between the data value and the data resource utility value is expressed as follows:
[0035]
[0036] Where p represents the data value, p w X(q) represents the maximum acceptable price for the user, X(q) represents the utility value of the data resource, and β is a constant greater than 0.
[0037] Optionally, the cost is related to data quality, but not to data value;
[0038] The expected revenue is a function of data quality and data value.
[0039] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data resource pricing method described in embodiments of this application.
[0040] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to perform the steps of the data resource pricing method disclosed in embodiments of this application.
[0041] The data resource pricing method, apparatus, electronic device, and storage medium provided in this application determine the data quality evaluation value of the data resource to be priced based on the attributes of the data resource according to a data quality evaluation function. They then determine the data resource utility value of the data resource to be priced based on the data quality evaluation value according to a data resource utility function. Finally, they determine the value of the data resource to be priced based on the correlation between data value and data resource utility value. This correlation is determined when the relationship between profit, expected revenue, and cost maximizes profit. Since the data resource utility value can reflect users' purchasing intentions and thus reflect market changes, the data value determined based on the correlation between data value and data resource utility value can accurately reflect market changes and maximize profits, thereby improving the accuracy of data value determination. Attached Figure Description
[0042] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0043] Figure 1 This is a flowchart of a data resource pricing method provided in an embodiment of this application;
[0044] Figure 2 This is a schematic diagram of the structure of a data resource pricing device provided in an embodiment of this application;
[0045] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application. Detailed Implementation
[0046] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0047] Figure 1 This is a flowchart of a data resource pricing method provided in an embodiment of this application, such as... Figure 1 As shown, the method includes steps 110 to 130.
[0048] Step 110: Based on the data quality evaluation function, determine the data quality evaluation value of the data resource to be priced according to its attributes.
[0049] Among them, the data quality evaluation function is used to evaluate the data quality of data resources, which is closely related to the attributes of data resources. Data quality is an indicator that measures the degree to which data meets the needs of a business scenario. Data quality refers to the ability of data resources to achieve preset business goals in a specific application scenario. Its core value lies in supporting the effective operation of information systems and optimizing decision-making processes. This concept emphasizes that data should possess a set of attributes that meet user needs, rather than isolated technical parameters.
[0050] Data resource attributes can include at least one of the following: consistency, completeness, timeliness, accuracy, validity, and uniqueness. Consistency refers to data adhering to unified standards and maintaining a consistent format across the data set. Completeness refers to the absence of missing data information, such as a missing record for a specific field. Incomplete data has significantly reduced value, making it a fundamental evaluation criterion for data quality. Timeliness refers to the time interval between data generation and its availability for viewing, also known as data latency. Accuracy refers to the absence of anomalies or errors in the recorded data; common data inaccuracies include garbled characters. Validity refers to the requirement that data values and formats conform to data definitions or business definitions. Uniqueness means that for a given data item or set of data, there are no duplicate data values; values must be unique, such as ID data.
[0051] The data resources to be priced can be structured data. Structured data, also known as row data, is data logically expressed and implemented using a two-dimensional table structure, strictly adhering to data format and length specifications, and is primarily stored and managed through relational databases. Structured data has a clear, predefined data model and follows a consistent order. Structured data has three main characteristics: it has a clear meaning; it has a strict, consistent order; and it has a clear data type.
[0052] Data quality, as a key indicator for measuring the value of data resources, directly affects the pricing and market transactions of data resources. Therefore, it is necessary to accurately evaluate the data quality of data resources. This application's embodiments can quantify the attributes of the data resource to be priced, and based on the weight of each attribute, perform a weighted summation of the attributes to obtain the data quality evaluation value of the data resource to be priced.
[0053] In some embodiments of this application, the attributes include accuracy, completeness, and redundancy;
[0054] The process of determining the data quality evaluation value of the data resource to be priced based on the data quality evaluation function and according to the attributes of the data resource to be priced may include:
[0055] Based on the first weight of accuracy, the second weight of completeness, and the third weight of redundancy, the accuracy, completeness, and redundancy are weighted and summed to obtain the data quality evaluation value of the data resource to be priced.
[0056] Specifically, the accuracy of the data resource to be priced refers to the proportion of correct data in the total data volume. The completeness of the data resource to be priced refers to the proportion of complete data (excluding missing and meaningless data) in the total data volume. The redundancy of the data resource to be priced refers to the proportion of non-repeating data in the total data volume. Meaningless data can be data that does not match its corresponding field, such as a field for temperature but data for "Xiaoming".
[0057] The data resources to be priced are structured data and can be stored in a two-dimensional data table format. The accuracy of the data resources to be priced can be expressed by the following formula:
[0058]
[0059] Among them, Qs z N represents the accuracy of the data resource to be priced. 错误单元格 N represents the amount of erroneous data in the data resource to be priced. 数据列 N represents the number of columns in the data table storing data resources to be priced. 数据行 N represents the number of rows in the data table storing the data resources to be priced. 数据列 ×N 数据行 This indicates the total amount of data resources to be priced.
[0060] The completeness of the data resources to be priced can be expressed by the following formula:
[0061]
[0062] Among them, Qs w N represents the completeness of the data resource to be priced. 空值单元格 N represents the amount of missing (i.e., no value) data in the data resource to be priced. 无意义单元格 This indicates the amount of meaningless data in the data resource to be priced.
[0063] The redundancy of data resources awaiting pricing can be expressed by the following formula:
[0064]
[0065] Among them, Qs r N represents the redundancy of the data resources to be priced. 重复单元格 This indicates the amount of duplicate data in the data resource to be priced.
[0066] The calculation formulas for accuracy, completeness, and redundancy mentioned above are based on the example of representing the data resource to be priced as a two-dimensional data table. When multiple two-dimensional data tables exist, the corresponding data volumes in each two-dimensional data table can be summed to obtain the accuracy. When the data resource to be priced exists in multiple two-dimensional data tables, to calculate the accuracy, the total amount of erroneous data in all two-dimensional data tables is summed, and the total data volume of the data resource to be priced is summed. The ratio of the total amount of erroneous data to the total data volume is the proportion of erroneous data. The difference between the number 1 and the proportion of erroneous data is determined as the accuracy of the data resource to be priced. To calculate the completeness of the data resource to be priced, the total amount of incomplete data is summed, including missing and meaningless data in all two-dimensional data tables. The total data volume of the data resource to be priced is summed, and the ratio of the total amount of incomplete data to the total data volume is the proportion of incomplete data. The difference between the number 1 and the proportion of incomplete data is determined as the completeness of the data resource to be priced. When calculating the redundancy of the data resource to be priced, the total amount of duplicate data can be obtained by summing the duplicate data in all two-dimensional data tables. The total amount of data in the data resource to be priced can be obtained by summing the data in all two-dimensional data tables. The ratio of the total amount of duplicate data to the total amount of data is the proportion of duplicate data. The difference between the number 1 and the proportion of duplicate data is determined as the redundancy of the data resource to be priced.
[0067] The first weight for accuracy, the second weight for completeness, and the third weight for redundancy are preset values. The first, second, and third weights are all values between 0 and 1, and the sum of the three weights is 1.
[0068] Based on the first, second, and third weights, the accuracy, completeness, and redundancy of the data resource to be priced are weighted and summed to obtain the data quality evaluation value of the data resource to be priced. That is, the data quality evaluation function can be expressed as follows:
[0069] Qs = w z Qs z +w w Qs w +w r Qs r
[0070] Where Qs represents the data quality evaluation value of the data resource to be priced, w z w represents the first weight. w Indicates the second weight, w r This indicates the third weight.
[0071] Step 120: Based on the data resource utility function, determine the data resource utility value of the data resource to be priced according to the data quality evaluation value.
[0072] Utility refers to the degree of satisfaction a consumer (user) derives from consuming a good. The magnitude of utility depends on the consumer's subjective psychological evaluation and is determined by the intensity of the consumer's desire.
[0073] The utility of data resources is reflected in their practical application value in areas such as enterprise decision support, market analysis, and product development. Since the utility of data resources reflects users' subjective psychological evaluation of them, and users' subjective psychological evaluation of data resources reflects market changes, the utility value of data resources can reflect market changes.
[0074] In some embodiments of this application, the data resource utility function is a monotonically increasing function with respect to data quality, the data resource utility value is always positive, and the data resource utility function is a convex function. The data resource utility function can be defined as X(q), which is a monotonically increasing function with respect to data quality q; the higher the data quality, the greater the data resource utility value. The data resource utility value is always positive, i.e., X(q) > 0. The data resource utility function X(q) is a convex function, and the marginal utility of data resource utility decreases monotonically with increasing data quality. Marginal utility refers to the increase (or decrease) in benefit from a good or service when one unit is added (or removed). In other words, it is the change in additional satisfaction or utility brought about by each additional unit consumed when consuming more of a certain good or service. Economics generally holds that as the quantity of a good or service increases, marginal utility will gradually decrease; this is known as the law of diminishing marginal utility.
[0075] The data resource utility function can be determined based on the following conditions: the data resource utility function is a monotonically increasing function with respect to data quality, the data resource utility value is always positive, and the data resource utility function is a convex function. There are many data resource utility functions that meet the above conditions, and one can be selected for application.
[0076] After determining the data quality evaluation value of the data resource to be priced, substituting the data quality evaluation value into the data resource utility function yields the data resource utility value of the data resource to be priced.
[0077] In some embodiments of this application, the data resource utility function is represented as follows:
[0078]
[0079] Where α1 and α2 are constants, α1>0, α2<0, q represents data quality (in specific calculations, it is the data quality evaluation value), and X(q) represents the data resource utility value.
[0080] In one alternative implementation, α1 and α2 are determined by linear regression based on the user's utility survey value of the data resource samples in the data resource sample set and the data quality evaluation value of the data resource samples.
[0081] The aforementioned data resource utility function is determined based on practical experience. The constants α1 and α2 in the data resource utility function can be determined using linear regression based on a data resource sample set. The data resource sample set is a pre-prepared collection of data resources. By using the aforementioned data quality evaluation function to determine the data quality evaluation value of each data resource sample in this set, a utility survey of users can be conducted using this data resource sample set. This yields the user's utility survey value for each data resource sample in the set. Then, based on the utility survey value and data quality evaluation value of each data resource sample in the set, the constants α1 and α2 in the aforementioned data resource utility function can be determined using linear regression.
[0082] Step 130: Based on the correlation between data value and data resource utility value, determine the value of the data resource to be priced according to the data resource utility value. The correlation is determined based on the relationship between profit, expected revenue and cost to maximize the profit.
[0083] The pricing of data resources must comprehensively consider factors such as accuracy, completeness, timeliness, scarcity, and privacy sensitivity. To maximize profits, data providers need to price their data resources reasonably to attract buyers and ensure their market competitiveness. In the process of pricing data resources, a positive correlation can be established between data value and data resource utility. A higher data resource utility indicates that users are more inclined to purchase the data resource, thus increasing its value; conversely, a lower data resource utility indicates that users are less inclined to purchase it, resulting in lower data value.
[0084] The relationship between data value and data resource utility is determined based on the relationship between profit, expected revenue, and cost, and is based on the user's willingness to purchase to maximize the profit.
[0085] After determining the utility value of the data resource to be priced, its value can be directly determined based on the correlation between data value and data resource utility value. This determined value is related to users' willingness to buy, reflects market changes, and can maximize profits for the data resource to be priced.
[0086] In some embodiments of this application, the relationship between the data value and the data resource utility value is expressed as follows:
[0087]
[0088] Where p represents the data value, p w X(q) represents the maximum acceptable price for the user, X(q) represents the utility value of the data resource, and β is a constant greater than 0.
[0089] The cost is related to data quality but not to data value; the expected revenue is a function of data quality and data value.
[0090] The relationship between profit, expected revenue, and cost can be represented using a profit model for data resources, which can be expressed as follows:
[0091] Profit = Revenue - Cost
[0092] Let the data quality of the data resource be q, and the profit when the data value is p, be denoted as L(p,q). Expected revenue can be denoted as S(p,q), and cost as C(p,q). The above profit model can then be expressed as:
[0093] L(p,q)=S(p,q)-C(p,q)
[0094] The cost of data resources, C(p,q), is the cost of data governance. The higher the data quality, the higher the governance cost, but it is unrelated to the pricing of data resources, i.e., the data value p, i.e., C(p,q) = C(q).
[0095] The expected revenue of the data resource to be priced is related to the number of users and the probability of each user purchasing at a price of p. In other words, the expected revenue of the data resource can be expressed as follows:
[0096] S(p,q)=p×N 用户数 ×R
[0097] Where, N 用户数 R represents the number of users of the data resource to be priced, and R represents the probability of a user purchasing at a price of p.
[0098] The maximum acceptable price for users is linearly positively correlated with the utility value of data resources. That is, the maximum acceptable price for users can be expressed as follows:
[0099] p w =β×X(q)
[0100] Where, p w X(q) represents the maximum acceptable price for the user, X(q) represents the utility value of the data resource, and β is a constant.
[0101] The probability density function of a user's purchase intention at price p can characterize the user's willingness to purchase data resources at price p. The probability density function of a user's purchase intention at price p is denoted as f(p), and f(p) can be expressed as follows:
[0102]
[0103] The probability density function of a user's purchase intention at price p represents the price of the data resource, p, being less than or equal to the user's maximum acceptable price p. w Users are willing to buy when the price of data resources exceeds the user's maximum acceptable price p. w Users are unwilling to buy at that time.
[0104] When a data resource is priced at p, a user cannot purchase it at a price less than p. Therefore, the probability of a user purchasing the data resource at a price less than p is 0. The probability of a user purchasing the data resource at a price of p can be expressed as follows:
[0105]
[0106] Where R represents the probability of a user purchasing at price p, f(p) represents the probability density function of the user's purchase intention at price p, and p w This indicates the user's maximum acceptable price.
[0107] Substituting the above-obtained expression for the user's purchase probability at price p into the formula for expected income, we can obtain the expected income as follows:
[0108] S(p,q)=p×N 用户数 (p w -p)
[0109] Therefore, substituting this expected revenue into the above profit model, we can obtain the profit model as follows:
[0110] L(p,q)=S(p,q)-C(p,q)=p×N 用户数 (p w -p)-C(q)
[0111] As can be seen from the above formula, after the data quality evaluation value is determined, the profit L(p,q) is a quadratic function of price p, and this quadratic function opens downwards. Therefore, the value of price p when the profit L(p,q) is obtained by differentiating the above formula, that is, when... At this point, the profit reaches its maximum value, and the price (i.e., data value) p of the data resource can be obtained as:
[0112]
[0113] That is, the value of the data resources determined above is the data value corresponding to the maximization of profit. The data resource utility function is expressed as... At that time, the value p of the data resource can be expressed as follows:
[0114]
[0115] Where p represents the value of the data resource, β is a constant, q represents the evaluation value of the data resource, and α1 and α2 are constants, where α1>0 and α2<0.
[0116] The data resource pricing method provided in this application determines the data quality evaluation value of the data resource to be priced based on the attributes of the data resource according to a data quality evaluation function. It then determines the data resource utility value of the data resource to be priced based on the data quality evaluation value according to a data resource utility function. Finally, it determines the value of the data resource to be priced based on the correlation between data value and data resource utility value. This correlation is determined when the relationship between profit, expected revenue, and cost maximizes profit. Since data resource utility value can reflect users' purchasing intentions and thus reflect market changes, the data value determined based on the correlation between data value and data resource utility value can accurately reflect market changes and maximize profits, thereby improving the accuracy of data value determination.
[0117] The data resource pricing method provided in this application can flexibly adapt to market changes and reflect the value of data resources in different application scenarios. Data resource pricing also involves the formulation of data transaction rules, including data product valuation and pricing rules, and transaction methods for data ownership and usage rights. In this process, data resource stratification and classification strategies, lifecycle strategies, and the application of new technologies, such as query-based pricing methods, are all important technical means to achieve effective data resource pricing. The application of these strategies and technologies helps improve the transparency and fairness of data resource pricing and promotes the healthy development of the data factor market.
[0118] This application presents a data resource pricing method that focuses on addressing the issues of quantifying data quality evaluation, quantifying dataset resource utility evaluation, and accurately pricing data resources. This aims to adapt to the rapid development of the data trading market and the demand for efficient data resource allocation in the era of big data. The method constructs a data quality evaluation function by comprehensively considering factors such as data accuracy, completeness, timeliness, scarcity, and privacy sensitivity. This function quantifies the quality of data resources, and the method evaluates the utility of data resources through a data resource utility function. The utility function is designed as a monotonically increasing and convex function, ensuring that higher data resource quality corresponds to greater utility, while marginal utility diminishes. Furthermore, the method proposes a profit model for data resource sales. This model considers data governance costs and the maximum acceptable price for users, and determines a profit-maximizing pricing strategy through the probability density function of user purchase intention.
[0119] Overall, the technical solution of this application provides a scientific, flexible and market-adaptive strategy for the reasonable pricing of data resources. It can effectively reflect the value of data resources in different application scenarios, provide a fair and transparent pricing reference for both parties in data transactions, and promote the development of the data element market.
[0120] Figure 2 This is a schematic diagram of the structure of a data resource pricing device provided in an embodiment of this application, as shown below. Figure 2 As shown, the device includes:
[0121] The data quality determination module 210 is used to determine the data quality evaluation value of the data resource to be priced based on the attributes of the data resource to be priced, according to the data quality evaluation function.
[0122] The data utility determination module 220 is used to determine the data resource utility value of the data resource to be priced based on the data resource utility function and the data quality evaluation value.
[0123] The data value determination module 230 is used to determine the value of the data resource to be priced based on the correlation between data value and data resource utility value, wherein the correlation is determined based on the relationship between profit, expected revenue and cost to maximize the profit.
[0124] Among them, the data quality evaluation function is used to evaluate the data quality of data resources, which is closely related to the attributes of data resources. Data quality is an indicator that measures the degree to which data meets the needs of a business scenario. Data quality refers to the ability of data resources to achieve preset business goals in a specific application scenario. Its core value lies in supporting the effective operation of information systems and optimizing decision-making processes. This concept emphasizes that data should possess a set of attributes that meet user needs, rather than isolated technical parameters.
[0125] Data resource attributes can include at least one of the following: consistency, completeness, timeliness, accuracy, validity, and uniqueness. Consistency refers to data adhering to unified standards and maintaining a consistent format across the data set. Completeness refers to the absence of missing data information, such as a missing record for a specific field. Incomplete data has significantly reduced value, making it a fundamental evaluation criterion for data quality. Timeliness refers to the time interval between data generation and its availability for viewing, also known as data latency. Accuracy refers to the absence of anomalies or errors in the recorded data; common data inaccuracies include garbled characters. Validity refers to the requirement that data values and formats conform to data definitions or business definitions. Uniqueness means that for a given data item or set of data, there are no duplicate data values; values must be unique, such as ID data.
[0126] Utility refers to the degree of satisfaction a consumer (user) derives from consuming a good. The magnitude of utility depends on the consumer's subjective psychological evaluation and is determined by the intensity of their desire. The utility of data resources is reflected in their practical application value in areas such as business decision support, market analysis, and product development. Since the utility of data resources reflects users' subjective psychological evaluation of them, and users' subjective psychological evaluation of data resources reflects market changes, the utility value of data resources can reflect market changes.
[0127] The relationship between data value and data resource utility is determined based on the relationship between profit, expected revenue, and cost, and is based on the user's willingness to purchase to maximize the profit.
[0128] After determining the utility value of the data resource to be priced, its value can be directly determined based on the correlation between data value and data resource utility value. This determined value is related to users' willingness to buy, reflects market changes, and can maximize profits for the data resource to be priced.
[0129] Optionally, the attributes include accuracy, completeness, and redundancy;
[0130] The data quality determination module is specifically used for:
[0131] Based on the first weight of accuracy, the second weight of completeness, and the third weight of redundancy, the accuracy, completeness, and redundancy are weighted and summed to obtain the data quality evaluation value of the data resource to be priced.
[0132] The accuracy of the data resource to be priced refers to the proportion of correct data in the total data volume. The completeness of the data resource to be priced refers to the proportion of complete data (excluding missing and meaningless data) in the total data volume. The redundancy of the data resource to be priced refers to the proportion of non-repeating data in the total data volume. Specific calculation methods can be found in the method implementation examples, and will not be elaborated here.
[0133] Optionally, the data resource utility function is a monotonically increasing function with respect to data quality, the data resource utility value is always positive, and the data resource utility function is a convex function.
[0134] Optionally, the data resource utility function is represented as follows:
[0135]
[0136] Where α1 and α2 are constants, α1>0, α2<0, q represents data quality, and X(q) represents data resource utility value.
[0137] Optionally, α1 and α2 are determined by linear regression based on the user's survey value of the utility of the data resource samples in the data resource sample set and the data quality evaluation value of the data resource samples.
[0138] Optionally, the relationship between the data value and the data resource utility value is expressed as follows:
[0139]
[0140] Where p represents the data value, p w X(q) represents the maximum acceptable price for the user, X(q) represents the utility value of the data resource, and β is a constant greater than 0.
[0141] Optionally, the cost is related to data quality, but not to data value;
[0142] The expected revenue is a function of data quality and data value.
[0143] The data resource pricing device provided in this application embodiment is used to implement the steps of the data resource pricing method described in this application embodiment. The specific implementation of each module of the device is described in the corresponding steps, and will not be repeated here.
[0144] The data resource pricing device provided in this application determines the data quality evaluation value of the data resource to be priced based on the attributes of the data resource according to a data quality evaluation function. It then determines the data resource utility value of the data resource to be priced based on the data quality evaluation value according to a data resource utility function. Finally, it determines the value of the data resource to be priced based on the correlation between data value and data resource utility value. This correlation is determined when the relationship between profit, expected revenue, and cost maximizes profit. Since the data resource utility value can reflect users' purchasing intentions and thus reflect market changes, the data value determined based on the correlation between data value and data resource utility value can accurately reflect market changes and maximize profits, thereby improving the accuracy of data value determination.
[0145] Figure 3 is a structural diagram of an electronic device provided in an embodiment of the present application, such as Figure 3 As shown, the electronic device 300 may include one or more processors 310 and one or more memories 320 connected to the processors 310. The electronic device 300 may also include an input interface 330 and an output interface 340 for communicating with another device or system. Program code executed by the processor 310 may be stored in the memory 320.
[0146] The processor 310 in the electronic device 300 calls the program code stored in the memory 320 to execute the data resource pricing method in the above embodiment, that is, to execute:
[0147] Based on the data quality evaluation function, the data quality evaluation value of the data resource to be priced is determined according to its attributes.
[0148] Based on the data resource utility function, the data resource utility value of the data resource to be priced is determined according to the data quality evaluation value;
[0149] Based on the correlation between data value and data resource utility value, the value of the data resource to be priced is determined according to the data resource utility value. The correlation is determined based on the relationship between profit, expected revenue and cost to maximize the profit.
[0150] Optionally, the attributes include accuracy, completeness, and redundancy;
[0151] The process of determining the data quality evaluation value of the data resource to be priced based on the data quality evaluation function and according to the attributes of the data resource to be priced includes:
[0152] Based on the first weight of accuracy, the second weight of completeness, and the third weight of redundancy, the accuracy, completeness, and redundancy are weighted and summed to obtain the data quality evaluation value of the data resource to be priced.
[0153] Optionally, the data resource utility function is a monotonically increasing function with respect to data quality, the data resource utility value is always positive, and the data resource utility function is a convex function.
[0154] Optionally, the data resource utility function is represented as follows:
[0155]
[0156] Where α1 and α2 are constants, α1>0, α2<0, q represents data quality, and X(q) represents data resource utility value.
[0157] Optionally, α1 and α2 are determined by linear regression based on the user's survey value of the utility of the data resource samples in the data resource sample set and the data quality evaluation value of the data resource samples.
[0158] Optionally, the relationship between the data value and the data resource utility value is expressed as follows:
[0159]
[0160] Where p represents the data value, p w X(q) represents the maximum acceptable price for the user, X(q) represents the utility value of the data resource, and β is a constant greater than 0.
[0161] Optionally, the cost is related to data quality, but not to data value;
[0162] The expected revenue is a function of data quality and data value.
[0163] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the data resource pricing method as described in this application.
[0164] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the data resource pricing method as described in this application.
[0165] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus embodiments, since they are fundamentally similar to the method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.
[0166] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0167] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0168] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0169] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0170] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.
[0171] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.
[0172] The above provides a detailed description of a data resource pricing method, apparatus, electronic device, and storage medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
[0173] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
Claims
1. A data resource pricing method, characterized in that, include: Based on the data quality evaluation function, the data quality evaluation value of the data resource to be priced is determined according to its attributes. Based on the data resource utility function, the data resource utility value of the data resource to be priced is determined according to the data quality evaluation value; Based on the correlation between data value and data resource utility value, the value of the data resource to be priced is determined according to the data resource utility value. The correlation is determined based on the relationship between profit, expected revenue and cost to maximize the profit.
2. The method according to claim 1, characterized in that, The attributes include accuracy, completeness, and redundancy; The process of determining the data quality evaluation value of the data resource to be priced based on the data quality evaluation function and according to the attributes of the data resource to be priced includes: Based on the first weight of accuracy, the second weight of completeness, and the third weight of redundancy, the accuracy, completeness, and redundancy are weighted and summed to obtain the data quality evaluation value of the data resource to be priced.
3. The method according to claim 1, characterized in that, The data resource utility function is a monotonically increasing function with respect to data quality, the data resource utility value is always positive, and the data resource utility function is a convex function.
4. The method according to claim 3, characterized in that, The data resource utility function is expressed as follows: Where α1 and α2 are constants, α1>0, α2<0, q represents data quality, and X(q) represents data resource utility value.
5. The method according to claim 4, characterized in that, α1 and α2 are determined by linear regression based on the utility survey values of users on the data resource samples in the data resource sample set and the data quality evaluation values of the data resource samples.
6. The method according to any one of claims 1-5, characterized in that, The relationship between the value of data and the utility value of data resources is expressed as follows: Where p represents the data value, p w X(q) represents the maximum acceptable price for the user, X(q) represents the utility value of the data resource, and β is a constant greater than 0.
7. The method according to claim 6, characterized in that, The cost mentioned is related to data quality, but not to data value; The expected revenue is a function of data quality and data value.
8. A data resource pricing device, characterized in that, include: The data quality determination module is used to determine the data quality evaluation value of the data resource to be priced based on the attributes of the data resource to be priced, according to the data quality evaluation function. The data utility determination module is used to determine the data resource utility value of the data resource to be priced based on the data resource utility function and the data quality evaluation value. The data value determination module is used to determine the value of the data resource to be priced based on the correlation between data value and data resource utility value, wherein the correlation is determined based on the relationship between profit, expected revenue and cost to maximize the profit.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the data resource pricing method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the data resource pricing method according to any one of claims 1 to 7.