Data product pricing method and device, electronic equipment and storage medium

Abstract

The invention discloses a data product pricing method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining product information of a to-be-priced data product; wherein the product information at least comprises the cost of the to-be-priced data product; determining a sales profit formula of the to-be-priced data product based on the cost; substituting a pre-trained demand function into the sales profit formula to obtain a target function; performing derivation calculation on the target function to obtain the demand quantity when the profit is maximized; and substituting the demand quantity when the profit is maximized into a demand function to obtain a pricing price corresponding to the to-be-priced data product. Therefore, the optimal solution of the price of the to-be-priced data product is determined through a profit maximization formula in combination with the demand function, and the accuracy of data product pricing is effectively improved according to the optimal solution.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a pricing method and device for data products, an electronic device, and a storage medium. Background Art

[0002] Data products are data sets used for transactions and the information services derived from the data sets. The price of data products is the core mechanism for the market to effectively allocate resources. It is also the most sensitive and effective means of achieving value distribution. Therefore, the pricing of data products is particularly important.

[0003] In the existing technology, there is a method to formulate the reference price range of data product transactions through manual and machine learning. However, the method based on machine learning and human-machine collaboration generally requires data transaction history data as a training set for machine learning in order to train good machine learning. In addition, the historical data of data products is not rich, and the historical transaction freeze is unreasonable. In particular, the emerging new data products lack historical data. Therefore, from the two dimensions of operability and accuracy, it leads to the current dilemma of difficult pricing of data products. Summary of the invention

[0004] Based on the above-mentioned deficiencies of the prior art, the present application provides a pricing method and device, an electronic device, and a storage medium for a data product. In the method provided in the present application, the purpose is to improve the automated pricing of large-scale data product transaction scenarios to solve the problem of inaccurate pricing of data products brought about by the prior art.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] The first aspect of the present application provides a pricing method for a data product, including:

[0007] Acquire product information of the data product to be priced; wherein the product information at least includes the cost of the data product to be priced;

[0008] Determine a sales profit formula for the data product to be priced based on the cost;

[0009] Substituting the pre-trained demand function into the sales profit formula to obtain the target function;

[0010] Derivative calculation is performed on the objective function to obtain the demand quantity when maximizing profit;

[0011] Substitute the demand quantity when maximizing the profit into the demand function to obtain the pricing price corresponding to the data product to be priced.

[0012] Optionally, in the above-mentioned data product pricing method, the demand function training method includes:

[0013] Construct theoretical model of demand function;

[0014] Obtain test prices corresponding to multiple groups of samples of sample data products and their corresponding overall user demand; wherein a group of samples corresponds to a test price and its corresponding overall user demand;

[0015] Based on the plurality of test prices and their corresponding overall user demand, a linear regression algorithm is used to perform fitting calculation on the demand function theoretical model to obtain a demand function and a first optimal parameter and a second optimal parameter corresponding to the demand function;

[0016] Using the model evaluation index, determining whether the index levels of the demand function, the first optimal parameter, and the second optimal parameter all meet preset levels;

[0017] If the indicator levels of the demand function, the first optimal parameter, and the second optimal parameter all meet the preset levels, determining the demand function as a trained demand function;

[0018] If the indicator levels of the demand function, the first optimal parameter and the second optimal parameter do not meet the preset levels, the multiple groups of samples of the sample data product are iterated in quantity, and the test prices corresponding to the multiple groups of samples of the sample data product and their corresponding overall user demand are returned.

[0019] Optionally, in the above-mentioned data product pricing method, the step of obtaining test prices corresponding to multiple groups of samples of the sample data product and their corresponding overall user demand includes:

[0020] Get all users who purchased sample data products and the number of users;

[0021] Performing N rounds of sampling on all users who purchased the sample data product to obtain N groups of samples; wherein a group of samples includes at least n users;

[0022] The samples are priced using a plurality of preset price strategies to obtain a test price corresponding to each group of the samples;

[0023] Calculate the purchase rate of the sample data product for each group of samples at the corresponding test price during the preset test period to obtain the purchase rate corresponding to each group of samples;

[0024] For each group of samples, the purchase rate corresponding to the samples is multiplied by the number of users to obtain the overall user demand corresponding to the test price of a group of samples.

[0025] Optionally, in the above-mentioned data product pricing method, performing N rounds of sampling on all users who purchase the sample data product to obtain N groups of samples includes:

[0026] Determine the number of sample groups from which all users who purchase the sample data product are drawn;

[0027] Determine the number of users in the sample group using confidence level and allowable error range;

[0028] According to the number of sample groups and the number of users, a sampling method without replacement is used to perform N rounds of sampling on all users who purchase the sample data product to obtain N groups of samples.

[0029] Optionally, in the above-mentioned pricing method for data products, the derivative calculation of the objective function to obtain the demand quantity for maximizing profit includes:

[0030] Using the theorem of function extreme value, determine the extreme value point of the objective function;

[0031] Based on the extreme value point of the objective function, the objective function is derived to obtain the demand when the profit is maximized.

[0032] A second aspect of the present application provides a pricing device for a data product, including:

[0033] An information acquisition unit, used to acquire product information of the data product to be priced; wherein the product information at least includes the cost of the data product to be priced;

[0034] A profit determination unit, used to determine a sales profit formula for the data product to be priced based on the cost;

[0035] A first substitution unit is used to substitute the pre-trained demand function into the sales profit formula to obtain the target function;

[0036] A derivation unit is used to perform derivation calculation on the objective function to obtain the demand quantity for maximizing profit;

[0037] The second substitution unit is used to substitute the demand quantity when maximizing the profit into the demand function to obtain the pricing price corresponding to the data product to be priced.

[0038] Optionally, in the above-mentioned data product pricing device, the following is further included:

[0039] Model building unit, used to build a demand function theoretical model;

[0040] An acquisition unit is used to acquire test prices corresponding to multiple groups of samples of the sample data product and their corresponding overall user demand; wherein a group of samples corresponds to a test price and its corresponding overall user demand;

[0041] A fitting unit, configured to perform fitting calculation on the demand function theoretical model using a linear regression algorithm based on the plurality of test prices and their corresponding overall user demand quantities, to obtain a demand function and a first optimal parameter and a second optimal parameter corresponding to the demand function;

[0042] A testing unit, used to determine whether the indicator levels of the demand function, the first optimal parameter and the second optimal parameter all meet preset levels by using a model evaluation indicator;

[0043] A first determining unit, configured to determine the demand function as a trained demand function if the indicator levels of the demand function, the first optimal parameter, and the second optimal parameter all meet a preset level;

[0044] An iteration unit is used to iterate the quantity of multiple groups of samples of the sample data product if the indicator levels of the demand function, the first optimal parameter and the second optimal parameter do not meet the preset levels, and return multiple test prices for obtaining the multiple groups of samples of the sample data product and their corresponding overall user demand.

[0045] Optionally, in the above-mentioned data product pricing device, the acquisition unit includes:

[0046] A data acquisition unit, used to acquire all users who purchased the sample data product and the number of users;

[0047] A sampling unit, configured to perform N rounds of sampling on all users who purchased the sample data product to obtain N groups of samples; wherein a group of samples includes at least n users;

[0048] A pricing unit, used to price the samples using a plurality of preset price strategies to obtain a test price corresponding to each group of samples;

[0049] The first calculation unit is used to count the purchase rate of the sample data product purchased by each group of the samples at the corresponding test price during a preset test period to obtain the purchase rate corresponding to each group of the samples;

[0050] The second calculation unit is used to multiply the purchase rate corresponding to each group of samples by the number of users to obtain the overall user demand corresponding to the test price of a group of samples.

[0051] Optionally, in the above-mentioned data product pricing device, the sampling unit includes:

[0052] A second determining unit is used to determine the number of sample groups of all users who purchase the sample data product;

[0053] A third determining unit, used to determine the number of users in the sample group according to the confidence level and the allowable error range;

[0054] The extraction unit is used to perform N rounds of extraction on all users who purchase the sample data product according to the number of sample groups and the number of users, using a sampling method without replacement, to obtain N groups of samples.

[0055] Optionally, in the above-mentioned data product pricing device, the derivation unit includes:

[0056] A fourth determining unit, configured to determine the extreme value point of the objective function by using the theorem of the extreme value of the function;

[0057] The third calculation unit is used to perform a derivative calculation on the objective function based on the extreme value point of the objective function to obtain the demand when the profit is maximized.

[0058] A third aspect of the present application provides an electronic device, including:

[0059] Memory and processor;

[0060] Wherein, the memory is used to store programs;

[0061] The processor is used to execute the program, and when the program is executed, it is specifically used to implement a pricing method for a data product as described in any one of the above items.

[0062] A fourth aspect of the present application provides a computer storage medium for storing a computer program, which, when executed, is used to implement a pricing method for a data product as described in any one of the above.

[0063] The present application provides a pricing method for data products, which obtains product information of the data product to be priced, wherein the product information at least includes the cost of the data product to be priced, and then determines the sales profit formula of the data product to be priced based on the cost, and then substitutes the pre-trained demand function into the sales profit formula to obtain the target function, and then derives the target function to obtain the demand when the profit is maximized, and finally substitutes the demand when the profit is maximized into the demand function to obtain the pricing price corresponding to the data product to be priced. Thus, the optimal solution of the price of the data product to be priced is determined through the profit maximization formula and combined with the demand function, and then the accuracy of data product pricing is effectively improved according to the optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0065] Figure 1 A flowchart of a data product pricing method provided in an embodiment of the present application;

[0066] Figure 2 A flowchart of a method for training a demand function provided in an embodiment of the present application;

[0067] Figure 3 A flowchart of a method for obtaining multiple test prices and their corresponding demand quantities provided in an embodiment of the present application;

[0068] Figure 4 A flowchart of a method for extracting a user provided in an embodiment of the present application;

[0069] Figure 5 A flowchart of a method for constructing a formula for solving a demand quantity when maximizing profit provided in an embodiment of the present application;

[0070] Figure 6 A schematic diagram of the structure of a pricing device for a data product provided in another embodiment of the present application;

[0071] Figure 7 A schematic diagram of the structure of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0072] 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 only 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.

[0073] In this application, 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 such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0074] The present application embodiment provides a pricing method for a data product, such as Figure 1 As shown, the specific steps include:

[0075] S101. Obtain product information of the data product to be priced.

[0076] Among them, the product information may include the cost of the data product to be priced.

[0077] Specifically, for data products to be priced, we first need to understand the cost of the data products in order to determine a suitable pricing strategy to achieve profitability and improve competitiveness. Therefore, we need to obtain the cost information of the data products to be priced in advance.

[0078] S102. Determine the sales profit formula of the data product to be priced based on the cost.

[0079] Specifically, the formula for determining sales profit is: Π=(PC)*Q.

[0080] Among them, C is the cost of the data product to be priced, P is the pricing price, and Q is the demand.

[0081] Optionally, the formula for determining sales profit is determined in advance through expert experience.

[0082] S103. Substitute the pre-trained demand function into the sales profit formula to obtain the target function.

[0083] Specifically, the expression of the pre-trained demand function is: Q = a ※ -b ※ P, after transformation, P = a ※ / b ※ -Q / b ※ .

[0084] Then the transformed demand function expression P = a ※ / b※ -Q / b ※ Substituting into the formula for determining sales profit, the expression of the objective function is:

[0085] Π=(a ※ / b ※ -Q / b ※ -C)*Q=-Q 2 / b ※ +a ※ Q / b ※ -CQ

[0086] Among them, a ※ 、b ※ and C are known parameters, and Q is the demand. It should be noted that P is re-expressed using Q in the objective function, so that there is only one unknown solution in the expression formula of the objective function, which is convenient for subsequent derivation.

[0087] Optionally, the present application embodiment provides a method for training a demand function, such as Figure 2 As shown, the following steps are included:

[0088] S201. Construct a theoretical model of demand function.

[0089] It should be noted that since the theoretical model of the demand function is of great significance for market analysis, pricing strategy and economic forecasting, it is necessary to construct a basic demand function theoretical model in advance to understand the relationship between the price and demand of data products, so that the pricing of data products can be scientifically treated in the future. Specifically, it can be constructed based on the demand for data products, because the demand for a certain data product refers to the number of data products that users are willing and able to pay for at a certain price level and within a certain period of time. Moreover, when other factors remain unchanged, the change in demand is only related to price, so the demand function theoretical model can be constructed based on demand Q and price P.

[0090] Specifically, the demand function theoretical model is expressed as: Q = a-bP.

[0091] Then, if we select N price levels, there will be N different levels of demand. i When the demand is Q i , the demand function theoretical model can be transformed into the form of a linear regression equation: Q i =a-bP i +ε i , i = 1, 2, ..., N. For the linear regression equation, the coefficients a and b can be determined by a model fitting method based on the sample demand and sample price information.

[0092] S202: Obtain test prices corresponding to multiple groups of samples of sample data products and their corresponding overall user demand.

[0093] Among them, a group of samples corresponds to a test price and its corresponding overall user demand.

[0094] Optionally, in another embodiment of the present application, a specific implementation of step S202 is as follows: Figure 3 As shown, the following steps are included:

[0095] S301. Obtain all users who purchased sample data products and the number of users.

[0096] It should be noted that the demand needs to be determined based on the number of users who purchase the sample data product. In addition, in the embodiment of the present application, a local determination method is also used to determine the demand for all users to purchase the sample data product. Therefore, it is necessary to obtain all users who purchase the sample data product and the number of users in advance.

[0097] S302: Perform N rounds of sampling on all users who purchase the sample data product to obtain N groups of samples.

[0098] A set of samples may include n users.

[0099] For users who purchase sample data products, the purchase rate of all users can be inferred from the purchase rate of local users. Therefore, it is necessary to perform N rounds of sampling on all users who purchase sample data products to obtain local users, that is, N groups of samples.

[0100] Optionally, in another embodiment of the present application, a specific implementation of step S302 is as follows: Figure 4 As shown, the following steps are included:

[0101] S401: Determine the number of sample groups of all users who purchase sample data products.

[0102] Optionally, based on the need to construct a demand function, it is necessary to first determine the number of sample groups of all users who purchase sample data products. The number of sample groups can be preliminarily determined based on expert judgment and historical experience. Specifically, the experience of existing regression models can be used, with k representing the number of variables in the demand fitting function, and then k is used to preliminarily determine the number of sample groups as: N = 3k + 1.

[0103] S402: Determine the number of users in the sample group according to the confidence level and the allowable error range.

[0104] The number of sample points n (number of users) can be determined by the confidence level and the allowable error range. Specifically, the following calculation formula can be used:

[0105] n=(t / 2Δ p )2

[0106] Among them, t is the critical value corresponding to the confidence level. The critical value can be found after setting the confidence level before sampling. p It is the maximum error range allowed for sampling, which can be determined through expert judgment and historical experience before sampling.

[0107] S403: According to the number of sample groups and the number of users, a sampling method without replacement is used to perform N rounds of sampling on all users who purchase the sample data product to obtain N groups of samples.

[0108] Therefore, according to the number of sample groups and the number of users determined in step S401 and step S402, a sampling method without replacement is adopted for all users who purchase the sample data product, and N rounds of sampling are performed, with n samplings in each round, thereby forming N groups of samples, and each group of samples has n sample points (users), so that each group of samples effectively represents the user demand situation.

[0109] S303: pricing the samples using a plurality of preset price strategies to obtain a test price corresponding to each group of samples.

[0110] It is understandable that in order to evaluate the user demand elasticity and market response at different price levels and find the best pricing strategy, it is necessary to implement different pricing strategies for different sample groups to complete user diversion. i , the corresponding number of users is denoted as T S i , the corresponding price is set as P i .

[0111] Optionally, another embodiment of the present application also provides a feasible preset test price strategy, and the test prices corresponding to N groups can be {C, (1+20%)C, (1+40%)C, ..., (1+20(N-1)%)C}, that is, the test prices are preset with the cost as the anchor point, and the test price for the Nth group is preset to "(1+20(N-1)%)C".

[0112] S304. Calculate the purchase rate of the sample data product for each group of samples at the corresponding test price during the preset test period to obtain the corresponding purchase rate for each group of samples.

[0113] It should be noted that after completing user diversion, it is necessary to enter the purchase test phase to allow users at different prices to make purchases on their own, thereby obtaining the purchase rate at different prices. The preset test period can be determined based on expert judgment and historical experience. Generally, it should be at least 3 days and no longer than 14 days.

[0114] Then, for each test price, T S i sample users, the number of users who purchased the data product during the statistical test period is expressed as Q S i , the purchase rate is denoted as R S i , then R S i =Q S i / T S i , so that the purchase rate corresponding to different prices can be obtained.

[0115] S305 . For each group of samples, multiply the purchase rate corresponding to the sample by the number of users to obtain the overall user demand corresponding to the test price of a group of samples.

[0116] It should be noted that in the embodiment of the present application, the overall situation is inferred from the sample, so the price is P i When all users participating in data trading activities purchase the data product, the quantity is expressed as Q i , Q i =T*R S i That is, for any price level P i , a corresponding total user demand Q has been inferred i .

[0117] S203. Based on the multiple test prices and their corresponding overall user demands, a linear regression algorithm is used to perform fitting calculations on the demand function theoretical model to obtain the demand function and the first optimal parameters and the second optimal parameters corresponding to the demand function.

[0118] Specifically, after obtaining multiple test prices P i and its corresponding total user demand Q i , the linear regression model solution method (such as the least squares method) can be used to fit the demand function theoretical model in step S201 and calculate the optimal parameters, thereby determining the regression equation of the demand function. Therefore, the optimal parameter of a can be calculated as a ※ , the optimal parameter of b is b ※ , the corresponding fitted demand function regression equation is:

[0119] Q i =a ※ -b ※ P i +ε i , i=1,2,…,N

[0120] S204: Using the model evaluation index, determine whether the index levels of the demand function, the first optimal parameter, and the second optimal parameter all meet the preset levels.

[0121] It should be noted that in order to accurately obtain the pricing of data products, it is necessary to test the fitted demand function and the calculated first optimal parameters and second optimal parameters. Specifically, the commonly used indicators such as existing R-squared, adjusted R-square, mean square error (MSE), mean absolute error (MAE), and existing test methods such as F test and T test can be used to test whether the indicator levels of the demand function, the first optimal parameter and the second optimal parameter meet the preset levels. If the indicator levels of the demand function, the first optimal parameter and the second optimal parameter all meet the preset levels, it means that the demand function fitted at this time can accurately price the data product to be priced, so step S205 is executed. If the indicator levels of the demand function, the first optimal parameter and the second optimal parameter do not meet the preset levels, it means that the demand function needs to be iteratively trained, so step S206 is executed.

[0122] S205: Determine the demand function as the trained demand function.

[0123] Specifically, when the index levels of the demand function, the first optimal parameter, and the second optimal parameter all meet the preset levels, a ※ (the first optimal parameter) and b ※ (The second optimal parameter) is the coefficient determined by fitting, that is, the demand function is determined accordingly: Q = a ※ -b ※ P.

[0124] S206: Perform quantity iteration on multiple groups of samples of the sample data product.

[0125] It should be noted that when the indicator levels of the demand function, the first optimal parameter and the second optimal parameter do not meet the preset levels, it means that the demand function test has not passed. Therefore, an iterative approach can be adopted, that is, increasing the number of sample groups of sample data products and adding a new round of extraction, so as to obtain a test price of a group of samples of a new round of sample data products and its corresponding overall user demand, and then construct the demand function. Therefore, after iterating the quantity of multiple groups of samples of sample data products, it is necessary to return to execute step S202.

[0126] S104. Derivative calculation is performed on the objective function to obtain the demand quantity for maximizing profit.

[0127] It should be noted that in order to maximize the transaction revenue of data products, maximize the sales profit of data products, and thus obtain the optimal pricing of data products, the objective function can be derived to determine the demand when the sales profit is maximized. The expression of the derivative formula is:

[0128]

[0129] Optionally, in another embodiment of the present application, a specific implementation of step S104 is as follows: Figure 5 As shown, the following steps are included:

[0130] S501. Determine the extreme value point of the objective function using the theorem of function extreme value.

[0131] It is understandable that according to the theorem of function extreme value, the objective function is ※ The point is differentiable, and Q ※ The corresponding sales profit is the extreme point of the objective function, so the formula for finding the extreme point of the objective function is:

[0132]

[0133] Based on the extreme point, the sales profit can be maximized, so that the pricing of the data product to be priced can be optimized. Therefore, it is necessary to determine the extreme point of the objective function in advance.

[0134] S502: Based on the extreme value point of the objective function, the objective function is derived to obtain the demand for maximizing profit.

[0135] It can be understood that according to step S501, Q ※ The corresponding sales profit is the extreme point of the objective function, then the formula for finding the extreme point based on step S501 is:

[0136]

[0137] The objective function can be derived, that is, -2Q / b ※ +a ※ / b ※ When C is 0, the objective function can maximize sales profit, and Q is Q ※ .

[0138] Therefore -2Q ※ / b ※ +a ※ / b ※ -C=0, thus getting Q ※ =(a ※ -b ※ C) / 2, where a ※ 、b※ and C are known values, and then we can get the demand quantity Q when maximizing profit ※ .

[0139] S105. Substitute the demand quantity when maximizing profits into the demand function to obtain the pricing price corresponding to the data product to be priced.

[0140] It is understandable that the demand quantity Q that maximizes profit is ※ =(a ※ -b ※ C) / 2 is substituted into Q=a ※ -b ※ P is calculated, and the optimal price solution P for the data product to be priced can be obtained. ※ , that is, the pricing price P of the data product to be priced ※ , that is: P ※ =(a ※ +b ※ C) / 2b ※ .

[0141] The present application provides a pricing method for data products, which obtains product information of the data product to be priced, wherein the product information at least includes the cost of the data product to be priced, and then determines the sales profit formula of the data product to be priced based on the cost, and then substitutes the pre-trained demand function into the sales profit formula to obtain the target function, and then derives the target function to obtain the demand when the profit is maximized, and finally substitutes the demand when the profit is maximized into the demand function to obtain the pricing price corresponding to the data product to be priced. Thus, the optimal solution of the price of the data product to be priced is determined through the profit maximization formula and combined with the demand function, and then the accuracy of data product pricing is effectively improved according to the optimal solution.

[0142] Another embodiment of the present application provides a pricing device for a data product, such as Figure 6 As shown, it includes the following units:

[0143] The information acquisition unit 601 is used to acquire product information of the data product to be priced, wherein the product information at least includes the cost of the data product to be priced.

[0144] The profit determination unit 602 is used to determine the sales profit formula of the data product to be priced based on the cost.

[0145] The first substitution unit 603 is used to substitute the pre-trained demand function into the sales profit formula to obtain the target function.

[0146] The derivation unit 604 is used to derive the maximum profit formula to obtain the maximum profit demand.

[0147] The second substitution unit 605 is used to substitute the demand quantity when maximizing profit into the demand function to obtain the pricing price corresponding to the data product to be priced.

[0148] It should be noted that the specific working process of the above-mentioned units in the embodiment of the present application can refer to steps S101 to S105 in the above-mentioned method embodiment, and will not be repeated here.

[0149] Optionally, a pricing device for a data product provided in another embodiment of the present application further includes:

[0150] Model building unit, used to build demand function theoretical model.

[0151] The acquisition unit is used to acquire the test prices corresponding to multiple groups of samples of the sample data product and the corresponding overall user demand, wherein one group of samples corresponds to one test price and the corresponding overall user demand.

[0152] The fitting unit is used to fit the demand function theoretical model based on multiple test prices and their corresponding overall user demand quantities using a linear regression algorithm to obtain the demand function and the first optimal parameter and the second optimal parameter corresponding to the demand function.

[0153] The testing unit is used to use the model evaluation index to determine whether the index levels of the demand function, the first optimal parameter and the second optimal parameter all meet the preset levels.

[0154] The first determination unit is used to determine the demand function as a trained demand function if the indicator levels of the demand function, the first optimal parameter and the second optimal parameter all meet preset levels.

[0155] The iteration unit is used to iterate the quantity of multiple groups of samples of the sample data product if the indicator levels of the demand function, the first optimal parameter and the second optimal parameter do not meet the preset levels, and return multiple test prices of the multiple groups of samples of the sample data product and their corresponding overall user demand.

[0156] Optionally, in a pricing device for a data product provided in another embodiment of the present application, the acquiring unit includes:

[0157] The data acquisition unit is used to acquire all users who purchased the sample data product and the number of users.

[0158] The sampling unit is used to perform N rounds of sampling on all users who purchase the sample data product to obtain N groups of samples, wherein a group of samples includes at least n users.

[0159] The pricing unit is used to price the samples using a plurality of preset price strategies to obtain the test price corresponding to each group of samples.

[0160] The first calculation unit is used to count the purchase rate of the sample data product of each group of samples at the corresponding test price during the preset test period to obtain the purchase rate corresponding to each group of samples.

[0161] The second calculation unit is used to multiply the purchase rate corresponding to each group of samples by the number of users to obtain the overall user demand corresponding to the test price of a group of samples.

[0162] Optionally, in a data product pricing device provided in another embodiment of the present application, the sampling unit includes:

[0163] The second determining unit is used to determine the number of sample groups of all users who purchase the sample data product.

[0164] The third determining unit is used to determine the number of users in the sample group according to the confidence level and the allowable error range.

[0165] The extraction unit is used to perform N rounds of extraction on all users who purchase the sample data product according to the number of sample groups and the number of users, using a sampling method without replacement, to obtain N groups of samples.

[0166] Optionally, in a pricing device for a data product provided in another embodiment of the present application, the derivation unit 604 includes:

[0167] The fourth determining unit is used to determine the extreme value point of the target function by using the theorem of the extreme value of the function.

[0168] The third calculation unit is used to perform a derivative calculation on the objective function based on the extreme value point of the objective function to obtain the demand when the profit is maximized.

[0169] Another embodiment of the present application provides an electronic device, such as Figure 7 As shown, including:

[0170] Memory 701 and processor 702 .

[0171] The memory 701 is used to store programs.

[0172] The processor 702 is used to execute a program. When the program is executed, it is specifically used to implement a pricing method for a data product provided in any one of the above embodiments.

[0173] Another embodiment of the present application provides a computer storage medium for storing a computer program. When the computer program is executed, it is used to implement a pricing method for a data product provided in any of the above embodiments.

[0174] Computer storage media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0175] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0176] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A pricing method for a data product, characterized in that: include: Acquire product information of the data product to be priced; wherein the product information at least includes the cost of the data product to be priced; Determine a sales profit formula for the data product to be priced based on the cost; Substituting the pre-trained demand function into the sales profit formula to obtain the target function; Derivative calculation is performed on the objective function to obtain the demand quantity when maximizing profit; Substitute the demand quantity when maximizing the profit into the demand function to obtain the pricing price corresponding to the data product to be priced.

2. The method according to claim 1, characterized in that The training method of the demand function comprises: Construct theoretical model of demand function; Obtain test prices corresponding to multiple groups of samples of sample data products and their corresponding overall user demand; wherein a group of samples corresponds to a test price and its corresponding overall user demand; Based on the plurality of test prices and their corresponding overall user demand, a linear regression algorithm is used to perform fitting calculation on the demand function theoretical model to obtain a demand function and a first optimal parameter and a second optimal parameter corresponding to the demand function; Using the model evaluation index, determining whether the index levels of the demand function, the first optimal parameter, and the second optimal parameter all meet preset levels; If the indicator levels of the demand function, the first optimal parameter, and the second optimal parameter all meet the preset levels, determining the demand function as a trained demand function; If the indicator levels of the demand function, the first optimal parameter and the second optimal parameter do not meet the preset levels, the multiple groups of samples of the sample data product are iterated in quantity, and the test prices corresponding to the multiple groups of samples of the sample data product and their corresponding overall user demand are returned.

3. The method according to claim 2, characterized in that The obtaining of the test prices corresponding to the multiple groups of samples of the sample data product and the corresponding overall user demand includes: Get all users who purchased sample data products and the number of users; Performing N rounds of sampling on all users who purchased the sample data product to obtain N groups of samples; wherein a group of samples includes at least n users; The samples are priced using a plurality of preset price strategies to obtain a test price corresponding to each group of the samples; Calculate the purchase rate of the sample data product for each group of samples at the corresponding test price during the preset test period to obtain the purchase rate corresponding to each group of samples; For each group of samples, the purchase rate corresponding to the samples is multiplied by the number of users to obtain the overall user demand corresponding to the test price of a group of samples.

4. The method according to claim 3, characterized in that The method of performing N rounds of sampling on all users who purchased the sample data product to obtain N groups of samples includes: Determine the number of sample groups from which all users who purchase the sample data product are drawn; Determine the number of users in the sample group using confidence level and allowable error range; According to the number of sample groups and the number of users, a sampling method without replacement is used to perform N rounds of sampling on all users who purchase the sample data product to obtain N groups of samples.

5. The method according to claim 1, characterized in that The step of performing a derivative calculation on the objective function to obtain the demand quantity when maximizing the profit includes: Using the theorem of function extreme value, determine the extreme value point of the objective function; Based on the extreme value point of the objective function, the objective function is derived to obtain the demand when the profit is maximized.

6. A pricing device for a data product, characterized in that: include: An information acquisition unit, used to acquire product information of the data product to be priced; wherein the product information at least includes the cost of the data product to be priced; A profit determination unit, used to determine a sales profit formula for the data product to be priced based on the cost; A first substitution unit is used to substitute the pre-trained demand function into the sales profit formula to obtain the target function; A derivation unit is used to perform derivation calculation on the objective function to obtain the demand quantity for maximizing profit; The second substitution unit is used to substitute the demand quantity when maximizing the profit into the demand function to obtain the pricing price corresponding to the data product to be priced.

7. The device according to claim 6, characterized in that Also includes: Model building unit, used to build a demand function theoretical model; An acquisition unit is used to acquire test prices corresponding to multiple groups of samples of the sample data product and their corresponding overall user demand; wherein a group of samples corresponds to a test price and its corresponding overall user demand; A fitting unit, configured to perform fitting calculation on the demand function theoretical model using a linear regression algorithm based on the plurality of test prices and their corresponding overall user demand quantities, to obtain a demand function and a first optimal parameter and a second optimal parameter corresponding to the demand function; A testing unit, used to determine whether the indicator levels of the demand function, the first optimal parameter and the second optimal parameter all meet preset levels by using a model evaluation indicator; A first determining unit, configured to determine the demand function as a trained demand function if the indicator levels of the demand function, the first optimal parameter, and the second optimal parameter all meet a preset level; An iteration unit is used to iterate the quantity of multiple groups of samples of the sample data product if the indicator levels of the demand function, the first optimal parameter and the second optimal parameter do not meet the preset levels, and return multiple test prices for obtaining the multiple groups of samples of the sample data product and their corresponding overall user demand.

8. The device according to claim 7, characterized in that The acquisition unit comprises: A data acquisition unit, used to acquire all users who purchased the sample data product and the number of users; A sampling unit, configured to perform N rounds of sampling on all users who purchased the sample data product to obtain N groups of samples; wherein a group of samples includes at least n users; A pricing unit, used to price the samples using a plurality of preset price strategies to obtain a test price corresponding to each group of samples; The first calculation unit is used to count the purchase rate of the sample data product purchased by each group of the samples at the corresponding test price during a preset test period to obtain the purchase rate corresponding to each group of the samples; The second calculation unit is used to multiply the purchase rate corresponding to each group of samples by the number of users to obtain the overall user demand corresponding to the test price of a group of samples.

9. An electronic device, characterized in that: include: Memory and processor; Wherein, the memory is used to store programs; The processor is used to execute the program, and when the program is executed, it is specifically used to implement a pricing method for a data product as described in any one of claims 1 to 5.

10. A computer storage medium, characterized in that: Used to store a computer program, which, when executed, is used to implement a pricing method for a data product as described in any one of claims 1 to 5.

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