CN114926214B - Model construction-based product pricing method, device, equipment and storage medium - Google Patents
Model construction-based product pricing method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, and discloses a product pricing method based on model construction, which comprises the following steps: acquiring historical product pricing data, and performing data cleaning on the historical product pricing data to obtain standard product pricing data; performing data segmentation on the standard product pricing data, and performing training operation and verification operation on a pre-constructed regression model according to the segmented standard product pricing data to obtain a price previewing model; responding to the data of the product to be priced input by a user, and utilizing the price previewing model to conduct price previewing on the product pricing data to obtain first pricing; a second pricing input by the user is received, and standard pricing is calculated based on the first pricing and the second pricing, and the identity information of the user. The invention also provides a product pricing device, equipment and storage medium based on the model construction. The invention can improve the accuracy of product pricing.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a product pricing method and apparatus based on model construction, an electronic device, and a readable storage medium.
Background
Product pricing refers to the process of putting a reasonable price on a product according to the needs of a customer. For example, a certain insurance institution carries out the process of premium quotation for the personal safety of the old according to the physical condition, age and other factors of the old.
The current common product pricing method is generally to price by manpower, such as pricing according to experience of management system or business staff, and the method is easy to cause the condition that the product is over-priced, so that customers are not full, or the product is over-priced, and the benefits of enterprises are jeopardized. In addition, the efficiency of manually pricing products is not high, and often long-term consideration is required to give quotations according to the customer's situation.
Disclosure of Invention
The invention provides a product pricing method, a device, electronic equipment and a computer readable storage medium based on model construction, and aims to improve the accuracy of product pricing.
In order to achieve the above object, the present invention provides a product pricing method based on model construction, comprising:
acquiring service demands, acquiring historical product pricing data according to the service demands, and performing data cleaning on the historical product pricing data to obtain standard product pricing data;
performing data segmentation on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, and performing training operation and verification operation on a pre-built regression model according to the product pricing data training set and the product pricing data testing set to obtain a price pre-modeling model;
responding to the data of the product to be priced input by a user, and utilizing the price previewing model to conduct price previewing on the product pricing data to obtain first pricing;
A second pricing input by the user is received, and standard pricing is calculated based on the first pricing and the second pricing, and the identity information of the user.
Optionally, the training operation includes:
Calculating the pricing data training set by using a preset regression equation to obtain a regression model of the pricing data training set;
calculating a loss value of the regression model by using a preset loss function;
And adjusting parameters of the regression model according to the loss values, and performing iterative training on the regression model after the parameters are adjusted until a preset termination condition is met, and obtaining a price previewing model according to the regression model after the parameters are adjusted.
Optionally, the verifying operation includes:
inputting the product pricing data test set into the price previewing model to generate a pricing result;
Judging whether the pricing result is correct data or not according to the data labels in the product pricing data test set;
And when the pricing result is judged to be not correct data, carrying out parameter adjustment on the price previewing model according to the pricing result, and returning to the step of inputting the product pricing data test set into the price previewing model to generate the pricing result until the pricing result is judged to be correct data, so as to obtain the trained price previewing model.
Optionally, the data segmentation is performed on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, including:
Calculating an average value of the standard product pricing data;
the standard pricing product data are classified according to the average value to obtain standard pricing product data above the average value and standard pricing product data below the average value;
Respectively labeling the standard pricing product data above the average value and the standard pricing product data below the average value to obtain positive product data and negative product data;
Setting the proportion of the positive product data and the negative product data in a product pricing data training set and a product pricing data testing set;
And randomly extracting data from the positive product data and the negative product data according to the proportion to obtain a product pricing data training set and a product pricing data testing set.
Optionally, the data cleaning of the historical product pricing data to obtain standard product pricing data includes:
Carrying out structuring processing on the historical product pricing data, and carrying out tabulation processing on the structured historical product pricing data to obtain a historical product pricing data table;
carrying out data backup on the historical product pricing data table to obtain a historical product pricing data backup table;
repeating value deletion and deletion, filling or conversion treatment of missing values are carried out on the historical product pricing data backup table, so that a cleaning data table is obtained;
Calculating the data standard deviation of each field in the cleaning data table, and marking the data which is larger than the data standard deviation preset multiple in the data corresponding to the field as an abnormal value;
Processing the abnormal value according to a preset rule to obtain a standard product pricing data table;
and extracting data in the standard product pricing data table to obtain standard product pricing data.
Optionally, the calculating standard pricing according to the first pricing and the second pricing, and the identity information of the user includes:
acquiring the user grade of the user according to the identity information of the user;
confirming the weight duty ratio of the first pricing and the second pricing according to a preset user grade preference comparison table;
and obtaining standard pricing according to the weight ratio.
Optionally, the obtaining standard pricing according to the weight ratio includes:
The standard pricing S is calculated using the following formula:
S＝A*X+B*Y
Wherein A, B is a first pricing and a second pricing, and X, Y is a weight duty cycle of the first pricing and a weight duty cycle of the second pricing, respectively.
In order to solve the above problems, the present invention further provides a product pricing device based on model construction, the device comprising:
The regression model training module is used for acquiring service demands, acquiring historical product pricing data according to the service demands, performing data cleaning on the historical product pricing data to obtain standard product pricing data, performing data segmentation on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, and performing training operation and verification operation on a pre-built regression model according to the product pricing data training set and the product pricing data testing set to obtain a price previewing model;
the price previewing module is used for responding to the to-be-priced product data input by the user, and performing price previewing on the product pricing data by utilizing the price previewing model to obtain first pricing;
And the standard pricing calculation module is used for receiving second pricing input by the user and calculating standard pricing according to the first pricing and the second pricing and the identity information of the user.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one computer program; and
And a processor executing the computer program stored in the memory to implement the model-based build product pricing method described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the model-based build product pricing method described above.
According to the product pricing method, device, equipment and storage medium based on model construction, corresponding historical product pricing data are obtained according to service requirements, the historical product pricing data are subjected to data cleaning, the historical product pricing data after the data cleaning are subjected to data segmentation to obtain a product pricing data training set and a product pricing data testing set, so that accuracy of model training is improved, accuracy of product pricing is improved, further, a pre-built regression model is trained and verified according to the product pricing data training set and the product pricing data testing set to obtain a price previewing model, time required by product pricing is shortened, and efficiency of product pricing is improved; and finally, inputting the data of the product to be priced, which is input by the user, into the price previewing model to obtain a first price, and generating standard price according to the second price input by the user and the identity information of the user, thereby ensuring the rationality of the product price and improving the accuracy of the product price. Therefore, the product pricing method, device, equipment and storage medium based on the model construction provided by the embodiment of the invention improve the accuracy of product pricing.
Drawings
FIG. 1 is a flow chart of a model-based product pricing method according to one embodiment of the invention;
FIG. 2 is a schematic block diagram of a model-based product pricing device according to one embodiment of the invention;
FIG. 3 is a schematic diagram of an internal structure of an electronic device implementing a model-based product pricing method according to an embodiment of the present invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a product pricing method based on model construction. The execution subject of the model-based product pricing method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the model-based build product pricing method may be performed by software or hardware installed on a terminal device or a server device, and the software may be a blockchain platform. The server may include an independent server, and may also include a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (ContentDelivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, which is a schematic flow chart of a model-based product pricing method according to an embodiment of the invention, in an embodiment of the invention, the model-based product pricing method includes:
S1, acquiring service demands, acquiring historical product pricing data according to the service demands, and performing data cleaning on the historical product pricing data to obtain standard product pricing data;
In the embodiment of the present invention, the service requirement may be a product item selected by the user, such as personal safety a.a.r., a pension, etc. The historical product pricing data may be historical offer data for a product item and corresponding user data, such as historical offer data for an pension insurance product and corresponding user data for the physical condition, age, etc. of the user.
In the embodiment of the invention, because some historical product pricing data in the historical product pricing data are influenced by environmental factors or human factors and cannot be used for normal model training, abnormal data in the historical product pricing data need to be cleaned before training the historical product pricing data so as to improve the accuracy of model and product pricing.
In an alternative embodiment of the invention, the pricing accuracy can be ensured by deleting, filling or treating the abnormal data as a special data.
In detail, the step of obtaining the historical product pricing data according to the business requirement, and performing data cleaning on the historical product pricing data to obtain standard product pricing data includes:
Carrying out structuring processing on the historical product pricing data, and carrying out tabulation processing on the structured historical product pricing data to obtain a historical product pricing data table;
carrying out data backup on the historical product pricing data table to obtain a historical product pricing data backup table;
repeating value deletion and deletion, filling or conversion treatment of missing values are carried out on the historical product pricing data backup table, so that a cleaning data table is obtained;
calculating the data standard deviation of each field in the cleaning data table, and marking the data which is larger than the preset multiple of the data standard deviation of the field in the data corresponding to the field as an abnormal value;
Processing the abnormal value according to a preset rule to obtain a standard product pricing data table;
and extracting data in the standard product pricing data table to obtain standard product pricing data.
In the embodiment of the invention, the data type of the historical product pricing data is obtained, the corresponding hierarchical structure of each historical product pricing data is identified, and the historical product pricing data is classified according to the hierarchical structure, so that the structural processing of the historical product pricing data is realized.
In the embodiment of the invention, the historical product pricing data backup table is obtained by carrying out data backup on the historical product pricing data table, so that the situation that the original data is possibly damaged when the data is cleaned is avoided.
In an alternative embodiment of the present invention, the processing the outlier according to a preset rule may be converting the outlier into an average value to represent or converting the outlier into a value with three times of standard deviation.
In the embodiment of the invention, the standard product pricing data is obtained by adding, deleting and modifying the abnormal data in the historical product pricing data backup table so as to ensure the accuracy and usability of the data.
S2, carrying out data segmentation on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, and executing training operation and verification operation on a pre-constructed regression model according to the product pricing data training set and the product pricing data testing set to obtain a price pre-modeling model;
In the embodiment of the invention, the product pricing data training set and the product pricing data testing set can be data sets obtained by dividing the standard product pricing data according to a certain proportion, wherein the product pricing data training set is used for model training, and the product pricing data testing set is used for model testing.
In the embodiment of the invention, the error of the model in the real environment is called generalization error, and further, in the process of model construction, the trained model is finally required to be deployed in the real environment, and the trained model is expected to obtain a good prediction effect on real data, namely, the lower the generalization error of the trained model is expected to be, the better. Therefore, it is desirable to divide the existing standard product pricing data into training and testing sets to reduce generalization errors.
In an alternative embodiment of the present invention, in order to prevent data snoop bias, data is randomly divided into a product pricing data training set and a product pricing data testing set, so as to avoid knowing too much about sample characteristics in the product pricing data testing set, prevent artificial selection of the product pricing data testing set which is helpful for the model, and avoid too optimistic verification results of the model, but in practice, the expected problem is not reached.
In detail, the data segmentation is performed on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, which comprises the following steps:
Calculating an average value of the standard product pricing data;
the standard pricing product data are classified according to the average value to obtain standard pricing product data above the average value and standard pricing product data below the average value;
Respectively labeling the standard pricing product data above the average value and the standard pricing product data below the average value to obtain positive product data and negative product data;
Setting the proportion of the positive product data and the negative product data in a product pricing data training set and a product pricing data testing set;
And randomly extracting data from the positive product data and the negative product data according to the proportion to obtain a product pricing data training set and a product pricing data testing set.
In the embodiment of the invention, sklearn can be utilized to randomly extract data from positive product data and negative product data, so as to obtain a product pricing data training set and a product pricing data testing set. Wherein sklearn is a very powerful machine learning library provided by Python third parties, having methods that can be used for supervised and unsupervised learning.
In the embodiment of the invention, the standard product pricing data is classified and labeled so as to facilitate sklearn machine learning libraries to identify the standard product pricing data class, thereby more effectively performing data segmentation.
Because the standard product pricing data are randomly distributed, a large amount of data which is larger or smaller than the average value easily form the product pricing data training set or the product pricing data testing set, so that model training or model testing effect is poor, and the accuracy of a model is reduced. Therefore, the embodiment of the invention sets the proportion of the positive product data and the negative product data in the product pricing data training set and the product pricing data testing set in advance, and supervises and learns by utilizing sklearn machine learning libraries to generate a reasonable product pricing data training set and a reasonable product pricing data testing set.
Further, in the embodiment of the present invention, the regression model may be a data model for numerical prediction, and there are a multiple linear regression model, a compound curve regression model, a cubic curve regression model, and the like.
In the embodiment of the invention, firstly, the value to be predicted in the regression model is determined, secondly, a proper model is selected, the parameters of the model are determined by utilizing an optimization algorithm, and finally, the product pricing data training set and the product pricing data testing set are input into the model for model training and verification.
In the embodiment of the invention, the trained price previewing model is obtained by training and verifying the regression model, so that the accuracy of the model is improved, the occurrence of quotation errors and the like caused by insufficient accuracy of the model is reduced, and the user experience is improved.
In detail, the training operation includes:
Calculating the pricing data training set by using a preset regression equation to obtain a regression model of the pricing data training set;
calculating a loss value of the regression model by using a preset loss function;
And adjusting parameters of the regression model according to the loss values, and performing iterative training on the regression model after the parameters are adjusted until a preset termination condition is met, and obtaining a price previewing model according to the regression model after the parameters are adjusted.
Further, the verifying operation includes:
inputting the product pricing data test set into the price previewing model to generate a pricing result;
Judging whether the pricing result is correct data or not according to the data labels in the product pricing data test set;
And when the pricing result is judged to be not correct data, carrying out parameter adjustment on the price previewing model according to the pricing result, and returning to the step of inputting the product pricing data test set into the price previewing model to generate the pricing result until the pricing result is judged to be correct data, so as to obtain the trained price previewing model.
In the embodiment of the present invention, the preset regression equation may be a linear regression equation, a compound curve regression equation, a cubic curve regression equation, and the like. The loss function is dependent on the regression model, for example, the loss function may be a square loss function when the regression model is a linear regression model, and the loss function may be a logarithmic loss function when the regression equation is a logistic regression model.
And S3, responding to the to-be-priced product data input by the user, and utilizing the price previewing model to conduct price previewing on the product pricing data to obtain first pricing.
In the embodiment of the invention, the data of the product to be priced can be the data according to which the product to be priced is needed. For example, if a user wants to purchase a pension, the user's physical condition, age, etc. data needs to be acquired.
In an alternative embodiment of the invention, the trained price previewing model can be utilized to preview the price of the product which the user wants to purchase, so that the situation that the price is too high or too low is reduced, the user experience is poor or the benefit of enterprises is damaged, the time of product quotation is also reduced, and the product quotation efficiency is improved.
And S4, receiving second pricing input by the user, and calculating standard pricing according to the first pricing, the second pricing and the identity information of the user.
In an embodiment of the present invention, the second pricing may be an expected pricing of the user.
According to the embodiment of the invention, the standard pricing is calculated according to the first pricing and the second pricing, so that on one hand, the benefit of enterprises is not damaged, and on the other hand, good product purchasing experience of users is provided, and therefore discontents of the users are reduced.
In detail, the calculating standard pricing according to the first pricing and the second pricing and the identity information of the user includes:
acquiring the user grade of the user according to the identity information of the user;
confirming the weight duty ratio of the first pricing and the second pricing according to a preset user grade preference comparison table;
and obtaining standard pricing according to the weight ratio.
In the embodiment of the invention, the identity information of the user can be a consumption record, a name and the like of the user. The user's user level may be the importance of the user in the enterprise customer value hierarchy. The preset user grade preference comparison table may be a preference strength corresponding to the corresponding user grade, for example, when the user grade is grade 1, the first pricing weight is specified to be 0.9, the second pricing weight is specified to be 0.1, when the user grade is grade 2, the first pricing weight is specified to be 0.85, the second pricing weight is specified to be 0.15, and so on.
Further, the obtaining standard pricing according to the weight ratio includes:
The standard pricing S is calculated using the following formula:
S＝A*X+B*Y
Wherein A, B is a first pricing and a second pricing, and X, Y is a weight duty cycle of the first pricing and a weight duty cycle of the second pricing, respectively.
According to the product pricing method based on model construction, corresponding historical product pricing data are obtained according to service requirements, the historical product pricing data are subjected to data cleaning, the historical product pricing data subjected to data cleaning are subjected to data segmentation to obtain a product pricing data training set and a product pricing data testing set, so that accuracy of model training is improved, accuracy of product pricing is improved, further, a pre-constructed regression model is trained and verified according to the product pricing data training set and the product pricing data testing set, a price previewing model is obtained, time required by product pricing is shortened, and efficiency of product pricing is improved; and finally, inputting the data of the product to be priced, which is input by the user, into the price previewing model to obtain a first price, and generating standard price according to the second price input by the user and the identity information of the user, thereby ensuring the rationality of the product price and improving the accuracy of the product price. Therefore, the product pricing method based on the model construction provided by the embodiment of the invention improves the accuracy of product pricing.
As shown in fig. 2, a functional block diagram of a model-based product pricing device of the present invention is shown.
The model-based product pricing device 100 of the present invention may be installed in an electronic device. Depending on the functions implemented, the model-based product pricing means may comprise a regression model training module 101, a price previewing module 102 and a standard pricing calculation module 103, which may also be referred to as a unit, a series of computer program segments capable of being executed by the processor of the electronic device and performing fixed functions, stored in the memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
The regression model training module 101 is configured to obtain a service requirement, obtain historical product pricing data according to the service requirement, perform data cleaning on the historical product pricing data to obtain standard product pricing data, perform data segmentation on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, and perform training operation and verification operation on a pre-built regression model according to the product pricing data training set and the product pricing data testing set to obtain a price previewing model.
In the embodiment of the present invention, the service requirement may be a product item selected by the user, such as personal safety a.a.r., a pension, etc. The historical product pricing data may be historical offer data for a product item and corresponding user data, such as historical offer data for an pension insurance product and corresponding user data for the physical condition, age, etc. of the user.
In the embodiment of the invention, because some historical product pricing data in the historical product pricing data are influenced by environmental factors or human factors and cannot be used for normal model training, abnormal data in the historical product pricing data need to be cleaned before training the historical product pricing data so as to improve the accuracy of model and product pricing.
In an alternative embodiment of the invention, the pricing accuracy can be ensured by deleting, filling or treating the abnormal data as a special data.
In detail, the step of obtaining the historical product pricing data according to the business requirement, and performing data cleaning on the historical product pricing data to obtain standard product pricing data includes:
Carrying out structuring processing on the historical product pricing data, and carrying out tabulation processing on the structured historical product pricing data to obtain a historical product pricing data table;
carrying out data backup on the historical product pricing data table to obtain a historical product pricing data backup table;
repeating value deletion and deletion, filling or conversion treatment of missing values are carried out on the historical product pricing data backup table, so that a cleaning data table is obtained;
calculating the data standard deviation of each field in the cleaning data table, and marking the data which is larger than the preset multiple of the data standard deviation of the field in the data corresponding to the field as an abnormal value;
Processing the abnormal value according to a preset rule to obtain a standard product pricing data table;
and extracting data in the standard product pricing data table to obtain standard product pricing data.
In the embodiment of the invention, the data type of the historical product pricing data is obtained, the corresponding hierarchical structure of each historical product pricing data is identified, and the historical product pricing data is classified according to the hierarchical structure, so that the structural processing of the historical product pricing data is realized.
In the embodiment of the invention, the historical product pricing data backup table is obtained by carrying out data backup on the historical product pricing data table, so that the situation that the original data is possibly damaged when the data is cleaned is avoided.
In an alternative embodiment of the present invention, the processing the outlier according to a preset rule may be converting the outlier into an average value to represent or converting the outlier into a value with three times of standard deviation.
In the embodiment of the invention, the standard product pricing data is obtained by adding, deleting and modifying the abnormal data in the historical product pricing data backup table so as to ensure the accuracy and usability of the data.
In the embodiment of the invention, the product pricing data training set and the product pricing data testing set can be data sets obtained by dividing the standard product pricing data according to a certain proportion, wherein the product pricing data training set is used for model training, and the product pricing data testing set is used for model testing.
In the embodiment of the invention, the error of the model in the real environment is called generalization error, and further, in the process of model construction, the trained model is finally required to be deployed in the real environment, and the trained model is expected to obtain a good prediction effect on real data, namely, the lower the generalization error of the trained model is expected to be, the better. Therefore, it is desirable to divide the existing standard product pricing data into training and testing sets to reduce generalization errors.
In an alternative embodiment of the present invention, in order to prevent data snoop bias, data is randomly divided into a product pricing data training set and a product pricing data testing set, so as to avoid knowing too much about sample characteristics in the product pricing data testing set, prevent artificial selection of the product pricing data testing set which is helpful for the model, and avoid too optimistic verification results of the model, but in practice, the expected problem is not reached.
In detail, the data segmentation is performed on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, which comprises the following steps:
Calculating an average value of the standard product pricing data;
the standard pricing product data are classified according to the average value to obtain standard pricing product data above the average value and standard pricing product data below the average value;
Respectively labeling the standard pricing product data above the average value and the standard pricing product data below the average value to obtain positive product data and negative product data;
Setting the proportion of the positive product data and the negative product data in a product pricing data training set and a product pricing data testing set;
And randomly extracting data from the positive product data and the negative product data according to the proportion to obtain a product pricing data training set and a product pricing data testing set.
In the embodiment of the invention, sklearn can be utilized to randomly extract data from positive product data and negative product data, so as to obtain a product pricing data training set and a product pricing data testing set. Wherein sklearn is a very powerful machine learning library provided by Python third parties, having methods that can be used for supervised and unsupervised learning.
In the embodiment of the invention, the standard product pricing data is classified and labeled so as to facilitate sklearn machine learning libraries to identify the standard product pricing data class, thereby more effectively performing data segmentation.
Because the standard product pricing data are randomly distributed, a large amount of data which is larger or smaller than the average value easily form the product pricing data training set or the product pricing data testing set, so that model training or model testing effect is poor, and the accuracy of a model is reduced. Therefore, the embodiment of the invention sets the proportion of the positive product data and the negative product data in the product pricing data training set and the product pricing data testing set in advance, and supervises and learns by utilizing sklearn machine learning libraries to generate a reasonable product pricing data training set and a reasonable product pricing data testing set.
Further, in the embodiment of the present invention, the regression model may be a data model for numerical prediction, and there are a multiple linear regression model, a compound curve regression model, a cubic curve regression model, and the like.
In the embodiment of the invention, firstly, the value to be predicted in the regression model is determined, secondly, a proper model is selected, the parameters of the model are determined by utilizing an optimization algorithm, and finally, the product pricing data training set and the product pricing data testing set are input into the model for model training and verification.
In the embodiment of the invention, the trained price previewing model is obtained by training and verifying the regression model, so that the accuracy of the model is improved, the occurrence of quotation errors and the like caused by insufficient accuracy of the model is reduced, and the user experience is improved.
In detail, the training operation includes:
Calculating the pricing data training set by using a preset regression equation to obtain a regression model of the pricing data training set;
calculating a loss value of the regression model by using a preset loss function;
And adjusting parameters of the regression model according to the loss values, and performing iterative training on the regression model after the parameters are adjusted until a preset termination condition is met, and obtaining a price previewing model according to the regression model after the parameters are adjusted.
Further, the verifying operation includes:
inputting the product pricing data test set into the price previewing model to generate a pricing result;
Judging whether the pricing result is correct data or not according to the data labels in the product pricing data test set;
And when the pricing result is judged to be not correct data, carrying out parameter adjustment on the price previewing model according to the pricing result, and returning to the step of inputting the product pricing data test set into the price previewing model to generate the pricing result until the pricing result is judged to be correct data, so as to obtain the trained price previewing model.
In the embodiment of the present invention, the preset regression equation may be a linear regression equation, a compound curve regression equation, a cubic curve regression equation, and the like. The loss function is dependent on the regression model, for example, the loss function may be a square loss function when the regression model is a linear regression model, and the loss function may be a logarithmic loss function when the regression equation is a logistic regression model.
The price previewing module 102 is configured to perform price previewing on the product pricing data by using the price previewing model in response to the product data to be priced input by the user, so as to obtain first pricing.
In the embodiment of the invention, the data of the product to be priced can be the data according to which the product to be priced is needed. For example, if a user wants to purchase a pension, the user's physical condition, age, etc. data needs to be acquired.
In an alternative embodiment of the invention, the trained price previewing model can be utilized to preview the price of the product which the user wants to purchase, so that the situation that the price is too high or too low is reduced, the user experience is poor or the benefit of enterprises is damaged, the time of product quotation is also reduced, and the product quotation efficiency is improved.
The standard pricing calculating module 103 is configured to receive a second pricing input by the user, and calculate standard pricing according to the first pricing and the second pricing, and identity information of the user.
In an embodiment of the present invention, the second pricing may be an expected pricing of the user.
According to the embodiment of the invention, the standard pricing is calculated according to the first pricing and the second pricing, so that on one hand, the benefit of enterprises is not damaged, and on the other hand, good product purchasing experience of users is provided, and therefore discontents of the users are reduced.
In detail, the calculating standard pricing according to the first pricing and the second pricing and the identity information of the user includes:
acquiring the user grade of the user according to the identity information of the user;
confirming the weight duty ratio of the first pricing and the second pricing according to a preset user grade preference comparison table;
and obtaining standard pricing according to the weight ratio.
In the embodiment of the invention, the identity information of the user can be a consumption record, a name and the like of the user. The user's user level may be the importance of the user in the enterprise customer value hierarchy. The preset user grade preference comparison table may be a preference strength corresponding to the corresponding user grade, for example, when the user grade is grade 1, the first pricing weight is specified to be 0.9, the second pricing weight is specified to be 0.1, when the user grade is grade 2, the first pricing weight is specified to be 0.85, the second pricing weight is specified to be 0.15, and so on.
Further, the obtaining standard pricing according to the weight ratio includes:
The standard pricing S is calculated using the following formula:
S＝A*X+B*Y
Wherein A, B is a first pricing and a second pricing, and X, Y is a weight duty cycle of the first pricing and a weight duty cycle of the second pricing, respectively.
FIG. 3 is a schematic diagram of an electronic device implementing a model-based product pricing method of the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a model-based build product pricing program.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in an electronic device and various types of data, such as code of a product pricing program constructed based on a model, etc., but also to temporarily store data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules (e.g., model-based product pricing programs, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device and process data.
The communication bus 12 may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Optionally, the communication interface 13 may comprise a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The model-based build product pricing program stored by the memory 11 in the electronic device is a combination of computer programs that, when run in the processor 10, can implement:
acquiring service demands, acquiring historical product pricing data according to the service demands, and performing data cleaning on the historical product pricing data to obtain standard product pricing data;
performing data segmentation on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, and performing training operation and verification operation on a pre-built regression model according to the product pricing data training set and the product pricing data testing set to obtain a price pre-modeling model;
responding to the data of the product to be priced input by a user, and utilizing the price previewing model to conduct price previewing on the product pricing data to obtain first pricing;
A second pricing input by the user is received, and standard pricing is calculated based on the first pricing and the second pricing, and the identity information of the user.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring service demands, acquiring historical product pricing data according to the service demands, and performing data cleaning on the historical product pricing data to obtain standard product pricing data;
performing data segmentation on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, and performing training operation and verification operation on a pre-built regression model according to the product pricing data training set and the product pricing data testing set to obtain a price pre-modeling model;
responding to the data of the product to be priced input by a user, and utilizing the price previewing model to conduct price previewing on the product pricing data to obtain first pricing;
A second pricing input by the user is received, and standard pricing is calculated based on the first pricing and the second pricing, and the identity information of the user.
Further, the computer standard storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (5)
1. A method of pricing a product based on model construction, the method comprising:
Acquiring service demands, acquiring historical product pricing data according to the service demands, and performing data cleaning on the historical product pricing data to obtain standard product pricing data, wherein the historical product pricing data is historical quotation data of specific product items and corresponding user data, and the user data comprises physical conditions and ages of users;
performing data segmentation on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, and performing training operation and verification operation on a pre-built regression model according to the product pricing data training set and the product pricing data testing set to obtain a price pre-modeling model;
responding to the data of the product to be priced input by a user, and utilizing the price previewing model to conduct price previewing on the product pricing data to obtain first pricing;
receiving second pricing input by a user, and calculating standard pricing according to the first pricing and the second pricing and the identity information of the user;
Wherein the training operation comprises: calculating the pricing data training set by using a preset regression equation to obtain a regression model of the pricing data training set; calculating a loss value of the regression model by using a preset loss function; adjusting parameters of the regression model according to the loss values, and performing iterative training on the regression model after the parameters are adjusted until preset termination conditions are met, and obtaining a price previewing model according to the regression model after the parameters are adjusted;
The data segmentation is carried out on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, and the method comprises the following steps: calculating an average value of the standard product pricing data; the standard product pricing data are classified according to the average value to obtain standard product pricing data above the average value and standard product pricing data below the average value; respectively labeling the standard product pricing data above the average value and the standard product pricing data below the average value to obtain positive product data and negative product data; setting the proportion of the positive product data and the negative product data in a product pricing data training set and a product pricing data testing set; randomly extracting data from positive product data and negative product data according to the proportion to obtain a product pricing data training set and a product pricing data testing set;
The step of data cleaning the historical product pricing data to obtain standard product pricing data comprises the following steps: carrying out structuring processing on the historical product pricing data, and carrying out tabulation processing on the structured historical product pricing data to obtain a historical product pricing data table; carrying out data backup on the historical product pricing data table to obtain a historical product pricing data backup table; repeating value deletion and deletion, filling or conversion treatment of missing values are carried out on the historical product pricing data backup table, so that a cleaning data table is obtained; calculating the data standard deviation of each field in the cleaning data table, and marking the data which is larger than the data standard deviation preset multiple in the data corresponding to the field as an abnormal value; processing the abnormal value according to a preset rule to obtain a standard product pricing data table; extracting data in the standard product pricing data table to obtain standard product pricing data;
Said calculating standard pricing based on said first pricing and said second pricing, and identity information of said user, comprising: acquiring the user grade of the user according to the identity information of the user; confirming the weight duty ratio of the first pricing and the second pricing according to a preset user grade preference comparison table; obtaining standard pricing according to the weight ratio;
and obtaining standard pricing according to the weight ratio, wherein the standard pricing comprises the following steps: the standard pricing S is calculated using the following formula:
Wherein A, B is a first pricing and a second pricing, and X, Y is a weight duty cycle of the first pricing and a weight duty cycle of the second pricing, respectively.
2. A model-based build product pricing method as recited in claim 1, wherein the verifying operation comprises:
inputting the product pricing data test set into the price previewing model to generate a pricing result;
Judging whether the pricing result is correct data or not according to the data labels in the product pricing data test set;
And when the pricing result is judged to be not correct data, carrying out parameter adjustment on the price previewing model according to the pricing result, and returning to the step of inputting the product pricing data test set into the price previewing model to generate the pricing result until the pricing result is judged to be correct data, so as to obtain the trained price previewing model.
3. Model-based product pricing means for implementing a model-based product pricing method according to any of claims 1 to 2, comprising:
The regression model training module is used for acquiring service demands, acquiring historical product pricing data according to the service demands, performing data cleaning on the historical product pricing data to obtain standard product pricing data, performing data segmentation on the standard product pricing data to obtain a product pricing data training set and a product pricing data testing set, and performing training operation and verification operation on a pre-built regression model according to the product pricing data training set and the product pricing data testing set to obtain a price previewing model;
the price previewing module is used for responding to the to-be-priced product data input by the user, and performing price previewing on the product pricing data by utilizing the price previewing model to obtain first pricing;
And the standard pricing calculation module is used for receiving second pricing input by the user and calculating standard pricing according to the first pricing and the second pricing and the identity information of the user.
4. An electronic device, the electronic device comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the model-based build product pricing method according to any one of claims 1 to 2.
5. A computer readable storage medium storing a computer program, which when executed by a processor implements a model-based build product pricing method according to any of claims 1 to 2.
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