Dynamic pricing method, device and system based on intelligent pricing model

Abstract

The invention relates to the technical field of intelligent pricing, in particular to a dynamic pricing method, device and system based on an intelligent pricing model, and the method comprises the steps: determining the pricing configuration parameters of an object according to the category and / or identifier of the object needing to be priced, and generating the basic price of the object in combination with the price parameters of the object, such as the cost price; based on the category and / or identifier of the object, the pricing configuration parameter, the price parameter, the basic price and the price parameter of the competitive product object, the price adjustment coefficient of the object is analyzed, and finally the pricing of the object is generated according to the price adjustment coefficient of the object and the basic price of the object. That is, the object is priced from multiple dimensions of the type / identifier of the object, the basic price, the price parameter, the pricing configuration parameter and the price parameter of the competing object, so that the pricing accuracy and efficiency of the object are improved, and the demand of quickly and dynamically responding to the market change is facilitated.

Description

Technical Field

[0001] This invention relates to the field of smart pricing technology, and in particular to a method, apparatus and system for dynamic pricing based on a smart pricing model. Background Technology

[0002] As market demand changes and factors such as production and inventory levels influence commodity prices, adjustments are frequently necessary to meet market demands.

[0003] Currently, when it is necessary to price a product, a simple analysis is often conducted from the perspectives of the product's cost, the expected profit margin, and the fixed profit margin markup, in order to determine the product's price.

[0004] However, practice has shown that the aforementioned pricing method considers only a single or simple combination of dimensions, resulting in low accuracy in pricing analysis. Furthermore, it is a static pricing method that cannot dynamically respond to market changes. Therefore, there is an urgent need to propose a new intelligent pricing method to improve the accuracy and efficiency of product pricing and to dynamically respond to market changes. Summary of the Invention

[0005] This invention provides a method, apparatus, and system for dynamic pricing based on an intelligent pricing model, which can improve the accuracy and efficiency of commodity pricing and enable rapid dynamic response to market changes.

[0006] To address the aforementioned technical problems, a first aspect of this invention discloses a method for dynamic pricing based on a smart pricing model, the method comprising: Based on the information of the target object obtained, the pricing configuration parameters of the target object are determined. The target object is the object that needs to have its price adjusted. The information of the target object includes the category of the target object and / or the identifier of the target object. Based on the pricing configuration parameters of the target object and the obtained price parameters of the target object, generate the base price of the target object; Based on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the obtained price parameters of the target object's competitors, the price adjustment coefficient of the target object is determined; The target price of the target object is generated based on the price adjustment coefficient of the target object and the base price of the target object.

[0007] As an optional implementation, in the first aspect of the present invention, determining the price adjustment coefficient of the target object based on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the obtained price parameters of the target object's competitors includes: performing time-series feature analysis on the information of the target object, the price parameters of the target object, and the base price of the target object based on a pre-trained intelligent pricing model to obtain the price time-series features of the target object; Based on the intelligent pricing model, feature analysis is performed on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the price parameters of the target object's competitors to obtain the price market characteristics of the target object. Based on the intelligent pricing model, feature analysis is performed on the category of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the price parameters of the target object's competitors to obtain the price risk characteristics of the target object. Based on the intelligent pricing model, feature analysis is performed on the price time series characteristics, price market characteristics, and price risk characteristics of the target object to obtain the price adjustment coefficient of the target object.

[0008] As an optional implementation, in the first aspect of the present invention, before generating the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object, the method further includes: Calculate the number of pricing groups for the pricing configuration parameters of the target object, and determine whether the number of pricing groups is greater than or equal to a preset threshold for the number of pricing groups; When it is determined that the number of pricing groups is less than the preset pricing group threshold, the operation of generating the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object is triggered. When it is determined that the number of pricing groups is greater than or equal to the preset pricing group threshold, the current scene type of the target object is collected, and based on the current scene type of the target object, the target pricing configuration parameters that match the current scene type of the target object are filtered from all the pricing configuration parameters. The step of generating the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object includes: Based on the target pricing configuration parameters of the target object and the obtained price parameters of the target object, the base price of the target object is generated.

[0009] As an optional implementation, in the first aspect of the present invention, the step of filtering target pricing configuration parameters that match the current scenario type of the target object from all the pricing configuration parameters according to the current scenario type of the target object includes: When the current scenario type of the target object is the first scenario type, for any of the pricing configuration parameters, the pricing importance of the pricing configuration parameter is determined according to the pricing importance of each sub-pricing configuration parameter in the pricing configuration parameter, and the pricing configuration parameter with the highest pricing importance is selected from all the pricing configuration parameters according to the pricing importance of all the pricing configuration parameters, and is used as the target pricing configuration parameter that matches the current scenario type of the target object; When the current scenario type of the target object is the second scenario type, for any of the pricing configuration parameters, based on the current scenario type of the target object, target sub-pricing configuration parameters that match the current scenario type of the target object are selected from all the sub-pricing configuration parameters in the pricing configuration parameters; Based on the pricing importance of each of the target sub-pricing configuration parameters, the pricing configuration parameter corresponding to the target sub-pricing configuration parameter with the highest pricing importance is selected from all the target sub-pricing configuration parameters and used as the target sub-pricing configuration parameter that matches the current scenario type of the target object.

[0010] As an optional implementation, in the first aspect of the present invention, before generating the target price of the target object based on the price adjustment coefficient of the target object and the base price of the target object, the method further includes: Based on the current status of the target object, determine all replenishment factors of the target object, obtain replenishment data for each replenishment factor, and determine the replenishment parameters of each replenishment factor based on the replenishment data of each replenishment factor. Based on the category and / or identifier of the target object, determine the replenishment weight corresponding to each replenishment factor; The replenishment coefficient of the target object is determined based on the replenishment parameters of each replenishment factor and their respective replenishment weights. The step of generating the target price of the target object based on the price adjustment coefficient of the target object and the base price of the target object includes: The target price of the target object is generated based on the price adjustment coefficient of the target object, the base price of the target object, and the replenishment coefficient of the target object.

[0011] As an optional implementation, in the first aspect of the present invention, the method further includes: Obtain the price parameters of the competing products, and determine the price competitiveness parameters of the target product based on the price parameters of the competing products and the current pricing of the target product. Obtain the profit achievement rate, inventory turnover rate, and risk coefficient of the target object; The profit urgency of the target object is determined based on the profit achievement rate of the target object, the inventory turnover rate of the target object, and the predetermined profit coefficient. Based on the risk coefficient of the target object, the profit urgency of the target object, and the price competitiveness parameter of the target object, the pricing configuration parameters of the target object are adjusted to obtain the adjusted pricing configuration parameters of the target object. The adjusted pricing configuration parameters of the target object are used to perform price adjustment operations on similar objects of the target object.

[0012] As an optional implementation, in the first aspect of the present invention, the method further includes: Acquire price fluctuation data of the target object within a target time period, and analyze the price fluctuation data of the target object to obtain the price volatility of the target object; Obtain the inventory change data corresponding to the target object, and analyze the inventory change data corresponding to the target object to obtain the inventory turnover rate of the target object; Obtain the system load information corresponding to the target object; Based on the price volatility of the target object, the inventory turnover rate of the target object, and the system load corresponding to the target object, an adjustment operation is performed on the price adjustment cycle corresponding to the target object to obtain the adjusted price adjustment cycle of the target object. Specifically, when the real-time time reaches the start time corresponding to the price adjustment cycle of the target object, a price adjustment operation is performed on the target object.

[0013] A second aspect of this invention discloses an apparatus for dynamic pricing based on a smart pricing model, the apparatus comprising: The determination module is used to determine the pricing configuration parameters of the target object based on the information of the target object obtained. The target object is an object that needs to be price adjusted. The information of the target object includes the category of the target object and / or the identifier of the target object. The generation module is used to generate the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object; The determining module is further configured to determine the price adjustment coefficient of the target object based on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the obtained price parameters of the target object's competitors. The generation module is further configured to generate a target price for the target object based on the price adjustment coefficient of the target object and the base price of the target object.

[0014] As an optional implementation, in a second aspect of the present invention, the determining module determines the specific method of the price adjustment coefficient of the target object based on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the obtained price parameters of the target object's competitors, including: performing time-series feature analysis on the information of the target object, the price parameters of the target object, and the base price of the target object based on a pre-trained intelligent pricing model to obtain the price time-series features of the target object; Based on the intelligent pricing model, feature analysis is performed on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the price parameters of the target object's competitors to obtain the price market characteristics of the target object. Based on the intelligent pricing model, feature analysis is performed on the category of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the price parameters of the target object's competitors to obtain the price risk characteristics of the target object. Based on the intelligent pricing model, feature analysis is performed on the price time series characteristics, price market characteristics, and price risk characteristics of the target object to obtain the price adjustment coefficient of the target object.

[0015] As an optional implementation, in a second aspect of the invention, the apparatus further includes: The calculation module is used to calculate the number of pricing groups of the pricing configuration parameters of the target object before the generation module generates the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object; The judgment module is used to determine whether the number of pricing groups is greater than or equal to a preset pricing group threshold; when it is determined that it is less than the preset pricing group threshold, the generation module is triggered to perform the operation of generating the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object; The data acquisition module is used to acquire the current scene type of the target object when it is determined that the number of pricing groups is greater than or equal to the preset pricing group threshold. The filtering module is used to filter target pricing configuration parameters that match the current scenario type of the target object from all the pricing configuration parameters, based on the current scenario type of the target object; The specific method by which the generation module generates the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object includes: Based on the target pricing configuration parameters of the target object and the obtained price parameters of the target object, the base price of the target object is generated.

[0016] As an optional implementation, in a second aspect of the invention, the specific method by which the filtering module filters target pricing configuration parameters that match the current scenario type of the target object from all the pricing configuration parameters, based on the current scenario type of the target object, includes: When the current scenario type of the target object is the first scenario type, for any of the pricing configuration parameters, the pricing importance of the pricing configuration parameter is determined according to the pricing importance of each sub-pricing configuration parameter in the pricing configuration parameter, and the pricing configuration parameter with the highest pricing importance is selected from all the pricing configuration parameters according to the pricing importance of all the pricing configuration parameters, and is used as the target pricing configuration parameter that matches the current scenario type of the target object; When the current scenario type of the target object is the second scenario type, for any of the pricing configuration parameters, based on the current scenario type of the target object, target sub-pricing configuration parameters that match the current scenario type of the target object are selected from all the sub-pricing configuration parameters in the pricing configuration parameters; Based on the pricing importance of each of the target sub-pricing configuration parameters, the pricing configuration parameter corresponding to the target sub-pricing configuration parameter with the highest pricing importance is selected from all the target sub-pricing configuration parameters and used as the target sub-pricing configuration parameter that matches the current scenario type of the target object.

[0017] As an optional implementation, in a second aspect of the present invention, the determining module is further configured to, before the generating module generates the target price of the target object based on the price adjustment coefficient of the target object and the base price of the target object, determine all replenishment factors of the target object based on the current situation of the target object, obtain replenishment data for each replenishment factor, and determine the replenishment parameters of the replenishment factor based on the replenishment data of each replenishment factor; The determining module is further configured to determine the replenishment weight corresponding to each replenishment factor based on the category and / or identifier of the target object, and to determine the replenishment coefficient of the target object based on the replenishment parameters of each replenishment factor and their respective replenishment weights; The specific method by which the generation module generates the target price of the target object based on the price adjustment coefficient and the base price of the target object includes: The target price of the target object is generated based on the price adjustment coefficient of the target object, the base price of the target object, and the replenishment coefficient of the target object.

[0018] As an optional implementation, in a second aspect of the present invention, the determining module is further configured to obtain the price parameters of the competitor object, and determine the price competitiveness parameters of the target object based on the price parameters of the competitor object and the current pricing of the target object; The determining module is also used to obtain the profit achievement rate of the target object, the inventory turnover rate of the target object, and the risk coefficient of the target object; The determining module is further configured to determine the profit urgency of the target object based on the profit achievement rate of the target object, the inventory turnover rate of the target object, and a pre-determined profit coefficient. The device further includes: The first adjustment module is used to perform an adjustment operation on the pricing configuration parameters of the target object based on the risk coefficient of the target object, the profit urgency of the target object, and the price competitiveness parameter of the target object, so as to obtain the adjusted pricing configuration parameters of the target object. The adjusted pricing configuration parameters of the target object are used to perform price adjustment operations on similar objects of the target object.

[0019] As an optional implementation, in a second aspect of the present invention, the determining module is further configured to acquire price fluctuation data of the target object within a target time period; The device further includes: The analysis module is used to analyze the price fluctuation data of the target object to obtain the price volatility of the target object; The determining module is also used to obtain inventory change data corresponding to the target object; The analysis module is also used to analyze the inventory change data corresponding to the target object to obtain the inventory turnover rate of the target object; The determining module is also used to obtain the system load status corresponding to the target object; The second adjustment module is used to perform an adjustment operation on the price adjustment cycle corresponding to the target object based on the price volatility of the target object, the inventory turnover rate of the target object and the system load of the target object, so as to obtain the adjusted price adjustment cycle of the target object. Specifically, when the real-time time reaches the start time corresponding to the price adjustment cycle of the target object, a price adjustment operation is performed on the target object.

[0020] A third aspect of this invention discloses an intelligent pricing system, the system comprising: Memory containing executable program code; A processor coupled to memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in any of the methods for dynamic pricing based on a smart pricing model disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in any of the methods for dynamic pricing based on a smart pricing model disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In this embodiment of the invention, the pricing configuration parameters of the object are determined based on the category and / or identifier of the object to be priced, and a base price is generated by combining the object's price parameters, such as cost price. Then, the price adjustment coefficient of the object is analyzed based on the object's category and / or identifier, pricing configuration parameters, price parameters, base price, and the price parameters of its competitors. Finally, the object's price is generated based on the object's price adjustment coefficient and its base price. That is, the object is priced from multiple dimensions, including the object's type / identifier, base price, price parameters, pricing configuration parameters, and the price parameters of its competitors, which improves the accuracy and efficiency of object pricing, thereby facilitating a rapid and dynamic response to market changes. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a method for dynamic pricing based on a smart pricing model disclosed in an embodiment of the present invention; Figure 2This is a flowchart illustrating another method for dynamic pricing based on a smart pricing model disclosed in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a device for dynamic pricing based on an intelligent pricing model disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of another device for dynamic pricing based on a smart pricing model disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an intelligent pricing system disclosed in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] This invention discloses a method, apparatus, and system for dynamic pricing based on an intelligent pricing model. It determines the pricing configuration parameters of the object based on its category and / or identifier, and generates a base price by combining these parameters with the object's price parameters, such as cost price. Then, based on the object's category and / or identifier, pricing configuration parameters, price parameters, base price, and the price parameters of competing objects, it analyzes the object's price adjustment coefficient. Finally, based on the object's price adjustment coefficient and base price, it generates the object's final price. This approach prices the object from multiple dimensions, including object type / identifier, base price, price parameters, pricing configuration parameters, and the price parameters of competing objects, improving the accuracy and efficiency of object pricing and facilitating rapid and dynamic response to changing market demands. Detailed explanations follow.

[0029] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for dynamic pricing based on an intelligent pricing model, as disclosed in an embodiment of the present invention. This method can be applied to any scenario requiring intelligent pricing analysis, such as a goods rental scenario, provided that the scenario is equipped with a device for executing the method. This device includes one of the following: an intelligent pricing device, an intelligent pricing system (local system or cloud system), and an intelligent pricing server (local server or cloud server). Figure 1 As shown, the method may include the following operations: 101. Based on the information of the target object obtained, determine the pricing configuration parameters of the target object. The target object is the object that needs to be price adjusted. The information of the target object includes the category of the target object and / or the identifier of the target object.

[0030] In this embodiment of the invention, optionally, the target object can be understood as a physical item and / or a service without a physical form. Optionally, the category of the target object includes, but is not limited to, one of the existing market categories such as 3C products, clothing, toys, shoes, and daily necessities. Further optionally, the information of the target object also includes, but is not limited to, at least one of the following: the target object's grade, its function, its color (for physical items), and its batch number. The more information the target object contains, the more accurate the determination of its pricing configuration parameters will be, thereby further improving the accuracy of its target price.

[0031] In this embodiment of the invention, the target object can optionally be any object that requires intelligent pricing. It should also be noted that parallel intelligent pricing analysis can be performed on multiple target objects simultaneously, improving load resource utilization.

[0032] In this embodiment of the invention, optionally, the pricing configuration parameters for the target object include at least two of the following: profit configuration parameters, pricing power configuration parameters, and risk configuration parameters. Further optionally, the proportions of each parameter in the pricing configuration parameters differ for target objects of different categories. For example, for a target object in the 3C category, the profit configuration parameter is greater than the pricing power configuration parameter; for a target object in the apparel category, the profit configuration parameter is less than the pricing power configuration parameter.

[0033] 102. Generate the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object.

[0034] In this embodiment of the invention, optionally, the price parameters of the target object include the cost price and operating price of the target object. It should be noted that the relevant data of the object in this invention is automatically collected or received from another end (such as a merchant's end), such as data in Excel format. Optionally, after collecting or receiving the relevant data, it is read in blocks to reduce memory overflow. For any data block, it is validated, such as using regular expressions. If the validation fails, it is discarded, for example, if the price contains illegal characters. Further optionally, failed data blocks are logged (e.g., line number, reason for failure). If the validation passes, the validated data block is converted to a standardized format and stored in a cache queue. The relevant data of the target object is retrieved from this cache queue. The types of object-related data include, but are not limited to, categories and / or identifiers (such as numbers), cost prices, operating prices, inventory levels, and other types of target object-related data mentioned in this invention. This block-based data integration, reading, and analysis improves data processing efficiency and accuracy, thereby improving the query efficiency and accuracy of relevant data for pricing objects, and further improving the collection / acquisition efficiency and accuracy of relevant data for pricing objects.

[0035] In this embodiment of the invention, optionally, generating a base price for the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object includes: From all sub-parameters of the pricing configuration parameters of the target object, filter the target sub-parameter with the highest value and the type of that target sub-parameter; When the type of the target sub-parameter is profit type, the base profit rate of the obtained target object is increased according to the value of the target sub-parameter, and the base price of the target object is generated based on the increased base profit rate and the price parameter of the obtained target object. When the type of the target sub-parameter is price force type, obtain the pricing price of the competitor object, and generate the base price of the target object based on the pricing price of the competitor object, the base profit margin of the target object, and the price parameters of the target object.

[0036] As can be seen, the embodiments of the present invention can also select a matching basic pricing method based on the magnitude of the profit configuration parameter and the price configuration parameter in the pricing configuration parameters, determine the basic price of the pricing object, improve the accuracy and reliability of the basic price analysis, and thus help to further improve the accuracy and reliability of the price adjustment of the pricing object.

[0037] 103. Based on the target object's information, pricing configuration parameters, price parameters, base price, and price parameters of its competitors, determine the target object's price adjustment coefficient.

[0038] In this embodiment of the invention, optionally, data from multiple competitor objects within a target time period (e.g., 30 days) is crawled from a competitor object platform. The data for each competitor object includes its price parameters, specifically its listed price. Furthermore, the data for each competitor object also includes its promotional data and / or inventory data. This improves the coverage of price parameters for competitor objects. Moreover, during the data crawling process, anti-crawling strategies are implemented to prevent the target object's related data from being crawled by anti-crawling techniques.

[0039] 104. Generate the target price of the target object based on the price adjustment coefficient and the base price of the target object.

[0040] In this embodiment of the invention, the target price of the target object is the price to be adjusted.

[0041] As can be seen, the embodiments of the present invention determine the pricing configuration parameters of an object by determining the category and / or identifier of the object to be priced as needed, and generate its base price by combining the object's price parameters, such as cost price. Then, based on the object's category and / or identifier, pricing configuration parameters, price parameters, base price, and the price parameters of its competitors, the price adjustment coefficient of the object is analyzed. Finally, the object's price is generated based on the object's price adjustment coefficient and its base price. That is, the object is priced from multiple dimensions, including the object's type / identifier, base price, price parameters, pricing configuration parameters, and the price parameters of its competitors, which improves the accuracy and efficiency of object pricing, thereby facilitating a rapid and dynamic response to the changing market demands.

[0042] In this embodiment of the invention, optionally, the price adjustment coefficient of the target object is determined based on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the obtained price parameters of the target object's competitors. This includes: performing time-series feature analysis on the information of the target object, the price parameters of the target object, and the base price of the target object based on a pre-trained intelligent pricing model to obtain the time-series price features of the target object. Based on the intelligent pricing model, feature analysis is performed on the target object's information, pricing configuration parameters, price parameters, base price, and price parameters of its competitors to obtain the target object's price market characteristics. Based on the intelligent pricing model, feature analysis is performed on the target object's category, pricing configuration parameters, price parameters, base price, and price parameters of its competitors to obtain the target object's price risk characteristics. Based on the intelligent pricing model, feature analysis is performed on the price time series characteristics, price market characteristics, and price risk characteristics of the target object to obtain the price adjustment coefficient of the target object.

[0043] In this embodiment of the invention, optionally, the price parameters of the target object also include the daily pricing price and sales volume within the target time period. The time-series price characteristics of the target object include, but are not limited to, at least one of the following: price sequence, price difference, and periodicity indicator over the past target time period. The price sequence represents the daily weighted average price of the target object within the target time period. The price difference represents the daily price change rate of the target object within the target time period. The periodicity indicator represents the ratio of the average price of the target object on a first type of date (e.g., non-working days) to the average price on a second type of date (e.g., working days) within the target time period, wherein the sum of the first type of date and the second type of date equals the target time period.

[0044] In this embodiment of the invention, optionally, the price market characteristics of the target object include, but are not limited to, multiple factors such as the price factor of competitors, price competitiveness index, competitor inventory saturation, and market popularity. The price factor of competitors includes price standard deviation and / or price variance. Specifically, regarding the price factor: it is determined by the price parameters of each competitor, the average price of all competitors, and the number of competitors for all competitors. Regarding the price competitiveness index: it is determined by obtaining the mean price and / or median price parameters of all competitors, obtaining the current pricing of the target object, and determining the price competitiveness index based on the current pricing of the target object and the mean price and / or median price of competitors. Regarding competitor inventory saturation: Calculate the average inventory level of all competitors and the average daily sales volume of all competitors. Determine the competitor inventory saturation based on the average inventory level and average sales volume. Alternatively, determine the inventory saturation of each competitor based on its inventory level and average daily sales volume, and calculate the average inventory saturation of all competitors as the competitor inventory saturation level. Or, use the average of the competitor inventory saturation levels obtained through the aforementioned methods as the competitor inventory saturation level. Regarding market popularity: Determine market popularity based on the search volume and click volume of competitors on various platforms.

[0045] In this embodiment of the invention, the optional price risk characteristics of the target object include, but are not limited to, multiple factors such as inventory turnover rate, lease default rate, risk coefficient, cost volatility, and replenishment coefficient. Specifically, for inventory turnover rate: the target object's inventory turnover rate is determined based on its total sales volume and average inventory / inventory ratio within the target time period. For lease default rate: the target object's lease default rate is determined based on the number of defaulted orders and total orders within the target time period. For risk coefficient: the risk coefficient is determined based on the target object's historical return rate for the same period. For cost volatility: the cost volatility is determined based on the prices from all suppliers of the target object. For replenishment coefficient, please refer to the description in the relevant sections of this invention; it will not be repeated here.

[0046] In this embodiment of the invention, optionally, the intelligent pricing model is pre-trained from the basic pricing model. Furthermore, the intelligent pricing model can be further optimized based on the obtained price adjustment coefficients; that is, the optimization of the intelligent pricing model is a dynamic process to further improve its determination accuracy, generalization ability, and robustness. Optionally, the basic pricing model may include, but is not limited to, models capable of pricing analysis such as LSTM / Transformer models and random forest models.

[0047] As can be seen, the embodiments of the present invention can also sequentially analyze the time-series characteristics, market characteristics and risk characteristics corresponding to the pricing object, and jointly analyze the price adjustment coefficient of the pricing object with the three characteristics, thereby improving the accuracy and reliability of the price adjustment coefficient analysis, which is conducive to further improving the pricing accuracy and reliability of the pricing object.

[0048] In an optional embodiment, before generating the target price of the target object based on the price adjustment factor and the base price of the target object, the method may further include the following steps: Based on the current status of the target object, determine all replenishment factors of the target object, obtain the replenishment data for each replenishment factor, and determine the replenishment parameters for each replenishment factor based on the replenishment data for each replenishment factor. Determine the replenishment weight corresponding to each replenishment factor based on the category and / or identifier of the target object; Based on the replenishment parameters of each replenishment factor and their corresponding replenishment weights, the replenishment coefficient of the target object is determined, wherein the sum of all replenishment weights is equal to 1. Specifically, the target price of the target object is generated based on the price adjustment coefficient and the base price of the target object, including: The target price of the target object is generated based on the price adjustment coefficient, the base price, and the replenishment coefficient of the target object.

[0049] In this optional embodiment, all replenishment factors may include, but are not limited to, at least two of the following: inventory level factor, supplier delivery cycle factor, and activity status factor. Specifically, for the inventory level factor: the inventory level corresponding to the target object is obtained, and the replenishment parameters for the inventory level factor are derived based on the inventory level corresponding to the target object and a preset inventory level corresponding to a pre-determined target object. For the supplier delivery cycle factor: the supplier delivery cycle corresponding to the target object is obtained, and the replenishment parameters for the supplier delivery cycle factor are derived based on the supplier delivery cycle corresponding to the target object and a preset supplier delivery cycle corresponding to a pre-determined target object. For the activity status factor: the activity status (e.g., promotional status) corresponding to the target object is obtained as the replenishment parameters for the activity status factor.

[0050] In this optional embodiment, the current status of the target object can be used to represent the promotional situation. For example, for major promotions, it includes inventory level factors, supplier delivery cycle factors, and activity status factors; for non-major promotions, it only includes inventory level factors and supplier delivery cycle factors.

[0051] In this optional embodiment, the weight of the same replenishment factor may vary for target objects of different categories and / or identifiers. For example, the replenishment weight of the inventory factor may be greater for best-selling objects with high demand, and less for others.

[0052] As can be seen, this optional embodiment improves the accuracy of the replenishment coefficient analysis of the pricing object by analyzing various replenishment factors and their weights. Furthermore, by combining the accurately analyzed replenishment coefficient with the aforementioned pricing factors, the pricing of the pricing object is comprehensively analyzed, thereby further improving the accuracy and reliability of the pricing analysis.

[0053] In another alternative embodiment, the method may further include the following steps: Obtain the pricing parameters of competitors and, based on these parameters and the target product's current pricing, determine the target product's price competitiveness parameters. Obtain the profit achievement rate, inventory turnover rate, and risk coefficient of the target object; The profit urgency of the target object is determined based on the profit achievement rate, inventory turnover rate, and pre-determined profit coefficient of the target object. Based on the target object's risk coefficient, profit urgency, and price competitiveness parameters, the pricing configuration parameters of the target object are adjusted to obtain the adjusted pricing configuration parameters of the target object. The adjusted pricing configuration parameters for the target object are used to perform price adjustment operations on similar objects of the target object. Specifically, the pricing configuration parameters in steps 102 and 103 are updated to the adjusted pricing configuration parameters to analyze the base price and adjustment coefficient.

[0054] In this optional embodiment, the price parameter of the competitor object may include the current pricing of all competitor objects crawled on various platforms. Specifically, the average / median price of the current pricing of all competitor objects is calculated, and the price competitiveness parameter of the target object is determined based on the average and / or median price and the current pricing of the target object. Further optionally, the number of all competitor objects is analyzed; if it is greater than or equal to a preset number of competitor objects (e.g., 3000), the current pricing of some of the highest-priced and / or lowest-priced competitor objects is removed before determining the price competitiveness parameter.

[0055] In this optional embodiment, for a description of inventory turnover rate and risk coefficient, please refer to the foregoing related descriptions, which will not be repeated here.

[0056] In this optional embodiment, the profit achievement rate corresponding to the target object is used to represent the ratio between the profit of the target object in the target time period and the preset profit.

[0057] In this optional embodiment, the determined profit coefficient may include a profit achievement rate coefficient and an inventory turnover rate coefficient, and the sum of the two is equal to 1. Specifically, a first profit urgency is determined based on the profit achievement rate coefficient and the profit achievement rate, a second profit urgency is determined based on the inventory turnover rate coefficient and the inventory turnover rate, and the profit urgency of the target object is determined based on the first profit urgency and the second profit urgency.

[0058] In this optional embodiment, optionally, the pricing configuration parameters of the target object are adjusted based on the risk coefficient, profit urgency, and price competitiveness parameters of the target object, to obtain the adjusted pricing configuration parameters of the target object, including: Based on the profit urgency and risk coefficient of the target object, determine the basic profit adjustment coefficient of the target object; based on the basic profit adjustment coefficient and the profit urgency, determine the profit adjustment coefficient of the target object. The analysis compares the target object's price competitiveness parameter with the preset price competitiveness parameter. Based on this comparison and the target object's profit adjustment coefficient, adjustments are made to the pricing configuration parameters to obtain the adjusted pricing configuration parameters. Specifically, if the comparison indicates that the price competitiveness parameter is greater than or equal to the preset price competitiveness parameter, the profit configuration parameter is increased based on the profit adjustment coefficient, and the price competitiveness configuration parameter is decreased. In this case, the risk configuration parameter can remain unchanged. Conversely, if the comparison indicates that the price competitiveness parameter is less than the preset price competitiveness parameter, the profit configuration parameter is decreased based on the profit adjustment coefficient, and the price competitiveness configuration parameter is increased. In this case, the risk configuration parameter can remain unchanged.

[0059] As can be seen, this optional embodiment adaptively and dynamically adjusts the profit configuration parameters, price power configuration parameters, and risk configuration parameters of the pricing object based on the price of the competitor object, the profit achievement rate of the pricing object, the inventory turnover rate, and the risk coefficient. This achieves a balance among the three configuration parameters, making them adaptable to the market of the pricing object and improving the accuracy of its basic pricing determination, thereby realizing accurate dynamic pricing of the object.

[0060] In yet another optional embodiment, the method may further include the following steps: Obtain price fluctuation data of the target object within the target time period, and analyze the price fluctuation data of the target object to obtain the price volatility of the target object; Obtain and analyze the inventory change data corresponding to the target object to obtain the inventory turnover rate of the target object; Obtain the system load information corresponding to the target object; Based on the price volatility of the target object, the inventory turnover rate of the target object, and the system load of the target object, an adjustment operation is performed on the price adjustment cycle of the target object to obtain the adjusted price adjustment cycle of the target object. Specifically, when the real-time time reaches the start time corresponding to the price adjustment cycle of the target object, a price adjustment operation is performed on the target object.

[0061] In this optional embodiment, system load may optionally include CPU utilization. Further, it may also include, but is not limited to, at least one of memory utilization, I / O read / write speed, and network bandwidth utilization. The more information included in the system load metrics, the more accurate the analysis of price adjustment cycles will be.

[0062] In this optional embodiment, the price adjustment cycle can be analyzed based on one, two, or three of the following: the price volatility of the target object, the inventory turnover rate of the target object, and the system load corresponding to the target object. For example, regarding price volatility, if the volatility exceeds a threshold, a price adjustment is immediately triggered, i.e., the present invention adjusts the current price of the target object immediately. Similarly, regarding inventory turnover rate, if its week-on-week decrease exceeds a threshold, the present invention adjusts the current price of the target object at regular intervals. Likewise, regarding system load, if its occupancy rate exceeds a threshold, the present invention adjusts the current price of the target object at other regular intervals.

[0063] As can be seen, this optional embodiment improves the accuracy of price adjustment execution by monitoring market fluctuations, inventory levels, and load conditions to adaptively and dynamically adjust the price adjustment cycle of the pricing object. This helps to further improve the accuracy, efficiency, and flexibility of price adjustments, enabling a more efficient and precise response to real-time market bidding demands.

[0064] In yet another optional embodiment, the method may further include the following steps: Collect historical actual profit data of the target object within a historical time period (e.g., within 30 days); Based on the target object's historical actual profit data, determine the actual profit margin within the historical time period, and determine whether the actual profit margin is greater than or equal to the preset profit margin. When the result is yes, collect the target object's historical change data within the historical time period, including multiple data such as historical actual profit data, historical inventory change data, object's own cost change data, operating cost change data, sales change data, and competitor's pricing price change data. Based on the target object's category and / or identifier, determine the target object's initial profit margin and multiple profit-influencing factors; among these, all profit-influencing factors include multiple factors such as the object's own costs, operating costs, life cycle, inventory levels, and competitor pricing. Based on the collected historical change data of the target object, a corresponding profit impact weight is set for each profit influencing factor; the historical change data of the target includes historical change data and historical actual profit data. Analyze each profit-influencing factor and its corresponding profit-influencing weight among all profit-influencing factors to obtain the profit change rate of the target object, and determine the basic profit rate of the target object based on the profit change rate and the initial profit rate.

[0065] As can be seen, this optional embodiment first analyzes the profit margin over a period of time, and when the analysis shows that it exceeds expectations, it adjusts the weight of the profit factors that affect the profit of the pricing object based on the historical change data of the pricing object, thereby calculating the profit impact factor on the profit of the pricing object, and then combining it with its initial profit margin to determine the basic profit margin of the pricing object in this instance, which improves the accuracy of determining the basic profit margin, thereby helping to improve the accuracy of this intelligent pricing.

[0066] In this optional embodiment, optionally, determining the actual profit rate within a historical time period based on the target object's historical actual profit data includes: Based on the length of the historical time period and the category and / or identifier of the target object, the historical time period is divided into multiple sub-historical time periods; Based on the time sequence of each sub-historical period, the historical actual profit data is divided into sub-historical actual profit data that match the corresponding sub-historical period, and each sub-historical actual profit data is analyzed to obtain the actual profit rate of the corresponding sub-historical period. Analyze the sales performance of the target object in each sub-historical time period and obtain the current sales performance of the target object; The sales situation for each sub-historical period is compared and analyzed with the current sales situation to obtain the sales period analysis results for each sub-historical period. Based on the sales period analysis results corresponding to each sub-historical period, a corresponding profit weight is set for the actual profit margin of all sub-historical periods. Based on the actual profit margin and corresponding profit weight of each sub-historical period, the actual profit margin of the target object within the historical period is determined.

[0067] In this optional embodiment, for target objects with peak sales periods, their historical time periods can be divided. Further optionally, different sales periods have different impacts on profits; for example, peak sales periods have a greater impact on profit margins, and this impact is positively correlated. That is, if the current sales period is a peak sales period, then the profit weight within the sub-historical time period where the sales period is a peak sales period is set to a larger value.

[0068] As can be seen, this optional embodiment can also divide the historical time period into time segments and analyze the actual profit rate of each time segment. Based on the actual profit rate of each time segment and the impact of that time segment on the profit rate, it can comprehensively analyze the actual profit rate of the pricing object in the past time segment, thereby improving the accuracy and reliability of its profit rate analysis. This is conducive to improving the accuracy of whether the basic profit rate adjustment operation needs to be performed.

[0069] Example 2 Please see Figure 2 , Figure 2 This is a flowchart illustrating another method for dynamic pricing based on a smart pricing model disclosed in an embodiment of the present invention. This method can be applied to any scenario requiring smart pricing analysis, such as a goods rental scenario, provided that the scenario is equipped with a device for executing the method. This device includes one of the following: a smart pricing device, a smart pricing system (local system or cloud system), and a smart pricing server (local server or cloud server). Figure 2 As shown, the method may include the following operations: 201. Based on the information of the target object obtained, determine the pricing configuration parameters of the target object. The target object is the object that needs to be price adjusted. The information of the target object includes the category of the target object and / or the identifier of the target object.

[0070] 202. Calculate the number of pricing groups for the pricing configuration parameters of the target object, and determine whether the number of pricing groups is greater than or equal to the preset pricing group threshold; if it is determined to be less than the preset pricing group threshold, trigger step 204; if it is determined to be greater than or equal to the preset pricing group threshold, trigger step 203.

[0071] In this embodiment of the invention, optionally, the preset pricing group threshold can be a fixed value of 2, or it can be flexibly determined according to the actual market situation, such as 3.

[0072] 203. Collect the current scenario type of the target object, and based on the current scenario type of the target object, filter out one set of target pricing configuration parameters that match the current scenario type of the target object from all pricing configuration parameters.

[0073] 204. Generate the base price of the target object based on its pricing configuration parameters and the obtained price parameters. When the price configuration parameter is greater than or equal to a preset pricing group threshold, the pricing configuration parameter becomes the target pricing configuration parameter.

[0074] 205. Based on the target object's information, pricing configuration parameters, price parameters, base price, and price parameters of its competitors, determine the target object's price adjustment coefficient.

[0075] 206. Generate the target price of the target object based on the price adjustment coefficient and the base price of the target object.

[0076] For further descriptions of steps 201 and 204-206 in this invention, please refer to the detailed description of steps 101-104 in Embodiment 1, which will not be repeated here.

[0077] It is evident that implementation Figure 2 The described method determines the pricing configuration parameters of an object based on its category and / or identifier, and generates a base price by combining the object's price parameters, such as cost price. Then, based on the object's category and / or identifier, pricing configuration parameters, price parameters, base price, and the price parameters of competing objects, it analyzes the object's price adjustment coefficient. Finally, based on the object's price adjustment coefficient and base price, it generates the object's final price. This method prices the object from multiple dimensions, including object type / identifier, base price, price parameters, pricing configuration parameters, and competitor price parameters, improving the accuracy and efficiency of object pricing and facilitating rapid and dynamic response to changing market demands. Furthermore, when multiple sets of pricing configuration parameters are matched, the most suitable parameter is automatically selected based on the object's category and / or current scenario type, improving the accuracy of pricing configuration parameter analysis. This further enhances the accuracy of determining the object's base price, and consequently, improves the accuracy of price adjustments.

[0078] In this embodiment of the invention, optionally, based on the current scenario type of the target object, the target pricing configuration parameters that match the current scenario type of the target object are selected from all pricing configuration parameters, including: When the current scenario type of the target object is the first scenario type, for any pricing configuration parameter, the pricing importance of the pricing configuration parameter is determined according to the pricing importance of each sub-pricing configuration parameter in the pricing configuration parameter. Based on the pricing importance of all pricing configuration parameters, the pricing configuration parameter with the highest pricing importance is selected from all pricing configuration parameters and used as the target pricing configuration parameter that matches the current scenario type of the target object. When the current scenario type of the target object is the second scenario type, for any pricing configuration parameter, based on the current scenario type of the target object, select the target sub-pricing configuration parameter that matches the current scenario type of the target object from all the sub-pricing configuration parameters in the pricing configuration parameters; Based on the pricing importance of each target sub-pricing configuration parameter, the pricing configuration parameter corresponding to the target sub-pricing configuration parameter with the highest pricing importance is selected from all target sub-pricing configuration parameters and used as the target sub-pricing configuration parameter that matches the current scenario type of the target object.

[0079] In this embodiment of the invention, optionally, the current scenario type of the target object includes at least one of the following: object lifecycle scenario, object promotion scenario, and inventory scenario. The object lifecycle scenario includes the mature stage or non-mature stage, where the non-mature stage includes one of the initial stage, growth stage, decline stage, and recession stage. The object promotion scenario includes regular sales period or irregular sales period, where the irregular sales period includes one of the recession stage, general promotion stage, and major promotion stage. The inventory scenario includes an insufficient inventory scenario (i.e., demand exceeds supply) or a non-insufficient inventory scenario, where the non-insufficient inventory scenario includes a suitable inventory scenario (i.e., supply and demand balance) or an excessive inventory scenario (i.e., supply exceeds demand). The first scenario type includes at least one of the mature stage, regular sales period, and insufficient inventory scenario, and the second scenario type includes at least one of the non-mature stage, non-insufficient inventory scenario, and irregular sales period.

[0080] As can be seen, the embodiments of the present invention can also select different configuration strategies to match the corresponding pricing configuration parameters for the pricing object according to different current scenario types. The pricing configuration parameter containing the sub-pricing configuration parameter with the highest pricing importance or the most matching sub-pricing configuration parameter improves matching efficiency while accurately matching the most suitable pricing configuration parameter for the pricing object.

[0081] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of a device for dynamic pricing based on an intelligent pricing model, disclosed in an embodiment of the present invention. This device can be applied to any scenario requiring intelligent pricing analysis, such as a goods rental scenario, and includes one of the following: an intelligent pricing device, an intelligent pricing system (local system or cloud system), and an intelligent pricing server (local server or cloud server). Figure 3 As shown, the device may include: The determination module 301 is used to determine the pricing configuration parameters of the target object based on the information of the target object obtained. The target object is the object that needs to be price adjusted. The information of the target object includes the category of the target object and / or the identifier of the target object. The generation module 302 is used to generate the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object; The determination module 301 is also used to determine the price adjustment coefficient of the target object based on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object and the obtained price parameters of the target object's competitors. The generation module 302 is also used to generate the target price of the target object based on the price adjustment coefficient of the target object and the base price of the target object.

[0082] It is evident that implementation Figure 3 The described device determines the pricing configuration parameters of an object by considering its category and / or identifier, and generates its base price by combining the object's price parameters, such as cost price. Then, based on the object's category and / or identifier, pricing configuration parameters, price parameters, base price, and the price parameters of competing objects, it analyzes the object's price adjustment coefficient. Finally, based on the object's price adjustment coefficient and its base price, it generates the object's price. This means that pricing an object is performed from multiple dimensions, including the object's type / identifier, base price, price parameters, pricing configuration parameters, and the price parameters of competing objects, improving the accuracy and efficiency of object pricing and facilitating rapid and dynamic response to changing market demands.

[0083] In this embodiment of the invention, optionally, the determining module 301 determines the specific method of the price adjustment coefficient of the target object based on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object and the price parameters of the target object's competitors, including: performing time-series feature analysis on the information of the target object, the price parameters of the target object and the base price of the target object based on a pre-trained intelligent pricing model to obtain the price time-series features of the target object; Based on the intelligent pricing model, feature analysis is performed on the target object's information, pricing configuration parameters, price parameters, base price, and price parameters of its competitors to obtain the target object's price market characteristics. Based on the intelligent pricing model, feature analysis is performed on the target object's category, pricing configuration parameters, price parameters, base price, and price parameters of its competitors to obtain the target object's price risk characteristics. Based on the intelligent pricing model, feature analysis is performed on the price time series characteristics, price market characteristics, and price risk characteristics of the target object to obtain the price adjustment coefficient of the target object.

[0084] As can be seen, the embodiments of the present invention can also sequentially analyze the time-series characteristics, market characteristics and risk characteristics corresponding to the pricing object, and jointly analyze the price adjustment coefficient of the pricing object with the three characteristics, thereby improving the accuracy and reliability of the price adjustment coefficient analysis, which is conducive to further improving the pricing accuracy and reliability of the pricing object.

[0085] In an optional embodiment, Figure 4 This is a schematic diagram of another device for dynamic pricing based on a smart pricing model disclosed in an embodiment of the present invention, as shown below. Figure 4 As shown, the device may further include: The calculation module 303 is used to calculate the number of pricing groups of the pricing configuration parameters of the target object before the generation module 302 generates the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object; The judgment module 304 is used to determine whether the number of pricing groups is greater than or equal to the preset pricing group threshold; when it is determined that it is less than the preset pricing group threshold, the generation module 302 is triggered to perform the operation of generating the basic price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object; The acquisition module 305 is used to acquire the current scene type of the target object when it is determined that the number of pricing groups is greater than or equal to the preset threshold. The filtering module 306 is used to filter target pricing configuration parameters that match the current scenario type of the target object from all pricing configuration parameters based on the current scenario type of the target object. The generation module 302 generates the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object in the following specific ways: Based on the target pricing configuration parameters of the target object and the obtained price parameters of the target object, generate the base price of the target object.

[0086] It is evident that implementation Figure 4 When multiple sets of pricing configuration parameters are matched, the device automatically matches the most suitable pricing configuration parameters based on the category of the pricing object and / or the current scenario type, which improves the accuracy of the pricing configuration parameter analysis. This, in turn, helps to further improve the accuracy of determining the base price of the pricing object, and thus helps to further improve the accuracy of price adjustment of the pricing object.

[0087] In this optional embodiment, the filtering module 306 may filter target pricing configuration parameters that match the current scenario type of the target object from all pricing configuration parameters in the following specific ways: When the current scenario type of the target object is the first scenario type, for any pricing configuration parameter, the pricing importance of the pricing configuration parameter is determined according to the pricing importance of each sub-pricing configuration parameter in the pricing configuration parameter. Based on the pricing importance of all pricing configuration parameters, the pricing configuration parameter with the highest pricing importance is selected from all pricing configuration parameters and used as the target pricing configuration parameter that matches the current scenario type of the target object. When the current scenario type of the target object is the second scenario type, for any pricing configuration parameter, based on the current scenario type of the target object, select the target sub-pricing configuration parameter that matches the current scenario type of the target object from all the sub-pricing configuration parameters in the pricing configuration parameters; Based on the pricing importance of each target sub-pricing configuration parameter, the pricing configuration parameter corresponding to the target sub-pricing configuration parameter with the highest pricing importance is selected from all target sub-pricing configuration parameters and used as the target sub-pricing configuration parameter that matches the current scenario type of the target object.

[0088] It is evident that implementation Figure 4 The described device can also select different configuration strategies to match the corresponding pricing configuration parameters for the pricing object according to different current scenario types. The pricing configuration parameter containing the sub-pricing configuration parameter with the highest pricing importance or the most suitable one improves the matching efficiency while accurately matching the most suitable pricing configuration parameter for the pricing object.

[0089] In another alternative embodiment, Figure 4 As shown, the determining module 301 is also used to determine all replenishment factors of the target object based on the current situation of the target object before the generating module generates the target price of the target object based on the price adjustment coefficient of the target object and the base price of the target object, and to obtain the replenishment data of each replenishment factor, and to determine the replenishment parameters of the replenishment factor based on the replenishment data of each replenishment factor. The determination module 301 is also used to determine the replenishment weight corresponding to each replenishment factor based on the category and / or identifier of the target object, and to determine the replenishment coefficient of the target object based on the replenishment parameters of each replenishment factor and their respective replenishment weights. The generation module 302 generates the target price of the target object based on the price adjustment coefficient and the base price of the target object in the following specific ways: The target price of the target object is generated based on the price adjustment coefficient, the base price, and the replenishment coefficient of the target object.

[0090] It is evident that implementation Figure 4The described device can also improve the accuracy of the replenishment coefficient analysis of the pricing object by analyzing multiple replenishment factors and their weights. Then, by combining the accurately analyzed replenishment coefficient with the aforementioned pricing factors, the pricing of the pricing object is comprehensively analyzed, which further improves the accuracy and reliability of the pricing analysis.

[0091] In yet another alternative embodiment, such as Figure 4 As shown, the determining module 301 is also used to obtain the price parameters of the competitor object, and determine the price competitiveness parameters of the target object based on the price parameters of the competitor object and the current pricing of the target object; The determination module 301 is also used to obtain the profit achievement rate, inventory turnover rate and risk coefficient of the target object; The determination module 301 is also used to determine the profit urgency of the target object based on the profit achievement rate of the target object, the inventory turnover rate of the target object, and the predetermined profit coefficient. like Figure 4 As shown, the device may further include: The first adjustment module 307 is used to perform an adjustment operation on the pricing configuration parameters of the target object based on the risk coefficient of the target object, the profit urgency of the target object and the price competitiveness parameters of the target object, so as to obtain the adjusted pricing configuration parameters of the target object. The adjusted pricing configuration parameters for the target object are used to perform price adjustment operations on similar objects of the target object.

[0092] It is evident that implementation Figure 4 The described device adaptively and dynamically adjusts the profit configuration parameters, price power configuration parameters, and risk configuration parameters of the pricing object based on the price of competing products, the profit achievement rate of the pricing object, the inventory turnover rate, and the risk coefficient. This achieves a balance among the three configuration parameters, making them adaptable to the market of the pricing object and improving the accuracy of its basic pricing determination, thereby realizing accurate dynamic pricing of the object.

[0093] In yet another alternative embodiment, such as Figure 4 As shown, the determining module 301 is also used to obtain price fluctuation data of the target object within the target time period; like Figure 4 As shown, the device may further include: Analysis module 308 is used to analyze the price fluctuation data of the target object to obtain the price volatility of the target object; The determination module 301 is also used to obtain inventory change data corresponding to the target object; Analysis module 308 is also used to analyze the inventory change data corresponding to the target object to obtain the inventory turnover rate of the target object; The determination module 301 is also used to obtain the system load status corresponding to the target object; The second adjustment module 309 is used to perform an adjustment operation on the price adjustment cycle corresponding to the target object based on the price volatility of the target object, the inventory turnover rate of the target object and the system load of the target object, so as to obtain the adjusted price adjustment cycle of the target object. Specifically, when the real-time time reaches the start time corresponding to the price adjustment cycle of the target object, a price adjustment operation is performed on the target object.

[0094] It is evident that implementation Figure 4 The described device can also adaptively and dynamically adjust the price adjustment cycle of the pricing object by monitoring market fluctuations, inventory status, and load status, thereby improving the accuracy of price adjustment execution and further enhancing the accuracy, efficiency, and flexibility of price adjustment to respond more efficiently and accurately to the real-time bidding demands of the market.

[0095] Example 4 Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an intelligent pricing system disclosed in an embodiment of the present invention. This system can be applied to any scenario requiring intelligent pricing analysis, such as a goods rental scenario. Figure 5 As shown, the system may include: Memory 401 storing executable program code; Processor 402 coupled to memory 401; Furthermore, it may also include an input interface 403 and an output interface 404 coupled to the processor 402; The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the method for dynamic pricing based on the smart pricing model described in Embodiment 1 or Embodiment 2.

[0096] Example 5 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the method for dynamic pricing based on a smart pricing model as described in Embodiment 1 or Embodiment 2.

[0097] Example 6 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the method for dynamic pricing based on a smart pricing model described in Embodiment 1 or Embodiment 2.

[0098] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0099] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0100] Finally, it should be noted that the method, apparatus, and system for dynamic pricing based on a smart pricing model disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic pricing based on an intelligent pricing model, characterized in that, The method includes: Based on the information of the target object obtained, the pricing configuration parameters of the target object are determined. The target object is the object that needs to have its price adjusted. The information of the target object includes the category of the target object and / or the identifier of the target object. Based on the pricing configuration parameters of the target object and the obtained price parameters of the target object, generate the base price of the target object; Based on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the obtained price parameters of the target object's competitors, the price adjustment coefficient of the target object is determined; The target price of the target object is generated based on the price adjustment coefficient of the target object and the base price of the target object.

2. The method for dynamic pricing based on a smart pricing model according to claim 1, characterized in that, The step of determining the price adjustment coefficient of the target object based on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the obtained price parameters of the target object's competitors includes: performing time-series feature analysis on the information of the target object, the price parameters of the target object, and the base price of the target object based on a pre-trained intelligent pricing model to obtain the price time-series features of the target object; Based on the intelligent pricing model, feature analysis is performed on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the price parameters of the target object's competitors to obtain the price market characteristics of the target object. Based on the intelligent pricing model, feature analysis is performed on the category of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the price parameters of the target object's competitors to obtain the price risk characteristics of the target object. Based on the intelligent pricing model, feature analysis is performed on the price time series characteristics, price market characteristics, and price risk characteristics of the target object to obtain the price adjustment coefficient of the target object.

3. The method for dynamic pricing based on a smart pricing model according to claim 1 or 2, characterized in that, Before generating the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object, the method further includes: Calculate the number of pricing groups for the pricing configuration parameters of the target object, and determine whether the number of pricing groups is greater than or equal to a preset threshold for the number of pricing groups; When it is determined that the number of pricing groups is less than the preset pricing group threshold, the operation of generating the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object is triggered. When it is determined that the number of pricing groups is greater than or equal to the preset pricing group threshold, the current scene type of the target object is collected, and based on the current scene type of the target object, the target pricing configuration parameters that match the current scene type of the target object are filtered from all the pricing configuration parameters. The step of generating the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object includes: Based on the target pricing configuration parameters of the target object and the obtained price parameters of the target object, the base price of the target object is generated.

4. The method for dynamic pricing based on a smart pricing model according to claim 3, characterized in that, The step of filtering target pricing configuration parameters that match the current scenario type of the target object from all the pricing configuration parameters, based on the current scenario type of the target object, includes: When the current scenario type of the target object is the first scenario type, for any of the pricing configuration parameters, the pricing importance of the pricing configuration parameter is determined according to the pricing importance of each sub-pricing configuration parameter in the pricing configuration parameter, and the pricing configuration parameter with the highest pricing importance is selected from all the pricing configuration parameters according to the pricing importance of all the pricing configuration parameters, and is used as the target pricing configuration parameter that matches the current scenario type of the target object; When the current scenario type of the target object is the second scenario type, for any of the pricing configuration parameters, based on the current scenario type of the target object, target sub-pricing configuration parameters that match the current scenario type of the target object are selected from all the sub-pricing configuration parameters in the pricing configuration parameters; Based on the pricing importance of each of the target sub-pricing configuration parameters, the pricing configuration parameter corresponding to the target sub-pricing configuration parameter with the highest pricing importance is selected from all the target sub-pricing configuration parameters and used as the target sub-pricing configuration parameter that matches the current scenario type of the target object.

5. The method for dynamic pricing based on a smart pricing model according to any one of claims 1, 2, and 4, characterized in that, Before generating the target price of the target object based on the price adjustment coefficient and the base price of the target object, the method further includes: Based on the current status of the target object, determine all replenishment factors of the target object, obtain replenishment data for each replenishment factor, and determine the replenishment parameters of each replenishment factor based on the replenishment data of each replenishment factor. Based on the category and / or identifier of the target object, determine the replenishment weight corresponding to each replenishment factor; The replenishment coefficient of the target object is determined based on the replenishment parameters of each replenishment factor and their respective replenishment weights. The step of generating the target price of the target object based on the price adjustment coefficient of the target object and the base price of the target object includes: The target price of the target object is generated based on the price adjustment coefficient of the target object, the base price of the target object, and the replenishment coefficient of the target object.

6. The method for dynamic pricing based on a smart pricing model according to any one of claims 1, 2, and 4, characterized in that, The method further includes: Obtain the price parameters of the competing products, and determine the price competitiveness parameters of the target product based on the price parameters of the competing products and the current pricing of the target product. Obtain the profit achievement rate, inventory turnover rate, and risk coefficient of the target object; The profit urgency of the target object is determined based on the profit achievement rate of the target object, the inventory turnover rate of the target object, and the predetermined profit coefficient. Based on the risk coefficient of the target object, the profit urgency of the target object, and the price competitiveness parameter of the target object, the pricing configuration parameters of the target object are adjusted to obtain the adjusted pricing configuration parameters of the target object. The adjusted pricing configuration parameters of the target object are used to perform price adjustment operations on similar objects of the target object.

7. The method for dynamic pricing based on a smart pricing model according to any one of claims 1, 2, and 4, characterized in that, The method further includes: Acquire price fluctuation data of the target object within a target time period, and analyze the price fluctuation data of the target object to obtain the price volatility of the target object; Obtain the inventory change data corresponding to the target object, and analyze the inventory change data corresponding to the target object to obtain the inventory turnover rate of the target object; Obtain the system load information corresponding to the target object; Based on the price volatility of the target object, the inventory turnover rate of the target object, and the system load corresponding to the target object, an adjustment operation is performed on the price adjustment cycle corresponding to the target object to obtain the adjusted price adjustment cycle of the target object. Specifically, when the real-time time reaches the start time corresponding to the price adjustment cycle of the target object, a price adjustment operation is performed on the target object.

8. A device for dynamic pricing based on an intelligent pricing model, characterized in that, The device includes: The determination module is used to determine the pricing configuration parameters of the target object based on the information of the target object obtained. The target object is an object that needs to be price adjusted. The information of the target object includes the category of the target object and / or the identifier of the target object. The generation module is used to generate the base price of the target object based on the pricing configuration parameters of the target object and the obtained price parameters of the target object; The determining module is further configured to determine the price adjustment coefficient of the target object based on the information of the target object, the pricing configuration parameters of the target object, the price parameters of the target object, the base price of the target object, and the obtained price parameters of the target object's competitors. The generation module is further configured to generate a target price for the target object based on the price adjustment coefficient of the target object and the base price of the target object.

9. An intelligent pricing system, characterized in that, The system includes: Memory containing executable program code; A processor coupled to memory; The processor calls the executable program code stored in memory to execute the method of dynamic pricing based on the smart pricing model as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the method for dynamic pricing based on a smart pricing model as described in any one of claims 1-7.

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