Digital product pricing and dividing processing system

Abstract

The invention relates to the technical field of data processing, and discloses a digital product pricing and dividing processing system, which comprises a data acquisition module, a data processing module, a dynamic pricing module, a weight calculation module and an income calculation module. Compared with the prior art which only depends on a fixed price strategy or a single division proportion, and especially under the conditions that user behavior data is complex and commodity price fluctuation is obvious, dynamic pricing and reasonable division cannot be realized, the price sensitivity model is constructed by combining historical consumption data and real-time interaction data of the user, so that the price sensitivity of the user is improved. And a dynamic pricing and profit distribution mechanism is introduced, so that digital product pricing and profit distribution based on real-time market change are realized, the problems of price stiffness and unpublic profit distribution are avoided, and the pricing flexibility, the profit distribution fairness and the overall operation efficiency of the platform are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a digital product pricing and sharing processing system. Background Art

[0002] At present, in digital product trading platforms, pricing and revenue sharing strategies usually rely on static models or simple rules, such as fixed price strategies and single sharing ratios. This approach has obvious shortcomings in practical applications. For example, fixed price strategies cannot dynamically reflect users' real-time consumption preferences and market price fluctuations, resulting in a lack of pricing flexibility; single sharing ratios cannot be dynamically adjusted according to suppliers' actual contributions, such as advertising investment and service quality, which can easily lead to a decline in suppliers' enthusiasm, thereby affecting the overall revenue of the platform; existing technologies cannot fully meet the needs of implementing dynamic and flexible pricing strategies and dynamically adjusting the sharing ratio between the platform and suppliers based on actual operating data when user behavior is complex and changeable and market prices fluctuate significantly. Therefore, there is an urgent need for a solution that can dynamically adjust commodity pricing and sharing weights based on user historical consumption data and real-time interaction data, and can still achieve real-time adjustment of pricing and fair and reasonable revenue distribution in a complex market environment, so as to improve the overall operating efficiency and user satisfaction of the platform. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a digital product pricing and sharing processing system, aiming to solve the technical problem that the prior art only relies on fixed price strategies or a single sharing ratio, especially under the conditions where user behavior data is complex and commodity prices fluctuate significantly, and cannot achieve dynamic pricing and reasonable sharing.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a digital product pricing and sharing processing system, comprising:

[0005] The data acquisition module is used to request the backend to obtain the database in advance, obtain the historical consumption data of all users from the transaction record table in the database, and obtain the real-time interaction data from the dynamic information table of commodity i in the database;

[0006] The data processing module is used to perform weighted average processing on the historical consumption data of all users to obtain average consumption data; normalize the average consumption data and real-time interaction data to obtain the feature vector F1 of the average consumption data and the feature vector F2 of the real-time interaction data; and integrate the feature vector F1 of the average consumption data and the feature vector F2 of the real-time interaction data to obtain the price sensitivity feature vector S of commodity i. i ;

[0007] The dynamic pricing module is used to obtain the basic price P0 of commodity i from the supplier price table in the database, obtain the historical transaction price of the commodity from the commodity historical transaction record table in the database, and calculate the commodity price fluctuation value H and the commodity price mean H 均值 ;

[0008] According to the price sensitive feature vector S of commodity i i Calculate the first price adjustment coefficient △P1, based on the commodity price fluctuation value H and the commodity price mean H 均值 Calculate the second price adjustment coefficient △P2;

[0009] The preliminary commodity price P is calculated by combining the first price adjustment coefficient △P1 and the second price adjustment coefficient △P2 first , the calculation formula is:

[0010] P first =P0+△P1+△P2

[0011] The initial commodity price P first The preset minimum price P of product i min When the initial commodity price P first Less than the preset minimum price P min When setting the final product price P i The preset minimum price P min ; When the initial commodity price P first Not less than the preset minimum price P min When setting the final product price P i is the initial commodity price P first ;

[0012] The weight calculation module is used to obtain the sales volume Q of product i from the product sales table in the database. i , obtain the user satisfaction U of product i from the product evaluation table in the database i ;

[0013] According to product sales Q i and user satisfaction U i Calculate the initial supplier share weight W i , the calculation formula is:

[0014]

[0015] Among them, W i is the initial supplier share weight, j is the index, and m is the total number of goods provided by the supplier;

[0016] Get the advertising investment ratio G of the supplier corresponding to product i i , according to the advertising investment ratio G iThe initial supplier share weight W i Make corrections to get the final supplier share weight W s , the correction formula is:

[0017] W s =W i ·(1+γ·G i )

[0018] Among them, γ is the participation adjustment coefficient;

[0019] Calculate the final platform share weight W d , the calculation formula is W d =1-W s ;

[0020] The revenue calculation module is used to divide the final supplier into weights W s And the final platform is divided into weights W d Calculate the final supplier profit S corresponding to product i s and the final platform revenue S p ;

[0021] Introducing the user return rate r has an impact on the final supplier's profit S s Feedback adjustment is carried out, and the final supplier benefits after adjustment Among them, ε is the return rate weight coefficient.

[0022] Preferably, in the data processing module, the price sensitive feature vector S of commodity i i The calculation formula is:

[0023] S i =w1·F1+w2·F2

[0024] Among them, S i is the price-sensitive feature vector of commodity i, F1 is the feature vector of average consumption data, F2 is the feature vector of real-time interaction data, w1 is the weight coefficient of the feature vector of average consumption data, and w2 is the weight coefficient of the feature vector of real-time interaction data.

[0025] Preferably, in the dynamic pricing module, the calculation formula of the first price adjustment coefficient △P1 is:

[0026] △P1=-α·S i

[0027] Among them, α is the sensitivity adjustment coefficient.

[0028] Preferably, in the dynamic pricing module, the calculation formula of the second price adjustment coefficient △P2 is:

[0029]

[0030] Among them, β is the historical volatility adjustment coefficient.

[0031] Preferably, in the data acquisition module, the historical consumption data includes the number of transactions, purchase amount and purchase time of the user in the past month; the real-time interaction data includes the product browsing time, click frequency and number of collections.

[0032] Preferably, in the revenue calculation module, the final supplier revenue S s The calculation formula is S s =P i Q i ·W s .

[0033] Preferably, in the revenue calculation module, the final platform revenue S p The calculation formula is S p =P i Q i ·W d .

[0034] The beneficial effect of the present invention is that compared with the prior art which only relies on a fixed price strategy or a single profit-sharing ratio, especially when the user behavior data is complex and the commodity prices fluctuate significantly, and cannot achieve the technical problem of dynamic pricing and reasonable profit-sharing, the present application combines the user's historical consumption data and real-time interaction data to construct a price sensitivity model, and introduces a dynamic pricing and profit-sharing weight adjustment mechanism, thereby realizing the pricing and profit distribution of digital products based on real-time market changes, thereby avoiding the problems of price rigidity and unfair profit-sharing, and improving pricing flexibility, profit distribution fairness and the overall operating efficiency of the platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A schematic flow chart of a first embodiment of a digital product pricing and sharing processing system provided by the present invention.

[0037] Figure 2 A schematic diagram of a device for a digital product pricing and sharing processing system provided by the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Embodiment 1: Figure 1 , which is a flow chart of the first embodiment of the digital product pricing and sharing processing system of the present invention, proposes the first embodiment of the digital product pricing and sharing processing system of the present invention.

[0040] In a first embodiment, the digital product pricing and sharing processing system includes:

[0041] The data acquisition module is used to request the backend to obtain the database in advance, obtain the historical consumption data of all users from the transaction record table in the database, and obtain the real-time interaction data from the dynamic information table of commodity i in the database;

[0042] It should be noted that historical consumption data includes the number of transactions, transaction amount and purchase time of users in the past certain period of time (for example, 1 month). These data can reflect the user's consumption habits and long-term purchasing preferences. Real-time interaction data includes the user's product browsing time, click frequency and number of collections. These data are used to capture the user's real-time interests and behavioral preferences.

[0043] It is understandable that historical consumption data focuses on reflecting users' long-term consumption behavior trends, while real-time interaction data pays more attention to users' short-term interest changes. The combination of these two types of data can fully reflect user behavior and provide comprehensive support for pricing models.

[0044] The data processing module is used to perform weighted average processing on the historical consumption data of all users to obtain average consumption data; normalize the average consumption data and real-time interaction data to obtain the feature vector F1 of the average consumption data and the feature vector F2 of the real-time interaction data; and integrate the feature vector F1 of the average consumption data and the feature vector F2 of the real-time interaction data to obtain the price sensitivity feature vector S of commodity i. i ;

[0045] It should be noted that weighted processing of the historical consumption data of all users can highlight important features and avoid the impact of excessive deviations in individual user data on the overall data.

[0046] It is understandable that since the data scales of different features may vary greatly, for example, the transaction amount may be in the hundreds of yuan, while the transaction frequency is only in the single digits, the data needs to be normalized to eliminate the dimensional difference. The normalization method uses minimum and maximum normalization.

[0047] The dynamic pricing module is used to obtain the basic price P0 of commodity i from the supplier price table in the database, obtain the historical transaction price of the commodity from the commodity historical transaction record table in the database, and calculate the commodity price fluctuation value H and the commodity price mean H 均值 ;

[0048] According to the price sensitive feature vector S of commodity i i Calculate the first price adjustment coefficient ΔP1, based on the commodity price fluctuation value H and the commodity price mean H 均值 Calculate the second price adjustment coefficient ΔP2;

[0049] The preliminary commodity price P is calculated by combining the first price adjustment coefficient ΔP1 and the second price adjustment coefficient ΔP2 first , the calculation formula is:

[0050] P first =P0+ΔP1+ΔP2

[0051] The initial commodity price P first The preset minimum price P of product i min When the initial commodity price P first Less than the preset minimum price P min When setting the final product price P i The preset minimum price P min ; When the initial commodity price P first Not less than the preset minimum price P min When setting the final product price P i is the initial commodity price P first ;

[0052] It should be noted that the commodity base price P0 is the original price of the commodity provided by the supplier, which is used to determine the starting point of the price adjustment. It is usually set based on the production cost of the commodity and the market benchmark. The first price adjustment coefficient ΔP1 is based on the price sensitivity characteristic vector S of the commodity. i Calculation reflects the user's sensitivity to price fluctuations of goods. For price-sensitive goods, ΔP1 is usually a negative value to lower prices and thus attract more consumers. The second price adjustment coefficient AP2 is a correction value calculated based on the commodity price fluctuation value H. It is used to dynamically adapt to market price changes and avoid the impact of overpricing or overpricing on market competitiveness.

[0053] For example, suppose the base price of commodity i is P0 = 100 yuan, the price sensitivity feature vector Si = 0.5, the price sensitivity adjustment coefficient α = 20, the price fluctuation value calculated from the historical price data is H = 0.1, the price fluctuation adjustment coefficient β = 30, and the preset minimum price of the commodity is P min = 90 yuan, calculate the first price adjustment coefficient ΔP1 = -α·S i = -10 yuan; calculate the second price adjustment coefficient: Calculate the initial commodity price P first =P0+ΔP1+AP2=93 yuan; Compare the initial price with the lowest price: P first =93 yuan ≥ P min =90, therefore, the final price of the product is 93 yuan.

[0054] The weight calculation module is used to obtain the sales volume Q of product i from the product sales table in the database. i , obtain the user satisfaction U of product i from the product evaluation table in the database i ;

[0055] According to product sales Q i and user satisfaction U i Calculate the initial supplier share weight W i , the calculation formula is:

[0056]

[0057] Among them, W i is the initial supplier share weight, j is the index, and m is the total number of goods provided by the supplier;

[0058] Get the advertising investment ratio G of the supplier corresponding to product i i , according to the advertising investment ratio G i The initial supplier share weight W i Make corrections to get the final supplier share weight W s , the correction formula is:

[0059] W s =W i ·(1+γ·G i )

[0060] Among them, γ is the participation adjustment coefficient;

[0061] Calculate the final platform share weight W d , the calculation formula is W d =1-W s ;

[0062] It should be noted that the initial supplier share weight W iIt is the sales volume Q of the product. i and user satisfaction U i This process is designed to reflect the direct impact of commodity market performance on the supplier's share ratio. Suppliers of commodities with higher sales and higher user satisfaction will receive higher share weights, thereby encouraging suppliers to provide better quality commodities and services. i Used to modify the initial supplier share weights to comprehensively reflect the supplier's contribution in marketing and promotion; by adjusting G i , which can dynamically improve the enthusiasm of suppliers in platform promotion and form healthy competition.

[0063] It should be understood that the modified supplier share weight W s Directly affects the final profit distribution of suppliers, and the platform share weight W d It reflects the proportion of platform revenue. This profit-sharing mechanism avoids the unfair distribution problem caused by single weight calculation by introducing multi-dimensional factors, while encouraging suppliers to continuously optimize sales, service quality and advertising investment.

[0064] The revenue calculation module is used to divide the final supplier into weights W s And the final platform is divided into weights W d Calculate the final supplier profit S corresponding to product i s and the final platform revenue S p ;

[0065] Introducing the user return rate r has an impact on the final supplier's profit S s Feedback adjustment is carried out, and the final supplier benefits after adjustment Among them, ε is the return rate weight coefficient.

[0066] It should be noted that the final supplier profit S s and platform revenue S p It is obtained by distributing the total revenue of the goods according to the final supplier share weight and the platform share weight. Through this calculation, the fairness of profit distribution is ensured, and the supplier's contribution and the role of the platform are fully considered. The user return rate is introduced to make feedback adjustments to the final supplier's profits. The purpose is to further reflect the impact of user experience on supplier profits, reduce the proportion of profits of suppliers with high return rates, and thus encourage suppliers to improve product quality and service levels.

[0067] In addition, the present invention also provides a digital product pricing and sharing processing device, please refer to Figure 2A digital product pricing and sharing processing device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a digital product pricing and sharing processing system in the above-mentioned embodiment 1. A digital product pricing and sharing processing device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A digital product pricing and sharing processing device is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. A digital product pricing and sharing processing device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of a digital product pricing and sharing processing device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow a digital product pricing and sharing processing device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a digital product pricing and sharing processing device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0068] The present invention also provides a computer program product, including a computer program, which implements the steps of the digital product pricing and sharing processing system as described above when the computer program is executed by a processor. The computer program product provided by the present invention can solve the technical problem of digital product pricing and sharing processing. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the digital product pricing and sharing processing system provided by the above embodiment, which will not be described in detail here.

[0069] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the system of the embodiments disclosed in the present invention are executed.

[0070] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0071] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A digital product pricing and sharing processing system, characterized in that the system include: The data acquisition module is used to request the backend to obtain the database in advance, obtain the historical consumption data of all users from the transaction record table in the database, and obtain the real-time interaction data from the dynamic information table of commodity i in the database; The data processing module is used to perform weighted average processing on the historical consumption data of all users to obtain average consumption data; normalize the average consumption data and real-time interaction data to obtain the feature vector F1 of the average consumption data and the feature vector F2 of the real-time interaction data; and integrate the feature vector F1 of the average consumption data and the feature vector F2 of the real-time interaction data to obtain the price sensitivity feature vector S of commodity i. i ; The dynamic pricing module is used to obtain the basic price P0 of commodity i from the supplier price table in the database, obtain the historical transaction price of the commodity from the commodity historical transaction record table in the database, and calculate the commodity price fluctuation value H and the commodity price mean H 均值 ; According to the price sensitive feature vector S of commodity i i Calculate the first price adjustment coefficient ΔP1, based on the commodity price fluctuation value H and the commodity price mean H 均值 Calculate the second price adjustment coefficient ΔP2; The preliminary commodity price P is calculated by combining the first price adjustment coefficient ΔP1 and the second price adjustment coefficient ΔP2 first , the calculation formula is: P first =P0+ΔP1+ΔP2 The initial commodity price P first The preset minimum price P of product i min When the initial commodity price P first Less than the preset minimum price P min When setting the final product price P i The preset minimum price P min ; When the initial commodity price P first Not less than the preset minimum price P min When setting the final product price P i is the initial commodity price P first ; The weight calculation module is used to obtain the sales volume Q of product i from the product sales table in the database. i , obtain the user satisfaction U of product i from the product evaluation table in the database i ; According to product sales Q i and user satisfaction U i Calculate the initial supplier share weight W i , the calculation formula is: Among them, W i is the initial supplier share weight, j is the index, and m is the total number of goods provided by the supplier; Get the advertising investment ratio G of the supplier corresponding to product i i , according to the advertising investment ratio G i The initial supplier share weight W i Make corrections to get the final supplier share weight W s , the correction formula is: W s =W i ·(1+γ·G i ) Among them, γ is the participation adjustment coefficient; Calculate the final platform share weight W d , the calculation formula is W d =1-W s ; The revenue calculation module is used to divide the final supplier into weights W s And the final platform is divided into weights W d Calculate the final supplier profit S corresponding to product i s and the final platform revenue S p ; Introducing the user return rate r has an impact on the final supplier's profit S s Feedback adjustment is carried out, and the final supplier benefits after adjustment Among them, ε is the return rate weight coefficient.

2. A digital product pricing and sharing processing system as claimed in claim 1, characterized in that: In the data processing module, the price-sensitive feature vector S of commodity i i The calculation formula is: S i =w1·F1+w2·F2 Among them, S i is the price-sensitive feature vector of commodity i, F1 is the feature vector of average consumption data, F2 is the feature vector of real-time interaction data, w1 is the weight coefficient of the feature vector of average consumption data, and w2 is the weight coefficient of the feature vector of real-time interaction data.

3. A digital product pricing and sharing processing system as claimed in claim 1, characterized in that: In the dynamic pricing module, the calculation formula for the first price adjustment coefficient ΔP1 is: ΔP1=-α·S i Among them, α is the sensitivity adjustment coefficient.

4. A digital product pricing and sharing processing system as claimed in claim 1, characterized in that: In the dynamic pricing module, the calculation formula for the second price adjustment coefficient ΔP2 is: Among them, β is the historical volatility adjustment coefficient.

5. A digital product pricing and sharing processing system as claimed in claim 1, characterized in that: In the data acquisition module, historical consumption data includes the number of transactions, purchase amount and purchase time of users in the past month; real-time interaction data includes product browsing time, click frequency and number of collections.

6. A digital product pricing and sharing processing system as claimed in claim 1, characterized in that: In the profit calculation module, the final supplier profit S s The calculation formula is S s =P i Q i ·W s .

7. A digital product pricing and sharing processing system as claimed in claim 1, characterized in that: In the revenue calculation module, the final platform revenue S p The calculation formula is S p =P i Q i ·W d .

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