An internet-based group purchase package intelligent settlement system

Abstract

The application relates to the technical field of intelligent settlement, and particularly discloses a group purchase package intelligent settlement system based on the Internet, which comprises the following modules: an information acquisition module which acquires the price of an original package and the number of commodities in a group purchase package, and calculates the unit price of the commodities; an information processing module which acquires the consumption record of a user, acquires the time, price and number of commodities for verification, and calculates the time interval, average verification time and average group purchase price; an information calculation module which calculates the purchase capacity coefficient and the number fluctuation value, and sets a first recommended package; and an information output module which calculates the unit price of different recommended packages according to a price function, calculates the price of the recommended package, and screens out the recommended package. The application provides a group purchase package recommendation method, and the group purchase package recommendation method enables the user to have more opportunities for selection and better satisfies the group purchase demand of the user.

Description

Technical Field

[0001] This invention relates to the field of intelligent settlement technology, specifically to an internet-based intelligent settlement system for group-buying packages. Background Technology

[0002] With the rapid development of some group-buying platforms, group buying has become an emerging shopping model. Most merchants will offer some very favorable group-buying packages on these platforms to attract customers with lower prices.

[0003] Once a group-buying package is added to a user's shopping cart, the system will provide the user with some new group-buying packages. The user can choose whether to change the package according to their own needs. The group-buying packages provided by the system come from the merchant's group-buying package database, which contains a large amount of group-buying package information. The group-buying package database can meet the user's needs for group-buying packages.

[0004] In existing technologies, group-buying packages on group-buying platforms are set by merchants, and users choose from these packages. Users who purchase a package on the platform need to redeem it at a physical store. Staff add the package to the user's shopping cart by entering a redemption code. However, the packages on group-buying platforms are relatively fixed, making it difficult for users to find a suitable one. When users redeem at the physical store, the system recommends packages. Therefore, there is an urgent need for a method to filter out suitable group-buying packages for users, achieving accurate recommendations and enabling users to choose packages that better meet their needs. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent settlement system for group-buying packages based on the Internet, thereby solving the above-mentioned technical problems.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] An internet-based intelligent settlement system for group-buying packages includes:

[0008] Information Acquisition Module: Marks the group-buying package purchased by the user as the original package and obtains the price S of the original package. ori The number of items N in the group purchase package ori And calculate the unit price P of the product. ori =S ori / N ori ;

[0009] Information processing module: Merchants obtain the consumption records of users who purchased group-buying packages in this store, and obtain the redemption time T of the i-th group-buying package. i Group purchase package price Si The number of items N in the group-buying package i where i is a positive integer;

[0010] Calculate the purchase time interval ΔT i =T i -T i-1 , where ΔT i Let represent the time interval between the redemption of the i-th group-buying package and the (i-1)-th group-buying package, and calculate the average redemption time. The average group-buying price of group-buying packages purchased by users Where I represents the total number of times a group-buying package has been purchased;

[0011] Information Calculation Module: Calculates the user's purchasing power coefficient X based on the average redemption time and average group purchase price. The specific formula for calculating the purchasing power coefficient X is as follows:

[0012] ;

[0013] Wherein, γ represents the preset first adjustment coefficient;

[0014] Calculate commodity quantity fluctuation value , where N ave This represents the average number of items purchased by a user in a group-buying package, and 2J first-recommended packages are set based on the fluctuation value of the number of items, where J is a preset threshold for the number of items in the first-recommended package, and the number of items in the j-th first-recommended package is... , where ρ is the preset change in the quantity of goods;

[0015] Information output module: based on price function Calculate the unit price P(NT) of the j-th first recommended package. j ), where μ represents the preset price change coefficient;

[0016] Calculate the recommended price ST for the j-th first recommended package. j =P(NT) j ) NT j The first recommended package contains NT worth of items. j ;

[0017] Obtain the merchant's package database, filter out group-buying packages with similar prices and quantities to the recommended packages, and display these recommended packages to customers.

[0018] As a further aspect of the present invention: a method for selecting group-buying packages from the group-buying package database that are similar in price and quantity to the recommended packages, specifically includes:

[0019] The price fluctuation range of the j-th first recommended package [ST] j -A,ST j +A] and commodity quantity fluctuation range [NT] j -B, NT j +B], where A is the preset price fluctuation coefficient and B is the preset commodity quantity fluctuation coefficient;

[0020] The group-buying packages in the database are filtered, and those whose prices fall within the price fluctuation range are selected. j -A,ST j The quantity of goods is within the range of +A and the quantity of goods is within the fluctuation range of [NT]. j -B, NT j The group-buying packages within [+B] are marked as candidate packages. Calculate the comprehensive value K of the i-th candidate package. i =|ST j -S i_pre |+|NT j -N i_pre | Select the candidate package with the lowest overall value as the recommended package, where S i_pre N represents the price of the i-th candidate package. j_pre This represents the number of items in the i-th candidate package.

[0021] As a further aspect of the present invention: if the overall value of the candidate packages is equal, the candidate package with the lower price is selected as the recommended package.

[0022] As a further aspect of the present invention: if the purchase time interval ΔT i ≤η, the i-th group purchase package and the (i-1)-th group purchase package are regarded as one group purchase package, and the price and quantity of goods of the i-th group purchase package and the (i-1)-th group purchase package are added together, where η represents the preset minimum time interval.

[0023] As a further aspect of the present invention: calculating the average group-buying price S ave At that time, the group-buying package purchased by the user this time will not be included in the average group-buying price S. ave The calculation.

[0024] As a further aspect of the present invention: when the fluctuation value of the commodity quantity f=0, if the commodity quantity N ori =N ave If the condition is met, then the subsequent steps will be stopped and no package recommendations will be made.

[0025] If the quantity of goods N ori ≠N ave If so, the subsequent steps will proceed normally.

[0026] As a further aspect of the present invention: obtaining the quantity NT of the j-th recommended package. j The number of items N in the group-buying package offered by the merchant x Calculate the difference in the quantity of goods, NC=NT j -N x If the difference in the number of goods NC is less than the preset minimum number of goods N min At this point, the lower-priced package is recommended.

[0027] As a further aspect of the present invention: when setting the recommended package quantity threshold J, ensure that the quantity NT of the j-th item is calculated in subsequent steps. j >0 and unit price P (NT) j )>0.

[0028] As a further aspect of the present invention, it includes:

[0029] Coupon module: Retrieves coupon information from the user's account, including coupon usage requirements and the total number of coupons YH;

[0030] Based on the usage requirements of the coupons, select the packages that meet the usage requirements from the merchant's package database and mark them as the second category of recommended packages. Select the lowest-priced second category recommended package as the recommended package.

[0031] Each coupon corresponds to a recommended package. YH recommended packages are selected and displayed to customers.

[0032] The beneficial effects of this invention are as follows: This invention automatically generates recommended packages by obtaining users' group-buying package information. First, it obtains the group-buying package information purchased by the user this time, marks this package as the original package, and obtains the corresponding price and quantity of goods. The purpose is to understand the user's travel desires and make appropriate adjustments accordingly. If the price of the group-buying package purchased this time is high, it indicates that the user's travel desires are high. Therefore, when setting recommended packages, reasonable adjustments should be made to achieve the purpose of accurate recommendations.

[0033] Obtain users' purchase history for group-buying packages, calculate the average redemption time, and the average redemption time indicates the user's level of interest in the product. A shorter average redemption time indicates more frequent purchases of group-buying packages. Calculate the average group-buying price S. ave The purpose is to understand the user's spending power, so that when recommending group-buying packages, the user's spending power can be taken into account, and recommendations can be made only if the user's spending power allows, thereby increasing the success rate of recommendations.

[0034] The user's preference coefficient is calculated using a formula. The formula shows that when the average group-buying price remains constant, the shorter the average redemption time, the higher the preference coefficient. Furthermore, the average group-buying price S... ave Increasing the value of a product will also increase the liking coefficient X. This is understandable because if a user is willing to spend a lot of time and money on a product, it means that the user is interested in that product. Therefore, this formula can express the user's liking for a product in data form, which can specifically show the user's liking for the product. It also makes it easier to set the number of products in the recommended package and the unit price of the products.

[0035] The unit price of the recommended package is calculated using a price function. The formula shows that the price function is piecewise, meaning the quantity of items in the recommended package will fluctuate. Common sense dictates that the more items, the lower the price, and vice versa. The formula uses the original unit price P of the items in the package. ori Based on this, the unit price of the goods is adjusted upwards or downwards. After calculating the unit price, the price of the recommended package is calculated based on the quantity of goods and the unit price, and a recommendation is made. If the user thinks that all the recommended packages are not suitable, the original package is selected. If there is a more preferred recommended package, after the user selects it, the corresponding recommended package is added to the shopping cart and the checkout is completed. At this time, the coupons in the user's account are obtained. If there are coupons that meet the usage conditions, the coupon with the largest discount amount is selected for use.

[0036] In summary, this invention provides a method for recommending group-buying packages, which gives users more choices and thus better meets their group-buying needs. Attached Figure Description

[0037] The invention will now be further described with reference to the accompanying drawings.

[0038] Figure 1 This is a schematic diagram of the structure of an intelligent settlement system for group-buying packages based on the Internet according to the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0040] Please see Figure 1 As shown, this invention is an internet-based intelligent settlement system for group-buying packages, comprising:

[0041] Information Acquisition Module: Marks the group-buying package purchased by the user as the original package and obtains the price S of the original package. ori The number of items N in the group purchase package ori And calculate the unit price P of the product. ori =S ori / N ori ;

[0042] Information processing module: Merchants obtain the consumption records of users who purchased group-buying packages in this store, and obtain the redemption time T of the i-th group-buying package. i Group purchase package price S i The number of items N in the group-buying package i where i is a positive integer;

[0043] Calculate the purchase time interval ΔT i =T i -T i-1 , where ΔT i Let represent the time interval between the redemption of the i-th group-buying package and the (i-1)-th group-buying package, and calculate the average redemption time. The average group-buying price of group-buying packages purchased by users Where I represents the total number of times a group-buying package has been purchased;

[0044] Information Calculation Module: Calculates the user's purchasing power coefficient X based on the average redemption time and average group purchase price. The specific formula for calculating the purchasing power coefficient X is as follows:

[0045] ;

[0046] Wherein, γ represents the preset first adjustment coefficient;

[0047] Calculate commodity quantity fluctuation value , where N ave This represents the average number of items purchased by a user in a group-buying package, and 2J first-recommended packages are set based on the fluctuation value of the number of items, where J is a preset threshold for the number of items in the first-recommended package, and the number of items in the j-th first-recommended package is... , where ρ is the preset change in the quantity of goods;

[0048] Information output module: based on price function Calculate the unit price P(NT) of the j-th first recommended package. j ), where μ represents the preset price change coefficient;

[0049] Calculate the recommended price ST for the j-th first recommended package. j =P(NT) j ) NT jThe first recommended package contains NT worth of items. j ;

[0050] Obtain the merchant's package database, filter out group-buying packages with similar prices and quantities to the recommended packages, and display these recommended packages to customers.

[0051] It should be noted that the system automatically generates recommended packages by obtaining users' group-buying package information. First, it obtains the group-buying package information that the user purchased this time, marks this package as the original package, and obtains the corresponding price and quantity of goods. The purpose is to understand the user's travel desires and make appropriate adjustments accordingly. If the price of the group-buying package purchased this time is relatively high, it indicates that the user's travel desires are high. Therefore, when setting recommended packages, reasonable adjustments should be made to achieve the goal of accurate recommendations.

[0052] Obtain users' purchase history for group-buying packages, calculate the average redemption time, and the average redemption time indicates the user's level of interest in the product. A shorter average redemption time indicates more frequent purchases of group-buying packages. Calculate the average group-buying price S. ave The purpose is to understand the user's spending power, so that when recommending group-buying packages, the user's spending power can be taken into account, and recommendations can be made only if the user's spending power allows, thereby increasing the success rate of recommendations.

[0053] The user's preference coefficient is calculated using a formula. The formula shows that when the average group-buying price remains constant, the shorter the average redemption time, the higher the preference coefficient. Furthermore, the average group-buying price S... ave Increasing the value of a product will also increase the liking coefficient X. This is understandable because if a user is willing to spend a lot of time and money on a product, it means that the user is interested in that product. Therefore, this formula can express the user's liking for a product in data form, which can specifically show the user's liking for the product. It also makes it easier to set the number of products in the recommended package and the unit price of the products.

[0054] The unit price of the recommended package is calculated using a price function. The formula shows that the price function is piecewise, meaning the quantity of items in the recommended package will fluctuate. Common sense dictates that the more items, the lower the price, and vice versa. The formula uses the original unit price P of the items in the package. ori Based on this, the unit price of the goods is adjusted upwards or downwards. After calculating the unit price, the price of the recommended package is calculated based on the quantity of goods and the unit price, and a recommendation is made. If the user thinks that all the recommended packages are not suitable, the original package is selected. If there is a more preferred recommended package, the corresponding recommended package is added to the shopping cart after the user selects it, and the checkout is completed.

[0055] In another preferred embodiment of the present invention, the method for selecting group-buying packages from the group-buying package database that are similar in price and quantity to the recommended packages specifically includes:

[0056] The price fluctuation range of the j-th first recommended package [ST] j -A,ST j +A] and commodity quantity fluctuation range [NT] j -B, NT j +B], where A is the preset price fluctuation coefficient and B is the preset commodity quantity fluctuation coefficient;

[0057] The group-buying packages in the database are filtered, and those whose prices fall within the price fluctuation range are selected. j -A,ST j The quantity of goods is within the range of +A and the quantity of goods is within the fluctuation range of [NT]. j -B, NT j The group-buying packages within [+B] are marked as candidate packages. Calculate the comprehensive value K of the i-th candidate package. i =|ST j -S i_pre |+|NT j -N i_pre | Select the candidate package with the lowest overall value as the recommended package, where S i_pre N represents the price of the i-th candidate package. j_pre This represents the number of items in the i-th candidate package.

[0058] It is worth noting that by accessing the merchant's group-buying package database and calculating the overall value, the lower the overall value, the more suitable the package is for the requirements. Therefore, the package with the lowest overall value is selected as the recommended package.

[0059] In another preferred embodiment of the present invention, if the overall values ​​of the candidate packages are equal, the candidate package with the lower price is selected as the recommended package.

[0060] It is worth noting that in practice, if two candidate packages have the same overall value and both have the lowest overall value, the lower-priced candidate package will be selected as the recommended package in order to minimize the user's expenses.

[0061] In another preferred embodiment of the present invention, if the purchase time interval ΔT i ≤η, the i-th group purchase package and the (i-1)-th group purchase package are regarded as one group purchase package, and the price and quantity of goods of the group purchase package are added together. The price and quantity of goods of the i-th group purchase package and the (i-1)-th group purchase package are added together respectively, where η represents the preset minimum time interval.

[0062] It is understandable that when users purchase goods, they find that there are no suitable group-buying packages available, so they choose to purchase multiple group-buying packages to combine. Since the interval between two group-buying packages is very short, processing them separately will lead to a shorter average redemption time, resulting in a larger error in the final result and affecting the accuracy of the final result.

[0063] In another preferred embodiment of the present invention, the average group-buying price S is calculated. ave At that time, the group-buying package purchased by the user this time will not be included in the average group-buying price S. ave The calculation.

[0064] It's important to note that although a user has purchased a group-buying package, it's uncertain whether this package is the most suitable for that user. Therefore, further recommendations are needed, and it's necessary to determine if the user chooses to change their package. If the user chooses to change their package, the corresponding price will also change. Therefore, the average group-buying price S needs to be calculated. ave At that time, the group purchase package purchased this time will not be included in the calculation.

[0065] In another preferred embodiment of the present invention, when the fluctuation value of the commodity quantity f=0, if the commodity quantity N ori =N ave If the condition is met, then the subsequent steps will be stopped and no package recommendations will be made.

[0066] If the quantity of goods N ori ≠N ave If so, the subsequent steps will proceed normally.

[0067] It should be noted that if a merchant has a fixed group-buying package for a long time, and the user always chooses the same package, the data is too singular to observe patterns based on historical purchase records. If the group-buying package purchased by the user this time has not changed, there is no need to recommend a new group-buying package to the user, and the subsequent steps should be stopped. If the package has changed, it means that the user wants to try a new package, and the subsequent steps should be performed normally.

[0068] In another preferred embodiment of the present invention, the quantity NT of the j-th recommended package is obtained. j The number of items N in the group-buying package offered by the merchant x Calculate the difference in the quantity of goods, NC=NT j -N x If the difference in the number of goods NC is less than the preset minimum number of goods N min At this point, the lower-priced package is recommended.

[0069] Understandably, if the quantity and price of the recommended package are very close to the group-buying package set by the merchant, the package with the lower price will be given priority as the recommended package to ensure that users can purchase a suitable group-buying package at a lower price.

[0070] In another preferred embodiment of the present invention, when setting the recommended package quantity threshold J, it is ensured that the quantity NT of the j-th item is calculated in subsequent steps. j >0 and unit price P (NT) j )>0.

[0071] It's important to note that when setting recommended packages, it's crucial to ensure that the quantity of items in the group-buying package is greater than 0. If the threshold value J for the recommended package quantity is set too high, it can lead to negative unit prices for items in the recommended package, which is illogical. Therefore, it's necessary to ensure that the quantity NT of the j-th item is [not specified]. j >0 and unit price P (NT) j )>0.

[0072] This invention is an internet-based intelligent settlement system for group-buying packages, and also includes:

[0073] Coupon module: Retrieves coupon information from the user's account, including coupon usage requirements and the total number of coupons YH;

[0074] Based on the usage requirements of the coupons, select the packages that meet the usage requirements from the merchant's package database and mark them as the second category of recommended packages. Select the lowest-priced second category recommended package as the recommended package.

[0075] Each coupon corresponds to a recommended package. YH recommended packages are selected and displayed to customers.

[0076] It's important to note that when using group-buying platforms, users may receive coupons from the platform or merchants. Using these coupons can reduce the cost of purchasing group-buying packages. To help users choose more affordable packages, we recommend packages based on the coupons. This not only allows users to use the coupons promptly and prevents them from expiring, but also enables them to purchase suitable packages at lower prices, thus improving the user experience.

[0077] Furthermore, the more packages recommended, the more options users have to choose from, and the greater the probability that users will select their preferred package.

[0078] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. An internet-based intelligent settlement system for group-buying packages, characterized in that, include: Information Acquisition Module: Marks the group-buying package purchased by the user as the original package and obtains the price S of the original package. ori The number of items N in the group purchase package ori And calculate the unit price P of the product. ori =S ori / N ori ; Information processing module: Merchants obtain the consumption records of users who purchased group-buying packages in this store, and obtain the redemption time T of the i-th group-buying package. i Group purchase package price S i The number of items N in the group-buying package i where i is a positive integer; Calculate the purchase time interval ΔT i =T i -T i-1 , where ΔT i Let represent the time interval between the redemption of the i-th group-buying package and the (i-1)-th group-buying package, and calculate the average redemption time. The average group-buying price of group-buying packages purchased by users Where I represents the total number of times a group-buying package has been purchased; Information Calculation Module: Calculates the user's purchasing power coefficient X based on the average redemption time and average group purchase price. The specific formula for calculating the purchasing power coefficient X is as follows: ； Wherein, γ represents the preset first adjustment coefficient; Calculate commodity quantity fluctuation value , where N ave This represents the average number of items purchased by a user in a group-buying package, and 2J first-recommended packages are set based on the fluctuation value of the number of items, where J is a preset threshold for the number of items in the first-recommended package, and the number of items in the j-th first-recommended package is... , where ρ is the preset change in the quantity of goods; Information output module: based on price function Calculate the unit price P(NT) of the j-th first recommended package. j ), where μ represents the preset price change coefficient; Calculate the recommended price ST for the j-th first recommended package. j =P(NT) j ) NT j The first recommended package contains NT worth of items. j ; Obtain the merchant's package database, filter out group-buying packages with similar prices and quantities to the recommended packages, and display these recommended packages to customers.

2. The intelligent settlement system for group-buying packages based on the Internet according to claim 1, characterized in that, The method for selecting group-buying packages from the database that are similar in price and quantity to the recommended packages includes: The price fluctuation range of the j-th first recommended package [ST] j -A,ST j +A] and commodity quantity fluctuation range [NT] j -B, NT j +B], where A is the preset price fluctuation coefficient and B is the preset commodity quantity fluctuation coefficient; The group-buying packages in the database are filtered, and those whose prices fall within the price fluctuation range are selected. j -A,ST j The quantity of goods is within the range of +A and the quantity of goods is within the fluctuation range of [NT]. j -B, NT j The group-buying packages within [+B] are marked as candidate packages. Calculate the comprehensive value K of the i-th candidate package. i =|ST j -S i_pre |+|NT j -N i_pre | Select the candidate package with the lowest overall value as the recommended package, where S i_pre N represents the price of the i-th candidate package. i_pre This represents the number of items in the i-th candidate package.

3. The intelligent settlement system for group-buying packages based on the Internet according to claim 2, characterized in that, If the overall value of the candidate packages is equal, the package with the lower price will be given priority as the recommended package.

4. The intelligent settlement system for group-buying packages based on the Internet according to claim 1, characterized in that, If the purchase time interval ΔT i ≤η, the i-th group purchase package and the (i-1)-th group purchase package are regarded as one group purchase package, and the price and quantity of goods of the i-th group purchase package and the (i-1)-th group purchase package are added together, where η represents the preset minimum time interval.

5. The intelligent settlement system for group-buying packages based on the Internet according to claim 1, characterized in that, Calculate the average group purchase price S ave At that time, the group-buying package purchased by the user this time will not be included in the average group-buying price S. ave The calculation.

6. The intelligent settlement system for group-buying packages based on the Internet according to claim 1, characterized in that, When the fluctuation value of the commodity quantity f=0, if the commodity quantity N ori =N ave If the condition is met, then the subsequent steps will be stopped and no package recommendations will be made. If the quantity of goods N ori ≠N ave If so, the subsequent steps will proceed normally.

7. The intelligent settlement system for group-buying packages based on the Internet according to claim 1, characterized in that, Get the quantity NT of the j-th recommended package. j The number of items N in the group-buying package offered by the merchant x Calculate the difference in the quantity of goods, NC=NT j -N x If the difference in the number of goods NC is less than the preset minimum number of goods N min At this point, the lower-priced package is recommended.

8. The intelligent settlement system for group-buying packages based on the Internet according to claim 1, characterized in that, When setting the recommended package quantity threshold J, ensure that the quantity NT of the j-th item is calculated in subsequent steps. j >0 and unit price P (NT) j )>0.

9. The intelligent settlement system for group-buying packages based on the Internet according to claim 1, characterized in that, include: Coupon module: Retrieves coupon information from the user's account, including coupon usage requirements and the total number of coupons YH; Based on the usage requirements of the coupons, select the packages that meet the usage requirements from the merchant's package database and mark them as the second category of recommended packages. Select the lowest-priced second category recommended package as the recommended package. Each coupon corresponds to a recommended package. YH recommended packages are selected and displayed to customers.

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