Shop product distribution method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of data processing, in particular to a shop product distribution method and device, computer equipment and a storage medium. The method comprises the following steps of: extracting product types, product transaction prices and product transaction time which are lower than a preset transaction threshold value from business information of a to-be-analyzed shop, extracting passenger flow data which are lower than the preset transaction threshold value, and analyzing the to-be-analyzed shop according to the product types, the product transaction prices, the product transaction time and the passenger flow data, and matching the corresponding distribution adjustment strategy and distributing the to-be-analyzed shops. Therefore, the business information of the shops can be comprehensively analyzed, and the products can be reasonably distributed.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method, device, computer device and storage medium for product stocking in stores. Background Art

[0002] Product stocking in stores is to lay appropriate products in the store in reasonable quantities, prices and display methods through the selection of goods, planning of display positions, inventory allocation and dynamic adjustment strategies.

[0003] Most of the current product stocking methods in stores rely on manual experience judgment or static analysis based on historical transaction data for stocking decisions. Usually, the transaction data statistics of a fixed time period (such as a single day or a single week) are used, and stocking plans are formulated in combination with indicators such as inventory turnover rate. However, such methods have certain defects. First, it is difficult to capture the impact of passenger flow changes on immediate transaction opportunities. Second, it is difficult to analyze the coordination of multi-dimensional data and passenger flow trends, resulting in a lag in stocking adjustment behind the actual business scenario changes.

[0004] Therefore, how to comprehensively analyze the business information of stores to reasonably conduct product stocking has become an urgent problem to be solved. Summary of the Invention

[0005] Embodiments of the present invention provide a method, device, computer device and storage medium for product stocking in stores to solve the problem of how to comprehensively analyze the business information of stores to reasonably conduct product stocking.

[0006] In a first aspect, an embodiment of the present invention provides a method for product stocking in stores, including: Obtain the business information of the store to be analyzed and the passenger flow data detected by sensors, extract the transaction data in the business information, and analyze the transaction data and the passenger flow data within a preset business time to obtain a transaction change curve and a passenger flow change curve; Extract the first marked transaction data in the transaction change curve that is lower than a preset transaction threshold, and extract the product type, product transaction price, and product transaction time in the business information corresponding to the first marked transaction data; According to the passenger flow change curve, determine the passenger flow data corresponding to the transaction time of the first marked transaction data as the second marked passenger flow data; Match the corresponding stocking adjustment strategy according to the product type, the product transaction price, the product transaction time, and the second marked passenger flow data, and conduct stocking for the store to be analyzed according to the stocking adjustment strategy.

[0007] In a second aspect, an embodiment of the present invention provides a device for product stocking in stores, including: A data analysis module, configured to obtain the business information of a store to be analyzed and the passenger flow data detected by sensors, extract the transaction data from the business information, and analyze the transaction data and the passenger flow data within a preset business hours to obtain a transaction change curve and a passenger flow change curve; An information extraction module, configured to extract the first marked transaction data that is lower than a preset transaction threshold in the transaction change curve, and extract the product type, product transaction price, and product transaction time corresponding to the first marked transaction data in the business information; A data marking module, configured to determine, according to the passenger flow change curve, that the passenger flow data corresponding to the transaction time of the first marked transaction data is the second marked passenger flow data; A product stocking module, configured to match a corresponding stocking adjustment strategy according to the product type, the product transaction price, the product transaction time, and the second marked passenger flow data, and perform product stocking on the store to be analyzed according to the stocking adjustment strategy.

[0008] In a third aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned store product stocking method is implemented.

[0009] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned store product stocking method is implemented.

[0010] The beneficial effects of the present invention compared with the prior art are as follows: By obtaining the business information of the store to be analyzed and the passenger flow data detected by sensors, extracting the transaction data from the business information, analyzing the transaction data and the passenger flow data within a preset business hours to obtain a transaction change curve and a passenger flow change curve, extracting the first marked transaction data that is lower than a preset transaction threshold in the transaction change curve, extracting the product type, product transaction price, and product transaction time corresponding to the first marked transaction data in the business information, determining, according to the passenger flow change curve, that the passenger flow data corresponding to the transaction time of the first marked transaction data is the second marked passenger flow data, matching a corresponding stocking adjustment strategy according to the product type, the product transaction price, the product transaction time, and the second marked passenger flow data, and performing product stocking on the store to be analyzed according to the stocking adjustment strategy. By extracting the product type, product transaction price, and product transaction time that are lower than a preset transaction threshold from the business information of the store to be analyzed, and extracting the passenger flow data that is lower than a preset transaction threshold, matching a corresponding stocking adjustment strategy according to the product type, the product transaction price, the product transaction time, and the passenger flow data, and performing product stocking on the store to be analyzed. Thus, the business information of the store is comprehensively analyzed to reasonably perform product stocking. Brief Description of the Drawings

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0012] Figure 1 It is a schematic diagram of the application environment of a method for stocking products in a store provided in the first embodiment of the present invention; Figure 2 It is a schematic flowchart of a method for stocking products in a store provided in the second embodiment of the present invention; Figure 3 It is a schematic flowchart of a method for stocking products in a store provided in the third embodiment of the present invention; Figure 4 It is a schematic flowchart of a method for stocking products in a store provided in the fourth embodiment of the present invention; Figure 5 It is a schematic flowchart of a method for stocking products in a store provided in the fifth embodiment of the present invention; Figure 6 It is a schematic flowchart of a method for stocking products in a store provided in the sixth embodiment of the present invention; Figure 7 It is a schematic diagram of the structure of a device for stocking products in a store provided in the seventh embodiment of the present invention; Figure 8 It is a schematic diagram of the structure of a computer device provided in the eighth embodiment of the present invention. Detailed Embodiments

[0013] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0014] Such as Figure 1As shown in the figure, it is a schematic diagram of the application environment of a method for stocking products in a store provided by Embodiment 1 of the present invention. Among them, the client and the server are connected for communication. Users can provide conditions, requirements, operation instructions, etc. for stocking products in the store to the server through operating the client. The server is used to execute the method for stocking products in the store of the present invention according to the relevant content sent by the client. Among them, the client includes, but is not limited to, various computer devices such as personal computers, laptop computers, smart phones, tablet computers, and portable wearable devices. The computer device corresponding to the server can be realized by an independent server or a server cluster composed of multiple servers.

[0015] As Figure 2 shown in the figure, it is a schematic flowchart of a method for stocking products in a store provided by Embodiment 2 of the present invention. Among them, this method for stocking products in a store is applied to the Figure 1 server in the figure. This method for stocking products in a store may include the following steps: Step S201: Obtain the business information of the store to be analyzed and the passenger flow data detected by sensors, extract the transaction data from the business information, and analyze the transaction data and the passenger flow data within the preset business hours to obtain a transaction change curve and a passenger flow change curve.

[0016] Among them, this step requires collecting two types of data. One is the business information of the store to be analyzed, which covers various transaction-related data during the operation of the store, such as the transaction quantity and transaction data of different products. The other is the passenger flow data detected by sensors. The sensors can be devices installed at the store entrance or inside the store, used to count the number of customers entering the store and their activities inside the store, etc. From the obtained business information, specifically extract the transaction data. The transaction data is the key data reflecting the actual transaction completion situation of the store, such as the data of each transaction, the products traded, etc.

[0017] Among them, within the preset business hours range, analyze the transaction data and the passenger flow data. The analysis process involves operations such as sorting, counting, and calculating the data, and finally obtain a transaction change curve and a passenger flow change curve. The transaction change curve can intuitively show the change trend of the transaction data over time within the preset business hours. Similarly, the passenger flow change curve can present the change situation of the passenger flow data over time. For example, from 9 am to 9 pm, and then generate a curve to show the transaction and passenger flow change trends in different time periods.

[0018] Step S202: Extract the first marked transaction data in the transaction change curve that is lower than the preset transaction threshold, and extract the product category, product transaction price, and product transaction time corresponding to the first marked transaction data in the business information.

[0019] Among them, a transaction threshold is preset. This threshold is a pre-set transaction standard used to measure whether the transaction situation is ideal. Find the data lower than this transaction threshold from the transaction change curve, and mark these data as the first marked transaction data. These data represent the time periods with not-so-good transaction situations. For these first marked transaction data, extract the corresponding product categories, product transaction prices, and product transaction times from the business information. This helps to clarify which products have poor transaction situations, what their prices are, and the specific transaction times during the periods with poor transactions. For example, if the transaction data for a certain time period is lower than the expected value, it will be marked as data that needs attention, and then the corresponding product categories, prices, and times will be extracted to help analyze why these time periods or products perform poorly.

[0020] Step S203: According to the passenger flow change curve, determine the passenger flow data corresponding to the transaction time of the first marked transaction data as the second marked passenger flow data.

[0021] Among them, according to the transaction time corresponding to the first marked transaction data, find the passenger flow data corresponding to the same time in the passenger flow change curve and mark it as the second marked passenger flow data. The purpose of this is to associate the time periods with poor transactions with the passenger flow situation at that time in order to analyze the impact of passenger flow factors on transactions.

[0022] Step S204: According to the product categories, product transaction prices, product transaction times, and the second marked passenger flow data, match the corresponding product placement adjustment strategy, and according to the product placement adjustment strategy, conduct product placement for the store to be analyzed.

[0023] Among them, combine the previously extracted product categories, product transaction prices, product transaction times, and the second marked passenger flow data, and match the corresponding product placement adjustment strategy according to the pre-established rules or algorithms. For example, if it is found that a certain type of product has poor transactions during low passenger flow periods and with relatively high transaction prices, the possible matching strategy is to reduce the product placement quantity of this product or adjust its price.

[0024] Among them, based on the matched product placement adjustment strategy, conduct actual product placement operations for the store to be analyzed. This includes increasing the inventory of some products, reducing the inventory of some products, adjusting the display positions of products, etc., in order to achieve the purpose of optimizing the product placement in the store.

[0025] In the embodiment of the present application, by obtaining the business information of the store to be analyzed and the passenger flow data detected by sensors, extracting the transaction data in the business information, analyzing the transaction data and the passenger flow data within the preset business hours, obtaining the transaction change curve and the passenger flow change curve, extracting the first marked transaction data in the transaction change curve that is lower than the preset transaction threshold, extracting the product type, product transaction price, and product transaction time corresponding to the first marked transaction data in the business information, determining the passenger flow data corresponding to the transaction time of the first marked transaction data as the second marked passenger flow data according to the passenger flow change curve, matching the corresponding product placement adjustment strategy according to the product type, product transaction price, product transaction time, and the second marked passenger flow data, and performing product placement on the store to be analyzed according to the product placement adjustment strategy. By extracting the product type, product transaction price, product transaction time, and the passenger flow data that are lower than the preset transaction threshold from the business information of the store to be analyzed, and matching the corresponding product placement adjustment strategy according to the product type, product transaction price, product transaction time, and the passenger flow data, and performing product placement on the store to be analyzed. Thus, the business information of the store is comprehensively analyzed to reasonably perform product placement.

[0026] As Figure 3 shown, it is a schematic flowchart of a method for placing products in a store provided in Embodiment 3 of the present invention. In step S204, matching the corresponding product placement adjustment strategy according to the product type, product transaction price, product transaction time, and the second marked passenger flow data, and performing product placement on the store to be analyzed according to the product placement adjustment strategy may include the following steps: Step S301, if the second marked passenger flow data is higher than the preset passenger flow threshold, extract the age distribution ratio of customers in the business information.

[0027] Step S302, extract the proportion of the number of product transaction types corresponding to the age distribution ratio in the business information.

[0028] Step S303, adjust the product type placement ratio according to the proportion of the number of transaction product types to obtain the first adjustment result, and perform product placement on the store to be analyzed according to the first adjustment result.

[0029] Among them, first, the second marked passenger flow data (i.e., the passenger flow data corresponding to the time period with poor transactions) will be compared with a preset passenger flow threshold. The preset passenger flow threshold is a pre-set standard for measuring the amount of passenger flow. If the second marked passenger flow data is higher than this threshold, it means that during this time period with poor transactions, the number of customers entering the store is relatively large. When there is a situation of high passenger flow but poor transactions, in order to find out the factors that may affect transactions, it is necessary to further analyze the characteristics of customers. Extract the age distribution ratio of customers from the business information, that is, the proportion of customers in different age groups among all customers. For example, it can be obtained that customers aged 20 - 30 account for 30%, and customers aged 30 - 40 account for 40% and other data.

[0030] After obtaining the age distribution ratio of customers, then find out the proportion of the number of product transaction types corresponding to each age group from the business information. This means analyzing which types of products are purchased by customers in different age groups, and the proportion of the number of transactions of these products in the total number of transactions of customers in this age group. For example, the number of transactions of clothing products purchased by customers aged 20 - 30 accounts for 50% of their total number of transactions, and the number of transactions of electronic products accounts for 30% and so on. Through such analysis, the consumption preferences of customers in different age groups can be understood.

[0031] Among them, if the proportion of the number of transactions of a certain type of product for customers in a certain age group is relatively high, it means that this type of product is more popular in this age group, then the stocking proportion of this type of product can be appropriately increased; on the contrary, if the proportion of the number of transactions is relatively low, the stocking proportion of the corresponding product can be reduced. After such adjustment, a first adjustment result will be obtained, and this result is a new product stocking proportion plan. Finally, according to the first adjustment result, the actual stocking of the store to be analyzed is carried out. According to the new stocking proportion, increase or decrease the inventory of different types of products, and place the products reasonably in the store to meet the consumption needs of customers in different age groups and improve the transaction rate of the store.

[0032] Optionally, according to the business information, obtain the current stocking proportion of the product, and analyze whether the current stocking proportion conforms to the proportion of the number of product transaction types; If it does not conform to the proportion of the number of product transaction types, then adjust the current stocking proportion until it conforms to the proportion of the number of product transaction types to obtain the first adjustment result.

[0033] Among them, the current stocking proportion of the product is compared with the proportion of the number of product transaction types. If the two are consistent, it means that the current stocking situation can better meet the needs. If they are inconsistent, it means that the current stocking is unreasonable and needs to be adjusted.

[0034] In this embodiment, by deeply analyzing the customer age and product transaction situation in the case of high passenger flow but poor transactions, the product placement is adjusted targeted, so as to optimize the product placement strategy of the store.

[0035] As Figure 4 shown, it is a schematic flowchart of a method for placing products in a store provided in the fourth embodiment of the present invention. When matching the corresponding placement adjustment strategy according to the product type, product transaction price, product transaction time, and the second marked passenger flow data in step S204, and placing products for the store to be analyzed according to the placement adjustment strategy, the following steps may further be included: Step S401, if the second marked passenger flow data is higher than the preset passenger flow threshold, set a preset time interval, and divide the transaction time into a first transaction time interval of the first transaction product category, a second transaction time interval of the second transaction product category, and a third transaction time interval of the third transaction product category according to the time interval.

[0036] Step S402, adjust the product type according to the first transaction time interval, the second transaction time interval, and the third transaction time interval to obtain a third adjustment result, and place products for the store to be analyzed according to the third adjustment result.

[0037] Optionally, analyze the first transaction product category, the second transaction product category, and the third transaction product category to obtain the non-repeated product categories that have not been repeatedly transacted.

[0038] Adjust the quantity of the non-repeated products in the non-repeated product categories to obtain a third adjustment result, and place products for the store to be analyzed according to the third adjustment result.

[0039] Among them, a preset time interval is set. For example, it can be set to 4 hours, 5 hours, etc. The specific time interval should be determined according to the actual business characteristics and data situation of the store. Then, according to this time interval, the transaction time is divided. All transaction times are divided into a first transaction time interval of the first transaction product category, a second transaction time interval of the second transaction product category, and a third transaction time interval of the third transaction product category according to different product types. Here, the "first transaction product category", "second transaction product category", and "third transaction product category" can represent different commodity types in different time intervals. For example, the first transaction product category in the first transaction time interval may be food and beverages, the second product category in the second transaction time interval may be clothing, and the third transaction product category in the third transaction time interval may be daily necessities, etc. Through this division, the distribution law of transactions of different product categories in time can be clearly seen.

[0040] According to the divided first transaction time interval, second transaction time interval, and third transaction time interval, analyze the transaction situation of various products in different time periods. If it is found that the transaction of a certain type of product is very active in a certain time period, while the transaction is less in other time periods, the product variety distribution can be adjusted according to this rule. Among them, the purpose of analyzing these three types of transaction product categories is to find out the product categories that have not been repeatedly transacted, that is, those product categories that are cross - independent and do not exist between different categories. After determining the non - repeated product categories, the quantity of products in these non - repeated product categories should be increased within the corresponding transaction time interval. For example, if it is found that the transaction of food products is active from 9 to 10 in the morning, while the transaction is less in the afternoon, then the product distribution display and inventory of food products can be increased in the morning; if it is found that the transaction of clothing products is high from 3 to 4 in the afternoon, then the display and distribution quantity of clothing products can be optimized during this time period.

[0041] Among them, through the above - mentioned adjustment of product categories according to the transaction time interval, the third adjustment result is obtained. This result is a new product variety distribution plan, which takes into account the transaction rules of different products in different time periods. Finally, according to this third adjustment result, the actual distribution operation is carried out on the store to be analyzed, and the products are placed and the inventory is adjusted according to the new plan to improve the rationality of the product distribution in the store.

[0042] In this embodiment, through the refined analysis of the transaction time, the product variety distribution is adjusted according to the transaction rules of different products in different time periods, so as to more accurately meet the needs of customers at different times and improve the operation effect of the store.

[0043] As Figure 5 shown, it is a schematic flow chart of a method for distributing products in a store provided in Embodiment 5 of the present invention. In step S204, according to the product variety, the product transaction price, the product transaction time, and the second marked passenger flow data, a corresponding distribution adjustment strategy is matched, and according to the distribution adjustment strategy, the products are distributed to the store to be analyzed. It may further include the following steps: Step S501, if the quantity of the second marked passenger flow data is not higher than the preset passenger flow threshold, then according to the business information, the current business hours are obtained, and it is judged whether the current business hours are within the first transaction time interval. If it is within the first transaction time, then the first price reduction operation is performed on the products corresponding to the second transaction product category and the products corresponding to the third transaction product category.

[0044] Step S502: If the current business hours are not within the first transaction time interval, then determine whether the current business hours are within the second transaction time interval. If they are within the second transaction time interval, then perform a second price reduction operation on the products corresponding to the first transaction product category and the products corresponding to the third transaction product category.

[0045] Step S503: If the current business hours are not within the second transaction time interval, then determine whether the current business hours are within the third transaction time interval. If they are within the third transaction time interval, then perform a third price reduction operation on the products corresponding to the first transaction product category and the products corresponding to the second transaction product category.

[0046] Step S504: Adjust the product transaction pricing according to the first price reduction operation, the second price reduction operation, and the third price reduction operation to obtain a fourth adjustment result. According to the fourth adjustment result, stock the store to be analyzed.

[0047] Among them, when the second marked passenger flow data volume is not higher than the preset passenger flow threshold, it means that during this current period with poor transactions, the number of customers entering the store is small. At this time, obtain the current business hours according to the business information, and then determine whether this time is within the first transaction time interval. The first transaction time interval is a specific time period previously divided according to the transaction time pattern. For example, it may be from 10 am to 12 pm.

[0048] If the current business hours are within the first transaction time interval, then perform a first price reduction operation on the products corresponding to the second transaction product category and the products corresponding to the third transaction product category. Here, the second transaction product category and the third transaction product category are different types of products. For example, the second transaction product category is clothing, and the third transaction product category is daily necessities. By reducing the prices of these two types of products, attract the customers entering the store during this time period to make purchases, thereby optimizing the stocking of the store's products.

[0049] If the current business hours are not within the first transaction time interval, then continue to determine whether they are within the second transaction time interval. The second transaction time interval is also a specific time period, such as from 2 pm to 4 pm.

[0050] If they are within the second transaction time interval, then perform a second price reduction operation on the products corresponding to the first transaction product category and the products corresponding to the third transaction product category. For example, the first transaction product category is food, and the third transaction product category is daily necessities, and reduce the prices of these two types of products.

[0051] If the current business hours are neither within the first transaction time interval nor within the second transaction time interval, then determine whether they are within the third transaction time interval. The third transaction time interval is also a specific time period, such as from 7 pm to 9 pm.

[0052] If it is within the third transaction time interval, perform a third price reduction operation on the products corresponding to the first transaction product category and the products corresponding to the second transaction product category.

[0053] Among them, the price adjustments involved in the previous first price reduction operation, second price reduction operation, and third price reduction operation are integrated to obtain a fourth adjustment result, which optimally adjusts the prices of different product categories according to different transaction time intervals. According to the fourth adjustment result, stock the shops to be analyzed.

[0054] In this embodiment, by adjusting the prices of different product categories according to different transaction time intervals, the passenger flow of the shop is increased, and at the same time, the stocking strategy is optimized according to the price adjustment result to better meet customer needs.

[0055] Such as Figure 6 shown, is a flowchart of a method for stocking shop products provided in Embodiment 6 of the present invention. In step S204, according to the product type, product transaction pricing, product transaction time, and second marked passenger flow data, match the corresponding stocking adjustment strategy, and stock the shop to be analyzed according to the stocking adjustment strategy. It may further include the following steps: Step S601, if the amount of the second marked passenger flow data is not higher than a preset passenger flow threshold, extract the product categories that meet a preset age ratio in the product transaction type quantity ratio.

[0056] Step S602, increase the quantity of the product categories to obtain a fifth adjustment result, and stock the shop to be analyzed according to the fifth adjustment result.

[0057] Among them, in the overall logic of step S204, when the amount of the second marked passenger flow data is not higher than the preset passenger flow threshold, it means that within the currently investigated time period, the number of customers entering the shop to be analyzed is small, and the shop may face poor transaction situations. The product transaction type quantity ratio reflects the proportion of different product categories in transactions. The preset age ratio is a pre-set ratio standard related to the consumption preferences of customers of different age groups. For example, if it is found that the transaction proportion of toy products just meets the preset age ratio for the children's age group, then the toy products are the product categories that meet the conditions. After determining the product categories that meet the preset age ratio, increase the quantity of these product categories. The basis for this operation is that since these product categories meet the preset age ratio in the transaction proportion, it indicates that they have a certain market demand among customers of the corresponding age groups. Increasing their quantity can provide more choices for customers. For example, if toy products meet the conditions, the quantity of different styles and types of toys can be increased.

[0058] In this embodiment, from the perspective of the age ratio and the transaction share ratio of product categories, the number of product categories that meet a specific age ratio is increased to optimize the product stocking strategy of the store.

[0059] As Figure 7 shown, it is a schematic diagram of a store product stocking device provided in the seventh embodiment of the present invention. The store product stocking device corresponds one-to-one to the store product stocking method in the above embodiment. The store product stocking device includes a data analysis module 71, an information extraction module 72, a data marking module 73, and a stocking module 74. The detailed description of each functional module is as follows: The data analysis module 71 is used to obtain the business information of the store to be analyzed and the passenger flow data detected by the sensor, extract the transaction data in the business information, and analyze the transaction data and the passenger flow data within the preset business hours to obtain a transaction change curve and a passenger flow change curve; The information extraction module 72 is used to extract the first marked transaction data lower than the preset transaction threshold in the transaction change curve, and extract the product type, product transaction price, and product transaction time corresponding to the first marked transaction data in the business information; The data marking module 73 is used to determine the passenger flow data corresponding to the transaction time of the first marked transaction data as the second marked passenger flow data according to the passenger flow change curve; The stocking module 74 is used to match the corresponding stocking adjustment strategy according to the product type, product transaction price, product transaction time, and the second marked passenger flow data, and perform stocking on the store to be analyzed according to the stocking adjustment strategy.

[0060] Optionally, the above stocking module 74 includes: An age distribution determination unit, which is used to extract the age distribution ratio of customers in the business information if the second marked passenger flow data is higher than the preset passenger flow threshold; A product transaction type extraction unit, which is used to extract the product transaction type quantity ratio corresponding to the age distribution ratio in the business information; A first adjustment unit, which is used to adjust the stocking ratio of product types according to the product transaction type quantity ratio to obtain a first adjustment result, and perform stocking on the store to be analyzed according to the first adjustment result.

[0061] Optionally, the above first adjustment unit includes: A stocking ratio judgment subunit, which is used to obtain the current stocking ratio of the product according to the business information and analyze whether the current stocking ratio conforms to the product transaction type quantity ratio; A ratio adjustment subunit, which is used to adjust the current stocking ratio until it conforms to the product transaction type quantity ratio to obtain a first adjustment result if it does not conform to the product transaction type quantity ratio.

[0062] Optionally, the above-mentioned product placement module 74 further includes: A time interval determination unit, configured to set a preset time interval if the second marked passenger flow data is higher than a preset passenger flow threshold, and divide the transaction time into a first transaction time interval for the first transaction product category, a second transaction time interval for the second transaction product category, and a third transaction time interval for the third transaction product category according to the time interval; A third adjustment unit, configured to adjust the product types according to the first transaction time interval, the second transaction time interval, and the third transaction time interval to obtain a third adjustment result, and perform product placement on the store to be analyzed according to the third adjustment result.

[0063] Optionally, the above-mentioned third adjustment unit includes: A non-duplicate product determination subunit, configured to analyze the first transaction product category, the second transaction product category, and the third transaction product category to obtain a non-duplicate product category with non-duplicate transactions; A non-duplicate adjustment subunit, configured to adjust the quantity of non-duplicate products in the non-duplicate product category to obtain a third adjustment result, and perform product placement on the store to be analyzed according to the third adjustment result.

[0064] Optionally, the above-mentioned product placement module 74 further includes: A first reduction unit, configured to, if the second marked passenger flow data volume is not higher than a preset passenger flow threshold, obtain the current business hours according to the business information, determine whether the current business hours are within the first transaction time interval, and if within the first transaction time, perform a first reduction operation on the prices of the products corresponding to the second transaction product category and the products corresponding to the third transaction product category; A second reduction unit, configured to, if the current business hours are not within the first transaction time interval, determine whether the current business hours are within the second transaction time interval, and if within the second transaction time interval, perform a second reduction operation on the prices of the products corresponding to the first transaction product category and the products corresponding to the third transaction product category; A third reduction unit, configured to, if the current business hours are not within the second transaction time interval, determine whether the current business hours are within the third transaction time interval, and if within the third transaction time interval, perform a third reduction operation on the prices of the products corresponding to the first transaction product category and the products corresponding to the second transaction product category; A fourth adjustment unit, configured to adjust the product transaction pricing according to the first reduction operation, the second reduction operation, and the third reduction operation to obtain a fourth adjustment result, and perform product placement on the store to be analyzed according to the fourth adjustment result.

[0065] Optionally, the above-mentioned product placement module 74 further includes: A category extraction unit, configured to extract product categories that meet a preset age ratio in the product transaction category quantity ratio if the second marked passenger flow data volume is not higher than a preset passenger flow threshold. A fifth adjustment unit, configured to increase the quantity of product categories to obtain a fifth adjustment result, and perform product stocking for the store to be analyzed according to the fifth adjustment result.

[0066] For the specific limitations of the store product stocking device, reference may be made to the limitations of the store product stocking method in the foregoing text, which will not be elaborated here. Each module in the above store product stocking device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0067] As Figure 8 shown, it is a schematic structural diagram of a computer device provided in Embodiment 8 of the present invention. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a store product stocking method.

[0068] In an embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the store product stocking method in the above embodiment. For example Figures 2 to 6 shown, to avoid repetition, it will not be elaborated here. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in this embodiment of the store product stocking device. For example Figure 7 shown, the functions of the data analysis module 71, the information extraction module 72, the data marking module 73, and the stocking module 74 will not be elaborated here to avoid repetition.

[0069] In an embodiment, a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, it implements the store product stocking method in the above embodiment. As Figures 2 to 6 shown, to avoid repetition, it will not be elaborated here. Alternatively, when the computer program is executed by the processor, it implements the functions of each module / unit in the above embodiment of the store product stocking device. For example Figure 7The functions of the data analysis module 71, information extraction module 72, data marking module 73, and product placement module 74 shown are not described in detail here to avoid repetition. The computer-readable storage medium can be non-volatile or volatile.

[0070] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0071] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0072] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A method for distributing products in a store, characterized in that: include: Obtaining business information of the store to be analyzed and customer flow data detected by sensors, extracting transaction data from the business information, analyzing the transaction data and customer flow data within a preset business hour, and obtaining a transaction change curve and a customer flow change curve; Extracting first marked transaction data below a preset transaction threshold from the transaction change curve, and extracting product types, product transaction prices, and product transaction times corresponding to the first marked transaction data from the business information; According to the passenger flow change curve, determining the passenger flow data corresponding to the transaction time of the first marked transaction data as the second marked passenger flow data; According to the product type, the product transaction price, the product transaction time and the second marked customer flow data, a corresponding distribution adjustment strategy is matched, and according to the distribution adjustment strategy, the store to be analyzed is distributed.

2. The method for distributing products in a store according to claim 1, characterized in that: The matching of a corresponding distribution adjustment strategy according to the product type, the product transaction price, the product transaction time and the second marked customer flow data, and distribution of the product to be analyzed according to the distribution adjustment strategy, includes: If the second marked passenger flow data is higher than the preset passenger flow threshold, extracting the age distribution ratio of customers in the business information; Extract the product transaction type quantity ratio corresponding to the age distribution ratio in the business information; According to the quantity ratio of the types of products sold, the distribution ratio of the product types is adjusted to obtain a first adjustment result, and according to the first adjustment result, the store to be analyzed is distributed.

3. The method for distributing products in a store according to claim 2, characterized in that: The adjusting the distribution ratio of the product types according to the quantity ratio of the transaction product types to obtain a first adjustment result includes: According to the business information, the current distribution ratio of the product is obtained, and whether the current distribution ratio is consistent with the product transaction type quantity ratio; If it does not meet the product transaction type quantity ratio, adjust the current distribution ratio until it meets the product transaction type quantity ratio, and obtain a first adjustment result.

4. The method for distributing products in a store according to claim 1, characterized in that: The step of matching a corresponding distribution adjustment strategy according to the product type, the product transaction price, the product transaction time and the second marked customer flow data, and distributing the product to the store to be analyzed according to the distribution adjustment strategy, further includes: If the second marked customer flow data is higher than the preset customer flow threshold, a preset time interval is set, and according to the time interval, the transaction time is divided into a first transaction time interval of the first transaction product category, a second transaction time interval of the second transaction product category, and a third transaction time interval of the third transaction product category; The product categories are adjusted according to the first transaction time interval, the second transaction time interval, and the third transaction time interval to obtain a third adjustment result, and the store to be analyzed is distributed with products according to the third adjustment result.

5. The method for distributing products in a store according to claim 4, characterized in that: The step of adjusting the product category according to the first transaction time interval, the second product transaction time, and the third transaction time interval to obtain a third adjustment result, and distributing the product to be analyzed according to the third adjustment result includes: Analyze the first transaction product category, the second transaction product category, and the third transaction product category to obtain non-repeated product categories with non-repeated transactions; The number of non-repeating products in the non-repeating product category is adjusted to obtain a third adjustment result, and the store to be analyzed is stocked according to the third adjustment result.

6. The method for distributing products in a store according to claim 4, characterized in that: The step of matching a corresponding distribution adjustment strategy according to the product type, the product transaction price, the product transaction time and the second marked customer flow data, and distributing the product to the store to be analyzed according to the distribution adjustment strategy, further includes: If the second marked customer flow data volume is not higher than the preset customer flow threshold, the current business hours are obtained according to the business information, and it is determined whether the current business hours are within the first transaction time interval; if they are within the first transaction time interval, a first price reduction operation is performed on the products corresponding to the second transaction product category and the products corresponding to the third transaction product category; If the current business hours are not within the first transaction time interval, determining whether the current business hours are within the second transaction time interval; if they are within the second transaction time interval, performing a second price reduction operation on the products corresponding to the first transaction product category and the products corresponding to the third transaction product category; If the current business hours are not within the second transaction time interval, determining whether the current business hours are within the third transaction time interval, and if they are within the third transaction time interval, performing a third price reduction operation on the products corresponding to the first transaction product category and the products corresponding to the second transaction product category; According to the first reduction operation, the second reduction operation, and the third reduction operation, the product transaction price is adjusted to obtain a fourth adjustment result, and according to the fourth adjustment result, the store to be analyzed is distributed with the product.

7. The method for distributing products in a store according to claim 2, characterized in that: The step of matching a corresponding distribution adjustment strategy according to the product type, the product transaction price, the product transaction time and the second marked customer flow data, and distributing the product to the store to be analyzed according to the distribution adjustment strategy, further includes: If the amount of the second marked customer flow data is not higher than the preset customer flow threshold, extracting product categories that meet the preset age ratio from the product transaction type quantity ratio; Increase the number of the product categories to obtain a fifth adjustment result, and distribute the products to the shops to be analyzed according to the fifth adjustment result.

8. A device for distributing products in a store, characterized in that: include: A data analysis module, used to obtain business information of the shops to be analyzed and customer flow data detected by sensors, extract transaction data from the business information, analyze the transaction data and customer flow data within preset business hours, and obtain a transaction change curve and a customer flow change curve; an information extraction module, configured to extract first marked transaction data below a preset transaction threshold value from the transaction change curve, and extract product types, product transaction prices, and product transaction times corresponding to the first marked transaction data from the business information; A data marking module, configured to determine, according to the passenger flow change curve, passenger flow data corresponding to the transaction time of the first marked transaction data as second marked passenger flow data; The distribution module is used to match the corresponding distribution adjustment strategy according to the product type, the product transaction price, the product transaction time and the second marked customer flow data, and distribute the products to the store to be analyzed according to the distribution adjustment strategy.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for distributing product goods in a store as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for distributing product in a store as claimed in any one of claims 1 to 7 is implemented.

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