CN113554400A - Inventory data updating method, device, equipment and storage medium - Google Patents
Inventory data updating method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a storage medium for updating inventory data, wherein the method comprises the following steps: responding to a feedback signal which is sent by a shop terminal and aims at a push purchase order, and inquiring the purchase number of a commodity SKU and the corresponding commodity SKU in the push purchase order; according to the commodity SKU and the purchase number thereof, inquiring the historical purchase record of the store terminal to obtain a target goods supply warehouse; inquiring the available stock quantity of commodity SKUs in the target goods supply warehouse; judging whether the available inventory quantity meets the purchase quantity in the pushed purchase order or not, and if so, sending an inquiry signal; in response to a lock signal for the interrogation signal transmitted by the store terminal, a portion of the available inventory quantity of the targeted supply warehouse is converted to a locked inventory quantity in accordance with the purchase data of the pushed purchase order. The system has the advantage that the purchasing requirement of the shop is ensured by directionally locking the inventory of a certain supply warehouse in response to the specific requirement of the shop.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for updating inventory data.
Background
In the related art, orders of a plurality of shops are collected in an internet platform mode, then uniform purchasing and logistics goods taking are carried out on the orders to a supplier, and then a carrier vehicle distributes the orders to the corresponding shops according to the purchasing orders, so that the warehousing cost of shops such as convenience stores is reduced, and the purchasing flexibility is improved.
Because the purchasing quantity in the mode has certain randomness, the conventional method for updating the inventory data periodically (24 hours or 12 hours) has the problem of lagging the inventory data, so that residual inventory is still displayed when a shop user joins a shopping cart, and when a purchasing order is generated (because the types of purchasing products are more, a certain time is needed between the time of joining the shopping cart and the time of collecting all the purchasing products), enough inventory is not matched with the purchasing order, so that the whole order-combining business related to the purchasing order cannot be realized.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present application propose inventory data update methods, apparatuses, electronic devices, and computer storage media to solve the technical problems mentioned in the background section above.
As a first aspect of the present application, some embodiments of the present application provide an inventory data update method, including: responding to a feedback signal which is sent by a shop terminal and aims at a push purchase order, and inquiring the purchase number of a commodity SKU and the corresponding commodity SKU in the push purchase order; according to the commodity SKU and the purchase number thereof, inquiring the historical purchase record of the store terminal to obtain a target goods supply warehouse; inquiring the available stock quantity of commodity SKUs in the target goods supply warehouse; judging whether the available inventory quantity meets the purchase quantity in the pushed purchase order, if so, sending an inquiry signal for inquiring whether to lock the inventory capacity required by the pushed purchase order to the store terminal, and if not, returning to inquire the historical purchase record of the store terminal to obtain another target goods supply warehouse; in response to a lock signal for the interrogation signal transmitted by the store terminal, a portion of the available inventory quantity of the targeted supply warehouse is converted to a locked inventory quantity in accordance with the purchase data of the pushed purchase order.
As a second aspect of the present application, some embodiments of the present application provide an inventory data updating apparatus, including: the system comprises a first query module, a second query module and a control module, wherein the first query module is used for responding to a feedback signal which is sent by a shop terminal and aims at a pushed purchase order, and querying the purchase number of a commodity SKU and a corresponding commodity SKU in the pushed purchase order; the second query module is used for querying the historical purchasing record of the store terminal according to the commodity SKU and the purchasing number thereof so as to obtain a target goods supply warehouse; the third inquiry module is used for inquiring the available stock quantity of the commodity SKU in the target supply warehouse; the judging module is used for judging whether the available inventory quantity meets the purchase quantity in the pushed purchase order, if so, sending an inquiry signal for inquiring whether to lock the inventory capacity required by the pushed purchase order to the store terminal, and if not, returning to inquire the historical purchase record of the store terminal to obtain another target goods supply warehouse; and the locking module is used for responding to a locking signal aiming at the inquiry signal sent by the shop terminal and converting a part of the available inventory quantity of the target supply warehouse into the locked inventory quantity according to the purchase data of the pushed purchase order.
As a third aspect of the present application, some embodiments of the present application provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
As a fourth aspect of the present application, some embodiments of the present application provide a computer storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The beneficial effect of this application lies in: the need for store procurement is assured by targeting inventory of a supply warehouse in response to the specific needs of the store.
More specifically, some embodiments of the present application may produce the following specific benefits: firstly, the pertinence of inventory locking is improved by sending a pushed purchase order to a shop; secondly, the locking action is closer to historical data and a purchase order to be generated by selecting a target goods supply warehouse; and thirdly, by the dynamic judgment condition of the available inventory quantity, the locking failure caused by the data delay of the locking is reduced, and the contradiction between the virtual locking and the actual situation is avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it.
Further, throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a flow diagram of a method for updating inventory data according to one embodiment of the present application;
FIG. 2 is a flow diagram of a portion of the steps in a method for updating inventory data according to one embodiment of the present application;
FIG. 3 is a flow diagram of a portion of the steps in a method for updating inventory data according to one embodiment of the present application;
FIG. 4 is a flow diagram of a portion of the steps in a method for updating inventory data according to one embodiment of the present application;
FIG. 5 is a flow diagram of a portion of the steps in a method for updating inventory data according to one embodiment of the present application;
FIG. 6 is a flow diagram of a portion of the steps in a method for updating inventory data according to one embodiment of the present application;
FIG. 7 is a block diagram of an inventory data update apparatus according to one embodiment of the present application;
FIG. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are for illustration purposes only and are not intended to limit the scope of the present application.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present application are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this application are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
The names of messages or information exchanged between a plurality of devices in the embodiments of the present application are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1, an inventory data updating method as an embodiment includes the steps of:
s1: and responding to a feedback signal which is sent by one shop terminal and aims at one pushed purchase order, and inquiring the purchase number of the commodity SKU and the corresponding commodity SKU in the pushed purchase order.
S2: according to the commodity SKU and the purchase number thereof, the historical purchase record of the store terminal is inquired to obtain a target supply warehouse.
S3: the inventory quantity available for the item SKU in the targeted supply warehouse is queried.
S4: and judging whether the available inventory quantity meets the purchase quantity in the pushed purchase order, if so, sending an inquiry signal for inquiring whether to lock the inventory capacity required by the pushed purchase order to the store terminal, and if not, returning to the step of inquiring the historical purchase record of the store terminal to obtain another target supply warehouse.
S5: in response to a lock signal for the interrogation signal transmitted by the store terminal, a portion of the available inventory quantity of the targeted supply warehouse is converted to a locked inventory quantity in accordance with the purchase data of the pushed purchase order.
As specific steps, the inventory data updating method of the above embodiment further includes the following steps:
s0: and generating a pushed purchase order according to the historical data of the shop corresponding to the shop terminal.
Step S0 is for generating a push purchase order targeted according to the history of the store from the history data of the store.
As a more specific aspect, step S0 may include the steps of: collecting inventory data and sales data of stores; generating shop state characteristic data according to the stock data and the sales data of the shop; inputting the store state characteristic data into an order prediction model so that the order prediction model outputs recommended purchasing data and a corresponding prediction confidence coefficient; and judging whether the prediction confidence coefficient is larger than a preset prediction confidence coefficient threshold value or not, and if the prediction confidence coefficient is larger than the prediction confidence coefficient threshold value, generating a pushed purchase order at least according to the recommended purchase data.
Specifically, the method for acquiring the store inventory data and the store sales data of the stores comprises the step of acquiring the store inventory data and the store sales data of the current day of the stores.
In order to provide the suggestion of purchase orders for the shop in time, the system preferably triggers the execution of the automated order generation method of the present application daily, i.e. the system runs the program daily according to user settings or system settings. As an alternative, the system can enable different shops to have different times for receiving and pushing the purchase orders, and the abnormal condition of locking the inventory caused by centralized operation is avoided.
As another preferable scheme, a trigger condition may be set, and when the trigger condition is satisfied, a program for implementing the automated order generation method is automatically executed.
For example, after the store passes through daily inventory and uploads store inventory data and store sales data, a program is automatically triggered; or, when the shop user uses the user terminal to send a request for the automatic order requirement to the server (the user can click a recommendation button in the APP interface of the user terminal), the program is triggered.
The store inventory data and the store sales data may be managed by a program on a system platform, or may be connected to other goods statistics programs owned by the store through a data port developed by the system.
Store inventory data and sales data as referred to herein refer to the SKU code and corresponding quantity data, respectively, of the items in inventory or sold that day.
According to a general analysis scheme, generally, historical store inventory data and sales data are adopted to respectively train a machine learning module, then the store inventory data and the sales data are respectively predicted, then the situation of possible goods taking is judged according to the prediction result, and then corresponding order suggestions and the like are generated according to the situation of goods shortage.
However, due to uncertainty of sales data, unless all goods are included in the input data, the input data is huge and invalid data is large, and finally the trained machine learning model cannot converge.
In addition, although the store inventory data is relatively stable with respect to the sales data, the machine learning model cannot be converged due to the problem that the store inventory data has many categories of products, as well.
For the above reasons, there is a great technical obstacle to directly adopting store inventory data and sales data for machine learning model training and prediction.
Based on the above, the technical scheme of the application adopts a new technical concept, and specifically, as one scheme, after the store inventory data and the store sales data are collected; and acquiring the SKU and the sales quantity of the commodities with the sales quantity positioned in the first five digits and the existing inventory quantity corresponding to the first five commodities from the sales data (the current day), and forming a matrix with five rows and three columns, wherein the matrix is used as shop state characteristic data.
As an extension, the number of rows of the matrix may be set according to the size of the store scale, and in a store with a large transaction amount, the number of categories of products sold per day is large, and the number of rows of the matrix may be tens of rows or tens of rows.
As a preferred solution, the total number of categories of merchandise in the store inventory is S, and the head value M = S × K is calculated, where K is the head percentage, which is determined according to the value interval in which S is located.
When S is more than 0 and is more than or equal to 300 percent, the value range of the head percentage is 7 percent; when S is more than 300 and is more than or equal to 1000, the value range of the head percentage is 15 percent; when S is more than 0 and is more than or equal to 300 percent, the value range of the head percentage is 20 percent. The number of rectangle lines N is equal to the rounding value of the header value M.
The order prediction model can be trained by adopting two schemes, and store state characteristic data introduced above are input. The difference is the source of the output data in the training set.
The first mode is as follows: and processing the historical data of the shop, wherein the actual historical purchase order is used as output data, the output data is a matrix formed by commodity SKU and purchase quantity in the purchase order, the matrix is determined to have two columns, the specific number of rows is generated according to the actual situation of the purchase order, and the shop state characteristic data of the day before the actual historical purchase order is generated is used as input data, so that a group of training data is formed. With such an arrangement, the order prediction model outputs a matrix of commodity SKUs and procurement quantities.
The method has the advantages that a purchase order mode can be directly generated, but due to the uncertainty of an output matrix, an order prediction model is equivalent to an empirical model, the final output is greatly influenced by the sequence of input training sets and the setting of model parameters during training, the accuracy fluctuation is large, a high confidence threshold value needs to be set to ensure that the output has reference significance, and the problem of program circulation is caused during running. In this way, the order prediction model may be a CNN neural network model as a preferred scheme.
The second mode is as follows: the order prediction model is constructed into a prediction machine learning model, namely, the shop state characteristic data before the current day is used as input data, the shop state characteristic data on the current day is used as output data, and model training is carried out on the order prediction model, so that the output data and the output data are regular data and a determined matrix, and training convergence is easy. In this way, as a preferred scheme, the order prediction model may adopt a BP neural network model.
Preferably, the order prediction model is constructed in a second way, and the output is not direct purchase order data, but store status characteristic data (actually a matrix) of the next day as described above, and in the matrix, SKU, sales and inventory of the front-ranked items that may appear on the second day are predicted.
The system passes through the sales quantity and inventory quantity data of all the commodities in the prediction matrix to judge whether the commodities meet the preset relative relationship. Specifically, the correlation is specifically that the number of sales of the stores is greater than the number of stocks of the stores x a balance coefficient; wherein, the value range of the equilibrium coefficient is 0.27 to 0.7.
For example, the balance coefficient takes a value of 0.5, and at this time, when the correlation is determined that the sales quantity is more than 50% of the stock quantity, the commodity is selected to the purchase order.
As a specific solution, an order prediction model is trained for each store, that is, one order prediction model corresponds to one store or one store terminal device (there may be multiple terminal devices in one store). The data for each store is input into a corresponding order prediction model.
Preferably, if the prediction confidence is smaller than or equal to the prediction confidence threshold, returning to the step of collecting the inventory data and the sales data of the shop, namely processing by using the order prediction model again and outputting new output data and confidence.
Preferably, if the prediction confidence level can not meet the prediction confidence level threshold all the time, for example, if the prediction confidence level can not meet the threshold after the preset times, the purchase order is directly generated according to the shop state characteristic data of the current day.
Through the forming and sending mode of the pushed purchase order, the system can initiate active recommendation to the shop terminal, and therefore the shop is promoted to carry out purchase pushing according to the actual conditions of the shop.
In addition, in step S0, the system generates a push purchase order including a plurality of products, that is, a plurality of commodity SKUs and corresponding purchase numbers, and steps S1 to S5 are performed for one commodity SKU in the push purchase order, and steps S1 to S5 need to be executed multiple times if there are a plurality of commodity SKUs.
As a further application scenario, the method performed by the above steps S1 to S5 may also be used to handle an operation of locking available inventory actively initiated by the store terminal. For example, the store terminal may be configured to have an interactive interface, such as a touch screen, by which the user of the store terminal can trigger operations for joining shopping carts and inventory reservations, or the store terminal may have an image capture device, by which the user can trigger a system server to perform steps similar to steps S1 to S5 by taking pictures of merchandise for identification.
As a concrete solution, the shop terminal device may be configured as a smartphone.
If the above method is applied to a locking operation actively initiated by a store user, the above steps S1 to S5 should be replaced with:
responding to a feedback signal sent by a shop terminal, and inquiring and pushing a commodity SKU in a purchase order and the purchase number of the corresponding commodity SKU; according to the commodity SKU and the purchase number thereof, inquiring the historical purchase record of the store terminal to obtain a target goods supply warehouse; inquiring the available stock quantity of commodity SKUs in the target goods supply warehouse; judging whether the available inventory quantity meets the purchase quantity in the feedback signal, if so, sending an inquiry signal for inquiring whether to lock the inventory capacity required by pushing the purchase order to the shop terminal, and if not, returning to inquire the historical purchase record of the shop terminal to obtain another target goods supply warehouse; converting a portion of the available inventory quantity of the targeted supply warehouse to a locked inventory quantity according to the purchase data of the pushed purchase order in response to a lock signal for the interrogation signal sent by the store terminal
As an extension scheme, the image-based commodity retrieval method in the embodiment of the scheme of triggering inventory locking by acquiring pictures through a shop terminal comprises the following steps:
s101: and responding to a retrieval trigger signal uploaded by the shop terminal, and extracting the commodity picture in the retrieval trigger signal.
S102: and inputting the commodity picture into a commodity identification model so that the commodity identification model outputs a commodity SKU.
S103: and querying a plurality of package SKUs corresponding to the commodity SKU and the purchase price corresponding to the package SKU according to the SKU.
S104: and feeding back the package SKU and the purchase price to the shop terminal.
S105: in response to a selection trigger uploaded by the store terminal, shopping cart entry data is generated, the shopping cart entry data including a package SKU, a purchase price, and a recommended purchase quantity.
The purchase price referred to here is the purchase price of the current e-commerce platform, namely the price updated immediately, and the defect that the purchase price similar to collection and shopping cart expiration causes 'order sharing' is expired is avoided. By adopting the scheme, the user can immediately place the order and improve the convenience of placing the order.
As a concrete solution, the shop terminal has a touch screen and a camera, which may be configured as one smart phone. The retrieval trigger signal of the store terminal can be generated by the operation of collecting the commodity picture or the operation result. The specific application scenes are two, wherein one is that the shop user collects the commodity pictures of the shop to realize automatic identification and ordering operation, and the other is that the shop user collects the commodity pictures of other shops or places to expand the commodity category.
In order to be suitable for the two application scenarios, the above-mentioned commodity retrieval method based on images preferably further comprises the following steps: extracting the acquisition position data of the commodity picture in the retrieval trigger signal; extracting the equipment coding data of the shop terminal in the retrieval trigger signal; acquiring a shop registration position corresponding to the equipment coded data according to the equipment coded data; judging whether the collected position data accords with the store registered positions, if so, inquiring the store group according to the store registered positions, otherwise, acquiring the store registered positions of other nearest stores according to the positions in the collected position data, and inquiring the store group positions according to the store registered positions of other stores; and determining and selecting the commodity identification model according to the shop grouping position.
With this configuration, it is determined whether the user is at his/her shop or at another location, and different product identification models are selected depending on the location. It is well known that training of neural network models requires training set data to have certain stable characteristics and to have sufficient data volume.
If only the historical data of one shop terminal is used for generating training set data, the problem of insufficient data quantity is caused, and if the historical data of all shop terminals in the system is used for training, model training can not be converged due to the fact that the characteristics of commodity picture data are too dispersed, or the accuracy of the model after the training is finished is poor.
In view of the above, the present application provides a solution to the above problems by means of store groups. Through research, the stores with the same size have certain similarity in commodity placing and have certain similarity in purchasing behavior if the stores are similar in regions.
Therefore, in order to train the commodity identification model more conveniently and identify the commodity picture better, a certain shop group is formed based on the characteristics, so that shops in the shop group can use the historical data of each other for reference, enough training data can be obtained, meanwhile, the training data have certain relevance, and the model training is convenient.
Particularly, when searching for the commodity pictures outside the shop, the commodity identification model is obtained by searching the shop group to which the nearest shop belongs to search, so that the searching efficiency and the searching accuracy are improved.
Preferably, the image-based product search method further includes the steps of: dividing the store terminals into different store groups according to at least the store registration positions of the store terminals; and configuring a commodity identification model for each store group, wherein the commodity identification model is formed by training historical commodity picture data and historical commodity SKUs (stock-keeping units) uploaded by store terminals in the store group as input data and output data respectively.
As a preferable scheme, the collected position data of the commodity picture and the equipment coded data of the shop terminal in the search trigger signal are extracted, the obtained equipment coded data are compared with the corresponding shop registration positions, whether the collected position data accord with the shop registration positions or not is judged, if yes, the shop group is inquired according to the shop registration positions, if not, the shop group position of the nearest other shop is obtained according to the position in the collected position data, and the shop group position is inquired according to the shop registration positions of the other shops.
In view of the above, as a further preferable aspect, the above-mentioned image-based product retrieval method further includes the steps of: dividing the store terminals into different store groups according to at least the store registration positions of the store terminals; and configuring a commodity identification model for each store group, wherein the commodity identification model is formed by training historical commodity picture data and historical commodity SKUs (stock-keeping units) uploaded by store terminals in the store group as input data and output data respectively.
As a specific scheme, the specific method for the shop grouping comprises the following steps:
and acquiring historical purchase order data of the shop terminal.
And calculating the average order value of the shop terminal according to the historical purchase order data.
And establishing a three-dimensional coordinate system by taking the average order value as a third dimension.
And acquiring coordinate values of the shop terminal in the three-dimensional coordinate system, wherein the other two-dimensional coordinates of the three-dimensional coordinate system are two-dimensional coordinates of the shop registration position of the shop terminal in the plane map.
And performing K-Means clustering operation by using the coordinate values of the shop terminal in the three-dimensional coordinate system.
And dividing the shop groups according to the K-Means clustering operation result.
More specifically, the historical orders referred to herein are purchase orders for stores. And establishing a three-dimensional coordinate system by taking the average order value as a third dimension. And the other two dimensions are used for establishing a two-dimensional coordinate system according to the geographic position, and coordinate values of the shop in the two-dimensional coordinate system are combined with the average order value of the third dimension to form the three-dimensional coordinate system. For example, let us assume that the coordinate axis of the three-dimensional coordinate system is X, Y, Z, wherein the coordinate of the store on the X, Y axis is divided into the position coordinate of the store on the plane map, and the Z-axis coordinate is the average order value of the store. The average order value is the average value of the order values of all purchase orders of the shop in the observation period. Coordinate values of the shop in the above three-dimensional coordinate system are acquired. The location coordinates may be obtained from maps and positioning data, and the average value of the order may be obtained from calculating historical data. Preferably, the observation period is quarterly or annual. Therefore, the characteristics of the shop can be reflected more stably for a longer time period.
The store group is essentially a set of stores, which can be expressed as the ID of the store from the data representation perspective; of course, the name of the store may be expressed, but the characters themselves are not suitable for data processing, and preferably, the unique store ID code of the store is used, or the unique account ID of the store user may be used, and the roles of the two are the same.
Step S102 specifically includes the following steps: the commodity identification model outputs a plurality of detection frames and commodity SKUs corresponding to the detection frames; judging whether the confidence coefficient of each detection frame and the commodity SKU meets a confidence coefficient threshold value, if so, retaining the commodity SKU, and if not, sending the detection frame to the store terminal to prompt the user to acquire the image again; except for duplicate item SKUs, the same SKU only holds one detection box for confidence threshold determination.
When the commodity pictures are collected, a plurality of images of the same commodity may appear, and only one of the images needs to be reserved. As a preferable embodiment, the confidence degrees corresponding to a plurality of detection frames of the same product may be generated, and the confidence degree determination may be performed using the one with the highest confidence degree.
In addition, it should be noted that the product identification model of the present application may be a convolutional neural network model, which can implement the functions of generating a detection frame and classifying images (classification corresponds to product SKU) in the detection frame, and such a convolutional neural network model is a technical solution known to those skilled in the art of image identification.
Continuing to refer to fig. 2, as a specific scheme, step S2 specifically includes the following steps: inquiring the terminal number of the shop terminal sending the feedback signal; extracting an order number of the pushed purchase order contained in the feedback signal; extracting a pushed purchase order corresponding to the order number from historical data of the pushed purchase order sent to the shop terminal according to the terminal number and the order number; and extracting the commodity SKU in the pushed purchase order and the purchase number of the corresponding commodity SKU.
Through the method, the corresponding and fed-back detail information of the push purchase order can be obtained from the plurality of push purchase orders.
Continuing to refer to fig. 3, as a specific scheme, step S3 specifically includes the following steps:
s31: and inquiring a plurality of historical purchasing records containing the commodity SKU within a set time range according to the commodity SKU.
In view of the variation of suppliers and logistics, if the historical data of shops are called, a lot of data noises such as invalid warehouse addresses and the like are generated. Therefore, to improve the effectiveness of data and to improve data processing efficiency. Step 31 only queries and invokes historical procurement data over a time frame. Alternatively, the time range is set to one month or one quarter.
S32: and judging whether the purchase number of the corresponding commodity SKU in the historical purchase record is more than or equal to the purchase data in the pushed purchase order, if so, taking the goods supply warehouse in the historical purchase record as a spare goods supply warehouse, and if not, switching to judging the next historical purchase record.
S33: duplicate alternate supply warehouses are eliminated, with only one remaining for the same supply warehouse.
Step S32 is to select an entry for procurement data, and since there may be multiple procurements from the same supplier, the sourcing warehouse is obtained through step S33.
S34: and selecting one of the different alternative goods supply warehouses which is closest to the shop corresponding to the shop terminal as a target goods supply warehouse.
As a more specific solution of step S34, a sequence table is generated by sorting the plurality of candidate supply warehouses from far to near according to the distances between the inquired candidate supply warehouses and the store terminal, and the candidate supply warehouse closest to the inquired candidate supply warehouse is used as the target supply warehouse.
Continuing to refer to fig. 4, as a specific scheme, step S4 specifically includes the following steps: sorting the distances of the alternative goods supply warehouses according to the corresponding store distance from the alternative goods supply warehouse to the store terminal; and transferring the historical purchase records of the inquiry shop terminal back to the next candidate supply warehouse as a target supply warehouse according to the distance sequence of the candidate supply warehouses.
With this specific solution, when the determination result in step S4 is negative, the next standby supply warehouse located next to the next nearest standby supply warehouse is queried and determined again as another target supply warehouse.
With continued reference to FIG. 5, the specific step of querying the target supply warehouse for the available inventory quantity of the product SKU in step S5 includes: inquiring the available inventory quantity directly corresponding to the commodity SKU; querying the same family SKU of the corresponding commodity according to the commodity SKU; the available inventory quantity for the family SKU is converted to an available inventory quantity for the item SKU. It should be noted that the SKUs of the same family refer to the same product, but the packing specifications of the SKUs are different, so that the SKUs correspond to different SKUs, for example, a certain soda water has 12 bottles, one SKU and 24 bottles, and the SKUs correspond to 24 bottles and another SKU, and the two SKUs are the SKUs of the same family.
With continued reference to fig. 6, the step of determining whether the available inventory quantity satisfies the purchase quantity in the push purchase order in step S5 specifically includes the following steps: calling historical data of available inventory quantity of a goods supply warehouse in the current time period; calculating the average value of the historical data of the available inventory quantity in the current time period; calculating the ratio k of the current value of the available inventory quantity in the current time period to the average value; calculating the ratio h of the current value of the available inventory quantity in the current time period to the numerical value of the purchase quantity in the recommended purchase order; and judging whether the ratio k is larger than or equal to the ratio h, if so, the available inventory quantity meets the purchase quantity in the push purchase order, and otherwise, the available inventory quantity does not meet the purchase quantity in the push purchase order.
Of course, a similar scheme may be used if the store client actively triggers, except that the number of purchases is actively set by the store user in the feedback signal, rather than pushing the number of purchases in the purchase order. As an extension, the number of purchases may also be obtained by the store user modifying the push purchase order through the store terminal.
As a specific scheme, the query signal in step S4 is in a specific form, and the lock confirmation interface provided by the APP includes specific one or more item SKUs and purchase quantity, and provides a "lock confirmation" button for the user to click. If the shop user clicks the 'confirm lock' button, the shop terminal is triggered to send a lock signal, then the shop terminal receives system data, and an interface for obtaining the locked inventory is displayed, wherein the interface comprises the just displayed commodity SKU, the purchase quantity and a lock code, and a two-dimensional code containing the information can be preferably provided, but specific warehouse information is not informed to the shop terminal.
As an expansion scheme, the locking confirmation interface also displays a payment information, a user can pay a basic fee for locking the inventory, if the shop user purchases the locked inventory before the time line is set, the basic fee is counted into the purchase fee or the logistics fee, if the time line is exceeded, a timing deduction fee is generated, the time length exceeding the set time line is charged, the time deduction fee is deducted on the basis of the basic fee, and after the basic fee is deducted, the system releases the corresponding locking inventory quantity. The timeline is typically set to 24 hours after confirmation of lock.
Referring to fig. 7, an inventory data update apparatus according to an embodiment includes: the system comprises a first query module, a second query module and a control module, wherein the first query module is used for responding to a feedback signal which is sent by a shop terminal and aims at a pushed purchase order, and querying the purchase number of a commodity SKU and a corresponding commodity SKU in the pushed purchase order; the second query module is used for querying the historical purchasing record of the store terminal according to the commodity SKU and the purchasing number thereof so as to obtain a target goods supply warehouse; the third inquiry module is used for inquiring the available stock quantity of the commodity SKU in the target supply warehouse; the judging module is used for judging whether the available inventory quantity meets the purchase quantity in the pushed purchase order, if so, sending an inquiry signal for inquiring whether to lock the inventory capacity required by the pushed purchase order to the store terminal, and if not, returning to inquire the historical purchase record of the store terminal to obtain another target goods supply warehouse; and the locking module is used for responding to a locking signal aiming at the inquiry signal sent by the shop terminal and converting a part of the available inventory quantity of the target supply warehouse into the locked inventory quantity according to the purchase data of the pushed purchase order.
Referring to fig. 8, an electronic device 800 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 801 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data necessary for the operation of the electronic apparatus 800 are also stored. The processing apparatus 801, the ROM802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.: output devices 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 808 including, for example, magnetic tape, hard disk, etc.: and a communication device 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 illustrates an electronic device 800 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 8 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present application include a computer program product comprising a computer program embodied on a computer storage medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through communications device 809, or installed from storage device 808, or installed from ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of some embodiments of the present application.
It should be noted that the computer storage media described above in some embodiments of the present application can be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In some embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present application, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer storage medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (hypertext transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer storage medium may be included in the electronic device, or may be separately present without being incorporated in the electronic device. The computer storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: responding to a feedback signal which is sent by a shop terminal and aims at a push purchase order, and inquiring the purchase number of a commodity SKU and the corresponding commodity SKU in the push purchase order; according to the commodity SKU and the purchase number thereof, inquiring the historical purchase record of the store terminal to obtain a target goods supply warehouse; inquiring the available stock quantity of commodity SKUs in the target goods supply warehouse; judging whether the available inventory quantity meets the purchase quantity in the pushed purchase order, if so, sending an inquiry signal for inquiring whether to lock the inventory capacity required by the pushed purchase order to the store terminal, and if not, returning to inquire the historical purchase record of the store terminal to obtain another target goods supply warehouse; in response to a lock signal for the interrogation signal transmitted by the store terminal, a portion of the available inventory quantity of the targeted supply warehouse is converted to a locked inventory quantity in accordance with the purchase data of the pushed purchase order.
Computer program code for carrying out operations for embodiments of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and including the conventional procedural programming languages: such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.
For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the present application and is provided for the purpose of illustrating the general principles of the technology. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present application is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present application are mutually replaced to form the technical solution.
Claims (10)
1. An inventory data updating method, comprising:
responding to a feedback signal which is sent by a shop terminal and aims at a push purchase order, and inquiring commodity SKU in the push purchase order and the purchase number corresponding to the commodity SKU;
according to the commodity SKU and the purchase number thereof, inquiring the historical purchase record of the store terminal to obtain a target goods supply warehouse;
querying the available inventory quantity of the commodity SKU in the target supply warehouse;
judging whether the available inventory quantity meets the purchase quantity in the pushed purchase order, if so, sending an inquiry signal for inquiring whether to lock the inventory capacity required by the pushed purchase order to the shop terminal, and if not, returning to inquire the historical purchase record of the shop terminal to obtain another target supply warehouse;
in response to a lock signal for the interrogation signal sent by the store terminal, converting a portion of the available inventory quantity of the targeted supply warehouse to a locked inventory quantity in accordance with the purchase data of the pushed purchase order.
2. The inventory data updating method as claimed in claim 1, wherein said querying for a product SKU of a push purchase order and a purchase number corresponding to said product SKU in response to a feedback signal sent by a store terminal for said push purchase order comprises:
inquiring a terminal number of the shop terminal which sends the feedback signal;
extracting the order number of the pushed purchase order contained in the feedback signal;
extracting the push purchase order corresponding to the order number from historical data of the push purchase order sent to the shop terminal according to the terminal number and the order number;
and extracting the commodity SKU in the push purchase order and the purchase number corresponding to the commodity SKU.
3. The inventory data updating method as claimed in claim 1, wherein the querying the historical purchase record of the store terminal to obtain a targeted supply warehouse according to the SKU and the purchase number thereof comprises:
inquiring a plurality of historical purchasing records containing the commodity SKU within a set time range according to the commodity SKU;
judging whether the purchase number corresponding to the commodity SKU in the historical purchase record is larger than or equal to the purchase data in the pushed purchase order, if so, taking a goods supply warehouse in the historical purchase record as an alternative goods supply warehouse, and if not, turning to judging the next historical purchase record;
removing the repeated alternative supply warehouses, and only one supply warehouse is reserved;
and selecting one of the different alternative goods supply warehouses which is closest to the shop corresponding to the shop terminal as the target goods supply warehouse.
4. The inventory data updating method of claim 3, wherein the switch back querying the store terminal's historical procurement records to obtain another of the targeted sourcing warehouses comprises:
sorting the distance of the candidate goods supply warehouses according to the distance from the candidate goods supply warehouses to the stores corresponding to the store terminals;
and the historical purchase records of the store terminal are searched in a transferring way, and the next alternative goods supply warehouse is called as the target goods supply warehouse according to the distance sequence of the alternative goods supply warehouse.
5. The inventory data updating method of claim 1, wherein the querying the quantity of inventory available for the item SKU in the targeted supply warehouse comprises:
inquiring the available inventory quantity directly corresponding to the commodity SKU;
querying the same family SKU of the corresponding commodity according to the commodity SKU; converting the available inventory quantity of the family SKU to an available inventory quantity of the item SKU.
6. The inventory data updating method of claim 1, wherein the determining whether the available inventory quantity satisfies a purchase quantity in the push purchase order comprises:
invoking historical data of the available inventory quantity of the sourcing warehouse for a current time period;
calculating an average value of the historical data of the available inventory quantities in the current time period;
calculating a ratio k of a current value of the available inventory quantity to the average value for a current time period;
calculating the ratio h of the current value of the available inventory quantity in the current time period to the numerical value of the purchase quantity in the recommended purchase order;
and judging whether the ratio k is larger than or equal to the ratio h, if so, the available inventory quantity meets the purchase quantity in the push purchase order, and if not, the available inventory quantity does not meet the purchase quantity in the push purchase order.
7. The inventory data updating method according to claim 1, further comprising:
and generating a pushed purchase order according to the historical data of the shop corresponding to the shop terminal.
8. An inventory data updating apparatus comprising:
the system comprises a first query module, a second query module and a third query module, wherein the first query module is used for responding to a feedback signal which is sent by a shop terminal and aims at a push purchase order, and querying commodity SKU in the push purchase order and the purchase number corresponding to the commodity SKU;
the second query module is used for querying the historical purchasing record of the shop terminal according to the commodity SKU and the purchasing number thereof so as to obtain a target goods supply warehouse;
a third query module, configured to query the target supply warehouse for the available inventory quantity of the product SKU;
the judging module is used for judging whether the available inventory quantity meets the purchase quantity in the pushed purchase order, if so, sending an inquiry signal for inquiring whether to lock the inventory capacity required by the pushed purchase order to the shop terminal, and if not, returning to inquire the historical purchase record of the shop terminal to obtain another target supply warehouse;
and the locking module is used for responding to a locking signal aiming at the inquiry signal and sent by the shop terminal, and converting a part of the available inventory quantity of the target supply warehouse into a locked inventory quantity according to the purchase data of the pushed purchase order.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the processors to implement the method of any one of claims 1 to 7.
10. A computer storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the method of any of claims 1 to 7.
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