CN112396365A - Inventory item prediction method and device, computer equipment and storage medium - Google Patents

Inventory item prediction method and device, computer equipment and storage medium Download PDF

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CN112396365A
CN112396365A CN201910748524.1A CN201910748524A CN112396365A CN 112396365 A CN112396365 A CN 112396365A CN 201910748524 A CN201910748524 A CN 201910748524A CN 112396365 A CN112396365 A CN 112396365A
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order
sales
inventory
statistical processing
inventory item
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CN112396365B (en
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刘垚
储孝国
曾庆维
石新晨
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SF Technology Co Ltd
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    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G06Q10/0875—Itemisation or classification of parts, supplies or services, e.g. bill of materials
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
    • G06Q30/0635—Processing of requisition or of purchase orders

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Abstract

The embodiment of the invention discloses a stock list prediction method, a stock list prediction device, computer equipment and a storage medium, and relates to the technical field of data processing. The inventory item prediction method comprises the following steps: acquiring historical order data of a merchant which is delivered from a target warehouse within a first time period; carrying out statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse; and predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, and outputting an inventory item list. According to the embodiment of the invention, the stock single in the target warehouse in the future is predicted through the statistical processing result of the historical order data of the target warehouse shipment, so that the stock single meeting the warehouse requirement is determined, the efficiency, the accuracy and the flexibility of determining the stock single can be improved, and the management cost is reduced.

Description

Inventory item prediction method and device, computer equipment and storage medium
Technical Field
The invention relates to the technical field of data processing, in particular to a stock list prediction method, a device, computer equipment and a storage medium.
Background
In the internet era, many users select commodities on line through the internet. For merchants, in general, the types of commodities sold at the same time are often hundreds of thousands, the sales volume of each commodity is not only high or low, but also the sales conditions of different regions may have significant differences. In each warehouse, if all the items sold by the merchant (one item corresponds to one SKU (stock Keeping unit)), the SKU is the smallest available unit for storing inventory control, such as a mobile phone, and different colors, colors and configurations correspond to different SKUs, so that not only the problems of increase of transportation cost and overstock of inventory are brought, but also the management is difficult. The existing method for determining the inventory items for each warehouse is to select the inventory items according to experience or sequentially select the inventory items from top to bottom according to the sales volume ratio, and the methods for determining the inventory items for the warehouse are often not optimal and are not flexible to adjust, so that the efficiency, the accuracy and the flexibility of determining the inventory items for the warehouse are reduced, and the management cost is increased.
Disclosure of Invention
Embodiments of the present invention provide a method and an apparatus for predicting inventory items, a computer device, and a storage medium, which can improve efficiency, accuracy, and flexibility of determining inventory items, reduce management cost, and solve the problems of low efficiency, low accuracy, poor flexibility, and high cost in determining inventory items for a warehouse in the prior art.
The embodiment of the invention provides an inventory item prediction method, which comprises the following steps:
acquiring historical order data of a merchant which is delivered from a target warehouse within a first time period;
carrying out statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse;
and predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, and outputting an inventory item list.
Further, the statistical processing of the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse includes:
counting the total number of orders in the historical order data and the total sales volume of all the single products in the historical order data;
counting the sales volume of each single product in each order in the historical order data;
determining the existence state of each single product in each order in the historical order data;
processing the sales volume of each single product in each order and the existence state of each single product in each order to obtain an order single product processing result;
wherein the statistical processing result comprises the total order number, the total sales of all the single products and the processing result of the single products of the order.
Further, the processing the sales volume of each item in each order and the presence status of each item in each order to obtain an order item processing result includes:
performing matrix conversion on the sales volume of each single product in each order to obtain a sales volume matrix;
performing binary conversion on the existing state of each single product in each order to obtain a binary matrix;
wherein the order sheet processing result comprises the sales matrix and the binary matrix.
Further, the predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result and outputting an inventory item list comprises:
acquiring a preset order satisfaction rate and a preset sales satisfaction rate;
and predicting the inventory items delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, the preset order satisfaction rate and the preset sales volume satisfaction rate, and outputting an inventory item list.
Further, before the step of predicting the inventory items that are shipped from the target warehouse within the future second time period by the merchant according to the statistical processing result and outputting the inventory item list, the inventory item prediction method further includes:
constructing an inventory item prediction model;
predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, and outputting an inventory item list, wherein the predicting comprises the following steps: and inputting the statistical processing result into the inventory item prediction model, predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future, and outputting an inventory item list.
Further, the building of the inventory item prediction model comprises:
setting an order satisfaction rate, a sales satisfaction rate and a binarization variable;
setting the predicted minimum number of the inventory single products as an objective function;
constructing a constraint condition of the objective function according to the relation among the order satisfaction rate, the sales satisfaction rate and the binarization variable;
and constructing the inventory item prediction model according to the target function and the constraint conditions of the target function.
Further, the inventory item prediction model includes a sales matrix parameter, a binary matrix parameter, an order total number parameter, and a total sales parameter of all items, and the constructing the constraint condition of the objective function according to the relationship among the order satisfaction rate, the sales satisfaction rate, and the binarization variable includes:
setting the relation between the sales matrix parameter and the binarization variable as a first constraint condition of the objective function;
setting the relation between the binary matrix parameter and the binary variable as a second constraint condition of the objective function;
setting the relation among the sales matrix parameters, the sales satisfaction rate and the total sales parameters of all the single products as a third constraint condition of the objective function;
setting the relation among the binary matrix parameter, the order satisfaction rate and the order total number parameter as a fourth constraint condition of the objective function.
The embodiment of the invention also provides an inventory item prediction device, which comprises:
the order acquisition unit is used for acquiring historical order data of the merchant in the first time period after the merchant delivers goods from the target warehouse;
the statistical processing unit is used for performing statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse;
and the predicting unit is used for predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result and outputting an inventory item list.
Further, the statistical processing unit includes:
the order sales amount counting unit is used for counting the total number of orders in the historical order data and the total sales amount of all the single products in the historical order data;
the single-product sales amount counting unit is used for counting the sales amount of each single product in each order in the historical order data;
the single product state determining unit is used for determining the existence state of each single product in each order in the historical order data;
the order sheet processing unit is used for processing the sales volume of each sheet in each order and the existence state of each sheet in each order to obtain an order sheet processing result;
wherein the statistical processing result comprises the total order number, the total sales of all the single products and the processing result of the single products of the order.
Further, the order sheet processing unit includes:
the sales matrix determining unit is used for performing matrix conversion on the sales of each single product in each order to obtain a sales matrix;
the binary matrix determining unit is used for performing binary conversion on the existing state of each single product in each order to obtain a binary matrix;
wherein the order sheet processing result comprises the sales matrix and the binary matrix.
Further, the prediction unit includes:
the first satisfaction rate acquisition unit is used for acquiring a preset order satisfaction rate and a preset sales satisfaction rate;
and the first single prediction unit is used for predicting the inventory single delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, and a preset order satisfaction rate and a preset sales volume satisfaction rate, and outputting an inventory single list.
Further, the inventory item prediction device further comprises:
the construction unit is used for constructing an inventory item prediction model;
the prediction unit is further configured to: and inputting the statistical processing result into the inventory item prediction model, predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future, and outputting an inventory item list.
Further, the building unit includes:
the parameter variable setting unit is used for setting the order satisfaction rate, the sales satisfaction rate and the binarization variable;
an objective function setting unit for setting the predicted minimum number of inventory items as an objective function;
the constraint condition setting unit is used for constructing a constraint condition of the objective function according to the relation among the order satisfaction rate, the sales satisfaction rate and the binarization variable;
and the model building unit is used for building the inventory item prediction model according to the target function and the constraint conditions of the target function.
Further, the constraint condition setting unit includes:
a first condition setting unit, configured to set a relationship between the sales matrix parameter and the binarization variable as a first constraint condition of the objective function;
a second condition setting unit, configured to set a relationship between the binary matrix parameter and the binary variable as a second constraint condition of the objective function;
a third condition setting unit, configured to set a relationship among the sales matrix parameter, the sales satisfaction rate, and the total sales parameter of all the singles as a third constraint condition of the objective function;
and the fourth condition setting unit is used for setting the relationship among the binary matrix parameters, the order satisfaction rate and the order total number parameters as a fourth constraint condition of the objective function.
An embodiment of the present invention further provides a computer device, where the computer device includes: one or more processors; a memory; and one or more applications, wherein the processor is coupled to the memory, the one or more applications stored in the memory and configured to be executed by the processor to perform any of the methods of inventory item prediction described above.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is loaded by a processor to execute any one of the above methods for predicting inventory items.
The method and the device for forecasting the inventory items of the target warehouse in the future acquire the historical order data of the target warehouse shipment, perform statistical processing on the historical order data to obtain a statistical processing result, forecast the inventory items of the target warehouse shipment in the future according to the statistical processing result, and output an inventory item list. The inventory items in the target warehouse in the future are predicted through the statistical processing result of the historical order data of the target warehouse shipment, so that the inventory items meeting the warehouse requirements are determined, the efficiency, accuracy and flexibility of determining the inventory items can be improved, and the management cost is reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart illustrating a method for predicting inventory items according to an embodiment of the present invention;
FIG. 2 is a sub-flow diagram of a method for predicting inventory items according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating a method for predicting inventory items according to another embodiment of the present invention;
FIG. 4 is a schematic sub-flow chart of a method for predicting inventory items according to another embodiment of the present invention;
FIG. 5 is a schematic block diagram of an inventory item prediction device provided by an embodiment of the present invention;
FIG. 6 is a schematic block diagram of an inventory item prediction device provided in accordance with another embodiment of the present invention;
FIG. 7 is a schematic block diagram of a computer device provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise. In addition, the terms "first" and "second" are used to distinguish a plurality of elements from each other. For example, a first constraint may be referred to as a second constraint, and similarly, a second constraint may be referred to as a first constraint, without departing from the scope of the invention. The first constraint and the second constraint are both constraints, but they are not the same constraint.
In the present disclosure, the word "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the invention. In the following description, details are set forth for the purpose of explanation. It will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and processes are not shown in detail to avoid obscuring the description of the invention with unnecessary detail. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
The embodiment of the invention provides an inventory item prediction method, an inventory item prediction device, computer equipment and a storage medium. The inventory item prediction method runs in equipment, and the equipment can be a server or a terminal, such as equipment of a mobile phone, a Pad, a desktop computer and the like. The following are detailed below.
Fig. 1 is a schematic flow chart of a stock single item prediction method according to an embodiment of the present invention, where a specific flow of the stock single item prediction method is as follows:
101, historical order data of a merchant from a target warehouse during a first time period is obtained.
The historical order data of the goods delivered from the target warehouse by the merchant in the first time period can be stored in the local, or in the database, or in other devices, and the like, so that the historical order data of the goods delivered from the target warehouse by the merchant in the first time period can be obtained directly from the local, or from the database, or an obtaining request is sent to other devices, and the obtaining is carried out from other devices.
The merchant may be an offline merchant or an online merchant. For example, when a client buys an order, an off-line merchant inputs and stores order data to form order data; after an online merchant such as an e-commerce merchant places an order, order data is stored in a database of an e-commerce platform. The merchant may be one merchant or a plurality of merchants. For example, a merchant corresponds to a plurality of e-commerce stores, the plurality of e-commerce stores share a warehouse, and then the historical order data of the merchant for delivering goods from the target warehouse is obtained, the historical order data of one of the e-commerce stores for delivering goods from the common warehouse may be obtained, or the historical order data of all the e-commerce stores for delivering goods from the common warehouse may be obtained. For example, a plurality of merchants correspond to a plurality of e-commerce stores, the plurality of e-commerce stores are different in sold product, and the plurality of e-commerce stores share one warehouse, the historical order data of the e-commerce store of one of the merchants for shipment from the common warehouse may be obtained, or the historical order data of the store stores of the plurality of merchants for shipment from the common warehouse may be obtained. For another example, a merchant corresponds to a plurality of warehouses, and one of the warehouses is used as a target warehouse. For example, if the plurality of warehouses are located in different locations, such as a city a and a city B, historical order data for the merchant to stock from the target warehouse is obtained, including obtaining historical order data for the merchant to stock in the city a warehouse or obtaining historical order data for the merchant to stock in the city B warehouse.
Wherein the first time period may be half a year, a quarter, a plurality of quarters, etc. For example, if stock orders are to be predicted for a target warehouse in the second quarter, historical order data of the target warehouse in the second quarter of the last year, historical order data of the target warehouse in the first quarter of the present year, or historical order data of the target warehouse in past seasons may be obtained.
The historical order data of the goods delivered from the target warehouse in the first time period comprises all the orders delivered from the target warehouse in the first time period, the single goods included in each order and the quantity corresponding to each single goods. Wherein, a single item corresponds to a SKU (Stock Keeping Unit), which is the smallest available Unit for Keeping inventory control, such as a mobile phone, and different SKUs are used for different models, colors and configurations.
Table 1 is an example of historical order data, where order numbers include order1, order2, and individual numbers include s1, s2, s 3. In an order with order number order1, the number of items s1 (sales) is 2 and the number of items s2 (sales) is 2; in an order with order number order2, the number of items s2 (sales) is 1 and the number of items s3 (sales) is 1. It should be noted that the data in table 1 are only examples to facilitate understanding of the scheme of the present invention, and do not constitute a limitation on the historical order data. In practice, more orders, more items, more quantities per item, etc. may be included in the historical order data, and more data, such as dates, delivery times, etc., may be included in each order.
TABLE 1 historical order data example
Order number Single number Quantity (sales volume)
order1 s1 2
order1 s2 2
order2 s2 1
order2 s3 1
And 102, carrying out statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse.
The statistical processing method comprises statistics, inquiry, data conversion and the like.
In one embodiment, as shown in FIG. 2, step 102, comprises the steps of:
and 201, counting the total number of orders in the historical order data and the total sales volume of all the items in the historical order data.
Wherein, the total number of orders in the statistical historical order data comprises: and eliminating the same order numbers in the historical order data, and performing accumulation statistics on the rest order numbers to obtain the total order number in the historical order data. For example, the total number of orders in the historical order data in table 1 is 2.
Counting the total sales of all the items in the historical order data, including: and adding the sales amount corresponding to each single product in each order to obtain the total sales amount of all the single products in the historical order data. For example, in table 1, the total sales of all the individual products in the history order data obtained by adding the numbers corresponding to the number fields is 6.
And 202, counting the sales volume of each single product in each order in the historical order data.
The sales volume of each individual in each order refers to the sales volume corresponding to each individual in each order. Specifically, the step of counting the sales volume of each individual item in each order in the historical order data includes: and acquiring data in each order, and counting the sales volume of each single product in each order according to the data in each order. Or obtaining an order number from historical order data; inquiring whether the same order number still exists; if the same order number exists, acquiring data corresponding to all the same order numbers, and taking the data corresponding to all the same order numbers as the data of one order; and counting the sales volume of each single product in the order until all order numbers are acquired. Wherein the sales volume corresponding to the non-existing item in the order is set to 0. For example, in table 1, after the order number order1 in the first piece of data is obtained, whether the order number of the second piece of data is order1 is queried, the data in two orders 1 are used as data of one order, and the sales volume of each order in the order is counted, where the sales volume of s1 is 2, the sales volume of s2 is 2, and the sales volume of s3 is 0, so that all the order numbers are obtained completely.
The presence status of the individual items in each order in the historical order data is determined 203.
The presence status of the items in each order refers to the status of whether the items are present in each order. Specifically, the step of determining the presence status of the individual items in each order in the historical order data comprises: acquiring data in each order, and judging whether each single product exists in the order or not according to the data in each order; and determining the existence state of each single product in each order according to the judgment result. And if the judgment result indicates that a certain single product exists, determining that the certain single product exists in the order, and if the judgment result indicates that the certain single product does not exist, determining that the certain single product does not exist in the order. For example, in order1 of Table 1, singles s1, s2 are present and singles s3 are not present.
And 204, processing the sales volume of each single in each order and the existence state of each single in each order to obtain an order single processing result.
The statistical processing result of the historical shipment data of the target warehouse comprises the following steps: total number of orders, total sales of all items, and order item processing results.
It should be noted that the total sales volume for all of the items refers to the total sales volume for all of the items in the entire order.
Specifically, the step of processing the sales volume of each item in each order and the presence status of each item in each order to obtain the processing result of the order items includes: performing matrix conversion on the sales volume of each single product in each order to obtain a sales volume matrix; performing binary conversion on the existing state of each single product in each order to obtain a binary matrix; the order sheet processing result comprises the sales matrix and the binary matrix. The sales matrix is specifically an order form sales matrix, and the binary matrix is specifically an order form binary matrix.
Performing matrix conversion on the sales volume of each individual product in each order to obtain a sales volume matrix, wherein the matrix conversion comprises: and acquiring one order from all orders, converting the sales volume of each single product in the order into one row or one column of the matrix, acquiring the next order, converting the sales volume of each single product in the next order into the next row or the next column of the matrix until all orders are counted, and taking the obtained matrix as a sales volume matrix. For example, after matrix conversion is performed on the sales volume of each individual item in order1, a corresponding row or column in the sales volume matrix is: 2,2,0.
Table 2 is an example of a sales matrix. The sales matrix is obtained by matrix conversion according to the data in table 1.
TABLE 2 sales matrix example
Sales matrix s1 s2 s3
order1 2 2 0
order2 0 1 1
The method for obtaining the binary matrix by performing binary conversion on the existence state of each single product in each order comprises the following steps: acquiring one order from all orders, performing binary conversion on the existence state of each single product in the order, and taking the result of the binary conversion on the existence state of each single product in the order as one row or one column of a matrix; and obtaining a next order, performing binary conversion on the existence state of each single product in the next order, taking the result of the binary conversion on the existence state of each single product in the next order as the next row or the next column of the matrix until all orders are subjected to the binary conversion, and taking the obtained matrix as a binary matrix. Wherein, the step of performing binary conversion on the existing state of each single product in the order comprises the following steps: and if a certain single product exists in the order, setting the existence state of the certain single product in the order to be 1, otherwise, setting the existence state of the certain single product in the order to be 0 until the existence state of each single product in the order is set. For example, in table 1, in the order with order number order1, the presence state of s1 is 1, the presence state of s2 is 1, and the presence state of s3 is 0, and after matrix conversion, the corresponding row or column in the binary matrix is: 1,1,0. The existence status of each item in the order may be represented by other characters or numbers, such as 1 and 2 for existence and nonexistence of the item.
Table 3 is an example of a binary matrix. The binary matrix is obtained by performing binary conversion based on the data in table 1.
TABLE 3 binary matrix example
Binary matrix s1 s2 s3
order1 1 1 0
order2 0 1 1
In one embodiment, the step 102 further includes 205.
And 205, taking the total number of orders, the total sales volume of all the single products and the processing result of the single products of the orders in the historical order data as the statistical processing result of the historical shipment data of the target warehouse.
Therefore, the statistical processing result of the historical shipment data of the target warehouse includes: total order number, total sales of all the single products, and processing result (including sales matrix and binary matrix) of the single products of the order.
It should be noted that the order of executing steps 202 and 203 is not specifically limited, and step 202 may be executed first, or step 203 may be executed first, or steps 202 and 203 are executed synchronously; the execution sequence of steps 201 and 204 is not specifically limited, and steps 202, 203 and 204 may be executed first, and then step 201 is executed, or steps 203, 202 and 204 are executed first, and then step 201 is executed, or steps 202, 203 and 204(203, 202 and 204) and step 201 are executed synchronously.
In other embodiments, the statistical processing of the historical shipment data for the target warehouse may also include other data.
And 103, predicting the inventory items which are delivered from the target warehouse by the merchant in the future second time period according to the statistical processing result, and outputting an inventory item list.
It should be noted that, in the embodiment of the present invention, the prediction of future inventory items according to the historical order data is based on the fact that the sales amount of each item in the first preset time period and the sales amount of each item in the second preset time period in the future do not change much.
The first time period and the second time period may be the same or different, for example, the first time period is half a year, and the second time period may be half a year, or a quarter.
In one embodiment, step 103 comprises: acquiring a preset order satisfaction rate and a preset sales satisfaction rate; and predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, and the preset order satisfaction rate and sales satisfaction rate, and outputting an inventory item list.
The order satisfaction rate refers to the number of orders which can be satisfied divided by the total number of the orders, and if all the single item target warehouses in one order have corresponding quantity of stocks, the order is determined to be satisfied; the sales fulfillment rate is the total sales corresponding to the orders that can be fulfilled divided by the total sales corresponding to all orders. For example, with respect to the data in table 1, if only the items s1 and s2 are present in a certain target warehouse, and the number of items s1 is 2 and the number of items s2 is 2, the order1 is satisfied and the order1 cannot be satisfied, so that the order satisfaction rate is 1/2 ═ 0.5 and the sales satisfaction rate is (2+2)/(2+2+1+1) ═ 2/3. Note that 0< order satisfaction rate < ═ 1, and 0< sales fulfillment rate < ═ 1.
In one embodiment, the prediction of inventory items may be made by an inventory item prediction model.
Specifically, step 103 includes: and inputting the statistical processing result into an inventory item prediction model, predicting the inventory items delivered from the target warehouse by the merchant in a second time period in the future, and outputting an inventory item list.
In one embodiment, step 103 comprises: acquiring a preset order satisfaction rate and a preset sales satisfaction rate; and inputting the statistical processing result, the preset order satisfaction rate and the preset sales satisfaction rate into the inventory item preset model so as to predict inventory items which are delivered from the target warehouse by the merchant in the future second time period and output an inventory item list.
Therein, it can be understood that the order satisfaction rate and the sales satisfaction rate are also designed in the stock item prediction model. The preset order satisfaction rate and the preset sales satisfaction rate may be preset, and may be the same as or different from the order satisfaction rate and the sales satisfaction rate in the stock item prediction model. It can be understood that if the order satisfaction rate and the sales satisfaction rate in the inventory item prediction model are directly used, the preset order satisfaction rate and sales satisfaction rate do not need to be obtained. If the order satisfaction rate and the sales satisfaction rate in the stock single prediction model are required to be modified, the preset order satisfaction rate and the preset sales satisfaction rate are obtained. It is understood that the order satisfaction rate and the sales satisfaction rate in the stock item prediction model do not necessarily satisfy the needs of the user, and therefore, the order satisfaction rate and the sales satisfaction rate need to be reset, and the preset order satisfaction rate and the preset sales satisfaction rate need to be obtained. The embodiment of the invention provides the method for obtaining the preset order satisfaction rate and the preset sales satisfaction rate so as to modify the order satisfaction rate and the sales satisfaction rate in the stock single product prediction model, improve the practicability of the stock single product prediction model and improve the user experience.
In one embodiment, the inventory unit testing model comprises a mixed integer linear programming model. Wherein, the linear programming model has the integral number required by partial or all decision variables, and is called as mixed integer linear programming model.
According to the embodiment of the invention, the inventory item meeting the requirement of the target warehouse is determined by carrying out statistical processing on the historical order data of the shipment in the target warehouse and predicting the inventory item of the target warehouse in the future according to the statistical processing result, so that the efficiency, accuracy and flexibility of determining the inventory item can be improved, and the management cost is reduced.
In one embodiment, the prediction of the inventory items is made by an inventory item prediction model that includes a mixed integer linear programming model. Fig. 3 is a schematic flow chart of a method for predicting inventory items according to an embodiment of the present invention. The method comprises the following specific processes:
301, a mixed integer linear programming model is constructed.
And (3) constructing an objective function and constraint conditions of the objective function by setting parameters to construct a mixed integer linear programming model. The target function and the constraint condition of the target function may include the set parameters.
In one embodiment, as shown in FIG. 4, step 301 comprises the following steps:
401, a set related to the mixed integer linear programming model is defined, and parameters related to the mixed integer linear programming model are set.
It should be noted that other inventory item prediction models may include other sets and/or parameters if used. In this embodiment, a mixed integer linear programming model is used as an example to facilitate understanding of the scheme in the embodiment of the present invention.
The set involved in the mixed integer linear programming model is defined as shown in table 4. Corresponding to table 1, the I set includes order1, order 2; the J set includes s1, s2, s 3.
TABLE 4 sets of mixed integer linear programming models involved
Figure BDA0002166389510000131
Figure BDA0002166389510000141
Parameters related to the mixed integer linear programming model comprise a sales matrix parameter a, a binary matrix parameter b, an order total number parameter M, a total sales parameter S of all single products and the like. The parameters involved in the mixed integer linear programming model are shown in table 5.
TABLE 5 parameters involved in the Mixed integer Linear programming model
Figure BDA0002166389510000142
And 402, setting an order satisfaction rate, a sales satisfaction rate and a binarization variable.
It is noted that the order fulfillment rate and the sales fulfillment rate are also parameters in some embodiments as in the mixed integer linear programming model and are therefore also present in table 5. The order satisfaction rate and the sales satisfaction rate in the mixed integer linear programming model can be set to specific values, so that if the order satisfaction rate and the sales satisfaction rate set in the mixed integer linear programming model need to be modified, the preset order satisfaction rate and the preset sales satisfaction rate need to be obtained when stock single product prediction is performed. In other embodiments, the order fulfillment rate and the sales fulfillment rate in the mixed integer linear programming model may be in the form of parameters, so that the preset order fulfillment rate and sales fulfillment rate need to be obtained when stock single product prediction is performed. Wherein the order fulfillment rate corresponds to a service level and the sales fulfillment rate corresponds to a sales level. In the embodiment of the invention, the service level, namely the order satisfaction rate, is required firstly, and the sales satisfaction rate is further required. This is because there may be a plurality of feasible solutions when the order satisfaction rate is satisfied, and a better feasible solution can be obtained from the plurality of feasible solutions by adding the constraint of the sales satisfaction rate.
For example, for the data in Table 1, there are two orders order1 and order 2. If only the order satisfaction rate is set, assuming that the order satisfaction rate is 50%, it is sufficient if any one of the order1 or the order2 is satisfied. If order1 is satisfied, the sales satisfaction rate is (2+2)/(2+2+1+1) ═ 2/3; if order2 is satisfied, the sales satisfaction rate is (1+1)/(2+2+1+1) ═ 1/3. If only the order satisfaction rate is met, there are two feasible solutions, and the sales satisfaction rates corresponding to the feasible solutions are different, such as 2/3 and 1/3. For the merchant, the larger the number of the sold products is, the better the product is, so the sales satisfaction rate is further introduced to enable the merchant to obtain a better feasible solution from the feasible solutions. If the sales fulfillment rate is set to 50%, a more optimal feasible solution is that order1 is fulfilled and singlets s1 and s2 are selected.
The binary variables of the set mixed integer linear programming model are shown in table 6.
TABLE 6 binary variables of the mixed integer Linear programming model
Figure BDA0002166389510000151
And 403, setting the predicted minimization of the number of the inventory items as an objective function.
The minimization of the predicted inventory item quantity is set as an objective function, namely the minimization of the predicted inventory SKU quantity, and the minimization of the type quantity of the inventory item can also be understood as the minimization of the type quantity of the inventory item. The set objective function is shown in table 7.
And 404, constructing a constraint condition of the objective function according to the relation among the order satisfaction rate, the sales satisfaction rate and the binarization variable.
Wherein step 404 comprises: and constructing a constraint condition of the objective function according to the relationship among the set parameters, the order satisfaction rate, the sales satisfaction rate and the binarization variables. Specifically, constructing a constraint condition of the objective function according to the relationship among the set parameters, the order satisfaction rate, the sales satisfaction rate and the binarization variables, comprises: setting a relation between the sales matrix parameter and the binarization variable as a first constraint condition of the objective function; setting a relation between the binary matrix parameter and the binary variable as a second constraint condition of the target function; setting the relation among the sales matrix parameters, the sales satisfaction rate and the total sales parameters of all the single products as a third constraint condition of the objective function; and setting the relation among the binary matrix parameters, the order satisfaction rate and the order total number parameters as a fourth constraint condition of the objective function.
Specifically, setting a relationship between a sales matrix parameter and a binarization variable as a first constraint condition of an objective function, including: setting a sales matrix parameter a and a binary variable xjAnd a binary variable xiiAs a first constraint of the objective function.
Specifically, setting a relationship between a binary matrix parameter and a binary variable as a second constraint condition of the objective function, including: setting a binary matrix parameter b and a binary variable xjAnd a binary variable xiiAs a second constraint of the objective function.
Specifically, setting a relationship among the sales matrix parameters, the sales satisfaction rate, and the total sales parameters of all the singles as a third constraint condition of the objective function, including: and setting the relationship among the sales matrix parameter a, the sales satisfaction rate beta and the total sales parameter S of all the single products as a third constraint condition of the objective function. For example, it is understood that the third constraint means that the total sales volume of the individual items of the order to be satisfied is set to be equal to or greater than the sales volume satisfaction rate multiplied by the total sales volume of all the individual items.
Specifically, setting a relationship among a binary matrix parameter, an order satisfaction rate, and an order total number parameter as a fourth constraint condition of the objective function, including: and setting the relation among the binary matrix parameter b, the order satisfaction rate alpha and the order total number parameter M as a fourth constraint condition of the objective function. For example, it is understood that the fourth constraint means that the number of orders to be satisfied is set to be equal to or greater than the order satisfaction rate multiplied by the total number of orders.
TABLE 7 objective function and constraint of objective function
Figure BDA0002166389510000161
Figure BDA0002166389510000171
Table 7 shows specific settings of the objective function and the first, second, third, and fourth constraints of the objective function. Although not shown in table 7, it is understood that the values of i and j both belong to integers.
The constraint condition (1), the constraint condition (2), the constraint condition (3) and the constraint condition (4) respectively correspond to a first constraint condition, a second constraint condition, a third constraint condition and a fourth constraint condition. The formulas in the third constraint condition and the fourth constraint condition are easy to understand, and the formulas in the first constraint condition and the second constraint condition can be understood to be both true for each order.
For example, for order1 in table 1, the left side of the formula in the first constraint is a (order1-s1) x (s1) + a (order1-s2) x (s2) + a (order1-s3) x (s3), where a (order1-s1), a (order1-s2), a (order1-s3) correspond to the first row of data in the sales matrix, respectively 2, 2, 0, and thus, the left side of the formula is 2 x (s1) +2 x (s2) +0 x (s 3); the right side of the formula in the first constraint is (a (order1-s1) + a (order1-s2) + a (order1-s 3)). zeta.order1＝4*ξorder1. Assuming the order1 is satisfied ξorder11, formula 2 x (s1) +2 x (s2)>4. Thus, if the order1 is satisfied, consider formula 2 x (s1) +2 x (s2)>The case of 4 holds true, singles 1 and singles 2 are to be selected. Similarly, for the order2, the formula constraint in the first constraint condition is also satisfied. The formula constraint in the second preset condition is similar to the formula constraint in the first constraint condition, and is not described herein again.
And 405, constructing a mixed integer linear programming model according to the target function and the constraint condition.
And constructing a mixed integer linear programming model according to the objective function and the constraint conditions of the objective function in the table 7, wherein the mixed integer linear programming model comprises the objective function and the constraint conditions of the objective function in the table 7.
It should be noted that, in this embodiment, the execution sequence of step 401 and step 402 is not limited, and step 402 may be executed first and then step 401 is executed, or step 401 and step 402 may be executed simultaneously.
This embodiment further defines a process that is specific to constructing a mixed integer linear programming model.
Historical order data for a merchant to ship from a target warehouse over a first time period is obtained 302.
303, carrying out statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse.
Please refer to step 102 in fig. 1 for a detailed flow of the statistical process. The statistical processing result comprises a sales matrix, a binary matrix, the total number of orders, the total sales of all the single products and the like.
The steps 302-303 in this embodiment are the same as the steps 101-102 in the embodiment of fig. 1, and please refer to the description in the embodiment of fig. 1 for details, which are not repeated herein.
And 304, inputting the statistical processing result into the mixed integer linear programming model, predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future, and outputting an inventory item list.
Specifically, the statistical processing result is input into the mixed integer linear programming model, and the mixed integer linear programming model is solved by using a mathematical programming solver, so that an optimal solution satisfying the order satisfaction rate and the sales satisfaction rate can be obtained.
For example, for the data in table 1, the order satisfaction rate is 1/2, the sales satisfaction rate is 1/2, the number of the stock items finally solved is 2, the stock item list includes s1 and s2, and the satisfied order is order 1.
In one embodiment, step 304 includes: acquiring a preset order satisfaction rate and a preset sales satisfaction rate; and inputting the statistical processing result, the preset order satisfaction rate and the preset sales satisfaction rate into the mixed integer linear programming model, predicting the inventory items which are delivered from the target warehouse by the merchant in the second time period in the future, and outputting an inventory item list. In this embodiment, the preset order satisfaction rate and the preset sales satisfaction rate are further obtained, and the order satisfaction rate and the sales satisfaction rate in the mixed integer linear programming model are replaced according to the obtained order satisfaction rate and the obtained sales satisfaction rate, that is, the order satisfaction rate and the sales satisfaction rate in the mixed integer linear programming model are modified, so that the practicability of the mixed integer linear programming model is improved, and the user experience is improved.
According to the embodiment, the inventory list is predicted according to the built mixed integer linear programming model, and the built mixed integer linear programming model relates to constraints of order satisfaction rate and sales satisfaction rate, so that the mixed integer linear programming model can obtain the optimal feasible solution from a plurality of feasible solutions when being solved, so as to obtain the optimal inventory list, namely, the least quantity of inventory items (namely, the quantity of inventory item types) is predicted. The method can predict the single products stored and transported in the target warehouse under the condition of not influencing the service level, improves the efficiency, accuracy and flexibility of determining the single products in the inventory, greatly reduces the quantity of the single products stored and transported in the target warehouse and reduces the management cost.
In order to better implement the inventory item prediction method in the embodiment of the invention, on the basis of the inventory item prediction method, the embodiment of the invention also provides an inventory item prediction device. The inventory item predicting device is integrated in equipment, and the equipment can be a server or a terminal, such as mobile phones, pads, desktop computers and other equipment.
Fig. 5 is a schematic block diagram of an inventory item prediction apparatus according to an embodiment of the present invention, which includes an order obtaining unit 501, a statistical processing unit 502, and a prediction unit 503.
The order obtaining unit 501 is configured to obtain historical order data of the merchant that is shipped from the target warehouse within the first time period.
And the statistical processing unit 502 is configured to perform statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse.
The statistical processing unit 502 includes an order sales statistical unit, an item status determination unit, and an order item processing unit. The order sales amount statistic unit is used for counting the total number of orders in the historical order data and the total sales amount of all the items in the historical order data. And the single product sales amount counting unit is used for counting the sales amount of each single product in each order in the historical order data. And the single product state determining unit is used for determining the existence state of each single product in each order in the historical order data. And the order single-product processing unit is used for processing the sales volume of each single product in each order and the existence state of each single product in each order to obtain an order single-product processing result, wherein the statistical processing result comprises the total number of the orders, the total sales volume of all the single products and the order single-product processing result. In one embodiment, the statistical processing unit 502 further includes: and the result determining unit is used for taking the total number of orders, the total sales volume of all the single products and the processing result of the single products of the orders in the historical order data as the statistical processing result of the historical shipment data of the target warehouse. The order sheet processing unit comprises a sales matrix determining unit and a binary matrix determining unit. And the sales matrix determining unit is used for performing matrix conversion on the sales of the single products in each order to obtain a sales matrix. And the binary matrix determining unit is used for performing binary conversion on the existence state of each single product in each order to obtain a binary matrix. The order sheet processing result comprises a sales matrix and a binary matrix.
And the predicting unit 503 is configured to predict the inventory items shipped from the target warehouse within the second time period in the future by the merchant according to the statistical processing result, and output an inventory item list.
In an embodiment, the prediction unit 503 includes a first satisfaction rate obtaining unit and a first singleton prediction unit. The first satisfaction rate obtaining unit is used for obtaining a preset order satisfaction rate and a preset sales satisfaction rate. And the first single predicting unit is used for predicting the inventory single delivered from the target warehouse in the future second time period by the merchant according to the statistical processing result and the preset order satisfaction rate and sales satisfaction rate, and outputting an inventory single list.
In one embodiment, the prediction of inventory items may be made by an inventory item prediction model. The prediction unit 503 is specifically configured to: and inputting the statistical processing result into an inventory item prediction model, predicting the inventory items delivered from the target warehouse by the merchant in a second time period in the future, and outputting an inventory item list.
In one embodiment, the prediction of inventory items may be made by an inventory item prediction model. The prediction unit 503 includes: a second satisfaction rate obtaining unit and a second unit prediction unit. The second satisfaction rate obtaining unit is used for obtaining a preset order satisfaction rate and a preset sales satisfaction rate. And the second single prediction unit is used for inputting the statistical processing result, the preset order satisfaction rate and the preset sales satisfaction rate into the inventory single preset model so as to predict inventory single shipped from the target warehouse by the merchant in a second time period in the future and output an inventory single list.
In one embodiment, the prediction of the inventory items is made by an inventory item prediction model that includes a mixed integer linear programming model. As shown in fig. 6, the schematic block diagram of an inventory item prediction apparatus according to an embodiment of the present invention includes a building unit 601, an order obtaining unit 602, a statistical processing unit 603, and a prediction unit 604.
The building unit 601 is configured to build a mixed integer linear programming model.
In one embodiment, the building unit 601 includes: the device comprises a parameter variable setting unit, an objective function setting unit, a constraint condition setting unit and a model building unit. The parameter variable setting unit is used for defining a set related to the mixed integer linear programming model and setting parameters related to the mixed integer linear programming model. And the parameter variable setting unit is also used for setting the order satisfaction rate, the sales satisfaction rate and the binarization variable. And the target function setting unit is used for setting the predicted minimized stock single product quantity as a target function. And the constraint condition setting unit is used for constructing the constraint condition of the objective function according to the relation among the order satisfaction rate, the sales satisfaction rate and the binarization variable. And the model construction unit is used for constructing a mixed integer linear programming model according to the objective function and the constraint condition. The constraint condition setting unit comprises a first condition setting unit, a second condition setting unit, a third condition setting unit and a fourth condition setting unit. The first condition setting unit is used for setting the relation between the sales matrix parameter and the binarization variable as a first constraint condition of the objective function. And the second condition setting unit is used for setting the relation between the binary matrix parameters and the binary variables as a second constraint condition of the target function. And the third condition setting unit is used for setting the relationship among the sales matrix parameters, the sales satisfaction rate and the total sales parameters of all the single products as a third constraint condition of the target function. And the fourth condition setting unit is used for setting the relationship among the binary matrix parameters, the order satisfaction rate and the order total number parameters as a fourth constraint condition of the objective function.
An order obtaining unit 602, configured to obtain historical order data of the merchant that is shipped from the target warehouse within the first time period.
And the statistical processing unit 603 is configured to perform statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse. The statistical processing unit 603 is the same as the statistical processing unit 502 in the above embodiment, and please refer to the description of the statistical processing unit 502 in the above embodiment, which is not repeated herein.
And the predicting unit 604 is configured to input the statistical processing result into the mixed integer linear programming model, predict the inventory items shipped from the target warehouse by the merchant in the second time period in the future, and output an inventory item list.
In one embodiment, the prediction unit 604 includes a third satisfaction rate obtaining unit and a third singleness prediction unit. And the third satisfaction rate acquiring unit is used for acquiring a preset order satisfaction rate and a preset sales satisfaction rate. And the third single product prediction unit is used for inputting the statistical processing result, the preset order satisfaction rate and the preset sales satisfaction rate into the mixed integer linear programming model, predicting the inventory single products which are delivered from the target warehouse by the merchant in the second time period in the future and outputting an inventory single product list.
It should be noted that, as will be clear to those skilled in the art, specific implementation processes of the above apparatus and each unit may refer to corresponding descriptions in the foregoing method embodiments, and for convenience and brevity of description, no further description is provided herein.
The embodiment of the present invention further provides a computer device, which integrates any one of the inventory item prediction devices provided by the embodiments of the present invention, and the computer device includes:
one or more processors;
a memory; and
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to perform the steps of the method for inventory singles prediction described in any of the embodiments above.
The embodiment of the invention also provides computer equipment which integrates any stock single product prediction device provided by the embodiment of the invention. Fig. 7 is a schematic diagram showing a structure of a computer device according to an embodiment of the present invention, specifically:
the computer device may include components such as a processor 701 of one or more processing cores, memory 702 of one or more computer-readable storage media, a power supply 703, and an input unit 704. Those skilled in the art will appreciate that the computer device configurations illustrated in the figures are not meant to be limiting of computer devices and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. Wherein:
the processor 701 is a control center of the computer apparatus, connects various parts of the entire computer apparatus using various interfaces and lines, and performs various functions of the computer apparatus and processes data by running or executing software programs and/or modules stored in the memory 702 and calling data stored in the memory 702, thereby monitoring the computer apparatus as a whole. Optionally, processor 701 may include one or more processing cores; preferably, the processor 701 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 701.
The memory 702 may be used to store software programs and modules, and the processor 701 executes various functional applications and data processing by operating the software programs and modules stored in the memory 702. The memory 702 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 702 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 702 may also include a memory controller to provide the processor 701 with access to the memory 702.
The computer device further includes a power supply 703 for supplying power to the various components, and preferably, the power supply 703 is logically connected to the processor 701 through a power management system, so that functions of managing charging, discharging, and power consumption are implemented through the power management system. The power supply 703 may also include any component including one or more of a dc or ac power source, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
The computer device may also include an input unit 704, the input unit 704 being operable to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
Although not shown, the computer device may further include a display unit and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 701 in the computer device loads the executable file corresponding to the process of one or more application programs into the memory 702 according to the following instructions, and the processor 701 runs the application program stored in the memory 702, thereby implementing various functions as follows:
acquiring historical order data of a merchant which is delivered from a target warehouse within a first time period;
carrying out statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse;
and predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, and outputting an inventory item list.
The computer device may implement the steps in any embodiment of the inventory item prediction method provided in the embodiment of the present invention, and therefore, beneficial effects that can be achieved by any inventory item prediction method provided in the embodiment of the present invention can be achieved, which are detailed in the foregoing embodiment and will not be described herein again.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, an embodiment of the present invention provides a storage medium, which may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like. Stored thereon, is a computer program that is loaded by a processor to perform the steps of any of the methods for inventory item forecasting provided by embodiments of the present invention. For example, the computer program may be loaded by a processor to perform the steps of:
acquiring historical order data of a merchant which is delivered from a target warehouse within a first time period;
carrying out statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse;
and predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, and outputting an inventory item list.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and parts that are not described in detail in a certain embodiment may refer to the above detailed descriptions of other embodiments, and are not described herein again.
In a specific implementation, each unit or structure may be implemented as an independent entity, or may be combined arbitrarily to be implemented as one or several entities, and the specific implementation of each unit or structure may refer to the foregoing method embodiment, which is not described herein again.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
The inventory item prediction method, the inventory item prediction device, the computer device and the storage medium provided by the embodiment of the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. An inventory item prediction method, comprising:
acquiring historical order data of a merchant which is delivered from a target warehouse within a first time period;
carrying out statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse;
and predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, and outputting an inventory item list.
2. The method for forecasting inventory items as claimed in claim 1, wherein the statistically processing the historical order data to obtain the statistical processing result of the historical shipment data of the target warehouse comprises:
counting the total number of orders in the historical order data and the total sales volume of all the single products in the historical order data;
counting the sales volume of each single product in each order in the historical order data;
determining the existence state of each single product in each order in the historical order data;
processing the sales volume of each single product in each order and the existence state of each single product in each order to obtain an order single product processing result;
wherein the statistical processing result comprises the total order number, the total sales of all the single products and the processing result of the single products of the order.
3. The method of predicting inventory items as recited in claim 2, wherein processing the sales volume of the items in each order and the presence status of the items in each order to obtain order item processing results comprises:
performing matrix conversion on the sales volume of each single product in each order to obtain a sales volume matrix;
performing binary conversion on the existing state of each single product in each order to obtain a binary matrix;
wherein the order sheet processing result comprises the sales matrix and the binary matrix.
4. The method of predicting inventory items as recited in claim 1, wherein said predicting inventory items that are shipped from said target warehouse by said merchant within a second time period in the future based on said statistical processing results and outputting an inventory item list comprises:
acquiring a preset order satisfaction rate and a preset sales satisfaction rate;
and predicting the inventory items delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, the preset order satisfaction rate and the preset sales volume satisfaction rate, and outputting an inventory item list.
5. The method of inventory item forecasting according to claim 1, wherein prior to the step of outputting the inventory item list based on the statistical processing result to forecast inventory items shipped by the merchant from the target warehouse within the second time period in the future, the method of inventory item forecasting further comprises:
constructing an inventory item prediction model;
predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result, and outputting an inventory item list, wherein the predicting comprises the following steps: and inputting the statistical processing result into the inventory item prediction model, predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future, and outputting an inventory item list.
6. The method of inventory item forecasting according to claim 5, wherein the constructing an inventory item forecasting model comprises:
setting an order satisfaction rate, a sales satisfaction rate and a binarization variable;
setting the predicted minimum number of the inventory single products as an objective function;
constructing a constraint condition of the objective function according to the relation among the order satisfaction rate, the sales satisfaction rate and the binarization variable;
and constructing the inventory item prediction model according to the target function and the constraint conditions of the target function.
7. The method of predicting inventory items as recited in claim 6, wherein the inventory item prediction model includes a sales matrix parameter, a binary matrix parameter, a total number of orders parameter, and a total sales parameter for all items, and the constructing constraints of the objective function according to relationships between an order fulfillment rate, a sales fulfillment rate, and a binary variable includes:
setting the relation between the sales matrix parameter and the binarization variable as a first constraint condition of the objective function;
setting the relation between the binary matrix parameter and the binary variable as a second constraint condition of the objective function;
setting the relation among the sales matrix parameters, the sales satisfaction rate and the total sales parameters of all the single products as a third constraint condition of the objective function;
setting the relation among the binary matrix parameter, the order satisfaction rate and the order total number parameter as a fourth constraint condition of the objective function.
8. An inventory item prediction device, comprising:
the order acquisition unit is used for acquiring historical order data of the merchant in the first time period after the merchant delivers goods from the target warehouse;
the statistical processing unit is used for performing statistical processing on the historical order data to obtain a statistical processing result of the historical shipment data of the target warehouse;
and the predicting unit is used for predicting the inventory items which are delivered from the target warehouse by the merchant in a second time period in the future according to the statistical processing result and outputting an inventory item list.
9. A computer device, characterized in that the computer device comprises:
one or more processors; a memory; and one or more applications, wherein the processor is coupled to the memory, the one or more applications stored in the memory and configured to be executed by the processor to implement the method of inventory item prediction of any of claims 1-7.
10. A storage medium having stored thereon a computer program to be loaded by a processor for performing the steps of the method of inventory item forecasting according to any one of claims 1 to 7.
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CN113128932A (en) * 2021-04-16 2021-07-16 北京京东振世信息技术有限公司 Warehouse stock processing method and device, storage medium and electronic equipment
CN113128932B (en) * 2021-04-16 2024-04-16 北京京东振世信息技术有限公司 Warehouse stock processing method and device, storage medium and electronic equipment
CN113537874A (en) * 2021-05-14 2021-10-22 深圳市富能新能源科技有限公司 Storage management method, system, terminal equipment and computer storage medium
CN113627662A (en) * 2021-08-03 2021-11-09 杭州拼便宜网络科技有限公司 Inventory data prediction method, device, equipment and storage medium
CN113627662B (en) * 2021-08-03 2024-05-28 杭州拼便宜网络科技有限公司 Inventory data prediction method, apparatus, device and storage medium
CN114997471A (en) * 2022-05-12 2022-09-02 北京沃东天骏信息技术有限公司 Information generation method and device, electronic equipment and computer readable medium
CN115034523A (en) * 2022-08-10 2022-09-09 深圳市感恩网络科技有限公司 Enterprise ERP integrated management system and method based on big data
CN115034523B (en) * 2022-08-10 2022-11-01 深圳市感恩网络科技有限公司 Enterprise ERP (enterprise resource planning) comprehensive management system and method based on big data
CN115660733A (en) * 2022-11-04 2023-01-31 鹏展万国电子商务(深圳)有限公司 Sales prediction system and method based on artificial intelligence

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