CN113554401A

CN113554401A - Inventory data management method, device, equipment and storage medium

Info

Publication number: CN113554401A
Application number: CN202110898696.4A
Authority: CN
Inventors: 秦广伟; 钱娱
Original assignee: Hangzhou Pinjie Network Technology Co Ltd
Current assignee: Hangzhou Pinjie Network Technology Co Ltd
Priority date: 2021-08-05
Filing date: 2021-08-05
Publication date: 2021-10-26

Abstract

The application discloses an inventory data management method, an inventory data management device, inventory data management equipment and a storage medium, wherein the method comprises the following steps: responding to a training request signal sent by one warehouse terminal, and inquiring inventory data indexes uploaded by other shop terminals related to the training request signal; feeding back the inquired stock data index to a warehouse terminal sending a training request signal; responding to a selection operation signal of the warehouse terminal to the inventory data index, and sending an assistance training signal and a center model to other shop terminals according to the selection operation signal; receiving training result data from other shop terminals; and updating the center model according to the training result data and distributing the center model to the requested warehouse terminal. The method has the advantages that the machine learning model, the model training data and the model related parameters are cooperated through the plurality of warehouse terminals, so that one warehouse terminal has a more perfect machine learning model for inventory data management.

Description

Inventory data management method, device, equipment and storage medium

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 managing 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.

When a supplier manages inventory data, the supplier often needs to make certain prediction according to historical data so as to determine the replenishment quantity and opportunity.

However, since the purchasing quantity in the above mode is random to some extent, and is different from the conventional mode of periodically purchasing multi-stage distribution batches, a supplier often cannot accurately predict the purchasing quantity according to the change of own data, and in the above mode, the shipment quantity of the supplier is also influenced by other suppliers, and the problem that the data quantity of training data is insufficient may be encountered, so that the inventory management based on effective prediction cannot be realized by the supplier according to the own data at present.

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 management methods, apparatuses, electronic devices, and computer readable 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 management method, including: responding to a training request signal sent by one warehouse terminal, and inquiring inventory data indexes uploaded by other shop terminals related to the training request signal; feeding back the inquired stock data index to a warehouse terminal sending a training request signal; responding to a selection operation signal of the warehouse terminal to the stock data indexes, and sending an assistance training signal and a center model to other shop terminals which upload the stock data indexes according to the stock data indexes selected by the selection operation signal; receiving training result data obtained by training the central model through inventory data of other shop terminals; and updating the center model according to the training result data and distributing the updated center model to the warehouse terminal which sends the training request signal.

As a second aspect of the present application, some embodiments of the present application provide an inventory data management apparatus, including: the query module is used for responding to a training request signal sent by one warehouse terminal and querying the inventory data index uploaded by other shop terminals related to the training request signal; the feedback module is used for feeding the inquired stock data index back to the warehouse terminal which sends out the training request signal; the transmitting module is used for responding to a selection operation signal of the warehouse terminal to the stock data indexes, and transmitting an assistance training signal and a center model to other shop terminals which upload the stock data indexes according to the stock data indexes selected by the selection operation signal; the receiving module is used for receiving training result data obtained by training the central model through inventory data of other shop terminals; and the distribution module is used for updating the center model according to the training result data and distributing the updated center model to the warehouse terminal which sends the training request signal.

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 readable medium on which a computer program is stored, 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: through the cooperation of a plurality of warehouse terminals in the machine learning model, the model training data and the relevant parameters of the model, one of the warehouse terminals has a more perfect machine learning model for inventory data management.

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.

In the drawings:

FIG. 1 is a flow diagram of a method of inventory data management according to one embodiment of the present application;

FIG. 2 is a flow diagram of a portion of the steps in a method for inventory data management according to one embodiment of the present application;

FIG. 3 is a flow diagram of another portion of the steps in a method for inventory data management according to one embodiment of the present application;

FIG. 4 is an architectural diagram of an inventory data management system according to one embodiment of the present application;

FIG. 5 is a schematic view of an operation interface of a warehouse terminal in the inventory data management method according to an embodiment of the present application;

FIG. 6 is a schematic view of another operational interface of a warehouse terminal in a method for inventory data management according to one embodiment of the present application;

FIG. 7 is a schematic view of yet another operational interface of a warehouse terminal in a method for inventory data management according to one embodiment of the present application;

FIG. 8 is a block diagram of an inventory data management device according to one embodiment of the present application;

fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

The meaning of the reference symbols in the figures:

inventory data management system 100,

warehouse terminals

101, 102, 103, system server 104.

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 management method according to an embodiment of the present invention includes the steps of:

s1: and responding to a training request signal sent by one warehouse terminal, and inquiring the inventory data index uploaded by other shop terminals related to the training request signal.

S2: and feeding back the inquired inventory data index to a warehouse terminal sending a training request signal.

S3: and responding to a selection operation signal of the warehouse terminal to the stock data indexes, and sending an assistance training signal and a center model to other shop terminals which upload the stock data indexes according to the stock data indexes selected by the selection operation signal.

S4: and receiving training result data obtained by training the central model through the inventory data of other shop terminals.

S5: and updating the center model according to the training result data and distributing the updated center model to the warehouse terminal which sends the training request signal.

As shown in fig. 4, fig. 4 shows an inventory data management system comprising: several warehouse terminals belonging to different users and a system server. Steps S1-S5 may be performed by the system server.

The warehouse terminal can be a desktop computer, and as a preferred scheme, the warehouse terminal is a smart phone. The warehouse terminal has certain data processing capacity and can send and receive corresponding signals, data and information through a mobile network. The system server is a system platform built by one or more computers or a system platform formed by cloud servers. The warehouse terminals can interact data with the system service, and data is not directly exchanged between the warehouse terminals.

Referring to fig. 5, fig. 5 shows an operation interface of the warehouse terminal (in this case, a smart phone). The user can select a desired stock data management function through the operation interface. Wherein, the local analysis of the inventory data is based on the training and input of the local data and the machine learning model so as to predict the data change which is likely to happen from the outside. The local analysis of the inventory data is to realize the indirect training of the machine learning model by cooperating with other warehouse terminals to obtain a more complete machine learning model.

Referring to fig. 6, fig. 6 illustrates another operation interface of the warehouse terminal. The interface is used for specific requirements of stock data collaborative analysis selected or/and filled by a user. As shown in fig. 6, the items that the user needs to operate include: item analysis, forecast time horizon, commodity SKU.

The analysis items and the prediction time range are presented in a selection box mode, when a user clicks the selection box, the selection box of the analysis items and the prediction time range can generate pull-down alternative items, and the user can select one of the items.

Specifically, the analysis items include: stock forecast, replenishment forecast, shipment forecast, unit price forecast. The prediction time range includes: 1 day in the future, 5 days in the future, 15 days in the future, 30 days in the future and 3 months in the future, it needs to be explained here that if the prediction time range is too long, the prediction accuracy and confidence coefficient are greatly reduced, and the reference value is lost. As a maximum of 3 months is provided.

The item SKU may be in the form of a fill-in box, which the user may manually fill in the item SKU, and upon filling in, automatically associate a historical fill-in record and system to record the item SKU to assist the user in filling in the complete value. As a preferred scheme, a user can search an interface through a "magnifying glass" icon to obtain a corresponding product SKU through a product name, a picture and the like, and as a preferred scheme, the user can obtain the corresponding product SKU through the product name and text information of a corresponding product specification, and further, can obtain the corresponding product SKU through shooting a product picture and scanning a barcode of the product.

After completion of the analysis project, prediction time range and filling of the commodity SKU, a machine learning model is actually assigned, namely, a user assigns a preset machine learning model through operation.

In addition, when a user needs to predict a plurality of commodity SKUs or needs to perform summary analysis, the prediction can be performed through a merging algorithm after a plurality of machine learning model analysis results are locally summarized.

In addition, as shown in fig. 6, the method of the present application provides two schemes for selecting an assisted warehouse terminal, in which a corresponding warehouse terminal is selected according to the operation of a user, that is, after the user clicks a "manual matching" button, the warehouse terminal may pop up a list interface (not shown in the figure) for the user to select the corresponding warehouse terminal or the inventory data index of the warehouse terminal.

For the most part, for non-professional demanding users, all suitable warehouse terminals or inventory data indexes of the warehouse terminals can be matched by the system in the background (system server) by clicking on "auto match".

As shown in fig. 7, fig. 7 shows an interface when the system server sends the queried inventory data index to the warehouse terminal for selection by the user. As shown in fig. 7, parameters of the summary items of the inventory data index query result are displayed in the interface, such as: warehouse name, number of data pieces, and update time, which can be selected by a user through a selection operation.

Specifically, the stock data index of the present application refers to index data, in which each piece of data stored in the warehouse terminal can express a difference between the piece of data and another piece of data, except for the data content itself, that is, each piece of data has a header index, the data itself can be found through the header index, the data is in the field of the database, the stock data index is different from a data address index such as a key value, and the data address index serves as a construction object of a data model, but the stock data index may be associated with a data structure of the data storage itself and the key value.

Preferably, the inventory data index includes the following components: warehouse ID, item SKU, data time, data ID.

Wherein the warehouse ID is used to distinguish between different warehouse terminals, i.e. data owners. Item SKUs are used to distinguish different items and are automatically associated upon query. The data time is the time of data generation or storage, and can be accurate to milliseconds. The data ID is a random code matched for each piece of data, and is used to distinguish the difference between the above data, which are all the same, although in general, the data time is difficult to repeat.

The warehouse ID, the commodity SKU and the data time can be sent to the system server in a plain code mode by the warehousing terminal and then sent to the other warehousing terminal by the system server. Although the data content cannot be known when the data ID is obtained, in order to further improve the data security, the data ID is subjected to hash encryption and then sent to the system server, and the system server is subjected to secondary hash encryption when being sent to another warehouse terminal.

As shown in fig. 7, the system server searches for the required data through the inventory data index according to the configuration of fig. 6, then summarizes the data under the corresponding warehouse name, and counts the number of data pieces and the latest update time in the data sets, and the update time is accurate to date.

The system server predicts the correspondence between the warehouse ID and other data in the warehouse, and can obtain information such as address data of the warehouse from the warehouse ID.

As a preferable scheme, for fig. 7, the user may click on an interface (not shown in the figure) of one of the warehouse names to obtain the warehouse detail page, so as to obtain data such as the warehouse address.

As shown in fig. 2, step S1 specifically includes the following steps:

s11: the training content data in the training request signal is parsed.

S12: one of a plurality of center models is selected based on the training content data.

S13: and querying the data characteristics of input data and output data required by the training of the central model according to the type of the central model.

S14: a query scope for the inventory data index is generated based on one or more of the data features.

S15: a commodity SKU is selected based on the training content data.

S16: a query range for the inventory data index is generated based on the item SKUs.

As a specific scheme, the training request signal is generated and triggered by the operation of the warehousing terminal, for example, in the interface shown in fig. 6, the training request signal is sent to the system server by clicking the "auto match" button. The training request signal contains training content data including analysis items, predicted time ranges, commodity SKUs, etc.

Different training content data can be combined to specify different center models, and each center model refers to a machine learning model stored in a system server in advance or updated, and a plurality of center models can be set according to training content. Preferably, each commodity SKU may have a corresponding center model.

After determining the center model, the system server needs to query the center model for data characteristics of input data and output data, where the data characteristics refer to types and formats of the input data and the output data. Therefore, as a preferable aspect, the inventory data index further includes: data type and data format. These two parts are also processed by hash encryption.

As a further preferable scheme, when determining the warehouse terminal that has sent the training request signal, S1 may send the training request signal to a warehouse terminal corresponding to a warehouse within a certain set range.

More specifically, the geographic range is determined not according to the position of the warehouse initiating the assistance, but according to the commodity SKU selected by the training request signal, the warehouse purchasing the commodity SKU is determined, then all warehouses providing commodities of the commodity SKU to the shops within a certain time range are inquired according to the shops and the commodity SKU, and all inquired warehouses serve as the set range for sending the training request signal.

As shown in fig. 3, step S3 specifically includes:

s31: and analyzing the selected stock data index in the selection operation signal.

S32: and selecting the shop terminal for transmitting the assistance training signal according to the inventory data index.

S33: and transmitting the inventory data index selected by the corresponding selected operation signal to the shop terminal selected to transmit the assistance training signal.

S34: one of the plurality of historical versions of the center model selected based on the training content data, which is most updated in time, is selected and transmitted to the selected shop terminal.

In brief, the system server not only informs other warehousing terminals (of course, the warehouse terminal may initiate model training and upload parameters, etc.) to start assistance and needs to provide indexes of required data and models themselves.

As a specific scheme, the training result data comprises model parameters, a model structure and parameter gradients of the central model.

Through the scheme, the data of each cooperative warehouse terminal cannot be local, only the training result is output to the system server, the system server updates the original central model after integrating the parameters, then the updated model is sent to the initiated warehouse terminal, and the warehouse terminal can adopt the central model to perform data prediction and the like.

As shown in fig. 8, an inventory data management apparatus according to an embodiment of the present application includes: the query module is used for responding to a training request signal sent by one warehouse terminal and querying the inventory data index uploaded by other shop terminals related to the training request signal; the feedback module is used for feeding the inquired stock data index back to the warehouse terminal which sends out the training request signal; the transmitting module is used for responding to a selection operation signal of the warehouse terminal to the stock data indexes, and transmitting an assistance training signal and a center model to other shop terminals which upload the stock data indexes according to the stock data indexes selected by the selection operation signal; the receiving module is used for receiving training result data obtained by training the central model through inventory data of other shop terminals; and the distribution module is used for updating the center model according to the training result data and distributing the updated center model to the warehouse terminal which sends the training request signal.

As shown in fig. 9, the 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 in accordance with 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 readable 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 readable medium described above in some embodiments of the present application may be a computer readable signal medium or a computer readable storage medium 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 readable medium that is not a computer readable storage medium and 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 readable 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 readable medium may be included in the electronic device or may exist separately without being incorporated in the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: responding to a training request signal sent by one warehouse terminal, and inquiring inventory data indexes uploaded by other shop terminals related to the training request signal; feeding back the inquired stock data index to a warehouse terminal sending a training request signal; responding to a selection operation signal of the warehouse terminal to the stock data indexes, and sending an assistance training signal and a center model to other shop terminals which upload the stock data indexes according to the stock data indexes selected by the selection operation signal; receiving training result data obtained by training the central model through inventory data of other shop terminals; and updating the center model according to the training result data and distributing the updated center model to the warehouse terminal which sends the training request signal.

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

1. An inventory data management method, comprising:

responding to a training request signal sent by one warehouse terminal, and inquiring inventory data indexes uploaded by other shop terminals related to the training request signal;

feeding back the inquired inventory data index to the warehouse terminal sending the training request signal;

responding to a selection operation signal of the warehouse terminal to the stock data indexes, and sending an assistance training signal and a center model to other shop terminals which upload the stock data indexes according to the stock data indexes selected by the selection operation signal;

receiving training result data obtained by training the central model through inventory data of other shop terminals;

and updating the center model according to the training result data and distributing the updated center model to the warehouse terminal which sends the training request signal.

2. The inventory data management method according to claim 1, wherein the querying the inventory data indexes uploaded by the other store terminals related to the training request signal in response to the training request signal transmitted from one store terminal includes:

analyzing the training content data in the training request signal;

selecting one of a plurality of the center models according to the training content data;

inquiring data characteristics of input data and output data required by the central model training according to the type of the central model;

generating a query scope for the inventory data index from one or more of the data features.

3. The inventory data management method according to claim 2, wherein the querying the inventory data indexes uploaded by the other store terminals related to the training request signal in response to the training request signal transmitted from one store terminal includes:

selecting a commodity SKU according to the training content data;

and generating a query range of the inventory data index according to the commodity SKU.

4. The inventory data management method according to claim 1, wherein the issuing of the assistance training signal and the center model to other store terminals that have uploaded the inventory data indexes selected in accordance with the selection operation signal in response to the selection operation signal of the warehouse terminal to the inventory data indexes comprises:

analyzing the selected stock data index in the selection operation signal;

and selecting the shop terminal for sending the assistance training signal according to the inventory data index.

5. The inventory data management method according to claim 4, wherein the issuing of the assistance training signal and the center model to other store terminals that have uploaded the inventory data indexes selected in accordance with the selection operation signal in response to the selection operation signal of the warehouse terminal to the inventory data indexes comprises:

and transmitting the corresponding inventory data index selected by the selection operation signal to the shop terminal selected to transmit the assistance training signal.

6. The inventory data management method according to claim 5, wherein the issuing of the assistance training signal and the center model to other store terminals that have uploaded the inventory data indexes selected in accordance with the selection operation signal in response to the selection operation signal of the warehouse terminal to the inventory data indexes comprises:

and selecting one of the plurality of historical versions of the center model selected according to the training content data, which is updated with the latest time, and sending the selected one to the selected shop terminal.

7. The inventory data management method of claim 1, wherein the training result data includes model parameters, model structures, and parameter gradients of the central model.

8. An inventory data management device comprising:

the query module is used for responding to a training request signal sent by one warehouse terminal and querying inventory data indexes uploaded by other shop terminals related to the training request signal;

the feedback module is used for feeding the inquired stock data index back to the warehouse terminal which sends the training request signal;

the transmitting module is used for responding to a selection operation signal of the warehouse terminal to the stock data indexes, and sending an assistance training signal and a center model to other shop terminals which upload the stock data indexes according to the stock data indexes selected by the selection operation signal;

the receiving module is used for receiving training result data obtained by training the center model through inventory data of other shop terminals;

and the distribution module is used for updating the center model according to the training result data and distributing the updated center model to the warehouse terminal which sends the training request signal.

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-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.