CN112950320A

CN112950320A - Automatic commodity online and offline method and device based on machine self-learning and electronic equipment

Info

Publication number: CN112950320A
Application number: CN202110237298.8A
Authority: CN
Inventors: 刘晓斌
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-03-03
Filing date: 2021-03-03
Publication date: 2021-06-11

Abstract

The invention provides a commodity automatic on-line and off-line method and device based on machine self-learning, electronic equipment and a recording medium, wherein the method comprises the following steps: establishing a commodity information database for storing basic commodity information, commodity performance data and commodity scores, wherein the commodity scores are indexes for evaluating the fitness of online sales of commodities; establishing a commodity online and offline recommendation model, wherein the model is based on a machine self-learning algorithm and can periodically calculate and update commodity scores of commodities in a commodity database according to historical commodity basic information and historical commodity performance data; and automatically uploading or downloading the commodities on the online shopping platform according to a preset commodity uploading and downloading rule, wherein the commodity uploading and downloading rule is associated with the commodity score. The invention can more accurately evaluate whether the commodities sold on the shopping platform are liked by the user, automatically and preferentially bring the commodities with higher popularity on line for the user to purchase, improve the user experience and the satisfaction degree of the shopping platform, do not need manual operation, improve the efficiency and save the labor and the cost.

Description

Automatic commodity online and offline method and device based on machine self-learning and electronic equipment

Technical Field

The invention belongs to the technical field of network sales, and particularly relates to a machine self-learning-based automatic commodity online and offline method and device and electronic equipment.

Background

With the rise of the shopping mode of purchasing commodities in advance by using the network or the telephone platform, consumers can easily purchase the commodities to be purchased without going out, and with the enlargement of the scale of the network shopping platform, the types and the quantity of the commodities to be sold are rapidly increased. The problem with this is that some goods are sold at a fast rate, and some are sold at a slower rate. In order to ensure the hotness of the goods sold on-line, the goods on-line need to be adjusted regularly or irregularly. At present, a statistical worker searches according to the sales condition of commodities by using a background system of an online shopping platform to obtain the sales condition sequence of the commodities, and then selects the commodities on and off the line according to the sequence.

The mode consumes a great deal of time and energy of statistic personnel due to the adoption of a manual statistic mode. Accordingly, there is a need for improvements in the prior art.

Disclosure of Invention

Technical problem to be solved

The invention aims to solve the problem that the efficiency of online and offline goods on an online shopping platform is too low in the prior art.

(II) technical scheme

In order to solve the technical problem, one aspect of the present invention provides a machine self-learning based method for automatically loading and unloading commodities onto and from an online shopping platform, which is characterized in that the method includes the following steps:

establishing a commodity information database, wherein the database is used for storing basic commodity information, commodity performance data and commodity scores, and the commodity scores are indexes used for evaluating the fitness of online sales of commodities;

establishing a commodity online and offline recommendation model, wherein the model is based on a machine self-learning algorithm and can periodically calculate and update commodity scores of commodities in a commodity database according to historical commodity basic information and historical commodity performance data;

and automatically uploading or downloading the commodities on the online shopping platform according to a preset commodity uploading and downloading rule, wherein the commodity uploading and downloading rule is associated with the commodity score.

According to a preferred embodiment of the present invention, the online shopping platform includes a merchant hosting the platform;

the method further comprises the following steps: and acquiring the basic information of the commodity from the merchant.

According to a preferred embodiment of the present invention, the commodity performance data includes commodity popularity data;

the method further comprises the following steps: network data related to the commodity is tracked, and commodity popularity data of the commodity is calculated according to the network data.

According to a preferred embodiment of the present invention, the network data related to the article includes at least one of search data, click data, message data, and advertisement data.

According to a preferred embodiment of the invention, the method further comprises:

generating a popularity commodity recommendation table according to the commodity popularity data;

and automatically feeding back a commodity list contained in the hot commodity recommendation table but not contained in the commodity information database to the merchant residing in the platform to request the merchant to provide attribute information of the commodity.

and establishing a commodity pricing model based on machine self-learning, and generating an online price of the commodity according to at least one of commodity basic information, commodity performance data and commodity scores by the commodity pricing model when the commodity is automatically online on the online shopping platform.

According to a preferred embodiment of the present invention, the automatically putting on or off the line of the goods on the online shopping platform according to the predetermined goods putting on or off the line rule further comprises:

and the online shopping platform is used for online commodities according to the sequence of the commodity scores from high to low.

The invention provides a machine self-learning based automatic commodity loading and unloading device in a second aspect, which comprises:

the system comprises an information storage module, a commodity information database and a commodity score evaluation module, wherein the information storage module is used for establishing a commodity information database, the database is used for storing commodity basic information, commodity performance data and a commodity score, and the commodity score is an index used for evaluating the fitness of online sales of commodities;

the score calculation module is used for establishing a commodity online and offline recommendation model, and the model is based on a machine self-learning algorithm and can periodically calculate and update the commodity score of commodities in the commodity database according to the basic information of historical commodities and the performance data of the historical commodities;

and the online control module is used for automatically online or offline commodities on the online shopping platform according to a preset commodity online and offline rule, and the commodity online and offline rule is associated with the commodity score.

A third aspect of the invention proposes an electronic device comprising a processor and a memory for storing a computer-executable program, which, when executed by the processor, performs the method.

The fourth aspect of the present invention also provides a computer-readable medium storing a computer-executable program, which when executed, implements the method.

(III) advantageous effects

According to the invention, a commodity information database and a commodity online and offline recommendation model are established, the model automatically calculates the commodity score according to the commodity information and the commodity popularity stored in the database, and automatically adjusts the online and offline of the commodity on a shopping platform according to the score and a preset rule. Whether the commodity sold on the shopping platform is liked by the user can be accurately evaluated, the commodity with higher popularity is automatically and preferentially put on the line for the user to purchase, the unsatisfactory commodity of the user is taken off the line, the user experience and the satisfaction degree of the shopping platform are improved, manual operation is not needed, the efficiency is improved, and the labor and the cost are saved.

Drawings

FIG. 1 is a schematic diagram of a scenario of a machine self-learning based automatic online and offline application of a commodity according to the present invention;

FIG. 2 is a flow chart of a method for automatically loading and unloading commodities based on machine self-learning according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an automatic loading and unloading device for commodities based on machine self-learning according to an embodiment of the present invention;

FIG. 4 is a schematic structural diagram of an electronic device of one embodiment of the invention;

fig. 5 is a schematic diagram of a computer-readable recording medium of an embodiment of the present invention.

Detailed Description

In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.

In describing particular embodiments, specific details of structures, properties, effects, or other features are set forth in order to provide a thorough understanding of the embodiments by one skilled in the art. However, it is not excluded that a person skilled in the art may implement the invention in a specific case without the above-described structures, performances, effects or other features.

The flow chart in the drawings is only an exemplary flow demonstration, and does not represent that all the contents, operations and steps in the flow chart are necessarily included in the scheme of the invention, nor does it represent that the execution is necessarily performed in the order shown in the drawings. For example, some operations/steps in the flowcharts may be divided, some operations/steps may be combined or partially combined, and the like, and the execution order shown in the flowcharts may be changed according to actual situations without departing from the gist of the present invention.

The block diagrams in the figures generally represent functional entities and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and/or processing unit devices and/or microcontroller devices.

The same reference numerals denote the same or similar elements, components, or parts throughout the drawings, and thus, a repetitive description thereof may be omitted hereinafter. It will be further understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these elements, components, or sections should not be limited by these terms. That is, these phrases are used only to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention. Furthermore, the term "and/or", "and/or" is intended to include all combinations of any one or more of the listed items.

In order to solve the technical problems, the invention provides a machine self-learning based automatic commodity online and offline method which is mainly executed by an online and offline system, wherein the online and offline system comprises software and hardware which are installed in a server. The server includes but is not limited to: a single server, a server cluster, a distributed server, a server cluster based on a cloud architecture, etc.

FIG. 1 is a schematic diagram of a machine self-learning-based automatic online and offline application scenario of a commodity according to the present invention, as shown in FIG. 1, a merchant enters an online shopping platform through a merchant configuration module and provides basic information of the commodity to be put on the shelf, a commodity information database is used for storing the basic information of the commodity provided by each merchant and sending the information to a system which can only go online and offline, the system which can only go online and offline calculates a score of the commodity according to the commodity information and commodity heat, determines which commodities can go online according to rules and the score of the online and offline, and feeds the commodities which can go online back to the commodity information database, the commodity information database sends the commodity information of the commodities to the online shopping platform through an online and offline recommendation module in a list form, the online shopping platform automatically performs an online or offline operation on the commodities on the list, and ensures that the commodities which go online in the shopping platform are high-quality commodities with high, the commodities with higher popularity are preferentially brought online for users to purchase, and the user experience and the satisfaction degree of the shopping platform are improved.

FIG. 2 is a flow chart of a method for automatically loading and unloading commodities based on machine self-learning according to an embodiment of the present invention. As shown in fig. 2, the method includes:

s101, establishing a commodity information database, wherein the database is used for storing basic commodity information, commodity performance data and commodity scores, and the commodity scores are indexes used for evaluating the fitness of online sales of commodities.

Specifically, a merchant can voluntarily reside in an online shopping platform, if the merchant wants to online a commodity on the online shopping platform after the merchant resides in the online shopping platform, basic commodity information can be submitted to the online shopping platform, the online shopping platform is provided with a commodity information database for storing the basic commodity information submitted by the merchant, and the basic commodity information comprises commodity name, commodity type, commodity appearance picture, commodity trademark, commodity production place, commodity price provided by the merchant and other information of the online shopping platform according to the basic commodity information, the basic commodity information comprises commodity name, commodity type, commodity appearance picture, commodity trademark, commodity production place, commodity price and other information, the commodity expression data comprises heat data of the commodity, the expression form of the heat of the commodity is commodity sales quantity, commodity sales amount, commodity click quantity, message or evaluation quantity and the like, and the complete coincidence can be searched on the online shopping platform and other online shopping platforms on the network, or commodity popularity information corresponding to the commodities with conformity greater than a preset conformity threshold value, and commodity scoring refers to the good scoring rate and public praise of the commodities on each online shopping platform on the basis of the commodity performance data, and whether the commodities are online can be evaluated according to the commodity scoring.

S102, establishing a commodity online and offline recommendation model, wherein the model is based on a machine self-learning algorithm and can periodically calculate and update commodity scores of commodities in the commodity database according to historical commodity basic information and historical commodity performance data.

Specifically, the online shopping platform establishes a commodity online and offline recommendation model, the model is based on a machine learning model, basic information of commodities on the online history and performance data of the commodities are input into the machine learning model, the commodities on the online history comprise the commodities of the online shopping platform and the commodities on other shopping platforms, each attribute in the commodity basic information is used as a one-dimensional feature, each item of data in the performance data can also be used as a one-dimensional feature, a corresponding algorithm is set, commodity scores of the commodities are output, parameters of the machine learning model are adjusted according to the difference value between the actual scores of the commodities on the online history and the output scores, and the commodity online and offline recommendation model is obtained.

After the commodity online and offline recommendation model is obtained, commodity information provided by the resident merchant and the obtained commodity performance data are input into the commodity online and offline recommendation model, and then the commodity score of the commodity can be obtained.

In this embodiment, a time period may be preset, for example, a week, each time period is changed periodically to replace the historical commodity as the sample, or the recent commodity performance data is used as the sample to update the parameters of the commodity online and offline recommendation model, so that the output result of the commodity online and offline recommendation model is more accurate.

S103, automatically uploading or downloading commodities on the online shopping platform according to a preset commodity uploading and downloading rule, wherein the commodity uploading and downloading rule is associated with the commodity score.

Specifically, after the scores of the commodities are obtained by using the commodity online and offline recommendation model, online and offline rules can be preset, for example, whether the commodities are online or not is determined according to the score threshold of the scores, the commodities with the scores higher than the score threshold can be online, and the commodities with the scores lower than the score threshold cannot be online.

The online and offline period can be set, for example, one day or one week, the online commodity is kept online for 24 hours on the shopping platform, the online commodity is automatically offline after 24 hours, the performance data of the commodity in the 24 hours is added into the previous performance data, the basic information and the performance data of the commodity are input into a commodity online and offline recommendation model again for grading, and whether the commodity is online or offline in the next period is determined according to the grade. Under the condition, the operation of an online shopping platform can be maintained as long as the online goods are automatically loaded and unloaded according to the rule every day, and the whole process does not need manual operation and is automatically completed by an online loading and unloading system.

In addition, the online and offline system can also generate a hot commodity recommendation table according to the hot data of each commodity in a period, if the fact that the information of the commodities in the hot commodity recommendation table stored in the commodity information database is incomplete is detected, the system automatically feeds back a commodity list which is contained in the hot commodity recommendation table but not contained in the commodity information database to a merchant who is resident in the platform, requests the merchant to provide attribute information of the commodities, and stores the attribute information in a corresponding position in the commodity information database.

Preferably, the online and offline system also establishes a commodity pricing model based on machine self-learning, takes at least one of the basic information, the commodity performance data and the commodity score of the commodity as the input of the pricing model, or sets different weights for the three characteristics according to the type of the commodity, inputs the three characteristics into the pricing model after setting the weights, outputs the online price of the commodity, the relation between the online price and the commodity heat can be calculated in advance through the data of historical online commodities, the most reasonable price can be calculated from the relation, the historical commodities are used as samples to train a machine learning model, adjusting parameters of the model through the difference between the output online price and the calculated price to obtain a pricing model, for determining the price of the new online commodity, the algorithm can follow the fact that the higher the popularity or the score of the commodity is, and the price of the commodity can be properly increased. Pricing rules may also be added to refine the price of the good, for example, by pricing higher than the price of the good offered by the merchant, but not by a certain percentage.

After pricing the online commodities, the online and offline system sorts the online commodities according to the grades from high to low, and each type of commodity is online according to the sorted sequence, and the commodity with the highest grade in each type of commodity is preferentially online.

According to the method, a commodity information database and a commodity online and offline recommendation model are established, a commodity score is automatically calculated by the model according to commodity information and commodity popularity stored in the database, and the online and offline of commodities on a shopping platform are automatically adjusted according to the score and a preset rule. Whether the commodity sold on the shopping platform is liked by the user can be accurately evaluated, the commodity with higher popularity is automatically and preferentially put on the line for the user to purchase, the unsatisfactory commodity of the user is taken off the line, the user experience and the satisfaction degree of the shopping platform are improved, manual operation is not needed, the efficiency is improved, and the labor and the cost are saved.

Those skilled in the art will appreciate that all or part of the steps to implement the above-described embodiments are implemented as programs (computer programs) executed by a computer data processing apparatus. When the computer program is executed, the method provided by the invention can be realized. Furthermore, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, such as a magnetic disk or a magnetic tape storage array. The storage medium is not limited to centralized storage, but may be distributed storage, such as cloud storage based on cloud computing.

Embodiments of the apparatus of the present invention are described below, which may be used to perform method embodiments of the present invention. The details described in the device embodiments of the invention should be regarded as complementary to the above-described method embodiments; reference is made to the above-described method embodiments for details not disclosed in the apparatus embodiments of the invention.

Fig. 3 is a schematic diagram of an automatic commodity loading and unloading device based on machine self-learning according to an embodiment of the present invention.

As shown in fig. 3, the apparatus 200 includes:

the information storage module 201 is used for establishing a commodity information database, wherein the database is used for storing basic commodity information, commodity performance data and commodity scores, and the commodity scores are indexes used for evaluating the fitness of online sales of commodities;

the score calculation module 202 is used for establishing a commodity online and offline recommendation model, and the model is based on a machine self-learning algorithm and can periodically calculate and update the commodity scores of commodities in the commodity database according to the basic information of historical commodities and the performance data of the historical commodities;

and the online control module 203 is used for automatically online or offline the goods on the online shopping platform according to a preset goods online and offline rule, wherein the goods online and offline rule is associated with the goods score.

the information storage module 201 further includes: and the commodity information acquisition unit is used for acquiring the basic commodity information from the merchant.

the information storage module 201 further includes: and the commodity heat acquisition unit is used for tracking the network data related to the commodity and calculating the commodity heat data of the commodity according to the network data.

According to a preferred embodiment of the present invention, the apparatus 200 further comprises:

the commodity recommendation module is used for generating a popularity commodity recommendation table according to the commodity popularity data;

and the commodity detection module is used for automatically feeding back the commodity list which is contained in the hotness commodity recommendation table but not contained in the commodity information database to the merchant residing in the platform and requesting the merchant to provide the attribute information of the commodity.

According to a preferred embodiment of the present invention, the apparatus 200 further comprises: and the pricing module is used for establishing a commodity pricing model based on machine self-learning, and when the commodities are automatically online on the online shopping platform, the commodity pricing model generates the online prices of the commodities according to at least one of the basic information of the commodities, the commodity performance data and the commodity scores.

According to a preferred embodiment of the present invention, the up-down line control module 203 comprises: and the commodity sequencing unit is used for carrying out online commodity according to the commodity scores of the commodities from high to low.

Those skilled in the art will appreciate that the modules in the above-described embodiments of the apparatus may be distributed as described in the apparatus, and may be correspondingly modified and distributed in one or more apparatuses other than the above-described embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.

In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, which includes a processor and a memory, where the memory is used for storing a computer executable program, and when the computer program is executed by the processor, the processor executes a commodity automatic online and offline method based on machine self-learning.

As shown in fig. 4, the electronic device is in the form of a general purpose computing device. The processor can be one or more and can work together. The invention also does not exclude that distributed processing is performed, i.e. the processors may be distributed over different physical devices. The electronic device of the present invention is not limited to a single entity, and may be a sum of a plurality of entity devices.

The memory stores a computer executable program, typically machine readable code. The computer readable program may be executed by the processor to enable an electronic device to perform the method of the invention, or at least some of the steps of the method.

The memory may include volatile memory, such as Random Access Memory (RAM) and/or cache memory, and may also be non-volatile memory, such as read-only memory (ROM).

Optionally, in this embodiment, the electronic device further includes an I/O interface, which is used for data exchange between the electronic device and an external device. The I/O interface may be a local bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, and/or a memory storage device using any of a variety of bus architectures.

It should be understood that the electronic device shown in fig. 4 is only one example of the present invention, and elements or components not shown in the above example may be further included in the electronic device of the present invention. For example, some electronic devices further include a display unit such as a display screen, and some electronic devices further include a human-computer interaction element such as a button, a keyboard, and the like. Electronic devices are considered to be covered by the present invention as long as the electronic devices are capable of executing a computer-readable program in a memory to implement the method of the present invention or at least a part of the steps of the method.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: establishing a commodity information database, wherein the database is used for storing basic commodity information, commodity performance data and commodity scores, and the commodity scores are indexes used for evaluating the fitness of online sales of commodities; establishing a commodity online and offline recommendation model, wherein the model is based on a machine self-learning algorithm and can periodically calculate and update commodity scores of commodities in a commodity database according to historical commodity basic information and historical commodity performance data; and automatically uploading or downloading the commodities on the online shopping platform according to a preset commodity uploading and downloading rule, wherein the commodity uploading and downloading rule is associated with the commodity score.

Fig. 5 is a schematic diagram of a computer-readable recording medium of an embodiment of the present invention. As shown in fig. 5, the computer-readable recording medium stores therein a computer-executable program, which, when executed, implements the above-mentioned machine-self-learning merchandise automatic online and offline method according to the present invention. The computer readable storage medium may include a propagated data signal with 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 readable storage medium may also be any readable medium that is not a 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 readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

From the above description of the embodiments, those skilled in the art will readily appreciate that the present invention can be implemented by hardware capable of executing a specific computer program, such as the system of the present invention, and electronic processing units, servers, clients, mobile phones, control units, processors, etc. included in the system. The invention may also be implemented by computer software for performing the method of the invention. It should be noted, however, that the computer software for executing the method of the present invention is not limited to be executed by one or a specific hardware entity, but may also be implemented in a distributed manner by hardware entities without specific details, for example, some method steps executed by a computer program may be executed by a mobile client, and another part may be executed by a smart meter, a smart pen, or the like. For computer software, the software product may be stored in a computer readable storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or may be distributed over a network, as long as it enables the electronic device to perform the method according to the present invention.

In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.

Claims

1. A machine self-learning based automatic commodity online and offline method is used for an online shopping platform, and is characterized by comprising the following steps:

2. The machine self-learning based commodity automatic online and offline method according to claim 1, wherein:

the online shopping platform comprises a merchant residing in the platform;

3. The machine self-learning based commodity automatic online and offline method according to claim 2, wherein:

the commodity performance data comprises commodity popularity data;

4. The machine self-learning based commodity automatic online and offline method according to claim 3, wherein:

the network data related to the commodity includes at least one of search data, dotting data, message data, and advertisement data.

5. The machine self-learning based merchandise automatic on-line and off-line method of claim 4, further comprising:

6. The machine self-learning based merchandise automatic on-line and off-line method according to any one of claims 1-5, further comprising:

7. The machine self-learning based automatic commodity online and offline method according to claim 1, wherein the automatic online or offline commodity on the online shopping platform according to a predetermined commodity online and offline rule further comprises:

8. The utility model provides a commodity automatic wiring device based on machine self-learning which characterized in that includes:

9. An electronic device comprising a processor and a memory, the memory for storing a computer-executable program, characterized in that:

the computer program, when executed by the processor, performs the method of any of claims 1-7.

10. A computer-readable medium storing a computer-executable program, wherein the computer-executable program, when executed, implements the method of any of claims 1-7.