Method, device and electronic device for automatically logging goods on and off the line based on machine self-learning

Abstract

The present invention provides a method, device, electronic device and recording medium for automatically listing and unlisting goods based on machine self-learning, the method comprising: establishing a product information database for storing basic product information, product performance data and product ratings, the product rating being an indicator for evaluating the suitability of products for online sales; establishing a product listing and unlisting recommendation model, the model being based on a machine self-learning algorithm, being able to regularly calculate and update the product ratings of the products in the product database based on historical basic product information and historical product performance data; automatically listing or unlisting goods on an online shopping platform according to predetermined product listing and unlisting rules, the product listing and unlisting rules being associated with the product ratings. The present invention can more accurately assess whether the goods sold on the shopping platform are liked by users, automatically prioritize the listing of more popular goods for users to purchase, improve user experience and satisfaction with the shopping platform, without the need for manual operation, improve efficiency, and save manpower and costs.

Description

Technical Field

[0001] The present invention belongs to the technical field of online sales, and more specifically relates to a method, device and electronic equipment for automatically launching and shutting down commodities based on machine self-learning. Background Art

[0002] With the rise of the shopping model of pre-ordering goods through the Internet or telephone platform, consumers can easily buy the goods they want without leaving home. With the expansion of the scale of online shopping platforms, the types and quantities of goods sold are growing rapidly. The problem that follows is that some goods sell very quickly, while others sell slowly. In order to ensure the popularity of goods sold online, it is necessary to adjust online goods regularly or irregularly. At present, statisticians use the backend system of the online shopping platform to search according to the sales of goods to obtain the sales ranking of the goods, and then select the online and offline goods according to the ranking.

[0003] The above method uses manual statistics, which consumes a lot of time and energy of statisticians. Therefore, it is necessary to improve the existing technology. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] The present invention aims to solve the problem of low efficiency in online shopping platforms in the prior art.

[0006] (II) Technical solution

[0007] In order to solve the above technical problems, one aspect of the present invention provides a method for automatically adding and removing goods online based on machine self-learning, which is used in an online shopping platform. The method comprises the following steps:

[0008] Establish a product information database, which is used to store basic product information, product performance data and product ratings. The product rating is an indicator used to evaluate the suitability of products for online sales;

[0009] Establish a product online and offline recommendation model, which is based on a machine self-learning algorithm and can regularly calculate and update the product ratings of the products in the product database based on historical product basic information and historical product performance data;

[0010] The product is automatically put online or taken offline on the online shopping platform according to a predetermined product put online or take offline rule, wherein the product put online or take offline rule is associated with the product rating.

[0011] According to a preferred embodiment of the present invention, the online shopping platform includes merchants settled in the platform;

[0012] The method further includes: obtaining basic information of the product from the merchant.

[0013] According to a preferred embodiment of the present invention, the commodity performance data includes commodity popularity data;

[0014] The method further includes: tracking network data related to the commodity, and calculating commodity popularity data of the commodity according to the network data.

[0015] According to a preferred embodiment of the present invention, the network data related to the commodity includes at least one of search data, dot data, message data, and advertisement data.

[0016] According to a preferred embodiment of the present invention, the method further comprises:

[0017] Generate a hot commodity recommendation table according to the commodity popularity data;

[0018] A list of commodities included in the popular commodity recommendation table but not included in the commodity information database is automatically fed back to the merchants settled in the platform, requesting the merchants to provide attribute information of the commodities.

[0019] According to a preferred embodiment of the present invention, the method further comprises:

[0020] A product pricing model based on machine self-learning is established. When a product is automatically launched on the online shopping platform, the product pricing model generates a product launch price based on at least one of the product basic information, product performance data and product rating.

[0021] According to a preferred embodiment of the present invention, the method of automatically listing or delisting products on the online shopping platform according to predetermined product listing or delisting rules further includes:

[0022] The online shopping platform lists the products in descending order of the product ratings.

[0023] The second aspect of the present invention provides a device for automatically loading and unloading commodities based on machine self-learning, comprising:

[0024] An information storage module is used to establish a product information database, which is used to store basic product information, product performance data and product ratings, which are indicators used to evaluate the suitability of products for online sales;

[0025] A rating calculation module is used to establish a product online and offline recommendation model. The model is based on a machine self-learning algorithm and can regularly calculate and update the product ratings of the products in the product database based on historical product basic information and historical product performance data;

[0026] The on-line and off-line control module is used to automatically put products online or offline on the online shopping platform according to predetermined product on-line and off-line rules, and the product on-line and off-line rules are associated with the product ratings.

[0027] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store a computer executable program, and when the computer program is executed by the processor, the processor executes the described method.

[0028] A fourth aspect of the present invention further proposes a computer-readable medium storing a computer-executable program, wherein when the computer-executable program is executed, the method described is implemented.

[0029] (III) Beneficial effects

[0030] The present invention establishes a product information database and a product launch and launch recommendation model. The model automatically calculates the product score based on the product information and product popularity stored in the database, and automatically adjusts the launch and launch of the product on the shopping platform based on the score and preset rules. It can more accurately assess whether the products sold on the shopping platform are liked by users, automatically prioritize the launch of more popular products for users to purchase, and take products that users are not satisfied with offline, thereby improving user experience and satisfaction with the shopping platform, without manual operation, improving efficiency, and saving manpower and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of an application scenario of automatic online and offline commodity loading and unloading based on machine self-learning of the present invention;

[0032] Figure 2 This is a flow chart of a method for automatically adding and removing products from the online store based on machine self-learning according to an embodiment of the present invention;

[0033] Figure 3 It is a schematic diagram of a device for automatically loading and unloading commodities based on machine self-learning according to an embodiment of the present invention;

[0034] Figure 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;

[0035] Figure 5 is a schematic diagram of a computer-readable recording medium according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0037] In the process of introducing specific embodiments, the detailed description of the structure, performance, effect or other characteristics is to enable those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can implement the present invention with a technical solution that does not contain the above-mentioned structure, performance, effect or other characteristics under certain circumstances.

[0038] The flowchart in the accompanying drawings is only an exemplary process demonstration, and does not mean that the solution of the present invention must include all the contents, operations and steps in the flowchart, nor does it mean that it must be executed in the order shown in the figure. For example, some operations / steps in the flowchart can be decomposed, some operations / steps can be combined or partially combined, etc. The execution order shown in the flowchart can be changed according to actual conditions without departing from the main purpose of the present invention.

[0039] Frames in the attached figure Figure 1 Generally, it refers to functional entities, which do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processing unit devices and / or microcontroller devices.

[0040] The same reference numerals in the drawings represent the same or similar elements, components or parts, and thus the repeated description of the same or similar elements, components or parts may be omitted below. It should also be understood that although the first, second, third and other attributives representing the numbers may be used herein to describe various devices, elements, components or parts, these devices, elements, components or parts should not be limited by these attributives. In other words, these attributives are only used to distinguish one from another. For example, the first device may also be called the second device, but it does not deviate from the essential technical solution of the present invention. In addition, the terms "and / or" and "and / or" refer to all combinations including any one or more of the listed items.

[0041] In order to solve the above technical problems, the present invention proposes a method for automatically logging in and out of a commodity based on machine self-learning, which is mainly executed by an on-line and off-line system, which includes software and hardware 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.

[0042] Figure 1 This is a schematic diagram of an application scenario of automatic online and offline commodity processing based on machine self-learning of the present invention. Figure 1As shown, merchants enter the online shopping platform through the merchant configuration module and provide basic information of the products to be listed. The product information database is used to store the basic information of the products provided by each merchant, and send this information to the online and offline system. The online and offline system calculates the product score according to the product information and product popularity, determines which products can be put online according to the online and offline rules and scores, and feeds back the products that can be put online to the product information database. The product information database sends the product information of these products to the online shopping platform in the form of a list through the online and offline recommendation module. The online shopping platform automatically puts the products on the list online or offline to ensure that the products put online on the shopping platform are high-quality and popular products, and gives priority to putting more popular products online for users to purchase, thereby improving user experience and satisfaction with the shopping platform.

[0043] Figure 2 This is a flow chart of a method for automatically adding and removing products from the online store based on machine self-learning according to an embodiment of the present invention. Figure 2 As shown, the method includes:

[0044] S101. Establish a product information database, which is used to store basic product information, product performance data, and product ratings. The product rating is an indicator for evaluating the suitability of products for online sales.

[0045] Specifically, merchants can voluntarily join the online shopping platform. After joining, if they want to put products on the online shopping platform, they can submit basic product information to the online shopping platform. The online shopping platform is equipped with a product information database to store the basic product information submitted by the merchants, and obtain the performance data and product rating of the product on this shopping platform and other online shopping platforms based on the basic product information. The basic product information includes the product name, product type, product appearance picture, product trademark, product production place, product price provided by the merchant and other information. The product performance data includes the popularity data of the product, and the product popularity is expressed in the form of product sales quantity, product sales, product click number, product message or evaluation number, etc. You can search for the product popularity information corresponding to the product that is completely matched or whose compliance is greater than the preset compliance threshold on this shopping platform and other online shopping platforms on the Internet. The product rating is based on the product performance data and refers to the product's praise rate and reputation on various online shopping platforms. According to the product rating, it can be evaluated whether to put the product online.

[0046] S102: Establish a product online and offline recommendation model, where the model is based on a machine self-learning algorithm and can regularly calculate and update product ratings of products in the product database based on historical product basic information and historical product performance data.

[0047] Specifically, the online shopping platform establishes a product online and offline recommendation model, which is based on a machine learning model. The basic information of historically online products and the performance data of the products are input into the machine learning model. The historically online products here include products on this online shopping platform as well as products on other shopping platforms. Each attribute in the basic information of the product is used as a one-dimensional feature, and each data in the performance data can also be used as a one-dimensional feature. A corresponding algorithm is set to output the product score of the product. The parameters of the machine learning model are adjusted according to the difference between the actual score of the historically online product and the output score to obtain a product online and offline recommendation model.

[0048] After obtaining the product online and offline recommendation model, the product information provided by the merchants and the obtained product performance data are input into the product online and offline recommendation model to obtain the product score of the product.

[0049] In this embodiment, a time period can be pre-set, such as one week, and historical products can be regularly replaced as samples in each time period. The performance data of recently launched products can also be used as samples to update the parameters of the product online and offline recommendation model to make the output results of the product online and offline recommendation model more accurate.

[0050] S103: Automatically launch or delist products on the online shopping platform according to a predetermined product launch and delisting rule, wherein the product launch and delisting rule is associated with the product rating.

[0051] Specifically, after obtaining the product ratings using the product online and offline recommendation model, online and offline rules can be pre-set. For example, whether a product is online or offline is determined based on a score threshold. Products with scores higher than the score threshold can be online, while products with scores lower than the score threshold cannot be online.

[0052] You can set an online and offline cycle, such as one day or one week. The online products will remain online on the shopping platform for 24 hours, and will automatically go offline after 24 hours. The performance data of the product within these 24 hours will be added to the previous performance data, and the basic information and performance data of the product will be re-entered into the product online and offline recommendation model for scoring. The score will be used to determine whether the product will be online or offline in the next cycle. In this case, as long as the products are automatically online and offline according to the rules every day, the operation of an online shopping platform can be maintained. The entire process does not require manual operation and is automatically completed by the online and offline system.

[0053] In addition, the online and offline systems can also generate a popular product recommendation table based on the popularity data of each product within a period. If it is detected that the information stored in the product information database for the products in the popular product recommendation table is incomplete, the system will automatically feedback the list of products included in the popular product recommendation table but not included in the product information database to the merchants stationed on the platform, requesting the merchants to provide the attribute information of the products and store it in the corresponding location in the product information database.

[0054] Preferably, the online and offline system also establishes a commodity pricing model based on machine self-learning, with at least one of the basic information of the commodity, commodity performance data and commodity rating as the input of the pricing model, and different weights can be set for the above three types of features according to the type of commodity. After setting the weights, the three types of features are input into the pricing model together, and the output is the online price of the commodity. The relationship between the online price and the popularity of the commodity can be calculated in advance through the data of historical online commodities, and the most reasonable price can be calculated from it. The historical commodities are used as samples to train the machine learning model, and the parameters of the model are adjusted by the difference between the output online price and the calculated price to obtain a pricing model for determining the price of the newly launched commodity. The algorithm can follow the popularity or higher the rating of the commodity, the more appropriate the price of the commodity can be. Pricing rules can also be added to accurately price the commodity, for example, the price should be higher than the commodity price provided by the merchant, but not higher by a certain proportion.

[0055] After pricing the products online, the online and offline systems will sort the products from high to low according to their scores, and put each category of products online in the sorted order, giving priority to the products with the highest scores in each category.

[0056] The method of this embodiment establishes a product information database and a product launch and launch recommendation model. The model automatically calculates the product score based on the product information and product popularity stored in the database, and automatically adjusts the launch and launch of the product on the shopping platform based on the score and preset rules. It can more accurately assess whether the products sold on the shopping platform are liked by users, automatically prioritize the launch of more popular products for users to purchase, and take products that users are not satisfied with offline, thereby improving user experience and satisfaction with the shopping platform, without manual operation, improving efficiency, and saving manpower and costs.

[0057] Those skilled in the art will appreciate that all or part of the steps to implement the above embodiments are implemented as a program (computer program) executed by a computer data processing device. When the computer program is executed, the above method provided by the present invention can be implemented. Moreover, the computer program can be stored in a computer-readable storage medium, which can be a readable storage medium such as a disk, an optical disk, a ROM, a RAM, or a storage array composed of multiple storage media, such as a disk or a tape storage array. The storage medium is not limited to centralized storage, and it can also be distributed storage, such as cloud storage based on cloud computing.

[0058] The following describes an apparatus embodiment of the present invention, which can be used to execute a method embodiment of the present invention. The details described in the apparatus embodiment of the present invention should be regarded as supplementary to the above method embodiment; details not disclosed in the apparatus embodiment of the present invention can be implemented with reference to the above method embodiment.

[0059] Figure 3 It is a schematic diagram of a device for automatically loading and unloading goods based on machine self-learning according to an embodiment of the present invention.

[0060] like Figure 3 As shown, the device 200 includes:

[0061] The information storage module 201 is used to establish a product information database, which is used to store basic product information, product performance data and product ratings, which are indicators for evaluating the suitability of products for online sales;

[0062] The rating calculation module 202 is used to establish a product online and offline recommendation model. The model is based on a machine self-learning algorithm and can regularly calculate and update the product ratings of the products in the product database based on historical product basic information and historical product performance data;

[0063] The on-line and off-line control module 203 is used to automatically put products online or offline on the online shopping platform according to predetermined product on-line and off-line rules, and the product on-line and off-line rules are associated with the product ratings.

[0064] According to a preferred embodiment of the present invention, the online shopping platform includes merchants settled in the platform;

[0065] The information storage module 201 further includes: a commodity information acquisition unit, configured to acquire the basic information of the commodity from the merchant.

[0066] According to a preferred embodiment of the present invention, the commodity performance data includes commodity popularity data;

[0067] The information storage module 201 further includes: a commodity popularity acquisition unit, which is used to track network data related to commodities and calculate commodity popularity data of commodities according to the network data.

[0068] According to a preferred embodiment of the present invention, the network data related to the commodity includes at least one of search data, dot data, message data, and advertisement data.

[0069] According to a preferred embodiment of the present invention, the device 200 further includes:

[0070] A product recommendation module, used to generate a hot product recommendation table according to the product popularity data;

[0071] The commodity detection module is used to automatically feed back a list of commodities included in the popular commodity recommendation table but not included in the commodity information database to the merchants settled in the platform, and request the merchants to provide attribute information of the commodities.

[0072] According to a preferred embodiment of the present invention, the device 200 also includes: a pricing module, which is used to establish a product pricing model based on machine self-learning. When a product is automatically launched on the online shopping platform, the product pricing model generates a product launch price based on at least one of the product basic information, product performance data and product ratings.

[0073] According to a preferred embodiment of the present invention, the online and offline control module 203 includes: a product sorting unit, which is used to put products online in descending order of their product ratings.

[0074] Those skilled in the art will appreciate that the modules in the above device embodiments may be distributed in the device as described, or may be changed accordingly and distributed in one or more devices different from the above embodiments. The modules in the above embodiments may be combined into one module, or may be further split into multiple submodules.

[0075] The following describes an electronic device embodiment of the present invention, which can be regarded as a specific physical implementation of the method and device embodiments of the present invention. The details described in the electronic device embodiment of the present invention should be regarded as a supplement to the above method or device embodiments; details not disclosed in the electronic device embodiment of the present invention can be implemented with reference to the above method or device embodiments.

[0076] Figure 4 It is a structural diagram of an electronic device according to an embodiment of the present invention, the electronic device includes a processor and a memory, the memory is used to store a computer executable program, when the computer program is executed by the processor, the processor executes a method for automatically taking goods online and offline based on machine self-learning.

[0077] like Figure 4 As shown, the electronic device is presented in the form of a general computing device. The processor may be one or more and work in coordination. The present invention does not exclude distributed processing, that is, the processor may be dispersed in different physical devices. The electronic device of the present invention is not limited to a single entity, but may also be the sum of multiple physical devices.

[0078] The memory stores a computer executable program, which is usually a machine-readable code. The computer-readable program can be executed by the processor to enable the electronic device to perform the method of the present invention, or at least part of the steps in the method.

[0079] The memory includes a volatile memory, such as a random access memory unit (RAM) and / or a cache memory unit, and may also be a non-volatile memory, such as a read-only memory unit (ROM).

[0080] Optionally, in this embodiment, the electronic device further includes an I / O interface, which is used for the electronic device to exchange data with an external device. The I / O interface can represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0081] It should be understood that Figure 4 The electronic device shown is only an example of the present invention, and the electronic device of the present invention may also include elements or components not shown in the above examples. For example, some electronic devices also include display units such as display screens, and some electronic devices also include human-computer interaction elements such as buttons, keyboards, etc. As long as the electronic device can execute the computer-readable program in the memory to implement the method of the present invention or at least part of the steps of the method, it can be considered as an electronic device covered by the present invention.

[0082] Through the description of the above implementation, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation 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 flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to perform the above method according to the present invention. When the computer program is executed by a data processing device, the computer-readable medium can implement the above method of the present invention, namely: establish a commodity information database, which is used to store commodity basic information, commodity performance data and commodity ratings, and the commodity rating is an indicator for evaluating the suitability of the commodity for online sales; establish a commodity up-and-down recommendation model, which is based on a machine self-learning algorithm and can regularly calculate and update the commodity rating of the commodity in the commodity database according to the historical commodity basic information and historical commodity performance data; automatically go online or offline on the online shopping platform according to the predetermined commodity up-and-down rules, and the commodity up-and-down rules are associated with the commodity ratings.

[0083] Figure 5 Schematic diagram of a computer readable recording medium according to an embodiment of the present invention. Figure 5 As shown, a computer executable program is stored in a computer-readable recording medium, and when the computer executable program is executed, the above-mentioned machine self-learning commodity automatic on-line and off-line method of the present invention is realized. The computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier, which carries a readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0084] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type 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).

[0085] Through the above description of the implementation mode, it is easy for those skilled in the art to understand 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 the electronic processing unit, server, client, mobile phone, control unit, processor, etc. contained in the system. The present invention can also be implemented by computer software that executes the method of the present invention. However, it should be noted that the computer software that executes the method of the present invention is not limited to being executed by one or a specific hardware entity, and it can also be implemented in a distributed manner by unspecified specific hardware, such as some method steps executed by the computer program can be executed on a mobile client, and another part can be executed in a smart meter, a smart recognition pen, etc. For computer software, the software product can be stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), or it can be distributed and stored on the network, as long as it enables the electronic device to execute the method according to the present invention.

[0086] In summary, the present invention can be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that general data processing devices such as microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0087] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the present invention is not inherently related to any specific computer, virtual device or electronic device, and various general devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for automatically adding and removing products from the online store based on machine self-learning, used in an online shopping platform, characterized in that: The method comprises the following steps: Establish a product information database, which is used to store basic product information, product performance data and product ratings. The product rating is an indicator used to evaluate the suitability of products for online sales; Establish a product online and offline recommendation model, which is based on a machine self-learning algorithm and can regularly calculate and update the product ratings of the products in the product database based on historical product basic information and historical product performance data; use each attribute of the basic information of the historically online products as a one-dimensional feature, and each data item in the performance data as a one-dimensional feature to input into the product online and offline recommendation model, and output the product rating of the product; Setting an online and offline cycle, whereby the online product will be automatically offline after the shopping platform maintains the online and offline cycle, and the performance data of the product during the online and offline cycle will be added to the previous performance data, and the basic information and performance data of the product will be re-entered into the product online and offline recommendation model for scoring, and whether the product will be offline in the next cycle will be determined based on the score; A popular product recommendation table is generated based on the popularity data of each product within a period. If it is detected that the information of the products in the popular product recommendation table stored in the product information database is incomplete, a list of products included in the popular product recommendation table but not included in the product information database will be automatically fed back to the merchants on the platform, requesting the merchants to provide the attributes of the products.

2. The method for automatically adding and removing products from the online store based on machine self-learning according to claim 1, characterized in that: The online shopping platform includes merchants settled in the platform; The method further includes: obtaining basic information of the product from the merchant.

3. The method for automatically adding and removing goods from the online store based on machine self-learning according to claim 2 is characterized in that: The commodity performance data includes commodity popularity data; The method further includes: tracking network data related to the commodity, and calculating commodity popularity data of the commodity according to the network data.

4. The method for automatically adding and removing goods from the online store based on machine self-learning according to claim 3 is characterized in that: The network data related to the commodity includes at least one of search data, mark data, message data, and advertisement data.

5. The method for automatically logging in and out of a commodity based on machine self-learning according to any one of claims 1 to 4, characterized in that: The method further comprises: A product pricing model based on machine self-learning is established. When a product is automatically launched on the online shopping platform, the product pricing model generates a product launch price based on at least one of the product basic information, product performance data and product rating.

6. The method for automatically adding and removing goods from the online store based on machine self-learning according to claim 1, characterized in that: The method of automatically putting products online or offline on the online shopping platform according to the predetermined product online and offline rules further includes: The online shopping platform lists the products in descending order of the product ratings.

7. A device for automatically loading and unloading commodities based on machine self-learning, characterized in that: include: An information storage module is used to establish a product information database, which is used to store basic product information, product performance data and product ratings, which are indicators used to evaluate the suitability of products for online sales; The scoring calculation module is used to establish a product online and offline recommendation model. The model is based on a machine self-learning algorithm and can regularly calculate and update the product scores of the products in the product database according to the historical basic information of the products and the historical performance data of the products. Each attribute of the basic information of the products that have been online in the past is used as a one-dimensional feature, and each data in the performance data is also used as a one-dimensional feature to input into the product online and offline recommendation model, and output the product score of the product. The online and offline control module is used to set the online and offline cycles. The online products will be automatically offline after they maintain the online and offline cycles on the shopping platform. The performance data of the products during the online and offline cycles are added to the previous performance data, and the basic information and performance data of the products are re-entered into the product online and offline recommendation model for scoring. According to the score, it is determined whether the product will be online or offline in the next cycle; a hot product recommendation table is generated according to the popularity data of each product in a cycle. If it is detected that the information stored in the product information database of the products in the hot product recommendation table is incomplete, the list of products included in the hot product recommendation table but not included in the product information database will be automatically fed back to the merchants settled in the platform, requesting the merchants to provide product attributes.

8. An electronic device, comprising a processor and a memory, wherein the memory is used to store a computer executable program, wherein: When the computer program is executed by the processor, the processor performs the method according to any one of claims 1 to 6.

9. A computer-readable medium storing a computer-executable program, characterized in that: When the computer executable program is executed, the method according to any one of claims 1 to 6 is implemented.

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