Cross-border e-commerce full-automatic product selecting and racking method, system and equipment based on multiple platforms

Abstract

The invention is suitable for the technical field of cross-border e-commerce, and particularly relates to a cross-border e-commerce full-automatic product selecting and shelving method, system and device based on multiple platforms. Wherein the target information comprises a target area and a target platform; obtaining pre-selected commodity information based on the target information; wherein the pre-selected commodity information comprises at least one pre-selected commodity name; obtaining on-shelf product information based on the pre-selected commodity information and the target platform; wherein the on-shelf product information comprises at least one piece of on-shelf commodity information, and the on-shelf commodity information comprises on-shelf commodity names, on-shelf commodity pictures and on-shelf commodity prices. According to the cross-border e-commerce full-automatic product selecting and shelving method, system and equipment based on multiple platforms, the problems that the shelving period is long, dynamic market demands are difficult to capture and market earlier opportunities are possibly missed due to manual analysis, product selecting and shelving can be solved.

Description

Technical Field

[0001] This application belongs to the field of cross-border e-commerce technology, and in particular relates to a fully automated product selection and listing method, system and equipment for cross-border e-commerce based on multiple platforms. Background Technology

[0002] Cross-border e-commerce refers to an international business activity in which trading entities belonging to different customs territories complete transactions and payment settlements through e-commerce platforms, and deliver goods through cross-border logistics to complete the transaction.

[0003] In related technologies, when users select products for overseas shopping platforms, the analysis, selection, and listing are usually done manually, resulting in long listing cycles and difficulty in capturing dynamic market demand, which may lead to missing market opportunities. Summary of the Invention

[0004] This application provides a method, system, and equipment for fully automated product selection and listing in cross-border e-commerce based on multiple platforms. This can improve the problem that manual analysis, product selection, and listing lead to long listing cycles and difficulty in capturing dynamic market demand, which may result in missing market opportunities.

[0005] In a first aspect, embodiments of this application provide a fully automated product selection and listing method for cross-border e-commerce based on multiple platforms, including: Obtain target information; wherein, the target information includes target region and target platform, the target region reflects the region where the user sells goods, and the target platform is the e-commerce platform where the goods are listed; Based on the target information, pre-selected product information is obtained; wherein, the pre-selected product information includes at least one pre-selected product name; Based on the pre-selected product information and the target platform, the product information to be listed is obtained; wherein, the product information to be listed includes at least one product information, the product information to be listed includes the product name, product image and product price.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The fully automated product selection and listing method for cross-border e-commerce based on multiple platforms provided in this application first obtains target information including target regions and target platforms to clarify the direction of product selection and listing, providing basic data for subsequent steps. Then, based on the target information, pre-selected product information is obtained, and potential best-selling products are initially screened by combining regional demand and platform characteristics, while irrelevant products are filtered out to improve market response speed. Finally, based on the pre-selected product information and the target platforms, listing product information is obtained to ensure that products conform to supply chain advantages, accurately price them to suit the target platforms, and generate product images that can attract users, shortening the product selection and listing cycle.

[0007] Secondly, embodiments of this application provide a fully automated product selection and listing system for cross-border e-commerce based on multiple platforms, including: An acquisition unit is used to acquire target information; wherein, the target information includes a target region and a target platform, the target region reflects the region where the user sells goods, and the target platform is the e-commerce platform where the goods are listed; A pre-selection unit is used to obtain pre-selected product information based on the target information; wherein the pre-selected product information includes at least one pre-selected product name; The listing unit is used to obtain listing product information based on the pre-selected product information and the target platform; wherein, the listing product information includes at least one listing product information, and the listing product information includes the listing product name, listing product image and listing product price.

[0008] Thirdly, embodiments of this application provide a fully automated product selection and listing device for cross-border e-commerce based on multiple platforms, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method described in any one of the first aspects above.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any one of the first aspects above.

[0010] Fifthly, embodiments of this application provide a computer program product that, when running on a multi-platform cross-border e-commerce fully automated product selection and listing device, causes the multi-platform cross-border e-commerce fully automated product selection and listing device to execute any one of the methods described in the first aspect above.

[0011] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a fully automated product selection and listing method for cross-border e-commerce based on multiple platforms, provided in an embodiment of this application. Figure 2This is a flowchart illustrating step S200 of a fully automated product selection and listing method for cross-border e-commerce based on multiple platforms provided in an embodiment of this application. Figure 3 This is a flowchart illustrating step S300 of a fully automated product selection and listing method for cross-border e-commerce based on multiple platforms provided in an embodiment of this application. Figure 4 This is a schematic diagram of the structure of a multi-platform cross-border e-commerce fully automated product selection and listing system provided in one embodiment of this application; Figure 5 This is a schematic diagram of the structure of a multi-platform cross-border e-commerce fully automated product selection and listing device provided in one embodiment of this application. Detailed Implementation

[0014] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0015] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0016] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0017] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0018] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0019] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0020] Cross-border e-commerce refers to an international business activity in which trading entities belonging to different customs territories complete transactions and payment settlements through e-commerce platforms, and deliver goods through cross-border logistics to complete the transaction.

[0021] In related technologies, when users select products for overseas shopping platforms, the analysis, selection, and listing are usually done manually, resulting in long listing cycles and difficulty in capturing dynamic market demand, which may lead to missing market opportunities.

[0022] To address the aforementioned issues, this application provides a method, system, and device for fully automated product selection and listing in cross-border e-commerce across multiple platforms. This method first acquires target information, including the target region and target platform, to clarify the direction of product selection and listing, providing foundational data for subsequent steps. Then, based on the target information, pre-selected product information is obtained, and potential best-selling products are initially screened by combining regional demand and platform characteristics, while irrelevant products are filtered out to improve market responsiveness. Finally, based on the pre-selected product information and the target platform, listing product information is obtained, ensuring that products align with supply chain advantages, accurately pricing to suit the target platform, and generating product images that attract users, thus shortening the product selection and listing cycle.

[0023] The fully automated product selection and listing method for cross-border e-commerce based on multiple platforms provided in this application embodiment can be applied to fully automated product selection and listing equipment for cross-border e-commerce based on multiple platforms. In this case, the fully automated product selection and listing equipment for cross-border e-commerce based on multiple platforms is the executing entity of the fully automated product selection and listing method for cross-border e-commerce based on multiple platforms provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of fully automated product selection and listing equipment for cross-border e-commerce based on multiple platforms.

[0024] For example, fully automated product selection and listing equipment for cross-border e-commerce based on multiple platforms can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers.

[0025] To better understand the fully automated product selection and listing method for cross-border e-commerce based on multiple platforms provided in this application, the specific implementation process of the fully automated product selection and listing method for cross-border e-commerce based on multiple platforms provided in this application will be described by way of example below.

[0026] Figure 1 This application provides an embodiment of a fully automated product selection and listing method for cross-border e-commerce based on multiple platforms. The method includes: S100, Obtain target information; where the target information includes target region and target platform, the target region reflects the region where the user sells the goods, and the target platform is the e-commerce platform where the goods are listed.

[0027] As we can understand, the target region reflects the area where a user wants to sell their products, or the region where they intend to sell their products. The target platform is a shopping platform that can provide e-commerce services to the target region. Obtaining target information can be done by receiving data transmitted by users, or by retrieving popular sales regions and platform trend data from industry reports through standardized interfaces (such as REST APIs) to provide users with recommendation options, but is not limited to these methods. Obtaining target information clarifies the direction of product selection and listing, providing foundational data for subsequent steps.

[0028] S200, obtain pre-selected product information based on target information; wherein, the pre-selected product information includes at least one pre-selected product name.

[0029] It's understandable that obtaining pre-selected product information based on target information could involve real-time collection of trending search terms from major search engines in the target region (such as Google and Yandex), combined with hashtag data from social media platforms (such as Facebook and Twitter) to build a trending keyword pool, and connecting to the target platform's API (such as Amazon MWS and eBay Trading API) to obtain data on the top 1000 best-selling products in the past 7 days, including product titles, categories, sales volume, ratings, etc. Then, by analyzing the trending keyword pool and product data, the product titles of products with sales exceeding a preset sales volume in the past 7 days corresponding to keywords with an average daily search frequency greater than 1000 times in the trending keyword pool can be identified as pre-selected product names. Alternatively, it could involve sending search terms with an average daily search frequency greater than 1000 times and data on the top 1000 best-selling products in the past 7 days to users and receiving data transmitted by users, but these methods are not limited to these. Obtaining pre-selected product information based on target information allows for the initial screening of potential best-selling products by combining regional demand and platform characteristics, filtering out irrelevant products, and improving market responsiveness.

[0030] In one possible implementation, please refer to Figure 2 S200, Based on the target information, obtain the pre-selected product information, including: S210, obtain hot word information based on the target area; wherein, the hot word information includes at least one hot word, and the hot word reflects the items that users in the target area are interested in.

[0031] It's understandable that obtaining trending keyword information based on a target region can involve collecting trending search terms from mainstream search engines in the target region (such as Google and Yandex), combining this with hashtag data from social media platforms (such as Facebook and Twitter) to build a trending keyword pool, and then filtering the data in the pool through data cleaning and language recognition (ensuring that the keywords in the pool are consistent with the language of the target region), confirming keywords with a frequency of ≥500 times per day as trending keywords. Alternatively, it can involve receiving data transmitted by users, but is not limited to these methods. Obtaining trending keyword information based on a target region can quickly capture sudden market demands (such as holiday promotions and influencer marketing), providing a basis for subsequent steps.

[0032] In one possible implementation, please refer to Figure 2 S210, obtain hot word information based on the target region, including: S211, obtain the search set of users in the target area within a preset time period; wherein the search set includes at least one search term, and the number of searches for the search term is greater than the preset number of searches.

[0033] It is understandable that the preset time period can be the past 5 days, or a user-defined time period, but it is not limited to these. The preset number of searches can be 500, 1000, or a user-defined value, but it is not limited to these. The search set of users within the target area within the preset time period can be obtained by acquiring the search keywords of mainstream search engines in the target area within the preset time period and identifying the search keywords with a search frequency greater than the preset number of searches as the search set; alternatively, it can be obtained by using interfaces such as the Twitter API or Instagram Graph API to identify hashtags that appear more frequently than the preset number of searches within the preset time period as the search set, but it is not limited to these methods. Obtaining the search set of users within the target area within the preset time period provides a basis for subsequent steps.

[0034] S212, determine whether each search term in the search set matches the basic attributes of the product. If the search term matches the basic attributes of the product, then the search term is confirmed as a hot word.

[0035] As we can understand it, determining whether a search term matches the basic attributes of a product is equivalent to determining whether the search term can be sold as a product (for example, natural phenomena such as holidays and weather cannot be products, while digital products such as mobile phones and computers can). If a search term matches the basic attributes of a product, it means that the search term can be sold as a product. Identifying search terms that match the basic attributes of a product as hot keywords provides a basis for subsequent steps.

[0036] S213, confirm all hot words as hot word information.

[0037] It is understandable that identifying all trending words as trending word information can provide a basis for subsequent steps.

[0038] S220, obtain popular product information based on the target platform; wherein, popular product information includes at least one best-selling product.

[0039] It is understandable that obtaining popular product information based on a target platform can be achieved by using the target platform's API to retrieve products with sales exceeding or equal to a preset sales volume within a predetermined time period and identifying them as best-selling products. Alternatively, it can involve an existing user selecting a product type (e.g., kitchenware, digital products), and then using the target platform's API to retrieve the sales volume of products within that selected product type within a predetermined time period, identifying products with sales exceeding or equal to the preset sales volume as best-selling products. However, this approach is not limited to these methods. Obtaining popular product information based on a target platform enables accurate identification of popular products on that platform, providing a basis for subsequent steps.

[0040] In one possible implementation, please refer to Figure 2 S220, based on the target platform, obtains information on popular products, including: S221, obtain a sales analysis table of best-selling products on the target platform whose sales volume exceeds the preset sales volume within a preset time period; wherein, the time nodes on the horizontal axis of the sales analysis table are in days, and the vertical axis is the sales volume of best-selling products. The sales analysis table reflects the relationship between the sales volume of best-selling products and time.

[0041] It is understandable that the preset time period can be 5 days, 7 days, or a user-defined value, but it is not limited to these. The preset sales volume can be 1000 units, 1500 units, or a user-defined value, but it is not limited to these. The sales analysis report reflects the daily sales volume of best-selling products. The sales analysis report can be obtained through the target platform's API interface to retrieve the daily sales volume of each product within the preset time period, or by receiving data transmitted by the user, but it is not limited to these methods. Obtaining the sales analysis report provides a basis for subsequent steps.

[0042] S222, Based on each sales analysis table, obtain the forecast information corresponding to the best-selling products for each sales analysis table; wherein, the forecast information includes information reflecting whether the sales volume of the best-selling products can continue to be maintained or continue to grow.

[0043] It's understandable that predictive information about the best-selling products corresponding to each sales analysis table can be obtained by analyzing the trend of the line graph within the sales analysis table (whether there are inflection points, changes in the slope of the line, etc.), or by sending each sales analysis table to the user and receiving the data transmitted by the user, but it's not limited to these methods. Obtaining predictive information about the best-selling products corresponding to each sales analysis table can provide users with a reference for considering whether to list similar products.

[0044] In one possible implementation, please refer to Figure 2 S222, Based on each sales analysis table, obtain the forecast information corresponding to the best-selling products for each sales analysis table, including: S2221, Analyze each sales analysis table separately, take the sales volume corresponding to the smallest time node in the sales analysis table as the initial sales volume, and take the sales volume corresponding to the largest time node in the sales analysis table as the final sales volume.

[0045] Understandably, assuming a preset time period of 5 days and the analysis date is December 6th, the smallest time point on the sales analysis table is December 2nd, and the largest time point is December 6th. Confirming the initial and final sales figures provides a basis for subsequent steps.

[0046] S2222: The number of days reflected in the preset time period is confirmed as the number of analysis days, the sum of the sales corresponding to each time node on the sales analysis table is confirmed as the total sales, and the value obtained by dividing the total sales by the number of analysis days is confirmed as the average sales.

[0047] It is understandable that summing the sales at each time point on the sales analysis table to determine the total sales, and then dividing the total sales by the number of days analyzed to determine the average sales, can provide a basis for subsequent steps.

[0048] For example, assuming the preset time period is 5 days, the sales volume is 100 units on the first day, 200 units on the second day, 300 units on the third day, 400 units on the fourth day, and 500 units on the fifth day. Then the number of days analyzed is 5, the total sales volume is 100 + 200 + 300 + 400 + 500 = 1500 units, and the average sales volume is 1500 / 5 = 500 units.

[0049] S2223, the value obtained by adding the final sales volume to the average sales volume is confirmed as the judgment value.

[0050] It is understandable that using the final sales volume plus the average sales volume as the judgment value can provide a basis for subsequent steps.

[0051] For example, assuming the final sales volume is 500 units and the average sales volume is 400 units, then the judgment value = 500 + 400 = 900.

[0052] S2224: If the initial sales volume is greater than or equal to the final sales volume and less than the judgment value, then the prediction information reflecting that the sales volume of the hot-selling product can continue to be maintained is obtained; if the initial sales volume is greater than or equal to the judgment value, then the prediction information reflecting that the sales volume of the hot-selling product can continue to grow is obtained.

[0053] It is understandable that if the initial sales volume is greater than or equal to the final sales volume but less than the judgment value, it means that the sales growth rate of the hot-selling product is gradually decreasing but there is still a sales market, and the popularity of the hot-selling product may decrease; if the initial sales volume is greater than or equal to the judgment value, it means that the sales growth rate of the hot-selling product remains unchanged or increases, and the popularity of the hot-selling product increases or remains the same.

[0054] S223 identifies best-selling products as hot-selling product information if forecasts indicate that sales of these products will continue to be maintained or grow.

[0055] It is understandable that identifying best-selling products that reflect the forecast information that their sales volume can continue to be maintained or continue to grow as popular product information means collecting products that can maintain sales volume in the future, providing a basis for subsequent steps.

[0056] S230: Match each hot word with each best-selling product, and determine whether the matched hot word is associated with the best-selling product. If the hot word is associated with the best-selling product, then confirm the product name corresponding to the best-selling product as the pre-selected product name.

[0057] Understandingly, matching each trending keyword with each best-selling product is essentially determining whether the item or event reflected by the trending keyword drove the sales of the best-selling product (e.g., portable power banks boosted power bank sales, celebrity-endorsed water bottles boosted water bottle sales, etc.). Methods for determining whether trending keywords match best-selling products can include using Word2Vec and BERT pre-trained models for cross-border e-commerce data to generate a domain-specific word vector space containing 500,000 words and then calculating semantic similarity; alternatively, sending each trending keyword and best-selling product to users and receiving their judgments before transmitting the data, but these methods are not limited to these. The association between trending keywords and best-selling products indicates a correlation between the increased sales of the product and the behavior of consumers in the target region, excluding abnormal data generated by manipulation such as fake orders. Confirming the product names corresponding to best-selling products as pre-selected product names provides a basis for subsequent steps.

[0058] S240, confirm all pre-selected product names as pre-selected product information.

[0059] It is understandable that confirming all the pre-selected product names as pre-selected product information can provide a basis for subsequent steps.

[0060] S300, based on pre-selected product information and target platform, obtains product information for listing; wherein, the product information for listing includes at least one product information, which includes the product name, product image and product price.

[0061] It's understandable that obtaining product listing information based on pre-selected product information and the target platform could involve searching for the average price of pre-selected product names on the target platform, then listing pre-selected products whose average price falls within the expected price range, confirming the average price as the listed product price, and generating product images using AI based on the listed product names. Alternatively, it could involve sending the pre-selected product information and the target platform to users and receiving data transmitted by users, but is not limited to these methods. Obtaining product listing information based on pre-selected product information and the target platform ensures that products align with supply chain advantages, allows for accurate pricing to suit the target platform, generates attractive product images, and shortens the product selection and listing cycle.

[0062] In one possible implementation, please refer to Figure 3 S300 obtains product listing information based on pre-selected product information and the target platform, including: S310: Analyze each pre-selected product name to determine whether the product reflected by the pre-selected product name belongs to the advantageous supply chain. If the product reflected by the pre-selected product name belongs to the advantageous supply chain, then confirm the pre-selected product name as the product name to be listed. The advantageous supply chain is the raw material supply chain that the user can provide.

[0063] It's understandable that determining whether the product reflected in the pre-selected product name belongs to an advantageous supply chain means determining whether the user has a cost advantage in producing the product reflected in the pre-selected product name. If the product reflected in the pre-selected product name belongs to an advantageous supply chain, it means that the user can produce the product reflected in the pre-selected product name at a lower price.

[0064] For example, suppose a user can buy latex at a lower price and pre-selects the product name as a latex pillow, then the latex pillow belongs to an advantageous supply chain.

[0065] S320 obtains the listed product price corresponding to each listed product name based on each listed product name and the target platform.

[0066] It's understandable that obtaining the price of each listed product based on its name and target platform could involve searching the target platform for the average price of the product as reflected in its name, or sending the product name and target platform to the user and receiving the data transmitted by the user, but it's not limited to these methods. Obtaining the price of listed products based on their names and target platforms can provide users with a reference for assessing gross profit margins and their own competitiveness, and provide a basis for subsequent steps.

[0067] In one possible implementation, please refer to Figure 3 S320, based on each listed product name and the target platform, obtains the listed product price corresponding to each listed product name, including: S321, after searching each listed product name on the target platform, multiple price sets corresponding to each listed product name are obtained; wherein, the price set includes multiple product prices corresponding to the listed product names, and the product prices in the price set are arranged in ascending order.

[0068] It's understandable that searching for the names of listed products on the target platform and obtaining the corresponding price sets is equivalent to collecting the prices of the same products on the target platform. Obtaining the price sets corresponding to each listed product name and arranging the product prices in each set in ascending order provides a basis for subsequent steps.

[0069] S322, the value obtained by multiplying the number of commodity prices in the price set by a preset ratio is determined as the front cutoff point, and the value obtained by subtracting the front cutoff point from the number of commodity prices in the price set is determined as the back cutoff point.

[0070] It is understandable that the preset ratio could be 30%, 35%, or a user-defined value, but it is not limited to these. Calculating the pre-truncation point and post-truncation point provides a basis for subsequent steps.

[0071] For example, assuming the price set contains 100 product prices and the preset ratio is 30%, then the first cutoff point = 100 * 0.3 = 30, and the second cutoff point = 100 - 30 = 70.

[0072] S323: Arrange the price set in order of commodity prices, delete all commodity prices from the starting position to the first cutoff point, and delete commodity prices from the second cutoff point to the last position. Then, divide the sum of the remaining commodity prices in the price set by the number of remaining commodity prices in the price set and use the result as the analysis price.

[0073] It is understandable that deleting all product prices from the starting position to the previous cutoff point and deleting product prices from the next cutoff point to the end position can remove most abnormal prices (prices that are too high or too low), ensuring the reliability of the analyzed prices and providing a reference for users' pricing.

[0074] For example, suppose there are ten commodity prices in the price set: 10, 20, 21, 48, 49, 50, 51, 52, 80, and 85. The first cutoff point is 3 and the second cutoff point is 7. Then the analyzed price = (48 + 49 + 50 + 51 + 52) / 5 = 50.

[0075] S324, determine whether each analysis price is within the preset price range, and confirm the analysis price within the preset price range as the listed product price.

[0076] It is understandable that the preset price range could be 45~55, 40~60, or a value customized by the user based on their own circumstances, but it is not limited to these. Confirming the analyzed price within the preset price range indicates that the listed product price aligns with the user's economic expectations, providing a basis for subsequent steps.

[0077] S330: Based on hot word information and the names of each listed product, obtain the images of the listed products corresponding to each listed product name.

[0078] It's understandable that generating corresponding product images based on trending keywords and product names could involve combining trending keywords with product names, determining the semantic coherence of the combined words, and then inputting the semantically coherent words into image or video generation AI to produce product images. Alternatively, it could involve sending the trending keywords and product names to users and receiving the image data transmitted by them, but is not limited to these methods. Generating corresponding product images based on trending keywords and product names effectively aligns with consumer trends and attracts consumer attention.

[0079] In one possible implementation, please refer to Figure 3 S330, based on hot keyword information and the names of each listed product, obtains the images of the listed products corresponding to each listed product name, including: S331. After extracting each adjective from the hot word information and marking them as collocations, each collocation is combined with each listed product name to obtain multiple combination words corresponding to each listed product name.

[0080] It's understandable that extracting adjectives from hot words can be done through part-of-speech tagging using natural language processing libraries like NLTK (Natural Language Toolkit) and spaCy, or through regular expression matching, but is not limited to these methods. Obtaining multiple combinations of words corresponding to each listed product name can provide a basis for subsequent steps.

[0081] For example, suppose there are two collocations, "red" and "enthusiastic," and two product names, "power bank" and "raincoat." Then, the combined words would be "red power bank," "red raincoat," "enthusiastic power bank," and "enthusiastic raincoat."

[0082] S332, Step a: Determine the semantic rationality of each combination of words.

[0083] It is understandable that the semantic rationality of compound words can be judged through natural language processing tools (such as NLTK and spaCy) to analyze the grammatical structure of the compound words, or by constructing a training set using a large amount of semantically rational compound word data and training it with machine learning algorithms (such as support vector machines, random forests, and neural networks) to obtain a semantic analysis model for judgment, but it is not limited to these methods. Judging the semantic rationality of compound words is to determine whether the product described by the compound words can be produced.

[0084] S333, Step b: If each combination word contains both semantically reasonable and semantically unreasonable combination words, or if the semantics of each combination word are all reasonable, then delete the semantically unreasonable combination words, and then perform a second combination of each combination word with the corresponding product name to obtain at least one combination word corresponding to each product name. Then repeat steps a and b. If there are no semantically reasonable combination words among the combination words, then obtain the product images corresponding to each product name based on the combination words before combination.

[0085] It is understandable that if there are semantically reasonable compound words among the various compound words, then it is possible to further add qualifiers (adjectives) to these compound words, thereby making the produced products more aligned with consumer preferences (for example, "red power bank" can be modified by adding the adjective "portable" to become "portable red power bank," etc.). If there are no semantically reasonable compound words among the various compound words, then it is impossible to add further adjectives to these compound words. The methods for obtaining product images corresponding to each listed product name based on the compound words before combination can include inputting each compound word into an image generation AI (Stable Diffusion, Midjourney, etc.) or a video generation AI (Sora, Runway, etc.) to obtain product images corresponding to the products corresponding to the compound words, or sending the compound words to the user and receiving the data transmitted by the user, etc., but are not limited to these methods.

[0086] For example, suppose there are four collocation words: 1, 2, 3, and 4, and three product names: A, B, and C. After the first combination, we get 12 collocation words: 1A, 2A, 3A, 4A, 1B, 2B, 3B, 4B, 1C, 2C, 3C, and 4C. After deleting semantically unreasonable collocation words, we have 9 collocation words: 2A, 3A, 4A, 1B, 3B, 4B, 1C, 2C, and 3C. After the second combination, we get 9 collocation words: 23A, 24A, 34A, 13B, 14B, 34B, 12C, 13C, and 23C. If the meanings of 23A, 24A, 34A, 13B, 14B, 34B, 12C, 13C, and 23C are all unreasonable, then based on 2A, 3A, 4A, 1B, ... 3B, 4B, 1C, 2C, 3C yield the product images corresponding to A, B, and C respectively (2A, 3A, 4A yield the product image corresponding to A; 1B, 3B, 4B yield the product image corresponding to B; 1C, 2C, 3C yield the product image corresponding to C); if the semantics of 23A, 13B, and 12C in 23A, 24A, 34A, 13B, 14B, 34B, 12C, 13C, 23C are unreasonable, then delete 23A, 13B, and 12C, and then combine 24A, 34A, 14B, 34B, 13C, and 23C again to obtain 234A, 134B, 123C, and repeat the steps of combination and semantic reasonableness judgment.

[0087] S340: After confirming each product name, the corresponding product image, and the product price as product information, confirm all product information as product information.

[0088] It is understandable that confirming all the product information to be listed can ensure that the products meet the advantages of the supply chain, accurately price them to suit the target platform, generate product images that can attract users, and shorten the product selection and listing cycle.

[0089] For example, after confirming all the listed product information as listed product information, each listed product information in the listed product information can be directly listed on the target platform, or the listed product information can be sent to the user and the user can select the listed product information they want to list, etc., but not limited to these.

[0090] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0091] Corresponding to the fully automated product selection and listing method for cross-border e-commerce based on multiple platforms described in the above embodiments, this application also provides a fully automated product selection and listing system for cross-border e-commerce based on multiple platforms. Each unit of this system can realize each step of the fully automated product selection and listing method for cross-border e-commerce based on multiple platforms. Figure 4 The diagram shows a structural block diagram of a multi-platform cross-border e-commerce fully automated product selection and listing system provided in this application embodiment. For ease of explanation, only the parts related to this application embodiment are shown.

[0092] Reference Figure 4 , the system comprises: The acquisition unit is used to acquire target information, which includes target region and target platform. The target region reflects the region where the user sells the goods, and the target platform is the e-commerce platform where the goods are listed.

[0093] A pre-selection unit is used to obtain pre-selected product information based on target information; wherein, the pre-selected product information includes at least one pre-selected product name.

[0094] The listing unit is used to obtain listing product information based on pre-selected product information and the target platform; wherein, the listing product information includes at least one listing product information, which includes the listing product name, listing product image and listing product price.

[0095] It should be noted that the information interaction and execution process between the above-mentioned units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0096] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0097] This application also provides a fully automated product selection and listing device for cross-border e-commerce based on multiple platforms. Figure 5 This is a structural diagram of a multi-platform, fully automated product selection and listing device for cross-border e-commerce, provided as an embodiment of this application. Figure 5 As shown, the fully automated product selection and listing equipment for cross-border e-commerce based on multiple platforms in this embodiment includes a control device 6. The control device 6 includes at least one processor 60. Figure 5 Only one is shown in the image), at least one memory 61 ( Figure 5 (Only one is shown in the image) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, it causes the multi-platform cross-border e-commerce fully automated product selection and listing device to implement the steps in any of the above embodiments of the multi-platform cross-border e-commerce fully automated product selection and listing method, or causes the multi-platform cross-border e-commerce fully automated product selection and listing device to implement the functions of each unit in the above system embodiments.

[0098] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 62 in the control device 6.

[0099] The control device 6 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The control device 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 5This is merely an example of a fully automated product selection and listing device for cross-border e-commerce based on multiple platforms. It does not constitute a limitation on fully automated product selection and listing devices for cross-border e-commerce based on multiple platforms. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, buses, etc.

[0100] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0101] In some embodiments, the memory 61 may be an internal storage unit of the control device 6, such as a hard disk or memory of the control device 6. In other embodiments, the memory 61 may be an external storage device of the control device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the control device 6. Furthermore, the memory 61 may include both internal storage units and external storage devices of the control device 6. The memory 61 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0102] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0103] This application provides a computer program product that, when run on a multi-platform cross-border e-commerce fully automated product selection and listing device, enables the multi-platform cross-border e-commerce fully automated product selection and listing device to implement the steps in any of the above method embodiments.

[0104] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a multi-platform cross-border e-commerce fully automated product selection and listing device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0105] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0106] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0107] In the embodiments provided in this application, it should be understood that the disclosed multi-platform cross-border e-commerce fully automated product selection and listing system, equipment, and method can be implemented in other ways. For example, the embodiments of the multi-platform cross-border e-commerce fully automated product selection and listing system and equipment described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0108] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A fully automated product selection and listing method for cross-border e-commerce based on multiple platforms, characterized in that, include: Obtain target information; wherein, the target information includes target region and target platform, the target region reflects the region where the user sells goods, and the target platform is the e-commerce platform where the goods are listed; Based on the target information, pre-selected product information is obtained; wherein, the pre-selected product information includes at least one pre-selected product name; Based on the pre-selected product information and the target platform, the product information to be listed is obtained; wherein, the product information to be listed includes at least one product information, the product information to be listed includes the product name, product image and product price.

2. The fully automated product selection and listing method for cross-border e-commerce based on multiple platforms as described in claim 1, characterized in that, The process of obtaining pre-selected product information based on the target information includes: Hot word information is obtained based on the target area; wherein, the hot word information includes at least one hot word, and the hot word reflects the items or events that users in the target area are interested in; Popular product information is obtained based on the target platform; wherein, the popular product information includes at least one best-selling product; Each of the hot words is matched with each of the best-selling products, and it is determined whether the matched hot words are associated with the best-selling products. If the hot words are associated with the best-selling products, the product name corresponding to the best-selling products is confirmed as the pre-selected product name. All of the pre-selected product names are confirmed as the pre-selected product information.

3. The fully automated product selection and listing method for cross-border e-commerce based on multiple platforms as described in claim 2, characterized in that, The process of obtaining hot word information based on the target region includes: Obtain a search set of users within the target area within a preset time period; wherein the search set includes at least one search term, and the number of searches for the search term is greater than a preset number of searches; Each search term in the search set is determined to match the basic attributes of the product. If the search term matches the basic attributes of the product, the search term is identified as the hot word. All of the aforementioned hot words are confirmed as hot word information.

4. The fully automated product selection and listing method for cross-border e-commerce based on multiple platforms as described in claim 2, characterized in that, The process of obtaining popular product information based on the target platform includes: Obtain a sales analysis table of best-selling products on the target platform whose sales volume exceeds a preset sales volume within a preset time period; wherein, the time nodes on the horizontal axis of the sales analysis table are in days, and the vertical axis is the sales volume of the best-selling products, and the sales analysis table reflects the relationship between the sales volume of the best-selling products and time. Based on each of the aforementioned sales analysis tables, forecast information corresponding to the best-selling products for each of the aforementioned sales analysis tables is obtained; wherein, the forecast information includes information reflecting whether the sales volume of the best-selling products can continue to be maintained or continue to grow; The best-selling products corresponding to the forecast information that reflects the continued or sustained growth of sales of the best-selling products are identified as the popular product information.

5. The fully automated product selection and listing method for cross-border e-commerce based on multiple platforms as described in claim 4, characterized in that, The step of obtaining forecast information for best-selling products corresponding to each of the sales analysis tables includes: Each of the aforementioned sales analysis tables is analyzed separately, and the sales volume corresponding to the smallest time node in the sales analysis table is taken as the initial sales volume, and the sales volume corresponding to the largest time node in the sales analysis table is taken as the final sales volume. The number of days reflected in the preset time period is confirmed as the number of analysis days, the sum of the sales corresponding to each time node on the sales analysis table is confirmed as the total sales, and the value obtained by dividing the total sales by the number of analysis days is confirmed as the average sales. The value obtained by adding the final sales volume to the average sales volume is confirmed as the judgment value. If the initial sales volume is greater than or equal to the final sales volume and less than the judgment value, then the prediction information reflecting that the sales volume of the hot-selling product can continue to be maintained is obtained; if the initial sales volume is greater than or equal to the judgment value, then the prediction information reflecting that the sales volume of the hot-selling product can continue to grow is obtained.

6. The fully automated product selection and listing method for cross-border e-commerce based on multiple platforms as described in claim 2, characterized in that, The process of obtaining the product information to be listed based on the pre-selected product information and the target platform includes: Each of the pre-selected product names is analyzed to determine whether the product reflected by the pre-selected product name belongs to the advantageous supply chain. If the product reflected by the pre-selected product name belongs to the advantageous supply chain, then the pre-selected product name is confirmed as the product name to be listed. The advantageous supply chain is the raw material supply chain that the user can provide. The price of each listed product is obtained based on each listed product name and the target platform; Based on the hot word information and the names of each of the listed products, images of the listed products corresponding to each of the listed product names are obtained; After confirming each of the listed product names, the corresponding listed product images, and the listed product prices as the listed product information, all of the listed product information is confirmed as the listed product information.

7. The fully automated product selection and listing method for cross-border e-commerce based on multiple platforms as described in claim 6, characterized in that, The step of obtaining the price of the listed product corresponding to each of the listed product names based on each of the listed product names and the target platform includes: After searching each of the listed product names on the target platform, multiple price sets corresponding to each of the listed product names are obtained; wherein, each price set includes multiple product prices corresponding to the listed product names, and the product prices in the price set are arranged in ascending order; The value obtained by multiplying the number of commodity prices in the price set by a preset ratio is determined as the front cutoff point, and the value obtained by subtracting the front cutoff point from the number of commodity prices in the price set is determined as the back cutoff point. After deleting all commodity prices from the starting position to the first cutoff point and from the second cutoff point to the end position in the price set according to the order of commodity prices, the sum of the remaining commodity prices in the price set is divided by the number of remaining commodity prices in the price set, and the resulting value is confirmed as the analysis price. Each analyzed price is determined to be within a preset price range, and the analyzed price within the preset price range is confirmed as the price of the listed product.

8. The fully automated product selection and listing method for cross-border e-commerce based on multiple platforms as described in claim 6, characterized in that, The step of obtaining product images corresponding to each of the listed product names based on the hot word information and each of the listed product names includes: After extracting each adjective from the hot word information and marking them as collocations, each collocation is combined with each of the listed product names to obtain multiple combination words corresponding to each of the listed product names. Step a: Determine the semantic rationality of each of the combined words; Step b: If each of the combined words contains both semantically reasonable and semantically unreasonable combined words, or if all of the combined words are semantically reasonable, then delete the semantically unreasonable combined words, and then perform a secondary combination of each of the combined words with the corresponding combined words of the listed product name to obtain at least one combined word corresponding to each of the listed product names, and then repeat steps a and b; if none of the combined words contain semantically reasonable combined words, then obtain the listed product image corresponding to each of the listed product names based on the combined words before combination.

9. A fully automated product selection and listing system for cross-border e-commerce based on multiple platforms, characterized in that, include: An acquisition unit is used to acquire target information; wherein, the target information includes a target region and a target platform, the target region reflects the region where the user sells goods, and the target platform is the e-commerce platform where the goods are listed; A pre-selection unit is used to obtain pre-selected product information based on the target information; wherein the pre-selected product information includes at least one pre-selected product name; The listing unit is used to obtain listing product information based on the pre-selected product information and the target platform; wherein, the listing product information includes at least one listing product information, and the listing product information includes the listing product name, listing product image and listing product price.

10. A fully automated product selection and listing device for cross-border e-commerce based on multiple platforms, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 8.

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