Commodity pushing method based on e-commerce platform image analysis

Abstract

The invention relates to the field of image pushing, and discloses an e-commerce platform image analysis pushing system, which comprises commodity shooting equipment, image processing equipment, storage equipment, a processor, display equipment and user terminal equipment, the commodity shooting equipment is used for shooting commodity images, is connected with the processor and transmits shot image data to the processor; the image processing equipment is used for processing the commodity image, is connected with the processor, and is used for receiving the image data transmitted by the processor and executing image optimization processing; and the storage equipment is connected with the commodity shooting equipment, the processor, the analysis equipment and the display equipment and is used for storing the commodity images. Through cooperative use of the storage device, the image processing device, the commodity shooting device and other components, extraction and analysis of visual features such as colors, textures and shapes of commodity images can be automatically completed, and commodity pushing to users is improved through image comparison.

Description

Technical Field

[0001] The present invention relates to the technical field of image push, and in particular to a commodity push method based on image analysis of an e-commerce platform. Background Art

[0002] In modern e-commerce ("e-commerce") platforms, millions of products are displayed online for consumers to choose from, covering a wide range of categories from daily consumer goods to luxury goods. E-commerce platforms provide merchants with an open and diverse sales channel, allowing consumers to browse, compare and purchase products anytime and anywhere. Typical e-commerce platforms include multiple modules such as product display, recommendation, user management and payment system. The core of e-commerce platforms is how to enable users to quickly find products that meet their needs, so as to improve users' shopping experience and the transaction conversion rate of the platform.

[0003] In terms of product recommendations, the recommendation systems of traditional e-commerce platforms are usually based on behavioral data such as user browsing history, purchase history, and product reviews, and push relevant products through algorithm matching.

[0004] However, these methods have obvious limitations when it comes to products that rely on visual appeal (such as clothing, furniture, decorations, etc.), and it is difficult to fully utilize the rich visual information in product images. The shortcomings of this recommendation method make it impossible for the platform to effectively capture users' potential preferences, reducing the accuracy of recommendations and users' acceptance of recommended content. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a product push method based on e-commerce platform image analysis, which solves the problems of poor accuracy and customer acceptance of image push on existing platforms.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an e-commerce platform image analysis and push system, including a product shooting device, an image processing device, a storage device, a processor, a display device, and a user terminal device; The product photographing device is used to photograph product images and is connected to the processor to transmit the photographed image data to the processor; An image processing device is used to process the product image, is connected to the processor, receives the image data transmitted by the processor and performs image optimization processing; A storage device connected to the product photographing device, the processor, the analysis device and the display device, and used to store product images; A processor, connected to the product photographing device, the image processing device, the storage device and the display device, respectively, for transmitting the photographed image to the image processing device and storing the processing result in the storage device; A display device, connected to the storage device, the processor and the user terminal device, for displaying the processing and analysis results of the product image; The user terminal device is connected to the display device and is used to receive and push the product images that have been analyzed and optimized.

[0007] Preferably, the processor is equipped with a shelf loading and unloading management module and an analysis device; The on-shelf and off-shelf management module automatically adjusts the display priority and on-shelf and off-shelf status of the products according to user behavior data and product inventory conditions; The analysis device is connected to the storage device and is used to analyze the commodity image data stored in the storage device to extract commodity feature information.

[0008] Preferably, the on-shelf and off-shelf management module includes: Data analysis module, used to monitor the number of clicks, views, conversion rate and inventory status of products in real time; The rule engine module is used to automatically adjust the display priority or shelf status of products according to preset conditions.

[0009] Preferably, the analysis equipment comprises: The feature extraction module is used to extract color, texture, and shape features from the product image to generate a feature vector of the product; Behavior analysis module, used to analyze user preferences based on user behavior data and generate product recommendation information based on user preferences; The image quality detection module is used to detect the resolution, clarity and lighting conditions of product images.

[0010] Preferably, the commodity photographing device includes but is not limited to a smart phone or a digital camera.

[0011] Preferably, the image processing device includes but is not limited to a desktop computer or a tablet computer, and is installed with image processing software.

[0012] Preferably, the storage device includes but is not limited to a cloud storage service or a local physical storage device.

[0013] Preferably, the display device includes but is not limited to a merchant management backend or a merchant mobile application for viewing the uploaded product images and analysis results.

[0014] Preferably, the user terminal device includes but is not limited to a mobile terminal and a computer, and is used to display the product images after analysis and optimization.

[0015] A method for pushing products based on image analysis on an e-commerce platform comprises the following steps: S1: Use a camera to capture product images and transmit the image data to a processor; S2: The image processing device performs cropping and optimization preprocessing operations on the product image, and stores the processing results in a storage device; S3: The analysis device extracts product images from the storage device and performs feature extraction, user behavior analysis, and quality inspection on the images; S4: storing the product image features and user behavior data analysis results in a storage device; S5: The on-shelf and off-shelf management module in the processor automatically adjusts the display priority and on-shelf and off-shelf status of the products based on user behavior data and inventory information; S6: The processor generates a personalized push product list based on product features and user preference data, and transmits it to the display device; S7: The display device displays the recommended product list to the merchant and the user terminal device, so that the user and the merchant can view the pushed products; S8: The user terminal device collects the user's click, browsing, and purchase information, and transmits it back to the processor to perform step S1.

[0016] The present invention provides a product push method based on e-commerce platform image analysis, which has the following beneficial effects: 1. The present invention can automatically extract and analyze visual features such as product image color, texture and shape through the coordinated use of storage devices, image processing devices, product photography devices and other components, and improve product push to users through image comparison.

[0017] 2. The present invention is based on the analysis of image features. The system can recommend more diverse products to users through visual similarities. In particular, when users browse specific types of products, the system can recommend other products with similar styles based on visual features.

[0018] 3. By integrating the user's click, browse and other behavioral data with the visual feature data of the goods, the system of the present invention can model the user's interests from multiple dimensions, thereby generating recommendation results that are more in line with the user's preferences. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a system architecture diagram of the e-commerce platform image analysis and push system proposed by the present invention; Figure 2 This is a processor architecture diagram of the e-commerce platform image analysis and push system proposed by the present invention; Figure 3 This is a diagram of the analysis device architecture of the e-commerce platform image analysis and push system proposed by the present invention; Figure 4 This is an architecture diagram of the on-shelf and off-shelf management module of the e-commerce platform image analysis and push system proposed by the present invention; Figure 5This is a method step diagram of a product push method based on e-commerce platform image analysis proposed by the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1: Please refer to the attached Figure 1 , an embodiment of the present invention provides an e-commerce platform image analysis and push system, including: The product photographing device is used to photograph product images and is connected to the processor to transmit the photographed image data to the processor; the product photographing device includes but is not limited to a smart phone or a digital camera.

[0022] Specifically, the product photography equipment has a high-resolution photography function to ensure that the product image is clear and complete, and can accurately present the product's color, texture, shape and other visual characteristics.

[0023] In this system, the product photography equipment not only undertakes the image acquisition function, but also is the key link to ensure the display effect of the product on the e-commerce platform. By acquiring high-quality images, the product photography equipment provides a reliable data basis for subsequent image processing and analysis. The images collected by the photography equipment are pre-processed by cropping, optimizing, denoising and other operations through the image processing module of the processor, so that the image quality meets the display standards of the e-commerce platform, and removes background interference and other factors to make the product details more prominent. Based on such clear product image data, the system can perform in-depth image feature analysis.

[0024] Specifically, the analysis device in the processor will extract the product's color, texture, shape and other visual features from the image through the feature extraction module to form a feature vector of the product to provide support for subsequent recommendation steps.

[0025] Combined with user behavior data, the system can further match these visual features with user preferences to achieve personalized recommendations based on images. This multi-dimensional fusion recommendation model allows the system to not only consider the user's historical browsing and clicking behavior, but also understand the user's potential interests more accurately through visual features, and ultimately push product recommendations of similar style or visual fit to the user.

[0026] Through the collaboration between product photography equipment, processors and analysis modules, the system can efficiently complete product image collection, feature extraction and personalized recommendations, improve the accuracy of recommendation results, and thus optimize user experience and enhance the display and recommendation effects of platform products.

[0027] Image processing equipment, used to process product images, connected to the processor, receiving image data transmitted by the processor and performing image optimization processing; image processing equipment includes but is not limited to desktop computers or tablet computers, and is installed with image processing software.

[0028] Specifically, the image processing device includes but is not limited to a desktop computer or tablet computer, on which a fully functional image processing software is installed. The image processing software can perform operations such as image cropping, denoising, brightness and contrast adjustment, color correction, and background optimization, so as to ensure that the display effect of the product image meets the standard requirements of the e-commerce platform and is visually attractive.

[0029] Image processing equipment plays a vital role in product display. First, the product image is pre-processed, for example, the redundant background is cut out to make the main body of the product more prominent. De-noising can remove the noise or background interference generated during the shooting process to improve the clarity and quality of the image.

[0030] Brightness, contrast and color correction operations can further improve the visual effect of the product, making it more attractive, and ensure that the displayed color is consistent with the real object, enhancing the user's trust in the product. In addition, the image processing device can also optimize different areas of the image according to the characteristics of the product type, such as enhancing the contrast of certain detail areas, so that users can see the texture and details of the product more clearly.

[0031] A storage device is connected to the product shooting device, processor, analysis device and display device, and is used to store product images; the storage device includes but is not limited to a cloud storage service or a local physical storage device.

[0032] Specifically, the storage device ensures smooth data transmission between the modules of the system by connecting with the product photography device, processor, analysis device and display device, and supports the centralized storage and call of product images and analysis data. Storage devices include but are not limited to cloud storage services or local physical storage devices. The specific choice depends on the required scale of the system, the amount of data and the access speed requirements. Cloud storage services have the advantages of high scalability, easy backup and access, and are suitable for large-scale data storage and cross-regional data sharing, while local physical storage devices provide higher controllability and security, and are suitable for data storage needs in small or local area networks.

[0033] In the system's operating process, the storage device first receives the original image data from the product shooting device, and through cooperation with the processor, stores the product image and analysis data optimized and processed by the processor into the storage device.

[0034] The storage device is responsible for saving the image files optimized by the image processing device to ensure that high-quality images can be called when needed for subsequent feature analysis and display. The storage device not only stores product images, but also stores product feature data generated by the analysis device, user preference data, and information related to on-shelf management.

[0035] The processor is connected to the product shooting device, the image processing device, the storage device and the display device respectively, and is used to transmit the shot image to the image processing device and store the processing result in the storage device; the processor is equipped with a shelf management module and an analysis device; Specifically, the processor is responsible for transmitting the original image data received from the product shooting device to the image processing device, starting the image optimization operation, and ensuring that the product image maintains an efficient and orderly flow during the transmission, optimization, and storage processes. After the image processing device completes the image optimization, the processor stores the optimized image data in the storage device for subsequent analysis, display, and recommendation.

[0036] The processor is not only responsible for the scheduling and transmission of data, but also installed with the on-shelf and off-shelf management module and analysis equipment, which enhances the intelligent management ability and recommendation function of the system. The on-shelf and off-shelf management module dynamically adjusts the display priority and on-shelf and off-shelf status of products by real-time monitoring of user behavior data and product inventory. This module includes a data analysis module and a rule engine module. The data analysis module is used to continuously monitor the key data of the product, such as the number of clicks, views, conversion rate, etc., and update the display effect of the product in real time; the rule engine module automatically determines the display priority of the product or whether it needs to be on or off the shelf based on preset conditions, such as inventory warning lines, click-through rates and other rules. Through the intelligent management of this module, the platform can respond to market demand in a timely manner and maintain dynamic adjustment of product display.

[0037] In addition, the analysis device in the processor is responsible for the in-depth feature analysis of the product image and supports the intelligent recommendation function of the system. The analysis device includes a feature extraction module, a behavior analysis module, and an image quality detection module, which are used to extract visual features such as color, texture, and shape in the product image, analyze user preference behavior data, and detect quality indicators such as image resolution and clarity.

[0038] The feature extraction module generates feature vectors of products, laying the foundation for personalized recommendations. The behavior analysis module combines user behavior data to perform preference analysis to achieve accurate matching of product recommendations. The image quality detection module ensures that product images meet the platform's visual standards.

[0039] The on-shelf and off-shelf management module automatically adjusts the display priority and on-shelf and off-shelf status of products based on user behavior data and product inventory status; it includes: a data analysis module for real-time monitoring of product clicks, page views, conversion rate and inventory status; a rule engine module for automatically adjusting the display priority or on-shelf and off-shelf status of products based on preset conditions.

[0040] Specifically, the data analysis module is responsible for continuously monitoring the key data of the product, including the number of clicks, views, conversion rate and inventory status of the product. The number of clicks and views can directly reflect the user's interest in the product, while the conversion rate indicates the possibility of the user's actual purchase, and the inventory status reflects the number of products that can be sold. By monitoring these indicators in real time, the data analysis module can generate accurate information about the popularity and market demand of the product. For example, when the number of clicks and views of a product is high and the conversion rate is ideal, it indicates that users have a great interest in the product; if the inventory is insufficient, the system can remind the operator to replenish the inventory in time to avoid missing sales opportunities due to out-of-stock products.

[0041] The rule engine module automatically adjusts the display priority or listing status of products based on the real-time data provided by the data analysis module and the preset conditions of the platform. The rule engine has a series of conditional rules, such as the number of clicks, conversion rate, inventory warning line, etc. of the product.

[0042] When the number of clicks on a product continues to increase and the inventory is low, the rule engine can prioritize the product or mark it as a hot-selling product; when the number of clicks and conversion rate of a product continue to be low, the rule engine will lower the display priority of the product or even automatically remove it from the shelves to optimize the allocation of platform resources. This intelligent rule engine makes the management of listing and delisting more flexible, and can dynamically adapt to user needs and market changes, ensuring that high-quality products have a higher chance of display, and that low-demand products can reasonably allocate resources.

[0043] Overall, the on-shelf and off-shelf management module realizes the automated management of product display through the organic combination of the data analysis module and the rule engine module, ensuring that the display priority and on-shelf and off-shelf status can be dynamically adjusted based on user behavior data and inventory information.

[0044] The analysis device is connected to the storage device and is used to analyze the product image data stored in the storage device to extract product feature information. The analysis device includes: a feature extraction module, which is used to extract color, texture, and shape features from the product image to generate a feature vector of the product; a behavior analysis module, which is used to analyze user preferences in combination with user behavior data and generate product recommendation information based on user preferences; and an image quality detection module, which is used to detect the resolution, clarity, and lighting conditions of the product image.

[0045] Specifically, the analysis device is a key component of this system. It is connected to the storage device to analyze the product image data stored in the storage device and extract the product feature information, thereby supporting personalized recommendations and optimization of product display. The analysis device includes a feature extraction module, a behavior analysis module, and an image quality detection module. Each module has clear functions and cooperates with each other to achieve comprehensive and accurate image data analysis.

[0046] The feature extraction module focuses on extracting visual feature information from product images, including key visual features such as color, texture, and shape. Color features reflect the main color and color matching of the product, texture features reveal the material and texture of the product, and shape features can highlight the outline and structure of the product. By extracting these visual features and generating feature vectors of the product, the system establishes visual labels for the product, providing data support for recommendations based on image similarity, so that users can see products that match their aesthetic preferences when browsing, thereby improving the accuracy of recommendations.

[0047] The behavior analysis module combines the user's browsing, clicking, purchasing and other behavior data to deeply analyze the user's preference patterns, thereby generating product recommendation information that matches the user's interests. The behavior analysis module can identify the user's visual preferences (such as color, style, etc.) by matching user behavior data with product features, thereby helping the system generate a personalized recommendation list. The operation of this module can effectively improve the relevance of recommendation results, so that the system not only relies on historical purchase behavior, but also can dynamically analyze and respond to the user's latest preferences, enhancing the user's shopping experience.

[0048] The image quality detection module is used to detect the resolution, clarity and lighting conditions of product images and identify image quality issues. Resolution detection can ensure high-definition display of images to avoid blur or distortion; clarity detection ensures that product details can be clearly presented to enhance the user's viewing experience; lighting condition detection ensures the color authenticity and brightness effect of the image, making the display effect of the product close to the real object. For images that do not meet the platform requirements, the image quality detection module will mark these images for reshooting or optimization.

[0049] A display device is connected to the storage device, the processor and the user terminal device, and is used to display the processing and analysis results of the product image; the display device includes but is not limited to a merchant management backend or a merchant mobile application for viewing the uploaded product images and the analysis results.

[0050] Specifically, the display device extracts the processed product images from the storage device and displays the key information of the product on the merchant side and the user side in combination with the results of the processor analysis. The merchant management backend or mobile terminal application is used as the implementation form of the display device to support merchants to review, manage and update the uploaded product images.

[0051] For example, merchants can check the clarity, resolution and color effects of product images through the background to understand whether they meet the platform requirements, and decide whether to reshoot or optimize the image based on the feedback from the image quality detection module. In addition, the display device also displays the product feature information (such as color, texture and shape, etc.) extracted by the analysis device, so that merchants can clearly understand how the system identifies the visual features of the product, providing support for better product classification and label management.

[0052] The display device also has the function of displaying product recommendation information to users. By connecting to the user's terminal device, the personalized recommended product images are pushed directly to the user interface. Users can browse the product recommendations that meet their preferences on the mobile application or web page to get a more intuitive shopping experience.

[0053] The user terminal device is connected to the display device and is used to receive and push the analyzed and optimized product images. The user terminal device includes but is not limited to a mobile terminal and a computer, which is used to display the analyzed and optimized product images.

[0054] Specifically, the user terminal device is the user interaction port of the system. By connecting to the display device, it receives and displays the analyzed and optimized product images, providing users with a personalized and high-quality shopping experience. User terminal devices include but are not limited to mobile terminals, computers, etc., and can adapt to a variety of device forms to ensure that users can access product recommendation information and optimized product images at any time in different scenarios.

[0055] In specific applications, the user terminal device will intuitively present product images that meet the user's interests and preferences on the user interface based on the recommendation information pushed by the display device. Through the product images displayed on the user terminal device, the user can clearly see the high-quality product images optimized by the image processing device. The system analyzes the device's feature extraction and multi-dimensional fusion recommendations of user behavior data, allowing users to more conveniently browse products that meet their visual preferences.

[0056] Embodiment 2: Please refer to the attached Figure 2 The embodiment of the present invention provides a method for pushing products based on e-commerce platform image analysis, comprising the following steps: S1: Use a camera to capture product images and transmit the image data to a processor; Specifically, in the product listing process, the product is first photographed from multiple angles and in high definition using a product photography device (such as a smartphone or professional camera) to ensure that the overall appearance and detailed features of the product can be captured.

[0057] S2: The image processing device performs cropping and optimization preprocessing operations on the product image, and stores the processing results in a storage device; Specifically, the image processing device performs multiple optimization processes on the received product image, including cutting out unnecessary background, adjusting brightness and contrast, removing noise, etc., so that the main body of the product is more prominent and clear in the image.

[0058] S3: The analysis device extracts product images from the storage device and performs feature extraction, user behavior analysis, and quality inspection on the images; Specifically, the feature extraction module extracts visual features such as color, texture, and shape from product images to generate feature vectors of the products. These visual features help the system understand the main characteristics of the products and provide a basis for subsequent personalized recommendations. In addition, the analysis device will combine user behavior data (such as clicks and browsing) to generate personalized recommendation information.

[0059] S4: storing the product image features and user behavior data analysis results in a storage device; Specifically, after the analysis is completed, the processor saves the product feature data and user behavior analysis results to the storage device. This step ensures that the product image and its feature information, as well as the user preference analysis data, are recorded so that the system can call on the data to support recommendations at any time.

[0060] S5: The on-shelf and off-shelf management module in the processor automatically adjusts the display priority and on-shelf and off-shelf status of the products based on user behavior data and inventory information; Specifically, the on-shelf and off-shelf management module intelligently adjusts the product display strategy through the rule engine and data analysis module to ensure that high-quality products have higher exposure opportunities, while products with low demand or tight inventory can be automatically removed from the shelves or adjusted in display.

[0061] S6: The processor generates a personalized push product list based on product features and user preference data, and transmits it to the display device; Specifically, the processor combines product features and user behavior data to generate a highly personalized product recommendation list by analyzing the user's visual preferences and purchasing habits. The recommendation list ensures that the products recommended by the system meet the user's current interests and needs. The recommendation list is then transmitted to a display device for merchants and users to view.

[0062] S7: The display device displays the recommended product list to the merchant and the user terminal device, so that the user and the merchant can view the pushed products; Specifically, the display device displays the generated recommended product list on the merchant management background and user terminal device, so that the merchant can view the uploaded and optimized effects of the product images and adjust the display strategy according to the priority recommended by the system.

[0063] S8: The user terminal device collects the user's click, browsing, and purchase information, and transmits it back to the processor to perform step S1.

[0064] Specifically, users interact with recommended products on terminal devices (such as clicking, browsing, and purchasing), and these behavioral data are collected in real time and transmitted back to the processor. The processor stores these feedback data in the storage device and further optimizes the recommendation effect by updating the recommendation algorithm of the analysis device. The collection and feedback of user behavior data form a closed-loop system, which enables the recommendation engine to continuously adapt to changes in user preferences and further improve the relevance and personalization of recommendations.

[0065] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. E-commerce platform image analysis and push system, characterized by: Including product shooting equipment, image processing equipment, storage equipment, processor, display equipment, and user terminal equipment; The product photographing device is used to photograph product images and is connected to the processor to transmit the photographed image data to the processor; An image processing device is used to process the product image, is connected to the processor, receives the image data transmitted by the processor and performs image optimization processing; A storage device connected to the product photographing device, the processor, the analysis device and the display device, and used to store product images; A processor, connected to the product photographing device, the image processing device, the storage device and the display device, respectively, for transmitting the photographed image to the image processing device and storing the processing result in the storage device; A display device, connected to the storage device, the processor and the user terminal device, for displaying the processing and analysis results of the product image; The user terminal device is connected to the display device and is used to receive and push the product images that have been analyzed and optimized.

2. The e-commerce platform image analysis and push system according to claim 1, characterized in that: The processor is equipped with a shelf loading and unloading management module and an analysis device; The on-shelf and off-shelf management module automatically adjusts the display priority and on-shelf and off-shelf status of the products according to user behavior data and product inventory conditions; The analysis device is connected to the storage device and is used to analyze the commodity image data stored in the storage device to extract commodity feature information.

3. The e-commerce platform image analysis and push system according to claim 2, characterized in that: The on-shelf and off-shelf management module includes: Data analysis module, used to monitor the number of clicks, views, conversion rate and inventory status of products in real time; The rule engine module is used to automatically adjust the display priority or shelf status of products according to preset conditions.

4. The e-commerce platform image analysis and push system according to claim 2, characterized in that: The analysis equipment comprises: The feature extraction module is used to extract color, texture, and shape features from the product image to generate a feature vector of the product; Behavior analysis module, used to analyze user preferences based on user behavior data and generate product recommendation information based on user preferences; The image quality detection module is used to detect the resolution, clarity and lighting conditions of product images.

5. The e-commerce platform image analysis and push system according to claim 1, characterized in that: The product photography device includes but is not limited to a smart phone or a digital camera.

6. The e-commerce platform image analysis and push system according to claim 1, characterized in that: The image processing device includes but is not limited to a desktop computer or a tablet computer, and is installed with image processing software.

7. The e-commerce platform image analysis and push system according to claim 1, characterized in that: The storage device includes but is not limited to a cloud storage service or a local physical storage device.

8. The e-commerce platform image analysis and push system according to claim 1, characterized in that: The display device includes but is not limited to a merchant management backend or a merchant mobile application for viewing the uploaded product images and analysis results.

9. The e-commerce platform image analysis and push system according to claim 1, characterized in that: The user terminal device includes but is not limited to a mobile terminal and a computer, and is used to display the product images that have been analyzed and optimized.

10. A method for pushing products based on e-commerce platform image analysis, according to the e-commerce platform image analysis and push system according to any one of claims 1-9, characterized in that: The following steps are involved: S1: Use a camera to capture product images and transmit the image data to a processor; S2: The image processing device performs cropping and optimization preprocessing operations on the product image, and stores the processing results in a storage device; S3: The analysis device extracts product images from the storage device and performs feature extraction, user behavior analysis, and quality inspection on the images; S4: storing the product image features and user behavior data analysis results in a storage device; S5: The on-shelf and off-shelf management module in the processor automatically adjusts the display priority and on-shelf and off-shelf status of the products based on user behavior data and inventory information; S6: The processor generates a personalized push product list based on product features and user preference data, and transmits it to the display device; S7: The display device displays the recommended product list to the merchant and the user terminal device, so that the user and the merchant can view the pushed products; S8: The user terminal device collects the user's click, browsing, and purchase information, and transmits it back to the processor to perform step S1.

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