Electronic commerce data processing system

Abstract

The invention relates to the technical field of e-commerce, and discloses an e-commerce data processing system, which comprises a server, an input terminal, a reading module, a user terminal, a data extraction module and a behavior recognition module, the output end of the input terminal is connected with an input module, the output end of the input module is connected to the input end of the server, the output end of the server is connected to the input end of the reading module, the output end of the reading module is connected with a scanning module, and the output end of the scanning module is connected with a picture recognition module and a character recognition module. Through the arrangement of the AI analysis module, the data arrangement module, the parameter analysis module and the like, scanning analysis of propaganda drawings and characters can be carried out before all commodities are input, when a customer checks the commodities through the user terminal, the behavior of the customer can be analyzed to judge functions or parameters concerned by the customer, and the customer experience is improved. And then other commodities with the same or similar functions and parameters are screened out to be displayed preferentially, so that the required commodities are pushed to the clients more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce, and in particular to an e-commerce data processing system. Background Art

[0002] E-commerce is a business activity centered on the exchange of goods using information network technology. It can also be understood as the activities of conducting transactions and related services in the form of electronic transactions on the Internet, Intranet and Value Added Network (VAN). It is the electronicization, networking and informatization of all aspects of traditional business activities. The value of e-commerce is to allow consumers to shop and pay online through the Internet, saving time and space for customers and enterprises, greatly improving transaction efficiency, and saving a lot of precious time, especially for busy office workers.

[0003] When customers need to shop on an e-commerce platform, they usually search for the products they need to buy on the platform according to their own needs, then browse the products, and finally select the appropriate product to place an order. In order to enable customers to quickly select the appropriate product, the platform of the prior art will push similar products through the system for customers to quickly browse.

[0004] However, the existing push method is to push according to the category of the product, while customers often care about the specific functions of the product when they actually choose it. When browsing, they also focus on whether different products support the function or whether they have advantages in the function. As a result, customers have to browse more content before they can choose the right product, and the push accuracy needs to be improved. Therefore, we propose an e-commerce data processing system. Summary of the invention

[0005] The purpose of the present invention is to provide an e-commerce data processing system, which is equipped with an AI analysis module, a data sorting module, a parameter analysis module, etc., so that promotional pictures and texts of all commodities can be scanned and analyzed before they are entered. When customers view commodities through user terminals, their behaviors can be analyzed to determine the functions or parameters that customers are concerned about, and then other commodities with identical or similar functions and parameters can be screened out for priority display, so that the commodities required by customers can be pushed more accurately, thereby solving the problems raised in the background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an e-commerce data processing system, comprising a server, an input terminal, a reading module, a user terminal, a data extraction module and a behavior recognition module; the output end of the input terminal is connected to the input module, and the output end of the input module is connected to the input end of the server; the output end of the server is connected to the input end of the reading module, the output end of the reading module is connected to the scanning module, and the output end of the scanning module is connected to the image recognition module and the text recognition module, the image recognition module and the text recognition module are respectively used to recognize the image content and text content of the promotion and extract them, the output ends of the image recognition module and the text recognition module are both connected to the AI ​​analysis module, the AI ​​analysis module is used to analyze the image information and the text information, and the output end of the AI ​​analysis module is connected to the data sorting module, which is used to sort the analyzed images and text information, the output end of the data sorting module is connected to the parameter classification module, and the output end of the parameter classification module A checking module is connected, and the output end of the checking module is connected to the input module, and the output end of the input module is connected to the input end of the server; the user terminal is connected to the server via the Internet, and a retrieval module is also provided on the input end of the user terminal for inputting retrieval content and retrieval keywords; the data extraction module is connected to the output end of the server, and a typesetting system is connected to the output end of the data extraction module for typeset the extracted commodities, and the output end of the typesetting system is connected to the user terminal, and then the typeset content is displayed on the user terminal for customers to browse; the output end of the user terminal is also connected to a behavior recognition module for identifying the functions and parameters of interest when customers browse commodities, the output end of the behavior recognition module is connected to a row position analysis module for analyzing and comparing the points of interest when customers browse commodities, the output end of the behavior analysis module is connected to a screening module for screening commodities that are more suitable for customers, and the output end of the screening module is connected to the typesetting system.

[0007] By adopting the above technical solution, before the products are entered into the system, the pictures and text contents introducing the products are identified and analyzed, and classified and summarized according to different parameters. When customers visit the e-commerce platform, the retrieved products are directly displayed in layout, and then the products are screened according to the functions and parameters that the customers focus on when browsing. After that, the products with functions or parameters that the customers are interested in are given priority, which has a more accurate push effect.

[0008] As a preferred implementation of the present invention, the server is connected to a database, and the database is used to store commodity information and sorted data information.

[0009] As a preferred implementation manner of the present invention, a feedback module is connected to the input end of the user terminal for providing real-time feedback of needs or suggestions from customers.

[0010] As a preferred implementation of the present invention, the behavior recognition module includes a commodity recognition module for recognizing the name and number of the commodity browsed by the customer.

[0011] As a preferred embodiment of the present invention, the behavior recognition module includes a timing module for identifying the total time a customer browses a product and the length of time a customer stays browsing corresponding functions and parameters.

[0012] As a preferred implementation of the present invention, the behavior recognition module also includes a counting module for identifying the total number of times a customer browses a product and the number of times the customer browses corresponding functions and parameters.

[0013] As a preferred embodiment of the present invention, a marking module is connected to the output end of the data extraction module, and the output end of the marking module is connected to the input end of the typesetting system. The marking module can highlight and display the advantageous functions or parameters of the product.

[0014] As a preferred implementation manner of the present invention, the user terminal is a mobile phone, a tablet computer or a personal computer.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. The e-commerce data processing system of the present invention, this innovative system constructs an integrated data processing architecture, the core of which integrates an advanced artificial intelligence (AI) analysis engine, a data preprocessing module, and a refined parameter analysis module. This system is designed to ensure that a comprehensive intelligent scanning and in-depth analysis process of multimedia content (including promotional images and copywriting) is implemented before each product is put on the shelves. When consumers browse products through the user-end interface, the system can use advanced user behavior analysis technology to capture and analyze user interaction data in real time, and accurately identify users' potential focus of interest or preference parameters. Subsequently, relying on its powerful product matching algorithm, the system will efficiently screen out product clusters with the same or highly similar functional characteristics and technical parameters, and prioritize and display these products based on the analysis results. This process not only greatly improves the personalization and accuracy of product recommendations, but also achieves efficient response and accurate satisfaction of user needs, thereby achieving a more intelligent and customized product push experience in the field of e-commerce.

[0017] 2. The e-commerce data processing system of the present invention is also provided with a feedback module, and users can conveniently submit feedback through the user terminal. The system will respond quickly and mark the product as "not interested" or a similar label. Subsequently, the system uses the feedback data to dynamically adjust its recommendation algorithm and display logic to ensure that in the subsequent browsing process, the display priority of such products is automatically blocked or reduced, thereby achieving more accurate and personalized product recommendations without interfering with the user experience.

[0018] 3. The e-commerce data processing system of the present invention analyzes user behavior data to accurately prioritize the products that users may be interested in in the display list, so as to enhance user experience and purchasing intention; at the same time, it also incorporates intelligent filtering and sorting optimization algorithms, which can automatically identify and process products with significantly short user browsing time, and dynamically adjust the display priority of these products, that is, remove them from the instant recommendation list or place them at the end of the list, thereby improving the relevance and effectiveness of the recommended content, and ultimately achieving a more accurate, efficient and personalized product push effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0020] Figure 1 It is a schematic diagram of the overall structure of the e-commerce data processing system of the present invention;

[0021] Figure 2 It is a schematic diagram of the structure of the behavior recognition module of the e-commerce data processing system of the present invention.

[0022] In the figure: 1. Server; 2. Entry terminal; 21. Input module; 3. Reading module; 31. Scanning module; 32. Image recognition module; 33. Text recognition module; 34. AI analysis module; 35. Data sorting module; 36. Parameter analysis module; 37. Verification module; 38. Entry module; 4. User terminal; 41. Internet; 42. Retrieval module; 43. Feedback module; 5. Data extraction module; 51. Typesetting system; 52. Marking module; 6. Behavior recognition module; 61. Behavior analysis module; 62. Screening module; 63. Timing module; 64. Counting module; 65. Product recognition module; 7. Database. DETAILED DESCRIPTION

[0023] See also Figure 1-2 The present invention provides a technical solution: an e-commerce data processing system, comprising a server 1, an input terminal 2, a reading module 3, a user terminal 4, a data extraction module 5 and a behavior recognition module 6; the output end of the input terminal 2 is connected to an input module 21, when a commodity needs to be input into the e-commerce platform, various information, promotional pictures and promotional texts of the commodity are input through the input terminal 2, the output end of the input module 21 is connected to the input end of the server 1, and then the data is input into the server 1 through the input module 21.

[0024] The output end of the server 1 is connected to the input end of the reading module 3. When analyzing and processing the commodity, the input commodity data is first read by the reading module 3. The output end of the reading module 3 is connected to the scanning module 31, and the output end of the scanning module 31 is connected to the image recognition module 32 and the text recognition module 33. The image recognition module 32 and the text recognition module 33 are respectively used to recognize the image content and the text content of the promotion and extract them. The output ends of the image recognition module 32 and the text recognition module 33 are both connected to the AI ​​analysis module 34. The AI ​​analysis module 34 is used to analyze the image information and the text information, so that all the functions and parameter information of the products introduced by the pictures and texts can be analyzed. Intelligent analysis is performed, and the output end of the AI ​​analysis module 34 is connected to a data sorting module 35, through which the analyzed pictures and text information are sorted, and the output end of the data sorting module 35 is connected to a parameter classification module, which can classify and mark the functions and parameters extracted and analyzed from different pictures and text information, the output end of the parameter classification module is connected to a checking module 37, and the output end of the checking module 37 is connected to an input module 38, the output end of the input module 38 is connected to the input end of the server 1, after the checking module 37 checks that there is no problem with the analysis content and the classified data, the classified information content is input to the server 1 through the entry module 38.

[0025] Server 1 is connected to database 7. As the core component of data storage, database 7 is responsible for storing detailed information of commodities and processed data information. Server 1, as the hub of data processing and request response, has the ability to retrieve and extract required data from database 7 on demand. It also supports maintenance operations such as supplementing, modifying and updating the data in database 7. This design ensures centralized management, efficient access and real-time updating of data, and provides solid data support and flexible data processing capabilities for the e-commerce platform.

[0026] The user terminal 4 can be a terminal device such as a mobile phone, a tablet computer or a personal computer. Through the communication medium of the Internet 41, a remote connection and data interaction with the server 1 are realized. The user terminal 4 has a built-in search module 42. As one of the interfaces for interaction between the user and the system, the module allows the user to input specific search content or keywords to initiate a query request for specific product information. The data extraction module 5 is connected to the output end of the server 1. After receiving the search request from the user terminal 4, the server 1 will trigger the operation of the data extraction module 5. As a key component inside the server 1, the data extraction module 5 is responsible for searching the product name, keywords and other conditions provided by the search module 42 in the server management. The data extraction module 5 can efficiently search the database 7 and extract the product information and related data that meet the user's needs. The output end of the data extraction module 5 is connected to the typesetting system 51, so that the extracted products can be typeset through the typesetting system 51, and the output end of the typesetting system 51 is connected to the user terminal 4. The output end of the data extraction module 5 is connected to the marking module 52, and the output end of the marking module 52 is connected to the input end of the typesetting system 51. For the typeset products, the marking module 52 can display the important functions or parameters analyzed by the AI ​​analysis module 34 in a striking manner, and then display the typeset content on the user terminal 4 for customers to browse. Then the customers can click on different product links at will to browse.

[0027] In order to analyze the behavior habits of customers during their browsing process and obtain more accurate push purposes, a behavior recognition module 6 is also connected to the output end of the user terminal 4 for identifying the functions and parameters that customers pay attention to when browsing products. The behavior recognition module 6 includes a product recognition module 65 for identifying the name and number of the product browsed by the customer, and also includes a timing module 63 for identifying the total time the customer browses the product and the length of time the customer stays at the corresponding functions and parameters. At the same time, the behavior recognition module 6 also includes a counting module 64 for identifying the total number of times the customer browses the product and the number of times the customer browses the corresponding functions and parameters.

[0028] The output end of the behavior recognition module 6 is connected to a row position analysis module, which obtains data on the functions and parameters of the products that the customer is interested in or different products that the customer is interested in, and analyzes and compares the customer's focus when browsing the products. The output end of the behavior analysis module 61 is connected to a screening module 62, which can screen the same or similar products based on the above analysis data to screen out products that are more suitable for the customer. The output end of the screening module 62 is connected to the typesetting system 51, so that the typesetting system 51 prioritizes the products that the customer is interested in and the same or similar products with the functions or parameters that the customer is interested in, and displays the products that the customer is not interested in at the end, so as to achieve a more accurate push purpose.

[0029] Due to the diversity of user behavior and personalized preferences, that is, different user groups or individuals often have unique brand preferences, which leads to them automatically excluding certain specific brands of goods based on personal preferences, past experience or brand loyalty when faced with a large number of product choices. However, although the current system already has highly complex data processing and analysis capabilities, in the absence of clear user feedback or sufficient behavioral data support, the system often finds it difficult to accurately capture and understand each user's personalized brand selection tendency. Therefore, a feedback module 43 is connected to the input end of the user terminal 4 for real-time customer feedback needs or suggestions, so that users can conveniently submit feedback through the feedback module 43 of the user terminal 4, and the system responds quickly by marking the product as "not interested" or a similar label. Subsequently, the system uses these feedback data to dynamically adjust its recommendation algorithm and display logic to ensure that in the subsequent browsing process, the display priority of such products is automatically blocked or reduced, thereby achieving more accurate and personalized product recommendations without interfering with the user experience.

[0030] When in use, the introduction pictures and texts of all products are entered through the input terminal 2, and then input into the server 1 through the input module 21. After that, the reading module 3 of the system reads the introduction pictures and texts of the products, scans the pictures and texts, and respectively identifies and extracts the pictures and text contents through the picture recognition module 32 and the text recognition module 33. Then, the AI ​​analysis module 34 analyzes and determines the contents of the picture and text data. After being sorted by the data sorting module 35, the picture and text contents are classified and summarized according to types through the parameter classification module. After that, after the data is verified to be accurate by the checking module 37, it is entered into the database 7 of the platform server 1 through the input module 38 for storage. When a customer visits the sales platform, he accesses it through the user terminal 4 through the Internet 41. Before accessing, he can search for the products he needs to buy through the retrieval module 42. Then, the data extraction module 5 reads all the data of the products of the search content, and typesets the products through the typesetting system 51 and displays them on the user terminal 4. If the user adds keywords when searching, the data extraction module 5 also adds keywords when extracting product information. The typesetting system 51 will include keywords. The products with the keyword are displayed in the front layout, and the marking module 52 is used to mark and display the content of the keyword prominently. When the user clicks to enter the product for browsing, the product identification module 65 of the behavior identification module 6 can identify the customer's product and identify the content where the customer stays to view for a long time and the content with a long viewing time. The viewing time and the number of views are respectively counted by the timing module 63 and the technical module 64. Since the parameter data of each product are analyzed and classified, the behavior analysis module 61 can be used to analyze the parameter content that the customer pays attention to according to the customer's browsing key, and other products that have advantages in the parameter can be screened out by the screening module 62, so that the typesetting system 51 will screen out the products that the customer is more interested in and display them in the front position, and more accurately push the products with functions and parameters that the customer needs, and display the products that the customer browses for a short time and do not have the parameters that the customer is interested in at the back. When the customer does not like the product or the brand of the product due to personal preferences during the browsing process, the feedback module 43 can be used for real-time feedback, so that the user terminal 4 directly blocks the product or the brand of the product.

Claims

1. An e-commerce data processing system, characterized in that: It comprises a server (1), an input terminal (2), a reading module (3), a user terminal (4), a data extraction module (5) and a behavior recognition module (6); the output end of the input terminal (2) is connected to an input module (21), and the output end of the input module (21) is connected to the input end of the server (1); The output end of the server (1) is connected to the input end of the reading module (3), the output end of the reading module (3) is connected to a scanning module (31), and the output end of the scanning module (31) is connected to an image recognition module (32) and a text recognition module (33), the image recognition module (32) and the text recognition module (33) are respectively used to recognize and extract the image content and text content of the publicity, the output ends of the image recognition module (32) and the text recognition module (33) are both connected to an AI analysis module (34), the AI ​​analysis module (34) is used to analyze the image information and the text information, and the output end of the AI ​​analysis module (34) is connected to a data sorting module (35) for sorting the analyzed image and text information, the output end of the data sorting module (35) is connected to a parameter classification module, the output end of the parameter classification module is connected to a checking module (37), and the output end of the checking module (37) is connected to an input module (38), and the output end of the input module (38) is connected to the input end of the server (1); The user terminal (4) is connected to the server (1) via the Internet (41), and a search module (42) is also provided on the input end of the user terminal (4) for inputting search content and search keywords; The data extraction module (5) is connected to the output end of the server (1), and the output end of the data extraction module (5) is connected to a typesetting system (51) for typeset the extracted commodities, and the output end of the typesetting system (51) is connected to the user terminal (4), so that the typeset contents are displayed on the user terminal (4) for customers to browse; The output end of the user terminal (4) is also connected to a behavior recognition module (6) for identifying the functions and parameters of interest of a customer when browsing a product. The output end of the behavior recognition module (6) is connected to a row position analysis module for analyzing and comparing the points of interest of a customer when browsing a product. The output end of the behavior analysis module (61) is connected to a screening module (62) for screening products that are more suitable for the customer, and the output end of the screening module (62) is connected to a typesetting system (51).

2. An e-commerce data processing system according to claim 1, characterized in that: The server (1) is connected to a database (7), and the database (7) is used to store commodity information and sorted data information.

3. An e-commerce data processing system according to claim 1, characterized in that: The input end of the user terminal (4) is connected to a feedback module (43) for providing real-time feedback of needs or suggestions from customers.

4. An e-commerce data processing system according to claim 1, characterized in that: The behavior recognition module (6) includes a commodity recognition module (65) for recognizing the name and number of the commodity browsed by the customer.

5. An e-commerce data processing system according to claim 1, characterized in that: The behavior recognition module (6) includes a timing module (63) for identifying the total time that a customer browses a product and the length of time that the customer stays while browsing the corresponding functions and parameters.

6. An e-commerce data processing system according to claim 1, characterized in that: The behavior recognition module (6) also includes a counting module (64) for identifying the total number of times a customer browses a product and the number of times the customer browses corresponding functions and parameters.

7. An e-commerce data processing system according to claim 1, characterized in that: The output end of the data extraction module (5) is connected to a marking module (52), and the output end of the marking module (52) is connected to the input end of the typesetting system (51).

8. An e-commerce data processing system according to claim 1, characterized in that: The user terminal (4) is a mobile phone, a tablet computer or a personal computer.

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