Multi-element intelligent cross-domain interaction system based on bottle body two-dimensional code

Abstract

The invention relates to a multi-element intelligent cross-domain interaction system based on a bottle body two-dimensional code. The system comprises a coded data acquisition module for acquiring coded data; and the storage query judgment module retrieves the local storage data of the communication equipment so as to generate a corresponding judgment result request. And the data visualization module receives the coded data and the judgment result request, mines a tourism geographic information data set through a data extraction algorithm, and converts the information into visual display content by applying a visualization algorithm. And the user recommendation module collects social feature parameters input by the user, comprehensively analyzes the parameters and the visual display content, and generates a personalized recommended user list. According to the invention, deep fusion of the bottle body two-dimensional code and multi-field functions is realized, the convenience of information acquisition and social interaction is greatly improved, and diversified requirements of the bottle body two-dimensional code in tourism and social contact are met.

Description

Technical Field

[0001] The present invention belongs to the field of information technology, and particularly relates to a multi-intelligent cross-domain interaction system based on bottle body two-dimensional codes. Background Art

[0002] With the development of information technology, there has emerged a multi-intelligent cross-domain interaction technology based on bottle body two-dimensional codes. In the usage scenarios of drinking water and beverages, the bottle body packaging of the same manufacturer, brand, and product series is highly similar, and it is extremely easy to be confused when multiple people use it at the same time. The existing signature recognition methods have poor recognition effects due to problems such as inconvenient carrying tools and unclear handwriting, which is not only not conducive to ensuring personal hygiene and health but also likely to cause waste of resources. In the field of dating and making friends, the number of young people getting married and dating in China has been decreasing year by year, and the number of births has continued to decline. Although there are numerous existing dating platforms, there is a lack of targeted and convenient dating channels, making it difficult to fully utilize daily life scenarios to create more dating opportunities for young people. In the tourism field, the information on specialized tourism websites is complex and miscellaneous, tourists face difficulties in making choices, and there is also a lack of an efficient and accurate way to reach tourists for the promotion of local tourism information. Traditional bottle body packaging is difficult to break the existing dilemmas in these fields and achieve cross-domain intelligent interaction. Summary of the Invention

[0003] Based on this, in view of the above technical problems, it is necessary to provide a multi-intelligent cross-domain interaction system based on bottle body two-dimensional codes that can break through the data barriers between different fields and achieve intelligent interaction, precise recommendation, and efficient management.

[0004] In a first aspect, the present application provides a multi-intelligent cross-domain interaction system based on bottle body two-dimensional codes, including:

[0005] An encoded data acquisition module, configured to acquire encoded data including an item identification code and a service entry address.

[0006] A storage query and judgment module, configured to query and judge the local storage data of the communication device to determine whether there is a binding record associated with the item identification code, and obtain a corresponding judgment result request.

[0007] A data visualization module, configured to parse the encoded data and the judgment result request, obtain a tourism geographic information data set using a data extraction algorithm, and generate a visual display content on the display interface of the communication device using a visualization algorithm.

[0008] A user recommendation module, configured to acquire social feature parameters input by a user; analyze the social feature parameters and the visual display content based on a machine learning algorithm, generate a recommended user list, and return it to the communication device.

[0009] In one embodiment, query the local stored data of the communication device to determine whether there is a binding record associated with the item identification code, and obtain a corresponding judgment result request, including:

[0010] Access the local storage database according to the item identification code for relevance verification, and generate a binding status identifier.

[0011] If the binding status identifier is in the unbound state, process the user identity data using an identity verification algorithm to generate a binding message carrying the user identity hash value and the item identification code.

[0012] If the binding status identifier is in the bound state, extract the historical operation timestamp from the binding record set, and generate an ownership query message carrying the item identification code and the timestamp sequence.

[0013] Select a corresponding transport protocol based on the binding message or the ownership query message for data encapsulation to obtain an encapsulated data packet.

[0014] Send the encapsulated data packet to the cloud server using a preset encryption channel, update the binding record set according to the message type or return the ownership information to obtain a corresponding judgment result request.

[0015] Among them, if not stored, generate a binding request including user identity data and the item identification code; if already stored, generate an ownership query request including the item identification code.

[0016] In one embodiment, parse the encoded data and the judgment result request, and use a data extraction algorithm to obtain a tourism geographic information data set, and generate visual display content on the display interface of the communication device using a visualization algorithm, including:

[0017] Parse the format of the encoded data to extract an initial data set including geographic coordinates, scenic spot names, and user ratings.

[0018] Process the initial data set based on preset data cleaning rules to obtain an intermediate data set with standardized geographic labels.

[0019] Call the corresponding data extraction algorithm according to the feature type in the judgment result request, and input the intermediate data set into the data extraction algorithm to generate a target data set.

[0020] Input the target data set into the visualization algorithm to obtain a rendering instruction set with coordinate mapping relationships.

[0021] Parse the vector graphic data and text annotation data in the rendering instruction set to generate a pixel matrix adapted to the display interface of the communication device.

[0022] Transfer the pixel matrix to the graphics buffer of the communication device, triggering the generation of a screen refresh signal to generate visual display content.

[0023] In one embodiment, call the corresponding data extraction algorithm according to the feature type in the judgment result request, and input the intermediate data set into the data extraction algorithm to generate a target data set, including:

[0024] Match according to the feature type in the judgment result request to obtain the corresponding data extraction algorithm.

[0025] Structurally process the intermediate data set using data conversion parameters to generate an initial target data set including a dynamic verification code.

[0026] Trigger the verification model to perform integrity verification on the initial target data set according to the dynamic verification code. If the dynamic verification code is consistent with the preset verification sequence, output the final target data set.

[0027] Input the final target data set into the data extraction algorithm to generate a target data set.

[0028] In one embodiment, analyze the social feature parameters and visual display content based on a machine learning algorithm to generate a recommended user list and return it to the communication device, including:

[0029] Use vectorization to process the social feature parameters to obtain feature data with a multi-dimensional spatial distribution; the social feature parameters include user interaction frequency and content preference category.

[0030] Extract the text labels and image features of the visual display content to construct a content feature matrix.

[0031] Perform an association mapping on the vectorized feature data and the content feature matrix to generate a comprehensive matching degree set of users and content.

[0032] Process the comprehensive matching degree set based on the collaborative filtering algorithm to obtain an initial recommended user list.

[0033] In one embodiment, after obtaining the initial recommended user list, it further includes:

[0034] Screen the initial recommended user list according to the real-time response data of the communication device, remove records with an interaction interval exceeding the preset threshold, and obtain a screened recommended user list.

[0035] Perform dynamic weight sorting on the screened recommended user list to obtain an updated recommended user list; the dynamic weight is jointly determined by user activity and content update frequency.

[0036] Send the updated recommended user list to the communication device, and perform content push operations according to the list order to obtain corresponding feedback data.

[0037] Judge the feedback data based on preset update conditions. If the update conditions are met, re-execute the vectorization processing flow and generate incremental feature data.

[0038] Use the incremental learning mechanism to fuse the incremental feature data with the historical feature data, and update the distribution state of the comprehensive matching degree set.

[0039] In a second aspect, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0040] Obtain encoded data including an item identification code and a service entry address.

[0041] Query the local storage data of the communication device to determine whether there is a binding record associated with the item identification code, and obtain a corresponding judgment result request.

[0042] Parse the encoded data and the judgment result request, use a data extraction algorithm to obtain a tourism geographic information data set, and use a visualization algorithm to generate visualization display content on the display interface of the communication device.

[0043] Obtain social feature parameters input by the user; analyze the social feature parameters and the visualization display content based on a machine learning algorithm, generate a recommended user list, and return it to the communication device.

[0044] In a third aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0045] Obtain encoded data including an item identification code and a service entry address.

[0046] Query the local storage data of the communication device to determine whether there is a binding record associated with the item identification code, and obtain a corresponding judgment result request.

[0047] Parse the encoded data and the judgment result request, use a data extraction algorithm to obtain a tourism geographic information data set, and use a visualization algorithm to generate visualization display content on the display interface of the communication device.

[0048] Obtain social feature parameters input by the user; analyze the social feature parameters and the visualization display content based on a machine learning algorithm, generate a recommended user list, and return it to the communication device.

[0049] The above-mentioned multi-intelligent cross-domain interaction system, computer device and storage medium based on the bottle body QR code mainly include the following four core functional modules: The encoded data acquisition module is responsible for collecting encoded data containing the item identification code and the service entry address. The storage query and judgment module retrieves the data locally stored in the communication device and judges whether there is a binding record associated with the item identification code, and then generates a corresponding judgment result request. The data visualization module receives the encoded data and the judgment result request, mines the tourism geographic information dataset through a data extraction algorithm, and then uses a visualization algorithm to convert this information into intuitive visual display content, which is presented on the display interface of the communication device. The user recommendation module collects the social feature parameters input by the user, and with the help of machine learning algorithms, comprehensively analyzes these parameters and the visual display content, generates a personalized recommended user list, and feeds it back to the communication device. It realizes the deep integration of the bottle body QR code and multi-domain functions, greatly improving the convenience of information acquisition and social interaction. Users can obtain tourism geographic information in one stop only by scanning the QR code on the bottle body, browse it in an intuitive visual interface, and at the same time can obtain accurate user recommendations based on their personal social characteristics to meet their diverse needs in tourism and social aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a structural block diagram of a multi-intelligent cross-domain interaction system based on the bottle body QR code provided by an embodiment of the present invention;

[0052] Figure 2 It is a flowchart of analyzing social feature parameters and visual display content based on machine learning algorithms to generate a recommended user list and return it to the communication device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] In order to make the purpose, technical solutions and advantages of the present application clearer, the following will further describe the present application in detail with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not used to limit the present application.

[0054] The multi - intelligent cross - domain interaction system based on the bottle - body QR code provided by the embodiments of the present application integrates functions in multiple fields. Through the bottle - body QR code, the physical and digital boundaries can be crossed to start a multi - dimensional interaction experience. The system uses intelligent algorithms to present product information, brand stories, promotional activities, etc. in rich forms such as pictures, texts, videos, AR / VR experiences, etc., realizing in - depth communication between products and consumers. It can insight into consumers' preferences based on the scanned code data to assist in precision marketing, can track the product flow in real - time, and in the field of user services, can conveniently answer consumers' questions. This cross - domain interaction greatly enhances the product added value and user participation, building an efficient communication bridge between enterprises and consumers. Exemplarily, the multi - intelligent cross - domain interaction system based on the bottle - body QR code provided by the embodiments of the present application can be applied to at least one of the following scenarios including but not limited to the following.

[0055] First, the multi - intelligent cross - domain interaction system based on the bottle - body QR code is applied to the scenario of multiple people using drinking water or beverages. In the scenario where multiple people share drinking water or beverages, since the bottle appearances of the same manufacturer, brand, and product series are often highly similar, it is very easy for water bottles to be confused. To effectively solve this problem, this solution sets QR codes on the drinking water bottles and beverage bottle bodies. Users only need to use their mobile phones carried with them to scan the QR code. The relevant information registered during the first scan will be permanently stored in the database of the manufacturer's website. When the same brand of beverage is scanned again later, based on the previously recorded information of the person who scanned the code, the system will automatically identify and display the owner of the bottle of beverage. In this way, various bottled waters and bottled beverages can be accurately distinguished, avoiding the risk of cross - infection caused by misappropriating water bottles from the source, effectively protecting the health of the public; at the same time, reducing the waste of beverages caused by misappropriation and effectively improving the resource utilization efficiency.

[0056] Second, the multi - intelligent cross - domain interaction system based on the bottle - body QR code is applied to the dating scenario. For users who have the need to make friends or find a spouse, after scanning the bottle - body QR code, they can register their personal dating information on a specially built dating platform. The platform uses advanced matching algorithms to accurately screen and recommend suitable dating partners based on the information filled in by users. In addition, scanning the QR code can also guide users to follow social media accounts such as the WeChat official account and Douyin account of the platform to obtain more dating information and interaction opportunities, greatly expanding the dating channels, significantly improving the dating efficiency, helping users find ideal partners in a broader social space, and building good interpersonal relationships.

[0057] Third, the multi-intelligent cross-domain interaction system based on the bottle body QR code is applied to the tourism scenario. When a user scans the QR code on the bottle body, they can conveniently obtain the rich tourism information contained therein. This information covers all aspects of eating, accommodation, transportation, sightseeing, shopping, and entertainment at the tourism destination, and also includes details of ongoing discount promotions in the local area. This QR code is like a comprehensive tourism information distribution center, providing users with comprehensive and practical tourism information. With this function, users do not need to screen information on numerous tourism websites, can plan their trips more efficiently, deeply understand local characteristics, fully enjoy a convenient and rich tourism experience, and at the same time open up a new way for the publicity and promotion of tourism destinations.

[0058] In one of the embodiments, as Figure 1 shown, the present application provides a multi-intelligent cross-domain interaction system based on the bottle body QR code, which may include:

[0059] An encoded data acquisition module 101, configured to acquire encoded data including an item identification code and a service entry address.

[0060] This module can quickly and accurately identify various common encoding formats, including but not limited to QR codes, barcodes, etc. In actual application scenarios, whether it is the encoding on the bottle body, label, or other carriers, this module can quickly capture and parse the information therein. The item identification code is used to accurately locate and distinguish different items and is a key identifier for product traceability and information management; the service entry address provides users with a convenient path to various service platforms, such as tourism information platforms, social interaction platforms related to the product, etc.

[0061] A storage query and judgment module 102, configured to query the local storage data of the communication device to determine whether there is a binding record associated with the item identification code, and obtain a corresponding judgment result request.

[0062] Specifically, by using an optimized data indexing algorithm, it is possible to quickly locate the data record related to the item identification code. When the item identification code is input, this module quickly traverses the local storage database to determine whether there is an associated binding record. This process not only improves the efficiency of data query but also reduces unnecessary data processing overhead. If a binding record is queried, the system will further obtain relevant detailed information, such as the user's historical operation records, personalized settings, etc.; if not queried, it will trigger a corresponding processing process, such as guiding the user to perform the first binding operation. Through this accurate query and judgment mechanism, the system can generate corresponding judgment result requests according to different judgment results, providing accurate data support for subsequent modules and ensuring that the system can make reasonable and effective decisions in different application scenarios.

[0063] The data visualization module 103 is used to parse the coded data and the judgment result request and obtain the tourism geographic information data set using the data extraction algorithm, and generate visual display content on the display interface of the communication device using the visualization algorithm.

[0064] First, the coded data and the judgment result requests from the storage query judgment module are deeply parsed to extract key data related to tourism geographic information. Advanced data extraction algorithms are used to filter, integrate, and construct tourism geographic information datasets from complex data structures, covering a rich range of content such as geographic locations, attraction descriptions, and travel guides. Subsequently, specialized visualization algorithms are used to transform these datasets into easy-to-understand graphics, charts, or maps. On the display interface of the communication device, careful adjustment of visualization parameters such as color, layout, and element size creates clear, beautiful, and highly interactive visual displays. This enables users to quickly access and understand complex tourism geographic information, providing intuitive and convenient support for their travel planning and decision-making, effectively improving the user experience and information acquisition efficiency.

[0065] The user recommendation module 104 is configured to obtain social feature parameters input by the user, analyze the social feature parameters and the visual display content based on a machine learning algorithm, generate a recommended user list, and return it to the communication device.

[0066] Specifically, by receiving social characteristic parameters input by users, such as interests, hobbies, age, gender, and social circles, the system comprehensively understands the user's personalized needs and preferences. Based on machine learning algorithms, this module conducts in-depth analysis of these social characteristic parameters and the visual display content generated by the data visualization module. By establishing complex user portrait models and behavior prediction models, the system explores the potential similarities and correlations between users. Based on the analysis results, the system generates a highly accurate list of recommended users. These recommended users have a high degree of match with the target user in terms of interests, hobbies, travel preferences, and other aspects. Finally, the recommended user list is returned to the communication device, providing users with more social opportunities, helping them expand their social circles, promoting interaction and communication between users, realizing the organic integration of travel and social interaction, and enhancing user participation and stickiness in the system.

[0067] The above-mentioned multi-intelligent cross-domain interaction system, computer device and storage medium based on the bottle body QR code mainly include the following four core functional modules: The encoded data acquisition module is responsible for collecting encoded data containing the item identification code and the service entry address. The storage query and judgment module retrieves the local storage data of the communication device to determine whether there is a binding record associated with the item identification code, and then generates a corresponding judgment result request. The data visualization module receives the encoded data and the judgment result request, mines the tourism geographic information dataset through a data extraction algorithm, and then uses a visualization algorithm to convert this information into intuitive visual display content, which is presented on the display interface of the communication device. The user recommendation module collects the social feature parameters input by the user, and with the help of machine learning algorithms, comprehensively analyzes these parameters and the visual display content, generates a personalized recommended user list, and feeds it back to the communication device. It realizes the deep integration of the bottle body QR code and multi-domain functions, greatly improving the convenience of information acquisition and social interaction. Users can obtain tourism geographic information in one stop by simply scanning the QR code on the bottle body, browse it in an intuitive visual interface, and at the same time can obtain accurate user recommendations based on their personal social characteristics to meet their diverse needs in tourism and social aspects.

[0068] In one embodiment, querying and judging the local storage data of the communication device to determine whether there is a binding record associated with the item identification code, and obtaining the corresponding judgment result request may include the following steps:

[0069] Step S201, access the local storage database according to the item identification code for relevance verification, and generate a binding status identifier.

[0070] If the binding status identifier is in the unbound state, process the user identity data using an identity verification algorithm to generate a binding message carrying the user identity hash value and the item identification code.

[0071] If the binding status identifier is in the bound state, extract the historical operation timestamp in the binding record set to generate an attribution query message carrying the item identification code and the timestamp sequence.

[0072] Step S202, select the corresponding transport protocol based on the binding message or the attribution query message for data encapsulation to obtain the encapsulated data packet.

[0073] Step S203, send the encapsulated data packet to the cloud server through a preset encrypted channel, update the binding record set according to the message type or return the attribution party information to obtain the corresponding judgment result request.

[0074] Among them, if not stored, a binding request including user identity data and the item identification code is generated; if stored, an attribution query request including the item identification code is generated.

[0075] First, it will access the local storage database based on the item identification code, perform a correlation verification operation, and then generate a binding status identifier with a key indication function. When the binding status identifier shows an unbound state, the system will rigorously process the user identity data using an identity verification algorithm to generate a binding message containing the user identity hash value and the item identification code; if the binding status identifier indicates a bound state, the system will extract the historical operation timestamp from the binding record set and generate an ownership query message carrying the item identification code and the timestamp sequence. Next, the system will select an appropriate transmission protocol for data encapsulation according to the specific type of the binding message or the ownership query message, thus obtaining an encapsulated data packet. Then, through a preset encryption channel, the data packet will be securely sent to the cloud server. The cloud server will perform corresponding processing according to the received message type: for the binding message, update the binding record set; for the ownership query message, return the ownership party information. Through this series of operations, a corresponding judgment result request is finally obtained. If the relevant records are not stored locally, the system generates a binding request containing the user identity data and the item identification code; if they are stored, it generates an ownership query request containing the item identification code.

[0076] This embodiment uses an identity verification algorithm and an encryption channel to effectively protect the security of user identity data and item association information, prevent data leakage and illegal access, and ensure user information security. In terms of data management, through accurate correlation verification, clear status identification, and an orderly record update mechanism, it realizes the efficient management of item-user relationship data, providing reliable data support for the system. From the perspective of user experience, whether it is the first binding or subsequent query of ownership, accurate results can be obtained quickly, improving the convenience and fluency of user operations, enhancing users' trust and willingness to use the system, and promoting the wide application of the system in related fields.

[0077] In one of the embodiments, parsing the encoded data and the judgment result request and using a data extraction algorithm to obtain a tourism geographic information data set, and using a visualization algorithm to generate visualization display content on the display interface of the communication device may include the following steps:

[0078] Step S301, perform format parsing on the encoded data to extract an initial data set including geographic coordinates, scenic spot names, and user ratings.

[0079] Step S302, process the initial data set based on preset data cleaning rules to obtain an intermediate data set with standardized geographic tags.

[0080] Step S303, call the corresponding data extraction algorithm according to the feature type in the judgment result request, and input the intermediate data set into the data extraction algorithm to generate a target data set.

[0081] Step S304: Input the target data set into the visualization algorithm to obtain a rendering instruction set with coordinate mapping relationships.

[0082] Step S305: Parse the vector graphic data and text annotation data in the rendering instruction set to generate a pixel matrix adapted to the display interface of the communication device.

[0083] Step S306: Transmit the pixel matrix to the graphic buffer of the communication device to trigger a screen refresh signal to generate the visualization display content.

[0084] First, perform a deep format parsing on the encoded data to accurately extract key information such as geographical coordinates, scenic spot names, and user ratings from it, and construct an initial data set. Then, process the initial data set according to the preset data cleaning rules to remove the error data, duplicate data, and irrelevant information in it, so as to obtain an intermediate data set with standardized geographical tags, ensuring the accuracy and standardization of the data. Subsequently, according to the feature type in the judgment result request, the system will intelligently call the corresponding data extraction algorithm, use the intermediate data set as the input, further mine and process the data, and generate the target data set. After that, input the target data set into the visualization algorithm, and through complex operations, obtain a rendering instruction set with coordinate mapping relationships. Then, parse the vector graphic data and text annotation data in the rendering instruction set and convert them into a pixel matrix adapted to the display interface of the communication device. Finally, transmit the pixel matrix to the graphic buffer of the communication device to trigger a screen refresh signal, and generate intuitive and clear visualization display content on the communication device.

[0085] Through multi-step data processing and conversion, the originally complex and disordered encoded data can be converted into intuitive and easy-to-understand visualization content, such as maps, charts, etc., enabling users to quickly obtain and understand tourism geographical information, greatly improving the efficiency and experience of information acquisition. In terms of data quality guarantee, data cleaning and the application of targeted data extraction algorithms ensure the accuracy, standardization, and effectiveness of the finally displayed data, avoiding interference from incorrect or invalid information to users. From the perspective of system function implementation, it provides a good data display foundation for subsequent functions such as user recommendation and tourism planning, and strongly supports the implementation and optimization of the overall system functions.

[0086] In one embodiment, calling the corresponding data extraction algorithm according to the feature type in the judgment result request and inputting the intermediate data set into the data extraction algorithm to generate the target data set may include the following steps:

[0087] Step S401: Match according to the feature type in the judgment result request to obtain the corresponding data extraction algorithm.

[0088] Step S402: Structurally process the intermediate data set using data conversion parameters to generate an initial target data set including a dynamic verification code.

[0089] Step S403: Trigger the verification model to perform integrity verification on the initial target data set according to the dynamic verification code. If the dynamic verification code is consistent with the preset verification sequence, output the final target data set.

[0090] Step S404: Input the final target data set into the data extraction algorithm to generate the target data set.

[0091] Specifically, first, perform precise matching according to the feature type in the judgment result request to obtain the corresponding dedicated data extraction algorithm. Subsequently, structurally process the intermediate data set using the preset data conversion parameters, and generate an initial target data set including a dynamic verification code during the processing. Then, the system triggers the verification model according to the dynamic verification code to strictly perform integrity verification on the initial target data set. Only when the dynamic verification code is completely consistent with the preset verification sequence, will the final target data set that meets the standard be output. Finally, input the final target data set into the data extraction algorithm obtained by the previous matching to generate the target data set required by the system. <>

[0092] In this embodiment, through the setting of the dynamic verification code and the verification model, integrity verification is performed on the initial target data set, effectively ensuring the accuracy and integrity of the data, and avoiding deviations in subsequent analysis and applications caused by data errors or omissions. In terms of the effectiveness of algorithm application, corresponding data extraction algorithms are matched according to different feature types, making data processing more targeted, capable of fully mining data value, and improving data processing efficiency and quality. From the perspective of the overall system operation, the accurate and reliable target data set provides a solid data foundation for subsequent functional modules such as data visualization display and user recommendation, strongly supports the stable operation of the system functions, improves the reliability and practicality of the system, and provides users with higher-quality and accurate services.

[0093] In one of the embodiments, as Figure 2 shown, analyzing the social feature parameters and visualization display content based on machine learning algorithms, generating a recommended user list and returning it to the communication device may include the following steps: [9]]

[0094] Step S501: Use vectorization to process the social feature parameters to obtain feature data with a multi-dimensional spatial distribution; the social feature parameters include user interaction frequency and content preference category.

[0095] Step S502: Extract the text labels and image features of the visualization display content to construct a content feature matrix.

[0096] Step S503: Perform an association mapping on the vectorized feature data and the content feature matrix to generate a comprehensive matching degree set of users and content.

[0097] Step S504: Process the comprehensive matching degree set based on the collaborative filtering algorithm to obtain an initial recommended user list.

[0098] First, perform vectorization processing on social feature parameters including user interaction frequency and content preference categories. Through this processing method, the originally scattered and difficult-to-unify social feature parameters are transformed into feature data with multi-dimensional spatial distribution, making the data easier to analyze and operate at the mathematical level. Then, extract text tags and image features from the visualized content to construct a content feature matrix, comprehensively and accurately depicting the key characteristics of the visualized content. Subsequently, perform an association mapping on the vectorized feature data and the content feature matrix, and generate a comprehensive matching degree set that can reflect the matching degree between users and content through specific calculation rules. Finally, use the collaborative filtering algorithm to deeply process the comprehensive matching degree set to obtain an initial recommended user list.

[0099] In this embodiment, through in-depth analysis and association of social feature parameters and visualized content, the generated recommended user list highly conforms to the personal interests and preferences of users, greatly improving the accuracy and relevance of recommendations, providing strong support for users to find like-minded people, and enhancing the user experience and satisfaction in the social scenario. From the perspective of the system's intelligence, the entire process uses advanced vectorization processing, feature extraction, and collaborative filtering algorithms to fully explore the potential value of user data and content data, reflecting the system's intelligence level, enabling the system to better adapt to the diverse needs of different users, enhancing the system's core competitiveness, and promoting the efficient implementation of social functions.

[0100] In one of the embodiments, after obtaining the initial recommended user list, the following steps may further be included:

[0101] Step S601: Screen the initial recommended user list according to the real-time response data of the communication device, remove records with an interaction interval exceeding a preset threshold, and obtain a screened recommended user list.

[0102] Step S602: Perform dynamic weight sorting on the screened recommended user list to obtain an updated recommended user list; the dynamic weight is jointly determined by user activity and content update frequency.

[0103] Step S603: Send the updated recommended user list to the communication device and perform content push operations according to the list order to obtain corresponding feedback data.

[0104] Step S604, judge the feedback data based on a preset update condition. If the update condition is met, re - execute the vectorization processing flow and generate incremental feature data.

[0105] Step S605, use the incremental learning mechanism to fuse the incremental feature data and the historical feature data, and update the distribution state of the comprehensive matching degree set.

[0106] First, screen the initial recommended user list according to the real - time response data of the communication device. By setting a preset threshold for the interaction interval, remove the records with an interaction interval exceeding this threshold, so as to obtain the screened recommended user list, ensuring that the recommended users have a higher possibility of interaction with the current user. Then, perform dynamic weight sorting on the screened recommended user list. Among them, the dynamic weight is jointly determined by the user activity and the content update frequency, thereby generating an updated recommended user list. Subsequently, send the updated recommended user list to the communication device, perform the content push operation in the list order, and collect the corresponding feedback data. After that, judge the feedback data based on a preset update condition. If the update condition is met, re - execute the vectorization processing flow to generate incremental feature data. Finally, use the incremental learning mechanism to fuse the incremental feature data and the historical feature data, and update the distribution state of the comprehensive matching degree set.

[0107] In this embodiment, through real - time screening and dynamic weight sorting, the recommended users and content pushed are more in line with the current needs and interests of users, improving the user's attention and participation in the recommended content, and enhancing the user's satisfaction with using the system. From the perspective of system performance improvement, dynamic update and incremental learning are carried out based on the feedback data, enabling the system to continuously adapt to changing user behaviors and data, optimize the recommendation algorithm, improve the accuracy and timeliness of recommendations, and ensure that the system always maintains an efficient and intelligent operating state, providing continuous high - quality recommendation services for users.

[0108] In one of the embodiments, the present application provides a method for managing the attribution of bottled drinks based on two - dimensional codes, which may include:

[0109] Print a two - dimensional code on the packaging label or the bottle cap of the drinking water bottle or beverage bottle. This two - dimensional code is associated with a unique item identification code. After the user scans the code for the first time, the system guides them to log in to a specified website through the communication device, bind the mobile phone number or WeChat number with the item identification code, and store it in the cloud server, using the global uniqueness of the mobile phone number as the user identity identifier. When scanning the code subsequently, the storage query and judgment module verifies the binding status of the item identification code and the user identity, and displays the information of the drink owner in real - time.

[0110] This embodiment accurately distinguishes the attribution of bottled water or beverages in a multi - person scenario, effectively avoiding the risk of cross - infection caused by misappropriation, improving the resource utilization efficiency, and ensuring the health and hygiene of users.

[0111] In one embodiment, the present application provides an intelligent tourism information push system based on two-dimensional codes, which may include:

[0112] After the user scans the two-dimensional code on the bottle label or the bottle cap of the drinking water bottle or beverage bottle, the encoded data acquisition module parses and obtains the encoded data containing the address of the tourism information service entrance. The data visualization module mines the tourism geographical information dataset of the target area from the cloud database through a data extraction algorithm, including scenic spot names, geographical coordinates, user ratings, promotional activities, etc. After being processed by the visualization algorithm, an interactive map, a scenic spot recommendation list, and real-time promotional information are generated on the communication device interface.

[0113] This embodiment provides a one-stop tourism information entrance for users, helping users efficiently plan their itineraries. At the same time, it provides a promotion channel for scenic spots to accurately reach tourists, improving the efficiency of tourism information dissemination.

[0114] In one embodiment, the present application provides a social interaction and dating platform based on two-dimensional codes, which may include:

[0115] The scanning operation triggers the social function entrance of the user recommendation module. The user can input social feature parameters such as age, hobbies, and dating needs through the communication device. Based on machine learning algorithms, the system associates and maps user features with the visualized dating tags (such as regions, interest tags), generates a list of recommended users with high matching degrees through collaborative filtering algorithms, and supports users to log in through social accounts such as WeChat and Douyin, expanding the dating channels.

[0116] This embodiment creates social opportunities by using daily consumption scenarios, improves the dating efficiency of the youth group, and helps alleviate the problem of the declining birth rate.

[0117] In one embodiment, the present application provides a brand information interaction and marketing system based on two-dimensional codes, which may include:

[0118] The two-dimensional code integrates information entrances such as the brand culture, quality control, and promotional activities of the drinking water (beverage) manufacturer. After the user scans the code, the encoded data acquisition module parses and obtains the service entrance address, guiding the user to visit the brand official website or exclusive page. The storage query judgment module dynamically updates the brand data on the cloud server according to the user's code scanning behavior. The data visualization module displays the brand story, production process traceability, and limited-time promotional activities in the form of pictures, texts, videos, AR, etc., and at the same time supports users to interact with the brand through functions such as comments and sharing.

[0119] This embodiment enhances the stickiness between the brand and consumers, improves the brand reputation, promotes the growth of product sales through precision marketing, and realizes the efficient dissemination of brand culture.

[0120] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0121] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the multi-intelligent cross-domain interaction system based on the bottle body QR code as described above are implemented.

[0122] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0123] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial descriptions of the method embodiments. The device embodiments described above are only illustrative. The components described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0124] The above-described embodiments only represent several implementation manners of the embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the embodiments of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A multi-intelligent cross-domain interaction system based on the QR code on the bottle body, characterized in that, The system includes: An encoded data acquisition module, configured to acquire encoded data including an item identification code and a service entry address; A storage query and judgment module, configured to query the local storage data of the communication device to determine whether there is a binding record associated with the item identification code, and obtain a corresponding judgment result request; A data visualization module, configured to parse the encoded data and the judgment result request, obtain a tourism geographic information data set by using a data extraction algorithm, and generate visualization display content on the display interface of the communication device by using a visualization algorithm; A user recommendation module, configured to acquire social feature parameters input by a user; analyze the social feature parameters and the visualization display content based on a machine learning algorithm, generate a recommended user list, and return it to the communication device.

2. The system according to claim 1, characterized in that, The querying and judging the local storage data of the communication device to determine whether there is a binding record associated with the item identification code, and obtaining a corresponding judgment result request includes: Accessing a local storage database according to the item identification code for correlation verification, and generating a binding status identifier; If the binding status identifier is in an unbound state, processing user identity data by using an identity verification algorithm to generate a binding message carrying a user identity hash value and the item identification code; If the binding status identifier is in a bound state, extracting a historical operation timestamp from the binding record set, and generating an attribution query message carrying the item identification code and a timestamp sequence; Selecting a corresponding transmission protocol based on the binding message or the attribution query message for data encapsulation to obtain an encapsulated data packet; Sending the encapsulated data packet to a cloud server through a preset encrypted channel, updating the binding record set according to the message type or returning attribution party information, and obtaining a corresponding judgment result request; Wherein, if not stored, a binding request including user identity data and the item identification code is generated; if stored, an attribution query request including the item identification code is generated.

3. The system according to claim 1, wherein The parsing the encoded data and the judgment result request, obtaining a tourism geographic information data set by using a data extraction algorithm, and generating visualization display content on the display interface of the communication device by using a visualization algorithm includes: Parsing the format of the encoded data, and extracting an initial data set including geographic coordinates, scenic spot names, and user ratings; Processing the initial data set based on preset data cleaning rules to obtain an intermediate data set with standardized geographic tags; Invoking a corresponding data extraction algorithm according to the feature type in the judgment result request, and inputting the intermediate data set into the data extraction algorithm to generate a target data set; Inputting the target data set into a visualization algorithm to obtain a rendering instruction set with coordinate mapping relationships; Parsing the vector graphic data and text annotation data in the rendering instruction set to generate a pixel matrix adapted to the display interface of the communication device; Transmitting the pixel matrix to the graphic buffer of the communication device, and triggering a screen refresh signal to generate visualization display content.

4. The system according to claim 3, wherein Invoking a corresponding data extraction algorithm according to the feature type in the judgment result request, and inputting the intermediate data set into the data extraction algorithm to generate a target data set, including: Performing matching according to the feature type in the judgment result request to obtain a corresponding data extraction algorithm; Structurally processing the intermediate data set by using data conversion parameters to generate an initial target data set including a dynamic verification code; Triggering a verification model to perform integrity verification on the initial target data set according to the dynamic verification code. If the dynamic verification code is consistent with a preset verification sequence, output the final target data set; Inputting the final target data set into the data extraction algorithm to generate a target data set.

5. The system according to claim 1, wherein Analyzing the social feature parameters and the visual display content based on a machine learning algorithm to generate a recommended user list and return it to the communication device, including: Performing vectorization processing on the social feature parameters to obtain feature data with a multi-dimensional spatial distribution; the social feature parameters include user interaction frequency and content preference category; Extracting text tags and image features of the visual display content to construct a content feature matrix; Performing association mapping on the vectorized feature data and the content feature matrix to generate a comprehensive matching degree set of users and content; Processing the comprehensive matching degree set based on a collaborative filtering algorithm to obtain an initial recommended user list.

6. The system according to claim 5, wherein After obtaining the initial recommended user list, it further includes: Filtering the initial recommended user list according to the real-time response data of the communication device, removing records with an interaction interval exceeding a preset threshold to obtain a filtered recommended user list; Performing dynamic weight sorting on the filtered recommended user list to obtain an updated recommended user list; the dynamic weight is jointly determined by user activity and content update frequency; Sending the updated recommended user list to the communication device and performing content push operations according to the list order to obtain corresponding feedback data; Judging the feedback data based on a preset update condition. If the update condition is satisfied, re-perform the vectorization processing process and generate incremental feature data; Using an incremental learning mechanism to fuse the incremental feature data with historical feature data to update the distribution state of the comprehensive matching degree set.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the system according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the system according to any one of claims 1 to 6.

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