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

Abstract

This application relates to a multi-domain intelligent interactive system based on bottle QR codes. The system includes: an encoded data acquisition module that collects encoded data; a storage, query, and judgment module that retrieves data stored locally on the communication device and generates corresponding judgment result requests; a data visualization module that receives the encoded data and judgment result requests, mines tourism geographic information datasets using data extraction algorithms, and then transforms this information into visualized content using visualization algorithms; and a user recommendation module that collects social feature parameters input by users, comprehensively analyzes these parameters and the visualized content, and generates a personalized list of recommended users. This system achieves deep integration of bottle QR codes with multiple domain functions, greatly improving the convenience of information acquisition and social interaction, and meeting diverse needs in tourism and social interaction.

Description

Technical Field

[0001] This invention belongs to the field of information technology, and in particular relates to a multi-dimensional intelligent cross-domain interactive system based on QR codes on bottles. Background Technology

[0002] With the development of information technology, multi-dimensional intelligent cross-domain interactive technology based on QR codes on bottles has emerged. (This technology is applicable to drinking beverages.)

[0003] In the context of water and beverage usage, bottle packaging from the same manufacturer, brand, and product line is highly similar, easily leading to confusion when multiple people use the product simultaneously. Existing signature recognition methods suffer from poor performance due to inconvenient tools and issues with handwriting clarity, which is detrimental to personal hygiene and health and wastes resources. In the dating and matchmaking sector, while there are numerous existing platforms, there is a lack of targeted and convenient channels, failing to fully leverage everyday scenarios to create more opportunities for young people to meet. In the tourism sector, specialized travel websites offer overwhelming information, making it difficult for tourists to choose, and the promotion of local tourism information lacks efficient and precise methods to reach tourists. Traditional bottle packaging cannot overcome these existing challenges and achieve cross-sectoral intelligent interaction. Summary of the Invention

[0004] Therefore, it is necessary to address the aforementioned technical issues by providing a multi-domain intelligent interactive system based on bottle QR codes that can break down data barriers between different fields and achieve intelligent interaction, accurate recommendation, and efficient management.

[0005] Firstly, this application provides a multi-dimensional intelligent cross-domain interactive system based on a bottle's QR code, including: The encoded data acquisition module is used to acquire encoded data, including item identification codes and service entry addresses.

[0006] The storage query and judgment module is used 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 the corresponding judgment result request.

[0007] The data visualization module is used to parse coded data and judgment result requests and to extract data using data extraction algorithms. Acquire tourism geographic information datasets and use visualization algorithms to generate visual content on the display interface of communication devices.

[0008] The user recommendation module is used to obtain social feature parameters input by users; it analyzes the social feature parameters and the visualized content based on machine learning algorithms, generates a list of recommended users, and returns it to the communication device.

[0009] In one embodiment, the local storage data of the communication device is queried to determine whether a binding record associated with an item identification code is stored, and a corresponding determination result request is obtained, including: Access the local storage database based on the item identification code to perform association verification and generate a binding status identifier.

[0010] If the binding status identifier is unbound, the user identity data is processed using the identity verification algorithm to generate a binding message carrying the user identity hash value and the item identification code.

[0011] If the binding status identifier is already bound, extract the historical operation timestamps from the binding record set and generate an ownership query message containing the item identifier code and timestamp sequence.

[0012] Based on the binding message or the attribution query message, the corresponding transport protocol is selected for data encapsulation to obtain the encapsulated data packet.

[0013] The encapsulated data packet is sent to the cloud server through a pre-defined encrypted channel. The binding record set is updated or the owner information is returned based on the message type to obtain the corresponding judgment result request.

[0014] If the data is not stored, a binding request including user identity data and item identification code is generated; if the data is stored, an ownership query request including item identification code is generated.

[0015] In one embodiment, the encoded data and the judgment result request are parsed and a data extraction algorithm is used.

[0016] Acquire tourism geographic information datasets and use visualization algorithms to generate visual content on the display interface of communication devices, including: The encoded data is parsed to extract the initial dataset, which includes geographic coordinates, attraction names, and user ratings.

[0017] The initial dataset is processed based on preset data cleaning rules to obtain intermediate data with standardized geographic labels. The corresponding data extraction algorithm is invoked based on the feature type requested in the judgment result request, and the intermediate dataset is input into the data extraction algorithm to generate the target dataset.

[0018] The target dataset is input into the visualization algorithm to obtain a set of rendering instructions with coordinate mapping relationships.

[0019] The vector graphics data and text annotation data in the rendering instruction set are parsed to generate a pixel matrix adapted to the display interface of the communication device.

[0020] The pixel matrix is ​​transmitted to the graphics buffer of the communication device, triggering a screen refresh signal to generate a visual display.

[0021] In one embodiment, the corresponding data extraction algorithm is invoked based on the feature type in the judgment result request, and the intermediate dataset is input into the data extraction algorithm to generate the target dataset, including: The corresponding data extraction algorithm is obtained by matching the feature types in the judgment result request.

[0022] The intermediate dataset is structured using data transformation parameters to generate an initial target dataset including a dynamic checksum.

[0023] The dynamic check code triggers the verification model to perform integrity verification on the initial target dataset. If the dynamic check code matches the preset verification sequence, the final target dataset is output.

[0024] The final target dataset is input into the data extraction algorithm to generate the target dataset.

[0025] In one embodiment, the analysis of social feature parameters and visual content based on machine learning algorithms generates a recommended user list and returns it to the communication device, including: By using vectorization to process social feature parameters, feature data with a multidimensional spatial distribution is obtained; social feature parameters include user interaction frequency and content preference category.

[0026] Text tags and image features of the visualized content are extracted to construct a content feature matrix.

[0027] The vectorized feature data and content feature matrix are correlated and mapped to generate a comprehensive matching set of users and content.

[0028] The initial list of recommended users is obtained by processing the comprehensive matching degree set based on the collaborative filtering algorithm.

[0029] In one embodiment, after obtaining the initial list of recommended users, the process further includes: The initial recommended user list is filtered based on the real-time response data of the communication equipment, and records with interaction intervals exceeding a preset threshold are removed to obtain the filtered recommended user list.

[0030] The filtered list of recommended users is dynamically weighted to obtain an updated list of recommended users; the dynamic weights are determined by both user activity and content update frequency.

[0031] The updated list of recommended users is sent to the communication device, and content push operations are performed according to the list order to obtain corresponding feedback data.

[0032] The feedback data is judged based on the preset update conditions. If the update conditions are met, the vectorization process is re-executed and incremental feature data is generated.

[0033] The incremental learning mechanism is used to fuse incremental feature data with historical feature data to update the distribution of the comprehensive matching degree set.

[0034] In one embodiment, the system further includes a multi-source data fusion engine for: Receive the encoded data, the judgment result request, the tourism geographic information dataset, and the social feature parameters; The encoded data, judgment result request, tourism geographic information dataset and social feature parameters are classified and labeled according to the preset data type identifier to obtain the labeled heterogeneous dataset; The heterogeneous dataset is structurally transformed using a unified data model to generate a fused dataset with a compatible data format; The fused dataset is stored in a local shared data buffer for concurrent access by the data visualization module and the user recommendation module.

[0035] In one embodiment, the system further includes a multimodal identity management module for: Obtain the user's identity information, which includes a mobile phone number hash value and social media account authorization credentials; Based on the feature type in the judgment result request, determine the scene identifier of the current interaction scenario, which includes a binding scenario, a tourism scenario, or a social scenario; Based on the scene identifier, the corresponding identity resolution strategy is invoked to extract the subset of user attributes required for the current scene from the identity information; The subset of user attributes is associated and mapped with the fused dataset to generate context data that is adapted to the scenario.

[0036] Secondly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: Obtain encoded data including item identification codes and service entry addresses.

[0037] The system queries the local storage data of the communication device to determine whether it stores binding records associated with item identification codes, and obtains the corresponding determination result request.

[0038] The system parses the encoded data and judgment result requests, uses data extraction algorithms to obtain a tourism geographic information dataset, and employs visualization algorithms to generate visual display content on the communication device's display interface.

[0039] Obtain social feature parameters input by the user; analyze the social feature parameters and the visualized content based on machine learning algorithms, generate a list of recommended users, and return it to the communication device.

[0040] Thirdly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps: Obtain encoded data including item identification codes and service entry addresses.

[0041] The system queries the local storage data of the communication device to determine whether it stores binding records associated with item identification codes, and obtains the corresponding determination result request.

[0042] The system parses the encoded data and judgment result requests, uses data extraction algorithms to obtain a tourism geographic information dataset, and employs visualization algorithms to generate visual display content on the communication device's display interface.

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

[0044] The aforementioned multi-domain intelligent interactive system, computer equipment, and storage medium based on bottle QR codes mainly comprises the following four core functional modules: The encoded data acquisition module collects encoded data containing item identification codes and service entry addresses. The storage query and judgment module retrieves data from the local storage of the communication device, determines whether there are any binding records associated with the item identification code, and generates a corresponding judgment result request. The data visualization module receives the encoded data and judgment result requests, mines tourism geographic information datasets through data extraction algorithms, and then uses visualization algorithms to transform this information into intuitive visual content, presenting it on the display interface of the communication device. The user recommendation module collects social characteristic parameters input by users, uses machine learning algorithms to comprehensively analyze these parameters and the visual content, generates a personalized list of recommended users, and feeds it back to the communication device. This achieves deep integration of bottle QR codes with multiple domain functions, greatly improving the convenience of information acquisition and social interaction. Users can obtain tourism geographic information in one stop simply by scanning the bottle QR code and browse it in an intuitive visual interface. They can also receive accurate user recommendations based on their personal social characteristics, meeting their diverse needs in tourism and social interaction. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 The structural framework of the multi-dimensional intelligent cross-domain interactive system based on bottle QR codes provided in the embodiments of the present invention; Figure 2 This is a flowchart illustrating how a machine learning algorithm is used to analyze social feature parameters and visualized content to generate a recommended user list and return it to a communication device, as provided in an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] The multi-domain intelligent cross-interaction system based on bottle QR codes provided in this application integrates functions from multiple fields. By using the bottle QR code, the physical and digital boundaries can be crossed, enabling a diverse interactive experience. Through intelligent algorithms, the system presents product information, brand stories, promotional activities, and other content in rich formats such as text, images, videos, and AR / VR experiences, achieving deep communication between products and consumers. It can gain insights into consumer preferences based on scanning data, facilitating precise marketing; it can track product flow in real time; and in the area of ​​user services, it can conveniently answer consumer questions. This cross-domain interaction greatly enhances product added value and user engagement, building an efficient communication bridge between businesses and consumers. Exemplarily, the multi-domain intelligent cross-interaction system based on bottle QR codes provided in this application can be applied to at least one scenario, including but not limited to the following.

[0050] First, this multi-functional intelligent cross-domain interactive system based on bottle QR codes is applied to scenarios where multiple people share drinking water or beverages. In such scenarios, bottles from the same manufacturer, brand, and product series often have highly similar appearances, leading to frequent confusion. To effectively address this issue, this solution incorporates QR codes on both drinking water and beverage bottles. Users simply scan the QR code with their mobile phones. The information registered during the first scan is permanently stored in the manufacturer's website database. Subsequent scans of the same brand of beverage will automatically identify and display the owner of the bottle based on the previously recorded scan information. This method accurately distinguishes between various types of bottled water and beverages, preventing cross-infection risks from mistakenly taking the wrong bottle and effectively protecting public health. Simultaneously, it reduces beverage waste caused by misuse, effectively improving resource utilization efficiency.

[0051] Secondly, this multi-dimensional intelligent cross-domain interactive system based on the bottle's QR code is applied to dating scenarios. Users seeking friendships or partners can scan the QR code to register their personal information on a dedicated dating platform. This platform utilizes advanced matching algorithms to accurately filter and recommend suitable potential partners based on the user's information. Furthermore, scanning the QR code also guides users to follow the platform's WeChat official account, Douyin account, and other social media accounts, providing more dating information and interaction opportunities. This significantly expands dating channels, improves dating efficiency, and helps users find their ideal partners and build fulfilling interpersonal relationships in a broader social sphere.

[0052] Third, this multi-dimensional intelligent cross-domain interactive system based on bottle QR codes is applied to tourism scenarios. When users scan the QR code on the bottle, they can easily access a wealth of tourism information. This information covers all aspects of the tourist destination, including eating, accommodation, transportation, sightseeing, shopping, and entertainment, as well as details of ongoing local discounts and promotions. The QR code acts as a comprehensive tourism information hub, providing users with comprehensive and practical travel information. With this function, users no longer need to sift through numerous travel websites, enabling them to plan their trips more efficiently, gain a deeper understanding of local characteristics, and fully enjoy a convenient and enriching travel experience. It also opens up new avenues for promoting tourist destinations.

[0053] In one embodiment, such as Figure 1 As shown, this application provides a multi-domain intelligent interactive system based on a bottle's QR code, which may include: The encoded data acquisition module 101 is used to acquire encoded data including item identification code and service entry address.

[0054] This module can quickly and accurately identify various common encoding formats, including but not limited to QR codes and barcodes. In practical applications, whether the encoding is on a bottle, label, or other carrier, this module can quickly capture and parse the information. Obtaining the item identification code is crucial for accurately locating and distinguishing different items, serving as a key identifier for product traceability and information management. The service entry address provides users with convenient access to various service platforms, such as product-related travel information platforms and social interaction platforms.

[0055] The storage query and judgment module 102 is used to query the local storage data of the communication device to determine whether it should be stored. The binding record associated with the item identification code is used to obtain the corresponding judgment result request.

[0056] Specifically, by using an optimized data indexing algorithm, data records related to item identification codes can be quickly located.

[0057] Once the item identification code is entered, the module quickly traverses the local storage database to determine if a corresponding binding record exists. This process not only improves data query efficiency but also reduces unnecessary data processing overhead. If a binding record is found, the system will further retrieve relevant detailed information, such as the user's historical operation records and personalized settings; if no record is found, the corresponding processing flow is triggered, such as guiding the user to perform the initial binding operation. Through this precise query and judgment mechanism, the system can generate corresponding judgment result requests based on 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.

[0058] The data visualization module 103 is used to parse the coded data and judgment result requests, obtain tourism geographic information datasets using data extraction algorithms, and generate visual display content on the communication device display interface using visualization algorithms.

[0059] First, the encoded data and the judgment result requests from the storage query judgment module are deeply analyzed to extract key data related to tourism geographic information. Using advanced data extraction algorithms, a tourism geographic information dataset is constructed by filtering and integrating data from complex data structures, covering rich content such as geographical locations, attraction descriptions, and travel guides. Then, professional visualization algorithms are used to transform these datasets into easily understandable graphics, charts, or maps. On the display interface of the communication device, clear, aesthetically pleasing, and highly interactive visualizations are generated through careful adjustment of visualization parameters such as color, layout, and element size. This enables users to quickly acquire and understand complex tourism geographic information, providing... It provides intuitive and convenient support for users' travel planning and decision-making, effectively improving user experience and information acquisition efficiency.

[0060] The user recommendation module 104 is used to obtain social feature parameters input by users; it analyzes the social feature parameters and the visualized content based on machine learning algorithms, generates a list of recommended users, and returns it to the communication device.

[0061] Specifically, by receiving social characteristic parameters input by users, such as interests, age, gender, and social circles, the system comprehensively understands users' personalized needs and preferences. Based on machine learning algorithms, this module performs in-depth analysis of these social characteristic parameters and the visualization content generated by the data visualization module. By establishing complex user profile models and behavior prediction models, it uncovers potential similarities and connections between users. Based on the analysis results, the system generates a highly accurate list of recommended users, who have a high degree of matching with the target users in terms of interests, travel preferences, etc. Finally, the list of recommended users is returned to the communication device, providing users with more social opportunities, helping them expand their social circles, promoting interaction and communication among users, achieving an organic integration of tourism and social interaction, and enhancing user participation and stickiness in the system.

[0062] The aforementioned multi-domain intelligent interactive system, computer equipment, and storage medium based on bottle QR codes mainly comprises the following four core functional modules: The encoded data acquisition module collects encoded data containing item identification codes and service entry addresses. The storage query and judgment module retrieves data from the local storage of the communication device, determines whether there are any binding records associated with the item identification code, and generates a corresponding judgment result request. The data visualization module receives the encoded data and judgment result requests, mines tourism geographic information datasets through data extraction algorithms, and then uses visualization algorithms to transform this information into intuitive visual content, presenting it on the display interface of the communication device. The user recommendation module collects social characteristic parameters input by users, uses machine learning algorithms to comprehensively analyze these parameters and the visual content, generates a personalized list of recommended users, and feeds it back to the communication device. This achieves deep integration of bottle QR codes with multiple domain functions, greatly improving the convenience of information acquisition and social interaction. Users can obtain tourism geographic information in one stop simply by scanning the bottle QR code and browse it in an intuitive visual interface. They can also receive accurate user recommendations based on their personal social characteristics, meeting their diverse needs in tourism and social interaction.

[0063] In one embodiment, querying the local storage data of the communication device to determine whether it stores a binding record associated with an item identification code, and obtaining the corresponding determination result request, may include the following steps: Step S201: Access the local storage database based on the item identification code to perform association verification and generate a binding status identifier.

[0064] If the binding status identifier is unbound, the user identity data is processed using the identity verification algorithm to generate a binding message carrying the user identity hash value and the item identification code.

[0065] If the binding status identifier is already bound, extract the historical operation timestamps from the binding record set and generate an ownership query message containing the item identifier code and timestamp sequence.

[0066] Step S202: Select the corresponding transmission protocol based on the binding message or the home query message to encapsulate the data and obtain the encapsulated data packet.

[0067] Step S203: The encapsulated data packet is sent to the cloud server using a preset encrypted channel. The binding record set is updated or the owner information is returned according to the message type to obtain the corresponding judgment result request.

[0068] If the data is not stored, a binding request including user identity data and item identification code is generated; if the data is stored, an ownership query request including item identification code is generated.

[0069] First, the system accesses the local storage database based on the item identification code, performs an association verification operation, and generates a binding status identifier, which serves as a crucial indicator. When the binding status identifier shows an unbound state, the system rigorously processes the user's identity data using an identity verification algorithm, generating a binding message containing the user's identity hash value and the item identification code. If the binding status identifier indicates that the data is already bound, the system extracts historical operation timestamps from the binding record set, generating an ownership query message carrying the item identification code and a timestamp sequence. Next, the system selects a suitable transmission protocol for data encapsulation based on the specific type of the binding message or ownership query message, thus obtaining an encapsulated data packet. Then, using a pre-defined encrypted channel, the data packet is securely sent to the cloud server. The cloud server processes the received message type accordingly: for binding messages, it updates the binding record set; for ownership query messages, it returns the owner information. Through this series of operations, the system ultimately obtains the corresponding judgment result request. If no relevant record is stored locally, the system generates a binding request containing the user's identity data and the item identification code; if it is stored, it generates an ownership query request containing the item identification code.

[0070] This embodiment utilizes identity verification algorithms and encrypted channels to effectively protect the security of user identity data and item association information, preventing data leakage and unauthorized access, and ensuring user information security. In terms of data management, accurate association verification, clear status indicators, and an orderly record update mechanism enable efficient management of item-user relationship data, providing reliable data support for the system. From a user experience perspective, whether it's initial binding or subsequent ownership queries, accurate results can be obtained quickly, improving the convenience and smoothness of user operations, enhancing user trust and willingness to use the system, and promoting its widespread application in related fields.

[0071] In one embodiment, parsing the encoded data and the judgment result request, obtaining a tourism geographic information dataset using a data extraction algorithm, and generating visual display content on the communication device display interface using a visualization algorithm may include the following steps: Step S301: Parse the format of the encoded data and extract the initial dataset including geographic coordinates, attraction names and user ratings.

[0072] Step S302: Process the initial dataset based on preset data cleaning rules to obtain an intermediate dataset with standardized geographic labels.

[0073] Step S303: Based on the feature type in the judgment result request, call the corresponding data extraction algorithm, and input the intermediate dataset into the data extraction algorithm to generate the target dataset.

[0074] Step S304: Input the target dataset into the visualization algorithm to obtain a rendering instruction set with coordinate mapping relationships.

[0075] Step S305: Parse the vector graphics data and text annotation data in the rendering instruction set to generate a pixel matrix adapted to the display interface of the communication device.

[0076] Step S306: The pixel matrix is ​​transmitted to the graphics buffer of the communication device, triggering a screen refresh signal to generate visual display content.

[0077] First, the encoded data undergoes deep format parsing to accurately extract key information such as geographic coordinates, attraction names, and user ratings, constructing an initial dataset. Next, the initial dataset is processed according to preset data cleaning rules to remove erroneous, duplicate, and irrelevant data, resulting in an intermediate dataset with standardized geographic labels, ensuring data accuracy and standardization. Then, based on the feature type requested in the judgment result request, the system intelligently invokes the corresponding data extraction algorithm, using the intermediate dataset as input, to further mine and process the data, generating the target dataset. Afterward, the target dataset is input into a visualization algorithm, undergoing complex calculations to obtain a rendering instruction set with coordinate mapping relationships. The vector graphics data and text annotation data in the rendering instruction set are then parsed and converted into a pixel matrix adapted to the communication device's display interface. Finally, this pixel matrix is ​​transmitted to the communication device's graphics buffer, triggering a screen refresh signal and generating intuitive and clear visual content on the communication device.

[0078] Through multi-step data processing and transformation, complex and disordered coded data can be transformed into intuitive and easy-to-understand visualizations, such as maps and charts, allowing users to quickly obtain and understand tourism geographic information, greatly improving the efficiency and experience of information acquisition. Regarding data quality assurance, the application of data cleaning and targeted data extraction algorithms ensures the accuracy, standardization, and effectiveness of the final displayed data, avoiding interference from erroneous or invalid information. From the perspective of system functionality, it provides a solid data display foundation for subsequent functions such as user recommendations and tourism planning, strongly supporting the implementation and optimization of the overall system functionality.

[0079] In one embodiment, invoking the corresponding data extraction algorithm based on the feature type in the judgment result request, and inputting the intermediate dataset into the data extraction algorithm to generate the target dataset, may include the following steps: Step S401: Match the feature types in the judgment result request to obtain the corresponding data extraction algorithm.

[0080] Step S402: The intermediate dataset is structured using data conversion parameters to generate an initial target dataset including a dynamic checksum.

[0081] Step S403: The verification model is triggered to perform integrity verification on the initial target dataset based on the dynamic check code. If the dynamic check code is consistent with the preset verification sequence, the final target dataset is output.

[0082] Step S404: Input the final target dataset into the data extraction algorithm to generate the target dataset.

[0083] Specifically, the system first performs precise matching based on the feature type in the judgment result request to obtain the corresponding dedicated data extraction algorithm. Then, it uses pre-set data transformation parameters to perform structured processing on the intermediate dataset, generating an initial target dataset containing a dynamic checksum. Next, the system triggers a verification model based on the dynamic checksum to perform rigorous integrity verification on the initial target dataset. Only when the dynamic checksum completely matches the preset verification sequence will the final target dataset that meets the standard be output. Finally, this final target dataset is input into the previously matched data extraction algorithm to generate the target dataset required by the system.

[0084] This embodiment verifies the integrity of the initial target dataset by setting dynamic checksums and a verification model, effectively ensuring the accuracy and completeness of the data and avoiding deviations in subsequent analysis and application due to data errors or missing data. Regarding the effectiveness of algorithm application, corresponding data extraction algorithms are matched according to different feature types, making data processing more targeted, fully mining data value, and improving data processing efficiency and quality. From the perspective of overall system operation, an accurate and reliable target dataset provides a solid data foundation for subsequent functional modules such as data visualization and user recommendation, strongly supporting the stable operation of system functions, improving system reliability and usability, and providing users with higher quality and more accurate services.

[0085] In one embodiment, such as Figure 2 As shown, the process of analyzing social feature parameters and visual content based on machine learning algorithms to generate a recommended user list and return it to the communication device may include the following steps: Step S501: Use vectorization to process social feature parameters to obtain feature data with multidimensional spatial distribution; social feature parameters include user interaction frequency and content preference category.

[0086] Step S502: Extract text tags and image features from the visualized content to construct a content feature matrix.

[0087] Step S503: The vectorized feature data and content feature matrix are correlated and mapped to generate a comprehensive matching degree set between users and content.

[0088] Step S504: Process the comprehensive matching degree set based on the collaborative filtering algorithm to obtain the initial recommended user list.

[0089] First, social feature parameters, including user interaction frequency and content preference categories, are vectorized. This process transforms the originally scattered and difficult-to-process social feature parameters into feature data with a multi-dimensional spatial distribution, making the data easier to analyze and compute mathematically. Next, text tags and image features are extracted from the visualized content to construct a content feature matrix, comprehensively and accurately characterizing the key features of the visualized content. Then, the vectorized feature data is correlated and mapped with the content feature matrix, and through specific calculation rules, a comprehensive matching score set reflecting the degree of matching between users and content is generated. Finally, a collaborative filtering algorithm is used to perform deep processing on the comprehensive matching score set to obtain an initial list of recommended users.

[0090] This embodiment generates a recommended user list that highly matches users' personal interests and preferences through in-depth analysis and correlation of social feature parameters and visualized content. This significantly improves the accuracy and relevance of recommendations, providing strong support for users to discover like-minded individuals and enhancing their experience and satisfaction in social scenarios. From the perspective of system intelligence, the entire process utilizes advanced vectorization processing, feature extraction, and collaborative filtering algorithms to fully explore the potential value of user and content data, demonstrating the system's intelligence level. This enables the system to better adapt to the diverse needs of different users, enhance its core competitiveness, and promote the efficient realization of social functions.

[0091] In one embodiment, after obtaining the initial list of recommended users, the following steps may also be included: Step S601: Filter the initial recommended user list based on the real-time response data of the communication device, remove records with interaction intervals exceeding a preset threshold, and obtain the filtered recommended user list.

[0092] Step S602: The filtered list of recommended users is dynamically weighted to obtain an updated list of recommended users; the dynamic weight is determined by both user activity and content update frequency.

[0093] In step S603, the updated list of recommended users is sent to the communication device, and the content push operation is performed according to the list order to obtain the corresponding feedback data.

[0094] Step S604: The feedback data is judged based on the preset update conditions. If the update conditions are met, the vectorization process is re-executed and incremental feature data is generated.

[0095] Step S605: Use the incremental learning mechanism to fuse incremental feature data and historical feature data to update the distribution of the comprehensive matching degree set.

[0096] First, the initial recommended user list is filtered based on real-time response data from the communication device. By setting a preset threshold for the interaction interval, records with interaction intervals exceeding this threshold are removed, resulting in a filtered recommended user list that ensures a higher probability of interaction between the recommended users and the current user. Next, the filtered recommended user list is dynamically weighted and sorted. The dynamic weights are determined by both user activity and content update frequency, generating an updated recommended user list. Subsequently, the updated recommended user list is sent to the communication device, and content push operations are performed according to the list order, while collecting corresponding feedback data. Then, the feedback data is evaluated based on preset update conditions. If the update conditions are met, the vectorization processing flow is re-executed to generate incremental feature data. Finally, an incremental learning mechanism is used to merge the incremental feature data with historical feature data, updating the distribution of the comprehensive matching degree set.

[0097] This embodiment, through real-time filtering and dynamic weight ranking, pushes recommended users and content that better match users' current needs and interests, increasing user attention and engagement with the recommended content and enhancing user satisfaction with the system. From the perspective of system performance improvement, dynamic updates and incremental learning based on feedback data enable the system to continuously adapt to changing user behavior and data, optimize the recommendation algorithm, improve the accuracy and timeliness of recommendations, and ensure that the system always maintains a highly efficient and intelligent operating state, providing users with consistently high-quality recommendation services.

[0098] In one embodiment, this application provides a method for managing the ownership of bottled beverages based on QR codes, which may include: A QR code is printed on the label or cap of the drinking water or beverage bottle. This QR code is associated with a unique item. Product identification code. After the user scans the code for the first time, the system guides them to log in to a designated website via a communication device, binding their mobile phone number or WeChat account with the product identification code and storing it on a cloud server. The globally unique mobile phone number serves as the user's identity identifier. Subsequent scans utilize a storage query module to verify the binding status between the product identification code and the user's identity, displaying the beverage's owner information in real time.

[0099] This embodiment accurately distinguishes the ownership of bottled water or beverages in multi-person scenarios, effectively avoiding the risk of cross-infection caused by mistaking, improving resource utilization efficiency, and protecting users' health and hygiene.

[0100] In one embodiment, this application provides a QR code-based intelligent tourism information push system, which may include: After the user scans the QR code on the label or cap of the drinking water or beverage bottle, the encoded data acquisition module... The system retrieves coded data containing the address of the tourism information service portal. The data visualization module uses data extraction algorithms to mine a dataset of tourism geographic information for the target area from a cloud database, including attraction names, geographic coordinates, user ratings, and promotional activities. After processing by visualization algorithms, it generates an interactive map, a list of recommended attractions, and real-time promotional information on the communication device interface.

[0101] This embodiment provides users with a one-stop travel information portal, helping them to plan their trips efficiently, while also providing scenic spots with a precise promotional channel to reach tourists and improve the efficiency of travel information dissemination.

[0102] In one embodiment, this application provides a QR code-based social interaction and dating platform, which may include: Scanning a QR code triggers the social features of the user recommendation module, allowing users to input social characteristics such as age, interests, and dating needs via their communication devices. Based on machine learning algorithms, the system maps user characteristics to visually displayed dating tags (such as location and interest tags), generating a highly matched list of recommended users through collaborative filtering. The system also supports user login via WeChat, Douyin, and other social media accounts, expanding users' dating channels.

[0103] In one embodiment, this application provides a QR code-based brand information interaction and marketing system, which may include: The QR code integrates information on the brand culture, quality control, and promotional activities of drinking water (beverage) manufacturers. (User) After scanning the QR code, the encoded data acquisition module parses the data to obtain the service entry address, guiding users to the brand's official website or dedicated page. The storage, query, and judgment module dynamically updates the brand data on the cloud server based on the user's scanning behavior. The data visualization module displays the brand story, production process traceability, and limited-time promotional activities in the form of images, videos, and AR, while also supporting user interaction with the brand through comments, sharing, and other functions.

[0104] This embodiment enhances brand-consumer engagement, improves brand reputation, promotes product sales growth through precise marketing, and achieves efficient dissemination of brand culture.

[0105] In one embodiment, the system also includes a multi-source data fusion engine to address the heterogeneity of multi-source data such as encoded data, judgment result requests, tourism geographic information datasets, and social feature parameters, thereby achieving unified management and efficient collaboration.

[0106] Specifically, the multi-source data fusion engine performs the following operations: It receives encoded data from the encoded data acquisition module, judgment result requests from the storage query judgment module, tourism geographic information datasets from the cloud server, and social feature parameters input by the user. These data have different formats, update frequencies, and storage locations, forming a heterogeneous dataset.

[0107] The data is categorized and labeled based on preset data type identifiers. These identifiers can be predefined according to the data source or content; for example, coded data can be labeled "Type A," judgment result requests "Type B," tourism geographic information datasets "Type C," and social feature parameters "Type D." Through this labeling, the system can quickly identify the attributes of various data types.

[0108] Subsequently, a unified data model was used to perform a structured transformation on the labeled heterogeneous dataset. The unified data model defines compatible data formats, including field mapping relationships and data type conversion rules. For example, geographic coordinates in tourism geographic information were converted to a system-wide unified coordinate format, and user preferences in social feature parameters were converted into standardized vectors. Through this transformation, a fused dataset with compatible data formats was generated.

[0109] The fused dataset is stored in a local shared data buffer. This buffer uses a caching mechanism to support concurrent reading from multiple modules, thereby avoiding redundant data processing overhead and improving the overall system response speed.

[0110] This embodiment achieves unified modeling and shared access to binding status data, tourism information data, and social feature data through a multi-source data fusion engine. This provides high-quality, standardized input for subsequent data visualization and user recommendations, significantly improving the system's data processing efficiency and collaborative capabilities.

[0111] In one embodiment, the system also includes a multimodal identity management module, which is used to intelligently identify and manage user identities and permissions according to different interaction scenarios, so as to ensure the security and scenario adaptability of data use.

[0112] Specifically, the multimodal identity management module performs the following operations: The system obtains the user's identity information. This identity information includes, but is not limited to: the user's identity hash value generated after processing the user's identity data through an identity verification algorithm, and the social media account authorization credentials obtained when the user logs in via social media account authorization. This information serves as the basis for the user's identity in different scenarios.

[0113] Based on the feature type in the judgment result request generated by the storage query judgment module, the scene identifier of the current interaction scenario is determined. The feature type reflects the purpose of the user's current operation. For example, if the judgment result request is a binding request, the scene identifier corresponds to a binding scenario; if the judgment result request is a homepage query request and subsequently triggers the display of tourism information, the scene identifier may correspond to a tourism scenario; if the user actively inputs social feature parameters, the scene identifier may correspond to a social scenario.

[0114] Based on the scene identifier, the corresponding identity resolution strategy is invoked to extract the subset of user attributes required for the current scene from the identity information. For example, in a binding scenario, only the user's identity hash value needs to be extracted to generate the binding message; in a tourism scenario, it may be necessary to extract the user's historical browsing preferences to assist in filtering tourism information; in a social scenario, social feature parameters such as the user's age and interest tags need to be extracted for the recommendation algorithm.

[0115] The extracted subset of user attributes is mapped to the aforementioned fused dataset to generate scenario-adapted contextual data. This contextual data will be passed to subsequent data visualization or user recommendation modules to ensure that each module can only access user information necessary for the current scenario, thus preventing privacy leaks.

[0116] This embodiment uses a multimodal identity management module to achieve dynamic adaptation and access control of user identities in different functional scenarios. This satisfies the user data needs of each module while maximizing the protection of user privacy and security, and enhances the intelligence and usability of the system.

[0117] In the above embodiments, the multi-source data fusion engine and the multimodal identity management module work together to construct an efficient, secure, and intelligent cross-domain interaction system. The multi-source data fusion engine solves the technical challenges of inconsistent data formats and low calling efficiency, providing a reliable data foundation for data visualization and user recommendation. The multimodal identity management module dynamically adjusts the exposure level of user identity information according to the scenario, achieving a balance between functional requirements and privacy protection. The synergistic effect of both enables the system to maintain high performance while ensuring user data security when dealing with complex and ever-changing user needs, further enhancing the inventiveness and practicality of this invention.

[0118] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially 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 on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed 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 performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0119] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the multi-intelligent cross-domain interactive system based on bottle QR codes as described above.

[0120] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0121] For the device embodiment, since it basically corresponds to the method embodiment, please refer to the method embodiment for relevant details. The embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.

[0122] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A multi-element intelligent cross-domain interaction system based on a bottle body two-dimensional code, characterized in that, The system includes: The encoded data acquisition module is used to acquire encoded data, including item identification codes and service entry addresses; The storage query and judgment module is used to query the local storage data of the communication device to determine whether the binding record associated with the item identification code is stored, and to obtain the corresponding judgment result request. The data visualization module is used to parse the coded data and judgment result request, obtain tourism geographic information dataset using data extraction algorithms, and generate visual display content on the display interface of the communication device using visualization algorithms. The user recommendation module is used to obtain social feature parameters input by the user; analyze the social feature parameters and the visualized content based on machine learning algorithms, generate a list of recommended users, and return it to the communication device.

2. The system of claim 1, wherein, The step of querying the local storage data of the communication device to determine whether a binding record associated with the item identification code is stored, and obtaining the corresponding determination result request, includes: Access the local storage database based on the item identification code to perform association verification and generate a binding status identifier; If the binding status identifier is unbound, the user identity data is processed using an identity verification algorithm to generate a binding message carrying the user identity hash value and the item identification code; If the binding status identifier is already bound, extract the historical operation timestamps from the binding record set and generate an attribution query message containing the item identification code and timestamp sequence; Based on the binding message or the attribution query message, the corresponding transmission protocol is selected for data encapsulation to obtain the encapsulated data packet; The encapsulated data packet is sent to the cloud server through a preset encrypted channel. The binding record set is updated or the owner information is returned according to the message type to obtain the corresponding judgment result request. If the item is not stored, a binding request including user identity data and the item identification code is generated; if the item is stored, an ownership query request including the item identification code is generated.

3. The system of claim 1, wherein, The process of parsing the encoded data and judgment result request, obtaining a tourism geographic information dataset using a data extraction algorithm, and generating visual display content on the communication device display interface using a visualization algorithm includes: The encoded data is parsed to extract an initial dataset including geographic coordinates, attraction names, and user ratings; The initial dataset is processed based on preset data cleaning rules to obtain an intermediate dataset with standardized geographic labels; Based on the feature type in the judgment result request, the corresponding data extraction algorithm is invoked, and the intermediate dataset is input into the data extraction algorithm to generate the target dataset; The target dataset is input into a visualization algorithm to obtain a set of rendering instructions with coordinate mapping relationships; The vector graphics data and text annotation data in the rendering instruction set are parsed to generate a pixel matrix adapted to the display interface of the communication device; The pixel matrix is ​​transmitted to the graphics buffer of the communication device, triggering a screen refresh signal to generate visual display content.

4. The system of claim 3, wherein, The step of calling the corresponding data extraction algorithm based on the feature type in the judgment result request, and inputting the intermediate dataset into the data extraction algorithm to generate the target dataset includes: Based on the feature type in the judgment result request, a matching algorithm is obtained to extract the corresponding data. The intermediate dataset is structured using data transformation parameters to generate an initial target dataset including a dynamic checksum. The dynamic check code triggers the verification model to perform integrity verification on the initial target dataset. If the dynamic check code is consistent with the preset verification sequence, the final target dataset is output. The final target dataset is input into the data extraction algorithm to generate the target dataset.

5. The system of claim 1, wherein, The process of analyzing the social feature parameters and visual content based on machine learning algorithms, generating a recommended user list, and returning it to the communication device includes: The social feature parameters are processed using vectorization to obtain feature data with a multidimensional spatial distribution; the social feature parameters include user interaction frequency and content preference category. Extract the text tags and image features of the visualized content to construct a content feature matrix; The vectorized feature data and content feature matrix are correlated and mapped to generate a comprehensive matching degree set between users and content; The initial list of recommended users is obtained by processing the comprehensive matching degree set using a collaborative filtering algorithm.

6. The system of claim 5, wherein, After obtaining the initial list of recommended users, the process also includes: The initial recommended user list is filtered based on the real-time response data of the communication device, and records with an interaction interval exceeding a preset threshold are removed to obtain the filtered recommended user list. The filtered list of recommended users is dynamically weighted to obtain an updated list of recommended users; the dynamic weights are determined by both user activity and content update frequency. The updated list of recommended users is sent to the communication device, and content push operation is performed according to the list order to obtain corresponding feedback data; The feedback data is judged based on the preset update conditions. If the update conditions are met, the vectorization process is re-executed and incremental feature data is generated. The incremental learning mechanism is used to fuse the incremental feature data and historical feature data to update the distribution state of the comprehensive matching degree set.

7. The system of claim 1, wherein, The system also includes a multi-source data fusion engine for: Receive the encoded data, the judgment result request, the tourism geographic information dataset, and the social feature parameters; The encoded data, judgment result request, tourism geographic information dataset and social feature parameters are classified and labeled according to the preset data type identifier to obtain the labeled heterogeneous dataset; The heterogeneous dataset is structurally transformed using a unified data model to generate a fused dataset with a compatible data format; The fused dataset is stored in a local shared data buffer for concurrent access by the data visualization module and the user recommendation module.

8. The system according to claim 7, characterized in that, The system also includes a multimodal identity management module, used for: Obtain the user's identity information, which includes a mobile phone number hash value and social media account authorization credentials; Based on the feature type in the judgment result request, determine the scene identifier of the current interaction scenario, which includes a binding scenario, a tourism scenario, or a social scenario; Based on the scene identifier, the corresponding identity resolution strategy is invoked to extract the subset of user attributes required for the current scene from the identity information; The subset of user attributes is associated and mapped with the fused dataset to generate context data that is adapted to the scenario.

9. A computer device comprising a memory and a processor, wherein the memory stores 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 9.

10. A computer-readable storage medium having stored thereon a computer program, 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 9.

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