Digital collectible aggregation display method and device, electronic equipment and storage medium

Abstract

The application provides a digital collectible aggregation display method and device, electronic equipment and storage medium, which can be used in the field of blockchain technology. The method comprises the following steps: sending an authorization request to a plurality of collectible platforms based on a pre-configured smart contract; receiving authorization feedback results of each collectible platform and writing the authorization feedback results of each collectible platform into a blockchain; reading each authorization feedback result on the blockchain, if the authorization feedback result indicates confirmation of authorization, obtaining a plurality of digital collectibles of the collectible platform corresponding to the authorization feedback result; and displaying a collectible display page based on the collectible information of each digital collectible. The method of the application integrates digital collectible resources of a plurality of other collectible platforms for centralized display, so that users can more conveniently browse and search digital collectibles of their interest, improving the information acquisition efficiency and use experience of users.

Description

Technical Field

[0001] This application relates to the field of blockchain technology, and in particular to a method, apparatus, electronic device, and storage medium for the aggregated display of digital collectibles. Background Technology

[0002] Digital collectibles refer to unique digital certificates generated using blockchain technology for specific works of art, enabling authentic and trustworthy digital issuance, purchase, collection, and use while protecting their digital copyright.

[0003] In existing technologies, the display process of digital collectibles involves using blockchain technology (such as public chains and consortium chains) to record and authenticate the digital collectibles on the blockchain, generating blockchain digital products. These blockchain-based digital collectibles are then directly displayed on a platform, allowing users to browse and search for collectibles of interest.

[0004] The current method of exhibiting digital artifacts involves dispersing them across multiple platforms. This means that users need to manage multiple accounts and be familiar with the operating rules and security mechanisms of different platforms, which is not conducive to users' browsing and access to digital artifacts and affects the user experience. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for the aggregated display of digital collectibles, in order to solve the technical problem that existing digital collectibles are distributed across different collectible platforms, which is not conducive to users' browsing and access to digital collectibles and affects user experience.

[0006] According to the first aspect disclosed in this application, this application provides a method for aggregating and displaying digital collectibles, including:

[0007] Authorization requests are sent to multiple collection platforms based on pre-configured smart contracts;

[0008] Receive authorization feedback results from various collection platforms and write the authorization feedback results from each collection platform into the blockchain;

[0009] Read the authorization feedback results on the blockchain. If the authorization feedback result indicates confirmation of authorization, then obtain multiple digital collectibles from the collectibles platform corresponding to the authorization feedback result.

[0010] Based on the collection information of each digital collection, a collection display page is displayed.

[0011] In one feasible implementation, based on the collection information of each digital collection, a collection display page is displayed, including:

[0012] If the user's browsing history can be obtained, the browsing history is input into a pre-built preference recognition model to obtain the user's preference tags; wherein, the preference recognition model is obtained by training a deep learning model;

[0013] Based on the first matching degree between the preference tags and the classification tags of each digital collectible, the digital collectibles are sorted in descending order of the first matching degree.

[0014] Based on the sorted information of each digital artifact, the artifact display page is shown.

[0015] In one feasible implementation, the method further includes:

[0016] If the user's browsing history cannot be obtained, then the access history of each digital collection is obtained; wherein, the access history includes at least one of the following: number of views, number of likes, number of collections, and number of shares;

[0017] Based on the access records of each digital artifact, the popularity of each digital artifact is determined;

[0018] The digital collectibles are sorted in descending order of popularity.

[0019] Based on the sorted information of each digital artifact, the artifact display page is shown.

[0020] In one feasible implementation, the method further includes:

[0021] When the query information input by the user is obtained, the keywords in the query information are extracted;

[0022] Based on the second matching degree between the keywords and the classification tags of each digital collectible, the digital collectibles are sorted in descending order of the second matching degree.

[0023] Based on the sorted information of each digital artifact, the artifact display page is shown.

[0024] In one feasible implementation, the method further includes:

[0025] The digital collection is input into a pre-built collection classification model to obtain the classification result of the digital collection; wherein, the feature classification model is trained based on a deep learning model, and the classification result includes classification labels under multiple preset classification dimensions;

[0026] Based on the classification results, a label file is generated;

[0027] The tag file is written to the blockchain, and a mapping is established between the tag file and the digital collection.

[0028] In one feasible implementation, before displaying the collection display page based on the collection information of each digital collection, the method further includes:

[0029] For each digital collectible, based on the ownership information in the collectible information, it is determined whether the digital collectible is an authorized exhibition work;

[0030] If the digital artifact is an authorized exhibition work, then the artifact information of the digital artifact is obtained.

[0031] In one feasible implementation, the method further includes:

[0032] If the digital collection is an unauthorized exhibition work, an exhibition permission request will be sent to the rights holder via a smart contract on the blockchain;

[0033] Obtain the permission feedback result of the exhibition permission request and write the permission feedback result into the blockchain;

[0034] Read the permission feedback result on the blockchain. If the permission feedback result indicates permission to exhibit, then obtain the collection information of the digital collection.

[0035] According to a second aspect disclosed in this application, this application provides an aggregation and display device for digital collectibles, comprising:

[0036] The request sending module is used to send authorization requests to multiple collection platforms based on pre-configured smart contracts;

[0037] The feedback receiving module is used to receive the authorization feedback results from various collection platforms and write the authorization feedback results from each collection platform into the blockchain.

[0038] The data acquisition module is used to read the various authorization feedback results on the blockchain. If the authorization feedback result indicates confirmation of authorization, then the module acquires multiple digital collectibles from the collectibles platform corresponding to the authorization feedback result.

[0039] The collection display module is used to display the collection page based on the collection information of each digital collection.

[0040] According to a third aspect disclosed in this application, this application provides an electronic device, including a processor and a memory communicatively connected to the processor;

[0041] The memory stores computer-executed instructions;

[0042] The processor executes computer execution instructions stored in the memory to implement the method described in any one of the first aspects.

[0043] According to the fourth aspect disclosed in this application, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the method described in any one of the first aspects.

[0044] According to the fifth aspect disclosed in this application, this application provides a computer program product, including a computer program, which, when executed, is used to implement the method described in any one of the first aspects.

[0045] Compared with the prior art, this application has the following advantages:

[0046] This application provides a method, apparatus, electronic device, and storage medium for the aggregated display of digital collectibles. By integrating digital collectible resources from multiple other collectible platforms, it enables the centralized display of digital collectibles from multiple platforms. This centralized display method allows users to more conveniently browse and search for digital collectibles of interest, reduces the complexity of switching between multiple collectible platforms, and greatly improves the efficiency of information acquisition and user experience. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0048] Figure 1 A flowchart illustrating a method for aggregating and displaying digital collectibles, provided as an embodiment of this application;

[0049] Figure 2 A flowchart illustrating another method for aggregating and displaying digital collectibles, provided as an embodiment of this application;

[0050] Figure 3 A schematic diagram of the structure of a digital collection aggregation display device provided in an embodiment of this application;

[0051] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0052] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0053] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0054] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize or refuse.

[0055] Furthermore, the technical solution involved in this application, which involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and the use of artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the results of automated decision-making, provides users with corresponding operation entry points for users to choose to agree to or reject the results of automated decision-making; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0056] Digital collectibles refer to unique digital certificates generated for specific works of art using blockchain technology. These certificates protect digital copyrights and enable authentic and reliable digital issuance, purchase, collection, and use. As a type of electronic collectible, digital collectibles come in various forms, including images, music, videos, 3D models, and electronic tickets. Blockchain technology generates different digital certificates for different collectibles, enabling digital issuance, trading, collection, and circulation. They are characterized by being immutable, indivisible, and issued in limited quantities, and are known as "NFTs (Non-Fungible Tokens)" in international markets.

[0057] In existing technologies, the display process of digital collectibles involves using blockchain technology (such as public chains and consortium chains) to record and authenticate the digital collectibles on the blockchain, generating blockchain digital products. This process ensures the uniqueness and authenticity of the digital collectibles, with each collectible receiving a unique identifier on the blockchain. The digital collectibles, once recorded on the blockchain, are then directly displayed on a platform, allowing users to browse and search for collectibles of interest.

[0058] Current methods of exhibiting digital collectibles involve distributing them across multiple platforms. This means users need to manage multiple accounts and be familiar with the operating rules and security mechanisms of different platforms. This not only increases management complexity but also potentially increases security risks due to users using the same passwords on different platforms or inadequate security measures. Because digital collectibles are distributed across different blockchains, cross-chain transactions may face high latency and costs. Limited interoperability between different blockchains leads to reduced transaction efficiency, and cross-chain transactions may involve additional fees and waiting times. Distributed digital collectibles may also face liquidity issues, meaning it may be difficult to find buyers or sellers on some platforms.

[0059] Therefore, existing digital collections are scattered across different platforms, which makes it difficult for users to browse and access digital collections and affects the user experience.

[0060] To address the aforementioned technical issues, this application proposes a method, apparatus, electronic device, and storage medium for the aggregated display of digital collectibles. By integrating digital collectible resources from multiple other collectible platforms for centralized display, users can more conveniently browse and search for digital collectibles of interest, thereby improving users' information acquisition efficiency and user experience.

[0061] It should be noted that the digital collection aggregation display method, device, electronic device and storage medium provided in this application can be used in the field of blockchain technology, or in any field other than blockchain. The application field of the digital collection aggregation display method, device, electronic device and storage medium in this application is not limited.

[0062] The technical solution of the digital collection aggregation and display method provided in this application will be described in detail below through specific embodiments. It should be noted that the following embodiments may exist alone or in combination with each other, and the same or similar content may not be described again in different embodiments.

[0063] It should be noted that the execution entity of the digital collection aggregation display method provided in this application embodiment is the server of the aggregation platform, and correspondingly, the digital collection aggregation display device is also set in the server of the aggregation platform.

[0064] Specifically, aggregation platforms can aggregate digital collectibles from different collectible platforms for unified display, thereby enhancing the user experience.

[0065] Figure 1 A flowchart illustrating a method for aggregating and displaying digital collectibles, as provided in this application embodiment, is shown below. Figure 1 In some embodiments, the method for aggregating and displaying digital collectibles includes the following steps:

[0066] S101 sends authorization requests to multiple collection platforms based on pre-configured smart contracts.

[0067] The initiator (aggregation platform) generates an authorization request by calling relevant functions in a deployed smart contract. This request typically includes the specific content of the authorization, the address of the recipient (collection platform), and other information. The generated authorization request is broadcast through the blockchain network to ensure that all relevant nodes receive it.

[0068] Specifically, smart contracts need to clearly define the terms and conditions of authorization, such as the scope and duration of authorization, as well as the operations after authorization.

[0069] S102 receives authorization feedback results from various collection platforms and writes the authorization feedback results from each collection platform into the blockchain.

[0070] Upon receiving an authorization request, the recipient invokes its own smart contract to verify the request. This verification typically includes checking the request's legitimacy, the initiator's permissions, and the reasonableness of the authorization terms. After verification, the recipient returns an authorization feedback result, indicating whether or not to confirm the authorization.

[0071] Specifically, if the authorization request is verified and successfully processed, the recipient will send an authorization confirmation message to the initiator. This message typically includes a unique authorization identifier (such as an authorization ID or transaction hash) so that the initiator can track and verify the authorization status. Furthermore, the authorization confirmation message also includes the specific terms and conditions of the authorization, as well as the validity period of the authorization.

[0072] Specifically, if the authorization request fails to pass verification or processing, the recipient will send an error message to the initiator. This message will detail the reason for the failure, such as invalid signature, insufficient permissions, or unreasonable terms.

[0073] Specifically, the aggregation platform establishes a new blockchain to record relevant information during the display process. Multiple digital collectible platforms (Platform A, Platform B, and Platform C) write the information authorized by users to the aggregation platform onto the blockchain. The aggregation platform then retrieves the relevant digital collectibles by reading the authorization information on the blockchain.

[0074] S103: Read the authorization feedback results on the blockchain. If the authorization feedback result indicates confirmation of authorization, then obtain multiple digital collectibles from the collectibles platform corresponding to the authorization feedback result.

[0075] If the authorization feedback result indicates that the collection platform confirms the authorization, then the digital collection on the collection platform can be obtained.

[0076] Specifically, if the collection platform's data is stored directly on the blockchain, and the aggregation platform has access to that blockchain, then the aggregation platform can directly retrieve the collection data by calling the data query function in the collection platform's smart contract. If the collection platform's data is stored off-chain (such as in a database or cloud storage), or if the aggregation platform cannot directly access the collection platform's blockchain, then it can access the collection data through oracles or middleware. The collection platform can also provide a dedicated API interface, allowing the aggregation platform to access its collection data via HTTP requests and other methods.

[0077] S104, based on the collection information of each digital collection, displays the collection display page.

[0078] After acquiring digital collectibles from other collectible platforms, a collectibles display page is shown to showcase the digital collectibles.

[0079] Specifically, the information on digital collectibles mainly includes basic information, content information, ownership information, and transaction information. Basic information includes the collectible name, author / creator, issuer, issuance date, and collectible ID. Content information includes the collectible description, preview images / dynamic displays, etc. Ownership information includes copyright ownership, usage rights, scope of authorization, and copyright statement. Transaction information includes the current holder, transaction history, and pricing information. Aggregation platforms can select some or all of the collectible information to display based on their needs. For example, basic display information for a digital collectible can be generated based on its preview image and name.

[0080] In this embodiment, by integrating digital collection resources from multiple other collection platforms, digital collections from multiple platforms can be centrally displayed. This centralized display method makes it easier for users to browse and search for digital collections they are interested in, reduces the complexity of switching between multiple collection platforms, and greatly improves the efficiency of information acquisition and user experience.

[0081] exist Figure 1 Based on the embodiments shown, the following is combined with Figure 2 The technical solution for the above-mentioned method of aggregating and displaying digital collectibles will be further introduced.

[0082] Figure 2 A flowchart illustrating another method for aggregating and displaying digital collectibles provided in this application embodiment is shown below. Figure 2 In some embodiments, the method for aggregating and displaying digital collectibles includes the following steps:

[0083] S101 sends authorization requests to multiple collection platforms based on pre-configured smart contracts.

[0084] S102 receives authorization feedback results from various collection platforms and writes the authorization feedback results from each collection platform into the blockchain.

[0085] S103: Read the authorization feedback results on the blockchain. If the authorization feedback result indicates confirmation of authorization, obtain the ownership information of each digital collectible.

[0086] S104. Based on the ownership information in the collection information of each digital collection, determine whether the digital collection is an authorized exhibition work.

[0087] After authorization by the platform, it is also necessary to obtain the ownership information of each digital collectible in order to confirm whether the rights holder of the digital collectible has authorized the exhibition.

[0088] Specifically, the ownership information of digital artifacts records the usage rights of the digital artifacts, so that it can be determined whether the digital artifacts are authorized for exhibition based on the ownership information.

[0089] S105. If the digital collection is an authorized exhibition work, then obtain the collection information of the digital collection.

[0090] If the digital artifact is an authorized exhibition work, the artifact information will be obtained for subsequent display.

[0091] S106 If the digital collection is an unauthorized exhibition work, an exhibition permission request will be sent to the rights holder via a smart contract on the blockchain.

[0092] If the digital collection is an unauthorized exhibition work, a smart contract is needed to send an exhibition permission request to the rights holder in order to request the rights holder to authorize the exhibition.

[0093] Specifically, the exhibition permit request includes data such as the exhibition period, exhibition authorization, collection number, and rights holder information.

[0094] S107: Obtain the permission feedback result of the exhibition permission request and write the permission feedback result into the blockchain.

[0095] S108: Read the permission feedback result on the blockchain. If the permission feedback result indicates permission to exhibit, then obtain the collection information of the digital collection.

[0096] If the rights holder agrees to authorize the exhibition of the digital artifact, the artifact information will be obtained for subsequent display.

[0097] S109, if the user's browsing history can be obtained, the browsing history is input into the pre-built preference recognition model to obtain the user's preference tags; wherein, the preference recognition model is obtained by training a deep learning model.

[0098] Specifically, a user's browsing history includes records of digital collectibles viewed, the time a user spends on a single collectible page, and user interactions, such as clicks like saving, liking, sharing, or clicking "view details" or "zoom in on image." If a user exhibits these behaviors on certain digital collectibles, it indicates that the user is more interested in those collectibles.

[0099] Specifically, the training of the preference recognition model can be achieved based on the following methods:

[0100] First, a large-scale and diverse collection of user browsing data needs to be collected as a sample dataset. This sample dataset contains browsing records from multiple users, and the browsing records are pre-labeled with real-world tags to ensure the sample data has corresponding preference labels. Specifically, these real-world tags correspond to the category tags of the digital collectibles.

[0101] The browsing history undergoes feature engineering to extract key features, such as converting text content into word vectors and statistically analyzing browsing category frequencies. These features, along with preference labels, are input into an initialized deep learning model (such as a neural network structure, which may include embedding layers, multiple fully connected layers, or recurrent neural networks to capture complex feature relationships). The loss between the model output and the true labels is calculated through forward propagation. The backpropagation algorithm is then used to automatically adjust the parameters of each layer of the model based on the loss gradient. This process is iterated continuously, using optimization algorithms (such as stochastic gradient descent) to gradually reduce the loss until the model's performance metrics (such as accuracy and recall) on the validation set reach a satisfactory level or no longer show significant improvement. At this point, model training is complete, and the trained deep learning model will have the ability to output appropriate preference labels based on newly input user browsing history.

[0102] S110, based on the first matching degree between the preference tag and the classification tag of each digital collectible, sort the digital collectibles in descending order of the first matching degree.

[0103] Specifically, regarding the matching degree between preference labels and classification labels, the preference labels and classification labels can first be vectorized. For example, word embedding technology can be used to convert the words in each label text into low-dimensional dense vectors, and then average pooling and other methods can be used to aggregate the word vectors into the vector of the entire label text. Alternatively, a pre-trained language model (such as BERT) can be used to directly obtain the context-related vectors of the label text. After obtaining the vectors of the two label texts, the matching degree can be measured by calculating their similarity. For example, cosine similarity can be used, which calculates the cosine value of the angle between the two vectors. The closer the cosine value is to 1, the higher the matching degree. Euclidean distance can also be used, where the smaller the distance, the closer the two label texts are in the vector space, i.e., the higher the matching degree. Finally, the degree of matching between the two labels is determined based on the calculation results.

[0104] S111, based on the sorted collection information of each digital collection, displays the collection display page.

[0105] After sorting the digital collection information, the digital collections that match the user's preferred tags will be displayed first to improve the user experience.

[0106] Optionally, after the aggregation platform acquires digital collectibles from multiple other platforms, it needs to categorize the digital collectibles and assign each digital collectible a corresponding category label, specifically including:

[0107] Step 1: Input the digital collection into the pre-built collection classification model to obtain the classification results of the digital collection; wherein, the feature classification model is trained based on a deep learning model, and the classification results include classification labels under multiple preset classification dimensions.

[0108] The collection classification model first extracts features from digital collections across multiple classification dimensions, such as major categories, images, music, and videos, as well as other dimensions like country, person, dynasty, festival, style, color, size, and dimensions. Based on these extracted features, the digital collections are then classified to obtain the classification results.

[0109] Specifically, the feature extraction model is trained based on a deep learning model, and its training process includes:

[0110] First, we collect and preprocess a digital collection sample dataset, augmenting the samples and normalizing the input features through data augmentation (rotation, scaling, etc.).

[0111] Secondly, design deep learning model architectures (such as CNNs for images, Transformers for sequential data, or multimodal fusion networks), and select appropriate loss functions (such as cross-entropy loss) and optimizers (such as Adam).

[0112] Next, the sample data is labeled with real labels and divided into training, validation and test sets. During training, the error between the prediction result and the real label is calculated through forward propagation, the model parameters are updated through backpropagation, and the performance (such as accuracy, F1 score, etc.) is monitored using the validation set to prevent overfitting (early stopping or regularization techniques can be used).

[0113] Finally, the model's generalization ability is evaluated on an independent test set, the optimal model weights are saved, and the model is deployed in a real-world application. Furthermore, throughout the model's training process, the hyperparameters (such as learning rate and batch size) need to be iteratively adjusted to optimize classification performance.

[0114] Step 2: Generate a label file based on the classification results.

[0115] Among them, a tag file is generated based on the classification results output by the collection classification model. The tag file records all the classification tags of the digital collection.

[0116] Step 3: Write the tag files to the blockchain and establish a mapping between the tag files and the digital collection.

[0117] In this process, after writing the tag file to the blockchain, a mapping is established between the tag file and the digital collectibles. This way, when obtaining the category tag of a digital collectible, the corresponding category tag can be obtained by reading the tag file on the blockchain.

[0118] S112, if the user's browsing history cannot be obtained, then the access history of each digital collection is obtained; wherein, the access history includes at least one of the following: number of views, number of likes, number of collections, and number of shares.

[0119] S113, based on the access records of each digital collectible, determine the popularity of each digital collectible.

[0120] Among these metrics, pageviews are the foundational indicator, reflecting the frequency with which users see digital collectibles. High pageview counts indicate significant exposure, attracting initial user attention. Likes reflect user recognition of the digital collectible's content or artistic value; more likes indicate greater emotional resonance or aesthetic appeal. Collections suggest users' intention to preserve and follow the digital collectible long-term, reflecting its continued appeal. Shares represent users actively recommending the digital collectible to others, indirectly demonstrating its reach and topicality. By assigning appropriate weights to each metric and weighting pageviews, likes, collections, and shares, a comprehensive score is calculated to determine the digital collectible's popularity. A higher score indicates greater popularity.

[0121] S114, based on the sorting of each digital collection in descending order of popularity.

[0122] S115, based on the sorted information of each digital collection, displays the collection display page.

[0123] The system displays digital collectibles based on their popularity, prioritizing those that are most popular with users to enhance the user experience.

[0124] S116, When the query information input by the user is obtained, extract the keywords from the query information.

[0125] In addition to automatic display, the system can also search for and display corresponding digital collectibles based on the user's query information. In this mode, the system first needs to obtain the user's query information and extract keywords from it, so that these keywords can be matched with the category tags of the digital collectibles.

[0126] Specifically, a collection search control is set up on the collection display page to make it easier for users to enter search information.

[0127] Specifically, natural language processing (NLP) technology can be used to parse the user input (such as text, phrases or questions) and extract core keywords (such as "ink painting", "science fiction", "limited edition" and potential synonyms (such as expanding "Guofeng" to "Chinese style" or "traditional art").

[0128] Specifically, if a user inputs multiple keywords (such as "dynamic digital collection of Dunhuang murals"), these keywords can be broken down and combined with corresponding tags, and the search scope can be narrowed down using logical AND relationships to ensure that the results accurately match all conditions.

[0129] S117. Based on the second matching degree between keywords and the classification tags of each digital collectible, sort the digital collectibles in descending order of the second matching degree.

[0130] This involves matching keywords with the category tags of digital collectibles and sorting the digital collectibles according to the degree of matching.

[0131] Specifically, by calculating the semantic similarity, co-occurrence frequency, and contextual relevance of keywords and tags, the tag combinations with the highest matching degree are selected. Finally, the results are weighted and optimized by combining user historical behavior data to ensure that the recommended tags not only match the input intent but also fit the user's preferences, thereby supporting the accurate query of digital collections in the future.

[0132] Specifically, keywords are matched with category tags. For example, entering "cyberpunk style" maps to the category tag "art style - cyberpunk", and entering "Chinese New Year limited edition" maps to the category tag "theme - festival - Chinese New Year" plus "rarity - limited edition".

[0133] S118, based on the sorted collection information of each digital collection, displays the collection display page.

[0134] In this regard, digital collectibles that better match the search results will be displayed first to improve the user experience.

[0135] In this embodiment, by classifying digital collectibles and assigning corresponding category tags, it is easy to match the category tags with user preference tags or user search keywords to sort and display digital collectibles, enabling users to find collectibles of interest more quickly and improving user satisfaction and engagement.

[0136] Figure 3 This is a schematic diagram of the structure of a digital collection aggregation and display device provided in an embodiment of this application. (See attached diagram.) Figure 3 The digital collection aggregation display device includes various functional modules for implementing the aforementioned digital collection aggregation display method, and any functional module can be implemented by software and / or hardware.

[0137] In some embodiments, the digital collection aggregation and display device 300 includes a request sending module 301, a feedback receiving module 302, a data acquisition module 303, and a collection display module 304. Wherein:

[0138] The request sending module 301 is used to send authorization requests to multiple collection platforms based on pre-configured smart contracts;

[0139] The feedback receiving module 302 is used to receive the authorization feedback results from each collection platform and write the authorization feedback results from each collection platform into the blockchain;

[0140] The data acquisition module 303 is used to read the various authorization feedback results on the blockchain. If the authorization feedback result indicates confirmation of authorization, it acquires multiple digital collectibles from the collectibles platform corresponding to the authorization feedback result.

[0141] The collection display module 304 is used to display the collection display page based on the collection information of each digital collection.

[0142] In some embodiments, the collection display module 304 is specifically used for:

[0143] If the user's browsing history can be obtained, the browsing history is input into a pre-built preference recognition model to obtain the user's preference tags; the preference recognition model is obtained by training a deep learning model.

[0144] Based on the first degree of matching between preference tags and the classification tags of each digital collectible, the digital collectibles are sorted in descending order of the first degree of matching.

[0145] Based on the sorted information of each digital artifact, the artifact display page is shown.

[0146] In some embodiments, the collection display module 304 is further configured to:

[0147] If the user's browsing history cannot be obtained, then the access history of each digital collection is obtained; the access history includes at least one of the following: number of views, number of likes, number of collections, and number of shares;

[0148] Based on the access records of each digital artifact, the popularity of each digital artifact is determined;

[0149] The digital collectibles are sorted in descending order of popularity.

[0150] Based on the sorted information of each digital artifact, the artifact display page is shown.

[0151] In some embodiments, the device 300 further includes a collection query module 305, which is specifically used for:

[0152] When the user inputs query information, extract the keywords from the query information;

[0153] Based on the second matching degree between keywords and the classification tags of each digital collectible, the digital collectibles are sorted in descending order of the second matching degree.

[0154] Based on the sorted information of each digital artifact, the artifact display page is shown.

[0155] In some embodiments, the device 300 further includes a collection classification module 306, which is specifically used for:

[0156] The digital artifacts are input into a pre-built artifact classification model to obtain the classification results of the digital artifacts; the feature classification model is trained based on a deep learning model, and the classification results include classification labels under multiple preset classification dimensions.

[0157] Generate a label file based on the classification results;

[0158] Write the tag files to the blockchain and establish a mapping between the tag files and digital collections.

[0159] In some embodiments, before displaying the collection display page based on the collection information of each digital collection, the collection display module 304 is further configured to:

[0160] Based on the ownership information of each digital artifact, determine whether the digital artifact is an authorized exhibition work.

[0161] If the digital artifact is an authorized exhibition work, then obtain the artifact information.

[0162] In some embodiments, the collection display module 304 is further configured to:

[0163] If the digital collection is an unauthorized exhibition work, an exhibition permission request will be sent to the rights holder via a smart contract on the blockchain;

[0164] Obtain the permission feedback result of the exhibition permission request and write the permission feedback result into the blockchain;

[0165] Read the permission feedback results on the blockchain. If the permission feedback results indicate permission for exhibition, then obtain the collection information of the digital collection.

[0166] The digital collection aggregation and display device 300 provided in this application embodiment is used to execute the technical solution provided in the aforementioned digital collection aggregation and display method embodiment. Its implementation principle and technical effect are similar to those in the aforementioned method embodiment, and will not be repeated here.

[0167] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing elements, entirely in hardware, or partially in software via processing elements and partially in hardware. For example, the collection display module 304 can be a separate processing element, or it can be integrated into a chip in the above device. Alternatively, it can be stored as program code in the memory of the above device, and its functions can be called and executed by a processing element. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through the integrated logic circuits in the hardware of the processor element or through software instructions.

[0168] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. (See attached diagram.) Figure 4 The electronic device 400 includes a processor 401 and a memory 402 communicatively connected to the processor 401;

[0169] Memory 402 stores instructions executed by the computer;

[0170] The processor 401 executes computer execution instructions stored in the memory 402 to implement the aforementioned technical solution of the method for aggregating and displaying digital collections.

[0171] In the aforementioned electronic device 400, the memory 402 and the processor 401 are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines, such as bus connections. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be classified as address buses, data buses, control buses, etc., but this does not mean that there is only one bus or one type of bus. The memory 402 stores computer execution instructions for implementing the aforementioned method of aggregating and displaying digital collections, including at least one software functional module that can be stored in the memory 402 in the form of software or firmware. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402.

[0172] The memory 402 includes at least one type of readable storage medium, not limited to Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 402 stores programs, which are executed by the processor 01 upon receiving execution instructions. Furthermore, the software programs and modules within the memory 402 may also include an operating system, which may include various software components and / or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.) and can communicate with various hardware or software components to provide an operating environment for other software components.

[0173] Processor 401 can be an integrated circuit chip with signal processing capabilities. The aforementioned processor 401 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor, or processor 401 can be any conventional processor.

[0174] The electronic device 400 is used to execute the technical solution provided in the aforementioned embodiment of the method for aggregating and displaying digital collections. Its implementation principle and technical effects are similar to those in the aforementioned method embodiment, and will not be repeated here.

[0175] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the aforementioned method for aggregating and displaying digital collectibles.

[0176] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer-readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0177] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium can be an integral part of the processor. Both the processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components within the control device of a digital artifact aggregation display apparatus.

[0178] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the aforementioned method for aggregating and displaying digital collectibles.

[0179] In the above embodiments, those skilled in the art will understand that the above method embodiments can be implemented entirely or partially by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless network, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0180] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0181] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the appended claims.

[0182] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for aggregating and displaying digital collectibles, characterized in that, include: Authorization requests are sent to multiple collection platforms based on pre-configured smart contracts; Receive authorization feedback results from various collection platforms and write the authorization feedback results from each collection platform into the blockchain; Read the authorization feedback results on the blockchain. If the authorization feedback result indicates confirmation of authorization, then obtain multiple digital collectibles from the collectibles platform corresponding to the authorization feedback result. Based on the collection information of each digital collection, a collection display page is displayed.

2. The method according to claim 1, characterized in that, Based on the collection information of each digital collection, a collection display page is displayed, including: If the user's browsing history can be obtained, the browsing history is input into a pre-built preference recognition model to obtain the user's preference tags; wherein, the preference recognition model is obtained by training a deep learning model; Based on the first matching degree between the preference tags and the classification tags of each digital collectible, the digital collectibles are sorted in descending order of the first matching degree. Based on the sorted information of each digital artifact, the artifact display page is shown.

3. The method according to claim 2, characterized in that, The method further includes: If the user's browsing history cannot be obtained, then the access history of each digital collection is obtained; wherein, the access history includes at least one of the following: number of views, number of likes, number of collections, and number of shares; Based on the access records of each digital artifact, the popularity of each digital artifact is determined; The digital collectibles are sorted in descending order of popularity. Based on the sorted information of each digital artifact, the artifact display page is shown.

4. The method according to claim 1, characterized in that, The method further includes: When the query information input by the user is obtained, the keywords in the query information are extracted; Based on the second matching degree between the keywords and the classification tags of each digital collectible, the digital collectibles are sorted in descending order of the second matching degree. Based on the sorted information of each digital artifact, the artifact display page is shown.

5. The method according to any one of claims 2-4, characterized in that, The method further includes: The digital collection is input into a pre-built collection classification model to obtain the classification result of the digital collection; wherein, the feature classification model is trained based on a deep learning model, and the classification result includes classification labels under multiple preset classification dimensions; Based on the classification results, a label file is generated; The tag file is written to the blockchain, and a mapping is established between the tag file and the digital collection.

6. The method according to any one of claims 2-4, characterized in that, Before displaying the collection showcase page based on the collection information of each digital collection, the method further includes: For each digital collectible, based on the ownership information in the collectible information, it is determined whether the digital collectible is an authorized exhibition work; If the digital artifact is an authorized exhibition work, then the artifact information of the digital artifact is obtained.

7. The method according to claim 6, characterized in that, The method further includes: If the digital collection is an unauthorized exhibition work, an exhibition permission request will be sent to the rights holder via a smart contract on the blockchain; Obtain the permission feedback result of the exhibition permission request and write the permission feedback result into the blockchain; Read the permission feedback result on the blockchain. If the permission feedback result indicates permission to exhibit, then obtain the collection information of the digital collection.

8. A digital collection aggregation and display device, characterized in that, include: The request sending module is used to send authorization requests to multiple collection platforms based on pre-configured smart contracts; The feedback receiving module is used to receive the authorization feedback results from various collection platforms and write the authorization feedback results from each collection platform into the blockchain. The data acquisition module is used to read the various authorization feedback results on the blockchain. If the authorization feedback result indicates confirmation of authorization, then the module acquires multiple digital collectibles from the collectibles platform corresponding to the authorization feedback result. The collection display module is used to display the collection page based on the collection information of each digital collection.

9. An electronic device, characterized in that, Includes a processor and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1 to 7.

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