Blockchain-based intelligent copyright monitoring method and platform for digital collections

Abstract

The present invention discloses a blockchain-based intelligent copyright monitoring method and platform for digital collections, which relates to data processing-related fields. The method includes: segmenting target digital collections; identifying infringement characteristics according to type; calling a content-aware hash algorithm library to perform infringement identification sensitivity testing and constructing an initial hash algorithm integration architecture; performing monitoring attention configuration and generating a preset hash algorithm integration architecture; performing multi-mode hash fusion processing on multiple collection blocks to obtain multiple block hash values ​​and upload them to the blockchain; obtaining a first detection collection according to type monitoring, performing multi-mode hash fusion processing and generating multiple detection hash values; performing similarity comparison and generating infringement monitoring reminder information. The method solves the technical problem of insufficient monitoring accuracy in existing digital collection copyright monitoring and achieves the technical effect of accurately identifying and monitoring digital collection infringement.

Description

Technical Field

[0001] This application relates to data processing related fields, and in particular to a blockchain-based intelligent copyright monitoring method and platform for digital collections. Background Art

[0002] With the rapid development of digital technology, the creation, trading, and collection of digital collectibles (such as digital artwork and virtual goods) have become increasingly popular, gradually becoming a vital component of the global cultural industry. However, the easy reproducibility, unlimited dissemination, and diversity of digital collectibles pose unprecedented challenges to copyright protection. Existing copyright monitoring methods mostly rely on a single hashing algorithm to identify infringement. While this method can provide basic protection in some cases, its limitations are becoming increasingly apparent. In particular, traditional methods often fail to effectively detect digital collectibles that have undergone minor modifications, resulting in frequent missed or false positives, which seriously undermines the accuracy and comprehensiveness of copyright protection.

[0003] Among the current related technologies, copyright monitoring of digital collections has the technical problem of insufficient monitoring accuracy. Summary of the Invention

[0004] This application provides a blockchain-based intelligent copyright monitoring method and platform for digital collections, adopts block processing for target digital collections, assigns monitoring weights to each block, identifies infringement characteristics according to the type of digital collections, generates preset infringement characteristics, performs sensitivity testing based on these characteristics, constructs and optimizes the initial hash algorithm integration architecture, performs multi-mode hash fusion processing on multiple collection blocks through the preset hash algorithm integration architecture, generates multiple block hash values, and uploads the generated block hash values ​​to the blockchain, etc., to achieve the technical effect of accurately identifying and monitoring infringement of digital collections.

[0005] This application provides a blockchain-based intelligent copyright monitoring method for digital collections, including:

[0006] The target digital collection is divided into blocks to generate multiple collection blocks and multiple block monitoring weights; infringement characteristics are identified according to the type of the target digital collection to generate preset infringement characteristics; based on the preset infringement characteristics, the content-aware hash algorithm library is called to perform infringement identification sensitivity testing, and an initial hash algorithm integration architecture is constructed according to the sensitivity test results; monitoring attention configuration is performed on the initial hash algorithm integration architecture based on the multiple block monitoring weights to generate a preset hash algorithm integration architecture; multi-mode hash fusion processing is performed on the multiple collection blocks through the preset hash algorithm integration architecture to obtain multiple block hash values ​​and upload them to the blockchain; a connection is established with a preset network platform, a first detection collection is obtained according to the type of the target digital collection, and the preset hash algorithm integration architecture is called to perform multi-mode hash fusion processing to generate multiple detection hash values; similarity comparison is performed on the multiple block hash values ​​and the multiple detection hash values ​​to generate infringement monitoring reminder information.

[0007] In a possible implementation, the target digital collection is divided into blocks to generate multiple collection blocks and multiple block monitoring weights, and the following processing is performed:

[0008] The target digital collection is divided into blocks according to a predetermined grid division to generate the multiple collection blocks; regional feature density detection is performed on the multiple collection blocks to generate multiple feature densities; complexity identification of color and texture features is performed on the multiple collection blocks to generate multiple complexity indices; weight configuration is performed based on the multiple feature densities and the multiple complexity indices to generate the multiple block monitoring weights, wherein the block monitoring weight is proportional to the feature density and the complexity index.

[0009] In a possible implementation, infringement characteristics are identified based on the type of the target digital collectible, preset infringement characteristics are generated, and the following processing is performed:

[0010] According to the type of the target digital collection, infringement records of digital collections of the same type are mined to generate a collection infringement record data set; infringement features are extracted from the collection infringement record data set to generate an infringement feature set; similarity comparison is performed on any two groups of features in the infringement feature set, and features with similarity greater than a preset similarity are clustered to generate the preset infringement features.

[0011] In a possible implementation, based on the preset infringement features, a content-aware hash algorithm library is called to perform an infringement identification sensitivity test. An initial hash algorithm integration architecture is constructed based on the sensitivity test results, and the following processing is performed:

[0012] Step 1: Extract the first infringement feature from the preset infringement features, and collect a historical infringement sample data set based on the first infringement feature as a constraint, wherein any set of historical infringement sample data includes historical genuine collection samples and historical infringing collection samples with corresponding relationships; Step 2: Use multiple content-aware hash algorithms in the content-aware hash algorithm library to hash and compare the historical genuine collection samples and the historical infringing collection samples, respectively, to generate multiple sets of hash value difference information; Step 3: Generate multiple first infringement identification sensitivity indicators based on the multiple sets of hash value difference information; Step 4: Based on the multiple first infringement identification sensitivity indicators, select the content-aware hash algorithm corresponding to the maximum indicator, and map and associate it with the first infringement feature to generate a first content-aware hash algorithm; and so on, iteratively repeat steps 1 to 4 until the second infringement feature to the Nth infringement feature in the preset infringement features is traversed, and the second content-aware hash algorithm to the Nth content-aware hash algorithm is obtained; and the initial hash algorithm integration architecture is generated using the first content-aware hash algorithm, the second content-aware hash algorithm, and the Nth content-aware hash algorithm.

[0013] In a possible implementation, the preset hash algorithm integrated architecture is used to perform multi-mode hash fusion processing on the multiple collection blocks, obtain multiple block hash values ​​and upload them to the blockchain, and perform the following processing:

[0014] The first content-aware hash algorithm, the second content-aware hash algorithm, and up to the Nth content-aware hash algorithm in the preset hash algorithm integrated architecture are used to perform feature processing in the pre-hashing stage on the multiple collection blocks, thereby generating multiple first pre-feature data sets, multiple second pre-feature data sets, and up to multiple Nth pre-feature data sets; hash calculations are performed on the multiple first pre-feature data sets, multiple second pre-feature data sets, and up to multiple Nth pre-feature data sets according to the monitoring attention configuration to generate the multiple block hash values.

[0015] In a possible implementation, the following processing is performed:

[0016] Read the first infringement identification sensitivity index, the second infringement identification sensitivity index, and the Nth infringement identification sensitivity index corresponding to the first content-aware hash algorithm, the second content-aware hash algorithm, and the Nth content-aware hash algorithm respectively; perform weight distribution on the first content-aware hash algorithm, the second content-aware hash algorithm, and the Nth content-aware hash algorithm based on the first infringement identification sensitivity index, the second infringement identification sensitivity index, and the Nth infringement identification sensitivity index to obtain a weight distribution result; establish a fusion optimization network based on the weight distribution result, connect it with the preset hash algorithm integrated architecture, fuse the multiple block hash values, and generate a preset fusion hash value; use the preset fusion hash value to perform infringement monitoring reminder on the first detected collection.

[0017] In a possible implementation, a similarity comparison is performed between the multiple block hash values ​​and the multiple detection hash values ​​to generate infringement monitoring reminder information, and the following processing is performed:

[0018] Based on the blockchain, copyright transaction information is obtained; the copyright transaction information is parsed to generate multiple permission features of multiple authorized parties; when the similarity between the multiple block hash values ​​and the multiple detection hash values ​​meets the preset infringement similarity, the first collection circulation information of the first detection collection is read; based on the multiple authorized parties and the multiple permission features, the first collection circulation information is verified, and if the verification fails, the infringement monitoring reminder information is generated.

[0019] This application also provides a blockchain-based intelligent copyright monitoring platform for digital collections, including:

[0020] A block processing module, which is used to perform block processing on the target digital collection and generate multiple collection blocks and multiple block monitoring weights; an infringement feature identification module, which is used to identify infringement features according to the type of the target digital collection and generate preset infringement features; an infringement identification sensitivity test module, which is used to call the content-aware hash algorithm library to perform infringement identification sensitivity test based on the preset infringement features and build an initial hash algorithm integration architecture based on the sensitivity test results; a monitoring attention configuration module, which is used to perform monitoring attention configuration on the initial hash algorithm integration architecture based on the multiple block monitoring weights and generate a preset hash algorithm set. a multi-mode hash fusion processing module, which is used to perform multi-mode hash fusion processing on the multiple collection blocks through the preset hash algorithm integrated architecture, obtain multiple block hash values ​​and upload them to the blockchain; multiple detection hash value generation modules, which are used to establish a connection with a preset network platform, monitor and obtain a first detection collection according to the type of the target digital collection, and call the preset hash algorithm integrated architecture to perform multi-mode hash fusion processing to generate multiple detection hash values; an infringement monitoring reminder information generation module, which is used to perform similarity comparison between the multiple block hash values ​​and the multiple detection hash values ​​to generate infringement monitoring reminder information.

[0021] The blockchain-based intelligent copyright monitoring method and platform for digital collections proposed in this application first divides the target digital collection into blocks to generate multiple collection blocks and multiple block monitoring weights, then identifies infringement features based on the type of the target digital collection to generate preset infringement features, then calls the content-aware hash algorithm library based on the preset infringement features to perform infringement identification sensitivity testing, constructs an initial hash algorithm integration architecture based on the sensitivity test results, and then performs monitoring attention configuration on the initial hash algorithm integration architecture based on multiple block monitoring weights to generate a preset hash algorithm integration architecture, then performs multi-mode hash fusion processing on multiple collection blocks through the preset hash algorithm integration architecture to obtain multiple block hash values ​​and upload them to the blockchain, then establishes a connection with the preset network platform, monitors and obtains the first detection collection according to the type of the target digital collection, and calls the preset hash algorithm integration architecture to perform multi-mode hash fusion processing to generate multiple detection hash values, finally performs similarity comparison between multiple block hash values ​​and multiple detection hash values ​​to generate infringement monitoring reminder information, thereby achieving the technical effect of accurately identifying and monitoring infringement of digital collections. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the platform according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact sequence. On the contrary, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0023] Figure 1 A flowchart of the blockchain-based intelligent copyright monitoring method for digital collections provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of the structure of the blockchain-based intelligent copyright monitoring platform for digital collections provided in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: block processing module 10, infringement feature recognition module 20, infringement recognition sensitivity testing module 30, monitoring attention configuration module 40, multi-mode hash fusion processing module 50, multiple detection hash value generation module 60, infringement monitoring reminder information generation module 70. DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0029] The present application embodiment provides a blockchain-based intelligent copyright monitoring method for digital collections, such as Figure 1 As shown, the method includes:

[0030] Step S100: Divide the target digital collection into blocks to generate multiple collection blocks and multiple block monitoring weights. Specifically, the original data of the target digital collection, such as images, videos, or photos, is obtained through a file reading interface. The target digital collection is divided into multiple small blocks (collection blocks) based on a preset block size (such as a fixed pixel size or time segment). For example, for images, the division is based on a pixel grid; for videos, the division is based on frames and intra-frame areas. Based on the complexity, uniqueness, and importance of each block (for example, the center area of ​​an image is more important than the edge area), a monitoring weight is assigned to each block through an algorithm.

[0031] In one possible implementation, the target digital collection is segmented to generate multiple collection blocks and multiple block monitoring weights. Step S100 further includes step S110, segmenting the target digital collection according to a predetermined segmentation grid to generate the multiple collection blocks. Specifically, a two-dimensional (for images) or three-dimensional (for videos) segmentation grid is determined based on the size of the target digital collection (e.g., pixel size of an image, duration and resolution of a video) and a preset block size. Based on the segmentation grid, the target digital collection is divided into multiple equal-sized collection blocks. Specifically, a collection block is a single portion of the target digital collection obtained after segmentation, and each collection block contains a portion of the collection data. Step S120, regional feature density detection is performed on each of the multiple collection blocks to generate multiple feature densities. Specifically, the SIFT feature extraction algorithm is applied to each collection block to extract key points or feature descriptors within the collection block. The number of feature points or the density of feature descriptors within each collection block is counted as the feature density of the collection block. The feature density reflects the complexity and uniqueness of the collection block's content. Step S130: Perform color and texture feature complexity identification on each of the multiple collection blocks to generate multiple complexity indices. Specifically, perform color and texture analysis on each collection block, quantify the color and texture complexity using histogram analysis, and calculate a complexity index for each collection block based on the color and texture analysis results. The complexity index is a numerical value that quantifies the color and texture complexity within the collection block, reflecting the richness and variability of the collection block's content. Step S140: Combine the multiple feature densities and the multiple complexity indices to perform weight assignment and generate multiple block monitoring weights, where the block monitoring weights are proportional to the feature density and complexity index. Specifically, a weight calculation rule is set that takes the feature density and complexity index as input and generates a block monitoring weight through weighted summation. Based on the weight calculation rule, a block monitoring weight is assigned to each collection block. The block monitoring weight is proportional to the feature density and complexity index, i.e., blocks with higher feature density and greater complexity receive higher block monitoring weights. This implementation method allocates block monitoring weights by comprehensively considering the feature density and complexity of collection blocks, which more accurately reflects the importance of each collection block in copyright monitoring.

[0032] Step S200 identifies infringement characteristics based on the type of the target digital artifact and generates pre-set infringement characteristics. Specifically, the type of the target digital artifact is determined through file type detection and content analysis (e.g., texture and color distribution in images, motion patterns in videos). Based on the type, relevant characteristics are selected from a pre-set infringement characteristic library, such as common tampering methods and common copy and paste traces.

[0033] In one possible implementation, infringement characteristics are identified based on the type of the target digital collection to generate preset infringement characteristics. Step S200 further includes step S210, where infringement records of digital collections of the same type are mined based on the type of the target digital collection to generate a collection infringement record dataset. Specifically, the type of the target digital collection to be analyzed is determined, such as artwork, photography, music, or video. A database storing digital collection infringement records is accessed. This database stores digital collection infringement records from various sources, including copyright protection agencies, legal proceedings, and network monitoring platforms. All relevant infringement records are filtered from the database based on the type of the target digital collection, including detailed information such as descriptions of the infringed works, information about the infringer, methods of infringement, and time of infringement. The filtered infringement records are organized into a collection infringement record dataset. Step S220, infringement characteristics are extracted from the collection infringement record dataset to generate an infringement feature set. Specifically, based on common patterns of infringement, a series of quantifiable characteristics are defined, such as the degree of modification of the infringing work, the form of infringement (e.g., direct copying, partial copying, adaptation), the time span of the infringement, and the scope of the infringement. By using technical means such as text analysis, image processing, and data analysis, these defined feature values ​​are extracted from the collection infringement record data set, and the extracted feature values ​​are organized into a feature set, where each feature corresponds to one or more specific numerical values ​​or descriptions. In step S230, similarity comparison is performed on any two groups of features in the infringement feature set, and features with similarity greater than a preset similarity are clustered to generate the preset infringement features. Specifically, the cosine similarity calculation method is used to calculate the similarity of any two groups of features in the infringement feature set. A preset similarity threshold is set to determine whether the two groups of features are similar enough to be considered as the same type of infringement features. Features with similarity greater than the preset threshold are grouped into one category to form different infringement feature clusters. Based on the clustering results, representative preset infringement features are summarized and summarized, and these features are used for infringement identification and early warning. This implementation method ensures the pertinence and accuracy of the analysis results by mining infringement records based on the type of digital collections. It comprehensively reflects the diversity and complexity of infringement behaviors by defining and extracting a series of infringement features. Through similarity comparison and cluster analysis, it automatically summarizes representative preset infringement features, improving the accuracy of the generation of preset infringement features, and thus improving the efficiency and accuracy of infringement identification.

[0034] In step S300, based on the pre-set infringement characteristics, a content-aware hash algorithm library is invoked to perform an infringement identification sensitivity test. An initial hash algorithm integration architecture is constructed based on the sensitivity test results. Specifically, based on the pre-set infringement characteristics, an algorithm (such as aHash, dHash, or pHash) is selected from the content-aware hash algorithm library. The content-aware hash algorithm library is a collection of multiple hash algorithms, each designed to identify specific characteristics or changes in digital content and has different application scenarios. For example, aHash is suitable for capturing global changes, dHash is suitable for detecting changes in edge features, and pHash is suitable for capturing the overall structure and characteristics of an image. Each algorithm is tested to evaluate its identification performance (such as sensitivity, false positive rate, and false negative rate) in different infringement scenarios. Based on the test results, the algorithm combination with the best performance is selected to construct the initial hash algorithm integration architecture. The initial hash algorithm integration architecture is a framework built on the content-aware hash algorithm library. It selects and integrates multiple suitable hash algorithms based on the type of target digital artifact, the expected infringement characteristics, and the sensitivity test results. This architecture is used to achieve efficient hash processing and fusion of digital artifact blocks.

[0035] In one possible implementation, based on the preset infringement features, a content-aware hash algorithm library is invoked to perform an infringement identification sensitivity test. An initial hash algorithm integration architecture is constructed based on the sensitivity test results. Step S300 further includes step S310: Step 1: Extracting a first infringement feature from the preset infringement features. Using the first infringement feature as a constraint, a historical infringement sample dataset is collected. Any set of historical infringement sample data includes samples of historical genuine artifacts and samples of historical infringing artifacts that have a corresponding relationship. Specifically, any feature from the preset infringement feature set is selected as the first infringement feature (e.g., watermark removal, cropping, rotation, etc.). Based on the selected first infringement feature, a dataset containing samples of historical genuine artifacts and samples of historical infringing artifacts is collected from a digital artifact infringement record database. These samples have a clear corresponding relationship, meaning each pair of samples represents an authentic artifact and its corresponding infringing copy. Step S320: Step 2: Using multiple content-aware hash algorithms from the content-aware hash algorithm library, hash processing and comparison are performed on the historical genuine artifact samples and the historical infringing artifact samples, generating multiple sets of hash value difference information. Specifically, multiple algorithms from a content-aware hashing algorithm library are used to hash samples of historical authentic and infringing collections, generating their respective hash values. The hash values ​​of the same pair of samples (authentic and infringing) are compared, and the differences between them are calculated to generate multiple sets of hash value difference information. Step S330, Step Three: Multiple first infringement identification sensitivity indices are generated based on the multiple sets of hash value difference information. Specifically, based on the multiple sets of hash value difference information, sensitivity indicators for each hash algorithm in identifying the first infringement feature are calculated, including the average, standard deviation, and minimum / maximum differences of the difference rate. The greater the hash value difference information, the higher the sensitivity of the hash algorithm. Step S340, Step Four: Based on the multiple first infringement identification sensitivity indices, the content-aware hashing algorithm corresponding to the highest index is selected and mapped and associated with the first infringement feature to generate a first content-aware hashing algorithm. Specifically, the hashing algorithm with the highest infringement identification sensitivity index is selected as the optimal algorithm for the first infringement feature. This algorithm is mapped and associated with the first infringement feature to generate a specific content-aware hashing algorithm (the first content-aware hashing algorithm). Step S350, and so on, iteratively repeat steps 1 to 4 until the second to the Nth infringement feature in the preset infringement feature set is traversed, and the second to the Nth content-aware hash algorithm is obtained. Specifically, steps S310 to S340 are repeated for each feature in the preset infringement feature set until all preset infringement features are traversed, and each iteration generates a content-aware hash algorithm for a specific infringement feature.In step S360, the initial hash algorithm integration architecture is generated using the first content-aware hash algorithm, the second content-aware hash algorithm, and so on, up to the Nth content-aware hash algorithm. Specifically, all content-aware hash algorithms targeting different infringement characteristics are combined to form an initial hash algorithm integration architecture. This architecture includes multiple hash algorithms, each with the highest sensitivity to a specific infringement characteristic. This implementation improves the accuracy of infringement detection by performing sensitivity testing on each pre-set infringement characteristic and selecting the hash algorithm that best suits that characteristic.

[0036] Step S400: Based on the multiple block monitoring weights, the initial hash algorithm integrated architecture is configured for monitoring and attention, generating a preset hash algorithm integrated architecture. Specifically, the block monitoring weights are mapped to the initial hash algorithm integrated architecture, and each collection block is assigned a different hash processing weight. Based on the weight distribution, the initial hash algorithm integrated architecture is fine-tuned to ensure that high-weight collection blocks receive more detailed hash processing.

[0037] In step S500, the preset hash algorithm integrated architecture performs a multi-mode hash fusion process on the multiple collection blocks, obtaining multiple block hash values ​​and uploading them to the blockchain. Specifically, the preset hash algorithm integrated architecture is applied to each collection block to perform a multi-mode hash fusion process, generating a unique block hash value. This block hash value and related information (such as block monitoring weight and digital collection ID) are then uploaded to the blockchain network using a blockchain client (such as an Ethereum wallet or Hyperledger Fabric node). Smart contracts are employed to ensure data immutability and traceability.

[0038] In one possible implementation, a multi-mode hash fusion process is performed on the multiple collection blocks using the preset hash algorithm integrated architecture to obtain multiple block hash values ​​and upload them to the blockchain. Step S500 further includes step S510, wherein the first content-aware hash algorithm, the second content-aware hash algorithm, and the Nth content-aware hash algorithm in the preset hash algorithm integrated architecture are used to perform feature processing in the pre-hashing stage on the multiple collection blocks, generating multiple first pre-hashing feature datasets, multiple second pre-hashing feature datasets, and multiple Nth pre-hashing feature datasets. Specifically, the first content-aware hash algorithm, the second content-aware hash algorithm, and the Nth content-aware hash algorithm are applied to each collection block to perform feature processing in the pre-hashing stage, extracting feature information useful for hash calculation, such as edges, corners, and textures, from the block. These features constitute the pre-hashing feature dataset. Step S520, hash calculations are performed on the multiple first pre-hashing feature datasets, multiple second pre-hashing feature datasets, and multiple Nth pre-hashing feature datasets according to the monitoring attention configuration to generate the multiple block hash values. Specifically, the pre-feature dataset generated in step S510 is weighted according to the weights of multiple block monitoring, so that the features of the high-weight area contribute more to the final hash value, thereby increasing the sensitivity of the hash value to important content. Based on the weighted pre-feature dataset, the corresponding hash algorithm is applied to perform hash calculations to generate block hash values, and the generated multiple block hash values ​​are uploaded to the blockchain for subsequent verification, retrieval, and copyright monitoring operations. This implementation method combines the advantages of multiple hash algorithms through multi-mode hash fusion processing, improves the accuracy of the hash value, and makes the hash value more sensitive to changes in important content through monitoring attention configuration and weighted processing, which is conducive to quickly locating key information in copyright monitoring scenarios.

[0039] Step S600, establish a connection with a preset network platform, monitor and obtain the first detection collection according to the type of the target digital collection, and call the preset hash algorithm integrated architecture to perform multi-mode hash fusion processing to generate multiple detection hash values. Specifically, establish a connection with the preset network platform through technical means such as API or web crawler to obtain digital collection data on the platform. The preset network platform is an online platform that can provide digital collection-related services and allow data transmission, processing and analysis, such as an online market, a digital collection trading platform, a copyright monitoring center, etc. According to the type of the target digital collection, the same or similar type of collection is screened out from the preset network platform as the first detection collection. The first detection collection is the digital collection that is currently being monitored to check whether there is any infringement. The preset hash algorithm integrated architecture is applied to the screened first detection collection for hash processing to generate multiple detection hash values.

[0040] Step S700, perform a similarity comparison between the multiple block hash values ​​and the multiple detection hash values ​​to generate infringement monitoring reminder information. Specifically, the multiple block hash values ​​of the target digital collection stored on the blockchain are compared one by one with the multiple detection hash values ​​of the first detection collection obtained from the preset network platform. Use a hash value similarity calculation library (such as hashlib, scipy.spatial.distance in Python, etc.) to calculate the similarity between each pair of hash values, set a similarity threshold to determine whether the two hash values ​​are similar enough to indicate possible infringement. If the similarity of a pair of hash values ​​exceeds this threshold, it is considered that there is a potential infringement relationship between the two collection blocks. For all hash value pairs whose similarity exceeds the threshold, relevant information (such as the collection block location, the source of the detection collection, the similarity score, etc.) is collected. Based on this information, detailed infringement monitoring reminder information is generated, including the possible infringement location, infringement degree assessment, and recommended follow-up measures. The embodiment of the present application adopts technical means such as dividing the target digital collection into blocks and assigning a monitoring weight to each block, identifying infringement characteristics according to the type of digital collection, generating preset infringement characteristics, performing sensitivity testing based on these characteristics, constructing and optimizing the initial hash algorithm integration architecture, performing multi-mode hash fusion processing on multiple collection blocks through the preset hash algorithm integration architecture, generating multiple block hash values, and uploading the generated block hash values ​​to the blockchain, thereby achieving the technical effect of accurately identifying and monitoring infringement of digital collections.

[0041] In one possible implementation, the multiple block hash values ​​are compared with the multiple detection hash values ​​for similarity to generate infringement monitoring alert information. Step S700 further includes step S710: obtaining copyright transaction information based on the blockchain. Specifically, the blockchain network is connected and the blockchain's API or SDK is used to query copyright transaction information related to the target digital collection. The copyright transaction information records detailed information about the copyright transfer or authorization transaction of the target digital collection and is an important basis for confirming copyright ownership, including transaction time, transaction parties, transaction amount, transaction content, etc. Step S720: parsing the copyright transaction information to generate multiple permission profiles for multiple authorizers. Specifically, the copyright transaction information is parsed to identify all authorizers (i.e., the original copyright owner and subsequent licensees). For each authorizer, corresponding permission profiles are generated based on their transaction information, including authorization scope (e.g., geographical and time restrictions) and usage methods (e.g., copying, distribution, display, etc.). Step S730: When the similarity between the multiple block hash values ​​and the multiple detection hash values ​​meets a preset infringement similarity, the first collection circulation information of the first detection collection is read. Specifically, if the similarity of at least one set of hash values ​​exceeds a preset infringement similarity threshold (e.g., 90%), it is considered that infringement may have occurred. After confirming the infringement, the circulation information related to the first detected collection (e.g., current holder, circulation history, transaction records, etc.) is read. Step S740 verifies the circulation information of the first collection based on the multiple authorized parties and the multiple permission characteristics. If the verification fails, the infringement monitoring reminder information is generated. Specifically, based on the circulation information of the first detected collection, it checks whether the current holder has legal authorization. All authorized parties and their permission characteristics are traversed to confirm whether the current holder's behavior exceeds the scope of authorization. If any violation is found (e.g., unauthorized use, copying, distribution, etc.), the verification fails. In the event of verification failure, an infringement monitoring reminder information is generated and sent to the copyright owner or relevant organization. This implementation method ensures the accuracy and effectiveness of infringement monitoring by parsing copyright transaction information and verifying the permission characteristics of the authorized party.

[0042] In one possible implementation, step S700 further includes step S750, reading the first, second, and Nth infringement identification sensitivity indicators corresponding to the first, second, and Nth content-aware hash algorithms, respectively. Specifically, the infringement identification sensitivity indicator for each content-aware hash algorithm (first through Nth) is obtained from a configuration file. Step S760, weights are assigned to the first, second, and Nth content-aware hash algorithms based on the first, second, and Nth infringement identification sensitivity indicators, to obtain a weight assignment result. Specifically, a weighted average strategy is used to assign weights to each algorithm based on its infringement identification sensitivity indicator. The weights reflect the importance of the algorithm in the fusion process. The weight assignment result is a weight vector, in which each element corresponds to the weight of a content-aware hash algorithm. Step S770, based on the weight assignment result, establishes a fusion optimization network, connects it to the preset hash algorithm integration architecture, and fuses the multiple block hash values ​​to generate a preset fused hash value. Specifically, based on the weight distribution result, a fusion optimization network is constructed. This network is a weighted summer for fusing the hash values ​​of multiple content-aware hash algorithms into a preset fusion hash value. The fusion optimization network is connected to the preset hash algorithm integration architecture, and the fusion optimization network is used to fuse the multiple block hash values ​​to generate a preset fusion hash value. Step S780, the preset fusion hash value is used to perform infringement monitoring reminders on the first detection collection. Specifically, the preset fusion hash value is compared with the detection hash value of the first detection collection (generated through the same fusion process). If the similarity between the two exceeds a preset threshold, it is considered that there is potential infringement, and infringement monitoring reminder information is generated and sent to the copyright owner or relevant stakeholders. This implementation method fuses multiple block hash values ​​into a more representative preset fusion hash value through the fusion optimization network, thereby achieving comprehensive monitoring of the copyright status of digital collections and improving the accuracy of infringement monitoring.

[0043] In the above, refer to Figure 1 The following describes in detail the intelligent monitoring method of digital collection copyright based on blockchain according to an embodiment of the present invention. Figure 2 The present invention describes a blockchain-based intelligent copyright monitoring platform for digital collections according to an embodiment of the present invention.

[0044] The blockchain-based intelligent copyright monitoring platform for digital collections, according to an embodiment of the present invention, addresses the technical issue of insufficient monitoring accuracy in existing digital collection copyright monitoring systems, achieving the technical effect of accurately identifying and monitoring digital collection copyright infringement. The blockchain-based intelligent copyright monitoring platform for digital collections includes a block processing module 10, an infringement feature recognition module 20, an infringement identification sensitivity testing module 30, a monitoring attention configuration module 40, a multi-mode hash fusion processing module 50, a multiple detection hash value generation module 60, and an infringement monitoring alert information generation module 70.

[0045] The block processing module 10 is used to block the target digital collection and generate multiple collection blocks and multiple block monitoring weights. The infringement feature identification module 20 is used to identify infringement features based on the type of the target digital collection and generate preset infringement features. The infringement identification sensitivity testing module 30 is used to call the content-aware hash algorithm library to perform infringement identification sensitivity testing based on the preset infringement features and construct an initial hash algorithm integration architecture based on the sensitivity test results. The monitoring attention configuration module 40 is used to configure the monitoring attention of the initial hash algorithm integration architecture based on the multiple block monitoring weights to generate a preset hash algorithm integration architecture. The multi-mode hash fusion processing module 50 is used to perform multi-mode hash fusion processing on the multiple collection blocks through the preset hash algorithm integration architecture to obtain multiple block hash values ​​and upload them to the blockchain. The multiple detection hash value generation module 60 is used to establish a connection with a preset network platform, monitor and obtain a first detection collection according to the type of the target digital collection, and call the preset hash algorithm integration architecture to perform multi-mode hash fusion processing to generate multiple detection hash values. The infringement monitoring reminder information generation module 70 is used to perform similarity comparison between the multiple block hash values ​​and the multiple detection hash values ​​to generate infringement monitoring reminder information.

[0046] The specific configuration of the block processing module 10 will be described in detail below. As described above, the target digital collection is subjected to block processing to generate multiple collection blocks and multiple block monitoring weights. The block processing module 10 may further include: a block processing unit for performing block processing on the target digital collection according to a predetermined grid division to generate the multiple collection blocks; a regional feature density detection unit for performing regional feature density detection on the multiple collection blocks to generate multiple feature densities; a complexity identification unit for performing complexity identification of color and texture features on the multiple collection blocks to generate multiple complexity indices; a weight configuration unit for performing weight configuration based on the multiple feature densities and the multiple complexity indices to generate the multiple block monitoring weights, wherein the block monitoring weight is proportional to the feature density and the complexity index.

[0047] The specific configuration of the infringement feature identification module 20 will be described in detail below. As described above, the infringement feature identification module 20 performs infringement feature identification based on the type of the target digital collectible and generates a preset infringement feature. The infringement feature identification module 20 may further include: an infringement record mining unit for mining infringement records of digital collectibles of the same type based on the type of the target digital collectible to generate a collection infringement record dataset; an infringement feature extraction unit for extracting infringement features from the collection infringement record dataset to generate an infringement feature set; and a feature clustering unit for performing similarity comparison between any two feature sets in the infringement feature set, clustering features with similarities greater than a preset similarity, and generating the preset infringement feature.

[0048] The specific configuration of the infringement identification sensitivity test module 30 will be described in detail below. As described above, based on the preset infringement features, the content-aware hash algorithm library is called to perform an infringement identification sensitivity test, and an initial hash algorithm integration architecture is constructed according to the sensitivity test results. The infringement identification sensitivity test module 30 may further include: Step 1: the execution unit is used to extract the first infringement feature from the preset infringement features, and collect historical infringement sample data sets based on the first infringement feature as a constraint, wherein any group of historical infringement sample data includes historical genuine collection samples and historical infringing collection samples with corresponding relationships; Step 2: the execution unit is used to use a plurality of content-aware hash algorithms in the content-aware hash algorithm library to perform hash processing and comparison on the historical genuine collection samples and the historical infringing collection samples, respectively, to generate multiple sets of hash value difference information; Step 3: the execution unit is used to extract the first infringement feature from the preset infringement features, and collect historical infringement sample data sets based on the first infringement feature as a constraint, wherein any group of historical infringement sample data includes historical genuine collection samples and historical infringing collection samples with corresponding relationships; Step 4: the execution unit is used to perform hash processing and comparison on the historical genuine collection samples and the historical infringing collection samples, respectively, to generate multiple sets of hash value difference information; Step 5: the execution unit is used to extract the first infringement feature from the preset infringement features, and collect historical infringement sample data sets based on the first infringement feature as a constraint, wherein any group of historical infringement sample data includes historical genuine collection samples and historical infringing collection samples with corresponding relationships; Step 6: the execution unit is used to extract the first infringement feature from the preset infringement features, and collect historical infringement sample data sets based on the first infringement feature as a constraint, wherein the historical infringement sample data includes historical genuine collection samples and historical infringing collection samples with corresponding relationships; Step 7: the execution unit is used to extract the first infringement feature from the preset infringement features, and collect historical infringement sample data sets based on the first infringement feature as a constraint, wherein the historical infringement sample data includes historical genuine collection samples and historical infringing collection samples with corresponding relationships; Step 8: the execution unit is used to extract the first infringement feature from The step three execution unit is used to generate multiple first infringement identification sensitivity indicators based on the multiple groups of hash value difference information; the step four execution unit is used to screen the content-aware hash algorithm corresponding to the maximum indicator based on the multiple first infringement identification sensitivity indicators, and map and associate it with the first infringement feature to generate a first content-aware hash algorithm; the iteration unit is used to iteratively repeat steps one to four by analogy, until the second infringement feature to the Nth infringement feature in the preset infringement feature is traversed, and the second content-aware hash algorithm to the Nth content-aware hash algorithm is obtained; the initial hash algorithm integrated architecture generation unit is used to generate the initial hash algorithm integrated architecture using the first content-aware hash algorithm, the second content-aware hash algorithm to the Nth content-aware hash algorithm.

[0049] The specific configuration of the multi-mode hash fusion processing module 50 will be described in detail below. As described above, the multi-mode hash fusion processing is performed on the multiple collection blocks using the preset hash algorithm integrated architecture to obtain multiple block hash values ​​and upload them to the blockchain. The multi-mode hash fusion processing module 50 may further include: a pre-stage feature processing unit for performing pre-stage hash processing feature processing on the multiple collection blocks using the first content-aware hash algorithm, the second content-aware hash algorithm, through the Nth content-aware hash algorithm in the preset hash algorithm integrated architecture, to generate multiple first pre-stage feature data sets, multiple second pre-stage feature data sets, through multiple Nth pre-stage feature data sets; and a hash calculation unit for performing hash calculations on the multiple first pre-stage feature data sets, multiple second pre-stage feature data sets, through multiple Nth pre-stage feature data sets according to the monitoring attention configuration to generate the multiple block hash values.

[0050] The specific configuration of the infringement monitoring and reminder information generation module 70 will be described in detail below. As described above, the multiple block hash values ​​are compared with the multiple detection hash values ​​for similarity to generate infringement monitoring and reminder information. The infringement monitoring and reminder information generation module 70 may further include: an infringement identification sensitivity index reading unit for reading the first infringement identification sensitivity index, the second infringement identification sensitivity index, and the Nth infringement identification sensitivity index corresponding to the first content-aware hash algorithm, the second content-aware hash algorithm, and the Nth content-aware hash algorithm, respectively; a weight allocation unit for weighting the first content-aware hash algorithm, the second content-aware hash algorithm, and the Nth content-aware hash algorithm based on the first infringement identification sensitivity index, the second infringement identification sensitivity index, and the Nth infringement identification sensitivity index to obtain a weight allocation result; a fusion unit for establishing a fusion optimization network based on the weight allocation result, connecting to the preset hash algorithm integrated architecture, fusing the multiple block hash values, and generating a preset fusion hash value; and an infringement monitoring and reminder unit for performing infringement monitoring and reminder on the first detection collection using the preset fusion hash value.

[0051] Among them, the infringement monitoring reminder information generation module 70 can further include: a copyright transaction information acquisition unit for acquiring copyright transaction information based on the blockchain; a copyright transaction information parsing unit for parsing the copyright transaction information and generating multiple permission characteristics of multiple authorized parties; a first collection circulation information reading unit for reading the first collection circulation information of the first detected collection when the similarity between the multiple block hash values ​​and the multiple detection hash values ​​meets the preset infringement similarity; a verification unit for verifying the first collection circulation information based on the multiple authorized parties and the multiple permission characteristics, and if the verification fails, generating the infringement monitoring reminder information.

[0052] The blockchain-based intelligent copyright monitoring platform for digital collections provided by the embodiments of the present invention can execute the blockchain-based intelligent copyright monitoring method for digital collections provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0053] Although this application makes various references to certain modules in the platform according to the embodiments of this application, any number of different modules can be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0054] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A blockchain-based intelligent copyright monitoring method for digital collections, characterized by: include: Divide the target digital collection into blocks to generate multiple collection blocks and multiple block monitoring weights; Identify infringement characteristics based on the type of the target digital collection and generate preset infringement characteristics; Based on the preset infringement features, calling the content-aware hash algorithm library to perform infringement identification sensitivity testing, and building an initial hash algorithm integration architecture according to the sensitivity test results; The initial hash algorithm integrated architecture is monitored and configured based on the plurality of block monitoring weights to generate a preset hash algorithm integrated architecture, including: Step 1: Extract the first infringement feature from the preset infringement features, and collect a historical infringement sample data set based on the first infringement feature as a constraint, wherein any set of historical infringement sample data includes historical genuine collection samples and historical infringing collection samples that have a corresponding relationship; Step 2: Using multiple content-aware hash algorithms in the content-aware hash algorithm library, hash processing and comparison are performed on the samples of the historical genuine collection and the samples of the historical infringing collection, respectively, to generate multiple sets of hash value difference information; Step 3: generating a plurality of first infringement identification sensitivity indicators based on the plurality of sets of hash value difference information; Step 4: Based on the multiple first infringement identification sensitivity indicators, a content-aware hash algorithm corresponding to the maximum indicator is selected, and the content-aware hash algorithm is mapped and associated with the first infringement feature to generate a first content-aware hash algorithm; By analogy, iteratively repeat steps 1 to 4 until the second infringement feature to the Nth infringement feature in the preset infringement feature is traversed, and the second content-aware hash algorithm to the Nth content-aware hash algorithm is obtained; Performing multi-mode hash fusion processing on the multiple collection blocks through the preset hash algorithm integrated architecture to obtain multiple block hash values ​​and upload them to the blockchain, including: performing feature processing in a pre-hashing stage on the plurality of collection blocks using the first content-aware hash algorithm, the second content-aware hash algorithm, and the Nth content-aware hash algorithm in the preset hash algorithm integrated architecture, to generate a plurality of first pre-feature data sets, a plurality of second pre-feature data sets, and a plurality of Nth pre-feature data sets; Performing hash calculations on the plurality of first pre-feature data sets, the plurality of second pre-feature data sets, and up to the plurality of Nth pre-feature data sets according to the monitoring attention configuration to generate the plurality of block hash values; Establishing a connection with a preset network platform, monitoring and obtaining a first detection collection according to the type of the target digital collection, and calling the preset hash algorithm integrated architecture to perform multi-mode hash fusion processing to generate multiple detection hash values; A similarity comparison is performed on the multiple block hash values ​​and the multiple detection hash values ​​to generate infringement monitoring reminder information.

2. The blockchain-based intelligent copyright monitoring method for digital collections according to claim 1, characterized in that: The target digital collection is divided into blocks to generate multiple collection blocks and multiple block monitoring weights, including: Divide the target digital collection into blocks according to a predetermined division grid to generate the plurality of collection blocks; Performing regional feature density detection on the plurality of collection blocks respectively to generate a plurality of feature densities; Performing color and texture feature complexity identification on the plurality of collection blocks to generate a plurality of complexity indices; The plurality of feature densities and the plurality of complexity indices are combined to perform weight configuration to generate the plurality of block monitoring weights, wherein the block monitoring weight is proportional to the feature density and the complexity indices.

3. The blockchain-based intelligent copyright monitoring method for digital collections according to claim 1, characterized in that: Infringement characteristics are identified based on the type of the target digital collection, and preset infringement characteristics are generated, including: Mining infringement records of digital collections of the same type according to the type of the target digital collection to generate a collection infringement record dataset; Extracting infringement features from the collection infringement record dataset to generate an infringement feature set; A similarity comparison is performed on any two groups of features in the infringement feature set, and features with similarities greater than a preset similarity are clustered to generate the preset infringement features.

4. The blockchain-based intelligent copyright monitoring method for digital collections according to claim 1, characterized in that: Based on the preset infringement features, the content-aware hash algorithm library is called to perform infringement identification sensitivity testing. Based on the sensitivity test results, an initial hash algorithm integration architecture is constructed, including: The initial hash algorithm integration architecture is generated using the first content-aware hash algorithm, the second content-aware hash algorithm, and up to the Nth content-aware hash algorithm.

5. The blockchain-based intelligent copyright monitoring method for digital collections according to claim 1, characterized in that: Also includes: Reading a first infringement identification sensitivity index, a second infringement identification sensitivity index, and an Nth infringement identification sensitivity index corresponding to the first content-aware hash algorithm, the second content-aware hash algorithm, and the Nth content-aware hash algorithm, respectively; performing weight allocation on the first content-aware hash algorithm, the second content-aware hash algorithm, and the Nth content-aware hash algorithm based on the first infringement identification sensitivity index, the second infringement identification sensitivity index, and the Nth infringement identification sensitivity index, to obtain a weight allocation result; Establishing a fusion optimization network based on the weight distribution result, connecting it with the preset hash algorithm integrated architecture, fusing the multiple block hash values, and generating a preset fusion hash value; The preset fusion hash value is used to perform infringement monitoring reminder on the first detected collection.

6. The blockchain-based intelligent copyright monitoring method for digital collections according to claim 1, characterized in that: Performing a similarity comparison between the multiple block hash values ​​and the multiple detection hash values ​​to generate infringement monitoring reminder information includes: Based on the blockchain, obtaining copyright transaction information; Parsing the copyright transaction information to generate multiple permission features of multiple authorized parties; When the similarity between the plurality of block hash values ​​and the plurality of detection hash values ​​meets a preset infringement similarity, reading the first collection circulation information of the first detection collection; The first collection circulation information is verified based on the multiple authorized parties and the multiple authority features, and if the verification fails, the infringement monitoring reminder information is generated.

7. The blockchain-based intelligent monitoring platform for digital collection copyrights is characterized by: The platform is used to implement the blockchain-based intelligent monitoring method for digital collection copyrights according to any one of claims 1 to 6, and the platform includes: A block processing module, which is used to perform block processing on the target digital collection to generate multiple collection blocks and multiple block monitoring weights; an infringement feature identification module, the infringement feature identification module being used to identify infringement features according to the type of the target digital collectible and generate a preset infringement feature; An infringement identification sensitivity testing module, wherein the infringement identification sensitivity testing module is used to call a content-aware hash algorithm library to perform an infringement identification sensitivity test based on the preset infringement features, and to construct an initial hash algorithm integration architecture according to the sensitivity test results; A monitoring attention configuration module, configured to perform monitoring attention configuration on the initial hash algorithm integrated architecture based on the plurality of block monitoring weights to generate a preset hash algorithm integrated architecture; A multi-mode hash fusion processing module, which is used to perform multi-mode hash fusion processing on the multiple collection blocks using the preset hash algorithm integrated architecture to obtain multiple block hash values ​​and upload them to the blockchain; Multiple detection hash value generation modules, each configured to establish a connection with a preset network platform, monitor and obtain a first detection artifact according to the type of the target digital artifact, and invoke the preset hash algorithm integration architecture to perform multi-mode hash fusion processing to generate multiple detection hash values; The infringement monitoring reminder information generation module is used to perform similarity comparison between the multiple block hash values ​​and the multiple detection hash values ​​to generate infringement monitoring reminder information.

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