A digital content management method, device and platform

Abstract

The application provides a digital content management method and device and a digital content management platform. The method comprises the following steps: determining a heat index of digital content in a first period, wherein the heat index is positively correlated with the heat of the digital content; predicting the heat of the digital content in a second period according to the heat index of the digital content in the first period, wherein the second period is the next heat period of the first period; if the heat of the digital content in the second period is less than a preset heat threshold, migrating the digital content from a main chain to a side chain in the case that the digital content is deployed on the main chain; and if the heat of the digital content in the second period is greater than or equal to the preset heat threshold, migrating the digital content from the side chain to the main chain in the case that the digital content is deployed on the side chain. Through the application, the waste of blockchain resources can be reduced.

Description

Technical Field

[0001] This application relates to the field of blockchain technology, and in particular to a digital content management method, device and digital content management platform. Background Technology

[0002] With the rapid development of internet technology and the digital cultural and creative industry, various types of digital content, such as images, audio and video, and cultural and creative works, have experienced explosive growth. The production, dissemination, and application scenarios of digital content are becoming increasingly diverse, and it has been integrated into people's lives and various industries. The massive emergence of digital content has spurred the demand for professional digital content management, leading to the development of digital content management platforms (i.e., digital content management systems).

[0003] Digital content management platforms provide users with one-stop services for digital content storage, forwarding, copyright registration, work retrieval, and authorized use, effectively controlling the lifecycle of digital content and improving its circulation efficiency and utilization value. However, most digital content management platforms adopt a centralized architecture, where digital content is centrally stored on a central server. This is prone to data tampering and difficulties in tracing its origin, and lacks safeguards in areas such as content ownership verification and circulation. To address these shortcomings, digital content management platforms are gradually integrating blockchain technology with their systems to achieve more comprehensive digital content management.

[0004] However, blockchain-based digital content management platforms still have shortcomings. Currently, these platforms store and process all digital content-related data indiscriminately, leading to a waste of blockchain resources. Summary of the Invention

[0005] To address the related technical issues, embodiments of this application provide a digital content management method, apparatus, and digital content management platform.

[0006] The technical solution of this application embodiment is implemented as follows: This application provides a digital content management method, the method comprising: Determine the popularity index of digital content in the first period, wherein the popularity index is positively correlated with the popularity of digital content; Based on the popularity index of the digital content in the first period, the popularity of the digital content in the second period is predicted, wherein the second period is the next popularity period after the first period. If the popularity of the digital content in the second period is less than a preset popularity threshold, then if the digital content is deployed on the main chain, the digital content will be migrated from the main chain to the side chain. If the popularity of the digital content in the second period is greater than or equal to the preset popularity threshold, then if the digital content is deployed on a sidechain, the digital content will be migrated from the sidechain to the main chain.

[0007] In the above scheme, determining the popularity index of digital content in the first period includes: The system acquires multi-dimensional information about digital content during the first period. This multi-dimensional information includes at least two of the following: access behavior information, interaction behavior information, circulation information, content category association information, and popularity decay information. The access behavior information represents the access popularity of digital content related to access behavior. The interaction behavior information represents the interaction popularity of digital content related to interaction behavior. The circulation information represents the circulation popularity of digital content related to circulation behavior. The content category association information represents the content category popularity of the content to which the digital content belongs. The popularity decay information represents the degree of decline in the popularity of digital content over time. Based on the multi-dimensional information of the digital content in the first period, the popularity index of the digital content in the first period is determined.

[0008] The method in the above scheme further includes: Receive the digital content uploaded by the user client; Determine the initial popularity of the digital content; Based on the initial popularity of the digital content, the digital content will be deployed on the main chain or side chain.

[0009] In the above scheme, determining the initial popularity of the digital content includes: Determine the average popularity and popularity distribution entropy corresponding to the content category to which the digital content belongs; wherein, the popularity distribution entropy reflects the popularity dispersion corresponding to the content category to which the digital content belongs; The initial popularity value of the digital content is determined based on the average popularity of the content category to which the digital content belongs and the popularity distribution entropy.

[0010] The method in the above scheme further includes: Based on the remaining resource quota corresponding to the main chain, rewards are set for multiple interactive behavior events for the digital content of the side chain; If any interactive event corresponding to any digital content on the sidechain is detected, a reward is issued to the user account that triggered the interactive event.

[0011] The method in the above scheme further includes: For each interactive behavior event, determine the growth rate of a measurement parameter corresponding to the interactive behavior event in adjacent incentive cycles, wherein the measurement parameter is used to measure the incentive effect of the corresponding interactive behavior event. The incentive weights corresponding to the interactive behavior events are adjusted based on the growth rate of the measured parameters, wherein the incentive weights are positively correlated with the reward size of the interactive behavior events.

[0012] In the above scheme, after predicting the popularity of the digital content in the second period, the method further includes: The circulation cost of the digital content in the second period is set based on its popularity during the second period.

[0013] The method in the above scheme further includes: Determine the average duration during which the overall popularity change for each content category reaches a preset proportion; Based on the average duration, set the popularity cycle for one or more digital content items in the corresponding content type.

[0014] This application embodiment also provides a digital content management device, applied to a blockchain-based digital content management platform, wherein the blockchain includes a main chain and side chains; the device includes: A determination module is used to determine the popularity index of digital content in the first period, wherein the popularity index is positively correlated with the popularity of digital content; A popularity prediction module is used to predict the popularity of the digital content in a second period based on the popularity index of the digital content in the first period, wherein the second period is the next popularity period after the first period. The control module is configured to migrate the digital content from the main chain to the side chain if the popularity of the digital content in the second period is less than a preset popularity threshold, provided that the digital content is deployed on the main chain; and to migrate the digital content from the side chain to the main chain if the popularity of the digital content in the second period is greater than or equal to the preset popularity threshold, provided that the digital content is deployed on the side chain.

[0015] This application also provides a digital content management platform, which is implemented based on blockchain, including a main chain and side chains; the digital content management platform is used to implement the method provided in this application.

[0016] The digital content management method, apparatus, and platform provided in this application establish a layered architecture of main chain and side chain in the blockchain to achieve differentiated management of cold and hot digital content. By determining the popularity index of digital content in the first period, the popularity of the digital content in the second period is predicted based on the popularity index in the first period. If the popularity of the digital content in the second period is less than a preset popularity threshold, then if the digital content is deployed on the main chain, it is migrated from the main chain to the side chain; if the popularity of the digital content in the second period is greater than or equal to the preset popularity threshold, then if the digital content is deployed on the side chain, it is migrated from the side chain to the main chain. Thus, by migrating digital content with changing popularity between the main chain and side chain, dynamic adaptation of digital content with different popularity to the blockchain's layered links can be achieved, enabling differentiated management of digital content with different popularity and rational allocation of blockchain resources. Furthermore, by pre-migrating digital content between the main chain and side chain based on predicted popularity, resource waste in the blockchain can be reduced. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the operation flow of a blockchain-based digital content management platform provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a digital content management method provided in an embodiment of this application; Figure 3 This is a schematic diagram of a digital content migration process provided in an embodiment of this application; Figure 4 This is a schematic diagram of the overall architecture of a digital content management platform system provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a digital content management device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the application will now be described in further detail with reference to the accompanying drawings.

[0019] Digital content encompasses various types of information carriers, including text, images, and videos, generated, stored, and disseminated in digital form. With the rapid development of internet technology and the digital cultural and creative industries, various digital content management platforms have emerged. Users upload digital content to these platforms through user interaction. These platforms not only provide online display and browsing of various digital content but also offer one-stop services such as storage, online distribution, copyright registration, work retrieval, and authorized use.

[0020] To optimize the trust and security mechanism of the digital content management platform, the platform is combined with blockchain technology. Based on the decentralized, immutable, fully traceable, and smart contract characteristics of blockchain, the security and openness of digital content processing are achieved.

[0021] For example, such as Figure 1 As shown, the operation process of a blockchain-based digital content management platform mainly includes the following core steps: The digital content owner uploads the source files and account information of the digital content to the centralized or decentralized service node of the digital content management platform; the digital content management platform uses smart contracts to encrypt the digital content and stores key information such as the hash value of the digital content on the blockchain, thereby achieving the immutability and traceability of digital content-related data based on the characteristics of blockchain. When a user accesses the digital content, the digital content management platform can use the key information such as the hash value stored on the blockchain to complete the retrieval and integrity verification of the digital content. In the digital content circulation process, the digital content management platform uses smart contracts to clarify the copyright ownership and usage rules of the digital content. The requester of digital content can obtain the right to use the digital content through the digital content management platform, and the digital content management platform automatically verifies the authorization conditions through smart contracts to realize the circulation of digital content. After the requester of digital content obtains the right to use the digital content, the digital content management platform automatically executes the revenue distribution according to the preset rules of the smart contract to ensure that all participants in the digital content receive revenue proportionally.

[0022] However, the value and transaction volume of current digital content exhibit a typical long-tail distribution, which means that existing blockchain-based digital content management platforms still have many inherent defects and technical shortcomings in practical applications.

[0023] First, the assessment method for the resource allocation required for digital content circulation (i.e., circulation costs, such as transaction fees) is unbalanced. The resource allocation required for digital content circulation is related to network congestion and resource consumption (such as gas consumption), but not directly related to the content value of the digital content. For digital content with low value and low circulation activity, the required resource allocation is prone to be too high, resulting in the circulation cost of digital content exceeding its value. In this situation, low user participation further reduces the circulation of digital content, causing digital content to fall into a vicious cycle of "high circulation costs - low user participation - lack of content liquidity - difficulty in realizing content value - further increase in circulation costs".

[0024] Meanwhile, the lack of differentiated management of digital content leads to a serious waste of storage and computing resources. Digital content management platforms fail to categorize the storage methods and circulation behavior of digital content. All related data is uniformly synchronized to all nodes on the blockchain, or static rules are used to classify digital content, such as mechanically dividing storage locations based solely on the size, type, or generation time of the content. This lack of differentiated management strategies for different types of digital content, such as failing to effectively distinguish between frequently accessed (cold) and frequently accessed (hot) digital content, results in less frequently accessed content consuming significant amounts of storage and computing resources, causing resource waste and impacting the service quality of the digital content management platform.

[0025] Finally, digital content management platforms lack incentive mechanisms and dynamic control mechanisms tailored to niche digital content. There is no effective business linkage mechanism between niche and popular digital content. Furthermore, niche digital content suffers from poor circulation, low exposure, and a lack of resources to support its independent development, further restricting its circulation.

[0026] Based on this, this application provides a digital content management scheme that sets up a layered architecture of main chain and side chain in the blockchain to achieve differentiated management of cold and hot digital content. By determining the popularity index of digital content in the first period, the popularity of the digital content in the next popularity period (i.e., the second period) is predicted based on the popularity index of the digital content in the first period. If the popularity of the digital content in the second period is less than a preset popularity threshold, then if the digital content is deployed on the main chain, it is migrated from the main chain to the side chain; if the popularity of the digital content in the second period is greater than or equal to the preset popularity threshold, then if the digital content is deployed on the side chain, it is migrated from the side chain to the main chain. Thus, by migrating digital content with fluctuating popularity between the main chain and side chains, dynamic adaptation of digital content with varying popularity to the blockchain's layered structure can be achieved. Since the main chain's processing performance is superior to that of side chains, using the main chain to manage popular digital content (i.e., content with popularity greater than or equal to a preset popularity threshold) satisfies its security requirements, while using side chains to manage less popular digital content (i.e., content with popularity less than a preset popularity threshold) satisfies its lightweight processing needs. This enables differentiated management of digital content with varying popularity and rational allocation of blockchain resources, improving the utilization rate of blockchain resources. Furthermore, by predicting popularity and migrating digital content between the main chain and side chains in advance, the waste of main chain resources can be reduced, and the probability of congestion on side chains can be lowered.

[0027] The digital content management platform in this embodiment includes multiple electronic devices. For example, these electronic devices may be storage nodes, computing nodes, etc., on a blockchain. The multiple electronic devices interact with each other and collaboratively process digital content via network communication links.

[0028] This application proposes a dual-chain collaborative processing mechanism, where a main chain and side chains manage digital content with varying levels of popularity. To reduce cross-chain communication congestion during peak access times when the side chain synchronizes data with the main chain, the main chain manages popular digital content, while the side chains manage less popular digital content.

[0029] The main chain and side chains are responsible for the storage, verification, and business processing of corresponding digital content, such as recording metadata and related business processing records. The main chain does not participate in the side chain's business processing of digital content; it only periodically receives the business processing records packaged by the side chain and updates the status information of the corresponding accounts, thus achieving lightweight data synchronization between the main chain and side chains. Simultaneously, the main chain and side chains are configured with different computing resources and block generation intervals. By constructing a differentiated dual-chain collaborative processing mechanism for hot and cold digital content, cold digital content achieves low-cost storage and business processing at the cost of minimal processing latency, improving overall business processing efficiency while realizing the rational allocation and efficient utilization of blockchain resources.

[0030] The following describes the digital content management method provided in the embodiments of this application, using a digital content management platform as the implementing entity. Figure 2 As shown, the method includes the following steps: Step 201: Determine the popularity index of digital content in the first period; Step 202: Based on the popularity index of digital content in the first period, predict the popularity of digital content in the second period, where the second period is the next popularity period after the first period. Step 203: If the popularity of digital content in the second period is less than the preset popularity threshold, then if the digital content is deployed on the main chain, the digital content will be migrated from the main chain to the side chain; if the popularity of digital content in the second period is greater than or equal to the preset popularity threshold, then if the digital content is deployed on the side chain, the digital content will be migrated from the side chain to the main chain.

[0031] In this embodiment, the digital content management platform periodically determines the popularity index for each piece of digital content. The period for calculating the popularity index can be called the popularity cycle. The popularity cycles for different content categories can be the same or different. The digital content management platform can set the popularity cycle according to the actual application scenario.

[0032] In some implementations, the digital content management platform determines the average duration for which the overall popularity change of each content category reaches a preset proportion, and then sets the popularity cycle for one or more digital contents in the corresponding content type based on the determined average duration.

[0033] For example, taking a preset ratio of 20%, the digital content management platform collects historical popularity data for one or more digital content items within each content category. It then fits a popularity change function to this historical data to determine the overall popularity change pattern for that content category. Based on this overall popularity change pattern, the platform determines the average duration for which the overall popularity change reaches 20%. It then sets the popularity cycle for that content category based on this average duration, ensuring the set cycle closely matches the sustained characteristics of the content category's overall popularity.

[0034] The digital content management platform periodically determines the popularity index for each piece of digital content. The popularity index reflects the popularity of the digital content. The popularity index is positively correlated with the popularity of the digital content. The higher the popularity index of a piece of digital content, the more popular it is. The lower the popularity index of a piece of digital content, the less popular it is.

[0035] Taking any piece of digital content as an example, the digital content management platform periodically determines the popularity index of that digital content. For instance, if the first period is any popularity cycle (such as the current popularity cycle), the digital content management platform determines the popularity index of that digital content in the first period. For example, the digital content management platform uses the access frequency of the digital content as the popularity index, and calculates the access frequency of the digital content in the first period to determine the popularity index of the digital content in the first period.

[0036] In some implementations, the digital content management platform can determine the popularity index of digital content in any popularity cycle (such as the first cycle) based on multi-dimensional information of the digital content.

[0037] Multi-dimensional information includes at least two of the following: access behavior information, interaction behavior information, circulation information, content category association information, and popularity decay information. Determining the popularity of digital content through multi-dimensional information allows for a more comprehensive integration of information from multiple dimensions to determine the popularity index, reducing the bias caused by single-dimensional information and improving the accuracy of the popularity index.

[0038] Access behavior information is used to represent the access popularity of digital content in relation to access behavior. For example, access behavior information can be determined through access behavior-related data such as the number of times digital content is accessed and retention time (e.g., the average browsing time of a user's single access to digital content).

[0039] Interactive behavior information is used to represent the popularity of digital content in relation to interactive behaviors. For example, interactive behavior information can be determined by the number of likes, comments, shares, and other interactive behaviors associated with digital content.

[0040] Circulation information is used to represent the circulation activity of digital content. For example, circulation information can be determined through data such as the number of times digital content is circulated, the volume of circulation, and the volatility.

[0041] Content category association information indicates the overall popularity of a content category to which digital content belongs; that is, it represents the overall popularity of multiple digital content items within the same content category. The higher the overall popularity of multiple digital content items within the same content category, the more likely an individual digital content item in that category is to attract attention. For example, content category association information can be determined using data such as reviews and ratings of the content category to which the digital content belongs.

[0042] Popularity decay information is used to represent the degree to which the popularity of digital content decreases over time. The popularity of a piece of digital content decreases continuously over time. For example, popularity decay information can be represented by a decay factor that decreases continuously over time.

[0043] In some implementations, when determining the popularity index of digital content in the first period, the digital content management platform can obtain multi-dimensional information about the digital content during that period and determine its popularity index based on this information. For example, the digital content management platform can weight the various dimensions of the multi-dimensional information to obtain the popularity index for that period.

[0044] For example, a digital content management platform can determine the popularity index of digital content in any popularity cycle using formula (1): Formula (1) in, This represents the popularity index of digital content during the popularity period t. This indicates the access popularity of digital content during the popularity period t (i.e., access behavior information). This represents the interaction popularity of digital content during the popularity period t (i.e., interaction behavior information). This indicates the circulation popularity (i.e., circulation information) of digital content during the popularity period t. This indicates the content category popularity (i.e., content category association information) of the digital content's category during the popularity period t. This represents the decay factor of digital content over a heat cycle t (i.e., heat decay information). They represent The weighting coefficients can be set according to the actual application scenario. This application embodiment does not restrict the value of each weighting coefficient.

[0045] In some implementations, access behavior information is related to the number of times digital content is accessed and its retention time. A digital content management platform can determine the access behavior information of digital content within a specific popularity period based on the number of times it is accessed and its retention time. For example, the platform obtains the number of times digital content is accessed and its retention time in the first period, and then standardizes these data to obtain standardized access counts and retention times. Finally, the platform weights these standardized access counts and retention times to obtain the access behavior information for the digital content in the first period.

[0046] The number of visits reflects how frequently users browse digital content; retention time reflects the degree of user interest in digital content. Determining access behavior information for digital content through the number of visits and retention time makes the access behavior information more objective and balanced, providing reliable data support for calculating the popularity index of digital content.

[0047] In some implementations, interactive behavior information is linked to interactive actions related to digital content. These actions include liking, saving, forwarding, and commenting. Digital content management platforms can determine the interactive behavior information of digital content within any given popularity period based on these related actions.

[0048] For example, a digital content management platform can determine the interactive behavior information of digital content in any popularity cycle through formula (2): Formula (2) in, This indicates the interactive behavior information of digital content within the popularity period t; This represents the number of likes received by digital content within a popularity period t; This represents the number of comments on digital content within a popularity period t; This represents the number of times digital content is forwarded within a popularity period t; This represents the forwarding conversion rate of digital content within a popularity period t. The forwarding conversion rate is the ratio of the number of access behaviors triggered by forwarding behavior to the number of forwards. They represent The corresponding weighting coefficients; This represents the marginal benefit parameter used to measure the degree of diminishing marginal benefit of the number of likes, and can be set according to the actual application scenario.

[0049] Interactive behaviors related to digital content can reflect users' level of interest in it. Evaluating the interactive behavior information of digital content through these behaviors allows for a more comprehensive measurement of the content's popularity in terms of engagement, thus providing reliable data support for calculating the popularity index of digital content.

[0050] In some implementations, circulation information is related to the holding conversion rate, average circulation volume, value volatility, and rate of return of digital content. A digital content management platform can determine the circulation information of digital content within a popularity cycle based on its holding conversion rate, average circulation volume, value volatility, and rate of return.

[0051] For example, a digital content management platform can determine the circulation information of digital content in any given popularity cycle using formula (3): Formula (3) in, This indicates the circulation information of digital content within a popularity period t; These represent the weighting coefficients corresponding to the holding conversion rate, average turnover, value volatility, and return rate, respectively, and can be set according to the actual application scenario.

[0052] The holding conversion rate represents the ratio of the number of times a series of digital content (such as non-fungible tokens (NFTs)) circulates within any given popularity period to the total number of digital content in that series that is in a circulating state. Circulation can be understood as the transfer of ownership, authorization of holding status, and changes. The holding conversion rate reflects the frequency of changes in the holding status of the series of digital content and the activity of circulation. For example, the holding conversion rate can be determined by formula (4): Conversion rate = Number of times the series of digital content is circulated / Total number of series of digital content Formula (4) Average circulation volume represents the number of times digital content circulates within a unit of time (such as 1 day, 1 hour, etc.) during any given popularity cycle.

[0053] Value volatility represents the fluctuation in the value of digital content. The value of digital content can be set according to specific application scenarios. For example, a digital content management system can set the value of digital content in response to user actions from the content holder.

[0054] For example, value volatility can be determined by formula (5): Formula (5) in, This represents the rate of change in the value of digital content over a popularity period t. , Indicates the popularity cycle of digital content Content value, Indicates the popularity cycle of digital content The value of its content; The preset value statistics duration is N, which is an integer multiple of the popularity period. This represents the average rate of change in the value of digital content over a statistical period N. It equals the average content value of digital content across various popularity cycles within the value statistics period N.

[0055] Yield represents the ratio between the value of digital content and its revenue over any given popularity cycle. Yield reflects the contribution capacity of digital content's resources (such as traffic resources). For example, yield can be determined using formula (6): Rate of return = / Content revenue formula (6) in, Indicates the popularity cycle of digital content The revenue from digital content can be understood as the difference between the resource revenue (such as traffic revenue) generated by the digital content and the resource cost consumed by the digital content. The rate of return reflects the content value corresponding to each unit of digital content revenue.

[0056] Circulation information reflects the circulation of digital content on digital content management platforms. By integrating digital content holding conversion rates, average circulation volume, value volatility, and yield, the circulation of digital content can be quantified more comprehensively, improving the accuracy of circulation information and providing reliable data support for calculating the popularity index of digital content.

[0057] In some implementations, content category association information can be determined through evaluation information of similar digital content within the same content category as the digital content. For example, content category association information includes sentiment score of similar digital content. The digital content management platform obtains comment text (i.e., evaluation information) of similar digital content within the same content category as the digital content. Then, the digital content management platform extracts sentiment words from the comment text and determines the sentiment score corresponding to each sentiment word. The higher the sentiment score of a sentiment word, the higher the level of acceptance for the digital content in that content category. Conversely, the lower the sentiment score of a sentiment word, the lower the level of acceptance for the digital content in that content category.

[0058] For example, a digital content management platform uses a pre-built sentiment dictionary to find individual sentiment words in a comment text and determine the sentiment feature score for each word. Then, the platform calculates the average sentiment feature score for each sentiment word in the comment text; this average can then be used as content category association information for the digital content. As another example, the platform can input the comment text into a pre-trained sentiment model (machine learning model) to perform sentiment analysis and obtain the sentiment feature score output by the model. This sentiment feature score can then be used as content category association information for the digital content.

[0059] Content category association information can reflect the degree of acceptance of digital content and can serve as a dimension related to the popularity of digital content. Since the digital content management platform already has a comment management mechanism, existing sentiment analysis functions can be reused to calculate sentiment characteristic scores, thereby reducing the additional overhead of the digital content management platform. This enriches the dimensions of digital content popularity assessment while ensuring the stability of the digital content management platform's operation.

[0060] In some implementations, heat decay information is represented by a decay factor that decreases over time. For example, heat decay information can be determined using formula (7): Formula (7) in, This represents the decay factor of digital content over a popularity period t. This indicates the time of heat assessment within the heat cycle t; Indicates the release time of digital content; This represents the time decay weighting coefficient; The larger the value, the faster the popularity of digital content decays over time. The smaller the value, the slower the rate at which the popularity of digital content decays over time.

[0061] Taking the determination of a digital content popularity index for the first period by a digital content management platform based on access behavior, interaction behavior, circulation information, content category association information, and popularity decay information in the first period as an example, after the digital content management platform determines these information, it standardizes them and then linearly weights each standardized dimension to obtain the popularity index for the first period. In some implementations, the digital content management platform can also normalize the popularity index, such as using the sigmoid function to normalize it, resulting in a normalized popularity index.

[0062] After determining the popularity index of digital content in the first period, in step 202, the digital content management platform predicts the popularity of digital content in the second period based on the popularity index of digital content in the first period. For example, the digital content management platform inputs the popularity index of digital content in the first period into a pre-trained popularity prediction model to obtain the popularity of digital content in the second period.

[0063] In some implementations, digital content management platforms predict the popularity of digital content in the second period based on its popularity index and popularity in the first period. Since the duration of digital content popularity is typically short, short-term popularity prediction is sufficient to meet the needs of popularity assessment. For example, the digital content management platform can use exponential smoothing to predict the popularity of digital content in the next popularity period (the second period) based on its popularity index and popularity in the first period. This adapts to the characteristics of rapid decline and sensitive changes in the popularity of digital content, achieving short-term popularity prediction with lower computational overhead.

[0064] For example, a digital content management platform can use formula (8) to predict the popularity of digital content in the next popularity cycle: Formula (8) in, This indicates the predicted popularity of digital content within the popularity period (t+1). This indicates the popularity of digital content during the popularity period t; This represents the popularity index of digital content during the popularity period t. , represents the preset smoothing coefficient, which can be set according to the actual application scenario.

[0065] After the digital content management platform predicts the popularity of digital content in the second period, in step 203, the digital content management platform compares the popularity of digital content in the second period with a preset popularity threshold, and migrates the digital content according to the comparison result, such as by migrating the digital content through a migration controller.

[0066] Understandably, the preset popularity threshold can be set according to the application scenario. The preset popularity threshold can be used to determine whether a piece of digital content is popular or not. If the popularity of a piece of digital content is less than the preset popularity threshold, it is considered to have low popularity and is classified as cold digital content. If the popularity of a piece of digital content is greater than or equal to the preset popularity threshold, it is considered to have high popularity and is classified as hot digital content.

[0067] If the popularity of digital content falls below a preset popularity threshold in the second period, the digital content management platform will migrate the digital content from the main chain to a side chain, provided the content is deployed on the main chain. Thus, if it is predicted that the popularity of any piece of digital content will decrease to below the preset threshold, indicating that the content is about to become "cold" content, migrating it from the main chain to the side chain in advance saves storage space and computing power on the main chain, reducing the resource consumption of "cold" content.

[0068] If the popularity of digital content in the second cycle is greater than or equal to a preset popularity threshold, the digital content management platform will migrate the digital content from the sidechain to the main chain if it is deployed on the sidechain. Thus, when it is predicted that the popularity of any piece of digital content will increase to be greater than or equal to the preset popularity threshold, that content is about to become "hot" content. In this case, migrating the content from the sidechain to the main chain in advance reduces the possibility of sidechain congestion due to high-concurrency access to digital content, improving overall service quality and user experience.

[0069] In some implementations, the initial popularity of digital content can be initialized using the average popularity and popularity distribution entropy of the content category to which the digital content belongs. For example, when new digital content is added, the digital content management platform determines the content category to which the digital content belongs, and then determines the average popularity and popularity distribution entropy corresponding to that content category. Based on the average popularity and popularity distribution entropy of that content category, the initial popularity of the new digital content is determined.

[0070] The average popularity of a content category reflects the average popularity of multiple digital content items within that category. The average popularity of any content category is equal to the ratio of the total popularity of all digital content in that category to the total number of digital content items.

[0071] The popularity distribution entropy corresponding to a content category reflects the degree of dispersion in popularity among multiple digital content items within that category. A higher popularity distribution entropy for any content category (e.g., close to 1) indicates a more even distribution of popularity among the digital content items within that category. Conversely, a lower popularity distribution entropy for any content category indicates greater differences in popularity among the digital content items within that category, suggesting that the popularity of that content category is mainly concentrated in a few trending digital content items.

[0072] The initial popularity of digital content can be determined by using the average popularity and popularity distribution entropy of the content category to which the digital content belongs. This can take into account the overall popularity level and popularity dispersion of the content category to which the digital content belongs, making the initial popularity of the digital content more accurate.

[0073] For example, a digital content management platform can determine the initial popularity of digital content using formula (9): Formula (9) in, Indicates the initial popularity of digital content; This indicates the average popularity of the content category to which the digital content belongs; Entropy represents the popularity distribution of digital content; , This represents the popularity percentage of digital content i within this content category, where i is a positive integer less than or equal to n; n represents the total number of digital content items within this content category.

[0074] In some implementations, when new digital content is added, the digital content management platform can deploy the digital content on the main chain or side chain based on its initial popularity.

[0075] For example, such as Figure 3 As shown, the user uploads digital content to the digital content management platform under user control, submitting personal identification information and metadata of the digital content (such as user account, password, and digital content name). The digital content management platform verifies the digital content. After verification, it stores the ownership confirmation record on the main chain and returns the corresponding digital rights token to the user, providing the user with proof of ownership of the digital content. The user can also set the circulation rules of the digital content and related terms of authorization (i.e., smart contracts). The digital content management platform receives the digital content uploaded by the user and predicts the initial popularity of the digital content through a popularity prediction module.

[0076] If the initial popularity of digital content is greater than or equal to a preset popularity threshold, the digital content management platform will store the source file corresponding to the digital content in a centralized cloud storage node, and deploy the circulation rules of digital content and the smart contract for authorized use on the main chain. By leveraging the high credibility, high concurrency and high security performance of the main chain, it will provide guarantees for the access, circulation and authorization of popular digital content.

[0077] If the initial popularity of digital content is less than a preset popularity threshold, the digital content management platform will store the source files of the digital content in a decentralized storage node (such as the Inter Planetary File System (IPFS)) and deploy the circulation rules of the digital content and the smart contracts for authorized use on the sidechain, thereby reducing the occupation of main chain resources by cold digital content.

[0078] In this embodiment, the digital content management platform adopts a layered collaborative architecture of main chain and side chain. Based on the differences in resource allocation between the main chain and side chain, differentiated processing of digital content is achieved, improving the overall operational efficiency and resource utilization rationality of the digital content management platform. Specifically, the main chain is allocated more computing resources, primarily used for handling high-frequency circulation of popular digital content, ensuring low latency and high reliability in the circulation of popular digital content; the cold chain is mainly used for low-frequency circulation of cold digital content, thereby reducing the resource consumption of the main chain and achieving layered scheduling and optimized allocation of resources.

[0079] To achieve collaboration and consistency between the main chain and the cold chain, the digital content management platform periodically packages all data generated by the cold chain within each synchronization cycle. Zero-knowledge proof technology is then used to verify the validity and correctness of the packaged data, generating corresponding proof documents. The digital content management platform submits these proof documents to the main chain and simultaneously submits a new state root to the main chain. This state root is a global state hash value generated based on all data transferred on the cold chain within the synchronization cycle.

[0080] After receiving the proof documents and state root submitted by the cold chain, the main chain verifies the validity of the proof documents. If the verification passes, the flow data of that batch is confirmed to be valid. The main chain then updates its own stored state root to complete the synchronization of the flow status between the main chain and the cold chain, ensuring the consistency and trustworthiness of the dual-chain data.

[0081] To reduce situations where the cost of digital content distribution exceeds its value, some implementations optimize distribution costs by linking them to the content's popularity. This allows the platform to set distribution costs for each popularity cycle based on its own performance. For example, after predicting the content's popularity in the second cycle based on its first cycle's popularity index, the platform can then set the distribution costs for the second cycle based on that popularity. Dynamically adjusting distribution costs based on content popularity makes the distribution process more rational.

[0082] For example, a digital content management platform can determine the circulation cost of digital content using formula (10): Formula (10) in, This represents the cost of distributing digital content; the first item, namely... This represents the cost elasticity term, used to balance the distribution costs of digital content with its value. This interconnectedness prevents the inverted phenomenon of digital content distribution costs; The elasticity adjustment coefficient represents the value of content and is used to adjust the degree of impact of content value on circulation costs. This represents the content value of digital content within the popularity period t; the second term, namely, This represents the popularity gain factor, which is used to correlate the circulation cost of digital content with its popularity, promote the development of digital content towards higher quality and higher popularity, and thus reduce the circulation cost of digital content. This represents the basic cost of digital content, which can be set according to the actual application scenario; This indicates the popularity of digital content within a popularity period t; and This indicates the preset adjustment coefficient.

[0083] Distribution costs refer to the resource allocation required for the distribution of digital content. When the ownership or usage rights of any digital content change, a certain amount of resources is consumed. User accounts must provide a certain amount of resources to the digital content management platform to acquire ownership or usage rights. Adjusting the distribution costs of digital content based on its popularity, and setting a negative correlation between distribution costs and popularity—that is, the higher the popularity of digital content, the lower the distribution costs—can promote high-quality development of digital content, thereby reducing the occurrence of distribution costs exceeding content value and making the distribution costs of digital content more reasonable.

[0084] The formula for calculating the circulation cost of digital content includes a basic cost item. This means that the circulation cost of digital content is related not only to its popularity but also to its basic cost. There is a positive correlation between the circulation cost and the basic cost; the higher the basic cost of the digital content, the higher its circulation cost. Generally, the basic cost corresponding to the main chain is higher than that corresponding to the cold chain. By configuring the main chain and cold chain with different basic costs, the excessively high proportion of circulation costs for low-popularity digital content can be reduced, thus promoting the circulation of digital content.

[0085] To boost the popularity of less popular digital content, some implementations incorporate incentive mechanisms. Digital content management platforms, based on the remaining resource quota on the main chain, reward sidechain digital content with various interactive events (such as reward points). For example, the digital content management platform uses the resource quota collected from providing digital content circulation services on the main chain as its operational resources for managing digital content. This collected resource quota can be stored in a resource quota pool. Taking the periodic adjustment of the incentive mechanism as an example, at the end of each incentive cycle, the platform allocates a portion (such as a preset percentage) of the remaining resource quota on the main chain as a reward for less popular digital content, generating reward points corresponding to the preset percentage of remaining resource quota.

[0086] When the digital content management platform detects any interactive event corresponding to any digital content on the sidechain, a reward is sent to the user account that triggered the interactive event. The reward sent to the user account can be used to redeem benefits such as featured recommendations and discount coupons for digital content. By setting up an incentive mechanism for less popular digital content, users actively participate in interactions with this content, not only increasing its popularity and revitalizing low-popularity content, breaking the vicious cycle of "high circulation costs - low user participation - lack of content liquidity - difficulty in realizing content value - further increase in circulation costs" trapped in less popular digital content, but also providing users with interactive rewards, thereby attracting more users to participate and forming a virtuous cycle.

[0087] For example, multiple interactive behavior events include ordinary interactive behavior events and advanced interactive behavior events. Ordinary interactive behavior events include liking events triggered by liking less popular digital content, sharing events triggered by sharing, and saving events triggered by saving. Advanced interactive behavior events include content value-added behavior events, platform building behavior events, and equity staking behavior events.

[0088] For example, advanced interactive behavior events are shown in Table 1: Table 1. List of Advanced Interactive Behavior Events

[0089] Different interactive events correspond to different reward points. For example, each ordinary interactive event is worth 1 point, while each advanced interactive event is worth 2 points.

[0090] In some implementations, the digital content management platform assigns a corresponding incentive weight to each interactive event; the higher the incentive weight, the higher the reward points for the interactive event. To achieve optimal or near-optimal incentive effects, the digital content management platform periodically measures the incentive effects of interactive events and adjusts the incentive weights for each interactive event accordingly. For example, the digital content management platform measures the incentive effects through an interaction module and uses an incentive regulator to provide feedback adjustments based on the incentive weights for each interactive event, thereby achieving adaptive adjustment of the reward points for each interactive event.

[0091] Taking two adjacent incentive cycles as an example, namely the first incentive cycle and the second incentive cycle, the first incentive cycle is the next incentive cycle after the second incentive cycle, and the incentive cycle is longer than the above-mentioned heat cycle.

[0092] For any piece of "cold" digital content, the digital content management platform determines the growth rate of a metric parameter for that content in the first incentive period compared to the second incentive period (the previous incentive period). A higher growth rate indicates a better incentive effect for the interactive event; a lower growth rate indicates a worse incentive effect. The digital content management platform adjusts the incentive weight corresponding to the interactive event based on the growth rate of the digital content's popularity between adjacent incentive periods. The incentive weight corresponding to the interactive event is positively correlated with the size of the reward for that interactive event.

[0093] The measurement parameters are used to measure the incentive effect of the interactive behavior event. For example, the measurement parameter for the above-mentioned ordinary interactive behavior event could be the growth rate of digital content popularity. The measurement parameter for the above-mentioned content value-added event could be the growth rate of digital content content value. The measurement parameter for the above-mentioned platform construction event could be the growth rate of the product of the number of platform users and the retention time of digital content. The measurement parameter for the above-mentioned equity staking event could be the growth rate of the amount of equity staked in digital content.

[0094] For example, the digital content management platform can adjust the incentive weight corresponding to any behavioral event using formula (11): New weight = Old weight (1+growth rate) (Regulatory factor) Smoothing factor / total weight formula (11) Among them, the new weight represents the incentive weight corresponding to the behavior event after adjustment; the old weight represents the incentive weight corresponding to the behavior event before adjustment; the growth rate represents the growth rate of the measurement parameter corresponding to the behavior event; the adjustment factor is used to control the degree of influence of the growth rate on the incentive weight, which can be set according to the actual application scenario; the smoothing factor is used to avoid the generation of extreme values, which can be set according to the actual application scenario; and the total weight represents the sum of the incentive weights corresponding to each interactive behavior event.

[0095] By periodically adjusting the incentive weights corresponding to various interactive behavioral events, the reward points corresponding to these events can be adjusted, thereby achieving adaptive adjustment of incentive points to achieve optimal or near-optimal incentive effects.

[0096] The digital content management method provided in this application addresses the serious imbalance in cost control for digital content circulation and the lack of differentiated storage and processing for both hot and cold digital content. It offers a blockchain-based dynamic dual-track digital content system architecture, optimizing processing speed and management methods by introducing an adaptive migration mechanism for hot and cold digital content and a cross-chain incentive mechanism. For example, the overall implementation of this digital content management platform system architecture is as follows: Figure 4 As shown, the innovation of this digital content management solution lies mainly in: First, a dual-chain collaborative management mechanism is provided, which classifies digital content according to its popularity and stores and processes cold and hot digital content differently to improve the processing efficiency of digital content-related businesses and optimize resource allocation.

[0097] Secondly, it provides a dynamic circulation cost setting strategy that comprehensively considers the content value and popularity of digital content, as well as the different basic costs of the main chain and the cold chain. This effectively solves the problem of excessively high circulation costs for low-popularity digital content, and achieves real-time enhancement of circulation costs for popular digital content and reduction of circulation costs for low-popularity digital content. Furthermore, a liquidity incentive scheme across digital content is provided. This scheme designs a liquidity incentive scheme across digital content, constructs a resource quota pool, achieves resource quota complementarity between hot and cold digital content, increases the overall popularity of digital content, enables low-popularity digital content to receive incentives, and breaks the vicious cycle of digital content going from low popularity to low value. Finally, taking into account the changing popularity of digital content, a dynamic digital content popularity measurement mechanism was established, and the storage, processing, and migration of digital content were periodically adjusted according to the popularity of digital content.

[0098] To implement the digital content management method provided in this application embodiment, this application embodiment also provides a digital content management device, applied to a blockchain-based digital content management platform, wherein the blockchain includes a main chain and side chains; as shown Figure 5 As shown, the device includes: The determining module 51 is used to determine the popularity index of digital content in the first period, wherein the popularity index is positively correlated with the popularity of digital content; The popularity prediction module 52 is used to predict the popularity of the digital content in a second period based on the popularity index of the digital content in the first period, wherein the second period is the next popularity period after the first period. The control module 53 is configured to migrate the digital content from the main chain to the side chain if the popularity of the digital content in the second period is less than a preset popularity threshold, and if the popularity of the digital content in the second period is greater than or equal to the preset popularity threshold, and if the digital content is deployed on the side chain, then migrate the digital content from the side chain to the main chain.

[0099] In some optional implementations, the determining module 51 is specifically used for: The system acquires multi-dimensional information about digital content during the first period. This multi-dimensional information includes at least two of the following: access behavior information, interaction behavior information, circulation information, content category association information, and popularity decay information. The access behavior information represents the access popularity of digital content related to access behavior. The interaction behavior information represents the interaction popularity of digital content related to interaction behavior. The circulation information represents the circulation popularity of digital content related to circulation behavior. The content category association information represents the content category popularity of the content to which the digital content belongs. The popularity decay information represents the degree of decline in the popularity of digital content over time. Based on the multi-dimensional information of the digital content in the first period, the popularity index of the digital content in the first period is determined.

[0100] In some alternative implementations, the control module 53 is further configured to: Receive the digital content uploaded by the user client; Determine the initial popularity of the digital content; Based on the initial popularity of the digital content, the digital content will be deployed on the main chain or side chain.

[0101] In some alternative implementations, the control module 53 is specifically used for: Determine the average popularity and popularity distribution entropy corresponding to the content category to which the digital content belongs; wherein, the popularity distribution entropy reflects the popularity dispersion corresponding to the content category to which the digital content belongs; The initial popularity value of the digital content is determined based on the average popularity of the content category to which the digital content belongs and the popularity distribution entropy.

[0102] In some alternative implementations, the control module 53 is further configured to: Based on the remaining resource quota corresponding to the main chain, rewards are set for multiple interactive behavior events for the digital content of the side chain; If any interactive event corresponding to any digital content on the sidechain is detected, a reward is issued to the user account that triggered the interactive event.

[0103] In some alternative implementations, the control module 53 is further configured to: For each interactive behavior event, determine the growth rate of a measurement parameter corresponding to the interactive behavior event in adjacent incentive cycles, wherein the measurement parameter is used to measure the incentive effect of the corresponding interactive behavior event. The incentive weights corresponding to the interactive behavior events are adjusted based on the growth rate of the measured parameters, wherein the incentive weights are positively correlated with the reward size of the interactive behavior events.

[0104] In some alternative implementations, the control module 53 is further configured to: The circulation cost of the digital content in the second period is set based on its popularity during the second period.

[0105] In some alternative implementations, the control module 53 is further configured to: Determine the average duration during which the overall popularity change for each content category reaches a preset proportion; Based on the average duration, set the popularity cycle for one or more digital content items in the corresponding content type.

[0106] It should be noted that the digital content management device provided in this application embodiment is only illustrated by the above-described division of program modules when performing digital content management. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the digital content management device and digital content management method provided in this application embodiment belong to the same concept, and their specific implementation process can be found in the method embodiment, which will not be repeated here.

[0107] This application embodiment also provides a digital content management platform, the platform being implemented based on blockchain, the blockchain including a main chain and side chains; the digital content management platform is used for: Determine the popularity index of digital content in the first period, wherein the popularity index is positively correlated with the popularity of digital content; Based on the popularity index of the digital content in the first period, the popularity of the digital content in the second period is predicted, wherein the second period is the next popularity period after the first period. If the popularity of the digital content in the second period is less than a preset popularity threshold, then if the digital content is deployed on the main chain, the digital content will be migrated from the main chain to the side chain. If the popularity of the digital content in the second period is greater than or equal to the preset popularity threshold, then if the digital content is deployed on a sidechain, the digital content will be migrated from the sidechain to the main chain.

[0108] In some alternative implementations, the digital content management platform is specifically used for: The system acquires multi-dimensional information about digital content during the first period. This multi-dimensional information includes at least two of the following: access behavior information, interaction behavior information, circulation information, content category association information, and popularity decay information. The access behavior information represents the access popularity of digital content related to access behavior. The interaction behavior information represents the interaction popularity of digital content related to interaction behavior. The circulation information represents the circulation popularity of digital content related to circulation behavior. The content category association information represents the content category popularity of the content to which the digital content belongs. The popularity decay information represents the degree of decline in the popularity of digital content over time. Based on the multi-dimensional information of the digital content in the first period, the popularity index of the digital content in the first period is determined.

[0109] In some alternative implementations, the digital content management platform is further used for: Receive the digital content uploaded by the user client; Determine the initial popularity of the digital content; Based on the initial popularity of the digital content, the digital content will be deployed on the main chain or side chain.

[0110] In some alternative implementations, the digital content management platform is specifically used for: Determine the average popularity and popularity distribution entropy corresponding to the content category to which the digital content belongs; wherein, the popularity distribution entropy reflects the popularity dispersion corresponding to the content category to which the digital content belongs; The initial popularity value of the digital content is determined based on the average popularity of the content category to which the digital content belongs and the popularity distribution entropy.

[0111] In some alternative implementations, the digital content management platform is further used for: Based on the remaining resource quota corresponding to the main chain, rewards are set for multiple interactive behavior events for the digital content of the side chain; If any interactive event corresponding to any digital content on the sidechain is detected, a reward is issued to the user account that triggered the interactive event.

[0112] In some alternative implementations, the digital content management platform is further used for: For each interactive behavior event, determine the growth rate of a measurement parameter corresponding to the interactive behavior event in adjacent incentive cycles, wherein the measurement parameter is used to measure the incentive effect of the corresponding interactive behavior event. The incentive weights corresponding to the interactive behavior events are adjusted based on the growth rate of the measured parameters, wherein the incentive weights are positively correlated with the reward size of the interactive behavior events.

[0113] In some alternative implementations, the digital content management platform is further used for: The circulation cost of the digital content in the second period is set based on its popularity during the second period.

[0114] In some alternative implementations, the digital content management platform is further used for: Determine the average duration during which the overall popularity change for each content category reaches a preset proportion; Based on the average duration, set the popularity cycle for one or more digital content items in the corresponding content type.

[0115] In an exemplary embodiment, this application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a memory that stores a computer program, which can be executed by a processor of a distributed storage system to complete the steps described in the above method. The computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM.

[0116] In an exemplary embodiment, this application also provides a computer program product, including a computer program that can be executed by a processor of a distributed storage system to perform the steps described above.

[0117] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of this application are included within the scope of protection of this application.

Claims

1. A digital content management method, characterized in that, A method applicable to a blockchain-based digital content management platform, wherein the blockchain includes a main chain and side chains; the method includes: Determine the popularity index of digital content in the first period, wherein the popularity index is positively correlated with the popularity of digital content; Based on the popularity index of the digital content in the first period, the popularity of the digital content in the second period is predicted, wherein the second period is the next popularity period after the first period. If the popularity of the digital content in the second period is less than a preset popularity threshold, then if the digital content is deployed on the main chain, the digital content will be migrated from the main chain to the side chain. If the popularity of the digital content in the second period is greater than or equal to the preset popularity threshold, then if the digital content is deployed on a sidechain, the digital content will be migrated from the sidechain to the main chain.

2. The method according to claim 1, characterized in that, The determination of the popularity index of digital content in the first period includes: The system acquires multi-dimensional information about digital content during the first period. This multi-dimensional information includes at least two of the following: access behavior information, interaction behavior information, circulation information, content category association information, and popularity decay information. The access behavior information represents the access popularity of digital content related to access behavior. The interaction behavior information represents the interaction popularity of digital content related to interaction behavior. The circulation information represents the circulation popularity of digital content related to circulation behavior. The content category association information represents the content category popularity of the content to which the digital content belongs. The popularity decay information represents the degree of decline in the popularity of digital content over time. Based on the multi-dimensional information of the digital content in the first period, the popularity index of the digital content in the first period is determined.

3. The method according to claim 1, characterized in that, The method further includes: Receive the digital content uploaded by the user client; Determine the initial popularity of the digital content; Based on the initial popularity of the digital content, the digital content will be deployed on the main chain or side chain.

4. The method according to claim 3, characterized in that, Determining the initial popularity of the digital content includes: Determine the average popularity and popularity distribution entropy corresponding to the content category to which the digital content belongs; wherein, the popularity distribution entropy reflects the popularity dispersion corresponding to the content category to which the digital content belongs; The initial popularity value of the digital content is determined based on the average popularity of the content category to which the digital content belongs and the popularity distribution entropy.

5. The method according to claim 1, characterized in that, The method further includes: Based on the remaining resource quota corresponding to the main chain, rewards are set for multiple interactive behavior events for the digital content of the side chain; If any interactive event corresponding to any digital content on the sidechain is detected, a reward is issued to the user account that triggered the interactive event.

6. The method according to claim 5, characterized in that, The method further includes: For each interactive behavior event, determine the growth rate of a measurement parameter corresponding to the interactive behavior event in adjacent incentive cycles, wherein the measurement parameter is used to measure the incentive effect of the corresponding interactive behavior event. The incentive weights corresponding to the interactive behavior events are adjusted based on the growth rate of the measured parameters, wherein the incentive weights are positively correlated with the reward size of the interactive behavior events.

7. The method according to claim 1, characterized in that, After predicting the popularity of the digital content in the second period, the method further includes: The circulation cost of the digital content in the second period is set based on its popularity during the second period.

8. The method according to claim 1, characterized in that, The method further includes: Determine the average duration during which the overall popularity change for each content category reaches a preset proportion; Based on the average duration, set the popularity cycle for one or more digital content items in the corresponding content type.

9. A digital content management device, characterized in that, An application is made to a blockchain-based digital content management platform, wherein the blockchain includes a main chain and side chains; the device includes: A determination module is used to determine the popularity index of digital content in the first period, wherein the popularity index is positively correlated with the popularity of digital content; A popularity prediction module is used to predict the popularity of the digital content in a second period based on the popularity index of the digital content in the first period, wherein the second period is the next popularity period after the first period. The control module is configured to migrate the digital content from the main chain to the side chain if the popularity of the digital content in the second period is less than a preset popularity threshold, provided that the digital content is deployed on the main chain; and to migrate the digital content from the side chain to the main chain if the popularity of the digital content in the second period is greater than or equal to the preset popularity threshold, provided that the digital content is deployed on the side chain.

10. A digital content management platform, characterized in that, The platform is implemented based on blockchain, which includes a main chain and side chains; the digital content management platform is used to perform the steps of the method according to any one of claims 1 to 8.

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