A digital creative IP content whole life cycle management and intelligent operation method

Abstract

The application provides a digital creative IP content full life cycle management and intelligent operation method, relates to the field of near field communication and artificial intelligence technology, and the method comprises the following steps: receiving original content and attaching metadata labels, adaptively transcoding and packaging standard data packets through a multimedia processing engine; performing three-layer funnel type intelligent auditing; adopting a Git-like repository for multi-version concurrent management; distributing to edge nodes according to timing or condition strategies; collecting user interaction behavior data in real time to generate feature vectors; and optimizing distribution strategies based on a hybrid recommendation algorithm. The corresponding system comprises a CMS module, a multimedia processing engine, an intelligent auditing unit, a version repository, a distribution module, a data collection platform, an operation module and an API gateway. The application realizes full life cycle closed loop management of IP content, shortens the period from production to online of IP content, and improves operation efficiency and user experience.

Description

Technical Field

[0001] This invention relates to the fields of near-field communication and artificial intelligence, and in particular to a method for the full lifecycle management and intelligent operation of digital cultural and creative IP content. Background Technology

[0002] In recent years, with the convergence of Near Field Communication (NFC) and Artificial Intelligence (AI) technologies, a new type of digital cultural and creative product has emerged: an interactive system combining NFC-triggered physical cultural and creative cards with online AI agents. A typical application scenario for this type of product is as follows: users can touch a cultural and creative card with a built-in NFC chip using an NFC-enabled mobile terminal (such as a smartphone) to activate the corresponding AI agent, engage in multi-round dialogues with the agent, trigger AR interactions, and complete learning tasks. This type of product deeply integrates the attributes of physical collectibles with digital interactive experiences, and has broad market prospects.

[0003] However, in the process of realizing the large-scale commercial operation of cultural and creative intelligent entities, existing content management systems have the following technical shortcomings in supporting the full lifecycle management of their IP content: 1. The IP content of cultural and creative intelligent entities involves multiple modalities, including character design texts, multi-turn dialogue materials, growth event scripts, AR interactive models, and high-definition promotional image sets. Traditional content management systems (CMS) typically serve only as static storage and publishing tools. The various stages of content production, review, storage, distribution, and operational feedback are independent, lacking unified end-to-end closed-loop management. This results in long content release cycles, low cross-departmental collaboration efficiency, and severe data silo problems.

[0004] 2. Existing CMS systems cannot effectively utilize the massive amounts of real-time interaction data between users and AI agents. Content distribution strategies often rely on the experience and judgment of operations personnel, lacking a data-driven dynamic optimization mechanism. This results in a highly homogenized user experience, an inability to achieve personalized interactive experiences, and negatively impacts user retention and product lifecycle value.

[0005] 3. Cultural and creative IP content includes various formats such as text, images, audio, video, and 3D AR models. Existing systems lack sufficient transcoding and adaptation capabilities when processing such heterogeneous content, making it difficult to automatically optimize content formats based on the performance parameters of different terminals. Furthermore, traditional manual review methods are costly and inefficient for the compliance review of AI-generated content (AIGC) and user-generated content (UGC).

[0006] 4. Multiple versions of the same IP intelligent agent often exist, requiring differentiated content distribution to different user groups, time windows, and geographical locations. Existing systems mostly adopt a "full replacement" or "single version" release model, which is difficult to support complex operational activities.

[0007] To address this, a method for full lifecycle management and intelligent operation of digital cultural and creative IP content is proposed. Summary of the Invention

[0008] In view of this, the present invention provides a method for full lifecycle management and intelligent operation of digital cultural and creative IP content, in order to solve or alleviate one of the technical problems existing in the prior art, and at least provide a beneficial option.

[0009] The technical solution of this invention is implemented as follows: a method for full lifecycle management and intelligent operation of digital cultural and creative IP content, comprising the following steps: Step S1: Receive the raw digital content uploaded by the IP content provider, and attach metadata tags to the raw digital content. The metadata tags include content type, associated IP identifier, applicable scenario and version tag. Through the multimedia processing engine, adaptively transcode and encapsulate the raw digital content according to the performance parameters of the target delivery terminal to generate standardized IP content data packets. Step S2: Perform a three-layer funnel-style intelligent audit on the IP content data packets: First layer: Initial machine screening based on a sensitive word database and image hashing algorithm; The second layer: calls a pre-trained multimodal review model for deep review; The third layer: Through the rules engine, decisions are made based on preset scenario-based compliance strategies, and approved content enters the release pool; Step S3: Using distributed object storage and a Git-like version repository, multi-version concurrent management is performed on the approved IP content data packets, supporting the creation, storage, and atomic switching of trunk branches and feature branches; Step S4: According to the configured timed or conditional distribution strategy, push the corresponding version of the IP content data packet to the edge CDN node at the specified time or under the specified conditions so that users can call it through the cultural and creative smart body triggered by NFC. Step S5: When the user interacts with the cultural and creative intelligent agent, the user behavior log is collected in real time through data tracking points, and the data is cleaned and feature aggregated by the real-time stream processing platform to generate the user and intelligent agent interaction feature vector. Step S6: Based on the user-agent interaction feature vector, run the hybrid recommendation algorithm to generate content recommendation results or distribution strategy adjustment parameters, and write them back to the distribution strategy database to optimize the content distribution strategy in step S4.

[0010] A further preferred embodiment: the adaptive transcoding in step S1 includes: For image content, transcode to WebP or AVIF format and generate multiple resolution versions; For video content, the slices are in HLS or DASH streaming media format; For AR model content, perform mesh reduction and texture compression to generate GLTF or GLB format; The transcoded content is then bound to the metadata tag and the associated NFC card ID, and encapsulated into the IP content data packet.

[0011] Further preferred: The multimodal review model in step S2 is built based on CNN and LSTM, and is used to analyze the correlation between text and images and detect potential biases in AI-generated content; The preset strategies of the rule engine include: disabling or enabling dialogue trees with specific content tags based on user age group or scene tags.

[0012] Further preferred: The version concurrency control in step S3 supports: There are multiple version branches at the same time, including the trunk branch and at least one feature branch; Switching between different versions is achieved through atomic operations, and the switching process does not affect ongoing user sessions.

[0013] A further preferred embodiment: the timing or conditional distribution strategy in step S4 includes: A time-window-based strategy is used to limit content replacement during holiday events or exhibitions; Strategies based on user profiles or geolocation are used to achieve differentiated content distribution for different user groups or exhibition areas; The distribution strategy is configured through a visual interface and is triggered for execution by the Quartz scheduling framework.

[0014] Further preferred: The user behavior logs collected in step S5 include: user ID, card ID, session round, dialogue intent tag, sentiment score, AR interaction duration, and content preference tag; the real-time stream processing platform adopts a Kafka and Flink combined architecture for data cleaning and feature aggregation.

[0015] Further preferred: The hybrid recommendation algorithm in step S6 includes: a user-based collaborative filtering algorithm, used to find other user groups with similar interests to the current user and recommend content they prefer; and a context-based Bandit algorithm, used to dynamically decide the content strategy for the next round of interaction by combining the sentiment of the current session and the interaction round.

[0016] Further preferred embodiments of the method include: providing a unified interface through an API gateway for interfacing with front-end applications, AR engines, and third-party systems; and linking with external AIGC content production tools through Webhook to achieve an automated closed loop from content generation to review and distribution.

[0017] In addition, the present invention also provides a digital cultural and creative IP content full lifecycle management and intelligent operation system, used to execute digital cultural and creative IP content full lifecycle management and intelligent operation methods, the system comprising: The CMS content management module is used for multi-source content access, metadata annotation, and version management. Multimedia processing engine for adaptive transcoding and encapsulation; The intelligent review unit includes a machine initial screening module, a multimodal review model, and a rule engine; Version control repository, used for Git-like concurrent version storage and switching; The intelligent distribution module includes a timed distribution unit and a conditional distribution unit; The data acquisition and processing platform includes a data tracking SDK and a real-time stream processing platform; The intelligent operations module is used to run the hybrid recommendation algorithm and update the distribution strategy; API gateway and Webhook interface for external integration.

[0018] A further preferred embodiment: a data feedback closed loop is formed between the intelligent operation module and the intelligent distribution module. After user behavior data is processed by the data collection and processing platform, it is input into the recommendation algorithm of the intelligent operation module. The output of the recommendation algorithm is used to dynamically adjust the distribution strategy of the intelligent distribution module. The change in the distribution strategy further affects the user's subsequent interaction experience.

[0019] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions: 1. This invention achieves closed-loop management of the entire lifecycle of IP content by constructing a complete technical chain from content access, intelligent transcoding, three-layer funnel review, version control, intelligent distribution to data feedback optimization. Compared to the fragmented model of traditional CMS systems, this invention automates the process from content production to operational feedback, shortening the cycle from IP content production to online deployment.

[0020] 2. This invention optimizes content distribution strategies by collecting real-time interaction data between users and AI agents and employing a hybrid recommendation model based on collaborative filtering and the context-aware Bandit algorithm. This data feedback closed-loop mechanism allows the AI ​​agent interaction content received by each user to be adapted in real-time based on their historical behavior and the current session context, thereby improving the average daily interaction time and the following month's retention rate.

[0021] 3. The three-layer funnel-shaped intelligent review mechanism proposed in this invention upgrades the traditional purely manual review mode to a human-machine collaborative intelligent review mode. The multimodal review model, based on CNN and LSTM networks, can simultaneously analyze the correlation between text and images as well as the potential biases of AI-generated content, reducing the workload of manual review.

[0022] 4. This invention adopts a Git-like version repository management mechanism, which supports concurrent storage and atomic switching of the main branch and multiple feature branches. This allows operators to develop and test multiple activity versions in parallel without affecting the stability of the main content, thereby improving operational flexibility and response speed.

[0023] 5. The multimedia processing engine of the present invention can automatically perform adaptive transcoding and encapsulation of images, videos, and AR 3D models according to the performance parameters of the target delivery terminal, ensuring that the content can obtain the optimal loading speed and presentation effect on different terminals, reducing user waiting time and improving the smoothness of interaction.

[0024] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system structure diagram of the present invention. Detailed Implementation

[0027] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0028] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] like Figures 1-2 As shown, this embodiment of the invention provides a method for the full lifecycle management and intelligent operation of digital cultural and creative IP content. This method achieves efficient operation of IP content by constructing a complete closed loop from content access, intelligent processing, review, version management to intelligent distribution and data feedback. Specifically, it includes the following steps: Step S1: Receive raw digital content uploaded by IP content providers. IP content providers include artists, copyright holders, and operators. Upload methods support API interfaces or web consoles. The raw digital content takes various forms, including character design text, multi-turn dialogue materials, AR interactive models, and high-definition promotional image sets. Metadata tags are automatically added to each piece of raw digital content. These metadata tags specifically include content type, associated IP identifier, applicable scenario, and version tag. Content type can be categorized as text, image, video, or AR model; the associated IP identifier corresponds to the unique ID of the intelligent agent; the applicable scenario indicates whether the content is used for an art exhibition, online community, or pop-up store; and the version tag distinguishes between the regular version and the event version. Subsequently, the multimedia processing engine is invoked to adaptively transcode and encapsulate the raw digital content based on the performance parameters of the target delivery terminal. Target delivery terminals include WeChat mini-programs, iOS or Android applications, AR glasses, etc., and performance parameters involve screen resolution, network bandwidth, and supported media formats. After processing, a standardized IP content data package with a unified format and direct callability is generated.

[0030] Step S2: Perform the following three-layer funnel-style intelligent review on the IP content data packets generated in the previous step to ensure that the content is legal, compliant, and meets the requirements of the scenario.

[0031] The first layer is machine-based initial screening. Based on a built-in sensitive word database and image hashing algorithm, the system quickly filters out content that clearly contains pornography, violence, political sensitivities, or other prohibited material. Once a sensitive word or blacklist hash value is matched, the image or text is marked as blocked and returned to the uploader.

[0032] The second layer is multimodal deep moderation. The system calls a pre-trained multimodal moderation model, which is built on CNN and LSTM networks and can jointly analyze the correlation between text and images to detect potential biases in AI-generated content.

[0033] The third layer is the rules engine adjudication. The rules engine makes the final decision based on preset scenario-based compliance policies. Examples of preset policies include: automatically disabling dialogue trees tagged with "romance" or "violence" for underage users; and allowing a certain degree of artistic nudity for art exhibitions. Approved content enters the publishing pool, while rejected content is returned with suggested modifications.

[0034] Step S3: Employ a distributed object storage system combined with a Git-like version control mechanism to manage multiple versions of approved IP content data packets concurrently. MinIO can be used for the distributed object storage. Version management specifically supports the creation of a main branch and multiple feature branches. The main branch stores stable, official versions, while feature branches, such as "Spring Festival Limited Edition Branch" or "Museum Collaboration Branch," are isolated and developed in parallel. The system supports atomic switching operations, replacing the entire version content at once to avoid intermediate states. The switching process does not affect ongoing user sessions: the user's current interaction still uses the old version, automatically switching to the new version only on the next request.

[0035] Step S4: Operations personnel can configure timed or conditional distribution strategies through a visual interface. Based on the configuration, the system will push the corresponding version of the IP content data packet to the edge CDN node at the specified time or under specified conditions to respond to content access requests triggered by users via NFC-enabled cultural and creative smart devices. Users can then activate the mini-program and access the content by tapping the card with their mobile phone.

[0036] Distribution strategies include the following types: Time-window based strategies: For example, automatically replacing the content with the "May Day Special" version between May 1st and May 5th, 2025. User profile or geolocation based strategies: For example, distributing a content branch containing the Palace Museum IP to users in Beijing, and distributing a content branch containing the Yu Garden IP to users in Shanghai. All distribution tasks are automatically triggered in the background by the Quartz scheduling framework, requiring no manual intervention.

[0037] Step S5: When a user wakes up the cultural and creative intelligent agent via NFC and interacts with it through multiple rounds of dialogue, AR interaction, and task completion, the front-end tracking SDK collects user behavior logs in real time. The logs include user ID, card ID, conversation rounds, dialogue intent tags, sentiment score, AR interaction duration, and content preference tags. The dialogue intent tags are assigned in real time by the natural language understanding module, the sentiment score is categorized as positive, neutral, or negative, and the content preference tags include, for example, "historical," "funny," or "lyrical." The collected raw log data undergoes data cleaning and feature aggregation on a real-time stream processing platform. The real-time stream processing platform uses a combination of Kafka message queues and the Flink computing engine. Data cleaning removes invalid logs and fills in missing fields, while feature aggregation calculates frequency, mean, and other metrics based on user ID, card ID, and time window, ultimately generating a structured user-agent interaction feature vector for subsequent recommendation algorithms.

[0038] Step S6: Based on the user-agent interaction feature vector generated in step S5, run the hybrid recommendation algorithm. This algorithm consists of two complementary parts.

[0039] The first part is a user-based collaborative filtering algorithm, which is used to find other user groups with similar interests to the current user and recommend content that similar users prefer, such as a corpus of obscure historical stories.

[0040] The second part is the context-based Bandit algorithm, which dynamically determines the content strategy for the next round of content push based on the current conversation's sentiment and interaction rounds. For example, when a user's low mood is detected, the algorithm switches to an encouraging dialogue; or a new topic is started in the third round of dialogue.

[0041] The algorithm's output of content recommendation results or distribution strategy adjustment parameters is written back to the distribution strategy database. Based on this, the system dynamically optimizes the content distribution strategy in step S4, such as increasing the distribution weight of a certain type of corpus within a specific user group. This forms a complete data-driven closed loop of "interaction, collection, recommendation, distribution, and re-interaction."

[0042] In this embodiment, the adaptive transcoding in step S1 specifically includes the following operations.

[0043] For image content, it is transcoded to WebP or AVIF format and generated in multiple resolution versions, including thumbnails, high-resolution images, and original images, to adapt to different screen sizes and network conditions.

[0044] For video content, the segments are in HLS or DASH streaming media format. HLS stands for HTTP Live Streaming, and DASH stands for Dynamic Adaptive Streaming over HTTP, to support adaptive bitrate playback.

[0045] For AR model content, mesh reduction and texture compression are performed. Mesh reduction refers to reducing the number of polygons, while texture compression, for example, compressing a 4K texture to 1K, ultimately generates GLTF or GLB format to ensure smooth rendering on mobile devices.

[0046] Finally, all the transcoded content is bound to metadata tags and associated NFC card IDs, and encapsulated together into a standardized IP content data package for easy subsequent version management and distribution.

[0047] Specifically, the multimodal moderation model in step S2 is built based on CNN and LSTM to analyze the correlation between text and images and detect potential biases in AI-generated content. The rule engine's preset strategies include disabling or enabling dialogue trees with specific content tags based on user age group or scene labels. For example, for users under 12, dialogue branches tagged with "fear" or "sadness" are automatically filtered.

[0048] Specifically, the version concurrency control in step S3 supports the following features.

[0049] There are multiple version branches, including the trunk branch and at least one feature branch. Feature branches can be developed and tested independently, and then merged into the trunk branch after completion.

[0050] Switching between different versions is achieved through atomic operations, and the switching process does not affect ongoing user sessions. The system employs a double-buffering mechanism: once a new version is loaded, the next user request is automatically routed to the new version.

[0051] Specifically, the timing or conditional distribution strategy in step S4 includes the following.

[0052] A time-window-based strategy is used to limit content replacement during festivals or exhibitions. For example, during a museum special exhibition, a limited-edition audio guide can only be accessed when triggered via NFC within the venue.

[0053] Strategies based on user profiles or geolocation are used to differentiate content distribution to different user groups or exhibition areas. For example, users in first-tier cities are given priority access to 4K high-definition content, while users in remote areas are given priority access to compressed versions.

[0054] The distribution strategy is configured through a visual interface, supports drag-and-drop calendars and condition combination logic, and is triggered by the Quartz scheduling framework.

[0055] Specifically, the user behavior logs collected in step S5 include user ID, card ID, session rounds, dialogue intent tags, sentiment scores, AR interaction duration, and content preference tags. The real-time stream processing platform uses a combination of Kafka and Flink architecture for data cleaning and feature aggregation.

[0056] Specifically, the hybrid recommendation algorithm in step S6 includes a user-based collaborative filtering algorithm and a context-based Bandit algorithm. The user-based collaborative filtering algorithm is used to find other user groups with similar interests to the current user and recommend content they prefer. The context-based Bandit algorithm is used to dynamically decide the content strategy for the next round of interaction by combining the sentiment of the current session and the interaction round.

[0057] Specifically, the method also includes providing a unified RESTful interface through an API gateway for integration with front-end applications, AR engines, and third-party systems. Front-end applications include mini-programs or apps, and third-party systems include CRM systems or data analytics systems. The method also includes linking with external AIGC content production tools via Webhooks to achieve an automated closed loop from content generation to review and distribution. External AIGC content production tools include, for example, Midjourney or ChatGPT. The specific process is as follows: users submit customized requests, AIGC tools generate content, which is pushed to this system via Webhooks. The system automatically completes the review, and after approval, it automatically distributes the content.

[0058] In addition, the present invention also provides a digital cultural and creative IP content full lifecycle management and intelligent operation system, the system including the following modules: The CMS content management module is responsible for multi-source content access, metadata annotation, and version management. Multi-source content access supports API, web upload, and FTP. Metadata annotation can be automatically extracted or manually applied. Version management covers operations such as creating, modifying, and deleting versions.

[0059] Multimedia processing engine: Responsible for adaptive transcoding and encapsulation, specifically including format conversion, compression and slicing of multimedia content such as images, videos and AR models, and outputting standardized IP content data packets.

[0060] The intelligent review unit comprises three sub-modules. The first sub-module is a machine-based initial screening module, implemented using a sensitive word database and image hashing algorithms. The second sub-module is a multimodal review model, based on CNN and LSTM networks. The third sub-module is a rules engine, used to execute scenario-based compliance policy decisions.

[0061] Version control repository: Implements Git-like concurrent version storage and switching, supports branch management, atomic commits, and version rollback.

[0062] The intelligent distribution module includes a timed distribution unit and a conditional distribution unit. The timed distribution unit uses the Quartz framework to trigger distribution within a time window. The conditional distribution unit triggers distribution based on conditions such as user profile, geographic location, or device type.

[0063] Data Acquisition and Processing Platform: This includes a data tracking SDK and a real-time stream processing platform. The data tracking SDK is embedded front-end acquisition code. The real-time stream processing platform uses a combined architecture of Kafka and Flink.

[0064] Intelligent Operations Module: Runs a hybrid recommendation algorithm, which includes collaborative filtering and contextual Bandit algorithm, and updates the distribution strategy database based on the calculation results.

[0065] API gateway and Webhook interface: used to provide a unified interface to the outside world and to link with external systems for event callback.

[0066] Specifically, a data feedback loop is formed between the intelligent operations module and the intelligent distribution module. User behavior data, after being processed by the data collection and processing platform, is input into the recommendation algorithm of the intelligent operations module. The output of the recommendation algorithm is used to dynamically adjust the distribution strategy of the intelligent distribution module, such as increasing the distribution weight of certain content in a certain group, or modifying the priority or traffic ratio in the timing rules. Changes in the distribution strategy further affect the subsequent user interaction experience, thus forming a positive cycle of continuous optimization.

[0067] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for full lifecycle management and intelligent operation of digital cultural and creative IP content, characterized in that, Includes the following steps: Step S1: Receive the raw digital content uploaded by the IP content provider and attach metadata tags to the raw digital content; The original digital content is adaptively transcoded and encapsulated based on the performance parameters of the target delivery terminal through a multimedia processing engine to generate standardized IP content data packets. Step S2: Perform intelligent auditing on the IP content data packets; Step S3: Using distributed object storage and a Git-like version repository, multi-version concurrent management is performed on the approved IP content data packets, supporting the creation, storage, and atomic switching of trunk branches and feature branches; Step S4: According to the configured timed or conditional distribution strategy, push the corresponding version of the IP content data packet to the edge CDN node at the specified time or under the specified conditions so that users can call it through the cultural and creative smart body triggered by NFC. Step S5: When the user interacts with the cultural and creative intelligent agent, the user behavior log is collected in real time through data tracking points, and the data is cleaned and feature aggregated by the real-time stream processing platform to generate the user and intelligent agent interaction feature vector. Step S6: Based on the user-agent interaction feature vector, run the hybrid recommendation algorithm to generate content recommendation results or distribution strategy adjustment parameters, and write them back to the distribution strategy database to optimize the content distribution strategy in step S4.

2. The method for full lifecycle management and intelligent operation of digital cultural and creative IP content according to claim 1, characterized in that, The adaptive transcoding in step S1 includes: For image content, transcode to WebP or AVIF format and generate multiple resolution versions; For video content, the slices are in HLS or DASH streaming media format; For AR model content, perform mesh reduction and texture compression to generate GLTF or GLB format; The transcoded content is then bound to the metadata tag and the associated NFC card ID, and encapsulated into the IP content data packet.

3. The method for full lifecycle management and intelligent operation of digital cultural and creative IP content according to claim 1, characterized in that, The multimodal review model in step S2 is built based on CNN and LSTM and is used to analyze the correlation between text and images and detect potential biases in AI-generated content. The preset strategies of the rule engine include: disabling or enabling dialogue trees with specific content tags based on user age group or scene tags.

4. The method for full lifecycle management and intelligent operation of digital cultural and creative IP content according to claim 1, characterized in that, Multi-version concurrency management support in step S3: There are multiple version branches at the same time, including the trunk branch and at least one feature branch; Switching between different versions is achieved through atomic operations, and the switching process does not affect ongoing user sessions.

5. The method for full lifecycle management and intelligent operation of digital cultural and creative IP content according to claim 1, characterized in that, The timing or conditional distribution strategy in step S4 includes: A time-window-based strategy is used to limit content replacement during holiday events or exhibitions; Strategies based on user profiles or geolocation are used to achieve differentiated content distribution for different user groups or exhibition areas; The distribution strategy is configured through a visual interface and is triggered for execution by the Quartz scheduling framework.

6. The method for full lifecycle management and intelligent operation of digital cultural and creative IP content according to claim 1, characterized in that, The user behavior logs collected in step S5 include: user ID, card ID, session round, dialogue intent tag, sentiment score, AR interaction duration, and content preference tag; the real-time stream processing platform adopts a Kafka and Flink combined architecture for data cleaning and feature aggregation.

7. The method for full lifecycle management and intelligent operation of digital cultural and creative IP content according to claim 1, characterized in that, The hybrid recommendation algorithm in step S6 includes: a user-based collaborative filtering algorithm, used to find other user groups with similar interests to the current user and recommend content they prefer; and a context-based Bandit algorithm, used to dynamically decide the content strategy for the next round of interaction by combining the sentiment of the current session and the interaction round.

8. The method for full lifecycle management and intelligent operation of digital cultural and creative IP content according to claim 1, characterized in that, The method also includes: providing a unified interface through an API gateway for connecting with front-end applications, AR engines, and third-party systems; and linking with external AIGC content production tools through Webhook to achieve an automated closed loop from content generation to review and distribution.

9. The method for full lifecycle management and intelligent operation of digital cultural and creative IP content according to claim 1, characterized in that, In step S2, the intelligent review adopts a three-layer funnel-style intelligent review, specifically including: The first layer involves machine screening based on a sensitive word database and image hashing algorithm. The second layer involves calling a pre-trained multimodal review model for deep review. The third layer uses a rules engine to make decisions based on preset scenario-based compliance strategies, and approved content enters the release pool.

10. A digital cultural and creative IP content full lifecycle management and intelligent operation system, applied to the digital cultural and creative IP content full lifecycle management and intelligent operation method according to any one of claims 1 to 9, characterized in that, The system includes: The CMS content management module is used for multi-source content access, metadata annotation, and version management. Multimedia processing engine for adaptive transcoding and encapsulation; The intelligent review unit includes a machine initial screening module, a multimodal review model, and a rule engine; Version control repository, used for Git-like concurrent version storage and switching; The intelligent distribution module includes a timed distribution unit and a conditional distribution unit; The data acquisition and processing platform includes a data tracking SDK and a real-time stream processing platform; The intelligent operations module is used to run the hybrid recommendation algorithm and update the distribution strategy; API gateway and Webhook interface for external integration.

11. The digital cultural and creative IP content full lifecycle management and intelligent operation system according to claim 10, characterized in that, A data feedback loop is formed between the intelligent operation module and the intelligent distribution module: After user behavior data is processed by the data collection and processing platform, it is input into the recommendation algorithm of the intelligent operation module. The output of the recommendation algorithm is used to dynamically adjust the distribution strategy of the intelligent distribution module. The change in the distribution strategy further affects the user's subsequent interaction experience.

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