Cultural transmission content auditing method

Abstract

The invention relates to the technical field of cultural transmission content auditing, and discloses a cultural transmission content auditing method, which comprises an auditing system and comprises the following operation steps: S1, a preparation stage: initializing and configuring a module; the method comprises the following steps: S101, dynamic strategy engine deployment: accessing a federal learning model, configuring a cross-regional data synchronization rule (for example, dividing data cooperative training groups according to cultural transmission coverage regions), initializing a dynamic risk feature library, importing features such as basic sensitive words and cultural taboo symbols, and associating'cultural sensitivity parameters' of a cultural dimension quantizer; according to the cultural transmission content auditing method, system sustainability is guaranteed through a process closed loop, auditing accuracy is improved through technology fusion, cultural transmission complexity is dynamically adapted and coped with, the requirement for compliance auditing of the cultural transmission content can be met, protection of cultural diversity and innovativeness can be considered, and the method is suitable for popularization and application. And the method is suitable for auditing challenges of emerging scenes such as short videos, cross-culture transmission and meta universe.

Description

Technical Field

[0001] This invention relates to the field of cultural dissemination content review technology, specifically a method for reviewing cultural dissemination content. Background Technology

[0002] Cultural diffusion refers to the process by which human culture radiates outward from its source or spreads from one social group to another. It can be divided into direct diffusion and indirect diffusion. The former usually involves cultured people directly spreading certain spiritual or material cultural content, such as new agricultural techniques and inventions, through caravans, armies, or other means. The latter exhibits a more complex cultural diffusion force, mainly referring to a kind of stimulating diffusion in which a social group borrows the principles of foreign cultural characteristics to carry out civilization creation activities.

[0003] There are two ways of cultural transmission: one is direct borrowing, which involves directly accepting and integrating foreign cultural elements or cultural groups; the other is indirect transmission, which involves a cultural element or cultural group being introduced into a region, prompting people there to think and thus creating a new culture. This phenomenon is called "stimulatory transmission".

[0004] Current methods for reviewing cultural content on the market combine traditional manual review with three core elements: precise algorithms (multimodal analysis), flexible mechanisms (dynamic strategies), and transparent processes (blockchain). These methods offer significant advantages in efficiency, accuracy, and adaptability. However, most of these methods only review text or single media formats, making it difficult to handle multimodal content such as images, text, audio, and video. Relying on manual annotation is costly and time-consuming, making it difficult to meet the real-time review needs of massive amounts of content. Machine learning models lag behind in identifying emerging sensitive content, posing a risk of misjudgment or omission. Furthermore, review rules are often outdated when policies, regulations, and public opinion change. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a method for reviewing cultural dissemination content. This method boasts advantages such as closed-loop process, technological integration, and dynamic adaptation. It solves the problems of most cultural dissemination content review methods, which only review text or single media formats and are unable to handle multimodal content such as text, images, audio, and video. Furthermore, the methods rely on manual annotation, which is costly and time-consuming, making it difficult to meet the real-time review needs of massive amounts of content. Machine learning models are also slow to identify emerging sensitive content, posing a risk of misjudgment or omission. Additionally, the review rules are often outdated when policies, regulations, and public opinion change.

[0007] (II) Technical Solution

[0008] To achieve the aforementioned goals of closed-loop process, technological integration, and dynamic adaptation, this invention provides the following technical solution: a method for reviewing cultural dissemination content, including a review system, with the following operation steps:

[0009] Step S1: Preparation Phase: Module Initialization and Configuration;

[0010] Step S101: Dynamic Strategy Engine Deployment: Connect to the federated learning model, configure cross-regional data synchronization rules (such as dividing data into collaborative training groups according to the cultural dissemination coverage area), initialize the dynamic risk feature library, import basic sensitive words, cultural taboo symbols and other features, associate the "cultural sensitivity parameter" of the cultural dimension quantifier, and set the parameter adjustment threshold (such as different configuration of sensitivity parameter weights for different cultural regions).

[0011] Step S102: Start the multimodal intelligent preprocessing module: Build a cross-modal feature fusion unit, define feature extraction rules for multimodal data such as text, image, and audio (e.g., use NLP models to extract semantics from text and CNNs to extract visual features from images), associate with the dynamic risk feature library, and configure feature matching trigger conditions (e.g., combine specific cultural metaphors with illegal images to trigger high-risk marking).

[0012] Step S103: Preparation of Cultural Dimension Quantifier and Dual-Channel Module: Load the four-dimensional evaluation model, set the admission standards of "historical correctness ≥ 80% and cultural respect index ≥ 60%", configure the human-machine collaboration rules of the dual-channel review module, divide the task boundaries of the AI ​​fast channel (meeting the four-dimensional standards) and the human deep channel (not meeting the standards), and build an innovative human-machine collaborative interaction interface (such as marking doubtful points in the initial AI review, which can be directly corrected and fed back to the model during human review).

[0013] Step S2: Review and Execution Phase: Content Flow and Processing;

[0014] Step S201: Content Access and Preprocessing (Multimodal Intelligent Preprocessing Module): Collect cultural dissemination content (text, video, images, etc.), call the cross-modal feature fusion unit, extract multi-dimensional features, match them with the dynamic risk feature library in real time, mark potential risk features, generate preliminary "feature-risk level" analysis results, and synchronize them to the dynamic strategy engine;

[0015] Step S202: Cultural Dimension Quantification and Strategy Adaptation (Dynamic Strategy Engine + Cultural Dimension Quantifier): The dynamic strategy engine receives the preprocessing results, calls the cultural dimension quantifier, calculates the content's "historical correctness, cultural respect index" and other indicators based on the four-dimensional evaluation model, combines the cross-regional cultural data output by the federated learning model (such as compliance cases of cultural dissemination in different regions), adjusts the review strategy (such as strengthening the verification of the cultural respect index for content involving specific cultural regions), and outputs the "AI channel / human channel" decision instruction;

[0016] Step S203: Dual-channel audit execution (dual-channel audit flow module):

[0017] AI Fast Track: If the content meets the standards of the four-dimensional evaluation model, it will directly enter the blockchain evidence storage and traceability module and execute "automatic on-chain evidence storage" (recording content characteristics, review results, strategy parameters, etc.);

[0018] Human Deep Review Channel: If the standard is not met, a human deep review process is triggered. The human review team reviews the results based on the detailed indicators output by the cultural dimension quantifier (such as the cultural respect index deduction items). After correcting the results, the human decision and review basis are simultaneously uploaded to the blockchain through the "human arbitration on-chain" module.

[0019] Step S204: Evidence Preservation and Traceability (Blockchain Evidence Preservation and Traceability Module): Whether it is AI automatic review or manual review, the "Full Process Review On-Chain Unit" is called to preserve data such as content hash value, review node, strategy parameters, and processing results on the blockchain. If a dispute arises later, the "Arbitration Smart Contract Unit" is triggered to retrieve the full process data on the blockchain and execute the dispute resolution according to the preset arbitration rules (such as the consensus of the cultural expert committee and historical case references).

[0020] Step S3: Optimization Iteration Phase: Dynamic Feedback and Model Upgrade;

[0021] Step S301: Feedback Data Collection (Dynamic Optimization Feedback Module): Regularly collect audit result data (such as AI misjudgment cases, manual review and correction content) and user culture conflict reports (cultural misunderstandings and compliance disputes reported by users), classify and label data types (model defect type, rule loophole type, cultural cognition difference type), and transmit them to the dynamic optimization feedback module.

[0022] Step S302: Model and Policy Optimization:

[0023] Incremental training of the model: The dynamic optimization feedback module inputs labeled data into the adaptive risk rating model to start incremental training (e.g., for cultural conflict cases, to enhance the model's ability to identify the cultural respect index);

[0024] Strategy rule optimization: Based on feedback data, adjust the dynamic risk feature library (supplement newly discovered culturally sensitive features) and the parameters of the four-dimensional assessment model (such as updating historical correctness judgment criteria according to cultural transmission trends), and synchronize them to the dynamic strategy engine to complete strategy iteration;

[0025] Step S303: Iteration Effect Verification: After the new strategy and model are launched, the review efficiency (AI channel processing volume), accuracy (false judgment rate), and cultural compatibility (number of user cultural conflict reports) before and after optimization are compared through the "dual loop iteration unit" to verify the optimization effect and form a "collection-optimization-verification" closed loop.

[0026] Furthermore, the review system includes a dynamic strategy engine, a multimodal intelligent preprocessing module, a cultural dimension quantifier, a dual-channel review flow module, a blockchain evidence storage and traceability module, and a dynamic optimization feedback module;

[0027] The dynamic strategy engine includes a three-layer collaborative review unit and a federated learning review model, which is used to receive feedback from multiple modules (such as the quantitative results of cultural dimensions and updates to the dynamic risk feature library), call the "three-layer collaborative review unit" and the "federated learning review model" to generate / adjust review strategies;

[0028] The multimodal intelligent preprocessing module includes a cross-modal feature fusion unit and a dynamic risk feature library. It is used to collect cultural content in various forms such as text, images, and audio. First, the cross-modal feature fusion unit extracts related features (such as text mentioning "traditional festivals" + images containing illegal symbols), and then compares them with the dynamic risk feature library to mark potential risks.

[0029] The cultural dimension quantifier includes a four-dimensional evaluation model (presumably including dimensions such as cultural compliance, dissemination value, risk level, and cultural respect). It is used to call the "four-dimensional evaluation model" to score cultural content from dimensions such as cultural compliance, dissemination value, risk level, and cultural respect, providing a quantitative basis for subsequent review decisions.

[0030] The dual-channel review module includes an innovative human-machine collaboration mechanism. Based on the quantitative results of the cultural dimension, the "innovative human-machine collaboration mechanism" is activated: low-risk, standardized content goes through the "AI fast track" for automatic review; high-risk, complex cultural controversy content (such as content involving cultural heritage disputes) is transferred to the "human deep channel" for human review and feedback to correct the AI ​​model.

[0031] The blockchain evidence storage and traceability module includes a full-process on-chain audit unit and an arbitration smart contract unit. The "full-process on-chain audit unit" records all data (hash value, processor, rule basis, etc.) from "submission → audit → decision". If a dispute arises, the "arbitration smart contract unit" automatically retrieves the on-chain data and arbitrates according to preset rules (cultural regulations, expert consensus).

[0032] The dynamic optimization feedback module includes a dual-loop iteration unit and an adaptive risk rating model. The dual-loop iteration unit simultaneously promotes "internal model iteration" (optimizing the AI ​​algorithm with the review results) and "external cultural feedback iteration" (updating rules in conjunction with new trends in cultural dissemination and policy adjustments). The "adaptive risk rating model" adjusts the content risk level in real time and outputs it to the dynamic strategy engine.

[0033] Furthermore, the three-tiered collaborative review unit is used to construct a hierarchical review process of "AI initial review → manual review → expert arbitration," enabling low-risk content to pass review quickly and high-controversy / high-value cultural content to undergo in-depth verification (such as content involving intangible cultural heritage, which requires expert confirmation of compliance). The federated learning review model is used to train the model using data from multiple platforms / regions (without disclosing the original data) and learn the review rules for different cultural scenarios (such as the different standards for domestic and international cultural exchange content), allowing the strategy to adapt to the needs of multicultural dissemination.

[0034] Furthermore, the cross-modal feature fusion unit is used to overcome the limitations of single-form review, identify content that is "textually compliant but contains cultural conflicts in images / audio" (such as anti-Japanese war themes accompanied by background music that distorts history), and solve the problem of missed judgment of multi-form cultural content. The dynamic risk feature library is used to update culturally sensitive points (emerging cultural memes, cross-cultural taboos, subcultural metaphors, etc.) in real time, so that the system can identify risks that "keep up with the times" (such as newly created culturally offensive words on the Internet), and avoid review delays caused by the iteration of cultural symbols.

[0035] Furthermore, the four-dimensional evaluation model is used to transform the abstract concept of "cultural influence" into calculable indicators (such as the "cultural respect index" which quantifies the degree to which content protects cultural diversity), upgrading the review process from "judging by experience" to "speaking with data," ensuring compliance while selecting content with cultural value (such as giving priority to high-quality traditional cultural popular science content).

[0036] Furthermore, the innovative human-machine collaboration mechanism uses AI to process massive amounts of basic content, improving review efficiency (such as compliance screening of batch cultural information), while human intervention focuses on complex cultural scenarios (such as cross-cultural misunderstandings and niche cultural disputes), avoiding AI misjudgments due to "not understanding the cultural context." At the same time, human intervention corrects data to feed back into the model, making the system "more culturally savvy" the more it is used.

[0037] Furthermore, the on-chain process of the core is used to ensure that the review of cultural dissemination is traceable and tamper-proof (especially important: compliance evidence must be retained for content involving cultural heritage), and to maintain the credibility of cultural dissemination. The arbitration smart contract is used to preset dispute resolution rules and automatically trigger the arbitration process.

[0038] Furthermore, the dual-loop iterative unit is used to enable the system to continuously evolve, both optimizing the technical model (for more accurate identification) and adapting to changes in the cultural ecology (such as emerging cultural forms and new policy requirements for cultural protection). The adaptive risk rating model is used to dynamically adjust content risk (such as reassessing the risk level of related content after a cultural event breaks out).

[0039] (III) Beneficial Effects

[0040] Compared with existing technologies, this invention provides a method for reviewing cultural dissemination content, which has the following beneficial effects:

[0041] 1. This method for reviewing cultural dissemination content ensures system sustainability through a closed-loop process, enhances review accuracy through technological integration, and dynamically adapts to address the complexity of cultural dissemination. It not only meets the "compliance review" requirements for cultural dissemination content but also takes into account the protection of cultural diversity and innovation, and adapts to the review challenges of emerging scenarios such as short videos, cross-cultural communication, and metaverse.

[0042] 2. This cultural dissemination content review method, through a closed-loop process, ensures full-link control from review to optimization. During the layered execution of the closed loop, it goes through the preparation stage (module initialization) → review execution (content flow) → optimization iteration (dynamic feedback), forming a complete cycle of "deployment-run-upgrade". During the evidence storage and traceability closed loop, the blockchain evidence storage module connects the entire review process (AI automatic / human arbitration results are all recorded on the chain), combined with "arbitration smart contracts", to achieve traceability and verifiability of "content-review-dispute resolution", ensuring the compliance and credibility of cultural dissemination review.

[0043] 3. This method for reviewing cultural dissemination content, through technological integration and multi-module collaboration, can overcome the limitations of single review. When multimodal and cultural adaptation are integrated, it is achieved through multimodal intelligent preprocessing (fusion of text, image, and audio features) + cultural dimension quantifier (four-dimensional model to evaluate cultural compliance). When human-machine collaboration and AI evolution are integrated, it is achieved through dual-channel review (AI fast channel + human deep channel) + dynamic optimization feedback (incremental model training and strategy rule optimization). It uses AI to improve the efficiency of basic review, and human intervention corrects misjudgments in complex cultural scenarios, while allowing the AI ​​model to continuously learn and evolve.

[0044] 4. This method for reviewing cultural dissemination content can adapt to the complexity and variability of cultural dissemination through dynamic adaptation. When the strategy is dynamically adjusted, the dynamic strategy engine links the federated learning model (cross-regional data synchronization) and the dynamic risk feature library (real-time updates of culturally sensitive points) to enable the review strategy to adapt to cultural differences and changes in cultural dissemination trends in different regions. When the risk is dynamically rated, the adaptive risk rating model relies on a dual-loop iteration (internal model optimization + external public opinion / cultural trend feedback) to adjust the content risk level in real time. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the review process for this invention;

[0046] Figure 2 This is a schematic diagram of the review system for this invention;

[0047] Figure 3 This is a schematic diagram of the connection of the dynamic strategy engine of the present invention;

[0048] Figure 4 This is a schematic diagram showing the connection between the four-dimensional evaluation model and the blockchain evidence storage and traceability module of this invention;

[0049] Figure 5 This is a schematic diagram showing the connection between the dynamic optimization feedback module and the adaptive risk rating model of the present invention;

[0050] Figure 6 This is a flowchart of the review process control and evidence storage branch logic of this invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Please see Figure 1-6 A method for reviewing cultural dissemination content, including a review system, with the following operation steps:

[0053] Step S1: Preparation Phase: Module Initialization and Configuration;

[0054] Step S101: Dynamic Strategy Engine Deployment: Connect to the federated learning model, configure cross-regional data synchronization rules (such as dividing data into collaborative training groups according to the cultural dissemination coverage area), initialize the dynamic risk feature library, import basic sensitive words, cultural taboo symbols and other features, associate the "cultural sensitivity parameter" of the cultural dimension quantifier, and set the parameter adjustment threshold (such as different configuration of sensitivity parameter weights for different cultural regions).

[0055] Step S102: Start the multimodal intelligent preprocessing module: Build a cross-modal feature fusion unit, define feature extraction rules for multimodal data such as text, image, and audio (e.g., use NLP models to extract semantics from text and CNNs to extract visual features from images), associate with the dynamic risk feature library, and configure feature matching trigger conditions (e.g., combine specific cultural metaphors with illegal images to trigger high-risk marking).

[0056] Step S103: Preparation of Cultural Dimension Quantifier and Dual-Channel Module: Load the four-dimensional evaluation model, set the admission standards of "historical correctness ≥ 80% and cultural respect index ≥ 60%", configure the human-machine collaboration rules of the dual-channel review module, divide the task boundaries of the AI ​​fast channel (meeting the four-dimensional standards) and the human deep channel (not meeting the standards), and build an innovative human-machine collaborative interaction interface (such as marking doubtful points in the initial AI review, which can be directly corrected and fed back to the model during human review).

[0057] Step S2: Review and Execution Phase: Content Flow and Processing;

[0058] Step S201: Content Access and Preprocessing (Multimodal Intelligent Preprocessing Module): Collect cultural dissemination content (text, video, images, etc.), call the cross-modal feature fusion unit, extract multi-dimensional features, match them with the dynamic risk feature library in real time, mark potential risk features, generate preliminary "feature-risk level" analysis results, and synchronize them to the dynamic strategy engine;

[0059] Step S202: Cultural Dimension Quantification and Strategy Adaptation (Dynamic Strategy Engine + Cultural Dimension Quantifier): The dynamic strategy engine receives the preprocessing results, calls the cultural dimension quantifier, calculates the content's "historical correctness, cultural respect index" and other indicators based on the four-dimensional evaluation model, combines the cross-regional cultural data output by the federated learning model (such as compliance cases of cultural dissemination in different regions), adjusts the review strategy (such as strengthening the verification of the cultural respect index for content involving specific cultural regions), and outputs the "AI channel / human channel" decision instruction;

[0060] Step S203: Dual-channel audit execution (dual-channel audit flow module):

[0061] AI Fast Track: If the content meets the standards of the four-dimensional evaluation model, it will directly enter the blockchain evidence storage and traceability module and execute "automatic on-chain evidence storage" (recording content characteristics, review results, strategy parameters, etc.);

[0062] Human Deep Review Channel: If the standard is not met, a human deep review process is triggered. The human review team reviews the results based on the detailed indicators output by the cultural dimension quantifier (such as the cultural respect index deduction items). After correcting the results, the human decision and review basis are simultaneously uploaded to the blockchain through the "human arbitration on-chain" module.

[0063] Step S204: Evidence Preservation and Traceability (Blockchain Evidence Preservation and Traceability Module): Whether it is AI automatic review or manual review, the "Full Process Review On-Chain Unit" is called to preserve data such as content hash value, review node, strategy parameters, and processing results on the blockchain. If a dispute arises later, the "Arbitration Smart Contract Unit" is triggered to retrieve the full process data on the blockchain and execute the dispute resolution according to the preset arbitration rules (such as the consensus of the cultural expert committee and historical case references).

[0064] Step S3: Optimization Iteration Phase: Dynamic Feedback and Model Upgrade;

[0065] Step S301: Feedback Data Collection (Dynamic Optimization Feedback Module): Regularly collect audit result data (such as AI misjudgment cases, manual review and correction content) and user culture conflict reports (cultural misunderstandings and compliance disputes reported by users), classify and label data types (model defect type, rule loophole type, cultural cognition difference type), and transmit them to the dynamic optimization feedback module.

[0066] Step S302: Model and Policy Optimization:

[0067] Incremental training of the model: The dynamic optimization feedback module inputs labeled data into the adaptive risk rating model to start incremental training (e.g., for cultural conflict cases, to enhance the model's ability to identify the cultural respect index);

[0068] Strategy rule optimization: Based on feedback data, adjust the dynamic risk feature library (supplement newly discovered culturally sensitive features) and the parameters of the four-dimensional assessment model (such as updating historical correctness judgment criteria according to cultural transmission trends), and synchronize them to the dynamic strategy engine to complete strategy iteration;

[0069] Step S303: Iteration Effect Verification: After the new strategy and model are launched, the review efficiency (AI channel processing volume), accuracy (false judgment rate), and cultural compatibility (number of user cultural conflict reports) before and after optimization are compared through the "dual loop iteration unit" to verify the optimization effect and form a "collection-optimization-verification" closed loop.

[0070] In the implementation of the case, the audit system includes a dynamic strategy engine, a multimodal intelligent preprocessing module, a cultural dimension quantifier, a dual-channel audit flow module, a blockchain evidence storage and traceability module, and a dynamic optimization feedback module;

[0071] The dynamic strategy engine includes a three-layer collaborative review unit and a federated learning review model, which are used to receive feedback from multiple modules (such as the quantitative results of cultural dimensions and updates to the dynamic risk feature library), call the "three-layer collaborative review unit" and the "federated learning review model" to generate / adjust review strategies;

[0072] The multimodal intelligent preprocessing module includes a cross-modal feature fusion unit and a dynamic risk feature library. It is used to collect cultural content in various forms such as text, images, and audio. First, the cross-modal feature fusion unit extracts related features (such as text mentioning "traditional festivals" + images containing illegal symbols), and then compares them with the dynamic risk feature library to mark potential risks.

[0073] The cultural dimension quantifier includes a four-dimensional assessment model (presumably including dimensions such as cultural compliance, dissemination value, risk level, and cultural respect). It is used to call the "four-dimensional assessment model" to score cultural content from dimensions such as cultural compliance, dissemination value, risk level, and cultural respect, providing a quantitative basis for subsequent review decisions.

[0074] The dual-channel review module includes an innovative human-machine collaboration mechanism. Based on the quantitative results of the cultural dimension, the "innovative human-machine collaboration mechanism" is activated: low-risk, standardized content goes through the "AI fast track" for automatic review; high-risk, complex cultural controversy content (such as content involving cultural heritage disputes) is transferred to the "human deep channel" for human review and feedback to correct the AI ​​model.

[0075] The blockchain evidence storage and traceability module includes a full-process on-chain audit unit and an arbitration smart contract unit. The "full-process on-chain audit unit" records all data (hash value, processor, rule basis, etc.) from "submission → audit → decision". If a dispute arises, the "arbitration smart contract unit" automatically retrieves the on-chain data and arbitrates according to preset rules (cultural regulations, expert consensus).

[0076] The dynamic optimization feedback module includes a dual-loop iteration unit and an adaptive risk rating model. The dual-loop iteration unit simultaneously promotes "internal model iteration" (optimizing AI algorithms with review results) and "external cultural feedback iteration" (updating rules based on new trends in cultural dissemination and policy adjustments). The "adaptive risk rating model" adjusts the content risk level in real time and outputs it to the dynamic strategy engine.

[0077] Among them, a closed-loop, adaptive, and traceable review system is formed by using a dynamic strategy engine as the core scheduling hub, combined with multimodal intelligent preprocessing, a cultural dimension quantifier, a dual-channel review flow, blockchain notarization, and dynamic optimization feedback. This structural connection can bring the following significant advantages:

[0078] 1. Improved review efficiency: AI + human collaboration optimization

[0079] AI fast track processes low-risk, standardized content (such as ordinary holiday greeting videos), with review speed reduced to milliseconds;

[0080] The manual review process focuses on high-risk and complex cultural disputes (such as whether adaptations of intangible cultural heritage are compliant), reducing the burden of manual review and improving overall efficiency.

[0081] The federated learning review model continuously optimizes AI decision-making, reduces the false positive rate, and decreases the need for review.

[0082] Compared to traditional manual review, the overall review efficiency is increased by more than 10 times, while the error rate is reduced by 50%.

[0083] 2. Enhanced cultural adaptability: Dynamic strategies + quantitative assessment

[0084] The Cultural Dimension Quantifier (Four-Dimensional Assessment Model) provides objective scores to avoid subjective bias, including cultural compliance (whether it complies with laws and regulations), dissemination value (whether it promotes cultural inheritance), risk level (whether it involves sensitive issues), and cultural respect (whether it offends specific groups).

[0085] The dynamic strategy engine adjusts the review rules in real time based on quantitative results, policy adjustments, and changes in public opinion (such as relaxing the review of relevant cultural content during a certain holiday).

[0086] It can adapt to the review needs of different regions and cultural backgrounds, reduce misjudgments caused by "one-size-fits-all" approaches, and enhance cultural inclusivity.

[0087] 3. Transparent and traceable auditing: Blockchain evidence storage + arbitration mechanism

[0088] The entire review process is recorded on the blockchain (submission, review, decision-making, appeal) to ensure data immutability and enhance credibility.

[0089] The arbitration smart contract automatically executes dispute arbitration (such as calling preset rules and expert consensus to make a ruling when a user files a complaint);

[0090] It can reduce the black box problem of review caused by human intervention, provide auditable review records, facilitate inspection by regulatory agencies, improve the efficiency of user complaint handling by 70%, and enhance the trustworthiness of the platform.

[0091] 4. Continuous optimization and self-evolution: dynamic feedback + dual-loop iteration

[0092] Internal model iteration: The AI ​​model is automatically optimized based on the review results (e.g., if a certain type of cultural content is frequently misjudged, the algorithm weights are adjusted).

[0093] External cultural feedback iteration: Dynamically update review standards based on policy adjustments and cultural trends (such as the rise of emerging subcultures);

[0094] The adaptive risk rating model adjusts the content risk level in real time to ensure that the review strategy keeps pace with the times;

[0095] The system can adapt to rapid changes in cultural dissemination (such as internet memes and emerging art forms) and reduce review errors caused by policy lags (such as the system being able to quickly adjust after a cultural symbol is redefined).

[0096] 5. Cross-platform and cross-cultural collaborative review

[0097] Federated learning models support collaborative training of data from different regions and platforms, avoiding data silos, and dynamic risk feature databases can share cross-cultural sensitive information (such as the different interpretations of a religious symbol in different regions);

[0098] Suitable for global content platforms (such as TikTok and Netflix), it enables localized moderation while maintaining global standards, reducing misjudgments caused by cultural differences (e.g., a gesture is friendly in country A but insulting in country B).

[0099] In the implementation of the case, the three-tiered collaborative review unit was used to build a hierarchical review process of "AI initial review → manual review → expert arbitration". Low-risk content was quickly approved, while high-controversy / high-value cultural content was deeply verified (such as content involving intangible cultural heritage, which requires expert confirmation of compliance). The federated learning review model was used to train the model by combining data from multiple platforms / regions (without disclosing the original data) and learn the review rules of different cultural scenarios (such as the different standards of domestic and foreign cultural exchange content), so that the strategy could adapt to the needs of multicultural dissemination.

[0100] Among these, tiered review can improve efficiency and reduce manual workload;

[0101] AI-powered initial screening: Quickly filters low-risk content (such as general cultural and scientific videos), with processing speeds down to milliseconds;

[0102] Manual review: For medium- and high-risk content (such as content involving religion or intangible cultural heritage), professional reviewers will make the judgment.

[0103] Expert arbitration: For highly controversial / high-value content (such as adaptations of intangible cultural heritage or transnational cultural disputes), the final decision is made by cultural scholars or legal experts;

[0104] 90% of low-risk content is processed automatically by AI, while humans only need to focus on 10% of complex cases. The overall review speed is increased by 5-10 times, reducing the cost of manual review. Experts only need to intervene in a very small number of controversial cases (<1%), avoiding "one-size-fits-all" approaches and ensuring that high-value cultural content (such as intangible cultural heritage) is not mistakenly deleted.

[0105] Federated learning enables cross-platform / cross-cultural collaboration, enhancing the model's generalization ability.

[0106] Data collaboration without sharing: Each platform / region uses federated learning to jointly train models without uploading raw data, thus protecting privacy;

[0107] Cultural differentiation adaptation: The model automatically learns the approval standards of different regions (such as the symbolic differences between the Chinese dragon and the Western dragon);

[0108] The model can be adapted to different regional cultural policies (e.g., Middle Eastern religious content vs. East Asian traditional culture), and can be trained using data from multiple regions to avoid misjudgments caused by single cultural samples (e.g., minority content being mistakenly labeled as sensitive). When the meaning of a cultural symbol changes (e.g., internet memes are given new meanings), federated learning can quickly and synchronously update the models on various platforms.

[0109] Typical application scenarios and actual test results are as follows:

[0110]

[0111]

[0112] This structure is achieved through hierarchical review and federated learning:

[0113] 1. Efficiency: AI handles most of the content, while human staff focus on key cases;

[0114] 2. Accuracy: Reduce cultural misjudgments and protect reasonable expression;

[0115] 3. Adaptability: Dynamically responding to policy and cultural changes;

[0116] 4. Globalization: Compatible with cultural standards from different regions.

[0117] It is applicable to scenarios such as short video platforms (such as TikTok), cultural IP management (such as intangible cultural heritage protection), and content review by multinational companies, promoting the intelligent and precise development of cultural content review.

[0118] In the implementation of the case, the cross-modal feature fusion unit is used to break through the limitations of single-form review, identify content that is "textually compliant but with hidden cultural conflicts in images / audio" (such as anti-Japanese war themes accompanied by background music that distorts history), solve the problem of missed judgment of multi-form cultural content, and the dynamic risk feature library is used to update culturally sensitive points in real time (emerging cultural memes, cross-cultural taboos, subcultural metaphors, etc.), so that the system can identify risks that "keep up with the times" (such as newly created culturally offensive words on the Internet), and avoid review delays caused by the iteration of cultural symbols;

[0119] Among these improvements, multimodal joint analysis and dynamic risk database updates have significantly enhanced the comprehensiveness and timeliness of cultural content review. The specific advantages and supporting data are as follows:

[0120] Reduced false negative rate:

[0121] Missed Detection Rate in Review Methods (Content Related to Cultural Conflicts)

[0122] Single-modal review 32%

[0123] Cross-modal fusion review 8%

[0124] The speed of new risk identification has been improved:

[0125]

[0126] Structured Advantage Matrix:

[0127]

[0128] This design is particularly well-suited for handling rapidly iterating subcultural content and implicit cultural conflicts, providing key technical support for emerging scenarios such as the metaverse and cross-border social networking.

[0129] In the implementation of the case, the four-dimensional assessment model is used to transform the abstract "cultural impact" into calculable indicators (such as the "cultural respect index" to quantify the degree of protection of cultural diversity by the content), so that the review is upgraded from "judging by experience" to "speaking with data", which not only ensures compliance, but also selects content with cultural value (such as giving priority to high-quality traditional cultural popular science content).

[0130] By quantifying the abstract cultural impact into four calculable dimensions, the review of cultural content has been made more scientific and standardized, while simultaneously ensuring compliance and protecting cultural values. The specific advantages and data verification are as follows:

[0131] Improved audit consistency:

[0132] Different assessment methods lead to different rates of discrepancy in auditor judgments.

[0133] Traditional experience judgment 42%

[0134] The four-dimensional model scored 9%.

[0135] Industry application results:

[0136]

[0137] The value matrix of quantitative auditing:

[0138]

[0139] This model is particularly suitable for scenarios that require balancing compliance and cultural diversity (such as the digitization of intangible cultural heritage and the review of cross-border cultural products), providing a feasible technical path for algorithms to promote good.

[0140] In the implementation of the case, an innovative human-machine collaboration mechanism was used to use AI to process massive amounts of basic content and improve the efficiency of review (such as compliance screening of batch cultural information), while human review focused on complex cultural scenarios (such as cross-cultural misunderstandings and niche cultural disputes) to avoid AI misjudgment due to "not understanding the cultural context". At the same time, human review corrected the data to feed back into the model, so that the system becomes more "culture-savvy" the more it is used.

[0141] The AI ​​preprocessing layer uses lightweight models (such as MobileNetV3+DistilBERT) to achieve millisecond-level initial screening, automatically filtering out obviously compliant content (accounting for approximately 85%). The human expert layer can focus on the 15% of complex cases (such as intangible cultural heritage adaptations and cross-cultural religious content), establishing a cultural expert annotation platform that supports a 200+ cultural tag system. The specific advantages and data verification are as follows:

[0142] Efficiency Breakthrough:

[0143]

[0144] This structure can reduce the AI ​​misjudgment rate from 12% to 4% (through a closed loop of human feedback), and achieve an accuracy rate of 92% in handling culturally controversial content (compared to 68% for traditional methods).

[0145] In the implementation of the case, the entire process of on-chain verification is used to ensure the traceability and tamper-proof nature of cultural dissemination review (especially important: compliance evidence must be retained for content involving cultural heritage), maintain the credibility of cultural dissemination, and use the arbitration smart contract to preset dispute resolution rules and automatically trigger the arbitration process.

[0146] Among them, the entire process is on-chain: the content hash value, review timestamp, decision basis and other evidence are stored on the chain, and IPFS + Ethereum dual storage is used to reduce costs by 60%;

[0147] Smart Arbitration Contract: Presets 14 types of cultural dispute resolution rules (such as the "20% adaptation" threshold for the secondary creation of intangible cultural heritage), and automatically triggers multi-party voting arbitration (cultural scholars + legal experts + community representatives).

[0148] In the implementation of the case, the dual-loop iterative unit is used to enable the system to continuously evolve, both to optimize the technical model (to identify more accurately) and to adapt to changes in the cultural ecology (such as emerging cultural forms and new policy requirements for cultural protection). The adaptive risk rating model is used to dynamically adjust the content risk (such as reassessing the risk level of related content after a cultural event breaks out).

[0149] Among them, the speed of type evolution: the timeliness of identifying new cultural phenomena is: traditional system: 3-6 months, this system: 7 days (through public opinion monitoring + federated learning);

[0150] Risk response capability: In the event of a traditional festival symbol being stigmatized, the system completed the following within 48 hours: the risk level was adjusted from P1 to P3, and 137 related audit rules were updated;

[0151] Comprehensive benefit matrix:

[0152]

[0153] Through few-shot learning, new cultural types can be processed in 7 days. Federated learning ensures data privacy across platforms while sharing model progress.

[0154] In summary, this method for reviewing cultural dissemination content ensures system sustainability through a closed-loop process, enhances review accuracy through technological integration, and dynamically adapts to the complexity of cultural dissemination. It not only meets the "compliance review" requirements of cultural dissemination content but also protects cultural diversity and innovation. It adapts to the review challenges of emerging scenarios such as short videos, cross-cultural communication, and metaverse. Through the closed-loop process, the entire chain from review to optimization is controllable. During the layered execution of the closed loop, it goes through a preparation phase (module initialization) → review execution (content flow) → optimization iteration (dynamic feedback), forming a complete cycle of "deployment-operation-upgrade." During the evidence storage and traceability closed loop, the blockchain evidence storage module connects the entire review process (both AI automatic and manual arbitration results are recorded on the blockchain). Combined with "arbitration smart contracts," it achieves traceability and verifiability of "content-review-dispute resolution," ensuring the compliance and credibility of cultural dissemination review.

[0155] Furthermore, through technological integration, the limitations of single-module review can be overcome through multi-module collaboration. When multimodal and cultural adaptation are integrated, it is achieved through multimodal intelligent preprocessing (fusion of text, image, and audio features) + cultural dimension quantifier (four-dimensional model assessment of cultural compliance). When human-machine collaboration and AI evolution are integrated, dual-channel review (AI fast channel + human deep channel) + dynamic optimization feedback (incremental model training, strategy rule optimization) is used to improve the efficiency of basic review with AI, while human intervention corrects misjudgments in complex cultural scenarios. At the same time, the AI ​​model continues to learn and evolve. Through dynamic adaptation, it can cope with the complexity and variability of cultural dissemination. When the strategy is dynamically adjusted, the dynamic strategy engine links the federated learning model (cross-regional data synchronization) and the dynamic risk feature library (real-time updates of cultural sensitivity points) to make the review strategy adaptable to the cultural differences and changes in cultural dissemination trends in different regions. When the risk is dynamically rated, the adaptive risk rating model relies on dual-loop iteration (internal model optimization + external public opinion / cultural trend feedback) to adjust the content risk level in real time.

[0156] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0157] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for reviewing cultural dissemination content, comprising a review system, characterized in that: The operation steps are as follows: Step S1: Preparation Phase: Module Initialization and Configuration; Step S101: Dynamic Strategy Engine Deployment: Connect to the federated learning model, configure cross-regional data synchronization rules (such as dividing data into collaborative training groups according to the cultural dissemination coverage area), initialize the dynamic risk feature library, import basic sensitive words, cultural taboo symbols and other features, associate the "cultural sensitivity parameter" of the cultural dimension quantifier, and set the parameter adjustment threshold (such as different configuration of sensitivity parameter weights for different cultural regions). Step S102: Start the multimodal intelligent preprocessing module: Build a cross-modal feature fusion unit, define feature extraction rules for multimodal data such as text, image, and audio (e.g., use NLP models to extract semantics from text and CNNs to extract visual features from images), associate with the dynamic risk feature library, and configure feature matching trigger conditions (e.g., combine specific cultural metaphors with illegal images to trigger high-risk marking). Step S103: Preparation of Cultural Dimension Quantifier and Dual-Channel Module: Load the four-dimensional evaluation model, set the admission standards of "historical accuracy ≥ 80% and cultural respect index ≥ 60%", configure the human-machine collaboration rules of the dual-channel review module, divide the task boundaries of the AI ​​fast channel (meeting the four-dimensional standards) and the human deep channel (not meeting the standards), and build an innovative human-machine collaborative interaction interface (such as marking doubtful points in the initial AI review, which can be directly corrected and fed back to the model during human review). Step S2: Review and Execution Phase: Content Flow and Processing; Step S201: Content Access and Preprocessing (Multimodal Intelligent Preprocessing Module): Collect cultural dissemination content (text, video, images, etc.), call the cross-modal feature fusion unit, extract multi-dimensional features, match them with the dynamic risk feature library in real time, mark potential risk features, generate preliminary "feature-risk level" analysis results, and synchronize them to the dynamic strategy engine; Step S202: Cultural Dimension Quantification and Strategy Adaptation (Dynamic Strategy Engine + Cultural Dimension Quantifier): The dynamic strategy engine receives the preprocessing results, calls the cultural dimension quantifier, calculates the content's "historical correctness, cultural respect index" and other indicators based on the four-dimensional evaluation model, combines the cross-regional cultural data output by the federated learning model (such as compliance cases of cultural dissemination in different regions), adjusts the review strategy (such as strengthening the verification of the cultural respect index for content involving specific cultural regions), and outputs the "AI channel / human channel" decision instruction; Step S203: Dual-channel audit execution (dual-channel audit flow module): AI Fast Track: If the content meets the standards of the four-dimensional evaluation model, it will directly enter the blockchain evidence storage and traceability module and execute "automatic on-chain evidence storage" (recording content characteristics, review results, strategy parameters, etc.); Human Deep Review Channel: If the standard is not met, a human deep review process is triggered. The human review team reviews the results based on the detailed indicators output by the cultural dimension quantifier (such as the cultural respect index deduction items). After correcting the results, the human decision and review basis are simultaneously uploaded to the blockchain through the "human arbitration on-chain" module. Step S204: Evidence Preservation and Traceability (Blockchain Evidence Preservation and Traceability Module): Whether it is AI automatic review or manual review, the "Full Process Review On-Chain Unit" is called to preserve data such as content hash value, review node, strategy parameters, and processing results on the blockchain. If a dispute arises later, the "Arbitration Smart Contract Unit" is triggered to retrieve the full process data on the blockchain and execute dispute resolution according to the preset arbitration rules (such as consensus of the cultural expert committee and reference to historical cases). Step S3: Optimization Iteration Phase: Dynamic Feedback and Model Upgrade; Step S301: Feedback Data Collection (Dynamic Optimization Feedback Module): Regularly collect audit result data (such as AI misjudgment cases, manual review and correction content) and user culture conflict reports (cultural misunderstandings and compliance disputes reported by users), classify and label data types (model defect type, rule loophole type, cultural cognition difference type), and transmit them to the dynamic optimization feedback module. Step S302: Model and Policy Optimization: Incremental training of the model: The dynamic optimization feedback module inputs labeled data into the adaptive risk rating model to start incremental training (e.g., for cultural conflict cases, to enhance the model's ability to identify the cultural respect index); Strategy rule optimization: Based on feedback data, adjust the dynamic risk feature library (supplement newly discovered culturally sensitive features) and the parameters of the four-dimensional assessment model (such as updating historical correctness judgment criteria according to cultural transmission trends), and synchronize them to the dynamic strategy engine to complete strategy iteration; Step S303: Iteration Effect Verification: After the new strategy and model are launched, the review efficiency (AI channel processing volume), accuracy (false judgment rate), and cultural compatibility (number of user cultural conflict reports) before and after optimization are compared through the "dual loop iteration unit" to verify the optimization effect and form a "collection-optimization-verification" closed loop.

2. The method for reviewing cultural dissemination content according to claim 1, characterized in that: The review system includes a dynamic strategy engine, a multimodal intelligent preprocessing module, a cultural dimension quantifier, a dual-channel review flow module, a blockchain evidence storage and traceability module, and a dynamic optimization feedback module. The dynamic strategy engine includes a three-layer collaborative review unit and a federated learning review model, which are used to receive feedback from multiple modules (such as the quantitative results of cultural dimensions and updates to the dynamic risk feature library), call the "three-layer collaborative review unit" and the "federated learning review model" to generate / adjust review strategies; The multimodal intelligent preprocessing module includes a cross-modal feature fusion unit and a dynamic risk feature library. It is used to collect cultural content in various forms such as text, images, and audio. First, the cross-modal feature fusion unit extracts related features (such as text about "traditional festivals" + image containing illegal symbols), and then compares them with the dynamic risk feature library to mark potential risks. The cultural dimension quantifier includes a four-dimensional evaluation model (presumably including dimensions such as cultural compliance, dissemination value, risk level, and cultural respect). It is used to call the "four-dimensional evaluation model" to score cultural content from dimensions such as cultural compliance, dissemination value, risk level, and cultural respect, providing a quantitative basis for subsequent review decisions. The dual-channel review module includes an innovative human-machine collaboration mechanism. Based on the quantitative results of the cultural dimension, the "innovative human-machine collaboration mechanism" is activated: low-risk, standardized content goes through the "AI fast track" for automatic review; high-risk, complex cultural controversy content (such as content involving cultural heritage disputes) is transferred to the "human deep channel" for manual review, and feedback is provided to correct the AI ​​model. The blockchain evidence storage and traceability module includes a full-process on-chain audit unit and an arbitration smart contract unit. The "full-process on-chain audit unit" records all data (hash value, processor, rule basis, etc.) from "submission → audit → decision". If a dispute arises, the "arbitration smart contract unit" automatically retrieves the on-chain data and arbitrates according to preset rules (cultural regulations, expert consensus). The dynamic optimization feedback module includes a dual-loop iteration unit and an adaptive risk rating model. The dual-loop iteration unit simultaneously promotes "internal model iteration" (optimizing AI algorithms with review results) and "external cultural feedback iteration" (updating rules in conjunction with new trends in cultural dissemination and policy adjustments). The adaptive risk rating model adjusts the content risk level in real time and outputs it to the dynamic strategy engine.

3. The method for reviewing cultural dissemination content according to claim 2, characterized in that: The three-tiered collaborative review unit is used to construct a hierarchical review process of "AI initial review → manual review → expert arbitration". Low-risk content is quickly approved, while high-controversial / high-value cultural content is deeply verified (such as content involving intangible cultural heritage, which requires expert confirmation of compliance). The federated learning review model is used to train the model by combining data from multiple platforms / regions (without disclosing the original data) and learn the review rules of different cultural scenarios (such as the different standards of domestic and foreign cultural exchange content), so that the strategy can adapt to the needs of multicultural dissemination.

4. The method for reviewing cultural dissemination content according to claim 2, characterized in that: The cross-modal feature fusion unit is used to overcome the limitations of single-form review, identify content that is "textually compliant but with implied cultural conflicts in images / audio" (such as anti-Japanese war themes accompanied by background music that distorts history), and solve the problem of missed judgment of multi-form cultural content. The dynamic risk feature library is used to update culturally sensitive points (emerging cultural memes, cross-cultural taboos, subcultural metaphors, etc.) in real time, so that the system can identify risks that "keep up with the times" (such as newly created offensive cultural terms on the Internet), and avoid review delays caused by the iteration of cultural symbols.

5. The method for reviewing cultural dissemination content according to claim 2, characterized in that: The four-dimensional evaluation model is used to transform the abstract concept of "cultural influence" into calculable indicators (such as the "cultural respect index" to quantify the degree to which content protects cultural diversity), upgrading the review process from "judging by experience" to "speaking with data," ensuring compliance while selecting content with cultural value (such as giving priority to high-quality traditional cultural popular science content).

6. The method for reviewing cultural dissemination content according to claim 2, characterized in that: The innovative human-machine collaboration mechanism uses AI to process massive amounts of basic content, improving review efficiency (such as compliance screening of batch cultural information), while human intervention focuses on complex cultural scenarios (such as cross-cultural misunderstandings and niche cultural disputes), avoiding AI misjudgments due to "not understanding the cultural context". At the same time, human intervention corrects data to feed back into the model, making the system "more culturally savvy" the more it is used.

7. The method for reviewing cultural dissemination content according to claim 2, characterized in that: The on-chain process of the core is used to ensure that the review of cultural dissemination is traceable and tamper-proof (especially important: compliance evidence must be retained for content involving cultural heritage), and to maintain the credibility of cultural dissemination. The arbitration smart contract is used to preset dispute resolution rules and automatically trigger the arbitration process.

8. The method for reviewing cultural dissemination content according to claim 2, characterized in that: The dual-loop iterative unit is used to enable the system to continuously evolve, both optimizing the technical model (for more accurate identification) and adapting to changes in the cultural ecology (such as emerging cultural forms and new policy requirements for cultural protection). The adaptive risk rating model is used to dynamically adjust content risk (such as reassessing the risk level of related content after a cultural event).

Images