Context-aware multimodal image and text content review system

Abstract

This invention discloses a context-aware multimodal image and text content review system, comprising: a multimodal data acquisition module for acquiring image and text data; a context-aware processing module for performing context analysis on the image and text data to generate a structured context feature set; a multimodal fusion review module connected to the multimodal data acquisition module and the context-aware processing module respectively, for fusing the image and text data with the structured context feature set to perform content review and generate results; and a result output and feedback module connected to the multimodal fusion review module for outputting the review results. This invention, through a dedicated context-aware processing module, deeply fuses the semantic association between images and text, enabling accurate identification of violations that are difficult to detect using traditional methods and rely on cross-modal contexts, fundamentally solving the problems of misjudgment and missed judgment caused by single modality or simple rule superposition.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence and content security technology, and in particular relates to a context-aware multimodal image and text content review system. Background Technology

[0002] With the explosive growth of internet and digital media content, efficient and accurate content moderation of multimedia information such as images, text, and videos has become a key technical requirement for maintaining cyberspace security and ensuring the compliant operation of platforms. Existing technical solutions mainly rely on the combination of computer vision and natural language processing technologies. On the image side, models based on deep convolutional neural networks (CNNs), such as ResNet and YOLO, are typically used to identify visually inappropriate elements such as pornography, violence, and specific logos. On the text side, pre-trained models based on the Transformer architecture, such as BERT and RoBERTa, are commonly used for sensitive word matching, sentiment analysis, or intent recognition. To achieve the moderation of mixed image and text content, the current mainstream practice is to input images and text into separate dedicated models for analysis, and then use a rule engine or a simple decision layer (such as logical "OR" or "AND" operations) to summarize the judgment results of the two modalities and output the final moderation conclusion. Furthermore, at the system architecture level, a common practice is to uniformly transmit the collected data to a cloud data center for processing, relying on the computing power of centralized servers to complete all analysis tasks.

[0003] However, the aforementioned existing technical solutions still have significant limitations and are difficult to meet the increasingly complex practical application needs. First, in terms of review accuracy, the isolated analysis of images and text followed by result splicing completely fails to understand the deep semantic connections and contextual relationships between the two, resulting in low recognition rates for content that relies on mutual interpretation of images and text, irony, or implicit implications (such as seemingly ordinary images paired with inflammatory text), lacking true semantic understanding capabilities. Second, in terms of system reliability, centralized cloud processing architectures are susceptible to network fluctuations and lack robust designs for hardware failures or extreme environments (such as high-temperature, high-humidity industrial scenarios), resulting in insufficient fault tolerance. Third, in terms of resource efficiency, transmitting all data to the cloud for processing is neither economical nor timely, especially unfriendly to edge scenarios with high real-time requirements, and the fixed processing mode cannot intelligently adapt to dynamically changing network conditions. Furthermore, in terms of engineering deployment, the system often adopts a tightly coupled design, making it difficult to flexibly replace or expand functional modules according to business needs, resulting in high upgrade and maintenance costs. Finally, in terms of long-term effectiveness, static content moderation models cannot utilize continuously generated feedback data for self-iterative optimization, making it difficult to quickly adapt to new forms and strategies of online content violations, and their capabilities easily become obsolete. Therefore, there is an urgent need for a new generation of multimodal content moderation systems that can deeply understand context, possess high reliability, are resource-efficient, easily expandable, and can continuously evolve. Summary of the Invention

[0004] To overcome the aforementioned shortcomings of existing technologies, this invention provides a context-aware multimodal image and text content review system, which solves the problems of existing image and text review technologies having difficulty understanding semantic relationships, and the systems being unreliable, poorly scalable, and unable to be continuously optimized.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A context-aware, multimodal image and text content moderation system includes: A multimodal data acquisition module is used to acquire text and image data; A context-aware processing module, connected to the multimodal data acquisition module, is used to perform context analysis on the image and text data and generate a structured context feature set. The multimodal fusion review module is connected to the multimodal data acquisition module and the context-aware processing module respectively, and is used to fuse the image and text data with the structured context feature set to perform content review and generate review results; The result output and feedback module is connected to the multimodal fusion review module and is used to output the review results.

[0006] Preferred options also include: A communication transmission module is connected between the multimodal fusion review module and the result output and feedback module, and is used to transmit the review result; The dual fault-tolerance module is connected to the multimodal data acquisition module, context-aware processing module, multimodal fusion audit module, and communication transmission module, and is used to monitor the operating status of each module and handle faults.

[0007] Preferably, the multimodal data acquisition module supports JPG, PNG, TXT, and DOCX format input, and standardizes the data into tensor data of a unified format through a format parsing engine; it has built-in anti-interference circuit, synchronous clock circuit, and data verification mechanism, and can work stably in environments with temperatures ranging from -40℃ to 85℃ and relative humidity ranging from 10% to 90%.

[0008] Preferably, the context-aware processing module adopts a layered architecture of edge preprocessing and cloud-based deep analysis; the edge is used for data denoising and primary feature extraction, while the cloud extracts multi-dimensional correlation features of the image and text data through sparse attention mechanism, BERT model and CNN-LSTM hybrid model respectively, and uses cross-modal attention mechanism to achieve feature alignment to generate the structured context feature set.

[0009] Preferably, the multimodal fusion review module adopts a deep learning architecture that includes a multimodal input layer, an attention fusion layer, and a classification output layer; the attention fusion layer adopts a weighted fusion strategy to dynamically fuse the standardized image and text data with the structured context feature set for compliance verification, anomaly location, and risk level quantification.

[0010] Preferably, the communication transmission module supports adaptive switching between wired and wireless communication modes, with a switching threshold of 100Mbps bandwidth; the audit results are transmitted using the AES-256 encryption algorithm and a dynamic key update mechanism with a cycle not exceeding 24 hours.

[0011] Preferably, the dual fault-tolerant protection module includes a hardware-level fault detection unit and a software-level anomaly repair algorithm. When an anomaly is detected, it can automatically trigger a restart or backup module switching operation and generate an encrypted anomaly log.

[0012] Preferably, the result output and feedback module is used to generate a standardized audit report that includes screenshots of the anomalies, risk evidence, and handling suggestions, and supports connection with third-party regulatory systems via RESTful API.

[0013] Preferably, the system further includes a cloud data processing unit; the cloud data processing unit includes: A distributed database is used to store the audit data generated by the multimodal fusion audit module and the exception logs generated by the dual fault tolerance guarantee module; The model iteration optimization unit, based on the data collected by the multimodal data acquisition module and the feedback information from the result output and feedback module, uses incremental training to iteratively optimize the deep learning model in the multimodal fusion review module.

[0014] Preferably, the multimodal data acquisition module, context-aware processing module, multimodal fusion review module, communication transmission module, result output and feedback module, and dual fault tolerance module all adopt standardized and modular design, supporting hot-swapping and independent upgrades.

[0015] The technical effects and advantages of the context-aware multimodal image and text content review system of this invention are as follows: 1. This invention, through a dedicated context-aware processing module, deeply integrates the semantic association between images and text, and can accurately identify violations that are difficult to detect by traditional methods and rely on cross-modal contexts (such as hints, irony, and contradictions between images and text). It fundamentally solves the problems of misjudgment and missed judgment caused by single modality or simple rule superposition.

[0016] 2. The industrial-grade protection and wide-range working environment design of the multimodal data acquisition module of this invention, combined with the real-time monitoring and automatic repair mechanism of the dual fault-tolerant protection module, ensures that the system can maintain high availability and uninterrupted stable operation under harsh conditions such as electromagnetic interference, extreme temperature and humidity fluctuations or occasional component failures.

[0017] 3. This invention adopts an edge-cloud layered processing architecture, which forwards tasks with high real-time requirements and postpones complex analysis, effectively reducing bandwidth pressure and cloud load. The communication transmission module supports intelligent switching between wired and wireless communication and efficient encryption, ensuring data security while achieving adaptive and optimal utilization of network resources.

[0018] 4. The entire system of this invention adopts a standardized and modular design, and each functional module supports hot-swapping and independent upgrades. This design enables the system to be quickly and flexibly configured and expanded according to different business scales and scenario requirements (such as from embedded terminals to cloud clusters), while significantly reducing the cost and complexity of later upgrades and maintenance.

[0019] 5. This invention relies on a cloud-based data processing unit, enabling the system to automatically collect review data and feedback results, and to periodically iteratively optimize the core review model using incremental learning technology. This allows the system's review capabilities to continuously improve with data accumulation, automatically adapting to new content formats and violation patterns, and maintaining technological advancement and high accuracy in the long term. Attached Figure Description

[0020] Figure 1This is a system framework diagram of the context-aware multimodal image and text content review system proposed in this invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] 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 entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes 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 limitation, an element defined by the phrase "includes..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] refer to Figure 1 This invention provides a context-aware multimodal image and text content moderation system, aiming to solve the problems of shallow semantic understanding, poor system robustness, difficulty in flexible expansion, and lack of self-evolution capability in the moderation of mixed image and text content in existing technologies. The system includes a multimodal data acquisition module, a context-aware processing module, a multimodal fusion moderation module, and a result output and feedback module that work sequentially and collaboratively, as well as an optional communication transmission module and a dual fault tolerance guarantee module. Specifically, the multimodal data acquisition module is responsible for collecting and standardizing image and text data; the context-aware processing module uses an edge-cloud layered architecture and models such as sparse attention, BERT, and CNN-LSTM to extract and align cross-modal correlation features to generate a structured context feature set; the multimodal fusion moderation module dynamically fuses raw data and context features through an attention fusion layer to achieve accurate moderation judgment including risk level quantification. Furthermore, the system supports modular hot-swappable design and independent upgrades, and continuously optimizes the model based on incremental learning through a cloud data processing unit. This invention represents a leap from single-modal feature matching to multi-modal contextual deep understanding, significantly improving the accuracy and interpretability of the review process, while ensuring the system's high reliability, scalability, and long-term effectiveness in complex environments.

[0024] Example 1

[0025] Minimal core system deployment: Purpose of implementation: This embodiment aims to verify the effectiveness of the core context-aware and integrated review mechanism of this invention, demonstrating its fundamental advantages over traditional isolated review methods. This deployment configuration corresponds to the minimum necessary technical solution.

[0026] Implementation System: The software system is deployed on a high-performance workstation (equipped with an NVIDIA RTX 4090 GPU). The system consists of four core software services: Data acquisition service: Monitor file directories and parse text and image content.

[0027] Context Analysis Service: Runs a trained multimodal joint coding model.

[0028] Fusion Review Service: Runs a neural network model that includes feature fusion and classification functions.

[0029] Results output service: Generates and displays audit reports.

[0030] Implementation steps: (1) Data Acquisition: The acquisition service detected a poster file uploaded by the user. The poster contains an image of a smiling cartoon character (JPG format) and a text slogan "Come and try it, it will make you forget all your troubles!" (TXT format). The service converts the image into a pixel tensor and the text into a sequence of word vectors.

[0031] (2) Context Analysis: The context analysis service receives the above data. Its internal joint coding model automatically analyzes the relationship between images (cartoon characters, friendly expressions) and text (suggestive phrases like "forget your troubles") through an attention mechanism, and outputs a comprehensive feature vector indicating that "the image and text together convey an inducing suggestion that relaxes the mind."

[0032] (3) Fusion Review: The fusion review service simultaneously receives the original image tensor, text vector, and the comprehensive feature vector generated in the previous step. The model performs a weighted fusion of the three, with the comprehensive feature vector assigned the highest weight. The model determines that the combination is suspected of misleading advertising, violating the platform's healthy guidance rules, and outputs the review result: {Conclusion: Violation; Risk Level: Medium; Abnormal Focus: Linkage between the overall text and the facial expression of the person in the image}.

[0033] (4) Results output: The results output service displays the above results in a visual manner on the auditor's console, prompting manual review.

[0034] Implementation results: This embodiment demonstrates that, through the collaboration of only four core modules, the system can accurately identify complex scenarios where individual images and text analysis yield no obvious violations, but the combination creates inappropriate inducements. This verifies the innovation and necessity of the core architecture of "context-aware processing" and "multimodal fusion review," and solves the core defects of traditional methods.

[0035] Example 2

[0036] Highly reliable end-to-end systems for industrial environments: Purpose of implementation: This embodiment demonstrates the complete application of the present invention in a demanding industrial safety production monitoring scenario with high reliability requirements, showcasing its comprehensive capabilities in hardware protection, layered computing, secure communication, fault tolerance, and self-evolution.

[0037] Implementation System: The system adopts a cloud-edge-device collaborative architecture: Edge side (workshop): Deploy industrial smart gateways (wide temperature range design, built-in ARM AI chip) to integrate data acquisition, lightweight preprocessing and fault detection units.

[0038] Network layer: An encrypted communication gateway that supports adaptive switching between industrial Ethernet and 5G private networks.

[0039] Cloud side: Deployed in a private cloud, it includes a high-performance GPU computing cluster, deep learning model service, distributed database, management console, and model training platform.

[0040] Implementation steps: (1) Extreme Environment Data Acquisition: A gateway installed in the chemical plant pump room (ambient temperature 45℃) acquires a video stream (image sequence) of equipment vibration via an industrial camera, and synchronously obtains the equipment log text ("Pump body P-101 vibration value continuously exceeds the standard, accompanied by abnormal noise") via OPC protocol. The verification mechanism within the gateway ensures that the image and text time stamps are aligned.

[0041] (2) Layered Intelligent Analysis: The gateway compresses the video and extracts keyframes, segments the logs, and encrypts and uploads a primary feature package. Upon receiving the package in the cloud, the deep analysis model is activated: the visual model identifies the abnormal shaking pattern of a specific pump in the video; the text model parses the device number and alarm level in the logs; the cross-modal alignment module associates the text description of "P-101" with the specific pump image area in the video, forming a precise contextual feature that "device P-101 visual anomaly and text alarm are highly matched".

[0042] (3) Dynamic fusion and decision-making: The fusion audit model integrates the original data with the above-mentioned precise features, determines it as "high risk of equipment failure", locates the specific equipment and time point, and the risk level is "high".

[0043] (4) Secure Transmission and High Availability: The audit results are sent back to the workshop control room in real time via a dedicated line encrypted with AES-256. During the process, if the simulated network is momentarily interrupted, the system's built-in fault tolerance mechanism immediately activates the local cache and switches to the backup analysis path to ensure that alarms are not lost. A red alarm and detailed report automatically pop up on the control room screen.

[0044] (5) Data closure and optimization: All data from this incident (raw sensor data, intermediate features, review results, and manual handling feedback) were stored in a cloud-based distributed database. One week later, the system automatically started incremental training using the accumulated new data, and the optimized model improved the accuracy of judging the correlation between images and text such as "excessive vibration" and "abnormal noise" by 8%.

[0045] Implementation results: This embodiment fully demonstrates the end-to-end capabilities from edge-based anti-interference data acquisition, cloud-based deep contextual understanding, secure and reliable transmission, seamless fault switching, and data-driven model optimization. The system meets the stringent requirements of industrial scenarios for stability, security, and continuous learning.

[0046] Example 3

[0047] Real-time content moderation platform for social media: Purpose of implementation: This embodiment verifies whether the present invention can maintain low latency and high accuracy in reviewing tens of thousands of high-concurrency social media content per second, and demonstrates the elastic expansion advantages brought by its modular architecture.

[0048] Implementation System: Build a microservices architecture on a public cloud (such as AWS). Core services (context analysis, converged auditing) are encapsulated as independently scalable Docker containers. The auditing service pool is supported by hundreds of GPU instances (such as NVIDIA T4), with a load balancer at the front end. All services communicate over a high-speed internal network.

[0049] Implementation steps: (1) High concurrency access: The platform simultaneously receives a massive stream of text and image posts from multiple social APIs.

[0050] (2) Efficient context extraction: For a post containing a blurry image of a medical instrument and the text “One course of treatment at home can cure diabetes”, a dedicated context analysis service calls a pre-trained model in milliseconds to extract conflicting contextual features between “blurry image information” and “the text makes an absolute promise of medical effect”.

[0051] (3) Elastic Fusion Audit: The load balancer distributes tasks to an instance in the audit service pool. This instance quickly completes the fusion judgment and outputs the conclusion "suspected of false medical advertising". During the "shopping festival", traffic surged, and the operation and maintenance platform automatically expanded the audit service pool by 50%, resulting in linear growth in throughput and stable average latency.

[0052] (4) Standardized output: After the review results (including violation codes, risk levels, and related evidence) are formatted, they are returned to the content scheduling system of the social platform in real time to trigger operations such as traffic restriction, removal or marking.

[0053] Implementation results: The system successfully handled the challenge of ultra-high concurrency, demonstrating the efficiency of its core algorithm and the remarkable resilience of its system architecture. Its modular design allows computing resources to be dynamically adjusted based on business traffic, achieving an optimal balance between cost and performance.

[0054] Example 4

[0055] Embedded edge auditing terminal: Purpose of implementation: This embodiment demonstrates how to lightweight the core context-aware capability of the present invention and integrate it into resource-constrained embedded devices (such as smart cameras and advertising screen terminals) to achieve low-latency localized real-time review.

[0056] Implementation System: It employs an all-in-one smart advertising screen with a built-in dedicated AI processing chip (such as Horizon Journey 3). The device integrates a camera and screen, and runs a highly streamlined, customized Linux system and a lightweight auditing software stack.

[0057] Implementation steps: (1) Local acquisition and processing: When the advertising screen plays commercial advertisements, its built-in system synchronously analyzes the final frame image displayed on the screen (such as a car drifting at high speed) and the superimposed advertising text ("Release your wildness, the horse route is yours to control!").

[0058] (2) Lightweight context modeling: Lightweight models running on the device (such as pruned multimodal Transformer) complete the analysis within 30 milliseconds and identify the reinforcing association features between “dangerous driving visuals” and “inflammatory copy that encourages dangerous driving”.

[0059] (3) Rapid local decision-making and interception: The integrated review module immediately determines that the advertisement content is in violation of regulations, with a risk level of "medium". The system does not rely on the network and directly triggers the content interception logic on the device side, suspends the playback of the advertisement, and replaces it with a backup public service advertisement.

[0060] (4) Log reporting: The key characteristics and audit results of the violation are compressed and encrypted, and then asynchronously uploaded to the central platform when the network is available.

[0061] Implementation results: This embodiment demonstrates that the technical solution of the present invention can be effectively tailored and optimized to adapt to edge computing scenarios, achieving efficient localized content security interception under conditions of extremely low latency and no need for cloud backhaul, thus greatly expanding its application scope.

[0062] Example 5

[0063] Multi-tenant review capability open platform: Purpose of implementation: This embodiment demonstrates how the present invention, as a basic service capability, can securely and flexibly provide customized auditing services for different types of external platforms (tenants) through standardized interfaces and protocols.

[0064] Implementation System: Build a multi-tenant SaaS-based review platform. The core of the platform is a unified review engine cluster. The front end uses an API gateway responsible for protocol adaptation, authentication, traffic management, and billing. The back end connects to independent, tenant-isolated data storage areas.

[0065] Implementation steps: (1) Multi-protocol adaptation access: An e-commerce platform (using HTTPS+JSON) and a live streaming platform (using WebSocket+Protobuf) simultaneously initiate review requests. The API gateway automatically identifies the protocols, decrypts and converts the formats, and sends the unified internal data format to the review engine.

[0066] (2) Unified core processing: The review engine calls context analysis and integrated review services to process the content. For example, for a product details page on an e-commerce platform (image: medicine bottle; text: family secret recipe), the engine identifies the violation association between "ordinary medicine images" and "text implying illegal medical effects".

[0067] (3) Customized Output and Feedback Loop: Audit results are returned via the API gateway in the format (JSON / Protobuf) agreed upon by the tenant. Simultaneously, the platform provides a management panel allowing each tenant to view their own data reports, customize risk label weights, and provide feedback on the accuracy of the audit results. This feedback data is stored in isolation and used for model fine-tuning specific to that tenant.

[0068] Implementation results: This embodiment demonstrates the system's openness and flexibility. By encapsulating a powerful auditing core into a configurable and interoperable service, this invention can serve diverse business scenarios in a "capability output" model, and form an independent optimization loop with the help of feedback data from each tenant, thereby improving customer stickiness and the level of intelligence of the service.

[0069] Comparative Example 1

[0070] Systems combining traditional rules with single-modal models: Implementation System: A widely used legacy system architecture is employed: a standalone open-source image recognition model (for identifying violent, pornographic, and other graphic content) and a keyword filtering system (for matching a sensitive word list). The results from both are aggregated into a decision engine based on simple rules (such as "logical OR").

[0071] Implementation steps: The cartoon poster in Example 1 was reviewed.

[0072] Image model: Recognized as "cartoon character" and "smiling", with no violation features, output "pass".

[0073] Text filtering system: Matching the keyword database, if no absolutely sensitive words such as "gambling and drugs" are found, output "pass".

[0074] Rule engine: Executes the rule "if both pass, then all pass", and finally determines that the content is compliant.

[0075] Implementation effect analysis: Traditional systems fail completely. They cannot understand the negative semantics—where 1+1>2—resulting from the combination of images (friendly cartoons) and text (manipulative cues). Their flaws are fundamental: Lacks deep semantic understanding: It only performs surface feature matching and has no ability to analyze contextual relationships.

[0076] Rigid rules: Rules and lexicons that rely on manual maintenance are difficult to deal with the ever-emerging new ways of violating the rules.

[0077] Unexplainable: Unable to provide a reasonable explanation for "why the violation occurred", only a yes / no conclusion is given.

[0078] Unable to handle contradictions and irony: For example, a beautiful landscape photo captioned "This is the world we are going to destroy" would be allowed by traditional systems because the image is compliant and the text contains no sensitive words. However, this invention can see the underlying conflict and harmful intentions.

[0079] Compared to Examples 1-5 and Comparative Example 1, the system architecture and review capabilities of the five examples of this invention form a fundamental and multi-layered stark contrast, comprehensively revealing the disruptive advantages of the context-aware and multimodal deep fusion mechanism proposed in this invention over traditional methods. The comparative example represents the current mainstream review paradigm, which relies on independent single-modal models (such as image classification and keyword filtering) for surface feature recognition, and then mechanically concatenates the results through simple logical rules (such as "logical OR"). This paradigm completely fails in the classic test scenario set in Example 1—a friendly cartoon image paired with a poster containing suggestive text—because it completely fails to understand the negative semantics of "1+1>2" generated by the combination of cross-modal elements. Its review logic remains at the surface matching stage, lacking the ability to analyze deeper semantics such as complex contextual relationships, irony, and contradictory rhetoric. In contrast, all embodiments of this invention are built upon a unified intelligent architecture: a dedicated context-aware processing unit deeply analyzes the semantic relationships between images and text to generate structured features that transcend the original data itself; then, a multimodal fusion review unit dynamically integrates the original information and contextual features to achieve accurate decision-making based on deep understanding. This fundamental innovation enables the system to accurately identify violations that rely on contextual relationships, which are inevitably missed by comparative systems. It fundamentally solves the core blind spot of traditional review methods, achieving a qualitative leap from "feature matching" to "semantic understanding."

[0080] Furthermore, the five embodiments systematically unfold and verify the superior performance and high flexibility of the core architecture of this invention from different dimensions and application scenarios, jointly drawing a complete technical map from core verification to full commercialization. Embodiment 1, as a minimal implementation, strips away all enhancement modules, purely and powerfully demonstrating the effectiveness and necessity of the two core mechanisms of "context awareness" and "multimodal fusion". Embodiments 2, 3, 4, and 5, on this basis, are like adding different "enhancing modules" and "application shells" to the core engine, respectively demonstrating the system's high reliability across the entire chain in harsh industrial environments (Embodiment 2), high performance and elastic scalability in dealing with massive concurrency on the Internet (Embodiment 3), lightweight and low-latency characteristics for adapting to resource-constrained edge devices (Embodiment 4), and openness and configurability as a standardized service to empower diversified third-party platforms (Embodiment 5). These five embodiments are not simple repetitions, but rather a comprehensive demonstration from four key engineering dimensions—reliability, performance, deployment form, and service model—showing how the same core invention can meet all-round needs from embedded terminals to cloud clusters, from closed intranets to open platforms, and from steady-state processes to high-concurrency traffic.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0082] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A context-aware multimodal image and text content review system, characterized in that, include: A multimodal data acquisition module is used to acquire text and image data; A context-aware processing module, connected to the multimodal data acquisition module, is used to perform context analysis on the image and text data and generate a structured context feature set. The multimodal fusion review module is connected to the multimodal data acquisition module and the context-aware processing module respectively, and is used to fuse the image and text data with the structured context feature set to perform content review and generate review results; The result output and feedback module is connected to the multimodal fusion review module and is used to output the review results.

2. The context-aware multimodal image and text content review system as described in claim 1, characterized in that, Also includes: A communication transmission module is connected between the multimodal fusion review module and the result output and feedback module, and is used to transmit the review result; The dual fault-tolerance module is connected to the multimodal data acquisition module, context-aware processing module, multimodal fusion audit module, and communication transmission module, and is used to monitor the operating status of each module and handle faults.

3. The context-aware multimodal image and text content review system as described in claim 1, characterized in that, The multimodal data acquisition module supports JPG, PNG, TXT, and DOCX format inputs. It standardizes the data into tensor data of a unified format through a format parsing engine. It has built-in anti-interference circuit, synchronous clock circuit, and data verification mechanism, and can work stably in environments with temperatures ranging from -40℃ to 85℃ and relative humidity ranging from 10% to 90%.

4. The context-aware multimodal image and text content review system as described in claim 1, characterized in that, The context-aware processing module adopts a layered architecture of edge preprocessing and cloud-based deep analysis; The edge is used for data denoising and primary feature extraction. The cloud uses sparse attention mechanism, BERT model and CNN-LSTM hybrid model to extract multi-dimensional correlation features of image and text data respectively, and uses cross-modal attention mechanism to realize feature alignment to generate the structured context feature set.

5. The context-aware multimodal image and text content review system as described in claim 1, characterized in that, The multimodal fusion review module adopts a deep learning architecture that includes a multimodal input layer, an attention fusion layer, and a classification output layer. The attention fusion layer adopts a weighted fusion strategy to dynamically fuse the standardized image and text data with the structured context feature set for compliance verification, anomaly location, and risk level quantification.

6. The context-aware multimodal image and text content review system as described in claim 2, characterized in that, The communication transmission module supports adaptive switching between wired and wireless communication modes, with a switching threshold of 100Mbps bandwidth; the audit results are transmitted using the AES-256 encryption algorithm and a dynamic key update mechanism with a cycle not exceeding 24 hours.

7. The context-aware multimodal image and text content review system as described in claim 2, characterized in that, The dual fault tolerance module includes a hardware-level fault detection unit and a software-level anomaly repair algorithm. When an anomaly is detected, it can automatically trigger a restart or backup module switching operation and generate an encrypted anomaly log.

8. The context-aware multimodal image and text content review system as described in claim 1, characterized in that, The output and feedback module is used to generate standardized audit reports that include screenshots of anomalies, risk evidence, and handling suggestions, and supports integration with third-party regulatory systems via RESTful API.

9. The context-aware multimodal image and text content review system as described in claim 1, characterized in that, The system further includes a cloud data processing unit; the cloud data processing unit includes: A distributed database is used to store the audit data generated by the multimodal fusion audit module and the exception logs generated by the dual fault tolerance guarantee module; The model iteration optimization unit, based on the data collected by the multimodal data acquisition module and the feedback information from the result output and feedback module, uses incremental training to iteratively optimize the deep learning model in the multimodal fusion review module.

10. The context-aware multimodal image and text content review system as described in claim 2, characterized in that, The multimodal data acquisition module, context-aware processing module, multimodal fusion review module, communication transmission module, result output and feedback module, and dual fault tolerance module all adopt standardized and modular design, supporting hot-swapping and independent upgrades.

Images