Complex scene-oriented end-to-end semantic extraction system

Abstract

The invention provides a complex scene-oriented end-to-end semantic extraction system, belongs to the technical field of artificial intelligence and natural language processing, and realizes cross-modal information association through a multi-source heterogeneous data fusion module to construct a dynamic semantic network model. A hierarchical attention mechanism is adopted to carry out context-aware coding on unstructured input, and unsupervised pre-training and a weak supervised fine tuning strategy are combined to optimize a feature representation space. And designing an adaptive inference engine, automatically switching semantic analysis paths based on scene complexity, and generating a structured output result. According to the method, the dependency on specific knowledge in the field is reduced, the semantic understanding generalization ability in a complex scene is remarkably improved, high-precision analysis performance can still be kept in a low-resource environment, meanwhile, calculation resource consumption is reduced, and the method is suitable for practical application scenes with multi-language mixing, serious noise interference and high real-time performance requirements.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence and natural language processing, and in particular to an end-to-end semantic extraction system for complex scenarios. BACKGROUND

[0002] Modern application scenarios involve data that is no longer limited to a single source or format. Text, speech, images, video, structured databases, sensor data, social media streams, and the like are intertwined, with information being scattered and complexly related. A large amount of valuable information is hidden in unstructured data such as natural language text, voice recordings, image / video descriptions, etc., and the extraction difficulty is much higher than that of structured data. There is a large amount of irrelevant, repetitive, and even erroneous information in the data, which interferes with the recognition of core semantics.

[0003] The end-to-end semantic extraction technology for complex scenarios is an inevitable product to meet the multi-source heterogeneous and deep semantic understanding needs in the information explosion era. It utilizes the powerful capabilities of deep learning and end-to-end learning to overcome the inherent defects of traditional pipeline methods, and has significant advantages in improving accuracy, simplifying systems, utilizing global context, enhancing robustness, and promoting multi-modal fusion. Although it still faces challenges such as computational cost and interpretability, it is undoubtedly the core driving force for the development of semantic understanding technology towards more intelligent and more general directions, and will play an increasingly key role in numerous intelligent applications. SUMMARY

[0004] To solve the above technical problems, the present application provides an end-to-end semantic extraction system for complex scenarios. To solve the problems of multi-stage pipeline error accumulation, cross-modal information fragmentation, and poor dynamic context adaptability in traditional semantic extraction.

[0005] The present application integrates deep learning, multi-modal learning, and cross-domain knowledge representation, and realizes direct and accurate mapping from raw input to structured semantics by constructing a unified encoding and decoding architecture, a multi-source heterogeneous data fusion mechanism, and a context perception model. Its core technology covers the pre-training and fine-tuning paradigm based on the Transformer architecture, providing powerful context semantic modeling capabilities. Cross-modal unified semantic space representation learning supports collaborative understanding of multiple sources of information such as text, images, and speech. And the end-to-end joint extraction mechanism for complex logic and implicit relationships synchronously processes multi-dimensional semantic elements such as entities, relationships, and events. This technology can be widely applied to intelligent question answering, knowledge graph construction, public opinion analysis, and other scenarios. It can address challenges such as noise interference, polysemy, and cross-modal association by replacing the traditional cascade pipeline with a unified model, significantly improving the robustness, accuracy, and generalization ability of semantic understanding

[0006] The technical solution of the present application is:

[0007] A complex scene-oriented end-to-end semantic extraction system, comprising:

[0008] A multi-modal data access and preprocessing module, which uniformly accesses heterogeneous data sources of text, image, voice, and video, and completes noise filtering, format standardization, and cross-modal alignment;

[0009] An end-to-end joint feature extraction module, which fuses visual, text, and voice features to generate a unified semantic representation vector, supporting joint extraction of entities and events;

[0010] A dynamic semantic modeling and optimization module, which dynamically adjusts semantic analysis strategies based on reinforcement learning to adapt to complex context changes;

[0011] A real-time feedback and adaptive learning module, which realizes low-latency scene adaptation through a closed-loop feedback iterative model;

[0012] An interpretable decision output module, which generates auditable semantic structures and decision-making basis;

[0013] A knowledge sedimentation and migration module, which realizes cross-scene capability migration and system self-evolution;

[0014] An application interface and visualization layer, which provides low-code APIs and interactive analysis interfaces.

[0015] Among them,

[0016] The multi-modal data access and preprocessing module comprises

[0017] Multi-source access interface: text source, web page or database is crawled through Scrapy, PDF or image is parsed using PDFMiner; video source, OpenCV decodes streaming media, FFmpeg processes video frames; voice source, WebRTC real-time acquisition, Librosa processes audio stream;

[0018] Noise cleaning: text, regular expression cleaning HTML tags, BERTCorrector correcting spelling errors; image, GAN-based denoising model repairing occluded or blurred areas; voice, spectral subtraction suppressing environmental noise, VAD endpoint detection eliminating silent segments;

[0019] Cross-modal alignment: CLIP model calculates the similarity between text and image, and dynamic attention mechanism synchronizes audio and video timestamps.

[0020] The end-to-end joint feature extraction module comprises

[0021] Visual feature engine: image, ViT extracts global features, Mask RCNN segments local objects; video, 3DCNN captures spatiotemporal features, TSN model models action sequences;

[0022] Text semantic encoding: long text processing, Longformer sliding window encoding, supporting 10k+ character documents; Multilingual adaptation, XLMRoBERTa cross-language embedding;

[0023] Cross-modal fusion: graph neural network constructs multi-modal semantic graph, node = entity / object, edge = cross-modal association; Gated attention mechanism dynamically weights the contribution of each modality.

[0024] Dynamic semantic modeling and optimization module, including

[0025] Joint decoder: multi-head pointer network, synchronously outputs entity boundaries and relationship types; Constraint decoding, inject domain rules;

[0026] Dynamic optimization mechanism: reinforcement learning framework, F1 value as reward signal, PPO algorithm optimizes multi-task weight; Online adversarial training, generate adversarial samples to improve robustness.

[0027] Real-time feedback and adaptive learning module, including

[0028] Confidence monitoring: logical consistency detection, knowledge graph verification of triple rationality; Uncertainty quantification, Monte Carlo Dropout calculates the prediction variance;

[0029] Incremental learning pipeline: flexible weight solidification, protect important parameters, avoid catastrophic forgetting; Active learning sampling, based on confidence to select high-value sample manual annotation;

[0030] Abnormal response: LSTMAutoencoder detects semantic drift; Automatically trigger fine-tuning, complete domain adaptation within 24 hours.

[0031] Interpretable decision output module, including

[0032] Ambiguity disambiguation: knowledge graph embedding, calculate entity context similarity; Bayesian inference, output probabilistic decision path;

[0033] Visual traceability: heat map positioning, GradCAM highlights key text fragments and image ROI regions; Causal chain generation, RDF triples exported as interactive knowledge graph.

[0034] Knowledge sedimentation and migration module, including

[0035] Rule neural collaboration: automatic rule mining, Apriori algorithm extracts high-frequency semantic patterns, neural network injection, rule conversion to model attention prior;

[0036] Meta-transfer learning: MAML framework, 5-sample adaptation to new domains; Parameterized knowledge base, store domain-specific weight matrix, support fast switching.

[0037] Application interface and visualization layer, comprising,

[0038] RESTful API: Input, raw multi-modal data; Output, JSONLD format semantic graph; Support gRPC streaming, delay <200ms;

[0039] Visualization console: 3D knowledge graph, PyVis dynamic display of entity relationship network; Decision traceability board, comparison of model version effects, annotation error attribution path

[0040] Automatic report: LaTeX engine generates structured analysis report.

[0041] The beneficial effects of the present application are

[0042] I. Improve decision-making quality and comprehensiveness

[0043] The intelligent procurement sourcing method considers multiple optimization objectives such as cost, risk, delivery speed and quality, etc., to provide a more balanced and comprehensive supplier selection solution for enterprises. This method not only focuses on finding the lowest cost option, but also evaluates the overall performance of suppliers from a broader perspective, including their long-term cooperation potential and potential risks. This comprehensive consideration helps enterprises make more informed decisions to ensure that each procurement meets the optimal state in multiple key indicators. In addition, this method can identify the most suitable suppliers for specific needs, thereby improving procurement efficiency and satisfaction.

[0044] II. Enhance the adaptability and flexibility of the system

[0045] The intelligent procurement sourcing method introduces a real-time feedback mechanism, enabling the system to adjust in real time according to the latest market data and changes in internal demand. This is beneficial for enterprises to respond quickly and update their procurement strategies in the face of rapidly changing market environments. By continuously learning and adapting to new information, the intelligent procurement sourcing method promotes the flexibility and agility of the enterprise procurement process. Whether it is market price fluctuations or changes in supplier performance, this method helps enterprises adjust strategies in a timely manner, maintain competitiveness, and effectively cope with various uncertainty factors.

[0046] III. Significantly improve work efficiency and automation level

[0047] The intelligent procurement sourcing method automates the entire process from data collection and feature extraction to the generation of final procurement recommendations. This process reduces reliance on manual operations and improves processing speed and accuracy. Through automated data processing and analysis tools, companies can obtain valuable information more quickly, supporting more efficient decision-making. Furthermore, the intelligent procurement sourcing method simplifies complex procurement processes, shortens the time cycle from demand identification to decision implementation, significantly improves overall work efficiency, and enables companies to maintain a competitive edge in the fierce market.

[0048] IV. Enhancing Transparency and Strengthening Trust

[0049] Intelligent sourcing methods utilize advanced visualization tools to present analytical results and rationale for recommendations, making the procurement decision-making process more transparent and understandable. This approach not only facilitates understanding and acceptance of decision outcomes by internal stakeholders but also helps build trust with external partners. Through clear and intuitive data presentation, all participants can better understand the logic and supporting evidence behind procurement decisions. Furthermore, intelligent sourcing methods can capture and visualize complex supply chain relationships, helping management gain a deeper understanding of every link in their supply chain network, thereby enabling more informed strategic decisions.

[0050] V. Strengthen risk management capabilities

[0051] Intelligent sourcing methods enhance enterprises' ability to predict and manage potential risks by learning from historical transaction data and continuously monitoring the current situation. This approach helps identify risk factors that may affect supply chain stability in advance and allows for corresponding preventative measures. For example, when abnormal supplier behavior or adverse market price fluctuations are detected, the system can immediately issue alerts and suggest appropriate response strategies. This proactive risk management approach helps enterprises reduce losses caused by unforeseen events, protect their interests from harm, and promote the healthy and stable development of the entire supply chain. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the system architecture of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but 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.

[0054] This invention provides an end-to-end semantic extraction system for complex scenarios, including...

[0055] I. Multimodal Data Access and Preprocessing Module

[0056] Function Description:

[0057] It unifies access to heterogeneous data sources such as text, images, voice, and video, and completes noise filtering, format standardization, and cross-modal alignment.

[0058] Specific algorithms and operations:

[0059] Multi-source access interfaces: Text source: crawls web pages or databases using Scrapy, and parses PDFs or images using PDFMiner. Video source: decodes streaming media using OpenCV, and processes video frames using FFmpeg. Audio source: real-time capture using WebRTC, and processes audio streams using Librosa.

[0060] Noise cleaning: For text, regular expressions are used to clean HTML tags, and BERT Corrector is used to correct spelling errors. For images, GAN-based denoising models (such as DnCNN) are used to repair occluded or blurred regions. For speech, spectral subtraction is used to suppress ambient noise, and VAD endpoint detection is used to remove silent segments.

[0061] Cross-modal alignment: The CLIP model calculates image-text similarity, and the dynamic attention mechanism synchronizes audio and video timestamps.

[0062] II. End-to-end Joint Feature Extraction Module

[0063] Function Description:

[0064] By integrating visual, textual, and speech features, a unified semantic representation vector is generated to support the joint extraction of entity relationship events.

[0065] Specific algorithms and operations:

[0066] Visual feature engine: For images, ViT extracts global features, and Mask R-CNN segments local objects. For videos, 3DCNN captures spatiotemporal features, and the TSN model models action sequences.

[0067] Text semantic encoding: Long text processing, Longformer sliding window encoding, supports documents of 10k+ characters. Multilingual adaptation, XLMRoBERTa cross-language embedding.

[0068] Cross-modal fusion: Graph neural networks (GAT) construct multimodal semantic graphs, where nodes = entities / objects and edges = cross-modal associations. Gated attention mechanisms (such as MCAN) dynamically weight the contributions of each modality.

[0069] III. Dynamic Semantic Modeling and Optimization Module

[0070] Function Description:

[0071] It dynamically adjusts semantic parsing strategies based on reinforcement learning to adapt to complex context changes.

[0072] Specific algorithms and operations:

[0073] Joint decoder: A multi-head pointer network that synchronously outputs entity boundaries and relationship types (e.g., company, merger, counterparty). Constraint decoding: Injects domain rules (e.g., "Merger events must include monetary attributes").

[0074] Dynamic optimization mechanism: A reinforcement learning framework is used, with the F1 score as the reward signal and the PPO algorithm optimizing multi-task weights. Online adversarial training generates adversarial examples (such as replacing synonyms to create ambiguity) to improve robustness.

[0075] IV. Real-time Feedback and Adaptive Learning Module

[0076] Function Description:

[0077] A closed-loop feedback iterative model is used to achieve low-latency scenario adaptation.

[0078] Specific algorithms and operations:

[0079] Confidence monitoring: Logical consistency detection, knowledge graph verification of the rationality of ternary combinations (e.g., "Apple acquires Microsoft" triggers a contradiction alert). Uncertainty quantification, Monte Carlo Dropout calculates prediction variance.

[0080] Incremental learning pipeline: Elastic weight solidification (EWC) protects important parameters and avoids catastrophic forgetting. Active learning sampling selects high-value samples for manual annotation based on confidence levels.

[0081] Anomaly Response: LSTMAutoencoder detects semantic shifts (such as performance degradation caused by new network terms). It automatically triggers fine-tuning, completing domain adaptation within 24 hours.

[0082] V. Explainable Decision Output Module

[0083] Function Description:

[0084] Generate auditable semantic structures and decision-making criteria.

[0085] Specific algorithms and operations:

[0086] Ambiguity disambiguation: Knowledge graph embedding to calculate entity context similarity (e.g., the association strength between "apple" and "phone" or "fruit" in a sentence). Bayesian inference to output probabilistic decision paths (brand label probability 92%).

[0087] Visual source tracing: heatmap localization, GradCAM highlighting of key text fragments and image ROI regions. Causal chain generation, exporting RDF triples into an interactive knowledge graph.

[0088] VI. Knowledge Accumulation and Transfer Module

[0089] Function Description:

[0090] Enable cross-scenario capability transfer and system self-evolution.

[0091] Specific algorithms and operations:

[0092] Rule-based neural collaboration: Automatic rule mining, Apriori algorithm extracts high-frequency semantic patterns (such as "diagnosed as [disease] must include [symptoms]"), neural network injection, rules are converted into model attention priors.

[0093] Meta-transfer learning: MAML framework, 5-sample adaptation to new domains (e.g., migrating from financial news to medical literature). Parameterized knowledge base, storing domain-specific weight matrices, supporting rapid switching.

[0094] VII. Application Interface and Visualization Layer

[0095] Function Description:

[0096] Provides a low-code API and an interactive analysis interface.

[0097] Specific algorithms and operations:

[0098] RESTful API: Input: Raw multimodal data. Output: Semantic graph in JSONLD format. Supports gRPC streaming processing with latency <200ms (1080P video stream).

[0099] Visual console: 3D knowledge graph, dynamically displaying entity relationship networks using PyVis. Decision attribution dashboard: compare model version performance, and automatically report error attribution paths: structured analysis reports generated by the LaTeX engine (including confidence level annotations and risk warnings).

[0100] This invention integrates deep learning, multimodal learning, and cross-domain knowledge representation. By constructing a unified encoding and decoding architecture, a multi-source heterogeneous data fusion mechanism, and a context-aware model, it achieves direct and accurate mapping from raw input to structured semantics. Its core technologies include a pre-trained fine-tuning paradigm based on the Transformer architecture, providing powerful contextual semantic modeling capabilities. It supports cross-modal unified semantic space representation learning, enabling collaborative understanding of multi-source information such as text, images, and speech. It also features an end-to-end joint extraction mechanism for complex logic and implicit relationships, simultaneously processing multi-dimensional semantic elements such as entities, relationships, and events. This technology can be widely applied in scenarios such as intelligent question answering, knowledge graph construction, and public opinion analysis. Addressing challenges such as noise interference, ambiguity, and cross-modal associations, it significantly improves the robustness, accuracy, and generalization ability of semantic understanding by replacing traditional cascaded pipelines with a unified model.

[0101] in

[0102] I. Multimodal Data Fusion and Preprocessing Module

[0103] Functionality: Real-time integration of heterogeneous data from multiple sources, including text, images, audio, and video, to build a unified semantic understanding foundation; further features include...

[0104] Cross-modal synchronization submodule:

[0105] Real-time association of heterogeneous data can be achieved through deep alignment networks (such as cross-modal attention mechanisms). For example, visual objects in images can be dynamically bound to textual descriptive entities to solve the problem of semantic separation between images and text.

[0106] Noise filtering and enhancement submodule:

[0107] Generative Adversarial Networks (GANs) are used to automatically clean up input noise (such as speech-to-text errors and image occlusion interference), and context-aware data augmentation is used to improve robustness in few-sample scenarios.

[0108] II. End-to-End Joint Semantic Modeling Module

[0109] Functionality: Based on a unified encoding and decoding architecture, it synchronously extracts multi-dimensional semantic elements such as entities, relationships, and events, avoiding cascading errors; further features include...

[0110] Dynamic Context Encoding Submodule:

[0111] Pre-trained Transformers (such as Unified Transformer) can be used to capture long-distance dependencies and implicit logic, such as identifying the participants, amounts, and causal chains of "mergers and acquisitions" events in financial texts.

[0112] Multi-task joint decoding submodule:

[0113] Design a multi-head decoder with shared parameters to generate structured semantic output (such as entity relation triples and event graphs) in parallel. For example, extract "disease symptoms and drugs" associations and treatment plan events simultaneously from medical reports.

[0114] Cross-modal semantic bridging submodule:

[0115] By fusing multimodal semantic nodes through graph neural networks, it supports complex reasoning (such as inferring the cause of equipment failure based on video footage and narration).

[0116] III. Real-time Feedback and Adaptive Optimization Module

[0117] Functionality: Dynamically iterates the model based on semantic understanding results to improve adaptability to complex scenarios; further features include...

[0118] Confidence feedback submodule:

[0119] Based on output logic consistency checks (such as knowledge graph verification) and user correction records, the confidence level is quantified and predicted. For example, low-confidence segments (such as ambiguous references to "its company") are automatically marked to trigger manual review.

[0120] Online incremental learning submodule:

[0121] The model parameters are updated in real time using lightweight fine-tuning techniques. For example, when the system misidentifies "apple" as a fruit rather than a brand, user feedback will drive the model to complete domain adaptation within 10 minutes.

[0122] IV. Complex Semantic Decision Making and Interpretable Module

[0123] Function: Generates executable semantic structures and ensures decision transparency; further includes

[0124] Polysemy disambiguation module:

[0125] By integrating knowledge graphs and contextual analysis to resolve ambiguities (such as "Beijing" as a location / organization), a probabilistic decision path is output (such as "organization label probability 82%)".

[0126] Explainable source tracing submodule:

[0127] Visualize semantic extraction of causal chains (such as highlighting textual evidence for "merger and acquisition amount" and cross-modal supporting images) to support compliance audits and model diagnostics.

[0128] V. Knowledge Accumulation and Transfer Module

[0129] Functionality: Enables cross-scenario semantic understanding capability transfer; further includes...

[0130] Rule-based neural synergy submodule:

[0131] Automatically extract domain rules from historical decisions (such as "prioritize extracting diagnostic conclusions from medical reports") and inject them into neural networks to guide reasoning in few-sample scenarios.

[0132] Cross-domain migration submodule:

[0133] Based on semantic meta-learning, we can adapt to new scenarios (such as migrating from financial sentiment analysis to industrial fault report parsing) and reduce the annotation requirements by 90%.

[0134] exist Figure 1 In the process, the data sequentially goes through the multimodal data access and preprocessing module, the end-to-end joint feature extraction module, the dynamic semantic modeling and optimization module, the interpretable decision output module, the real-time feedback and adaptive learning module, the application interface and visualization layer, the knowledge accumulation and transfer module, and then returns to the dynamic semantic modeling and optimization module in a closed loop.

[0135] The above description is merely a preferred embodiment of the present invention and is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. An end-to-end semantic extraction system for complex scenarios, characterized in that, include: The multimodal data access and preprocessing module unifies access to heterogeneous data sources of text, images, voice, and video, and completes noise filtering, format standardization, and cross-modal alignment. The end-to-end joint feature extraction module integrates visual, textual, and speech features to generate a unified semantic representation vector, supporting the joint extraction of entity relationship events; The dynamic semantic modeling and optimization module dynamically adjusts semantic parsing strategies based on reinforcement learning to adapt to complex context changes; The real-time feedback and adaptive learning module achieves low-latency scene adaptation through closed-loop feedback iterative model. An interpretable decision output module generates auditable semantic structures and decision-making rationale; The knowledge accumulation and transfer module enables cross-scenario capability transfer and system self-evolution. The application interface and visualization layer provide low-code APIs and interactive analysis interfaces.

2. The system according to claim 1, characterized in that, Multimodal data access and preprocessing module, including Multi-source access interface: Text source, crawling web pages or databases with Scrapy, and parsing PDFs or images with PDFMiner; Video source, decoding streaming media with OpenCV, and processing video frames with FFmpeg; Audio source, real-time capture with WebRTC, and processing audio streams with Librosa. Noise cleaning: For text, regular expressions are used to clean HTML tags, and BERT Corrector is used to correct spelling errors; for images, GAN-based denoising models are used to repair occluded or blurred areas; for speech, spectral subtraction is used to suppress environmental noise, and VAD endpoint detection is used to remove silent segments. Cross-modal alignment: The CLIP model calculates image-text similarity, and the dynamic attention mechanism synchronizes audio and video timestamps.

3. The system according to claim 1, characterized in that, End-to-end joint feature extraction module, including Visual feature engine: For images, ViT extracts global features, and Mask RCNN segments local objects; for videos, 3DCNN captures spatiotemporal features, and TSN model models action sequences. Text semantic encoding: Long text processing, Longformer sliding window encoding, supports documents with 10k+ characters; Multi-language adaptation, XLMRoBERTa cross-language embedding; Cross-modal fusion: Graph neural networks construct multimodal semantic graphs, where nodes = entities / objects and edges = cross-modal associations; gating attention mechanisms dynamically weight the contributions of each modality.

4. The system according to claim 1, characterized in that, The dynamic semantic modeling and optimization module includes... Joint decoder: Multi-head pointer network, synchronously outputs entity boundaries and relationship types; Constraint decoding and injection of domain rules; Dynamic optimization mechanism: reinforcement learning framework, with F1 score as reward signal, PPO algorithm to optimize multi-task weights; Online adversarial training generates adversarial examples to improve robustness.

5. The system according to claim 1, characterized in that, Real-time feedback and adaptive learning modules, including Confidence monitoring: Logical consistency detection, knowledge graph verification of the rationality of ternary combinations; uncertainty quantification, Monte Carlo Dropout calculation of prediction variance; Incremental learning pipeline: Flexible weights are fixed to protect important parameters and avoid catastrophic forgetting; Active learning sampling, selecting high-value samples for manual annotation based on confidence levels; Anomaly response: LSTMAutoencoder detects semantic drift; automatically triggers fine-tuning, and completes domain adaptation within 24 hours.

6. The system according to claim 1, characterized in that, Interpretable decision output module, including Ambiguity disambiguation: Knowledge graph embedding to calculate entity context similarity; Bayesian inference to output probabilistic decision paths; Visual source tracing: heat map positioning, GradCAM highlights key text fragments and image ROI regions; causal chain generation, RDF triples are exported as an interactive knowledge graph.

7. The system according to claim 1, characterized in that, The knowledge accumulation and transfer module includes Rule-based neural collaboration: Automatic rule mining, Apriori algorithm to extract high-frequency semantic patterns, neural network injection, rules are converted into model attention priors; Meta-transfer learning: MAML framework, 5 samples adapted to a new domain; A parameterized knowledge base stores domain-specific weight matrices and supports rapid switching.

8. The system according to claim 1, characterized in that, Application interface and visualization layer, including, RESTful API: Input: raw multimodal data; Output: semantic graph in JSONLD format; Supports gRPC streaming processing with latency <200ms; Visualization console: 3D knowledge graph, dynamically displaying entity relationship networks using PyVis; decision attribution dashboard, comparing model version performance and annotating error attribution paths. Automated Reporting: The LaTeX engine generates structured analysis reports.

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