10 results about "Contextual Associations" patented technology

This application provides a method and system for generating multilingual cultural content based on natural language understanding. The method first performs deep semantic analysis on the source language text content, extracting semantic structural elements, cultural feature elements, and contextual information. It then performs semantic alignment processing between these elements and a pre-defined multilingual cross-cultural knowledge graph. For semantic elements with cultural differences, it performs rule-constrained fuzzy reasoning to generate cultural adaptation control parameters to guide the content generation process. In the multilingual generation model, during content generation, the selection of vocabulary and expression methods are dynamically adjusted based on the cultural adaptation control parameters, outputting target language text content that conforms to the target language's cultural context. This solution enables cross-cultural dynamic adaptation generation based on fuzzy reasoning, thereby improving the cultural accuracy and contextual appropriateness of multilingual content output.

The application discloses a user intention recognition method based on multi-modal context perception, comprising the following steps: constructing an original data set by collecting multi-source modal data, extracting context correlation features by using multi-modal context attention perception technology, and establishing the correlation between intra-modal and inter-modal; dynamically adjusting the proportion of feature weights of each modal based on intention-oriented modal weight distribution technology, combining context-coupled intention recognition technology to generate a preliminary intention candidate set; performing semantic consistency verification through cross-modal semantic data intelligent processing technology, eliminating intention candidates corresponding to contradictory features, and screening and outputting accurate results through the user intention recognition link of context perception. Through the cooperation of different technologies, the application deeply integrates multi-modal context information, dynamically adapts to different scene requirements, optimizes the recognition process layer by layer, effectively improves the comprehensiveness and accuracy of user intention recognition, and is suitable for intention recognition requirements in various intelligent interaction scenes.

The application relates to the technical field of intelligent decision-making, and specifically discloses a log information desensitization method and device based on context semantics, equipment and a medium, the method comprising the following steps: acquiring a log source file and converting the log source file into a standardized log data structure; inputting the log source file into a pre-configured sensitive information feature library for screening and calculation, so as to obtain at least one field name and context information; comprehensively evaluating keyword features, separator features and position features in a weighted linear combination mode, so as to obtain a context correlation degree score; determining a sensitive field according to a comparison result, and searching for associated sensitive information; and performing desensitization processing on the sensitive information according to a preset desensitization strategy, so as to obtain a desensitized log file. The present scheme realizes deep mining and analysis of context semantics, can more accurately determine whether a field name is a sensitive field and associated sensitive information, effectively reduces misjudgment and missed judgment problems caused by semantic loss, and improves the reliability of sensitive information identification.

This invention relates to a network intrusion detection method based on bilayer BiLSTM knowledge distillation, belonging to the fields of deep learning and network security technology. The invention includes the following steps: First, a bilayer bidirectional long short-term memory (BiLSTM) network is used to extract temporal features of network traffic data to capture bidirectional contextual information in traffic packets. Second, a knowledge distillation architecture is introduced, using a pre-trained teacher model to pass soft-label knowledge to a lightweight student model, thereby compressing model parameters and enhancing the model's generalization performance in imbalanced data scenarios. Furthermore, by combining an attention mechanism to dynamically weight key spatial features, the feature extraction process is optimized, and deep fusion of multi-dimensional features is achieved through a fully connected layer. This invention significantly reduces system computational overhead and storage requirements while effectively improving the classification accuracy and stability of the lightweight detection network.

This invention discloses a sparse attention modeling method and system for long texts based on four-dimensional spatiotemporal coordinates, belonging to the field of large-scale model long text optimization and sparse attention technology. Existing full-scale attention mechanisms for large models suffer from high memory consumption, large inference latency in long texts, attention diffusion, and failure of long-range dependencies. Various sparse attention algorithms are mostly based on window truncation and fixed-interval sampling, which are empirical simplification strategies that easily lose key long-range information and disrupt the text's logical temporal structure. This invention utilizes four-dimensional topological constraints to construct an explicit sparse attention mask, abandoning the fixed-window sampling mode to achieve spatiotemporal topological adaptive sparse attention modeling. This invention preserves long-range temporal correlations through temporal causal constraints, isolates invalid cross-cycle interference through contextual branch constraints, preserves key dependencies in the inference chain through logical hierarchy constraints, isolates mixed information from multiple subjects through identity coordinates, and dynamically weights key token attention weights based on cognitive memory strength. This invention significantly reduces the computational power and memory overhead of long texts while fully preserving long-range causality, logic, and contextual correlations, completely solving the core pain points of degradation and information loss in traditional sparse attention for long texts, and significantly improving the stability and logical consistency of inference for millions of ultra-long texts.

The application discloses an API behavior perception modeling method and system based on semantic structure mapping and continuous learning, a device and a medium, and belongs to the technical field of API modeling. The method comprises the following steps: generating an interface semantic graph and a representation matrix through semantic analysis, combining user behavior modeling to extract behavior feature embedding and context association graph, fusing semantics and behavior features to form a joint representation, performing security risk analysis to identify abnormal behavior and assess risk levels, updating model parameters according to new data and feedback, monitoring model drift and adjusting behavior baseline through a continuous learning mechanism, and generating an interpretable report based on abnormal results, risk scores and multi-modal weights. Through the introduction of the API semantic structure mapping mechanism and behavior context modeling, the application realizes accurate perception and identification of interface call intentions and complex attack chains. Through the continuous learning mechanism, the application has the environment self-adaptation and long-term evolution ability, and effectively avoids model aging.

A system and methods for latent contextual threading for personalized dialogue through geometric manifold-based conversation management. The system maintains a personalized cognitive manifold as a geometric manifold in latent space that encodes user-specific dialogue patterns as navigable geometric structures. Multiple dialogue contexts are maintained as geometric trajectories within the manifold, with dialogue responses generated through manifold traversal rather than discrete context retrieval. A bidirectional adaptation system modifies the manifold's geometric structure based on user interactions. The system preserves dialogue continuity across session boundaries by serializing manifold geometry during session termination and restoring geometric positioning during session resumption. Dialogue coherence is evaluated through geometric analysis including curvature calculations and geodesic deviation measurements. The system maintains conversations through real-time manifold geometry modifications, providing dialogue experiences across session boundaries while maintaining contextual threading and personalized interaction patterns through geometric principles.

This invention discloses a vehicle sensitive data detection system based on multimodal semantic context analysis, specifically relating to the fields of network data security and automotive electronics technology. The system includes: a data packet upload module, a payload extraction module, a multimodal hybrid detection engine, a context association module, and a result generation and presentation module. The data packet upload module receives and verifies the PCAP file; the payload extraction module parses the data packet and extracts the application layer payload; the multimodal hybrid detection engine integrates keyword matching, regular expression matching, and local dictionary comparison units to perform parallel scanning of the payload text; the context association module binds the detection results with network quintuple information; and the result generation and presentation module generates a structured audit report and displays it visually. This invention achieves automated detection of sensitive information in automotive network data packets, improves the accuracy of identifying unique data such as VINs, geographic coordinates, and on-board diagnostic fault codes, and enhances the traceability and risk assessment capabilities of the detection results.

This method aims to address the shortcomings of existing semantic retrieval technologies, such as insufficient semantic understanding, limited logical reasoning capabilities, and a lack of contextual relevance in search results. By introducing a neuro-symbolic collaborative architecture, this method transforms textual knowledge into a symbolic knowledge structure and jointly models it with neuro-semantic embeddings to construct a hybrid memory index that combines semantic expression and logical relationships. The method comprises three core steps: text symbolization and index construction, query semantic parsing and symbolic structure generation, and neuro-symbolic retrieval and dynamic reasoning. This method effectively improves multi-hop reasoning capabilities, complex semantic understanding, and result interpretability, demonstrating higher accuracy and intelligence in complex semantic query scenarios.

This invention discloses a method and system for analyzing and quantifying children's classroom behavior through video images, belonging to the fields of computer vision and educational technology. The method includes: a video image acquisition step for acquiring and preprocessing classroom videos; a target tracking step for continuously locating target children and extracting skeletal key point coordinates using target detection and multi-target tracking algorithms; a behavior recognition step for constructing a spatiotemporal graph structure from the key point coordinates and identifying hyperactivity-related behaviors such as leaving one's seat, fidgeting, playing with objects, and looking around using a spatiotemporal graph convolutional network; a quantification and statistics step for calculating the frequency, duration, and interval distribution characteristics of behaviors; and a contextual association analysis step for synchronously recording teaching context labels, analyzing differences in behavior patterns under different contexts, and generating a context-behavior association matrix. This invention achieves a behavior recognition accuracy rate greater than 85% and outputs behavior statistics, time distribution graphs, and cross-contextual comparative analysis reports, making it suitable for educational research and tracking the effects of behavior interventions.