362 results about "Context sensing" patented technology

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a dynamic question and answer matching method and system based on context awareness. The method comprises the following steps: acquiring history of multiple rounds of dialogues and user background information, and generating a semantic understanding model of a current dialogue; based on the semantic understanding model and the current user input, matching the most relevant answer from the question and answer library, and dynamically adjusting the matching strategy; acquiring real-time feedback data of the user, updating a personalized recommendation algorithm based on the interest change of the user, and generating a personalized answer recommendation list; dynamically updating the content of the question and answer library according to the personalized recommendation list and the user behavior data; and inputting the user feedback and the updated question and answer library data into a personalized recommendation system to generate final personalized answer recommendation. Through context sensing and real-time dynamic adjustment, the matching precision of the question answering system is remarkably improved, the interaction experience of the user is improved, and the adaptability and flexibility of the system are enhanced.

The invention discloses a knowledge graph construction method for multi-modal data, and relates to the technical field of intelligent search optimization, and the method comprises the steps: matching similar knowledge entities based on a cross-modal consistency measurement method, combining redundant data through employing an information entropy deduplication strategy, carrying out semantic alignment through employing a graph convolution network, and dynamically adjusting a clustering structure, thereby achieving the optimization of the knowledge graph. The data integration quality of the knowledge graph is improved; aiming at the problem of lack of context sensing ability, search behavior data such as search keywords, click frequency and residence time are collected in real time, a search environment adaptation factor Gdf is calculated and evaluated, knowledge entity association weight is optimized by adopting reinforcement learning, and search suitability is improved; in order to solve the problem that multi-modal data is difficult to process, a cross-modal contrast learning method is adopted, a cross-modal matching degree index Mcf is calculated and evaluated, and the weight of each modal data is dynamically adjusted based on a logarithmic geometric mean, so that different modal data are fused in a shared vector space, and optimized modeling and retrieval of a cross-modal knowledge graph are realized.

The invention discloses an intelligent legal data processing method and system based on big data, and the method comprises the steps: S1, constructing a legal data collection framework, and carrying out the unified coding and representation of different data forms through a multi-modal fusion technology; s2, performing hierarchical semantic analysis on the cross-domain legal clauses to generate a high-dimensional semantic representation vector with context sensing capability; s3, performing node embedding, association analysis and reasoning optimization on the constructed legal knowledge graph to generate a dynamically updated multi-domain legal association network; s4, mapping and optimization among terms are realized through semantic comparison and multi-level rule verification; s5, performing modeling analysis on the dynamic trend and the potential risk factors of the legal data, and outputting a risk prediction and early warning strategy of the legal event; and S6, improving cross-data-source cooperative computing capability and data analysis efficiency through a distributed optimization strategy. The method has the advantages of high semantic understanding depth, high risk prediction accuracy and high privacy protection and cooperative computing efficiency.

The invention relates to the technical field of product quality inspection, in particular to a product quality detection system based on deep learning and machine vision, which comprises a multi-modal image acquisition module, a deep learning defect detection module and a dynamic resource scheduling module. The multi-modal image acquisition module is used for acquiring product images in all directions by using a high-resolution camera array and a self-adaptive light source; the deep learning defect detection module accurately identifies defects through dual-path residual denoising, multi-scale context sensing and graph topology defect classification; the dynamic resource scheduling module realizes reasonable scheduling of personnel and materials and balance of a production line according to a detection result, a skill map, an ant colony algorithm and the like; the augmented reality interaction module visually displays defects and provides maintenance navigation through holographic projection and multi-mode interaction; the self-evolution detection model can update the weight based on a new sample, and the multi-physics field simulation module simulates the influence of defects on the mechanical property of a product. The detection precision is high, the efficiency is high, and the product quality detection and production management level is remarkably improved.

The invention provides a multi-task printing scheduling optimization method based on AI intelligent strategy generation, and relates to the technical field of production workshop intelligent scheduling, and the method constructs and maintains a dynamic cognitive map, comprehensively perceives each entity state and complex association relationship of printing production in real time, utilizes an artificial intelligence engine, carries out context perception and reasoning based on the map, and improves the printing quality. The system executes the process in real time, analyzes event data, incrementally updates the state attribute of the cognitive map, evaluates the current execution effect, and adjusts the execution path and resource allocation of the flexible workflow in real time by applying a learning strategy; and continuous self-optimization and system self-evolution of a scheduling strategy model are realized through historical execution data, so that the problems of response rigidity, difficulty in realizing multi-target collaborative optimization and lack of fine-grained real-time adjustment in coping with emergencies in traditional scheduling are solved. And the scheduling flexibility, the overall efficiency, the product quality and the capability of coping with a complex and uncertain environment of printing production are remarkably improved.

The invention provides a data decision-making method and system based on multi-modal large model analysis, and the method comprises the steps: collecting multi-modal data, carrying out the preprocessing, and carrying out the multi-modal feature extraction and fusion; performing feature splicing and weighted combination according to the multi-modal features to generate initial situation features, and performing feature optimization on the initial situation features to obtain final situation features representing dynamic situation information under the current task and scene; performing specialized optimization on each modal feature by using the final situation feature to obtain optimized modal features, and splicing the optimized modal features into an optimized joint feature vector; and aligning the final modal specialization optimization features to generate a fused global feature. When specific modal analysis enters, global features are optimized according to specific tasks, and a decision result is generated and explained. According to the method, the higher context sensing capability, the more efficient modal fusion method and the more accurate task decision-making capability can be realized in the multi-modal data decision-making.

The invention relates to the technical field of semantic recognition, can be applied to business scenes such as financial science and technology and medical health, and discloses an interactive code generation method, device and equipment and a medium. And a visual preview interface is generated in combination with the context, multi-round modification through interactive adjustment or text editing is supported, and finally user interface element codes are generated. According to the method, natural language processing, context sensing and visual interactive correction are combined, so that the closed-loop process of interface codes from generation to dynamic adjustment is realized, the customization degree and the service adaptability of a generated result are improved, and the problems that an existing tool template is rigid, semantic understanding is deviated, and a multi-round correction mechanism is lacked are solved.

The invention relates to the technical field of intelligent retrieval, in particular to an enterprise RAG intelligent question answering system based on vector database reordering. The system specifically comprises: a document recall module, which retrieves a vector database to obtain candidate document blocks containing business metadata; the comprehensive scoring module is used for calculating a semantic correlation score by adopting a later-stage interaction architecture based on bidirectional token importance weighting, performing path semantic matching and context sensing rule evaluation according to a preset metadata ontology graph to obtain a service attribute score, and analyzing the evidence sub-graph to obtain a fact path score; fusing the semantic correlation score, the service attribute score and the fact path score to generate a comprehensive correlation score; and the sorting output module performs optimization resorting based on a preset punishment mechanism and the comprehensive correlation score to generate an optimized context set, and calls a generation model to output answers based on the optimized context set. According to the method, semantic accuracy, business compliance and fact reliability can be considered, and more trustworthy high-quality enterprise-level answers can be generated.

An intelligent (self-learning) sensor-aware and / or context-aware subsystem comprising (i) a System-on-a-Chip (SoC), (ii) a radio transceiver, (iii) a microphone, (iv) a voice processing module, (v) a (bio-inspired) neuromorphic event camera or a hyperspectral camera, (vi) a first set of computer implemental instructions in artificial neural networks (ANN) (which may include a transformer model / diffusion model or Poisson flow generative model++ (PFGM++)), (vii) a second set of computer implementable instructions to analyze and interpret contextual data and (viii) an autonomous artificial intelligence (AI) agent is disclosed.

The invention relates to the technical field of natural language processing, in particular to a knowledge graph fused reservoir emergency plan large language model review method. The method comprises the following steps: acquiring a to-be-examined reservoir emergency plan, and performing data structured processing; a first neural network model is combined with a multi-scale context sensing mechanism to comprehensively capture local and global semantic information, and knowledge graph representation of a reservoir emergency plan text is constructed; a second neural network model is adopted to process the chart type information, and a reservoir emergency plan multi-modal knowledge graph is constructed; and meanwhile, an open source large language model of a Transform architecture is finely adjusted by adopting comparative learning, a special reservoir emergency plan large language model is constructed, and complementary enhancement of language model semantic embedding and knowledge graph structure representation is realized through a bidirectional enhancement mechanism. And finally, in combination with a graph attention mechanism, embedding the multi-modal knowledge graph into the fine-tuned large language model for comprehensive review, and generating a review report.

The invention relates to the technical field of intelligent interaction systems, and discloses an intelligent interaction system and method based on a multi-modal large model. The system comprises a multi-modal input layer, a context sensing module, a cross-modal fusion module, a dynamic response generation module and a system monitoring module. The multi-modal input layer preprocesses multi-modal data; the context sensing module constructs a dynamic context vector; the cross-modal fusion module fuses the data to generate joint features; the dynamic response generation module generates a response strategy through hierarchical decision; and the system monitoring module monitors the key indexes and triggers an optimization mechanism. All the modules work cooperatively, multi-modal data deep fusion, precise context sensing, efficient dynamic response and system self-optimization are achieved, the accuracy, adaptability and stability of intelligent interaction are improved, and the method can be widely applied to multiple fields such as intelligent home and intelligent customer service.

An auditory operating system designed to facilitate context-aware, audio-based user interactions, particularly with artificial intelligence agents or applications. An auditory operating system shell serves as the primary interface, managing and coordinating multiple specialized agents that handle specific domains like music streaming, scheduling, or home automation. Using natural language processing, the auditory operating system shell identifies the appropriate agent or application for a user's command or query and ensures task execution and context preservation across interactions. The auditory operating system shell enforces privacy and stability by controlling agents' and applications' access to data and system privileges. The auditory operating system shell also supports dynamic context management, enabling seamless handoffs between agents when user requests span multiple domains. This auditory operating system shell may reduce the need for users to memorize specific wake words or commands, as the auditory operating system shell may determine the user's intent from speech or contextual cues.

The invention relates to a small target detection method and device and a storage medium, and the method comprises the steps: obtaining a to-be-processed image; inputting an image to be processed into the trained small target detection model; wherein the small target detection model comprises an enhanced feature extraction network and a detail feature optimization module; performing local feature extraction processing on the to-be-processed image through a plurality of context sensing aggregation units in the enhanced feature extraction network to generate a plurality of local feature maps and deep feature maps with different resolutions, and fusing the plurality of local feature maps to generate a global enhanced feature map; performing detail optimization processing on the input global enhanced feature map and the deep feature map through a detail feature optimization module to generate a prediction layer feature map; and obtaining a target detection result based on the prediction layer feature map. According to the invention, the problem of low detection precision of the small target is solved, the detection capability of the small target is enhanced, and the detection accuracy of the small target is remarkably improved.

A system for managing financial portfolio as well as for recommending personalized investment strategies, is disclosed. The system includes a first computing unit having an application interface, communicably connected to a central controller. The central controller includes a back-end server. The backend server includes a data receiving component adapted to receive the input data-sets from the first computing unit and real time data-sets from a plurality of data-sources. The backend server further includes a data analysis module adapted to process the real-time data to generate one or more actionable insights. The backend server furthermore includes a contextually intelligent portfolio management module adapted to utilize one or more contextual data related to the user, to monitors the user's investments and asset portfolios, and generate contextually relevant portfolio information for the user in a real-time. The backend server additionally includes a financial strategy implementation module adapted to utilize the actionable insights in combination with the contextually relevant portfolio information of the user to generate personalized investment strategies and recommendations for each user. In operation, a user generates and / or formulate at least one input query based at least in part on one or more input data-sets related to the user's financial portfolio. Thereafter, the input datasets are received at the back-end server which in turn are processed by the financial strategy implementation module in combination with one or more actionable insights generated by the data analysis module of the back-end server to identify a response to the input query, and / or provide one or more personalized investment related recommendation. The identified information and / or recommendation is elicited as a response to the input query and is presented and / or visualized on an output component of the first computing unit.

The invention provides an electrocardiogram data anomaly recognition method, device and equipment based on deep learning and a storage medium. An original electrocardiogram signal of a patient is collected, and a target electrocardiogram signal is obtained through multi-stage preprocessing. And identifying an abnormal signal in the target electrocardiogram signal by using a deep fusion learning model, and generating a preliminary identification result. And then, the preliminary recognition result is combined with a pre-trained machine learning model, mixed model processing is performed through a stack generalization fusion method, a Bayesian network is constructed based on the detailed information of the patient, a reasoning result with context sensing is obtained, and a target recognition result is finally generated. And generating an abnormal report according to the target identification result, and storing and displaying the abnormal report to the user in a classified manner according to a predefined standard. According to the method, high precision and personalized diagnosis of anomaly detection are ensured, and the reliability and efficiency of electrocardiogram analysis are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology and the like, and discloses a video stream processing method which comprises the steps of collecting current environment parameters, generating a mode switching instruction and determining a target processing mode; obtaining multi-dimensional context awareness data, and selecting a target detection model; key area coordinate parameters in the video frame sequence are extracted, and grading resolution parameters are determined; and based on the target processing mode, the target detection model and the grading resolution parameter, constructing a video processing strategy matrix, executing the video processing strategy matrix to perform coding processing on the video stream, and generating a target coding video stream. According to the invention, through intelligent mode switching based on the current environment parameters, dynamic adjustment of the video processing mode is realized, and the adaptive capacity of the system in a complex environment is improved; through target detection model selection in combination with multi-dimensional context awareness data, the detection precision is optimized, and the reliability of visual analysis is improved.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

The invention discloses a multi-source heterogeneous knowledge fusion question and answer solution system, which relates to the technical field of computers and comprises a knowledge base layer, a recall layer, an analysis and pruning layer and an answer generation layer. The knowledge base layer is used for constructing a dynamic heterogeneous entity fusion engine and a self-adaptive knowledge slice storage mechanism, and the dynamic heterogeneous entity fusion engine comprises a cross-modal entity alignment algorithm, relation topology completion and an incremental entity evolution model; the self-adaptive knowledge slice storage mechanism is used for performing semantic density perception slicing on the RAG document and constructing a three-level index tree; according to the method, through the technologies of cross-modal entity alignment, dynamic recall weight adjustment, context sensing entity priority correction, RAG cross-block reasoning enhancement, double-encoder conflict detection, knowledge graph guide generation and the like, knowledge fusion reasoning is achieved, the complex question answering accuracy is effectively improved, the conflict recognition rate is increased, and the manual maintenance cost is reduced.

The invention relates to the technical field of computers, and provides a multi-modal learning resource intelligent recommendation method and system based on an AI large model, and the method comprises the steps: carrying out the intention analysis of interaction behavior data and user information based on the AI large model, and obtaining a user feature vector; obtaining multi-modal resource data based on the user feature vector, and integrating the multi-modal resource data to obtain a resource representation vector; mining implicit association between resources and knowledge points and a mapping relation between user requirements and target skills based on a knowledge graph in combination with user feature vectors and resource representation vectors to obtain a knowledge network; generating candidate recommended learning resources based on the context awareness information and the knowledge network; and performing sorting optimization on the candidate recommendation learning resources based on the user feature vector and the resource representation vector in combination with the current stage index of the multi-modal resource data, and generating an initial resource recommendation list. According to the embodiment of the invention, the content features of the learning resources are comprehensively captured, and the resource recommendation accuracy is improved.

The invention relates to the technical field of medical image processing, and discloses a method for intelligently describing liver space-occupying lesion ultrasonic image content by using LLM. A liver ultrasonic image sequence, a patient historical medical record text and a blood biochemical index vector are obtained through a multi-modal data acquisition module, and features are extracted through a cross-modal contrast learning network to generate embedded vectors and align the embedded vectors. And inputting the aligned image embedding vector into a dynamic context sensing decoder, and generating a description text semantic mark sequence by using a layered multi-head attention mechanism. The confidence coefficient is evaluated through an uncertainty calibration module, and the text is optimized through a post-processing reordering mechanism when the confidence coefficient is lower than a threshold value. A real-time interaction optimization mechanism is further arranged, and the model is updated according to feedback of doctors. According to the method, multi-modal data are fused, description accuracy and reliability are improved, text quality is optimized, clinical requirements are met, and liver disease diagnosis is assisted.

The invention provides a design method and system of a touch interaction interface. Wherein user behavior habits are collected, key elements are extracted, a user participation degree evaluation system is constructed, and a behavior habit analysis report is generated; carrying out situation modeling processing by using a situation awareness layout optimization algorithm, carrying out adaptability analysis by adopting an environment adaptation technology, and generating environment adaptation interface configuration; a long-term preference tracking algorithm is used to record and learn personalized preference settings to add personalized components, a progressive interface evolution method is used to carry out progressive evolution processing, an interactive feedback mechanism is introduced step by step, an interactive module is added, and an optimized layout scheme is generated; and constructing a learning library according to the user behavior habits, importing interaction behavior data to perform behavior pre-judgment, adjusting interface elements and a response mode, and generating a touch interaction interface optimization strategy. According to the technical scheme provided by the invention, the man-machine interaction fluency and the user experience of the touch interaction interface are improved.

The invention discloses a personalized computer-aided decision-making method and system fusing multi-modal data, and relates to the field of personalized computer-aided decision-making, and the method comprises the steps: mapping multi-source heterogeneous modal data to a unified semantic embedding space, and obtaining a multi-modal unified representation vector set; carrying out three-layer progressive fusion on a feature layer, a situation layer and a decision layer of the multi-modal data to generate a global decision context vector; based on a cross attention mechanism, outputting a fused context sensing personalized vector; based on the behavior cloning model, outputting probability distribution on all decision options; according to the user feedback operation data, generating a user personalized decision strategy and performing dynamic optimization; and generating a structured decision report containing visual traceability information based on the hierarchical fusion process and decision reasoning logic. End-to-end intelligent generation from multi-source heterogeneous data to personalized decisions is realized, and a standardized process is converted into personalized customized decisions.

The invention discloses a satellite signal modulation recognition method based on an enhanced multi-scale feature attention network, and the method comprises the steps: obtaining a to-be-recognized satellite signal, inputting the to-be-recognized satellite signal into a trained enhanced multi-scale feature attention network, and obtaining a satellite signal modulation recognition result. The training process of the enhanced multi-scale feature attention network comprises the following steps: acquiring a satellite communication data set, and performing sample expansion to obtain an augmented sample; performing noise removal processing and feature extraction on the augmented sample to obtain denoised data features; inputting the de-noised data features into a global context sensing module, extracting modulation features of different frequencies and time scales, and performing feature fusion on the modulation features and the de-noised data features to obtain fusion features; and performing feature reconstruction and channel recovery on the fusion features, inputting the fusion features into a Softmax classifier, performing classification prediction, calculating classification loss, and optimizing network parameters through the classification loss. Under a complete blind recognition scene, the accuracy of a satellite signal modulation recognition result can be obviously improved.

The invention discloses an intelligent heterogeneous network fusion optimization method and device based on a large model, and the method comprises the steps: collecting real-time network state data, service demand data and user equipment data of various heterogeneous networks, and carrying out the preprocessing; key features capable of reflecting real-time changes of network states and service requirements are extracted from the preprocessed data; a self-adaptive multi-modal fusion algorithm is adopted, modal characteristics of heterogeneous network data are automatically identified, and weights and parameters in the fusion process are dynamically adjusted, so that the optimal fusion effect is achieved; building a large model driven by deep learning in combination with a cross-network context awareness optimization strategy; and the intelligent fusion module dynamically selects the optimal network path and transmission strategy according to the output result of the large model. According to the method and the device, intelligent fusion of the network is realized by adopting a self-adaptive multi-modal fusion algorithm and a cross-network context sensing optimization strategy based on a large model of deep learning.

A decision support system and a computer-implemented method of enterprise decision support include a data consolidation module, domain-specific machine learning models, an enterprise decision intelligence model, and a user interface layer. The data consolidation module collects data from both internal and external data sources. The domain-specific machine learning models generate decisions based on the collected data. The enterprise decision intelligence model integrates real-time trends and the decisions to provide context-aware recommendations. The enterprise decision intelligence model maintains a decision graph that connects decision variables of the domain-specific machine learning models in a causal relationship, with the domain-specific machine learning models interacting as an interconnected network. The decisions are influenced by affects of decision variables from other domain-specific machine learning models. The user interface layer facilitates interactive decision-making by way of the enterprise decision intelligence model and visualizing the recommendations.

The invention discloses a sleep staging method and system based on multimode signal fusion and context awareness. The sleep staging method based on multimode signal fusion and context sensing comprises the following steps: acquiring a multimode physiological data set; performing multi-scale feature extraction on the multi-modal physiological data set to obtain a multi-scale feature set; fusing the multi-scale feature set based on a lightweight model to obtain a fused feature set; classifying the fused feature set, and obtaining a sleep staging result based on a classification result; wherein the multi-scale feature extraction of the multi-modal physiological data set comprises the following steps: segmenting the multi-modal physiological data set into a frame sequence set; and performing historical context feature extraction, future context feature extraction simulation and timestamp feature extraction on the frame sequence set to obtain a context feature set. In this way, the staging continuity can be enhanced, and the misjudgment rate caused by burst signal fluctuation is reduced.

A system for filtering and presenting notifications received by an aircraft includes a processor, a display device, a communication device, and computer-readable memory. The computer-readable memory encoded with instructions that, when executed by the processor, cause the system to perform the following steps. The system receives one or more broadcast notifications. The notifications are, for example, Notices to Air Missions (NOTAMs). The system filters the one or more broadcasted notifications based upon applicability to a present flight plan to generate applicable notifications. The system filters the applicable notifications based upon one or more filtering criteria to assign a criticality to each of the applicable notifications. The system display, via the display device, the applicable notifications based upon the criticality of each of the applicable notifications. The system outputs alternate flight plans due to restrictions on the present flight plan.

The invention relates to the technical field of data analysis, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a user intention recognition method, device and equipment and a medium. Generating an intention recognition strategy of each user intention point according to the key intention verbal skill, constructing an intention recognition model, carrying out context semantic analysis on interaction text data of a target user, generating a context perception text, carrying out distributed coding on the key intention verbal skill in the intention recognition strategy, and obtaining intention strategy representation; and determining an attention weight according to the context sensing text and the intention strategy representation, identifying intention key data of the interactive text data, performing intention analysis on the intention key data by using an intention identification model, and generating a target intention of the target user. According to the invention, the accuracy and efficiency of intention recognition of the target user are improved.

The invention provides an equipment operation state anomaly detection method based on edge computing. According to the method, the accuracy, the robustness and the real-time response capability of equipment anomaly detection are improved. The method comprises the following steps: firstly, setting a channel confidence coefficient screening mechanism at an edge calculation end, and eliminating an abnormal transmission channel with drifting or fluctuation to guarantee data quality; then, fusing and modeling a structural dependency relationship among multiple channels by utilizing a graph neural network and a self-attention mechanism, and extracting a global context sensing vector; and finally, constructing a dynamic criterion system based on a multi-task state index, and realizing efficient anomaly judgment in combination with state difference scoring. According to the invention, through the integration of edge-side high-quality data screening, depth feature modeling and a fusion type detection mechanism, the intelligent level and field adaptability of equipment operation state abnormity identification are significantly improved.

Techniques for intelligent system optimization are provided. Capacity data of one or more exit devices at a physical location is received. Demand data that indicates a current demand for exit service at the physical location is received from one or more monitoring devices. A decision is generated based on the capacity data and the demand data using a machine learning (ML) model. Commands corresponding to the decision are transmitted to the one or more exit devices for implementation.