595 results about "Context awareness" patented technology

The invention discloses an intelligent programming auxiliary method and system based on a multi-modal AI language model, and belongs to the technical field of programming auxiliary tools. The method comprises the following steps: a multi-modal input processing stage; a dynamic context modeling stage; a hierarchical semantic analysis stage; in the code generation stage, codes are generated in two stages by adopting a Codex-Plus large model; the reinforcement learning driven code optimization stage is used for carrying out multi-objective optimization and reward function design on the codes generated in the code generation stage; a multi-dimensional feedback stage; an interaction and visualization stage; code semantic deep analysis, dynamic context sensing, multi-target optimization generation and real-time interactive feedback are realized by fusing code texts, natural language description, developer behavior data and a domain knowledge graph, and programming efficiency and code quality can be remarkably improved.

The invention provides a method for intelligently generating a complete dispatch report for financial unfavorable assets, relates to the field of management systems, and aims at deeply fusing multi-source heterogeneous data such as legal instruments and financial statements and forming a comprehensive context sensing model for target assets through multi-modal feature extraction, semantic alignment and knowledge graph construction technologies; a potential and non-dominant risk factor combination deeply coupled with asset characteristics is automatically mined by applying a genetic algorithm and other evolutionary calculation methods, and a self-adaptive risk assessment network is constructed to dynamically and quantitatively assess and predict the comprehensive risk level; carrying out attribution analysis on a risk assessment result by adopting an interpretable artificial intelligence model, and clearly revealing a key influence path and core data evidence; and according to a report logic framework and a narrative template which can be dynamically adjusted by a user, automatically outputting a financial non-performing asset full-duty survey report including deep analysis, risk early warning, diversified disposal suggestions and compliance review key points.

The invention discloses an intention classification method and device based on vector retrieval and context awareness and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of extracting business metadata and associating the business metadata with typical problem examples to generate a standardized service description document; encoding the standardized service description document into a high-dimensional semantic vector through a pre-training language model, and constructing a neighbor search index to store the high-dimensional semantic vector; splicing the user identity information and the current question text into an enhanced query statement, and encoding the enhanced query statement into a context-aware dynamic query vector through a semantic model; performing similarity retrieval based on the dynamic query vector to obtain candidate intelligent services, performing business domain filtering, context weighted sorting and dynamic priority rearrangement, and outputting target recommendation services; by collecting interactive behavior data of a target recommendation service, quality scoring is performed on service descriptions and problem examples based on a preset evaluation rule, and the service descriptions and the problem examples of which the quality scores are lower than a quality threshold value are updated.

The invention discloses a video feature extraction and multi-dimensional matching-based movie advertisement real-time pushing system, and belongs to the technical field of digital advertisements. According to the system, multi-modal analysis is carried out on visual, audio, text and semantic features of a movie through a video feature extraction module, a dynamic interest tag constructed by a user portrait analysis module is combined, and the weighted matching degree of a video scene, user preference and an advertisement tag is calculated by utilizing a multi-dimensional matching engine; and millisecond-level advertisement putting is completed through the real-time pushing decision module. According to the method, context awareness and streaming computing technologies are fused, the relevance between advertisements and content scenes is remarkably improved, cross-platform deployment is supported, the method is suitable for short videos, live broadcast and other real-time scenes, the advertisement click rate is increased by 55%-72% through tests, the response delay is lower than 200 ms, and the method has the technical advantages of being efficient, accurate and low in delay.

The invention relates to the technical field of advertisement putting and intelligent decision making, in particular to a favorite advertisement putting system for predicting the advertisement click rate, which comprises a context awareness intelligent adaptation unit and a deep enhancement decision making unit. By means of deep semantic analysis and situational inference engine processing, interest keywords are extracted by constructing a specific model, user situational portraits are constructed in combination with multi-source data, a preliminary advertisement set is screened by matching with an advertisement material library, a multi-agent architecture is constructed by a deep reinforcement decision unit, a master agent performs overall planning, slave agents are responsible for different advertisement types, and a user can perform multi-agent interaction. A multi-dimensional reward function system is designed, each agent collects feedback data such as operation of a user on an advertisement page, a reward value is calculated according to the feedback data, a strategy network is updated, a main agent integrates information to optimize an overall advertisement pushing strategy, accurate pushing of advertisements is achieved, and the advertisement click rate and the putting effect are effectively improved.

The invention provides an intelligent data quality monitoring method and system. The method comprises the following steps: performing data processing on multi-source heterogeneous data by utilizing a unified data model; inputting historical data as a training set into the pre-training model for training optimization to obtain an optimized pre-training model, and performing threshold determination on the standardized distribution data stream by using the optimized pre-training model in combination with an adaptive threshold module; inputting the processed abnormal event list into a context awareness and dynamic priority management module to carry out de-duplication optimization transmission; carrying out root analysis on the abnormal event list after the de-weighting optimization by utilizing a root analysis tool; generating a repair strategy for the root analysis result by using the assistance of a repair suggestion engine; and inputting the repair strategy and the visual report into a cross-platform monitoring interface, and generating a repair suggestion and an early warning notification. According to the method, the data consanguinity map is constructed based on the graph database, minute-level traceability of abnormal events is achieved, and the troubleshooting time is shortened by 50%.

The invention discloses a data anomaly detection method based on multi-scale feature extraction, relates to the technical field of data anomaly detection, and sequentially performs multi-source data acquisition and environment calibration, noise robustness multi-scale feature extraction, context awareness anomaly detection and sensor redundancy check, and adaptive feedback and collaborative update decision. Intelligent threshold learning and scene self-adaption are realized; the method comprises the following steps: firstly, acquiring and marking acceleration, current, electromagnetic and other multi-path signals, and constructing an environment label; then, utilizing a TCN and LSTM fusion model to extract short-term impact and long-term trend; according to the elevator working condition and the noise index, the threshold value is dynamically adjusted, and the fault is subjected to redundancy check; the model, the sensor weight and the threshold value are corrected through false alarm and missing alarm backflow; and finally, a monitoring-detection-feedback-updating-self-adaptive closed loop is constructed by means of reinforcement learning or meta learning cross-seasonal adaptation, high precision, low false alarm and high robustness are achieved, and elevator operation safety and maintenance efficiency are remarkably improved.

The embodiment of the invention provides an unmanned aerial vehicle aerial photography target detection method and device based on deep learning. The method is applied to the technical field of target detection, and comprises the following steps: acquiring aerial image data of an unmanned aerial vehicle, and preprocessing the image data; inputting the preprocessed image data into a deep learning-based feature extraction network, wherein the deep learning-based feature extraction network comprises a backbone network, a small target frequency domain enhancement module, a lightweight multi-scale modeling module, a hierarchical context sensing module and a convolution gating linear module; and the preprocessed image data is analyzed and processed through the feature extraction network based on deep learning, and a target detection result is output, so that the real-time performance and precision of target detection are improved.

The invention provides a Beidou electric power data intelligent acquisition and dynamic communication scheduling method and system. The method comprises the steps that a master station constructs a database and trains a data priority classification model, a channel quality prediction model and an intelligent compression model; a substation collects power data through an edge calculation unit, constructs a skyline candidate set to perform data screening, and performs intelligent classification marking by using a data priority classification model; performing reliability evaluation on the data stream by using a Gaussian mixture model, selecting a compression strategy according to a reliability score, a data type and a priority, and packaging into a data frame; determining a transmission strategy in combination with a channel quality prediction result and a context-aware intelligent switching protocol, and sending a data frame; the master station receives the data frame, performs integrity verification, decompresses and reconstructs the data frame, and feeds back a communication state for model updating; and monitoring the operation state, performing early warning based on the anomaly detection model, and triggering a self-healing strategy. According to the invention, the Beidou communication resource utilization rate and the system reliability are improved.

The invention relates to the technical field of artificial intelligence and machine learning, in particular to a government affair industry intelligent information retrieval and pushing system and method based on a large model, and the system comprises an intelligent semantic understanding and query analysis module, a semantic-driven efficient retrieval module, a generation-enhanced intelligent content generation module, and a personalized pushing and feedback optimization module. The method has the beneficial effects that a natural language query request input by a user is received through the intelligent semantic understanding and query analysis module, semantic analysis and intention recognition are performed by utilizing a pre-trained large language model, key information is extracted, and query logic is optimized through a context sensing mechanism. Then, a semantic-driven efficient retrieval module quickly retrieves document fragments most relevant to user query from mass data of government affair cloud, precise matching is achieved through semantic vectorization and an efficient vector retrieval technology, and a retrieval strategy is dynamically optimized in combination with user feedback.

The invention provides an intelligent recommendation method and system for an e-commerce platform, and the method comprises the steps: collecting user interaction behaviors and time-space context data in real time, and constructing a user behavior multi-modal feature matrix; extracting commodity multi-level features, and generating a commodity comprehensive feature matrix; identifying and predicting a user intention based on the user behavior feature matrix, and generating an intention distribution vector; a recommendation candidate set is obtained by combining the commodity feature matrix and utilizing a context awareness collaborative filtering enhancement technology; a multi-objective optimization function is constructed, and after the user intention vector is input, a personalized recommendation sequence is generated in combination with an optimization result and the candidate set; and user feedback is monitored in real time, online learning and reinforcement learning algorithms are adopted, and a recommendation strategy is continuously optimized based on user instant feedback and long-term satisfaction. According to the scheme, the recommendation accuracy, the diversity of recommendation results and the user experience can be improved.

The invention discloses a resource scheduling method of a server and electronic equipment, and relates to the technical field of network optimization. Time features based on historical load data and spatial features based on load correlation of adjacent servers are introduced in a feature extraction stage; the dynamic change rule of the server load and the collaborative influence of the cluster environment are comprehensively captured, and the input information richness and context sensing ability of the prediction model are improved. The method comprises the following steps: predicting load data at a target moment through a load prediction model, carrying out weighted calculation on a server load state in combination with a task type weight, quantitatively evaluating actual occupation demands of different task types on server resources, and dynamically adjusting a task allocation strategy based on an inverse relation between the load state and an allocation priority. According to the method and the device, the problem that a load balancing strategy cannot be optimized in a short time according to fluctuation of task loads and changes of resource consumption can be solved, and refined scheduling of server cluster resources and collaborative optimization of task allocation efficiency are realized.

A system for improving employee engagement and automating the integration of business processes. The system includes: a user interaction module configured to receive multimodal inputs, including natural language text and voice requests from employees via enterprise portals, mobile applications, voice-activated devices, and collaboration platforms, and to generate real-time responses via conversational output channels; a context processor that is operationally coupled with the user interaction module, wherein the context processor stores the interaction history, employee preferences, organizational role metadata, and task results in both short-term and long-term memory and dynamically derives context for controlling responses and initiating workflows; A natural language and intent processor that is communicatively linked to the context processor. The intent engine consists of large language models trained on company-specific lexicons to perform intent detection, entity extraction, ambiguity resolution, and sentiment or urgency classification from employee queries; a workflow orchestration layer in communication with the intent engine and the context processor, wherein the workflow orchestration layer includes a rule-based and AI-powered process execution engine configured to automatically initiate, route, and complete cross-functional business tasks, including approvals, escalations, and compliance checks, with workflows defined using modular templates that include conditional logic and time-based triggers; Webhooks and authentication protocols provide secure interoperability between the workflow orchestration layer and external enterprise platforms; a feedback and learning module that is operationally coupled with the workflow orchestration layer and the natural language understanding engine, wherein the feedback and learning module is configured to analyze task completion rates, response accuracy, latency metrics, and user feedback signals, and to retrain underlying language and workflow models for adaptive improvement in real time; and an administration console with role-based access controls, compliance dashboards, audit trails and interfaces for workflow configuration, whereby the administration console enables authorized personnel to monitor system operations, adjust interaction rules and enforce data protection restrictions across departments.

The invention discloses a large-model multi-scene antagonism dynamic evaluation system and method based on context perception strategy optimization, relates to the technical field of artificial intelligence safety evaluation, and aims to solve the problems of lack of multi-round context modeling, poor confrontation sample semantic consistency and lack of a feedback-driven optimization mechanism in the prior art. According to the method, a state space and an action space are constructed, an adversarial sample is dynamically generated by adopting a reinforcement learning strategy network, a high-quality sample set is expanded in combination with a semantic disturbance and screening mechanism, and a vulnerability knowledge base is constructed based on an interaction log to guide strategy optimization and security evaluation. The method can effectively improve the security evaluation coverage and vulnerability discovery capability of the model in a high-risk multi-round interaction scene, and is mainly used for security reinforcement and deployment support of large language models in the fields of medical treatment, customer service and the like.

The invention provides a virtual human design and application platform, method and device based on artificial intelligence, and relates to the technical field of virtual digital humans. The method comprises the steps of performing local anonymization on multi-modal input data on user equipment, encoding generated anonymized multi-modal features to obtain a multi-modal feature vector, and inputting the multi-modal feature vector into an emotion calculation model to obtain a user emotion intensity quantized value; inputting the multi-modal feature vector into a context sensing model, and generating a user intention vector after context correction in combination with a knowledge graph; generating an updated personality parameter matrix according to the user emotion intensity quantized value and the user intention vector; and outputting voice waveform data, facial muscle motion parameters and skeleton joint coordinate data based on the personality parameter matrix, and driving the virtual digital human three-dimensional model to perform real-time rendering. According to the scheme, the naturalness, emotional resonance and long-term user retention rate of virtual digital human interaction can be improved, and user privacy data security is protected.

The invention provides a cross-environment AI task collaborative execution method and device based on an MCP protocol and a storage medium, a plurality of different application programs deployed on a local system are packaged into uniform interface APIs, each application program corresponds to one uniform interface API, and the packaged uniform interface APIs support context awareness operation; the method comprises the following steps: constructing an MCP protocol at a cloud end, and obtaining a current state of each application program executing a task through a uniform interface API based on the MCP protocol, so that data among different application programs are kept consistent; an operation request input by a user is received at a cloud end, the operation request of the user is converted into an operation command of at least one application program based on an MCP protocol, the operation command is transmitted to a corresponding application program deployed on a local system through a uniform interface API, and the application program executes corresponding operation based on the operation command. According to the invention, a two-way operation path between the cloud AI and the local non-API software is established, and the success rate of cross-environment tasks is improved.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention discloses a virtual historical character dialogue method and system with role knowledge and context awareness, and relates to the technical field of man-machine interaction, and the method comprises the steps: constructing a multi-level role depth model; when a question of a current user is received, identifying information of a virtual scene where the current user is located, analyzing micro-expressions of the face of the user and voice rhythm characteristics of speech of the user, analyzing an emotional state and an interaction intention of the user based on a multi-modal fusion algorithm, and generating a user state vector; executing a dynamic Prompt construction program, extracting related information from the multi-level role depth model and the user state vector, and generating a structured Prompt; and inputting the structured Prompt into a large language model, generating a reply text conforming to role features based on questions of the current user, and driving a virtual character model. The method solves the problem that in the prior art, virtual historical figures cannot provide real immersion and credible interactive experience with emotional connection.

The invention relates to the technical field of road maintenance intelligence, and discloses a road intelligent maintenance decision-making system based on multi-dimensional evaluation and deep reinforcement learning, and the system comprises a geographic space data preprocessing module, a fuzzy TOPSI S evaluation module, a context awareness DQN strategy module, a credibility driving recommendation module, and a security strategy library module. The method comprises the following steps: generating a road health degree and a clustering label through multi-source data geographical weighted preprocessing and fuzzy TOPSI S dynamic weight evaluation; a reinforcement learning reward function is configured based on label differentiation, and a strategy space is explored in combination with noise; the confidence is verified through multiple models, a historical security policy is matched, and model optimization is driven through priority sampling injection samples. According to the method, the evaluation precision is improved through entropy weight-clustering dynamic weight, and flexible decision is realized in combination with reinforcement learning; and three-level security verification and closed-loop optimization are constructed to ensure that the risk is controllable, historical experience migration and multi-source data expansion are supported, and the cross-scene adaptive capacity is enhanced.

A system and method for real-time, identity-free personalization using deformable emotional trait vectors to dynamically adapt digital and voice-based experiences. Each user session is modeled as a behavioral object known as a Vectra, composed of fluidic traits—such as mass, viscosity, temperature, volatility, and texture—that evolve continuously in response to live behavioral, contextual, environmental, and voice-derived signals. These Vectras traverse a dynamically warped emotional space, the Vectraverse, influenced by ambient conditions including time of day, noise level, inventory urgency, and engagement rhythm. Gravitational pull toward predefined emotional goal attractors modulates system behavior, while a goal mutation engine reclassifies session intent when confidence decays or friction spikes. Outputs include tone modulation, content pacing, offer framing, and gamified reward logic—all executed without storing identity, login credentials, or historical profiles. The system supports modular deployment across voice, screen, signage, mobile, and in-room environments, and integrates with large language models, AI agents, and third-party personalization stacks via privacy-safe APIs and federated learning. Designed for zero-ID personalization, the platform enables emotionally intelligent, context-aware engagement across any surface or session.

The invention provides an agent collaborative question-answering method and system based on context awareness and authority control, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring user query, and triggering a user context manager to generate a session state user context object containing a permission label and preference; the central scheduler performs task decomposition based on the query intention and the context, dynamically selects an intelligent agent through a multi-dimensional scoring model and binds a knowledge source; the authority-aware agent execution engine executes triple authority verification and intermediate result desensitization processing in the whole process, and supports agent fault tolerance and audit log recording. The method realizes unification of knowledge security and access flexibility, supports multi-scene adaptive question and answer, and has good expandability and fault-tolerant capability.

The invention discloses a context awareness voice control lighting relieving method and system, and relates to the technical field of intelligent lighting control, and the method comprises the steps: obtaining a user voice instruction and environment data; performing semantic analysis on the user voice instruction, and extracting a control intention and a parameter requirement from the user voice instruction; determining target lighting parameters according to the control intention, the parameter requirements and the environment data; obtaining a current lighting state, and calculating a transition path from the current lighting state to the target lighting parameters; the transition path comprises a gradual change sequence of illumination parameters; and controlling the lighting equipment to perform lighting adjustment according to the transition path. According to the method, through technologies of multi-dimensional environment data fusion, deep semantic analysis, smooth transition control and the like, precise matching of the illumination parameters and user requirements is realized, and the user comfort and the system intelligence level are greatly improved.

The invention discloses an intelligent class case retrieval method and system based on reinforcement learning self-feedback, and the method comprises the following steps: S1, constructing a multi-level semantic understanding framework, carrying out the semantic understanding of a query case through the multi-level semantic understanding framework, and generating a complete semantic representation; s2, carrying out multi-dimensional similarity calculation and sorting optimization on the query case and historical discriminants in the candidate case library; s3, constructing a dynamic user portrait and optimizing a recommendation strategy; and S4, establishing multiple rounds of dialogues and context awareness, and carrying out visual display and feedback mechanism optimization. According to the intelligent class case retrieval method and class case retrieval system based on reinforcement learning self-feedback, a more accurate, efficient and reliable law intelligent retrieval system is finally achieved, judicial practice requirements are met, and quantitative and measurable technical progress is achieved in the aspects of retrieval accuracy, sorting quality, personalized service, law adaptability and the like.

The invention relates to the technical field of structured retrieval, and discloses a retrieval enhancement generation method and system based on LLM structured index and vector hybrid retrieval, and the retrieval enhancement generation method based on LLM structured index and vector hybrid retrieval comprises the steps of processing and analyzing a non-structural document, generating a hierarchical tree data extraction with document logic, and generating a hierarchical tree data extraction with document logic. The method comprises the following steps: obtaining a query demand, analyzing and identifying the query demand, self-matching any one or a combined retrieval strategy to quickly and accurately obtain a retrieval result in the complete logic positioning information, optimizing hierarchical tree data by adopting context perception, and establishing the complete logic positioning information in the hierarchical tree data through a reasoning path generation method. According to the method, through structured indexing, accurate positioning of specific chapters of the document, improvement of retrieval accuracy and support of multi-step logical reasoning, the long tail problem which cannot be processed by traditional RAG is solved, a clear and transparent retrieval path can be provided, and the system credibility is enhanced.

The invention relates to the crossing field of industrial control system (ICS) safety and artificial intelligence, and particularly discloses a PLC high-interaction honeypot system based on multi-agent task splitting and RAG enhancement, which adopts a localized multi-agent collaborative architecture based on edge computing and is composed of a high-simulation equipment layer and an intelligent decision-making layer. The high-simulation equipment layer comprises a PLC dynamic mirror image, an HMI interface and a sensor data generator, and an active trapping environment is constructed through protocol fingerprint confusion and virtual and real data fusion technologies. The decision-making layer deploys a multi-agent task scheduling engine, integrates four kinds of agents including protocol analysis, behavior analysis, threat assessment and response generation, and realizes attack context perception and strategy dynamic generation based on a local RAG knowledge base. The load balancing agent dynamically allocates tasks according to equipment resources, and cooperates with offline knowledge update (USB flash disk encryption synchronization threat features) to form a closed-loop defense system, thereby ensuring physical isolation of an industrial network and realizing high-fidelity active defense.

Systems, methods, client devices, and non-transitory computer-readable media are disclosed for displaying a context-aware avatar overlay editor within an active extended-reality environment to modify an avatar without suspending or navigating away from the extended-reality environment. For example, the disclosed systems can provide an integrated avatar overlay editor within an application corresponding to the extended-reality environment. Furthermore, the disclosed systems can utilize the integrated avatar overlay editor to provide, for display as an overlay user interface in the extended-reality environment, a context-aware avatar overlay editor. In addition, the disclosed systems can utilize a contextual recommendation engine to provide avatar modifications within the context-aware avatar overlay editor that are relevant to the active extended-reality environment. Indeed, the disclosed systems can receive selections within the context-aware avatar overlay editor and can modify an appearance of the avatar on-the-fly without suspending or leaving the extended-reality environment.

The invention provides an information resource matching recommendation method and system based on context awareness, and the method comprises the steps: firstly obtaining a context data set which is generated by real-time interaction of a target user and comprises user operation behavior data and scene awareness parameter data, and then carrying out the demand feature extraction of the context data set, the method comprises the following steps: acquiring a dynamic demand feature and a context association feature, matching the dynamic demand feature and the context association feature with a pre-stored information resource library to generate a real-time information resource matching result set, and optimizing a matching strategy of a target user matching model in real time according to a dynamic feedback parameter of the real-time information resource matching result set. According to the method, a resource matching strategy is optimized to obtain an optimized resource matching strategy, and finally, a target information resource set related to the dynamic demand characteristics is pushed to a target user terminal device based on the optimized resource matching strategy, so that more accurate and personalized information resource recommendation can be realized, and the recommendation quality and the efficiency of obtaining effective information by a user are improved.

Sanitizing data can be a cumbersome task, particularly when the volume of data is large, the content is sensitive, and / or the type of sanitation requires contextual determinations. Sanitizing large amounts of data is tedious and may often require highly trained personnel with clearances and / or other qualifications. In the systems and methods of the present disclosure, language models (LMs) are used to solve these and other technical issues with tools that may allow sanitizing data easily, with high versatility, context awareness, and / or low demand for computational resources. In particular, some of the disclosed systems and methods use a first language model and a second language model (being less resource-intensive than the first language model) to generate sanitized output data with improved efficiency and accuracy. This dual-model approach ensures that sensitive information is handled appropriately while optimizing computer resource usage.

The invention relates to the technical field of natural language processing, in particular to an NLP-based multi-source data fusion system and method.The NLP-based multi-source data fusion system comprises a data collecting and processing unit, an NLP processing unit, a data fusion unit and an output unit.The data collecting and processing unit collects multi-source heterogeneous data and executes standardization processing to obtain preprocessed data; the NLP processing unit extracts entity, relation and emotion features through deep semantic analysis, a context-aware entity relation graph is constructed by adopting a bidirectional attention mechanism model and a graph attention network, and the data fusion unit realizes cross-source entity ambiguity resolution through an iterative graph neural network based on a graph topological structure. The emotion confidence weight is dynamically distributed to generate a fusion vector, a rule feedback reconstruction map is extracted, and the output unit converts the fusion vector into a target format for output, so that the problem of semantic conflict of multi-source data is solved, and semantic coherence and availability of fusion data are improved.

The invention discloses an intelligent situation awareness vehicle-mounted atmosphere lamp control method and system and electronic equipment. The method comprises the following steps: step 1, scene state modeling of multi-source data fusion; step 2, coupling control of the dynamic spectrum and environmental parameters; step 3, generating an emotion-driven light sequence; step 4, dynamically adjusting the noise-coordinated lamplight; step 5, storing and calling personalized parameters; and step 6, optimizing a control strategy of feedback driving. Through multi-source data fusion modeling and comprehensive analysis of the driving scene, the user state and the environmental parameters, the state recognition accuracy is improved, misjudgment is avoided, dynamic spectrum and environmental parameter coupling, emotion driving and noise collaborative light adjustment are achieved, diversified requirements are met, a personalized atmosphere is created, fatigue is relieved, and safety is improved. User parameters can be stored and called, personalized services are enhanced, and more comfortable and convenient driving experience is brought.