639 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 provides a construction safety prompting method and system, and the method comprises the steps: carrying out the collection and fusion processing of multi-dimensional environment data of a construction site, and constructing a real-time updated environment digital twinborn model. And according to the model and individual physiological data of the workers, carrying out adaptive evaluation on the safety state of each worker to obtain an individual dynamic safety threshold. In combination with an environment digital twinborn model, an individualized dynamic safety threshold and real-time state data, a graph neural network reasoning technology is adopted to comprehensively evaluate the safety situation, and a potential risk trend is predicted. And according to a security situation assessment result and a risk prediction trend, generating adaptive early warning and intervention schemes for different risk levels by adopting a situation awareness grading processing mode, and providing accurate security prompts. According to the method, the safety state of the worker is monitored, predicted and early warned in real time according to the dynamic change of the construction site, the intelligent level and accuracy of construction safety management are improved, and the accident occurrence risk is reduced.

The invention discloses an urban greening system based on big data and an optimization method thereof, relates to the technical field of artificial intelligence, and solves the problems of difficulty in heterogeneous data integration, insufficient real-time analysis capability of dynamic data and low management efficiency in an existing urban greening system. Multi-source heterogeneous data are collected through an API interface and Internet of Things communication, and are cleaned, aligned, converted and fused through a distributed real-time data stream processing platform; image recognition and a machine learning algorithm are used for monitoring diseases and insect pests in real time, and a growth prediction model is trained in combination with an LSTM network, so that the quick response capability of the system to environmental changes is enhanced; according to the method, target weights are dynamically adjusted and optimized through the deep Q network, a multi-target optimization model is constructed in combination with mixed integer linear programming, context awareness and a genetic algorithm, intelligent optimization of irrigation, fertilization, pest control and greening layout is achieved, and the management efficiency and the resource utilization rate of an urban greening system are remarkably improved.

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 invention discloses an emergency communication and positioning system and method based on a Beidou satellite. The method comprises the following steps: S1, collecting data in real time; s2, analyzing the emergency state of the user by using a context awareness algorithm; s3, under the condition that the satellite communication signal is stable, transmitting the emergency information to a rescue center according to the calculated priority, if the satellite signal is unstable or interrupted, constructing a self-organizing network, and continuing to transmit the emergency information; s4, dynamically selecting an optimal path based on an adaptive path optimization algorithm inspired by an immune system, and further optimizing path selection through a space-time perception algorithm; s5, according to a priority management mechanism, the emergency information with high priority is processed preferentially, and feedback information is sent; s6, if the first emergency information transmission fails, the system starts a redundant data transmission mechanism; and S7, continuously tracking the state change of the user, and dynamically adjusting the rescue scheme. According to the invention, the situation awareness and immune system optimization algorithm is combined to realize efficient transmission and feedback of emergency information.

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 text travel data analysis, in particular to a text travel content recommendation system for analyzing preferences of text travel scenic spots, which comprises a culture gene bank construction and user interest modeling unit and a dynamic situation awareness and recommendation optimization unit. The data acquisition and preprocessing module extracts culture elements from a multi-channel text through optical character recognition and natural language processing, the culture gene extraction and fusion module constructs a knowledge graph and fuses similar elements to form a gene cluster, and the user interest model training module generates interest portraits based on user text travel platform data. In a dynamic situation awareness and recommendation optimization unit, a situation data acquisition and analysis module acquires and analyzes user situation data, a recommendation strategy generation and adjustment module generates a recommendation strategy according to a user state and interest by using a deep Q network algorithm, recommendation is optimized in combination with situation factors, and the accuracy of travel content recommendation is effectively 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.

An AI-driven system for personalized advertising in real time, where: ◯ an analytics unit to monitor and analyze user behavior in real time across multiple digital platforms such as websites, mobile applications, social media, and smart devices; the unit collects data on user engagement, browsing patterns, time spent, and content preferences to enable targeted, personalized advertising; ◯ a prediction module that predicts preferences and interests of a user, operatively connected to the user interaction data collection unit, wherein the prediction module uses machine learning models such as deep learning, recurrent neural networks (RNNs) and transformer-based architectures to predict interests of users based on historical interactions and inferred preferences; ◯ an emotion and sentiment analysis unit that assesses the user's mood in real time through computer vision, natural language processing (NLP) and voice analysis, whereby the analysis of facial expressions, voice pitch and linguistic mood is used to determine emotional states and receptivity to advertising content; ◯ an embodiment of a context awareness component in operational communication with the emotion analysis and mood unit, in some cases further augmented by various environmental and situational data such as the device type and its physical location, date and time, and the content processed in the device, processing methods, etc., in establishing adaptability and automatic ad placement to be as relevant as possible to the user and their status as prescribed; ◯ Use reinforcement learning algorithms and generative AI models to drive advertising with personalization engines. Creative elements, messaging, and presentations are dynamically adjusted in real time based on user responses to ensure advertising is personalized and always optimized for best performance; o a privacy-focused AI system with federated learning, differential privacy methods, and on-device AI processing to reduce targeted advertising while complying with international data protection laws such as the General Data Protection Regulation (GDPR) and California consumer privacy laws; o an operationally adapted ad delivery mechanism to engage with real-time bidding (RTB) networks, programmatic advertising exchanges and demand-side platforms (DSPs) and place advertisements through digital advertising networks, which guarantees the delivery of tailored advertising to the most relevant audience in real time; and o a contextual feedback loop in which the machine learning models used in the preference and interest prediction module and in advertising personalization The engine is continuously updated to reflect the latest user engagement data, improving personalization over time and optimizing advertising performance.

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 discloses a multi-modal large model dialogue method and system based on context awareness and a medium. Relates to the technical field of intelligent dialogues. According to the scheme, the dialogue request information and the perception feature information are generated in the context perception dialogue management model, and the answer strategy is determined according to the dialogue request information and the perception feature information; on the basis of various data of the user, system hot word auxiliary information provides personalized reply for the user and is more suitable for life links, accurate context perception is achieved, and enterprise product operation and development are served.

The invention discloses an optical remote sensing image salient target detection method based on double-branch progressive interaction and multi-direction feature enhancement. The method comprises the following steps: preprocessing an original data set; inputting the preprocessed image into a double-branch progressive interaction encoder for feature extraction, inserting a large windmill into a CNN branch to capture complex shape features, and realizing cross-branch interaction and weight adjustment by using a progressive interaction mechanism; inputting the extracted features into a semantic association enhancement module, generating a semantic association weight through a context awareness attention mechanism, and enhancing the semantic consistency of the significant target; inputting the enhanced features into a multi-scale adaptive fusion module, dynamically adjusting a feature fusion proportion, and introducing a deformable pooling operation to avoid loss of key features; and finally, inputting the fusion features into a decoder, recovering the image resolution through cross-layer connection, and generating a salient target detection result. According to the method, the problems of insufficient feature fusion and poor multi-scale target adaptability are effectively solved.

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 method for enhancing geographical name and address translation precision by using context awareness and a geographic library, relates to the technical field of intelligent translation, and constructs a dynamic collaborative ambiguity resolution framework by fusing a context awareness mechanism and multi-dimensional attributes of a geographic knowledge base so as to break through semantic limitation of traditional geographical name and address translation. Specifically, firstly, dynamic association information implied in an address text is captured by utilizing context semantic coding of a non-geographical name text fragment, and structured feature embedding is performed on candidate entities in combination with a geographic knowledge base to extract multi-dimensional attributes of the candidate entities, so that cross-modal disambiguation decision is realized in a semantic aggregation stage; through a geographic feature cross validation mechanism guided by context semantics, the problem of translation ambiguity caused by static rule dependence and geographic attribute segmentation in the prior art is effectively solved, so that a more accurate multi-language address translation effect is achieved.

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.

The invention belongs to the technical field of railway transportation safety, provides a railway contact net carrier cable tension online monitoring and early warning method and system, and aims at solving the technical problems that in the prior art, capturing is not comprehensive, and different environments are difficult to adapt. Acquiring a tension data stream by using a context awareness technique; analyzing the tension data flow by using a hidden Markov model to obtain a preliminary analysis result, processing by using a neural network, generating an abnormal mode recognition result, performing risk assessment by using a Bayesian network algorithm and a digital twin technology to obtain a preliminary fault estimation probability, and predicting a target fault estimation probability in combination with multi-context sensing data; a self-adaptive control algorithm is used for generating an adjusted alarm threshold value, when the monitored tension value exceeds the adjusted alarm threshold value, early warning information is generated in time, and processing is carried out through the block chain technology to obtain target early warning information; according to the technical scheme provided by the invention, the monitoring and early warning capabilities of the railway overhead line system carrier cable are comprehensively improved.

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 belongs to the technical field of vehicle-mounted human-computer interaction, and particularly relates to a vehicle-mounted music recommendation system based on context awareness, which covers the technologies of computer vision, recommendation systems, human-computer interaction and the like. The whole system is divided into two main parts: the first part is a context information collection module, and the module realizes real-time recognition of the emotion of a driver by collecting facial images and voice information of the driver; and the second part is a feedback module used for processing interaction between the driver and different emotion analysis results, including mapping rule reasoning and music recommendation. The system generates a personalized music recommendation list by analyzing the facial expression of the driver and the emotional characteristics extracted from the voice information, so as to reduce the music selection burden of the driver and prevent the occurrence of extreme emotions, thereby having important significance for guaranteeing the personal safety of the driver and the road traffic safety.