34 results about "Cognitive analysis" patented technology

Identity nodes within bidirectional dual node hashchain (BDNH) bundles generate hash keys by processing partial metadata and linking them through identity graphs. Translation nodes perform identity resolution, data anonymization, and re-keying by transforming original identifiers into anonymous IDs and re-key IDs. Laplace noise enabler capsules introduce Laplace noise into translation layer logs for differential privacy. BDNHs share metadata between adjacent bundles for secure and efficient data transmission. A cognitive analytics layer shared among all bundles verifies hashed encrypted data packets using a gossip protocol for real-time validation. An analytics workspace layer activates re-key IDs and creates digital tags for datasets using BERT transformers, linking data through knowledge graphs. The system manages parallel processing and includes machine learning modules, homomorphic encryption, federated learning, zero-knowledge proofs, attribute-based encryption, and anomaly detection. Bundles' AI engines work with the Laplace noise enablers to analyze data, optimize noise, and enhance the data clean room functionality.

The application relates to the technical field of artificial intelligence, and discloses a multi-dimensional cognitive state analysis method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a cognitive state classification system and a domain sample data set; determining target dialogue text; learning hierarchical structure features of the target dialogue text on a hyperbolic space based on the domain sample data set, and determining cognitive representations of cognitive state dimensions presented by the target dialogue text in the hyperbolic space; the cognitive state dimensions at least comprise four dimensions of emotion, thinking mode, position and intention; performing semantic training on the cognitive representations based on a preset large language model, and reasoning out a cognitive state image satisfying cognitive analysis text constraints and cognitive hierarchical structure constraints; the hierarchical overlap problem of the cognitive state is solved by using exponentially increasing space capacity, and geometric structure knowledge in the hyperbolic space is injected into the large language model, so that the joint modeling and reasoning capability of the model for the multi-dimensional cognitive state in a complex social scene is significantly improved.

The invention discloses a space-air-ground cooperative low-altitude security command and control method, system and device and a medium, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: generating a global dynamic situation map according to multi-source detection data; performing cognitive analysis on the global dynamic situation map to obtain a system configuration, a key unit and a current confrontation stage of the unmanned aerial vehicle swarm; based on the system configuration and the key unit, predicting a subsequent confrontation stage of the unmanned aerial vehicle swarm after the current confrontation stage and a corresponding swarm behavior characteristic; according to the current confrontation stage, the subsequent confrontation stages and the group behavior characteristics, a multi-stage induction signal sequence is generated, and induction signals differentially configured for the subsequent confrontation stages are set to trigger the unmanned aerial vehicle swarm to generate a preset state response by using the group behavior characteristics of the subsequent confrontation stages, the preset state response is planned as a favorable condition for executing the next-stage induction signal. The method has the effect of improving the concealment of confrontation.

The application discloses a network processing system based on artificial intelligence, comprising: an intelligent sensing layer: responsible for real-time collection of network equipment traffic, delay, topology raw data, and preliminary processing at the edge node to generate structured network state information and output to the cognitive analysis layer; cognitive analysis layer: after receiving the structured data of the intelligent sensing layer, four-dimensional modeling and intelligent analysis are performed on the network state; the optimized decision is generated and output to the adaptive execution layer; adaptive execution layer: responsible for converting the optimized decision into specific network configuration and operation according to the decision of the cognitive analysis layer, and feeding back the execution result to the man-machine cooperation layer, while receiving the administrator confirmation or adjustment instruction from the man-machine cooperation layer; man-machine cooperation layer: responsible for the interaction between the system and the administrator, and showing the real-time network state and the cause-effect chain of the AI decision; the administrator monitors the network state through the man-machine cooperation layer, confirms or rejects the AI suggestion, and feeds back the operation log to the cognitive analysis layer.

The invention discloses a personalized classical Chinese reading disorder diagnosis system based on a knowledge graph, and relates to the technical field of artificial intelligence education, the system comprises a server, the server is in communication connection with a data acquisition unit, a reading cognition analysis unit, a scene association analysis unit, an interaction analysis unit, a comprehensive diagnosis unit, a feedback output unit and a display terminal; by introducing a multi-modal data acquisition mechanism and a modular diagnosis structure, the automation, precision and dynamic response capability of classical Chinese reading disorder recognition can be effectively improved; compared with an existing static diagnosis means which only depends on manual evaluation, the real reading behavior of the user is dynamically collected and quantitatively analyzed from the three dimensions of vocabulary and sentence pattern, cultural association and behavior interaction, and a hierarchical obstacle signal and comprehensive cross judgment mechanism is established, so that the diagnosis result is more multi-dimensional and real-time, and the diagnosis accuracy is improved. And the method has more personalized adaptive capacity.

The invention discloses a voice image facial expression acquisition method based on a visual large model, and relates to the field of computer vision and intelligent cabin man-machine interaction. Uploading the collected video stream to a cloud server deployed with an artificial intelligence cognitive model based on a visual large model; receiving structured member information which is fed back by the cloud and contains member genders, ages and expression probabilities; executing multi-member optimization logic based on the information, and locking a target interaction object and extracting facial features by calculating a sorting score; and performing deformation on the parameterized model by utilizing facial features to construct a personalized grid, and converting real-time expression data into a weight to drive the grid to displace so as to generate a synchronous voice image. According to the method, accurate locking and high-fidelity expression driving of interaction objects in a complex scene are realized through an end-cloud collaboration and optimization strategy.

The application provides a method and system for dynamic modeling and closed-loop iteration of individual knowledge and cognitive patterns of scientific researchers, relating to the fields of computer technology, artificial intelligence and information processing technology. The method includes: obtaining explicit knowledge data and interaction behavior data of scientific researchers; performing enhancement and structuralization processing; constructing objective knowledge base information based on explicit knowledge data; constructing historical behavior graph based on interaction behavior data and performing metacognition analysis to extract cognitive pattern information; constructing a structured context assembler dominated by cognitive patterns; in response to a scientific task request, calling the assembler to generate a task-level scientific portrait structured context, and inputting it together with the task request into a large language model to generate personalized output; collecting multi-modal feedback data and updating the cognitive pattern to realize continuous iteration. Through deep twin modeling and closed-loop optimization of objective knowledge and subjective cognition, the application significantly improves the personalization and intelligence level of scientific research assistance.

The application belongs to the technical field of education evaluation, and discloses a cognitive analysis system focusing on ability change and question characteristics, which comprises an interactive coding network module, an ability acquisition module, an ability prediction module, an ability rationality module, a question prediction module, a knowledge point prediction module and a multi-task fusion module. The ability prediction module, the question prediction module and the knowledge point prediction module are used to respectively predict the probability of answering the next exercise correctly. Finally, the multi-task fusion module balances the three types of prediction probabilities through a fusion training mode to obtain the final probability of answering the next exercise correctly, and the final probability is used for cognitive analysis of the subject. The application can track the ability change of the subject in real time, provide dynamic ability value evaluation, and better reflect the ability development in the learning process.

The present application relates to the technical field of artificial intelligence, and discloses a method and system for cognitive analysis of emotional companions in a virtual-real fusion scene, comprising: firstly, sensing the emotional state of a user in the virtual-real fusion scene, identifying the emotional demand category in the interaction task, and querying the corresponding emotional response level; then, analyzing the emotional barrier limit triggered by each level, positioning the emotional fluctuation node of the user in the accompanying process, analyzing the interaction frequency and feedback time limit of the emotional companion at the fluctuation node, quantitatively calculating the perceived response amount, identifying the emotional feedback content based on the perceived response amount, analyzing the matching degree of the emotional feedback content with the virtual-real fusion scene, and finally determining the emotional accompanying state of the emotional companion to the user. The present application can improve the accurate judgment and continuous optimization of the emotional accompanying state of the user.

The invention belongs to the technical field of artificial intelligence, and discloses a meta-cognitive nonlinear emergence system and method based on man-machine symbiosis, and the system comprises a problem formatting and information extraction module, a core processing engine, and a result output and interaction interface. Wherein the core processing engine is composed of a multi-dimensional feature element cognitive analysis engine, a multi-dimensional feature nonlinear algorithm engine, a multi-dimensional feature analysis scanning engine, a lever point positioning engine, a man-machine collaborative learning engine and the like. The multi-dimensional feature analysis scanning engine performs parallel feature extraction and quantitative evaluation on the complex problem through six preset dimensions, and outputs a six-dimensional feature vector; the lever point positioning engine calculates and determines the most efficient intervention point based on the vector; the man-machine collaborative learning engine realizes deep collaboration and co-evolution of machines and human beings in the cognitive level. According to the method, panoramic analysis can be carried out on complex problems, broken points are accurately positioned, efficient intelligent support is provided for complex decisions, and the man-machine symbiosis mode can continuously optimize system performance and user thinking ability.

A Computer Device with Graphical User Interface for Augmented Cognitive Analysis

Artificial Intelligence (AI) may be used to generate insights to assist in building, debugging, and deploying AI models. In one or more embodiments, a user (e.g., AI developer) may pose a question (e.g., in a user interface), and a router and planner may wrap the question with AI model data and guidelines to send to an LLM. Based on the complex input—i.e., not just the user-posed question—the LLM based answer may provide additional insights and information that may not be readily apparent to the user. In one or more embodiments, the performance (e.g., drift) of the AI model may be tracked over time, and such tracking may be used to generate deeper answers through cognitive analysis. The answers may be displayed back on the user interface. Therefore, the embodiments herein can be leveraged as a co-pilot when developing AI models.

The invention relates to the technical field of data processing, in particular to an industrial cognitive analysis method and system based on vector and time sequence database driving, and the method comprises the steps: constructing a knowledge graph and variable manifold space double-layer architecture, mapping time sequence data into a potential state vector containing certainty and fluctuation characteristics through manifold embedding, and constructing a knowledge graph and variable manifold space double-layer architecture; performing isomorphic projection based on drift gradient in combination with the semantic vector, and further deducing a fault path by using a dynamic Bayesian graph neural network; the problems that continuous monitoring data and discrete knowledge entities have semantic gaps and are poor in anti-noise capacity are solved, the uncertainty of random disturbance of the industrial environment is accommodated, meanwhile, accurate semantic alignment of multi-modal data is achieved, the state gradual change trend can be sensitively captured, a fault evolution path conforming to logic causality can be dynamically restored, and the fault evolution efficiency is improved. And the diagnosis robustness and interpretability are obviously improved.

According to the batch database operation execution sequence optimization method based on time delay detection, random I / O is converted into quasi-sequence I / O, and the execution efficiency of operations such as large-scale deletion and updating is improved. GMM statistical modeling and LLM cognitive analysis are combined to form a dual verification framework, the storage performance level can be adaptively recognized, an abstract statistical peak value can be converted into the insight with clear business semantics, and the analysis depth and accuracy of the method are far better than those of a traditional method. The physical aggregation relationship of the data to be operated can be deduced only by detecting the data with a minimum proportion by adopting a strategy of'intelligent sampling + sequence correlation full-quantity mapping '. The mapping process can be efficiently completed in the database, and minimum disturbance and global optimization coverage of the production system are achieved. And in combination with a cognitive throttling mechanism based on a real-time load, high throughput, low delay and predictability of an optimization process are ensured, and the stability of a core service is ensured.

Systems and methods enhance data privacy and generate hash keys at identity nodes by processing partial metadata and linking these keys through identity graphs, ensuring no single node has the complete dataset. The method includes performing identity resolution, data anonymization, and re-keying at translation nodes, transforming identifiers into anonymous and re-key IDs. Laplace noise is introduced into translation layer logs using Laplace noise enabler capsules to ensure differential privacy. Metadata is shared between adjacent bundles using bidirectional dual node hashchains, establishing cryptographic links for secure data transmission. Hashed encrypted data packets are verified at a cognitive analytics layer using a gossip protocol for real-time data integrity validation. BERT transformers in an analytics workspace layer create digital tags for secure data sharing linked through knowledge graphs. Parallel data processing is managed using dual node hashchains and the gossip protocol, enhancing performance and resilience against cyber-attacks.

The invention relates to the field of industrial automation and data processing, and discloses an atomization AI interface packaging and calling method. In order to solve the problems that an existing industrial control system is high in coupling degree, AI is difficult to directly participate in control and dynamic scheduling and the like, the method comprises the steps that a stateless atomic energy unit is constructed based on AST and CFG analysis; the JSON Schema is adopted to carry out MCP protocol packaging; constructing a shared sensing data pool by utilizing multi-modal feature alignment and a vector database; on-demand activation and dynamic mapping of the capability are realized based on an LLM and ReAct reasoning framework; and finally, constructing an edge cloud collaborative three-layer architecture of perceptual analysis, execution control and cognitive analysis. According to the method, through atomic power semantic packaging, the problems that traditional machine software and CAM software are strongly coupled, and the capacity is difficult to combine according to needs are solved, the AI can understand the industrial control capacity and safely call the industrial control capacity, and the expandability, real-time collaboration and intelligent scheduling capacity of an industrial control system are remarkably improved.

Systems and methods for enhancing data privacy and security in digital communication and storage systems are disclosed and employ bidirectional dual node hashchains and Laplace noise enabler capsules to create a remodeled data clean room architectures that address performance issues and mitigates data leakage and cyber-attacks. Each bundle consists of identity nodes and translation nodes, which work in parallel to generate hash keys and share metadata through identity graphs. The translation nodes perform identity resolution, data anonymization, and re-keying processes, while Laplace noise enablers introduces differential privacy by adding noise to data logs. The system further includes a cognitive analytics layer that verifies encrypted data packets using a gossip protocol and an analytics workspace layer that utilizes BERT transformers for re-key ID activation and digital tag creation. This invention ensures secure metadata sharing, real-time validation, and optimized data processing performance, and is a robust, scalable, and privacy-preserving data management solution.

The invention relates to the field of cognition analysis, in particular to a cognition and emotion analysis method and device, electronic equipment and a medium, a first preset action associated with a specific cognition function is triggered by outputting an action indication instruction, and action information (used for representing a feedback action of the target object in response to the instruction) of the target object is obtained in real time; observable motion behaviors can be directly mapped into quantitative cognition and emotion indexes, specifically, a cognition score obtained through calculation based on motion information reflects the response speed of a target object to a motion execution instruction and the consistency degree (namely, the cognition function state) between a feedback motion of the target object and a first preset motion; and the emotion score represents the emotional state of the target object based on action characteristics (such as rhythm, variability and coordination).

The invention discloses a children behavior correction training system based on cognitive intervention, and relates to the technical field of children cognitive behavior intelligent correction. A data acquisition module acquires behavior data and browsing data of a child in the using process of the intelligent equipment in real time; the cognitive analysis module performs conjoint analysis on the behavior data and the browsing data based on a preset behavior-cognitive analysis model to generate child cognitive data; the child behavior analysis module performs correlation analysis on the child cognitive data and the original behavior data, and constructs structured child cognitive behavior data; the correction training module performs cognitive intervention analysis based on the child cognitive behavior data and in combination with child personal information, and determines a cognitive intervention behavior; and dynamically adjusting the training scheme according to the training acceptance index of the child. According to the method, refined modeling and personalized intervention are carried out on the child behaviors and cognitive states, and pertinence, adaptivity and continuous effectiveness of behavior correction training are improved.

This application discloses a method, system, equipment, and medium for low-altitude security command and control using a combined air-space-ground approach, relating to the technical field of unmanned aerial vehicles (UAVs). The method includes: generating a global dynamic situation map based on multi-source detection data; performing cognitive analysis on the global dynamic situation map to obtain the system configuration, key units, and current confrontation stage of the UAV swarm; predicting subsequent confrontation stages and corresponding group behavior characteristics of the UAV swarm after the current confrontation stage based on the system configuration and key units; and generating a multi-stage induction signal sequence based on the current confrontation stage, subsequent confrontation stages, and group behavior characteristics. The induction signals, configured differently for each subsequent confrontation stage, are set to trigger a preset state response of the UAV swarm using the group behavior characteristics of that subsequent confrontation stage. This preset state response is planned as a favorable condition for executing the next stage induction signal. This application has the effect of improving the concealment of confrontation.

The invention discloses a text reading cognitive analysis method and system, and belongs to the technical field of artificial intelligence education. According to the method, a macroscopic-mesoscopic-microscopic three-layer analysis architecture is constructed, so that a technical breakthrough from grammar surface analysis to cognitive deep analysis is realized. The method specifically comprises the steps of identifying a text type based on a static literary and schema model library; a three-layer architecture is adopted for structural analysis, a macroscopic layer extracts a chapter logic framework and generates a memory anchoring thinking body, a middle layer analyzes paragraph functions and writing techniques, and a microscopic layer quantitatively analyzes logic relations between sentences; and generating a logic chain visualization result and providing interactive training guidance. Compared with the prior art, the technical limitation that traditional text analysis can only process surface language features is broken through, and the technical problems of text logic structure deep analysis, cultural thinking difference recognition and personalized training generation are solved through an innovative three-layer analysis architecture and a memory anchoring thinking body technology.

Aspects of the present disclosure are directed to a cognition analysis and response system that can A) generate machine learning training data and use it to build a cognitive impairment machine learning model; B) identify cognitive impairments by applying the cognitive impairment machine learning model and performing a false positive / false negative analysis; and C) apply various automated adaptive measures for users with identified cognitive impairment. The cognition analysis and response system can apply the cognitive impairment machine learning model and can analyze these results to identify false positive and false negative cognitive impairment determinations by analyzing result confidence values, determining whether the result matches an expected progression of cognitive impairment, and / or analyzing external factors which may have caused incorrect results. The cognition analysis and response system can then validate or invalidate the result or exclude or apply weighting factors to parts of the input before regenerating the result.

The invention discloses a multi-agent collaborative text generation method and system. The method comprises the steps that a text generation task is received, and an intermediate state draft is generated; analyzing defects of the intermediate state draft through meta-cognition, and generating a natural language diagnosis report; performing semantic mapping on the report and the capability dimension of each optimization unit, dynamically generating a calling instruction to activate a target optimization unit, and forming a processing node set; interacting with each optimization unit in the processing node set to obtain a corresponding text modification suggestion set; and finally, optimizing the intermediate-state draft based on the text modification suggestion set, and outputting a final text. According to the method and the device, the problem of low efficiency when a large language model processes a complex text is solved, and the calculation power is distributed according to needs, so that the efficiency and the reliability of complex text generation are improved.

This invention relates to a method and system for real-time simultaneous interpretation of technical terms and complex concepts in live streaming, belonging to the field of live streaming interpretation. The method includes: establishing a conceptual potential field for technical terms and complex concepts; during the live stream, real-time acquisition of live audio and cognitive analysis based on the conceptual potential field to obtain the knowledge pressure of each concept; user-side decision-making based on the conceptual potential field and the knowledge pressure to achieve real-time simultaneous interpretation; and self-evolution based on user interaction feedback. This invention is a method for real-time perception of the knowledge pressure of concepts in the live streaming context, dynamic matching of user cognitive frequency, and intelligent selection of interpretation level and related recommendations based on the conceptual potential field.

The invention relates to a system and method for optimizing task assignments in an enterprise environment using real-time biometric data collected from wearable devices. The system comprises a wearable device for capturing biometric signals such as heart rate, EEG data, and other physiological metrics, a data processor to filter and validate the data, a cognitive analyzer to calculate cognitive metrics including cognitive load, stress levels, and focus, and a workflow optimizer to assign tasks based on these metrics. The workflow optimizer leverages machine learning algorithms to analyze the cognitive state of users, compare it with historical performance data, and adjust workload distribution dynamically. The system integrates with enterprise resource planning (ERP) systems to synchronize optimized task assignments across the organization. By monitoring cognitive metrics in real time, the invention ensures efficient workload management, reduces employee fatigue, and enhances overall productivity.

The invention belongs to the technical field of educational evaluation, and discloses a cognitive analysis system for attention ability change and question characteristics, which comprises an interactive coding network module, an ability acquisition module, an ability prediction module, an ability rationality module, a question prediction module, a knowledge point prediction module and a multi-task fusion module. Utilizing an ability prediction module, a question prediction module and a knowledge point prediction module to respectively predict the probability of answering the next exercise; and finally, the multi-task fusion module balances the three types of prediction probabilities in a fusion training mode to obtain the final probability of the next exercise, and the final probability is used for cognitive analysis of the subject. The ability change of the subject can be tracked in real time, dynamic ability value evaluation is provided, and ability development in the learning process is better reflected.

The invention provides a collaborative perception method and device based on multi-model fusion denoising and electronic equipment, and the method comprises the steps: carrying out the semantic cognition analysis of original point cloud data and meteorological information of a plurality of perception nodes based on a first model, and obtaining a semantic cognition analysis result. And based on the second model and the semantic cognition analysis result, performing spatial domain and frequency domain de-noising processing on the original point cloud data of the plurality of sensing nodes to obtain de-noised point cloud data of the plurality of sensing nodes. And based on a semantic cognition analysis result, performing fusion processing on the de-noised point cloud data of the plurality of sensing nodes to obtain collaborative point cloud data. And generating a semantic-level collaborative perception result based on the first model and the collaborative point cloud data.

A method, computer program product, and a system where a processor(s), obtains content from a meeting hosting system during a pre-defined interval. The processor(s) parses the textual content to identify potential keywords. The processor(s) iteratively cognitively analyzes the potential keywords to determine which potential keywords comprise seed keywords, where the seed keywords meet a maturity threshold for inclusion in a data structure, where the iterative cognitive analysis of each potential keyword of the potential keywords is repeated a pre-defined number of times, and where the iteratively cognitively analyzing includes generating and updating the data structure. The processor(s) outputs, based on completing the pre-defined number of times, the data structure comprising the seed keywords.

The invention relates to the field of data processing systems, in particular to a multimodal-based reproductive view cognitive analysis system, which comprises the following steps of: acquiring microscopic narrative data and macroscopic target data, and generating a topic popularity time sequence library by adopting a topic model; defining a rolling check window, and maintaining an active check model and a candidate check model trained based on different time window data in parallel; executing a model racing rule, and calculating a prediction residual error of the two in a rolling verification window; and according to the model handover rule, when the prediction residual error of the candidate model is lower than the prediction residual error of the active-service model, automatically updating the candidate model into the active-service model. According to the rolling window type model racing and automatic handover mechanism, an automatic verification and iteration process for predicting the timeliness of the correlation coefficient of the model is established; and the dynamic self-correction capability of the system is improved.