61 results about "Cognitive analysis" patented technology

The invention discloses a brand marketing planning scheme making system and method based on a knowledge graph, and relates to the technical field of brand marketing and cognitive analysis, and the system comprises a data preprocessing module, a drift monitoring and labeling module, and an evaluation regulation and control module. According to the system, multi-source heterogeneous text data is converted into a structured and semantic brand emotional relation graph, scattered user emotional expressions and public opinion concern points are converted into computable nodes and relations supporting time sequence analysis, the data organization is improved, the semantic hierarchy of a knowledge graph is enriched, and the user experience is improved. The cognitive evolution modeling availability is enhanced, the evolution direction and the risk signal are insighted, the time sensitivity and the evolution expression ability of the atlas are improved, the effect of converting from passive monitoring to active intervention is achieved, real-time response and high-credibility labeling of enterprise brand cognitive fluctuation are achieved, and the method is suitable for popularization and application. The timeliness, the accuracy and the intelligent response capability of an enterprise brand marketing strategy are improved, and a closed-loop adaptive regulation and control system is formed.

The invention discloses a network processing system based on artificial intelligence, and the system comprises an intelligent sensing layer which is responsible for collecting the flow, delay and topology original data of network equipment in real time, carrying out the initial processing at an edge node, generating structured network state information, and outputting the structured network state information to a cognitive analysis layer; the cognitive analysis layer is used for performing four-dimensional modeling and intelligent analysis on the network state after receiving the structured data of the intelligent perception layer; then generating an optimization decision and outputting the optimization decision to an adaptive execution layer; the self-adaptive execution layer is responsible for converting an optimization decision into specific network configuration and operation according to the decision of the cognitive analysis layer, feeding back an execution result to the man-machine collaboration layer, and receiving an administrator confirmation or adjustment instruction from the man-machine collaboration layer at the same time; the man-machine collaboration layer is responsible for interaction between the system and an administrator and displaying a real-time network state and an AI decision causal chain; and an administrator monitors the network state through the man-machine collaboration layer, confirms or rejects AI suggestions, and feeds back an operation log to the cognitive analysis layer.

The invention relates to the technical field of education, and discloses an AI-based learning weakness promotion training test question generation system, which comprises a data acquisition module used for acquiring emotional data and cognitive load data of students in real time, the emotional data comprises emotional fluctuation information of the students, and the cognitive load data comprises emotional fluctuation information of the students; the cognitive load data comprises question solving time and question answering error deviation information of the students; the emotion and cognition analysis module is used for analyzing the current emotion fluctuation and cognition load of the student based on the collected emotion data and cognition load data, and generating a corresponding emotion fluctuation value and a cognition load value; and the test question generation module is used for dynamically generating test questions suitable for the current learning state of the student according to the emotion fluctuation value and the cognitive load value of the student, and adjusting the difficulty and the type of the questions. According to the method, the difficulty and the type of the test questions are dynamically adjusted by combining emotion fluctuation and cognitive load analysis, so that cognitive overload and emotion pressure are effectively avoided, and the learning efficiency and the learning motivation are improved.

The invention discloses an advertisement generation evaluation system and method based on industrial big data, and belongs to the field of advertisement evaluation.The method comprises the steps that an advertisement information understanding model is constructed, advertisement data are imported into the advertisement information understanding model to extract advertisement semantic and visual features to recognize advertisement core focuses, and a product feature analysis model is constructed; product function parameters and selling point features are extracted through product data, product keywords are recognized, an audience cognition analysis model is constructed, audience understanding ability, information processing ability and attention focus features are extracted through user data, the language understanding and cognition level of target audiences is evaluated, and an advertisement product matching model is constructed; the matching condition of advertisement content, audiences and products is evaluated through an advertisement multi-mode semantic vector, an audience cognition vector, an advertisement focus vector and a product feature vector, a matching effect evaluation model is constructed, the defects of advertisement expression complexity, focus coverage and product adaptation degree are recognized, and the advertisement creation and delivery effect is improved.

In some aspects, the techniques described herein relate to a method including: digitizing a physical visual aid presented in a hybrid collaborative communication session to generate a digitized visual aid and data indicative of content of the physical visual aid; correlating the content of the physical visual aid with participant interactions within the hybrid collaborative communication session; and generating hybrid collaborative communication session content based upon the correlating the content of the physical visual aid with the participant interactions within the hybrid collaborative communication session.

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 invention discloses a big data-based text travel industry cognitive analysis system, and relates to the technical field of big data, and the system comprises a data collection module which is used for collecting related data, including scenic spot data, tourist behavior data, social media data and traffic data, of the text travel industry from a multi-source channel; the data preprocessing module is used for cleaning, denoising and formatting the collected data and ensuring the data security through a data encryption technology; the cognitive analysis module is used for performing semantic analysis, sentiment analysis and behavior pattern recognition on the preprocessed data based on a deep learning algorithm and a natural language processing technology to generate a cognitive map of the text travel industry; the intelligent decision-making module is used for predicting tourist demands through a machine learning model according to the cognitive map in combination with historical data and real-time data, optimizing resource configuration and providing personalized recommendation services; and the visual display module is used for displaying an analysis result to a user in a visual manner and supporting multi-dimensional data query and real-time monitoring.

The invention discloses a psychological experiment task-oriented subject response data modeling method and system, and belongs to the technical field of psychological experiment task data modeling, and the method comprises the following steps: collecting subject response data; performing reaction flow serialization, converting original data into a continuous time sequence, and recognizing a cognitive state transition point through a multi-scale recursive segmentation method based on dynamic entropy perception; dynamic cognitive model construction: adopting an improved drift diffusion model combining dynamic parameter decomposition and hierarchical Bayesian inference to obtain a dynamic cognitive analysis model reflecting cognitive strategy switching, fatigue accumulation and learning effect; and reaction data modeling: establishing a causal relationship among a state label, a cognitive parameter and a reaction behavior, and realizing quantitative modeling and prediction of a psychological experiment task. According to the scheme, fine modeling and dynamic analysis of the multi-dimensional psychological reaction process can be realized, and the interpretability and the application value of psychological experiment data are improved.

The invention relates to the technical field of smart power grids, and discloses a power distribution network fault positioning system and method, and the system comprises a causal generation module, an event inversion module, a dynamic pruning module, a causal collaborative verification module, and a meta-cognitive analysis module. The method comprises the following steps: iteratively calling a causal generation module through an event inversion module to reversely solve an optimal event parameter containing a fault position, and eliminating a physically unreasonable search space by utilizing a dynamic pruning module in iteration; then, the causal collaborative verification module verifies the result and calculates a diagnosis confidence coefficient, and when the confidence coefficient is lower than a threshold value, the event model is upgraded to reconstruct a complex fault; further, the system may generate a diagnosability map offline to identify a diagnostic blind spot. Through a generative inversion and model upgrading mechanism, accurate positioning of a fault and accurate reconstruction of a complex accident sequence are realized, a quantitative evaluation capability of a self-diagnosis capability is possessed, and an intelligent level of fault processing is substantially improved.

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 invention relates to a system (2) for analyzing human behavior, the system (2) comprising a plurality of sensors (4) for input data relating to human behavior, a plurality of monomodal behavioral analysis engines (6), and a cyber-cognitive analysis platform (8), each monomodal behavioral analysis engine (6) being capable of acquiring the input data relating to human behavior; the cyber-cognitive analysis platform (8) comprising a meta-engine (10) and at least two modules (12a-12f) configured to reproduce cognitive functions of a human brain, the meta-engine (10) being configured to aggregate the input data, to consolidate said input data, and to transmit said consolidated input data to said at least two modules (12a-12f);the meta-engine (10) being provided with a self-learning statistical analysis module (14) configured to analyze and correlate the input data from the plurality of single-modal behavioral analysis engines (6), and to provide an analysis of human behavior from said analyzed and correlated input data and data processed by said at least two modules (12a-12f). Abstract figure: Fig. 1;

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 present invention discloses an intelligent industrial design measurement and control device and method of application. The key points of the technical solution include the construction and application of an AIGC agile industrial design model, a human-machine collaborative design process, the construction of an application paradigm for industrial product generation scenarios, model optimization decisions based on cognitive analysis, and application case studies and verification. The AIGC model significantly improves design efficiency and innovation by rapidly generating and optimizing design solutions. Through precise demand analysis and optimization decisions, it ensures that the design solution is highly consistent with user expectations, thereby improving user satisfaction. Agile design methods and multiple iterative optimizations ensure that the design process is efficient and accurate, and improve design quality and reliability. Rapid generation and optimization of design solutions effectively shortens the product development cycle, thereby improving the company's market response speed and competitiveness.

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 application provides a knowledge graph-based cognitive ability analysis method and system, which can construct a corresponding knowledge graph according to the historical knowledge learning records of different target objects, determine the cognitive analysis error of the target object according to the knowledge graph, and correct the knowledge graph, so as to effectively avoid the occurrence of knowledge graph construction deviation caused by the data defects of the historical knowledge learning records, finally, re-determine the cognitive ability evaluation value of the target object according to the corrected knowledge graph, accurately and reliably evaluate the target object, and adjust the knowledge data learning progress of the target object according to the cognitive ability evaluation value, thereby maximizing the learning efficiency and learning quality of the target object.

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.

Described are methods and systems to provide an adaptive cognitive analytics platform to a user such that the user's data remains secure while enabling functionality adaptive to the user's needs. In some embodiments, a cognitive analytics application can be installed on the user's mobile device to analyze a plurality of data streams generated by sensors worn by the user. The cognitive analytics application is configured to allow the user to select and download one or more analytics programs from a plurality of vetted analytics programs depending on the user's desired functionality. The cognitive analytics application can be configured to determine the types of analytics program usable by the user based on the data streams available at the user's mobile device. To secure user data and protect the user's data privacy, the cognitive analytics application can prevent the plurality of analytics programs from transmitting monitored physiological data.

A device includes a memory configured to store one or more image-feature group (IFG) identifiers. The device also includes one or more processors coupled to the memory and configured to process, at a hierarchical vision encoder (HVE), an image frame of a sequence of image frames to generate image latent data. The image latent data corresponds to a downscaled representation of the image frame. The one or more processors are also configured to process the image latent data to generate a set of IFG identifiers. The one or more processors are further configured to add the set of IFG identifiers to image analysis data used to represent the sequence of image frames for image-based cognitive analysis.

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.

The invention discloses a fault self-healing and resource optimization method and system for a medical image SaaS platform, and the method comprises the steps: obtaining multi-source observation data in the operation process of the medical image SaaS platform, and constructing a digital twinborn model of the platform; based on the digital twinborn model and multi-source observation data, performing intelligent identification and root cause positioning on an abnormal state of the platform so as to execute cognitive analysis on a platform operation problem; and executing a preset corresponding fault repair operation and a resource optimization strategy according to the result of the cognitive analysis. The full-link transparency of the medical image SaaS platform is realized by constructing the digital twinborn model, the fault recovery time is greatly shortened, and the capabilities of the platform for automatically sensing, diagnosing and repairing system abnormity and adaptively optimizing resources are realized.

A Computer Device with Graphical User Interface for Augmented Cognitive Analysis

Aspects of the present disclosure are directed to a cognition analysis and response system that can A) identify and classify machine learning training data and use the training data 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 generate training data by using an unsupervised training process and verifying results with validations of training items that have been pre-classified. The cognition analysis and response system can then iterate through each set of inputs paired with cognitive impairment classification, applying a machine learning model and updating the model based on a comparison of the model output to the cognitive impairment classification.

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.

A device includes a memory configured to store sets of encoder output data. The device also includes one or more processors coupled to the memory and configured to add, to the memory, first encoder output data for image-based cognitive analysis, the first encoder output data representing a first image frame of a sequence of image frames. The one or more processors are configured to use a vision encoder to process a second image frame of the sequence of image frames to generate second encoder output data. The one or more processors are configured to, based on a comparison of the first image frame and the second image frame, determine whether to store the second encoder output data for image-based cognitive analysis.