49 results about "Cognitive computing" patented technology

A system and method for persistent cognitive computation with temporally synchronized multimodal processing implements a geometric approach to artificial intelligence through typed latent entities within a dynamic manifold substrate. The system maintains a latent manifold incorporating heterogeneous data modalities where local curvature reflects semantic density and typed entities are stratified according to structural properties. Temporal synchronization coordinates asynchronous multimodal data streams through generation of temporal alignment fields within the manifold that preserve semantic coherence across modal boundaries. Type-aware geometric operations enforce operation legality based on entity type and local manifold geometry, enabling structured recombination, compression, and traversal while preventing semantic distortion. The system executes synchronized manifold reorganization during idle periods through coordinated optimization operations including perturbation analysis and topological surgery. This architecture enables persistent memory through geometric encoding where frequently accessed concepts develop high-curvature regions and cognitive patterns emerge from usage-based manifold evolution.

The invention discloses a heterogeneous feature fusion-based multi-task traffic situation cognitive calculation method, and belongs to the field of intelligent traffic. The method specifically comprises the following steps: firstly, preprocessing multi-source traffic observation data to obtain normalized traffic flow, OD raster data and traffic state data; then, for a current frame, carrying out dynamic space-time convolution and diffusion diagram convolution on traffic flow and OD raster data to obtain node-level space-time features; meanwhile, carrying out feature extraction on the traffic state data by utilizing 3D convolution to obtain grid-level spatial-temporal features; and then, inputting the features and the common features into a heterogeneous cross attention fusion module to obtain a unified fusion feature h of the current frame, performing multi-task prediction, and respectively obtaining flow, OD and traffic state prediction results of future K steps. And finally, inputting the prediction result of each task and a true value to carry out multi-task loss calculation, carrying out parameter updating and completing model training. According to the invention, multi-task prediction can be realized by using multiple prediction heads.

The invention discloses a complex information chain modeling and self-adaptive reconstruction method based on cognitive calculation. The method comprises the steps that an information chain topology model is constructed according to nodes and links, and dynamic state parameters are configured; performing network cognitive evaluation based on the topology model and the state parameters, and generating a node efficiency index and a link reliability index; when a link failure is detected, determining an alternative path in the topology in which the failed link is removed based on the index; and according to the alternative path, reconstructing a topology model and updating parameters, and triggering new cognitive evaluation to form an adaptive closed loop. According to the method, the dynamic cognition and vulnerability analysis of the complex information chain can be realized, the adaptive reconstruction can be quickly completed when the link fails, and the robustness and task guarantee capability of the information chain in a dynamic confrontation environment can be remarkably improved.

The invention relates to the technical field of automatic driving, and discloses a Robotaxi-oriented VLA-world model fusion automatic driving system and a closed-loop training platform, and the system comprises an environment perception coding layer which is used for generating space-time aligned environment feature vectors; the mixed cognitive calculation core layer integrates a reactive sub-network, a judicial sub-network, a cognitive uncertainty evaluator and an anti-fact query interface; wherein the reactive sub-network is used for generating future trajectory distribution, and the judicial sub-network is used for calling the reactive sub-network to perform future state simulation through an anti-fact query interface when a trajectory distribution entropy value exceeds a threshold value, and executing thinking chain reasoning based on a simulation result to generate a control instruction. According to the method, deep closed loop of reactive prediction and judicial reasoning is realized through an anti-fact query mechanism, and the problems of cognitive splitting and reasoning short view existing when a traditional modular system processes a long-tail scene are solved.

The invention relates to the technical field of education and teaching. The invention provides a classroom teaching dynamic feedback and evaluation system integrated with multi-modal emotion calculation. The system comprises a multi-modal data acquisition module used for acquiring multi-source data in a classroom teaching environment and sending the multi-source data to an emotion-cognitive calculation center; the emotion-cognition calculation center is used for carrying out fusion analysis on the multi-source data, identifying the emotion state and the cognition state of the student, carrying out attribution analysis through an emotion-cognition coupling model, generating a coupling state label for describing the specific reason of the learning dilemma, and sending the coupling state label to the dynamic intervention engine; the dynamic intervention engine is used for matching and generating individual-level, group-level and system-level teaching intervention instructions from the teaching strategy knowledge base according to the received coupling state labels; and the visual feedback and evaluation module is connected with the emotion-cognition calculation center and the dynamic intervention engine, and is used for providing real-time classroom state visual display for the teacher and pushing personalized learning suggestions to the student terminal.

The invention provides an intention-driven semantic evolution mechanism based on a tensor semantic field and an application system thereof, and relates to the technical field of artificial intelligence, semantic networks and cognitive computing. According to the method, five types of semantic primitives including data, information, knowledge, intelligence and intention are expressed as high-order tensor nodes, a semantic tensor field network is constructed, and dynamic evolution and intention driving of semantics are achieved. The system comprises a multi-scale semantic aggregation mechanism, an intention weight diffusion algorithm, a semantic tensor evolution operator and a white box interpretation interface, supports full-link semantic processing from original data to high-level wisdom to intention constraint, and overcomes the defects in semantic representation and evolution, intention fusion and system interpretability in the prior art. And the generative AI system has stronger intention perception, semantic self-optimization and process transparency capabilities, and is suitable for applications such as a semantic perception large model platform, an AI cognitive map system and an interpretable language generator.

The invention relates to a neural electrophysiological signal analysis technology in the field of brain cognition calculation, in particular to a cross-individual EEG driving fatigue detection method based on multi-feature comparative learning. The method comprises the steps that PSD features and DE features are obtained based on electroencephalogram signal sample data; extracting a shallow PSD semantic feature, a shallow DE semantic feature, a deep PSD semantic feature and a deep DE semantic feature; obtaining a PSD prediction classification label and a DE prediction classification label based on the deep semantic features; defining a positive sample pair, a negative sample pair and a semi-positive sample pair, and constructing a contrast loss function; splicing the shallow semantic features to obtain a preliminary fusion representation, and obtaining a fusion feature vector based on the preliminary fusion representation, the deep PSD semantic features and the deep DE semantic features; and inputting the fused feature vector into a multi-feature decision maker to obtain a fatigue classification label of each sample. According to the method, the adaptability of the model to individual differences is improved, and the generalization ability of the model and the detection robustness are enhanced.

A system and method for implementing persistent cognitive computation through geometric representation of thought in a dynamic latent manifold. The system encodes inputs into a curved space characterized by time-evolving metric tensors, compression pressure fields derived from Ricci curvature, and goal potential fields that shape attention flow. Cognition occurs through geodesic traversal of this manifold, with attention following paths that minimize cognitive action while balancing semantic density and goal relevance. A Cognitive Dynamics Engine maintains manifold geometry, computing optimal trajectories and managing thought bundle operations including consolidation, expansion, and higher-order abstraction. During idle periods, autonomous dreaming processes reorganize the manifold through perturbation, recombination, and topological surgery. This architecture enables persistent memory through geometric encoding, where frequently accessed concepts develop high-curvature regions and cognitive shortcuts emerge from usage patterns, transforming artificial intelligence from stateless computation to structured motion through shaped memory space.

The invention discloses a lung cancer high-risk group screening questionnaire cognitive calculation correction method and system. The method comprises the following steps: acquiring questionnaire text data of a subject; constructing a BERT-QA model based on the clinical term knowledge graph, analyzing questionnaire text semantics, and extracting structured risk factor data; mapping the risk factor data to a topological space, and constructing a risk topological representation; calculating a dynamic weight coefficient of each risk factor based on an information entropy principle, and generating a risk assessment value of entropy weight optimization; carrying out calibration in combination with regional pollution map data; according to the method, deep learning, topology and fuzzy logic technologies are fused, intelligent analysis of questionnaire texts and dynamic weight calculation of risk factors are realized, environmental pollution factors are effectively integrated, the screening accuracy and individuation level are improved, and the method is suitable for large-scale popularization and application. The method provides technical support for accurate identification and early intervention of lung cancer high-risk groups, and has important clinical application value.

A system and method for implementing persistent cognitive computation through geometric representation augmented with holonomy-based experiential memory. The system encodes inputs into a curved latent manifold and maintains bounded sets of holonomy descriptors at each location, enabling two-component cognitive states comprising position and experiential context. Cognition occurs through holonomy-sensitive traversal where paths depend jointly on geometric structure and accumulated path-dependent constraints. Holonomy generators are created during traversal from prediction errors and constraint encounters, composed into consolidated descriptors, and undergo lifecycle management including reinforcement, decay, and irreversible export to residual constraint regions. This architecture escapes location-only representations by distinguishing cognitive states that occupy identical semantic positions but arise through different experiential histories. The system supports counterfactual reasoning through holonomy switching at fixed locations and preserves semantic memory as compressed transport deformation rather than stored trajectories, enabling scalable experiential learning where repeated patterns strengthen constraints while capacity remains bounded.

The application relates to the technical field of automatic driving and discloses a VLA-world model fusion automatic driving system and a closed-loop training platform for Robotaxi, which comprises an environment perception coding layer used for generating a space-time aligned environment feature vector; a hybrid cognitive computing core layer integrating a reactive subnetwork, a prudent subnetwork, a cognitive uncertainty evaluator and a counterfactual query interface; wherein the reactive subnetwork is used for generating a future trajectory distribution, the prudent subnetwork is used for simulating a future state through the counterfactual query interface to call the reactive subnetwork when the trajectory distribution entropy value exceeds a threshold value, and a control instruction is generated through thought chain reasoning based on a simulation result, the application realizes a deep closed loop of reactive prediction and prudent reasoning through a counterfactual query mechanism, and solves the problems of cognitive fragmentation and reasoning short-sightedness of a traditional modular system when processing long-tail scenes.

The application discloses a multi-modal false news detection method and device, and relates to the field of multi-modal cognitive computing. The method is used for providing in-depth mining and analysis of news information, realizing semantic enhancement of multi-modal information, and mining and application of news sentiment features. According to the BERT model, the first image caption information is subjected to feature coding to obtain a first caption representation and a first empirical representation. A first text representation is subjected to a self-attention mechanism, a full connection layer and averaging to obtain a sentiment representation of comprehensive analysis of the first text representation by all experts and a sentiment tendency evaluation value of comprehensive analysis of the first text representation by all experts. According to the attention weights of a plurality of different sentiment news processors and the first news processing features, news aggregation features for true or false classification are obtained. According to the news aggregation features for true or false classification and a false news detector, a prediction label of the first news is obtained.

This invention discloses a hierarchical continuous attractor network based on intrinsic time constant gradients, belonging to the fields of artificial neural networks and cognitive computing. The network includes a spatial input extraction module, multiple continuous attractor layers, a spatial projection module corresponding to each layer, and a wave packet decoding module. Each layer has a preset and fixed time constant during initialization, and the time constants of each layer monotonically increase from the bottom layer to the top layer. The same external spatial input is projected to each layer in parallel. The bottom layer tracks instantaneous spatial changes with a shorter relaxation time, while the top layer accumulates slowly varying spatial statistics with a longer relaxation time. The cyclic connection weights of each layer are initialized with translation-invariant functions to form a continuous attractor manifold, and are fine-tuned online by superimposing Hebbian enhancement and weight decay. This invention can form multi-timescale spatial representations, improving the stability and precision of representations in high-frequency spatial regions.

The embodiment of the invention provides a risk prevention and control rule configuration method and device, equipment, a medium and a program product. The method comprises the steps of obtaining voice data input by a user in response to a voice input operation of the user on a configuration page of a target financial service of a bank system; the voice data is used for describing configuration requirements related to risk prevention and control rules of the target financial service for the user; acquiring configuration information related to configuration items required by the configuration page from the voice data based on the related information of the configuration page; the related information is used for determining the context of the configuration page; filling the corresponding position of the configuration page with the configuration information; and in response to a determination operation of the user on the configuration page based on the configuration information, constructing a risk prevention and control rule of the target financial service based on the configuration information. The method is used for improving the efficiency of cognitive calculation decision system / platform configuration.

The invention relates to the field of cognitive computing, and discloses a computing co-processing hardware system and method based on user personality modeling, which comprises the following steps: aggregating records by taking task instances as boundaries, and executing time sequence correction on the aggregated records according to an operation triggering precedence relationship and a context dependency relationship; splitting the behavior data set according to the task stage switching points, and mapping the split behavior data to an operation type space; user cognitive feature description data is injected into a configuration entry of an information processing flow, and calling conditions, execution sequences and parallel relationships of information analysis nodes, result splicing nodes and intermediate data resident nodes are combined and set; continuously recording confirmation, revision and cross-stage calling behaviors of a user on a stage result; and according to the adjusted information processing node participation set, performing structured splitting and sequential recombination on the information data flow entering the system. The method has the advantage of improving the information matching accuracy.

The invention provides a method for evaluating the interactive cognitive ability of a driverless vehicle. The method comprises the following steps: constructing a hierarchical interactive scene library and collecting high-frequency data; constructing a cognitive entropy model; carrying out interactive cognitive calculation based on entropy reduction; and constructing a comprehensive scoring model, and carrying out comprehensive scoring and grading. According to the method for evaluating the interactive cognitive ability of the driverless vehicle, a clear optimization direction is provided for algorithm development, and an innovative implementation path is provided for evaluating the core ability of the driverless vehicle.

The invention relates to the technical field of big data and data visualization, and discloses a cognitive decision-making system fusing BI and AI capabilities, and the system comprises a data access and management module which accesses multi-modal data, carries out the preprocessing of the multi-modal data, and further carries out the full-life-cycle management; the index interaction and analysis module is used for performing index caliber unification processing on the data to obtain standardized index data; a data interaction panel is constructed, and an analysis result is visually displayed; the decision deduction module is used for performing cognitive calculation and dynamic deduction on the standardized index data and generating and outputting decision suggestions; executing a decision behavior to obtain actual effect data, and feeding back the actual effect data to a cognitive calculation process to realize continuous optimization of the module; and the authority workbench module is used for configuring a personalized Web interface according to role authority and integrating multiple functions so as to realize differentiated data access and operation.

The invention discloses a visual interface adaptive adjustment system based on user behavior analysis, and relates to the technical field of behavior analysis. The user behavior data acquisition module is used for acquiring multi-dimensional interaction data and encrypting and transmitting the multi-dimensional interaction data; the cognitive calculation engine module is used for mining behavior patterns and intentions based on deep learning; the environment perception and situation modeling module is used for perceiving an environment and a user state in real time; the self-adaptive interface generation module is used for dynamically adjusting layout, style and interaction; and the intelligent decision-making and continuous optimization module realizes intelligent decision-making through reinforcement learning. According to the system, user behaviors are accurately collected and analyzed, environment changes are sensed, and the visual interface layout and interaction are dynamically optimized; the task time can be remarkably shortened, the error rate is reduced, the user satisfaction degree, the use duration and the service conversion rate are improved, the user stickiness is enhanced, personalized experience is provided for users, and benefits are created for enterprises.

The invention provides a multi-mode-driven super-large-scale neural network controller and a danger early warning method thereof. The multi-mode-driven super-large-scale neural network controller comprises an audio and video acquisition module, a multi-sensor module, a data fusion module, an industrial data driving basic model, a controller decision module and an early warning and communication module. The intelligent controller and the method thereof have the beneficial effects that the intelligent controller and the method thereof integrate an audio and video system and multi-sensor information, perform cognitive calculation by using an audio and video neural network compatible with multiple data modalities, and complete environment intelligent evaluation and danger early warning; according to the invention, the audio and video neural network compatible with multiple data modals is applied to multi-modal data cognition and analysis of high-risk environments such as a coal mine for the first time; efficient fusion and cooperative processing of audio and video data and multi-sensor data are realized; an accurate and real-time danger early warning and accident response mechanism is provided, and the safety is remarkably improved; and an embedded platform is used to complete early warning of the intelligent controller.

The invention relates to the cross technical field of computer vision and natural language processing, and discloses a minority image description generation method based on a memory mechanism and cognitive calculation, which comprises the following steps of: extracting visual features through ConvNeXt, constructing national culture knowledge, executing a multi-level cognitive strategy on an input image, and carrying out national feature weighted fusion, so as to obtain the minority image description generation method based on the memory mechanism and the cognitive calculation. The method comprises the following steps of: obtaining a cognitive reasoning result, combining national culture knowledge with the cognitive reasoning result, constructing a logic cue word, splicing the logic cue word, visual features and the cognitive reasoning result, and inputting the spliced logic cue word, visual features and cognitive reasoning result into a multi-modal large-scale language model GLM-4V based on a Transform architecture to generate a description text. According to the method, high-precision understanding and description generation of cultural symbols, clothing utensils and scene semantics in minority images are achieved by fusing a dynamic generation strategy of a multi-modal large language model, and finally a natural language description text with accuracy, richness and culture depth is output.

The cognitive modeling method, device, and application for episodic memory task for early screening of cognitive impairment can effectively activate familiar and recall memory processes related to early damage in the disease. Its neural basis involves brain regions that are susceptible to early Alzheimer's disease. It has the characteristics of automation, strong objectivity, and high interpretability. The design complexity is moderate and it supports fully automatic presentation and scoring on a computer. It is very suitable for large-scale deployment in communities, primary healthcare institutions, or remote environments. The method includes: (1) data collection, which includes two stages: encoding stage and extraction stage; (2) constructing a cognitive computing model, which divides the cognitive process into a series of discrete processing steps. These steps form a decision tree leading to different possible classification results; (3) constructing five logistic regression classifiers and using 10-fold cross-validation to distinguish between cognitively normal individuals and individuals with mild cognitive impairment (MCI). Each model is developed using a different feature set.

In one aspect, the inventions include a system for control of a software defined computer network state system. First, an application plane layer is adapted to receive instructions regarding operation of the state system. Preferably, the application plane layer is coupled to an application plane layer interface. Second, a control plane layer includes an adaptive control unit, such as a cognitive computing unit, an artificial intelligence unit or a machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources. A title transfer network element is provided to transfer digital assets via a blockchain. The system may use domain transformations and difference engines.

The invention discloses an intelligent system paradigm operating system and an implementation method thereof. The system realizes six-stage system-level intelligent circulation of perception, cognition, decision making, execution, feedback and learning in an operating system layer. According to the system, a three-domain collaborative system is formed by a perception driving domain, a cognitive decision domain and an interaction expression domain, cross-domain communication, encryption verification and state synchronization are achieved through a unified data protocol and a trust bridge mechanism, and operation consistency, safety and verifiability of the system in a multi-terminal and multi-node environment are ensured. The system has adaptive optimization and continuous learning capabilities, can automatically adjust operation parameters and execution strategies according to task results and environmental changes, and loads an external function unit through a hierarchical expansion interface to realize function expansion and ecological level evolution. According to the invention, a unified structure basis and a system normal form are provided for a future artificial intelligence operating system, a cognitive computing platform and an autonomous evolution type intelligent system.

The present application relates to the technical field of enterprise team time management, in particular, the present application relates to a virtual team management method based on enterprise information sharing, the present application sets a time stamp for key task cooperation link and collects multiple types of data on the information sharing platform, a dynamic reminding threshold model is constructed by using the method of combining transfer learning and adversarial generative network, the social network factor of the task is considered, the quantum annealing algorithm is used to optimize the model parameters, in the task reminding stage, the model is used to monitor the task processing time in real time, the reminding frequency is dynamically adjusted based on cognitive computing, the busy state of the members is obtained by using blockchain and edge computing, the reminding time is postponed by using time series prediction and multi-agent negotiation, emotion computing and virtual reality technology are also used to trigger the reminder, so as to avoid interfering with the work of the members, improve the acceptance and attention of the members to the reminder, and significantly improve the cooperation efficiency of the key task of the virtual team, and optimize the virtual team management effect.

The application relates to the technical field of intelligent building systems, in particular to an electric control system and method for a combined air conditioning unit based on artificial intelligence, which comprises an environment monitoring module, a data analysis module, a prediction and control module, an energy management module, a user behavior recognition module, a maintenance optimization module and a context perception adjustment module; in the application, cognitive computing technology is introduced, complex environment data can be deeply analyzed and understood, the prediction and adaptation capability for subtle environment differences and sudden demand changes is improved, the air conditioning system can more accurately adjust its operation to meet the real-time environment and specific needs of users, through data analysis and machine learning technology, fault prediction and preventive maintenance can be more effectively carried out, thereby reducing system failure and maintenance costs, and better real-time reaction and automatic adjustment capability is exhibited under rapidly changing environmental conditions, ensuring that the operation of the air conditioning system is always in the optimal state.

A fractal cognitive computing node, a computer-implemented method for learning procedures, a computational cognition cluster, and a computational cognition architecture are provided. The FCN comprises a first input to receive a first input signal, a first output to provide a first output signal, a second input to receive a second input signal and a third input signal to receive a SA parameter. A memory of the FCN stores a collection of items. A processing unit implements a function that compares a combination of said first and second input signals with the stored collection and calculates a similarity measure for each compared item. The first output signal is calculated as a selection of the compared items having a similarity measure greater than said SA parameter. If the selection is empty, a new item is added to said memory. If not empty, the first output signal is set to said selection.

This invention discloses a multi-scale feature fusion method for defect recognition in injection molded parts, belonging to the field of computer vision and industrial product quality inspection technology. The method includes: constructing and preprocessing a dataset of defective injection molded parts images; extracting multi-scale feature maps using a deep convolutional neural network with an integrated visual cognitive computing module; enhancing small-target defect information by collaboratively modeling the consistency and differences of features through a parallel dual-branch feature fusion module; employing an attention-guided cross-level enhancement module to reweight the fused features through channels, highlighting key features; and finally, achieving accurate classification and localization of defects using a detection head network based on adaptive anchor boxes. This method can effectively improve the accuracy and recall rate of injection molded parts, especially for minute defects, and is suitable for online quality inspection on industrial production lines.

This invention discloses a method and system for dynamic mapping and assembly of AI agent capabilities based on employee permissions, belonging to the interdisciplinary technical field of artificial intelligence and enterprise-level security architecture. The method synchronizes the role tags and permission sets of an individual user in real time from the enterprise identity and access management center after the user logs into the enterprise's internal multi-agent collaborative network. It transforms the traditional IT permission model into system prompt fragments and available tool descriptors under an AI cognitive computing model through a mapping translation engine. It dynamically assembles a dedicated AI agent session instance for the user in an isolated sandbox, achieving cognitive-level physical masking—the AI ​​agent not only cannot perform unauthorized operations, but also has no unauthorized tools or external contact topologies in its perceptual domain. It achieves memory-level hot unloading and injection of tools by monitoring permission change events through a publish-subscribe mechanism. This invention fundamentally eliminates the risk of unauthorized attempts caused by the illusion of a large language model and significantly reduces inference costs.

The embodiment of the application discloses a kind of based on semantic cognition's Internet of Things terminal data classification method and its application, wherein based on semantic cognition's Internet of Things terminal data classification method includes: according to the semantic feature vector information of the reinforced application scene and business of Internet of Things terminal equipment, data fusion calculation model is constructed to carry out data fusion;Through the classification model trained based on the fusion feature of multi-level feature fusion, carry out cognitive calculation, classify the data fused using the data fusion calculation model, obtain the final application scene and the classification and result of associated business.It solves the problem that a large number of isolated and heterogeneous perception data are generated by Internet of Things perception device in prior art at all times, and a large number of data islands are formed.

The invention discloses an intelligent memory and target identification method based on micro-model self-learning, and relates to the technical field of intelligent and computer cognitive computing. Comprising the steps of initial target collection and feature extraction, constant-level micro-model memory construction, multi-model collaborative recognition and memory calling, micro-model autonomous updating and growth and memory management and recognition result output. According to the method, the intelligent exclusive memory device is constructed, and the tracking model and the micro-model self-learning ability are fused, so that the technical effects of constant-level space storage-target feature autonomous updating-recognition model growth while using are realized. According to the method, a certain micro-model scale is fixed, the traditional linear storage bottleneck is broken through, meanwhile, the continuous learning ability is achieved, and the storage and adaptation problems of the intelligent equipment in target memory and recognition are solved.