28 results about "Cognitive systems" patented technology

The invention particularly relates to a multi-level self-cognition system based on a large model, and relates to the technical field of large models. A neural symbol world model module; a large language model cognition core module; and a hierarchical decision planning system module. According to the method, deep integration of perception, cognition and decision making is achieved through the hierarchical fusion architecture, and compared with the prior art, the method has remarkable advantages; the multi-modal perception encoder adopts layered encoding and a cross-modal attention mechanism, so that the semantic alignment problem of multi-source perception data is effectively solved, and the understanding ability of the system to a complex scene is greatly improved; according to the neural symbol world model, the neural network and symbol reasoning are combined, the limitation of a pure neural network method in physical modeling is overcome, meanwhile, the calculation complexity of a pure symbol system is avoided, and efficient and accurate environment characterization and prediction are achieved.

The invention provides an industrial cognitive base system based on multi-modal comparative learning and an execution method, and the industrial cognitive base system based on multi-modal comparative learning is characterized in that a system architecture comprising a knowledge graph management module and a cross-modal coding fusion module is constructed; industrial field knowledge is dynamically injected into a multi-modal feature learning process in the form of a structured sub-graph, and a comparative learning mechanism introducing industrial semantic constraints is combined, so that a model obtained by final training can realize semantic alignment of multi-modal data; it can be ensured that generated unified semantic representation and reasoning results strictly conform to predefined industrial logic rules, and therefore the defect that in the prior art, a pure data driving method may generate results violating industrial common knowledge is effectively overcome; and the output reliability, the decision credibility and the practical application value of the industrial cognitive system in key tasks such as fault diagnosis and state monitoring are remarkably improved.

The invention provides a maze robot brain-like cognitive system based on an emotion and memory mechanism, and the system comprises a perception and emotion generation module which obtains the environment state of a robot, and calculates the reflective fear value and cognitive fear value of the robot; the memory module is used for recording short-time memory, working memory and long-time memory of the robot in combination with an anti-thought memory mechanism; the behavior decision-making module is used for comparing the evaluation function based on the cognitive fear value with the energy margin to obtain a behavior decision-making result; the learning module is used for exploring and learning a target node and an energy node to obtain an updated target guide table and an updated energy guide table; and the action selection module is used for updating the target guide table and the energy guide table according to the behavior decision result, and outputting the next action. According to the invention, the survival rate of the robot in the environment learning process can be improved, and long-time learning is realized; and the robot can be prevented from repeatedly exploring in an invalid area, the learning efficiency is improved, the energy consumption is reduced, and self-survival is ensured.

A device for decision support of a cognitive system based on data originating from a plurality of data sources, the device including a processing unit associated with each data source, each processing unit including an encoding unit configured to determine, from the data from the data source associated with the processing unit, a representation of data in a common representation space by applying a machine learning algorithm to the data, the device further including a data fusion unit configured to determine a representation model of an environment of the cognitive system by combining the data representations determined by the encoding units associated with the plurality of data sources through the application of a data fusion algorithm.

The application discloses a kind of memory hub methods and systems for multi-agent cognitive decision-making, comprising: obtaining the perception information, behavior result or decision context generated in the running process of cognitive system;The perception information is structured modeling, and memory unit with time attribute, access attribute and evolution attribute is generated;The memory unit is stored and managed in life cycle;Before or in the process of cognitive decision generation, according to the current decision context, select relevant historical experience from the memory unit to participate in decision formation;Based on the use effect of memory unit in the decision process, update the hierarchical state or call strategy of the memory unit, and construct the continuously evolving cognitive decision process based on historical experience feedback. Unified management and monitoring can be carried out on memory access behavior, permission configuration, life cycle state and abnormal conditions, and parameter configuration and state viewing can be supported during system operation.

The invention discloses a space-ground integrated electromagnetic spectrum hierarchical semantic situation multi-granularity cognitive system and method, and the system employs a cloud-edge-end hierarchical architecture, and comprises a space-based network, an air-based network and a ground-based network from top to bottom. Cloud computing nodes in the space-based network, edge computing nodes in the air-based network and terminal nodes in the ground-based network are subjected to layered molecular network processing; the method comprises the following steps: establishing a cloud-edge-end layered spectrum semantic situation multi-granularity cognitive mechanism, and collaboratively cognizing a spectrum semantic situation by cognitive granularity gradually coarsened from end to cloud; aiming at heterogeneity of a space-ground integrated network, nodes in air-space-ground network domains are subjected to hierarchical molecular networking based on space breadth, node density and frequency utilization characteristics, and a frequency spectrum hierarchical semantic situation multi-granularity cognitive mechanism in each network domain among the air-space-ground network domains is established. According to the method, the problems of spectrum sensing data explosion and resource shortage of computing nodes and communication links under a traditional spectrum situation sensing architecture are effectively solved.

The application discloses a Token marking method and a cognitive system for large language model input data, and belongs to the technical field of artificial intelligence. The application generates corresponding discrete coordinates for each Token input into the large language model, including: a time coordinate for providing a unified time reference; a context coordinate for identifying a dialogue time sequence; a logic coordinate for identifying the sequential position of the Token in a text or instruction, and the logic coordinate is independently numbered within the range defined by the time coordinate or the context coordinate; and a device coordinate for identifying an Internet of Things device, a role or a priority. The coordinates jointly constitute a unified discrete space-time cognitive framework, and provide a structured worldview for the large language model, so that the large language model can perform structured understanding based on multi-dimensional information such as time, context, logical sequence and identity. The application does not change the core architecture of the model, has strong compatibility, and can be widely applied to the fields of multi-round dialogue, intelligent customer service, industrial automation, smart home, Internet of Vehicles and the like.

The invention discloses a tensegrity structure autonomous state cognition system in a difficult-to-reach area, the system comprises a state reconstruction module and a man-machine interaction module, the state reconstruction module detects resistance change data of a cable in a tensegrity structure in real time based on a flexible sensor, predicts the real-time state of the structure by using a long-short-term memory network, and obtains the real-time state of the structure; reconstructing a three-dimensional shape and a mechanical behavior of the structure; the man-machine interaction module carries out word segmentation on the text and converts the text into a vector space with context perception so as to capture a semantic interdependence relationship, and a fine tuning data set is established through an aggregation sensor and image features; a pre-trained LLM model is obtained by combining parallel input of text prompt and time sequence coding, and training is carried out based on a fine tuning data set; in the reasoning process, the model uses reconstruction errors as abnormal scores for calculation, the anomalies are distinguished according to statistical distribution of the scores and a frequency threshold value of a specific field, and the anomalies are divided into sensor interference, sensor damage and rod deformation.

The application discloses an AI intelligent child camera system. The AI intelligent child camera system comprises a cognitive understanding engine, an association memory engine, a creation generation engine and a learning evolution engine. The cognitive understanding engine is used for performing preliminary identification and deep identification on a first target photo by using a dynamically switched end-cloud collaborative cognitive system; the association memory engine is used for associatively storing a new photo with an existing event; the creation generation engine is used for acquiring a child voice instruction and a corresponding second target photo, generating an animation short film corresponding to the child voice instruction from the second target photo, and generating an artistic image from a third target photo based on a decomposable flow matching; and the learning evolution engine is used for adjusting an AI model in the AI intelligent child camera system on the camera by using an orthogonal fine-tuning technology. An adaptive intelligent image interaction system capable of deeply understanding an individual world, intelligently reconstructing image narratives, actively cooperating with content creation, and growing together with children can be constructed.

The application provides a social emotional quotient quantitative criterion system and an AGI safety alignment method based on a four-dimensional discrete space-time cognitive system, which is a unified bottom layer base of general artificial intelligence (AGI) and is suitable for large language models, brain-like cognition, autonomous intelligent agents, brain-computer interfaces and all things interconnection systems. The application allocates a unique four-dimensional coordinate (T, C, L, D) to each Token / neural information element in the model, and constructs a full-link structured cognitive framework that is traceable, addressable, calibratable and constrained. The system adopts a double-bottom parallel architecture: bottom layer A: pure mathematical causal reasoning kernel, reasoning is completely free and cannot be intervened; bottom layer B: quantitative safety criterion bottom layer, only scoring, no reasoning, no pollution, no modification. The application realizes AGI safety alignment, anti-disguise, anti-evasion and anti-autonomous expansion from the root of the architecture by three-domain hard isolation (silicon-based endogenous domain, carbon-based interaction domain and external game domain), double-layer verification (output text + reasoning chain), execution gating (only lock actions, not thoughts), brain-computer interface default ban and cancellation of AI refusal right, and is a necessary bottom layer system of the next generation of strong cognitive AI.

This invention relates to the fields of basic research in cognitive neuroscience, large-scale artificial intelligence model architecture, brain-computer interfaces, and educational cognitive assessment. Specifically, it relates to a unified four-dimensional discrete-time spatiotemporal modeling method for basic neural information elements of the brain (neural impulses, neuronal cluster firing, and sensory neural signals). This method is based on a homologous and unified underlying theoretical framework and is an engineering embodiment of a unified four-dimensional discrete-time spatiotemporal cognitive system. It shares a completely homologous, isomorphic, and unified mathematical framework with the inventor's previously submitted "A Token Tagging Method and Cognitive System for Input Data of a Large Language Model." The only difference is that the AI ​​patent processes computational information elements (Tokens), while this patent processes biological information elements (neural impulses, sensory signals, and neuronal firing events). Together, they constitute a unified underlying architecture covering biological cognition and artificial intelligence.

The invention provides a model construction method and system for a communication interference cognitive system. The method comprises the following steps: constructing a communication interference cognitive reference set containing interference scene association features and interference response reference features; then, interference feature correlation modeling is carried out based on the communication interference cognitive reference set to form a correlation map; implementing cognitive model structure adaptation according to the correlation map to generate an initial interference cognitive model; carrying out interference scene adaptive iterative optimization on the initial model to form an optimized target interference cognitive model; and finally, outputting the target model for interference identification and response processing of the communication interference cognitive system. According to the method, the interference characteristics are comprehensively captured, the incidence relation is deeply mined, the model structure is continuously optimized, and the accuracy and adaptability of a communication interference cognitive system in a complex interference environment are effectively improved.

The application provides an industrial cognitive base system based on multi-modal contrast learning and an execution method, wherein the industrial cognitive base system based on multi-modal contrast learning constructs a system architecture comprising a knowledge graph management module and a cross-modal encoding fusion module, dynamically injects industrial field knowledge in the form of a structured sub-graph into a multi-modal feature learning process, and combines a contrast learning mechanism with the introduction of industrial semantic constraints, so that the model obtained through final training can not only realize semantic alignment of multi-modal data, but also ensure that the unified semantic representation and reasoning results generated by the model strictly comply with pre-defined industrial logic rules, thereby effectively overcoming the defects that the simple data-driven method in the prior art may produce results contrary to industrial common sense, and significantly improving the output reliability, decision confidence and practical application value of the industrial cognitive system in key tasks such as fault diagnosis and state monitoring.

The invention provides a public opinion information retrieval method based on bidirectional enhancement of a big language model and a knowledge graph. The method comprises the following steps: constructing a bidirectional enhancement mechanism, enhancing the big language model through the knowledge graph, and enhancing the knowledge graph through the big language model to obtain an enhanced big language model and the enhanced knowledge graph; acquiring real-time public opinion data in real time, and dynamically constructing a knowledge graph by adopting the bidirectional enhancement mechanism according to the real-time public opinion data; personalized retrieval and sorting are carried out, retrieval calculation is carried out on the knowledge graph and the content in the public opinion data retrieval pool according to user retrieval content, a retrieval result is obtained through sorting, and public opinion information retrieval based on bidirectional enhancement of the large language model and the knowledge graph is completed. According to the method, the crossing from a static knowledge base to a dynamic cognitive system can be realized, and second-level response knowledge reserve and deduction capability are provided for decision making; the qualitative change from a retrieval result to decision insight is realized, the user is effectively enabled, and the user stickiness is improved.

This invention discloses a cognitive decision-making method and system based on multidimensional situation coding, belonging to the field of artificial intelligence and cognitive computing technology. The method includes: acquiring input data and converting it into an event sequence, thereby generating a six-dimensional external polar field; driving a six-dimensional energy vector to converge from an initial neutral state to a stable state under the action of an energy evolution equation; binarizing the converged energy vector and matching it with a situation coding library to determine the current situation type; generating a structured description and action suggestions for the current situation; selecting a target situation type and planning an evolution path based on situation value weights; and outputting a cognitive decision report. This invention uses events as the native cognitive unit, which can be characters, words, phrases, sentences, or any combination thereof. Through a six-dimensional complete situation space and an energy dynamics emergence mechanism, it provides cognitive systems with endogenous interpretability, a unified situation coding framework, autonomous planning capabilities based on intrinsic value, and online self-evolution capabilities. It can be widely applied to intelligent decision support, complex system situation assessment, and other scenarios.

This invention discloses a cognitive architecture for human-computer interaction and robot-to-robot communication based on a four-dimensional discrete spatiotemporal cognitive system, belonging to the field of artificial intelligence human-computer interaction and robot-to-robot communication technology. The four-dimensional discrete spatiotemporal cognitive system is embedded in a large language model and a robot control system, including a time coordinate axis, a context coordinate axis, a logical coordinate axis, and a device coordinate axis. The method includes: the cognitive system automatically generating four-dimensional discrete coordinates for each token, where the time coordinate is based on real-time time, the context coordinate is based on the dialogue sequence number, the logical coordinate is based on the token's sequential position in the interaction information, and the device coordinate is based on the device's globally unique identifier; in the attention mechanism of the large language model and the robot control system, the attention weight is adjusted according to the coordinate differences between tokens. This invention uses the four-dimensional discrete spatiotemporal cognitive system as a fixed underlying architecture for human-computer interaction and robot-to-robot communication, effectively solving problems such as context confusion, timing control, and multi-device collaboration in multi-turn dialogues during human-computer interaction and robot-to-robot communication.

A mechanism is provided in a data processing system comprising a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to be specifically configured to implement a recognizer module for detecting user input in a cognitive environment. The recognizer module receives sensor signals from at least one wearable device being worn by a user. The recognizer module analyzes the sensor signals using a machine learning model to determine at least one user input indicator describing user input activity of the user. The recognizer module communicates the at least one user input indicator to a cognitive system executing within the cognitive environment. The cognitive system performs at least one cognitive action based on the at least one user input indicator.

The present application relates to a kind of intelligent reflecting surface IRS assisted cognitive wireless power transfer communication SWIPT system beamforming method, belong to wireless communication field.The method includes: S1: the IRS assisted cognitive SWIPT system scene transmission model is established.S2: with the minimum transmission power of secondary user transmitter as objective function, considering the minimum signal-to-noise ratio constraint of secondary user, power splitting coefficient and minimum energy collection constraint, intelligent reflecting surface phase shift constraint and the interference noise ratio threshold constraint of primary user, based on channel uncertainty, a joint optimization secondary user transmitter beam design, secondary user power splitting coefficient and intelligent reflecting surface phase shift design resource allocation model is established.S3: the non-convex problem with infinite constraints is converted into finite-dimensional deterministic convex optimization problem.S4: it is solved by converting into semi-definite programming problem.S5: for IRS phase shift design subproblem, penalty concave-convex process method is used to solve.

This invention relates to an incremental ontology-based cognitive system and method for intelligent agents with multi-dimensional ontology. The invention includes an ontology motion perception module, an ontology cognitive evaluation module, a memory pool module, an ontology knowledge mining module, and an ontology cognitive training module. The ontology motion perception module is used to obtain multimodal data; the ontology cognitive evaluation module is used to obtain ontology cognitive quantization error using the multimodal data; the memory pool module is used for data storage; the ontology knowledge mining module is used to establish an ontology knowledge base using multimodal data mining; and the ontology cognitive training module is used to construct an ontology cognitive model and train the ontology cognitive model under the guidance of the ontology cognitive quantization error and the ontology knowledge base. Compared with existing technologies, this invention has advantages such as multi-dimensional feature representation and perception of the ontology, improved accuracy of cognitive data, and reduced overall error.

Apparatus and associated methods relate to a synthetic cognitive system (SCS). In an illustrative example, the SCS may include a performance quantifier engine (PQE) configured to generate an action signal based on input data received from a user / agent experience. The SCS may, for example, include a synthetic cognitive identity (SCI). The SCI may, for example, include cognitive biases associated with an agent. The SCI may, for example, include goals associated with an agent. The SCI may, for example, include observations and inferences associated with an agent. The SCI may, for example, include hypotheses associated with an agent. The SCI may, for example, dynamically and independently update in real-time in response to the input data, such that the SCS generates responses dynamically to match with an evolution process of the user / agent experience. Various embodiments may advantageously simulate human-like cognitive and emotional processes, enabling personalized and adaptive interactions across multiple domains.

An abstraction system for generating standard customer profiles in a data processing system has a processing device and a memory. The abstraction system can receive customer data from a computing device over a network, perform unsupervised learning on the customer data to produce a plurality of customer clusters having a plurality of common features, and determine that a cluster represents a standard customer, and store a plurality of standard customer profiles based on the determined standard customer, where the standard customer profiles include a plurality of data distributions of the plurality of common features. The abstraction system also provides the standard customer profiles and additional standard customer profiles to a cognitive system for generating synthetic transaction data.

The invention discloses a universal robot cognitive architecture based on a world model and a control method thereof, and belongs to the technical field of robot control. Aiming at industry pain points that responsibility boundaries are fuzzy and decision-making processes are difficult to trace in large-scale application of robots, the invention provides a robot cognitive system constructed by taking the world as the center, and the robot cognitive system comprises eight functional modules: a first module is used for storing a physical world knowledge base; the second module is used for storing a robot body model; the third module is used for acquiring multi-modal sensing data; the fourth module is used for storing a life entity cognition model; the fifth module is used for storing a life-physical interaction rule base; the sixth module is used for storing a human exclusive knowledge base; the seventh module is used for storing a behavior specification and ethical constraint engine; and the eighth module is used for behavior execution and interaction control. A function-coupled whole is formed among the modules, a complete closed loop from world understanding to behavior generation is formed, and the seventh module is independent and preferential and performs compliance verification on all actions; the system is equipped with a traceable recording mechanism to ensure that marks are reserved in the whole key decision-making process. According to the method, responsibility is taken as a principal line for through design, so that the robot has explainable, traceable and responsible core capabilities on the basis of understanding the physical world, the life entity and the human society, and a technical guarantee is provided for the humanoid robot to enter home and industrial scenes.

Epistemology-Based, Adaptive Innovation Ecosystem with a Technological System Architecture Integrating Human, Artificial Intelligence, and Cognitive Systems This invention relates to the Original Epistemology-Based Innovation Ecosystem, a modular and transdisciplinary system built upon a technological architecture and centered on the Original Epistemology-Based Epistemic Core Engine. It integrates technological, artistic, social, and cultural domains within a unified structure grounded in scientific, ethical, and aesthetic principles. The system validates and transforms knowledge, generates meaning, and coordinates real-time cognitive adaptation. Integrated with the Zero-Principles Engine (Zero Time, Zero Energy, Zero Interrupt, Zero Budget, and Zero Waste) it enhances cognitive flow and regenerative capacity. The system can be implemented across a wide range of environments, including artificial intelligence architectures, digital platforms, physical innovation laboratories, and social transformation projects. It offers scalable, sustainable, and epistemically grounded innovation processes that redefine time and knowledge flow across individual, institutional, and global levels.

The invention discloses a robot cognitive system and method based on a physical dynamic-semantic causal collaborative field model, and belongs to the field of intelligent and robot autonomous decision making. The system comprises a multi-modal sensing unit, a collaborative field calculation unit, a cognitive decision module, a behavior execution unit and a dual-channel memory system. The core lies in a unified microgradable model which is constructed by the collaborative field calculation unit and is formed by coupling a physical dynamic field (psi p) and a semantic causal field (psi s) through a bidirectional collaborative protocol. Psi p is an implicit neural field embedded with physical equation constraints, and environment dynamic prediction is realized; psi is a dynamic attribute graph and represents the semantic and causal relationship. The two fields are interlocked and evolved through the protocols of physically-driven semantics and semantically-guided physics. According to the method, attention control based on field gradient, causal reasoning of physical inspiration and simulation verification planning in the field are realized. According to the method, the defects of slow response and poor adaptability of the traditional modular architecture are overcome, the end-to-end response delay in the test is less than or equal to 150ms (the actually measured mean value is 126ms), and the model migration training efficiency is improved to 4.2 times of that of a baseline model (that is, the training time is reduced by 76%).

Method for operating an autonomous vehicle (10), comprising: Operating a cognitive system (304) in response to a training data set (302) to generate a planned action for operating the autonomous vehicle (10); Evaluating the planned action to obtain a system performance rating; Updating the cognitive system (304) based on a comparison of the system performance rating with a human-based performance rating; and Operating the autonomous vehicle (10) using the cognitive system (304); wherein the human-based performance rating is obtained by evaluating a path driven by a human in relation to the training data set (302), wherein the evaluation is carried out by one or more humans; and / or the procedure further involves updating the cognitive system (304) by reducing a difference between the system performance rating and the human-based performance rating; and / or wherein an evaluation model generates the system performance rating, wherein the evaluation model includes at least one base metric weighted with a coefficient, and wherein the method further comprises adjusting the coefficient of the at least one base metric based on the comparison; and / or wherein the procedure further includes determining a complexity score which indicates a difficulty level of a driving scenario for the autonomous vehicle (10) and evaluating the planned action using the system performance rating, the human-based performance rating and the complexity score.

This invention discloses a cross-domain memory strength management and key-value (KV) cache optimization method based on a four-dimensional discrete-time spatiotemporal cognitive system, applicable to scenarios such as human-computer interaction, machine interaction, neuroscience, and large-scale model reasoning. This invention uses a unified modeling of information units based on four-dimensional discrete-time spatiotemporal coordinates, employs a cognitive heuristic model that integrates repetition reinforcement, cue matching, encoding depth, structural disorder, time decay, and interference dynamics to calculate memory strength, and applies this model to KV cache eviction management. This invention extends the four-dimensional discrete-time spatiotemporal cognitive system from a single cache scenario to the fields of human-computer interaction, machine interaction, and neuroscience, achieving unified cross-domain cognitive strength control; when applied to KV caching, it can significantly improve the retention rate of key information and the performance of long-context reasoning. This invention has strong versatility and controllable computational overhead, and can be widely adapted to scenarios such as artificial intelligence interaction, distributed machine communication and neuromorphic computing, brain-computer interfaces, and multi-agent communication.

The application discloses a kind of bacterial dysentery intelligent cognitive method and intelligent cognitive system based on ICP algorithm, belong to auxiliary diagnosis technical field, intelligent cognitive method includes: obtaining the relevant diagnosis basis of professional medical institutions about bacterial dysentery, establishes bacterial dysentery diagnosis basis standard information base;The relevant diagnosis basis of bacterial dysentery in bacterial dysentery diagnosis basis standard information base is normalized, and the feature information set of bacterial dysentery clinical classification is obtained;According to the feature information set of bacterial dysentery clinical classification, extract relevant diagnosis basis rule, establish bacterial dysentery diagnosis rule base;Using improved ICP algorithm, bacterial dysentery automatic registration model is established;According to bacterial dysentery automatic registration model, the information and data provided by target bacterial dysentery patient are registered and cognitively analyzed, and the corresponding cognitive report of bacterial dysentery case is generated, and diagnostic conclusion is given.