74 results about "Cognitive model" patented technology

The invention provides an intelligent scheduling and control method and device for an integrated energy system. According to the method, power, gas and heat resource operation data are acquired, multi-scale layered modeling is performed according to a time scale and a space scale, and a power resource state space model, a gas resource flow continuity model, a heat resource heat balance model and a multi-energy coupling characteristic constraint model are established; carrying out feature extraction and dimension reduction representation by adopting a deep auto-encoder network; cooperative training of multiple groups of cognitive models is carried out through a split hierarchical federal learning framework, and a global intelligent model is obtained; constructing a neural architecture search network with a hybrid bionic learning rule, setting a hierarchical scheduling target, and generating a hierarchical intelligent scheduling strategy; and a fault-tolerant control mechanism is constructed, and error detection and correction of operation deviation are realized. According to the invention, multi-time scale collaboration, collaborative learning under multi-device group privacy protection and high-reliability fault-tolerant control are realized, and the operation efficiency and reliability of the integrated energy system are remarkably improved.

The invention provides an intelligent interaction system and method based on a multi-stage cognitive mode, and the system comprises a multi-modal data collection module which is used for collecting user interaction data through a multi-modal sensor, and the data comprise language input, non-language behaviors, interface operation data, expressions, eye movement tracks and the like; and the cognitive feature analysis module is used for calling a deep learning model to perform feature extraction on the interaction data. According to the method, language, behavior, interaction, physiology and other data are fused through the multi-modal sensor, the cognitive driving vector is generated by using the deep learning model, the real-time cognitive state of the user is effectively captured, then the probability distribution of the cognitive stage is constructed in combination with Bayesian reasoning, the problems that in the prior art, only the cognitive level can be statically judged, and real-time updating is difficult are solved, and the user experience is improved. The accuracy and timeliness of user state perception are remarkably improved, and dynamic accurate recognition and continuous modeling in the cognitive stage are achieved.

The invention belongs to the technical field of intelligent education, and discloses a multi-modal artificial intelligence learning assistant based on an embedded platform, and the method comprises the steps: a data collection and processing module receives learning problems and student learning data, and carries out the standardization processing, and obtains a problem standardized data set; the cognitive model construction module constructs a learner cognitive model based on the problem standardized data set and student learning data, and marks knowledge gap nodes; the teaching scheme generation module determines a self-adaptive teaching scheme including knowledge depth parameters, a learning material combination strategy and an explanation expression strategy according to the structural features of the cognitive model; the learning sequence construction module constructs a customized learning sequence according to the knowledge gap nodes and the knowledge depth parameters; and the learning content generation module generates personalized learning content according to the learning sequence and the teaching scheme. Through the self-adaptive processing flow driven by the cognitive model, personalized teaching for different students is realized, and the learning efficiency and the learning effect are remarkably improved.

The invention discloses a quantum cognitive simulation system and method based on classical computation, and belongs to the technical field of quantum computation.A data processing module receives original data of external data input equipment, converts the original data into quantum state data after processing, and transmits the quantum state data to a quantum cognitive simulation computation module; the quantum cognitive simulation calculation module performs quantum cognitive simulation calculation by using a CPU cluster, a GPU array and a coprocessor, and stores a result in the storage module or directly transmits the result to the output module; and the output module performs format conversion on the calculation result and outputs the result to external display / control equipment. The quantum cognitive simulation system and method based on classical calculation aim at overcoming the defects of a traditional cognitive model in the aspects of processing decision fuzziness, context dependency and high-dimensional calculation efficiency, the effects of quantum superposition, interference, entanglement and the like can be efficiently simulated, quantitative calculation of related functions of human consciousness is achieved, and the quantum cognitive simulation system and method based on classical calculation can be applied to the field of quantum computing. And the performance in an actual application scene is optimized.

The invention discloses a subject cognition teaching knowledge construction and intelligent navigation system based on a knowledge graph, and relates to the field of education technology and intelligent teaching. The knowledge graph construction module is used for extracting knowledge points and association relationships thereof from subject teaching resources and constructing a structured knowledge graph; the personalized cognitive state vector generation module analyzes the mastering degree of knowledge points by means of a cognitive diagnosis model, and generates a state vector reflecting the cognitive level of an individual; the navigation strategy generation module adopts a hierarchical reinforcement learning method, and dynamically generates a navigation strategy containing knowledge point priorities and an optimal learning path by taking a learning target as guidance; and after the learning effect adjustment module pushes the strategy to the learner, acquiring knowledge point mastering state deviation and learning strategy consistency deviation in real time, and synchronously updating the edge weight of the knowledge graph and cognitive diagnosis model parameters. According to the system, through dynamic evolution of the knowledge graph and self-adaptive updating of the cognitive model, accurate optimization of a teaching strategy is realized, and the personalized learning navigation efficiency is improved.

The invention discloses a judgment document content induction and generation method based on image recognition, relates to the field of legal informatization and artificial intelligence, and aims to solve the problems of many cases and few people of judicial, low processing precision of traditional documents, poor adaptability and the like. The method comprises the following four steps: 1, element extraction: preprocessing and identifying a multi-modal case material through an improved OCR (CNN-Transform fusion model), and extracting key elements in combination with semantic error correction and a proprietary cognitive model (BiLSTM-CRF + knowledge graph); 2, element induction: loading rules according to the action cause, initializing elements and deducing associated elements, and carrying out threshold verification; 3, rule reasoning: circularly deducing core information of the constructed document, referring to current legal provisions, and recording a deduction log; and 4, generating a document, automatically matching court template filling contents, and outputting a compliant document after multi-dimensional verification. According to the method, the document generation efficiency and quality are greatly improved, various judicial requirements are met, and construction of a smart court is assisted.

The invention relates to the technical field of education, and discloses an online learning environment-oriented education interaction behavior recognition system, which is characterized in that a standardized cognitive model is constructed offline based on interaction data of a high-performance learning group; in the online stage, interaction events of individual learners are obtained in real time, high-dimensional cognitive trajectories representing cognitive states of the learners are generated, a trajectory dynamics analysis module executes double reference analysis, and internal dynamics indexes representing stability of the trajectories are obtained by calculating the maximum Lyapunov index, recognizing phase change points and the like; the external deviation measure of the difference between the trajectory and the normalized cognitive model is obtained by calculating the state deviation degree, the path efficiency and the like, and finally, the cognitive mode diagnosis module integrates the indexes and generates a structured diagnosis description according to a rule base. According to the method, through combination of internal dynamics and an external reference system, deep quantification and fine identification of the cognitive process of the learner can be realized, and an objective basis is provided for adaptive intervention.

The invention relates to the technical field of semantic cognition modeling and natural language interaction, in particular to an electric power digital twin question-answer interaction generation method based on semantic cognition modeling, which comprises the following steps of: accessing a power grid topological data flow in real time, analyzing equipment hierarchy, power supply attribution and physical connection relationship, and constructing a dynamic topological knowledge graph; the method comprises the following steps of: establishing a topological change monitoring channel, mapping the topological change monitoring channel to a semantic cognition model, embedding a topological hierarchy field and an association constraint rule, generating an enhanced semantic cognition model with topological association constraints, and establishing a topological change monitoring channel to update an association relationship in real time; receiving user natural language query, identifying keywords, extracting power supply attribution constraint rules, filtering invalid entities, and converting implicit topological semantics into combined query statements; and executing query to obtain topological associated data, verifying the power supply attribution consistency of a result set, filtering homonymy matching errors, triggering model updating and secondary verification during topology adjustment, and outputting question and answer responses conforming to topology constraints, so that the interaction accuracy and timeliness are improved.

The invention relates to a method and a system for constructing a traditional Chinese medicine lung cancer prevention and treatment system based on multi-source data fusion, belongs to the technical field of big data, integrates multi-source information such as global evidence-based literature, real world clinical data and expert decision support, constructs an AI-driven three-layer cognitive model, develops a clinical transformation platform, and provides a traditional Chinese medicine lung cancer prevention and treatment system. The method comprises the following steps: screening dominant treatment stages through an intelligent algorithm, optimizing a diagnosis and treatment scheme, establishing a quantitative correlation model of pathogenesis key elements and tumor metabolic activities, further constructing a syndrome-metabolism knowledge graph, forming a syndrome differentiation validity verification mechanism and a prescription linkage database, and finally designing a result transformation engine. New drug research and development and nosocomial preparation conversion are driven through an evidence chain, and evaluation standards are formulated in cooperation with traditional Chinese medicine syndrome improvement and western medicine solid tumor curative effect indexes. According to the method, the bottleneck problems of data splitting, dialectical staticizing and conversion in traditional research are solved, and full-chain innovation from cognitive modeling to clinical landing is achieved.

The invention is notably directed to a computer-implemented method of clustering anomalies detected in a computerized system. The proposed method makes use of an unsupervised cognitive model, executed based on input datasets to obtain clusters of anomalies. The method accesses input datasets, which correspond to detected anomalies of the computerized system. These anomalies span respective time windows. Each input dataset comprises a set of timeseries of key performance indicators. The key performance indicators of each input dataset extend over a respective time window. That is, each anomaly corresponds to a respective time window. This model includes a first stage, which includes an encoder designed to learn fixed-size representations of input datasets, and a second stage, which is a clustering stage. The model is executed based on the input datasets accessed, the first stage learning fixed-size representations of the input datasets and the second stage clustering the learned representations.

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 field of information scheduling, and particularly discloses an intelligent information scheduling operation scene cognitive model construction method. Comprising the following steps: multi-modal perception data acquisition, feature fusion and knowledge representation based on neural symbol reasoning, entropy reduction-oriented uncertainty quantification and cognitive depth enhancement, cognitive bidirectional alignment man-machine cooperation framework construction, and intelligent information scheduling strategy generation and dynamic adjustment based on a cognitive model. According to the method, neural symbol reasoning and multi-modal sensing are fused, an entropy reduction oriented uncertainty quantification mechanism is designed, a cognitive bidirectional alignment man-machine cooperation framework is constructed, normal form transformation of a scheduling system from passive response to active cognition is achieved, the accuracy, dynamic adaptability and man-machine cooperation efficiency of intelligent information scheduling are remarkably improved, and the method is suitable for popularization and application. The intelligent information scheduling technology is promoted to advance to a new height, and the increasing complex scheduling requirement is met.

The invention discloses a submission message generation method and system. The method comprises the following steps: firstly, constructing a multi-role developer cognitive map, and simulating information attention key points and cognitive modes of different roles; establishing cognitive models and decision preferences of different role developers based on the acknowledgement science theory and the large-scale development behavior data; then designing a code change importance perception algorithm based on an attention mechanism, simulating a human visual scanning and importance judgment process, and dynamically identifying key change contents concerned by a developer; and finally, establishing a context-aware message personalized generation framework, integrating multi-dimensional context information such as project history, team culture and personal preference, and adaptively adjusting a generation strategy and an output format of a submitted message. According to the method, the CMG problem is redefined from a'text generation task 'to a'cognitive assistance system', and an intelligent submission message really conforming to human cognitive habits is generated through a cognitive process and a decision mechanism of a deep modeling developer.

The invention provides a multi-personality decision simulation and game method for military artificial intelligence. Simulation of an AI opponent model on complex human behaviors is enhanced through a DIKWP semantic framework. The system firstly configures personality parameters based on a psychological model (such as MBTI, big five personalities), and maps the personality parameters to an AI cognitive model to construct a behavior preference model; then, a noise injection algorithm is adopted to simulate emotion and cognitive deviation, and unique deviation of different personalities in decision making is reproduced; the multi-agent self-gaming engine enables AIs with different personality characteristics to repeatedly game in a red-blue confrontation environment, optimizes a strategy and evaluates winning rate distribution. And the decision fusion analysis module summarizes the data, generates personality performance comparison and behavior pedigree diagrams under each tactical situation, and predicts opponent behaviors. The DIKWP semantic integration interface ensures that a personality simulation result can be seamlessly connected with a knowledge graph and a strategic intention module of an existing AI system. According to the method, AI opponent modeling is enriched on the semantic level, the decision simulation degree of a real commander is improved, and as a supplement of an existing AI simulation system, the ability of military AI in the aspects of strategic pre-judgment and cognitive game is enhanced.

The invention discloses a semantic planning and behavior generation system driven by autonomous consciousness. According to the system, a DIKWP mesh cognitive model is adopted, an intention / purpose semantic layer is embedded into an AI internal architecture, and autonomous decision making is achieved through five-layer semantic representation of data, information, knowledge, wisdom and intention. The system comprises a perception module, an intention generation module, a semantic planning module, a meta-cognition monitoring module, an execution module and the like. The AI can set a task target by itself and dynamically plan an action scheme based on a semantic state. Under the action of a double-circulation element cognitive mechanism, the AI can monitor the deviation between a behavior and a target in real time and automatically correct a plan. Compared with a traditional black box model, each step of decision of the AI has a traceable semantic basis and interpretation which can be understood by human beings, and the interpretability and controllability of the system are improved. The autonomous planning system can automatically adjust strategies according to environmental changes, ensures that AI behaviors always accord with expected targets and ethical safety constraints, is widely applicable to the fields of service robots, automatic driving, intelligent assistants and the like, and has important application values and commercial prospects.

The invention relates to the technical field of computer software, in particular to a human factor reliability analysis method and device based on a cognitive mechanism, and the method comprises the steps: refining a target nuclear power plant operation instruction text into executable operation behavior units; modeling the executable operation behavior unit according to the cognitive psychology model to obtain an operation cognitive model; repeatedly running different preset operation tasks for multiple times by using the operation cognition model so as to quantify and estimate human factor event occurrence time parameters; and inputting the human factor event occurrence time parameter and a preset cognitive failure probability into a pre-constructed human factor reliability analysis model to calculate a human factor error probability. Therefore, the problems that in an existing IDHEAS-ECA method, human factor event occurrence time mainly depends on subjective expert judgment, cognitive mechanism support is lacked, result precision is limited and the like are solved, and the method is suitable for operator behavior modeling and human factor error probability estimation in high-risk industries such as nuclear power plants and the like.

The invention provides a method and a system for constructing a personal digital awareness mirror image and realizing cross-generation intelligent inheritance, and solves the problems of cross-generation knowledge inheritance and the like, and the method comprises the following steps: S1, multi-modal data acquisition and double-track recording; s2, constructing a personalized digital behavior cognitive model; s3, model packaging and conditional access are carried out; and S4, multifunctional mode activation and interaction. The method has the advantages of cross-generation knowledge inheritance, good digital community treatment effect and the like.

The invention relates to the technical field of video processing, in particular to an outdoor sports teaching method, device and equipment and a storage medium, and the method comprises the steps: inputting initial multi-modal teaching data into a reinforcement learning task model for processing, and obtaining a target teaching video; comparing the target teaching video with the student action video to obtain a comparison result, and generating a teaching suggestion based on the comparison result; the training of the reinforcement learning task model comprises the following steps: training a basic learning cognitive model based on initial multi-modal teaching data to obtain a multi-modal closed-loop simulation generation model; processing the initial multi-modal teaching data based on a multi-modal closed-loop simulation generation model to obtain a to-be-compared teaching video; comparing the to-be-compared teaching video with the model teaching video to determine a target point location action video segment in the model teaching video; and training a multi-modal closed-loop simulation generation model based on the target point location action video segment and the initial multi-modal teaching data to obtain a reinforcement learning task model. And the accuracy of video output and teaching suggestions of the model can be improved.

The application discloses a story interactive active questioning method and system based on cognitive characteristics of children, and relates to the technical field of artificial intelligence. The method comprises the following steps: obtaining story content; performing multi-modal analysis on the story content to obtain an analysis result; constructing a four-dimensional cognitive model of a child according to the analysis result and historical interaction data of the child; selecting a target problem level in preset multi-level problems according to the four-dimensional cognitive model; generating an adaptive question corresponding to the target problem level; and updating the four-dimensional cognitive model and the multi-level problems according to the answer and emotional feedback of the child to the adaptive question. The application first analyzes the story content and then generates a corresponding adaptive question in combination with the cognition of the child, so that the generated story question matches the cognition of the child, the problems beyond the cognition of the child are reduced, the adaptability is improved, and the learning and understanding effect of the child is improved.

The invention discloses a behavior tree generation method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of computers.The method comprises the steps that an initial cue word corresponding to a virtual object and a target game environment where the virtual object is located are obtained; inputting the initial cue word into a pre-trained decision model to generate a first behavior tree corresponding to the virtual object; deploying the first behavior tree to a target game environment to obtain game match data; inputting the game playing data into a pre-trained cognitive model, and generating data description information corresponding to the game playing data; and optimizing the first behavior tree based on the data description information to obtain a target behavior tree of the virtual object in the target game environment. By implementing the technical scheme of the invention, automatic generation and optimization of the game virtual object behavior tree can be realized based on collaborative closed-loop iteration of the decision model and the cognitive model, and the intelligent level and the generation efficiency of the game virtual object are improved.

The invention discloses an intelligent course resource generation system and method.The intelligent course resource generation system comprises a demand perception module, a knowledge fusion engine, an intelligent generation core and a quality optimization ring, and the demand perception module is used for analyzing user portraits and course standard metadata; and the knowledge fusion engine is connected with the multi-source knowledge base and constructs a cross-modal knowledge graph. According to the method, personalized demands of learners are accurately adapted, and resource content depth and presentation forms are dynamically adjusted based on a deep cognitive model; the bottleneck of multi-modal teaching resource collaboration is broken through, and a teaching logic closed loop is formed by text elaboration, visual demonstration and interactive operation by means of a semantic-level cross-modal alignment technology; the cognitive overload risk is intelligently avoided, and an abstract concept is converted into a stepped cognitive path through an embedded load monitoring mechanism; and finally reconstructing an educational resource generation normal form, converting teaching experience into an iterable generation strategy, and forming an intelligent educational ecology with an adaptive evolution characteristic.

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 invention discloses a visual shared whiteboard system and method for solving a group collaboration mathematical problem, and belongs to the technical field of computer science and human-computer interaction crossing. The system comprises a multi-modal fusion input module used for converting input such as handwriting and voice into a structured mathematical object; the real-time semantic understanding and knowledge graph engine is used for constructing a collaborative cognitive model; the collaborative process management module based on the directed hypergraph is used for carrying out formalized modeling and navigation on a nonlinear problem solving process; the context-aware intelligent auxiliary module is used for providing verification, recommendation and guiding consensus achievement; and a multi-user cooperation engine ensures stable cooperation experience. According to the method, the problems of professional expression, real-time cooperation splitting, group thinking process management difficulty and the like are solved, and a unified, intelligent and efficient integrated cooperation environment is provided for group solving of complex mathematical problems.

The invention relates to a multi-stage cognitive modeling and parameter optimization method based on a visual pairing comparison task, and aims to solve the technical problems of incomplete cognitive process modeling, weak crowd distinguishing ability, task parameter empirical and the like in the existing VPC task evaluation technology. Precise evaluation of the cognitive function and optimization design of task parameters are achieved through a five-step method, wherein VPC task parameterization design and eye movement data collection are carried out; constructing a'familiarity accumulation-memory attenuation-novelty attention and utility 'three-stage cognitive model; performing backstepping on individual free parameters based on maximum likelihood estimation; carrying out multi-population cognitive parameter distribution modeling and subtype division; and task parameter closed-loop optimization based on simulation and efficiency discrimination. According to the method, quantitative distinguishing of learning efficiency, memory stability and novel preferences is achieved, cognitive parameter recovery precision and multi-crowd distinguishing efficiency are improved, a standardized and transplantable technical path is provided for early screening of cognitive impairment and crowd typing, and the method is suitable for cognitive neuroscience research and clinical cognitive evaluation scenes.

This invention provides an information processing device that enables the modeling of individual behavior from both external and internal perspectives. [Solution] The information processing device is an information processing device that generates a judgment model of the behavior of traffic participants when crossing a traffic environment and executes a simulation, and comprises: an acquisition unit that acquires characteristic information that characterizes traffic participants, which is the premise of the judgment model; a first generation unit that, when simulating the behavior of traffic participants when crossing a traffic environment, generates a behavior model of traffic participants as a reaction to the understanding of the surrounding situation as an external factor when crossing a traffic environment, based on the characteristic information; a second generation unit that, when simulating the behavior of traffic participants when crossing a traffic environment, generates a cognitive model of traffic participants as a reaction to the understanding of the surrounding situation as an internal factor when crossing a traffic environment, based on the characteristic information; and a third generation unit that generates a judgment model of the behavioral judgment of traffic participants when crossing a traffic environment, based on the behavior model and the cognitive model.

This disclosure provides a cognitive model training and exercise recommendation method, apparatus, device, and medium, relating to the field of data processing technology, and particularly to the fields of artificial intelligence and deep learning technology. The specific implementation scheme is as follows: Cognitive Model Training Method: From the historical answer interaction records of each sample exercise, multiple interaction representation vectors between each sample exercise and the tester are extracted. The training set and validation set of each sample exercise respectively include partial interaction representation vectors corresponding to each sample exercise. Based on the training set of each sample exercise, an inner layer training is performed on a preset cognitive model. Based on the validation set of multiple sample exercises, an outer layer training is performed on the preset cognitive model after the inner layer training. Exercise Recommendation Method: The representation parameters of each sample exercise obtained after training the preset cognitive model according to the above method are used to initialize the representation parameters of each sample exercise in the exercise library. Based on the representation parameters of each exercise in the exercise library, exercises are recommended to the target user.

The invention discloses a network intrusion detection method based on cognitive model optimization, which is a network intrusion detection method combining a cognitive heuristic algorithm and a neural network architecture. According to the method, firstly, an IDS model of unbalanced data is optimized, and then the efficient network intrusion detection method of a cognitive model and neural network hybrid architecture is implemented on the optimized IDS model. The training process of the deep learning network is optimized by utilizing a heuristic reasoning and memory updating mechanism of a cognitive model. Specifically, when the network model carries out prediction, the cognitive model provides an auxiliary decision through rule matching and reasoning, so that the detection accuracy is improved. According to the method, high classification accuracy can be kept under the condition that attack samples are limited. The network intrusion detection method provided by the invention has relatively high practicability and popularization value, and can be widely applied to the fields of network security, information protection and the like.

The invention relates to the technical field of traditional Chinese medicine information, discloses a cross-culture traditional Chinese medicine knowledge spreading method and system, and aims to solve the problems of semantic distortion and culture discount in traditional Chinese medicine knowledge cross-culture spreading. The method and the system are characterized in that a knowledge translation and multi-modal presentation framework driven by an audience cognition model is constructed, and the framework comprises a culture cognition model database, a culture cognition model generation module, a semantic deep mapping and narrative translation engine and a multi-modal presentation synthesis module. Wherein the engine carries out traditional Chinese medicine concept deconstruction, cross-cognitive domain analogy search and generates an explanatory narrative, and the module carries out multi-modal content synthesis and presentation according to a cultural cognition model. According to the technical scheme, culture barriers can be effectively overcome, and accurate, efficient and barrier-free transmission of traditional Chinese medicine knowledge in the global range is achieved.

The embodiment of the invention discloses a multi-semantic theme fused smart education cognitive terminal control method and device. A specific embodiment of the method comprises the following steps: preprocessing an original education dialogue text set to obtain a processed text set and an evaluated text set; performing topic clustering on the processed text set to obtain a sub-topic text set; performing multi-topic fusion on the sub-topic text set to obtain a topic text set; updating the initial education cognition model to obtain an education cognition model; generating a real-time theme identification set based on the education cognition model and the real-time education conversation; based on the evaluation text set, determining theme indexes of the real-time theme identification set; and according to the real-time theme identification set, generating an education control instruction, and according to the education control instruction, controlling the intelligent education cognition terminal to adjust the output content and the teaching aid transmission device to perform teaching aid transmission. According to the embodiment, response time can be reduced, and wrong transmission of teaching aids is avoided.

The application discloses a kind of intelligent prosthesis control method and system with feedback mechanism, and the application relates to intelligent prosthesis control technical field, comprising the following steps: S1: acquisition step: through the sensor cluster of prosthesis foot bottom, ankle joint and knee joint deployment, real-time acquisition terrain feature, joint motion parameter and residual limb electromyogram signal.The intelligent prosthesis control method and system with feedback mechanism, through the double driving architecture of biomechanics cognitive model and reinforcement learning strategy model, in combination with the double verification mechanism of mutual inspection arbitration unit, realizes the adaptive accurate matching of feedback signal type, intensity and timing under complex dynamic environment.Biomechanics model ensures that feedback parameters meet clinical safety boundary, reinforcement learning model optimizes environmental adaptability in real time, mutual inspection arbitration resolves model conflict through physiological tolerance screening and control stability prediction, solves the problems of control misalignment, too high user cognitive load and walking confidence decline caused by traditional static rule feedback.