20 results about "Knowledge state" patented technology

The application relates to the technical field of decision management, and provides a phosphorus mine industrial internet intelligent decision method and system based on a knowledge graph. Production data in a phosphorus mine industry is collected through intelligent sensors, and decision information is generated by performing edge processing on the production data through an edge computing node; knowledge tuples are generated based on the production data and the decision information; entity devices and their relationship information are mapped into device nodes and edges of a Bayesian network, a knowledge state code and a hidden state at a set time are generated based on a confidence weight of the device nodes; the knowledge state and probability of the device nodes at a next time are predicted based on the hidden state at the set time; and a dynamically evolved knowledge graph is generated based on the hidden state at the set time and the knowledge state and probability at the next time, so that the phosphorus mine industrial production is decided through the knowledge graph. The dynamically evolved knowledge graph generated by the scheme can assist decision making, and the efficiency and accuracy of dynamic decision making in the phosphorus mine industrial production are improved.

The application discloses a multi-dimensional student state knowledge tracking method fusing emotion and forgetting mechanism, relates to the field of intelligent education, and aims at the problems that the existing method ignores the dynamic influence of emotion on forgetting and lacks a learning feedback mechanism.The application proposes a multi-dimensional state collaborative evolution framework.The method firstly constructs an independent forgetting state which is explicitly regulated by emotion and establishes a knowledge state which is inhibited by the forgetting state;secondly, the knowledge learning gain is innovatively introduced into the emotion updating process, realizing deep coupling of the three-dimensional states of knowledge, emotion and forgetting;finally, the states are fused through cross-dimensional attention, and the basic prediction probability is dynamically calibrated by using subjective difficulty and a prediction uncertainty coefficient.The application effectively simulates a real cognitive psychological process, and significantly improves the accuracy, explainability and individualization level of knowledge state tracking.

The application discloses a kind of mathematical learning behavior tracking method and system based on big data analysis, it is related to big data analysis technical field. Including: acquisition learner is generated in the multi-source behavior data of digital mathematical learning platform;Mathematical expression analysis is carried out to draft track, generate expression syntax tree sequence, carry out difference operation to the expression syntax tree of adjacent time step, extract atomic operation sequence;Using time series behavior encoder, output knowledge state evolution vector;The knowledge state evolution vector is aligned with the pre-constructed mathematical knowledge graph, the probability of mastering of learner on each mathematical rule node is calculated and weighted rule violation sequence is generated;Using dependent edge, the weighted rule violation sequence is reversely propagated, and the basic missing rule is identified;According to basic missing rule and mastery probability, personalized learning path recommendation is generated.The application realizes the accurate tracking of mathematical learning behavior and individualized teaching.

The application discloses a kind of multi-dimensional evaluation method and system of VR teaching based on user behavior feedback, according to Bloom's taxonomy of educational objectives, construct the multi-dimensional evaluation framework covering cognition, skill and emotion / meta-cognition, and set observable behavior proxy index for each dimension;Through VR equipment, real-time collection of user multi-modal behavior data and teaching context, knowledge state is identified and backstepped by using Transformer and cognitive diagnosis model;Combining graph neural network and attention mechanism, multi-granularity evaluation of micro, meso and macro three layers fusion is realized;At the same time, based on historical behavior, individualized ability curve is constructed, novice and expert behavior are dynamically distinguished, evaluation standard is adaptively adjusted, and growth is emphasized rather than absolute score;Finally, the intelligent feedback for students and teachers is generated, and teaching intervention is driven.The system realizes the whole process, objective and individualized VR teaching evaluation, effectively improves teaching accuracy and learning effectiveness.

This invention discloses a model privacy risk assessment method based on user cognitive profile data, belonging to the field of privacy risk assessment. The method includes: acquiring user interaction data to be detected and querying a target cognitive profile model to obtain predicted output information of the interaction data; acquiring a knowledge state vector corresponding to the user interaction data; constructing a gray-box feature vector based on the predicted output information and the knowledge state vector; inputting the gray-box feature vector into a preset member inference detection model and outputting the probability that the user interaction data belongs to a member of the target model's training dataset; and determining whether the user interaction data is member data based on a comparison between the member probability and a preset threshold. This invention can more accurately capture the subtle differences between member data and non-member data, thereby significantly improving the accuracy of membership detection and solving the accuracy bottleneck problem caused by insufficient information in existing black-box methods.

The present application belongs to the field of artificial intelligence and data science technology, and discloses a programming knowledge tracking method and related device, comprising: based on an evaluation model, obtaining time dimension knowledge state information and question code and knowledge point relationship according to the learning sequence information of the learner, combining the question code and knowledge point relationship and the knowledge point hierarchical diagram to obtain knowledge point hierarchical dimension knowledge state information; and fusing the time dimension knowledge state information and the knowledge point hierarchical dimension knowledge state information of the learner, obtaining the knowledge point mastery evaluation vector of the learner according to the knowledge state latent vector of the learner, and then combining the to-be-predicted programming exercise and the knowledge point relationship to obtain the predicted answer result of the learner for the to-be-predicted programming exercise. In the loss function of the evaluation model, the knowledge point mastery constraint loss is combined. The present application improves the accuracy of programming knowledge tracking in the learning process by combining the knowledge point hierarchical diagram and the knowledge point mastery constraint loss.

The application discloses a kind of education experiment synthesis student intelligent agent construction method, system and electronic equipment, it is related to artificial intelligence education simulation field.The method includes: initialization configuration covers the multi-dimensional feature parameter set of cognitive ability, personality characteristics and knowledge state, and builds simulation environment including private reasoning channel;Double closed loop mechanism is introduced in simulation interaction: use in for consistency check closed loop, based on multi-dimensional feature matching or reward model, real-time correction "role deviation" behavior;With state dynamic evolution closed loop, combine positive cognitive gain, forgetting curve and "inherent characteristics-transient state" separation model, real-time update agent knowledge and psychological state;Synchronous generation includes the structured data set of behavior traceability label.The application solves the problem of behavior homogeneity and unexplainable in education simulation, and can generate high reliable and attribution ability scientific research data.

The application discloses a system for self-evolution and continuous learning of a theory reasoning intelligent agent; a first intelligent agent module receives scientific literature, performs theory reasoning on the scientific literature, and generates a corresponding theory summary; a cognitive dissonance monitoring module generates a dissonance signal when detecting inconsistency between the internal knowledge state and external behavior output of the first intelligent agent module; an endogenous target generation module receives the dissonance signal and generates an endogenous learning target for correcting the inconsistency through internal drive according to the dissonance signal; and a self-reflection and model updating module updates the model parameters of the first intelligent agent module according to the endogenous learning target to eliminate the inconsistency. The application enables the theory reasoning intelligent agent to get rid of the dependence on external artificial annotation or static reward model and has the capability of continuous self-evolution in a dynamic scientific research environment.

Embodiments are directed toward a blockchain system including a global state and an assemblage of blocks, each block representing a collection of state transformation records, each state transformation record describing a state transformation performed on the global state, where preceding blocks referenced by any given block contain state transformation records describing state transformations performed on the global state prior to the evaluation of the given block, and where at least one state transformation record is a zero-knowledge transformation record encoding a zero-knowledge state transformation description, which zero-knowledge transformation record comprises at least the following elements: paths identifying the locations of the elements of a discrete data subset, a revised data subset, and a transition proof implemented as a non-interactive zero-knowledge proof, which transition proof proves that the transition from the discrete data subset to the revised data subset follows the established rules of the blockchain system.

The application discloses a teacher matching method and system based on big data analysis, and relates to the technical field of data processing, which comprises the following steps: intercepting a state transition segment; searching a teacher historical teaching record according to a falling direction represented by the state transition segment, and reserving a historical teaching segment corresponding to the falling direction in a knowledge state trajectory before teaching; generating a teacher teaching effectiveness parameter indexed by a knowledge point range in which a mastery probability is converted from continuous falling to continuous rising according to the knowledge state trajectory before and after the historical teaching segment; and outputting a teacher matching result according to the result of point-by-point offsetting of the mastery probability falling amount in the state transition segment based on the teacher teaching effectiveness parameter. The application is beneficial to improving the adaptability of the teacher recommendation result to the knowledge state change process of students.

A deep knowledge tracing method based on learning behavior collaborative perception and heterogeneous relationship graph is composed of the steps of data set preprocessing, constructing a multi-factor interaction network, training the multi-factor interaction network, verifying the multi-factor interaction network and testing the multi-factor interaction network. The multi-factor interaction network is adopted in the method, the personalized learning behavior characteristics and exercise characteristics of learners are embedded, the attention enhanced time state updating module is used, the complex interaction relationship between different learning behavior characteristics and exercise characteristics is deeply analyzed, the key features influencing the knowledge state are extracted, and the knowledge state diagnosis of the learning behavior and exercise characteristics enhancement is realized. Compared with the existing knowledge tracing model, the method has good learning effect evaluation performance, high exercise answer prediction accuracy and strong generalization ability, and can be used for knowledge state diagnosis and personalized teaching intervention in an intelligent education system.

This invention belongs to the field of internet education application technology and relates to a time-aware, personalized, dynamic recommendation method for educational resources. It includes defining a user-resource interaction graph, constructing an educational domain knowledge graph, designing a dynamic encoding mechanism based on relative time intervals, designing a time-aware attention weighting calculation mechanism, initiating fine-tuning at a target time point, designing a learnable gating transition mechanism, designing a sliding window-based embedding interpolation update strategy to estimate the probability of user u interacting with item i, and employing an optimization objective based on Bayesian Personalized Ranking (BPR) loss during the pre-training and fine-tuning stages. This invention constructs a time-enhanced dynamic graph recommendation framework, introducing a time encoding mechanism and a graph structure fine-tuning mechanism based on the educational knowledge graph, to achieve accurate modeling of the learner's knowledge state evolution process, thereby improving the accuracy, real-time performance, and personalization of educational resource recommendations.

The application discloses a kind of based on difficulty perception's collaborative knowledge tracking method, belong to knowledge tracking technical field.The application includes: obtaining the difficulty parameter of each question in student historical answer data, and the difficulty parameter is standardized processing;Based on the historical answer of student and difficulty information, construct difficulty perception behavior characteristics;Calculate the similarity between target student and other students, and filter out the student similar to target student in difficulty perception behavior characteristics;According to the answer data of the similar student and the difficulty information of the target question, collaborative information screening is carried out;Based on the screened collaborative information, the learning state of target student is predicted.The method flow of the application is clear, easy to implement, can be directly deployed in existing online learning system, improves the overall performance of knowledge state prediction under the premise of not increasing additional hardware cost.

This application discloses a method, apparatus, device, and medium for assessing student knowledge status by integrating KAN and evidence theory, relating to the field of artificial intelligence technology. The method includes: constructing an enhanced feature representation that integrates question difficulty and discrimination; performing deep temporal modeling using a transformer encoder with an embedded memory decay operator; and employing KAN to map hidden state to beta-distributed evidence parameters, thereby achieving decoupled output of prediction probability and cognitive uncertainty. The beneficial effects include improving model parameter efficiency and interpretability, quantifying cognitive uncertainty to distinguish between true mastery and accidental guessing, enhancing the reliability of prediction results from the knowledge tracking model, and providing a credible assessment basis for smart education.

This invention provides a differentiated learning content search method based on knowledge state assessment, belonging to the field of intelligent education technology. The method includes: analyzing learning resources based on a pre-set knowledge graph, labeling related knowledge point nodes and difficulty levels, and constructing a three-dimensional virtual coordinate space to complete resource mapping; obtaining users' historical exam scores and browsing records to generate a knowledge state profile and personalized search vectors; parsing search keywords to generate constraints and initial search points, performing vector-oriented retrieval, and capturing suitable resources through Euclidean distance thresholds and constraints; generating personalized tags based on the captured vectors; calculating recommendation scores by integrating difficulty matching degree and interaction intensity, and outputting tagged results after sorting. This invention achieves end-to-end personalization from resource retrieval and sorting to presentation, providing differentiated learning content based on learners' actual ability levels, effectively solving the problems of homogeneous results and lack of adaptability in traditional search systems.

The application belongs to the technical field of computers and specifically discloses a programming knowledge tracking method based on code prediction enhancement, which comprises the following steps: based on historical programming questions and corresponding historical programming answers, extracting embedded representations, obtaining historical question embedded representations and historical answer embedded representations; based on the historical question embedded representations, the historical answer embedded representations and the embedded representations of target programming questions, predicting the embedded representations of the source code input by the measured object for the target programming questions through a multi-head attention mechanism model, and taking the predicted embedded representations of the source code as programming answer prediction results; and based on the embedded representations of the target programming questions, the programming answer prediction results and the current knowledge state of the measured object, predicting the answer correctness probability of the measured object for the target programming questions, wherein the current knowledge state is obtained by analyzing the time sequence evolution of the knowledge state based on the historical programming answers of the measured object. The application can accurately predict the programming performance of learners.

The application discloses a dynamic knowledge consistency management and gating control method based on a dependency graph, comprising the following steps: acquiring source knowledge data, downstream dependency data and knowledge state structure information, constructing a dependency graph, detecting that the source knowledge data is added, changed, invalidated, replaced or state-switched, determining an affected subgraph, performing differential consistency verification on downstream dependency nodes in the affected subgraph, generating a control result according to the verification result and the node type, and writing the control result into a management record to perform update control, query control, output control or state-switching control. The method can improve the consistency management efficiency, control accuracy and closed-loop control capability in a dynamic knowledge environment.

This invention provides a multi-agent virtual student simulation method and system for teacher Q&A training, relating to the field of intelligent education technology. The method includes: controlling an agent to analyze historical dialogues, dynamically predicting the virtual student's current knowledge state and generating an expected response state; the controlling agent determining, based on the expected response state and the current round of speech, whether to inject errors from an error pattern library and expected error results into the virtual student's current response; the controlling agent generating an ordered scheduling result for multiple functional agents based on the expected response state and expected error results; scheduling the work of multiple functional agents based on the ordered scheduling result; and the controlling agent aggregating the responses from multiple functional agents to generate the virtual student's current response and feeding it back to the teacher. This invention solves the technical problem that current large language models cannot realistically reproduce students' error performance and the gradual evolution of their cognition during problem-solving.

The invention belongs to the field of educational informatics and artificial intelligence and is a deep learning-based system for improving primary education and a method for optimizing personalized learning paths. The system collects data on students' learning and knowledge mastery behavior, models students' knowledge status using a deep learning model, and uses a knowledge graph to construct a dependency graph of knowledge points. The problem of generating learning paths is modeled as a Markov decision process, applying deep reinforcement learning for path finding and global optimization, with integrated consideration of learning benefit, cognitive load, retention of interest, and time constraints, so that dynamic learning paths are generated that meet individual differences.The system updates the dependency graph and the paths during the learning process in real-time, implements adaptive optimization, and displays the recommended paths and their rationale via a visualization interface of the knowledge graph, with support for teacher intervention. (Fig 9).