3187 results about "Learning data" patented technology

The invention discloses a personalized learning recommendation method based on a personalized knowledge graph. The method comprises the steps that the knowledge graph is constructed, and a learner fills in learning style self-description and learning target expected content; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of the students are analyzed to determine the personal basic conditions of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt the personalized learning features to the learning knowledge element sequence, and a personalized learning push path recommendation mode is constructed to assist the students in improving the learning effect.

The invention belongs to the technical field of intelligent learning systems, and particularly relates to a generative and multi-modal perception integrated intelligent agent learning system, which comprises the following steps of: establishing a dynamic knowledge graph of a learner by collecting various daily learning data, physiological indexes and basic data of the learner; the teaching strategy planning module is used for establishing a teaching strategy plan of a learner based on a dynamic knowledge graph of the learner, evaluating a specified index of the learner and a specified index of the system after the learner executes a specified time period based on the teaching strategy plan, and optimizing the teaching strategy planning module based on an evaluation result of the specified index of the multi-scale evaluation module. The teaching strategy planning of the learner is optimized, and the modules are coordinated and optimized based on the evaluation result of the specified index of the system. The technical problems that an online education system in the prior art is low in learning efficiency, insufficient in suggestion correlation, not integrated with a teaching feedback mechanism, incapable of reflecting real engineering capability and lack of a privacy protection mechanism are solved.

Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

The invention relates to a higher education personalized hierarchical teaching method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-dimensional learning data, carrying out the time-space alignment processing, and generating a synchronous multi-dimensional data set; performing spatial-temporal feature fusion and time sequence modeling on the data set by using a deep neural network, and constructing a dynamic student portrait; analyzing knowledge mastery degree features in the portrait through a semantic analysis model, and generating a personalized resource recommendation sequence in combination with the knowledge graph; based on the sequence and the portrait, planning a personalized learning path by using a path reasoning algorithm; and carrying out teaching hierarchy binding on the personalized resource recommendation sequence and the learning path to form a hierarchical teaching scheme. Accurate teaching support is provided for individual differences of students, and the teaching effect and learning experience are effectively improved.

The invention discloses a personalized learning recommendation method for a learner. The method comprises the steps that the learner completes learning style evaluation and learning target setting of the learner; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of students in the platform learning process are analyzed to determine the personal basic condition of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt personalized learning features to the learning knowledge element sequence, a personalized learning push path recommendation mode is constructed, and the learning recommendation efficiency is improved. And the learning effect can be improved.

The invention discloses a concept recommendation method based on large language model structure learning and knowledge representation, and belongs to the field of concept recommendation. The method comprises the following steps: firstly, acquiring learning data and a concept knowledge graph of a student account in a learning process, and preprocessing the learning data and the concept knowledge graph; performing text coding on an enhanced text generated by the large language model by using a pre-training language model to obtain a final hidden vector of each concept, further constructing an enhanced concept knowledge graph, and pre-training a GraphGPS concept encoder based on the enhanced concept knowledge graph and a double-view cross graph contrast learning method; synthesizing education data by a TabDDPM model to pre-train a deep knowledge tracking model; and splicing the concept representation of question embedding, answer embedding and structure perception and the student knowledge state representation to form a final representation, further obtaining a coding sequence, pre-training a sequence coding model and finely adjusting a concept recommendation matrix, and finally forming a concept recommendation model for performing concept recommendation.

The invention provides a wisdom education method and system based on student behavior sequence recommendation, and relates to the technical field of wisdom education. According to the method and the system, through an AI autonomous identification knowledge graph construction and innovative knowledge fusion method, a course knowledge system is structurally and hierarchically displayed, and a knowledge framework is clearly presented, so that knowledge graph framework construction of all-platform courses is facilitated, pre-repaired courses and subsequent courses are efficiently associated, and the construction efficiency is improved. And a clear and complete course learning path is provided for students. By adopting a recommendation algorithm based on a student behavior sequence, combining SASRec and introducing a self-attention mechanism of a sparse attention module, behavior data of students can be deeply analyzed, a learning path is adjusted according to real-time learning data, and personalized learning suggestions are provided for the students. According to the method and the system, multi-dimensional functions such as knowledge graph construction, a student behavior sequence recommendation algorithm and intelligent learning path optimization are integrated, and the method and the system have wide coverage and strong comprehensiveness.

The invention discloses an AIGC enabling auxiliary system for college second foreign language teaching and an implementation method. The system is cooperatively operated by five core modules; the intelligent voice recognition and user demand analysis module analyzes the learning demand of a learner by using a voice-to-text technology, and assists the customization of teaching content; the AIGC content generation module gathers multi-source data, generates personalized teaching resources by using a frontier model and continuously updates and evolves the personalized teaching resources; the learning progress management and evaluation module collects learning data, dynamically adjusts a personalized learning plan and provides evaluation and feedback; the real-time interaction and feedback module builds a multi-dimensional interaction channel, collects feedback of all parties, analyzes and processes the feedback and presents an application result; the multi-platform support module guarantees the stable operation of the system on a plurality of operating systems and devices, and efficiently completes data synchronization sharing and performance optimization adaptation. According to the invention, an all-around learning environment is established for second foreign language learners in universities, and the learning efficiency and experience are significantly improved.

The invention discloses a federal learning data processing method based on privacy protection, and the method comprises the following steps: S1, a client uses local data to train a generator of a generative adversarial network, and generates virtual data similar to the local data in distribution; s2, setting three privacy protection levels according to the privacy sensitivity of the local data; s3, performing model updating operation by using a homomorphic encryption technology; s4, utilizing a dynamic weight optimization mechanism based on a multi-arm bandit algorithm to obtain a priority processing data set; s5, preliminarily updating the global model, analyzing generated data through a reverse inference detection mechanism, and judging whether a privacy disclosure risk exists or not; s6, the server feeds back the updated global model to the client, and the client continues personalized model training in combination with local data; and S7, generating new virtual data, and carrying out the next round of global model training. According to the method, the generative adversarial network, differential privacy and homomorphic encryption are combined, so that efficient federated learning under privacy protection is realized.

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

The invention provides a personalized English education system and method based on multi-modal sentiment analysis. The system comprises a multi-modal interaction module for receiving and processing multi-modal data of students, an emotion recognition module for performing emotion analysis on the input multi-modal data, and a personalized module for evaluating real-time learning states of the students based on historical learning data of the students, and the core brain module is used for dynamically adjusting interactive feedback according to the emotional state and the real-time learning state. The emotion recognition module comprises emotion information fusion, the emotion information fusion adopts a weighting strategy, and the final output emotion state is adjusted through emotion consistency constraint and a conflict correction mechanism. According to the invention, through an emotion consistency loss function, a conflict correction mechanism and a knowledge graph-based super-outline control mechanism, the emotion and cognitive states of the students are accurately identified.

The invention discloses a course learning planning system based on a computer, and relates to the technical field of course learning planning, and the course learning planning system comprises a learning data acquisition module, a knowledge graph construction module, a learning index scoring module, a multi-index comprehensive evaluation module, a learning plan making module and a real-time progress feedback module. Wherein the modules are connected through electric signals; the learning data acquisition module is used for collecting learning data of students and determining evaluation indexes of the learning data. According to the course learning planning system based on the computer, by monitoring the learning progress and achievement of students, instant learning feedback is provided for the students, so that the students can quickly know the learning state of themselves, related learning resources and exercises are intelligently recommended according to the learning conditions of the students, the students are helped to consolidate knowledge and make up for deficiencies, and the learning efficiency is improved. Students can achieve a higher learning effect in a shorter time, and the learning efficiency is improved.

The invention discloses a training and evaluation system and method based on the competency of a pilot, and relates to the technical field of educational training, and the system comprises a learning progress monitoring module which is used for collecting the learning data, including the learning time and the task completion condition, of the pilot in the training process in real time, and carrying out the deep analysis of the data, according to the method, the learning progress and the potential difficulty of the pilots are recognized, and a learning progress report is formed, the individual difference and the potential difficulty of each pilot are recognized by monitoring the learning progress and performance of the pilots in real time, and then the training content, the training method and the training progress are adjusted; the targeted training mode ensures that the training content is always matched with the actual requirements of the pilots, the potential of each pilot can be played to the maximum extent, the overall training effect is improved, in addition, real-time feedback and suggestions provided by the system also help the pilots to correct errors and optimize learning strategies in time, and the training effect is further improved.

The invention relates to the technical field of intelligent education, and discloses an integrated English teaching method and system based on artificial intelligence. The method comprises the following steps: constructing a digital resource library; generating pre-class preview data based on the resource library and the initial cognitive level of the student, analyzing the pre-class preview data and updating the cognitive level; generating classroom courseware based on the resource library and the student updating cognitive level, and analyzing classroom learning data to update the cognitive level again; generating personalized after-class homework based on the resource library and the re-updated cognitive level of the student, and analyzing homework data to finally update the cognitive level; and forming a heavy and difficult comment report based on the resource library and the final cognitive level of the student. Through continuous data collection and analysis, dynamic adjustment of teaching content is realized, pre-class preview, classroom teaching and after-class review are closely combined to form a complete teaching closed loop, teaching pertinence and effectiveness are improved, personalized learning and precise teaching are realized, and the overall effect of English teaching is greatly improved.

The invention discloses a knowledge graph enhanced reasoning framework based on a graph neural network and a large language model. The framework comprises a vectorization representation module, a graph neural network coding module, a large language model interaction module and a joint reasoning module. The vectorization representation module is used for aligning the structured knowledge graph and the unstructured text and converting the structured knowledge graph and the unstructured text into unified vectorization representation so as to provide a basis for subsequent processing; the graph neural network coding module obtains graph structure characterization by utilizing low-dimensional embedding of graph neural network learning data; the big language model interaction module performs semantic completion on the knowledge graph entity relationship by means of a big language model to generate an enhanced context vector; and the joint reasoning module fuses the results of the two modules and outputs an enhanced knowledge graph tetrad. Through multi-module cooperation, the accuracy and integrity of knowledge graph reasoning are remarkably improved, and the method has wide application prospects in the fields of intelligent questions and answers, intelligent decisions and the like.

The invention discloses a short-cut denitrification-anaerobic ammonia oxidation system and an intelligent regulation and control method thereof, and belongs to the technical field of intelligent regulation and control of sewage treatment. Comprising the following steps: constructing and processing a machine learning data set to obtain a training set and a test set; based on the training set, a prediction model is established through a machine learning method, and prediction performance is evaluated to select an optimal prediction model; performing importance analysis on the data in the data set as feature data, and identifying key features; and taking the key features as optimization regulation and control parameters, and constructing an optimal control strategy through a particle swarm optimization algorithm. Compared with the prior art, the method has the beneficial effects that a system denitrification performance prediction model is constructed and optimized by utilizing a machine learning method by utilizing a large number of system operation parameters, microbial community structures and other multi-dimensional data, the operation characteristics of the system are fully reflected, and a unique optimal control strategy is designed and formed aiming at a specific scene; and the adaptability of the strategy is improved.

The invention discloses a large-model multi-modal data semantic representation alignment method, and the method comprises the steps: carrying out the fine-grained local feature extraction of first modal data and second modal data through employing a data processing technology based on deep learning, the method comprises the following steps: acquiring first modal data and second modal data, calculating similarity between fine-grained local features of the first modal data and the second modal data as an initial alignment loss function value, and then further combining global context semantic information to carry out semantic enhancement coding on the fine-grained local features of the first modal data and the second modal data to obtain an initial alignment loss function value; according to the method, a fine alignment loss function value is obtained through context-level semantic alignment measurement, and then model training is guided based on the fine alignment loss function value and an initial alignment loss function value, so that semantic representation alignment of multi-modal data is realized. In this way, the alignment precision of the multi-modal data on the semantic level can be remarkably improved, and a more accurate feature basis is provided for subsequent data fusion and application.

The invention provides a high-efficiency intelligent teaching resource scheduling algorithm and an implementation method thereof, and the method comprises the steps: constructing a judgment matrix according to a learner capability difference evaluation result, cooperation efficiency analysis data, knowledge graph coverage rate statistics and learning style matching degree score, carrying out the calculation to obtain a group heterogeneity index, solving the weight of each index through a feature value method, and carrying out the calculation of the group heterogeneity index. Carrying out weighted summation to obtain a group complementarity comprehensive index; acquiring a historical learning data sequence of a learner, extracting time sequence features, combining a current individual progress deviation monitoring result, individual resource demand evaluation and individual learning content suitability analysis, constructing an individual development trajectory prediction model, and predicting a future individual learning performance curve; and aiming at the group complementarity comprehensive index and a future individual learning performance prediction result, constructing a multi-objective optimization model of resource allocation, setting a group complementarity objective function and an individual satisfaction objective function, and solving to obtain a resource allocation scheme for balancing group and individual benefits.

The invention discloses an education experience system based on a virtual reality technology, which relates to the technical field of virtual reality and comprises a data acquisition module, a virtual reality content creation module, an interaction module, a data analysis module, an evaluation module and a central control module. The data acquisition module acquires and processes teaching materials; the virtual reality content creation module constructs a teaching scene according to materials; the interaction module generates a plot by means of a specific architecture, and interacts with a user by combining action, voice and touch units; the data analysis module collects multi-dimensional learning data, carries out deep analysis and then outputs a result; the evaluation module evaluates the learning effect and the teaching suitability by means of the analysis result and the user performance; the central control module coordinates data interaction and instruction transmission of the related modules as a whole and assists education development together.

The invention provides a learning data accurate mining method based on cognitive calculation driving. The learning data accurate mining method comprises the following steps of S1, performing multi-modal learning behavior data acquisition and heterogeneous integration; s2, a dynamic feature weight optimization step based on calculus; s3, performing cognitive state differential equation modeling; s4, a cognitive diagnosis hybrid model based on statistics; s5, incremental construction of the dynamic knowledge graph is carried out; s6, constructing a federated learning framework for privacy protection; s7, a cognitive intervention strategy is generated; s8, constructing a multi-granularity effect evaluation system; s9, a step of constructing an interpretability enhancement module; s10, a step of carrying out adaptive iterative optimization; the learning data accurate mining method based on cognitive calculation driving has the following advantages that the data utilization rate breaks through the limitation of a traditional method through federated learning and heterogeneous graph fusion; the attention prediction error is reduced through differential equation modeling, and the method is superior to all existing ARIMA / LSTM baseline models.

The invention discloses an integrated circuit junction temperature and thermal resistance evaluation system and method based on a digital twinning technology, and the method comprises the steps: extracting a temperature cloud picture from a simulation calculation result, and extracting a thermal flow line diagram from the temperature cloud picture based on a heat dissipation path, carrying out discrete sampling on the heat flow line diagram by using a deep learning data analysis and coding technology, and then carrying out embedded coding on each piece of sampled heat flow point data (a flow point position and a heat flow point temperature); in this way, the rectangular frame position data of the hot spot region is automatically identified according to the graph walk significant aggregation representation between the data embedding coding features of each heat flow point. Through the mode, the influence of a fine structure (such as a connecting wire, a welding spot and the like) on a heat transfer path can be captured, the complex heat dissipation requirement can be better met, and it is ensured that the heat dissipation requirement is effectively met.

The invention relates to the technical field of online education path optimization, and discloses an online education learning path optimization system based on big data and intelligent analysis, and the system comprises a data collection module which obtains basic data of a user; the user portrait generation module is used for generating a user portrait; the path generation module is used for generating an initial path candidate set according to the user portrait and screening the initial path candidate set to obtain a first learning path; the learning effect analysis module is used for obtaining a learning effect analysis result according to the historical learning performance data, the first learning data and a preset learning obstacle evaluation model; and the optimization module judges whether an obstacle exists or not, if yes, a plurality of nodes are added / reduced in the first learning path to obtain an optimized learning path, and otherwise, the first learning path is kept unchanged. The first learning path is adjusted according to the user portrait and the learning effect analysis result, personalized optimization is performed by integrating the learning effect and the learning habit, and improvement of the learning efficiency is facilitated.

The invention provides a network intrusion detection and defense system based on artificial intelligence, which belongs to the technical field of network security and comprises a three-dimensional behavior map modeling module, a multi-modal adversarial training module and an edge collaborative defense module. Wherein the three-dimensional behavior graph modeling module is used for constructing a dynamic interaction relation graph of a network entity (user / equipment / service) through a space-time fused graph convolutional network (SF-GCN) and calculating an adaptive behavior entropy value Hnet. According to the network intrusion detection and defense system based on artificial intelligence provided by the invention, the comprehensive detection rate is greatly improved, the false alarm rate is greatly reduced, and the missing report rate is greatly reduced; the end-to-end delay is effectively reduced, and the strategy generation speed is greatly increased; the model size is obviously reduced, the reasoning delay is obviously shortened, and the resource consumption is obviously reduced; dynamic defense strategies of finance, Internet of Things and other scenes are supported, and the blocking success rate is greatly improved; the protocol compliance is obviously improved, and the federal learning data leakage risk is greatly reduced.

The present invention proposes a 3D semantic segmentation method for performing 3D semantic segmentation on the basis of fusion data obtained through sensor fusion of cameras and LiDAR. The method may include the steps of: receiving an image photographed by a camera and point cloud data acquired from LiDAR, by a learning data generation device; generating a projection image expressing the point cloud data in polar coordinates of a size the same as those of the image, by the learning data generation device; and inputting the image and the projection image into an artificial intelligence (AI) machine-learned in advance to estimate a 2D segment map and a 3D segment map having dimensions as high as the number of types of classes to be predicted, by the learning data generation device.

The invention relates to the technical field of machine learning, in particular to a rock mass crustal stress field inversion method and system based on machine learning and a storage medium. The method comprises the following steps: S1, constructing a three-dimensional virtual model of a target rock mass, and obtaining geological data at each position in an inversion target area; s2, acquiring a geological type in the inversion area, and acquiring rock mass parameters of a rock mass corresponding to a sampling point; s3, a plurality of sets of test data are obtained through a hydraulic fracturing method and a drilling method and preprocessed, and learning data and detection data are obtained; s4, a rock mass stress prediction model is created, the rock mass stress prediction model comprises m sub-models, the learning data are used for training the sub-models, and the sub-models are verified based on the detection data. According to the invention, the problem of inaccurate inversion of the rock mass stress field is solved, and the inversion precision of the rock mass crustal stress field is improved.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

The invention discloses an electromagnetic detection data efficient three-dimensional inversion method based on operator learning, and belongs to the technical field of geophysical inversion, and the method comprises the steps: constructing an operator learning data set which comprises a plurality of electromagnetic forward modeling models and forward modeling responses of the electromagnetic forward modeling models; performing aviation electromagnetic frequency domain forward calculation training based on the operator learning data set by using a DeepONet network to obtain a trained aviation electromagnetic three-dimensional forward operator; inputting the initial uniform half-space model, replacing a generator with an aviation electromagnetic three-dimensional positive operator, and constructing a WGAN network by taking a KAN network as a discriminator to carry out aviation electromagnetic three-dimensional inversion; training is carried out based on the loss function and the data fitting difference convergence condition to update the KAN network and the uniform half-space model, and finally the updated uniform half-space model is obtained to serve as an inversion result. According to the method, more efficient and accurate three-dimensional aviation electromagnetic inversion can be realized, and the method is more suitable for geophysical inversion application under complex data distribution.

The invention discloses an intelligent teaching assistance system and method based on behavior data and a knowledge graph, and relates to the technical field of teaching assistance, and the method comprises the steps: evaluating the mastering degree of a student for different learned knowledge points, and obtaining knowledge mastering data; obtaining defect knowledge points from the knowledge mastering data, obtaining a knowledge graph of the student, and evaluating the learning influence degree of the knowledge points in the knowledge graph on learning of the defect knowledge points by the student; acquiring historical learning behavior records and historical knowledge mastering data of other students, and analyzing learning reference values of the other students to the student in combination with the historical learning behavior records and defect learning data of the student to obtain a target reference student; and acquiring historical learning data of the target reference student, generating teaching reference data of the student in combination with the defect learning data of the student, and pushing the teaching reference data to a course teacher of the student to assist the course teacher in teaching the student.

The invention discloses an AI-driven personalized foreign language learning content generation platform, and belongs to the technical field of artificial intelligence. Comprising the following modules: a data center and user portrait module used for comprehensively presenting personalized learning features and demands; the language ability evaluation module predicts the learning ability in combination with historical learning data and generates a standardized ability evaluation report; the intelligent content service module preferentially recommends the matched existing content, automatically generates personalized learning resources and dynamically adjusts the content difficulty; the learning path planning module continuously optimizes a path planning algorithm through a feedback closed loop to realize long-term formulation and dynamic adjustment of a learning strategy; the real-time monitoring and diagnosis module monitors and analyzes learning behaviors in real time, provides instant feedback and accurate error diagnosis, and continuously optimizes the learning effect based on data driving; and the multi-modal interaction module is used for providing scene-based adaptive interface display and ensuring interaction experience consistency and fluency among the functional modules.

The invention belongs to the technical field of artificial intelligence, and discloses a reinforcement learning method for improving mathematical ability of a large language model and a related device. The reinforcement learning method comprises the steps of obtaining a to-be-enhanced large language model and a reinforcement learning data set; performing fine tuning training on the to-be-enhanced large language model by adopting reinforcement learning, and performing process level labeling on answer prediction generated in reinforcement learning by applying Monte Carlo estimation during fine tuning training to obtain a fine-tuned large language model and a labeled data set; and training the process reward model based on the annotation data set to obtain a trained process reward model. According to the technical scheme disclosed by the invention, fine-grained errors existing in the reasoning process can be captured, and the mathematical ability of a large language model is enhanced; in addition, data annotation can be realized while reinforcement learning is carried out, and the collection cost of process-level annotation data is saved.