1935 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 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 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 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 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 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.

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 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.

The invention discloses a personalized intelligent education textbook generation method and device based on a large model, and the method comprises the steps: obtaining a knowledge point set, learning data of a learner, a learning target, and eye movement tracking data; determining a cognitive style vector of the learner according to the learning data and the eye movement tracking data; according to the cognitive style vector and the learning target, a learning path diagram of the learner for the knowledge point set is constructed, and the learning path diagram comprises learning nodes and jump relations among the multiple learning nodes; determining cognitive matching parameters of the knowledge points based on the cognitive style vector and the knowledge point attribute vector of each knowledge point in the knowledge point set, and determining presentation forms of the knowledge points according to the cognitive matching parameters; and based on the learning path diagram and the presentation form, generating a personalized intelligent education textbook through a preset large model. The learning efficiency of students is improved.

The invention relates to a federated learning method, system and device for personalized differential privacy protection and a medium. The method comprises the steps that a central server initializes global model parameters and issues the global model parameters to clients; each client sets an initial value and an extreme value of a personalized privacy budget based on data characteristics of the client; the client performs local training, cuts the gradient in the training process, and adds corresponding Gaussian noise processing based on the current personalized privacy budget; the central server performs weighted aggregation on the model parameters uploaded by the clients to update a global model, and issues the updated global model parameters to the clients for a new round of local training; and the central server dynamically adjusts the personalized privacy budget of each client based on the reward factor, and then allocates the personalized privacy budget to each client for local training again until a global model meeting a preset requirement is obtained. The method can be widely applied to the field of distributed machine learning data security.

The invention relates to a learning condition analysis method and system based on a large language model, a terminal and a medium, and relates to the field of education system technology, the method comprises the following steps: collecting learning data of students, the learning data comprising structured learning data and unstructured learning data, the structured learning data comprising examination scores and homework completion conditions, and the unstructured learning data comprising examination scores and homework completion conditions; the unstructured learning data comprises classroom behavior data, learning process data, student self-evaluation and mutual evaluation data and teacher teaching records; preprocessing the learning data to generate standardized data which can be used for analysis; input interaction information is received, analysis demand parameters are generated, and the interaction information comprises analysis demands of teachers or feedback information of students; based on a large language model, semantic understanding and deep analysis are carried out on the standardized data and the analysis demand parameters, and a learning condition analysis result is obtained; and displaying the study condition analysis result in a visual mode. The application has the effects of improving the teaching quality and meeting the actual teaching demand integrating degree.

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 provides a multi-agent collaborative teaching interaction method and system in an education scene, and relates to the technical field of intelligent education, and the method comprises the steps: building a knowledge point dependency graph; constructing an optimal teaching path set; forming a collaborative teaching network and calculating a teaching path overlapping degree matrix to divide teaching tasks; acquiring learning data to generate learning difficulty indication information; and matching the teaching path with the highest adaptation degree according to the difficulty information, and adjusting the corresponding intelligent agent to carry out supplementary explanation. According to the invention, efficient collaborative configuration of intelligent teaching resources can be realized, accurate teaching support is provided for personalized difficulties of learners, and the teaching efficiency and the learning effect are improved.

The invention belongs to the technical field of artificial intelligence education, and particularly relates to a student learning behavior prediction method based on artificial intelligence, and the method comprises the steps: obtaining student learning data and teacher teaching task data; constructing a student agent according to the student learning data, and constructing a teacher agent according to the teacher teaching task data; a dynamic teaching strategy generated by the teacher agent is input into the student agent, and the student agent predicts possible learning behavior change of the student based on a learned behavior mode in combination with a strategy-behavior causal model; the prediction result is linked with a teaching management mechanism, a dynamic learning file is generated, the teacher intelligent agent matches personalized learning resources through an intelligent recommendation algorithm according to the dynamic learning file, and the student intelligent agent receives feedback of students on the learning resources and performs targeted intervention actions in combination with feedback information. Therefore, the problems of insufficient causal reasoning ability, weak calibration ability, insufficient data fusion ability and the like in the prior art are solved.

The invention provides a robot control method and device, equipment and a storage medium, and relates to the technical field of robot control. The method comprises the steps of obtaining robot data of a robot executing a task, and determining a learning data set according to the data; building a first robot action prediction model based on the initial visual language model; obtaining a second robot action prediction model according to the data set and the first robot action prediction model; a robot time sequence, image data and a language instruction are taken and input into a second robot action prediction model, the time sequence data are input into a second language model through a preset time sequence model and a multi-layer projector, the image data are input into the second language model through a visual encoder and the projector, and the language instruction is input into the second language model through the first language model; the second language model outputs continuous actions to be executed; and completing the task according to continuous action control. And the time sequence model captures time sequence characteristics, so that the second robot action prediction model outputs continuous actions to be executed, and the operation smoothness and continuity are effectively guaranteed.

The invention discloses a personalized course recommendation method and system based on a knowledge graph, and relates to the technical field of course recommendation. The personalized course recommendation method and system based on the knowledge graph comprises the following steps: S1, acquiring user learning data by collecting front-end burying points and background access log data of a learning platform in real time, and preprocessing the user learning data; s2, constructing a behavior data relationship between the user and the knowledge points, evaluating a mastering condition of the user on the knowledge points, and constructing a time sequence knowledge point score set; s3, based on the time sequence knowledge point score set, identifying abnormally mastered knowledge points of the user in the current stage, analyzing whether the courses are worthy of recommendation, and partitioning candidate courses and generating recommended courses; and S4, quantifying the response difference between the recommendation course and the mastering effect, updating the recommendation tag and the user record, and perfecting the recommendation data circulation. The problem that in the prior art, learning effects are difficult to measure, and behavior-driven recommendation circulation is likely to happen is solved.

The invention relates to the technical field of education monitoring, in particular to an intra-subject equilibrium combination interval constitutive analysis method and system. The method comprises the following steps: obtaining subject learning data, and carrying out data standardization processing to obtain standardized subject learning data; performing interval structured modeling based on the standardized subject learning data to obtain a combined interval model; performing subject learning quality difference evaluation according to the combined interval model to obtain a hierarchical feature data set; performing subject core accomplishment relation modeling based on the standardized subject learning data to obtain a subject core accomplishment relation matrix; and performing subject learning quality evaluation according to the subject core literacy relation matrix and the hierarchical feature data set, and performing learning path optimization to obtain a subject optimization strategy. The pertinence and accuracy of teaching quality monitoring can be improved.

The invention discloses a federal learning data aggregation method for predicting traffic flow, a medium and equipment, and the method comprises the steps: setting a relation between a cloud server end and a vehicle end, and building a corresponding system; collecting vehicle data through the system for local training and data preprocessing; the vehicle side encrypts the preprocessed data through a homomorphic encryption technology and uploads the data to the cloud server side; carrying out weight distribution on the encrypted and uploaded data based on a kernel ridge regression technology; the encrypted update quantity is aggregated based on the adjusted weight, so that the update contribution to the global model is more similar, and the more reliable vehicle update quantity plays a more important role in the prediction process; and the aggregated encryption update quantity is decrypted, pre-training model updating is completed, finally, the server in the training wheel screens the weights of all vehicles, a part of vehicles with too low weights are eliminated, certain residual data similarity is ensured, and data aggregation of residual data is easier.

To increase the amount of information to be utilized.SOLUTION: An information processing system includes an information processing apparatus, a first terminal device that holds first user characteristic information, and a second terminal device that holds second user characteristic information. The information processing apparatus includes: an information collection unit which collects, from the first terminal device, first anonymized information generated by anonymizing the first user characteristic information, and collects, from the second terminal device, second anonymized information generated by anonymizing the second user characteristic information; a learning unit which generates a model configured to learn, by machine learning, a relationship between the first user characteristic information and the second user characteristic information, using the first anonymized information and the second anonymized information, as learning data, and output, on receipt of the first user characteristic information, estimated user characteristic information estimated from the relationship between the first user characteristic information and the second user characteristic information; and a model output unit which outputs the model to the first terminal device.SELECTED DRAWING: Figure 3

The invention relates to an abdominal CT multi-organ segmentation method based on a LoRA vertical domain MedSAM large model. The method comprises the following steps: step 1, preprocessing an abdominal CT image supervised learning data set; step 2, constructing a feature selection network; step 3, constructing a LoRA layer; step 4, performing interlayer context reasoning by using a mixed prompt strategy to realize abdomen CT automatic segmentation; the method has the advantages that through multi-feature information fusion, dynamic context prompting and self-adaptive LoRA fine tuning, the precision and robustness of the MedSAM large model in the abdomen multi-organ CT segmentation task are improved, multi-feature information can comprehensively represent organ features, context information can be effectively utilized through dynamic context prompting, and the accuracy and robustness of the multi-organ CT segmentation task are improved. Limited training data can be effectively utilized by self-adaptive LoRA fine tuning; according to an image encoder based on a MedSAM large model, a learnable LoRA layer is added to each Transform layer so as to achieve efficient parameter training, and for a mask decoder of the MedSAM large model, the LoRA layers are also added to a self-attention layer and a cross-attention layer at the same time.