1135 results about "Learning data" patented technology

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 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 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.

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.

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 belongs to the technical field of intelligent voice processing, and particularly relates to a spoken language pronunciation training and correcting system based on intelligent equipment, which acquires a user rhythm feature set including pitch change rate, accent intensity, pause duration and intonation contour by acquiring spoken language audio data sent by a user for a target text, and corrects the spoken language pronunciation training and correcting system. The method comprises the following steps: acquiring spoken language audio data, converting the spoken language audio data into a phoneme sequence aligned with target text time, generating a rhythm deviation degree report according to comparison evaluation of a user rhythm feature set and the phoneme sequence, an execution intonation mode, accent distribution, speech stream sound change and speech speed rhythm, and determining the rhythm deviation degree according to a rhythm problem type in the report in combination with user historical learning data. And forming and outputting a correction scheme including a text prompt, a targeted minimum contrast training unit and a listen-and-read simulation task, solving the problem of weak capability of correcting hyper-phoneme in a second language spoken language of an adult, and improving the authentic and fluency of the spoken language, thereby realizing efficient personalized learning.

The invention relates to the field of wisdom education, and particularly discloses a teaching strategy optimization model and grammar error early warning method based on data mining, and the method comprises the following steps: S1, obtaining original code data in a student code library in real time, and preprocessing the original code data to obtain a structured data matrix; s2, for the structured data matrix, generating a grammar parse tree through a grammar parser, positioning error nodes and extracting context features by using a node traversal algorithm, and constructing a single-error multi-dimensional feature vector; meanwhile, an error association rule base is constructed based on historical error data, a causal relationship across error types is identified, and dynamic knowledge graph data is formed. According to the technical scheme, association analysis and deep mining can be carried out on the multi-source learning data, dynamic solution suggestions for student individual errors or group generality errors are formed, and teachers are assisted to quickly adapt to dynamically changing learning requirements of the students.

The invention discloses a personalized teaching content generation method and system based on a digital portrait, and the method comprises the steps: collecting the multi-dimensional learning data of a student in real time, analyzing the data keyword of the student, constructing the portrait of the student, generating a teaching target based on the portrait of the student, and enabling the teaching target to comprise a teaching theme, exercises related to the theme, and knowledge points related to the theme. Converting the teaching target into an executable instruction of a large language model through a structured prompt engineering technology; the large language model generates initial teaching content according to the teaching instruction; verifying and optimizing the generated initial teaching content to ensure that the generated teaching content conforms to a teaching target and can adapt to the current cognitive level and learning requirements of students; and outputting the verified and optimized teaching content to the students and automatically adapting to the content presentation form according to the learning style preference of the students.

The invention discloses a complex metering sensor abnormal feature analysis method based on long-term accumulated data, and relates to the technical field of state monitoring and fault prediction. Static statistical characteristics and dynamic frequency characteristics of data and transient changes in non-stationary signals can be comprehensively captured, meanwhile, a model in which a double-layer long-short-term memory network is combined with an attention mechanism is constructed, short-term time sequence dependence in a first-layer LSTM learning data fragment and long-term evolution trend between second-layer LSTM learning fragments are constructed, and the time sequence dependence in a second-layer LSTM learning data fragment is constructed. The attention mechanism focuses on the key period, and the deep fusion from feature extraction to model construction enables the model to accurately identify the difference between normal data and abnormal data, thereby realizing the accurate detection of the sensor abnormality, and in the practical application, the normal fluctuation and real abnormality of the sensor can be effectively distinguished, and the accuracy of the sensor abnormality detection is improved. And reliable guarantee is provided for stable operation of the system.

The invention relates to a big language model enhanced knowledge adaptive learning planning system, and belongs to the field of intelligent education. The system comprises a knowledge center module, a learner portrait module, a path planning module and an intelligent learning guiding module, and the knowledge center module extracts entities and relationships from a multi-modal data source by using a large language model to construct a knowledge graph; the learner portrait module collects multi-dimensional learning data of the user and maps the multi-dimensional learning data to corresponding nodes of a knowledge graph, and dynamically deduces a learner portrait through a Bayesian knowledge tracking model; the path planning module generates an initial learning path based on the knowledge graph and the learner portrait, establishes a collaborative filtering analysis model, predicts and optimizes the expected effect of the current learner following the initial learning path in combination with a Bayesian knowledge tracking model, and finally generates a target learning path; and the intelligent learning guiding module generates a standardized knowledge card for each knowledge node on the target learning path through a security retrieval enhancement generation technology.

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

The invention relates to the technical field of wireless charging, and discloses a wireless charging power adaptive adjustment method and system based on machine learning, and the method comprises the steps: collecting and preprocessing multi-dimensional parameter data of a charging environment; learning the data by using a recurrent neural network, and establishing a correlation model of environmental factors and transmission loss; modeling a charging environment based on a graph structure, and extracting spatial relationship characteristics by using a graph neural network; a neighborhood sampling and dynamic pruning technology optimization model is adopted, and a hierarchical power decision system is constructed; adjusting the charging power by adopting feed-forward control according to the output of the decision-making system; according to the invention, by introducing the deep learning technology, accurate prediction and dynamic adjustment of the charging power are realized, and the charging efficiency and stability are improved; collaborative optimization of multiple charging points is realized by adopting graph structure modeling; and the calculation complexity is reduced through an optimization technology, so that the system is suitable for a large-scale application scene.

The invention provides an intelligent learning plan generation method and device based on a large model, and belongs to the technical field of educational informatization. Identifying entities and key terms; constructing a course knowledge graph; carrying out matching calculation with courses through mixed retrieval on the basis of a course knowledge graph and key information features, and carrying out dual-channel collaborative multi-channel retrieval in combination with vector similarity and keyword matching; the multi-target course arrangement information is optimized based on a genetic algorithm, course arrangement is carried out in combination with a time planner of a constraint satisfaction problem model, and a course time arrangement plan is generated; and related lecturers and students are informed of confirmed results. According to the method, the curriculums and student requirements are matched, the comprehensiveness and accuracy of curriculum retrieval are improved through weighted sorting, the curriculums meeting the learning plan requirements can be found more accurately, and the curriculum matching efficiency is improved. A better course arrangement scheme is found through global search, the resource utilization rate is improved, and a scientific and reasonable course time arrangement plan is generated.

The invention relates to the technical field of intelligent education, in particular to a personalized learning path planning method fusing artificial intelligence and education big data, and the method comprises the steps: collecting multi-modal education data of learning behavior data, cognitive process data and emotional state data from a learning terminal; constructing a learner portrait feature vector based on the preprocessed multi-modal education data; establishing a knowledge graph, and dynamically updating a weight matrix based on group learning data; and taking the learner portrait as a state space, taking a learning path decision as an action space, taking a learning effect as a reward function, generating a personalized learning path by adopting near-end strategy optimization, optimizing a path planning model, and generating a recommendation result. Personalized learning path planning is realized by constructing a multi-modal data fusion framework, a dynamic knowledge graph and an intelligent planning method.

The invention discloses a coastal flood control adaptive strategy optimization system based on reinforcement learning, and the system comprises an unmanned plane multi-dimensional data collection module which carries out the cooperative work through a multi-rotor unmanned plane group, and carries out the data collection; the deep learning data processing module is used for processing the data acquired by the unmanned aerial vehicle multi-dimensional data acquisition module, updating a three-dimensional terrain model, flood routing prediction, a flooding simulation diagram and abnormal signal real-time detection in real time; the reinforcement learning strategy generation module is used for state space design, action space expansion and reward function optimization; the unmanned aerial vehicle execution and feedback module is used for strategy execution tracking, post-disaster evaluation and model iteration; and the dynamic optimization closed loop unit comprises a daily mode subunit and an emergency mode subunit. According to the invention, the flood control strategy is sensed in real time, accurate decision is made, and dynamic optimization of the coastal flood control adaptive strategy is realized.

The invention discloses a vibration signal processing method based on self-adaptive wavelet packet and deep learning fusion, and belongs to the field of sewage plant equipment fault diagnosis. The method aims at solving the problems that traditional signal processing is poor in flexibility, the non-stationary signal processing capacity is weak, the deep learning data requirement is large, and the high-frequency weak feature capturing capacity is limited. According to the method, the high-frequency acceleration sensor is adopted, the vibration signals of the sewage plant equipment are accurately collected, the self-adaptive wavelet packet decomposition technology is applied, the primary function is dynamically selected, the number of decomposition layers is optimized, self-adaptive threshold noise reduction is achieved, and the signal processing quality is improved. Meanwhile, in combination with a one-dimensional convolutional neural network and a bidirectional LSTM model, local and global features of the signal are extracted respectively, and pre-processed data are formed through gating weighted fusion. According to the method, the signal-to-noise ratio and the weak fault detection rate are remarkably improved, feature redundancy and data requirements are reduced, the calculation efficiency and diagnosis accuracy are improved, the method is suitable for sewage plant equipment fault diagnosis, and the industrial applicability is enhanced.

The invention provides a knowledge pushing method and system based on a user portrait, and the method comprises the steps: receiving and analyzing a knowledge training request of a target user, and obtaining demand information; obtaining a user portrait, wherein the user portrait is constructed and updated based on the historical learning data of the target user; based on the user portrait and the demand information, matching knowledge points in a knowledge base to obtain a first knowledge point set and a second knowledge point set; the first knowledge point set comprises one or more target knowledge points matched with the demand information, and the second knowledge point set comprises associated knowledge points associated with the target knowledge points; and based on the user portrait, the demand information and the attribute information, performing combination and text extension on the target knowledge points and the associated knowledge points to generate a personalized knowledge set. According to the method, the personalized knowledge set adaptive to the user can be dynamically generated based on the actual demand of the user and the user portrait, and the user experience can be improved.

The invention discloses a microemulsion interfacial tension efficient prediction method and system based on active learning and molecular dynamics. According to the method, 217 molecular descriptors corresponding to each molecular structure are calculated by adopting an RDKit software package, and the descriptors are used for representing molecular structure characteristics and serve as input variables of a machine learning model, so that key structure information including molecular branching degree, polarity and the like is transmitted. For an oil-water-surfactant ternary interface system, the oil-water interfacial tension in the presence of a surfactant is simulated and calculated through molecular dynamics, and an IFT value is set as a model prediction target. An active learning mechanism is introduced, and iterative sample labeling in the molecular dynamics simulation process is guided; and integrating the obtained IFT data with the molecular descriptor features, constructing a machine learning data set, and training a random forest model. According to the method, the problem of screening a high-performance surfactant layer by a middle-phase microemulsion system can be solved, and the ultra-low oil-water interfacial tension can be rapidly and efficiently screened.

The invention belongs to the technical field of education course recommendation, and discloses a personalized learning path recommendation method based on a knowledge graph, which comprises the following steps: acquiring a knowledge point set according to a learning target, and constructing a basic knowledge graph; matching the first similar group, and obtaining learning records of the first similar group to form a first reference record set; marking difficulty values for the knowledge points according to the mastery degree in the first reference record set, and generating a knowledge point learning sequence; taking the first reference record set as a target reference set, and matching an optimal learning track for each knowledge point to obtain a comprehensive learning path; after learning records of the learner are obtained, matching a second similar group, and obtaining a second reference record set; and optimizing the comprehensive learning path according to the second reference record set, and adding review nodes for the knowledge points with low mastery degree. According to the method, knowledge point difficulty values are marked based on actual learning data, a learning sequence of cognitive load balance is generated, a dynamic recommendation process from general to individuation is realized, and learning efficiency and experience are improved.

A first system (10) includes an acquisition unit (11) that acquires data provided from a data providing apparatus such as an external server as inference data for a second system (20) to perform inference by an inference model, and a specifying unit (12) that specifies, from among data including the inference data 5acquired by the acquisition unit (11), data collected from the second system (20) that has performed inference by the inference model, as learning data for a learning model for constructing the inference model.

The invention provides an intelligent curriculum recommendation method and device based on a knowledge graph and a storage medium, and the method comprises the steps: constructing a multi-dimensional knowledge graph which comprises a curriculum skeleton knowledge sub-graph, a user capability sub-graph and a scene demand sub-graph; generating a knowledge dependence matrix based on the course skeleton knowledge sub-graph, and generating user learning sequence features based on historical learning data of the user; acquiring a learning target, a learning time constraint and a learning scene input by a user, and determining a first knowledge learning path and task sequence set and a second knowledge learning path and task sequence set of the user; and based on the user learning sequence features, the course skeleton knowledge sub-atlas, the user capability sub-atlas and the scene demand sub-atlas, using a fusion model to perform fusion processing on the first and second knowledge learning paths and the task sequence set to obtain a course meeting the user demand. The accuracy of course recommendation is improved.

The embodiment of the invention discloses a model optimization method based on transfer learning. The method comprises the following steps: acquiring to-be-migrated learning data, wherein the to-be-migrated learning data comprises source domain data associated with pathological characteristics of kidney diseases; semantic alignment processing is carried out on the source domain data and target domain data, the target domain data is kidney disease pathology data, and a semantic alignment result is obtained; determining a feature distribution result corresponding to the target domain data, and determining a migration weight corresponding to the source domain data based on the feature distribution result and the semantic alignment result; and optimizing the target domain model based on the migration weight, the source domain data and the target domain data to obtain an optimized target domain model. According to the embodiment of the invention, the accuracy and robustness of the target domain model in a kidney disease pathological diagnosis task and the generalization ability of different data distributions can be remarkably improved.

The invention specifically relates to the technical field of multi-modal large models, and discloses an automobile production teaching evaluation optimization system based on a large model, and the system comprises an industry knowledge obtaining module, a knowledge graph construction module, a teaching resource generation module, a capability data collection module, an intelligent capability evaluation module, and a resource matching cooperation module. An industry knowledge acquisition module acquires an automobile production and teaching fusion dynamic knowledge data set, a knowledge graph construction module constructs an automobile production and teaching knowledge graph, a teaching resource generation module generates teaching resources, and a capability data acquisition module acquires learning capability associated data of students. Student ability comprehensive scores are calculated through the intelligent ability evaluation module, personalized learning paths and learning materials are recommended for students through the resource matching cooperation module, talent lists are recommended for enterprises, automatic and high-frequency updating of teaching content is achieved, and intelligent scheduling of the whole process and intelligent upgrading of automobile production teaching evaluation are achieved. And the production and education docking efficiency is greatly improved.

The invention provides a learning progress and score management system based on multi-dimensional data integration. The learning progress and score management system comprises a data acquisition module, a data processing module, a deep analysis module, a suggestion pushing module and a management feedback module which are in communication connection in sequence, multi-dimensional learning data of students are collected for integration and preprocessing, behavior characteristics, relation characteristics and time sequence characteristic data are extracted for deep analysis, so that the learning progress of the students is judged, the association with scores is mined, the risk of subject hanging is predicted, the learning progress and the predicted scores are scored, and according to the scoring result, the learning progress of the students is determined. According to the invention, targeted learning suggestions are generated in combination with learning characteristics, interests, preferences and historical behaviors of different students, the learning suggestions are pushed to the students through multiple channels, the learning progress and scores of the students are effectively managed according to the learning suggestions, and the system is optimized according to collected feedback information.

A fingerprint information processing apparatus includes: an output unit that outputs a certainty factor that is an index indicating probability in which fingerprints indicated by a fingerprint image correspond to at least one of a plurality of pattern types, by using the fingerprint image and a learning model constructed by machine learning using learning data including a sample image indicating fingerprints; and a processing unit that performs processing based on the certainty factor.

An object is to accurately detect peaks of various compositions, even in a case of unseparated peaks in which peaks of a plurality of compositions are superimposed. A computer acquires waveform data D1 having a peak P1 in a composition A measured by a data analysis device (S10). Next, the computer acquires waveform data D2 having a peak P2 in a composition B measured by the data analysis device (S20). Next, waveform data D12 including unseparated peaks by superimposing the waveform data D1 including the acquired peak P1 and the waveform data D2 including the acquired peak P2 (S30) is generated. Next, the generated waveform data D12 of the unseparated peaks is input as learning data, and the waveform data D1 and D2 corresponding to the waveform data D12 are input as training data in Step S40. Next, machine learning is performed using the waveform data D12, D1, and D2, and a learned model for estimating an accurate separation method of unseparated peaks is constructed based on the trained result (S50).

The invention relates to the technical field of computers, and discloses a learning difficulty intelligent adjusting device and method, and the method comprises the steps: extracting key knowledge elements from pre-obtained user learning data, and constructing a learning graph; according to the learning map, analyzing the knowledge mastering degree and the learning ability of the user to obtain a user ability evaluation result; generating corresponding learning content in combination with the user capability evaluation result and the node association relationship in the learning map; in the learning process of the user, the updating condition of the knowledge elements and the change condition of the learning ability of the user are analyzed, and the learning graph is dynamically updated to obtain an updated learning graph; according to the updated learning map, the difficulty of the learning content is adjusted in real time, updated learning content is obtained, and intelligent adjustment of the learning difficulty is achieved; the learning content difficulty is intelligently adjusted, the requirements of different users in different learning stages are met, and the learning effect and the knowledge mastering degree are effectively improved.

Provided is a training system that performs training of a machine learning model that generates an impressing content or estimates a camera operation for capturing an impressing content. The training system includes a data collection device that collects data, and a training device that performs training of a machine learning model by using the data collected by the data collection device, in which the training device performs re-training of the machine learning model by using learning data that affects the training of the machine learning model to a predetermined degree or more, insufficient learning data, or data similar thereto, collected on the basis of a result of analyzing learning data that affects the training of the machine learning model.

To provide an image forming apparatus, an image processing method, and a program that can perform set-off correction according to various conditions related to read data.SOLUTION: An image forming apparatus comprises: a reading unit that performs a reading operation for a document to obtain first read data; an acquisition unit that acquires a learning condition related to the first read data; a generation unit that generates learning data including the first read data and learning condition to be used for learning processing through machine learning; and a correction unit that performs set-off correction for second read data to be corrected that is read by the reading unit, by using a learning model generated through the learning processing using the learning data.SELECTED DRAWING: Figure 4

The present disclosure generally relates to wireless communication systems, and more particularly, to an apparatus and method for integrated inference using dual-sided machine learning in wireless communication systems. A method of operating a user equipment (UE) in a wireless communication system includes: transmitting capability information of the UE to a network; receiving at least one of a structure or parameters of a reference model, or receiving a learning data set from the network according to the capability information of the UE; configuring a machine learning (ML) model directly on the UE or through a UE-side learning server based 10 on the received information; and performing integrated inference based on dual-sided machine learning models with the network using the configured machine learning model.