69 results about "Predictive learning" patented technology

The invention discloses a multi-source heterogeneous learning path planning method based on personalized constraints, which solves the problems of single path, data staticization and the like in the prior art, and comprises the following steps: acquiring multi-source behavior data of a learner, carrying out feature modeling, and constructing a high-dimensional behavior portrait; according to the high-dimensional behavior portrait, utilizing a neural collaborative filtering algorithm to predict the interest and mastering probability of a learner to any knowledge point, and generating a preliminary learning path; carrying out dominant and implicit evaluation on the knowledge mastering state of the learner by adopting a cognitive diagnosis model, and carrying out dynamic correction on the preliminary learning path to obtain a corrected learning path; performing dimension reduction on the multi-source behavior data by adopting a principal component analysis algorithm to extract a core factor, and forming an evaluation basis for path optimization; and constructing a multi-objective path function model, and obtaining an optimal dynamic learning path by adopting a multi-objective optimization path algorithm in combination with the corrected learning path and the core factor. The method has high practical value and popularization value in the technical field of learning path planning.

The invention relates to the technical field of education, in particular to an English personalized learning recommendation method based on big data, which comprises the following steps: collecting original data of English homework completion speed and answer accuracy of a learner, calculating average completion time and accuracy by using a statistical analysis method, identifying the deviation between learning ability and interest points, and recommending the learning ability to the learner. And obtaining a learning ability evaluation result. According to the invention, through predicting learning content demands, not only is the progress of a learner captured, but also future demands can be predicted, so that education resources are prepared in advance, prospective matching of teaching contents is realized, and through optimizing a teaching material sequence and contents, high matching of teaching materials and personalized learning demands is ensured; the use efficiency of educational resources and the maximization of the learning effect are remarkably improved, the learning path is dynamically adjusted, the learning adaptability is evaluated, the learning process is optimized, the flexible application of course content is enhanced, and more personalized learning experience and higher learning achievement are achieved.

The invention discloses a time enhanced knowledge tracking method based on dual-channel deentanglement, and belongs to the technical field of education data mining and cognitive modeling. According to the technical scheme, the method comprises the steps that time dynamic features and behavior reaction features in a learning interaction sequence are extracted and coded through a time domain encoder and a behavior domain encoder respectively; separating long-term trends and short-term fluctuations in the input features by using a multi-scale decoupling layer based on causal convolution; a time perception dual-channel attention module is adopted to independently decouple time and behavior characteristics after decoupling, and a nonlinear attenuation item based on a real interval is introduced to simulate memory forgetting; and finally, integrating dual-channel information through a gating fusion mechanism and predicting future answering performance of the learner. According to the method, optimization conflicts are effectively relieved, the robustness to a complex learning mode is enhanced, and knowledge state modeling which better accords with a cognitive law is realized.

Systems, devices and methods for optimising and managing distributed energy storage and flexibility resources on a localised and group aggregation basis, particularly around the determination, analysis and predictive learning of local data patterns, scoring availability for flexibility and risk profiles, to inform the optimisation of energy supply and behind the meter storage resources and local clusters of co-located or close resources within a community, low voltage network, feeder, neighbourhood or building. Said optimisation to involve scheduled, reactive and active management of data sources and local clusters of resources, for a range of goals such as price, energy supply, renewable leverage, asset value, constraint or risk management. Or where said optimisation achieves a local objective such as providing resources to off-set, aid local balancing or constraint management of larger local supplies and loads, or to aid active management of local energy demands and renewable supplies, storage resources, electric heat resources, electric vehicle charging resources or clusters of electric vehicle chargers, flexible loads in buildings.

The invention provides an auxiliary use method and device for learning electronic products, equipment and a medium. According to the method, under the condition that the use data of the user in the learning electronic product is obtained, the predicted learning time information of the user for each learning module is determined based on the obtained use data; the predicted learning time information can be used for indicating and predicting time required by the user to learn one to-be-learned content item in one learning module, so that time allocation recommendation information is determined based on the predicted learning time information of the user for each learning module; according to the method and the device, the learning time allocated on different learning modules by the user is recommended to the user through the time allocation recommendation information, so that the user can perform targeted learning on the content of each learning module according to the indication of the time allocation recommendation information, and the use efficiency of various learning resources in electronic learning products is improved.

The present invention relates to the technical field of knowledge tracing, and in particular to a method and system for predicting learning conditions of learners based on meta-paths, and an electronic device. In the method, after learner data, exercise book data, and learner answer interaction sequence data are obtained, firstly, a learner behavioral graph is constructed, and learning content preferences of learners are extracted; secondly, learning abilities of the learners are calculated; thirdly, features of the learning content preferences and features of the learning abilities are fused, and a learner heterogeneous information network based on meta-paths is constructed; next, embedding learning is performed on the leaner heterogeneous information network by using a layered graph attention mechanism, to extract leaner fused features; and finally, learning conditions of the learners are predicted by using a GRU-based knowledge tracing model. Compared with the prior art, the present invention has the advantages of more accurately predicting the probability that a learner will answer a next exercise correctly, evaluating and predicting a learning outcome of the learner, and thus providing the learner with multi-dimensional feedback and guidance.

The invention relates to the technical field of industrial data processing and intelligent control, and discloses an iron ore concentrate grinding medium intelligent regulation and control method and system based on real-time data processing, and the system comprises a sensing layer, an edge calculation layer, a cloud intelligent layer and an execution layer; according to the method, multi-dimensional data are obtained in real time and processed through a professional data preprocessing process, complex process characteristics are condensed into three core indexes including a particle size distribution index, a chemical component index and an energy consumption process index, and fuzzy worker experience is converted into an accurate numerical index; a reinforcement learning algorithm is introduced to enable the system to autonomously learn an optimal control strategy, so that the system has advanced intelligence of prediction, learning and global optimization; millisecond-level real-time response of index grading and medium regulation and control is achieved through the edge computing unit, timeliness of control is ensured, depth computing and model training are carried out based on cloud capacity, and automation and closed loop of the whole process from data collection to instruction execution are ensured.

The invention discloses a closed-loop transcranial magnetic stimulation brain regulation and control method based on multi-model fusion and individualized learning. Dynamic optimization and accurate regulation and control of TMS stimulation parameters are achieved. According to the technical scheme, the method comprises the following steps: collecting and preprocessing a multi-guide EEG signal in real time, and extracting frequency domain and time domain features and brain network topology indexes; the EEG state in the future 0.5-2 seconds is predicted by using depth sequential networks such as LSTM and the like, and an individualized stimulation parameter strategy is generated in combination with reinforcement learning and Bayesian optimization; tMS stimulation is triggered based on a prediction result, brain network indexes and reward evaluation, and long-term adaptive adjustment is realized through an individual memory pool. The traditional passive feedback mode is broken through, an active closed-loop mechanism of prediction-learning-regulation is realized, the TMS regulation efficiency and the individual adaptability are remarkably improved, and the method can be widely applied to scenes such as mental disease intervention, cognitive regulation and nerve rehabilitation.

The invention discloses an urban rainfall flood toughness deep learning evaluation method based on machine learning. The method comprises the following steps of 1, explaining time-space differentiation characteristics of urban rainfall flood; 2, analyzing and calculating urban rainfall flood toughness indexes; step 3, urban rainfall flood toughness evaluation is carried out; 4, constructing an urban rainfall flood disaster simulation model; and step 5, an evaluation prediction learning method based on machine learning. The method has the beneficial effects that the urban rainfall flood toughness can be accurately evaluated by utilizing a machine learning technology, and in-depth feasibility analysis can be carried out by virtue of a deep learning neural network model; according to the technology, multi-scale features can be learned and extracted in the space-time dimension, the nonlinear relation in urban rainfall flood toughness space-time data is effectively captured, the accuracy and stability of urban rainfall flood toughness prediction are improved, then the urban rainfall flood toughness is regulated and controlled, a corresponding urban emergency scheme is determined in time, and the overall toughness of a city is enhanced.

The invention provides an online course MOOC learning prediction method based on heterogeneous feature fusion, and belongs to the field of computer-aided intelligent education. The method comprises the following steps: acquiring an MOOC data set, and preprocessing the data set to obtain a test set; constructing an IHFNet network comprising a multi-agent adaptive static feature selector module, a hierarchical time sequence feature extractor module and a heterogeneous feature fusion module; a multi-agent adaptive static feature selector module screens key static features; the hierarchical time sequence feature extractor module extracts behavior time sequence features with high discrimination ability; the heterogeneous feature fusion module carries out adaptive fusion on the key static features and the behavior time sequence features and carries out classification prediction; an IHFNet network is trained; and collecting MOOC data of a to-be-predicted learner, and inputting the MOOC data to the trained IHFNet network for learning risk prediction. According to the invention, modeling is carried out by fusing the static features of the learner and the behavior time sequence features, the feature representation ability of the learner is enhanced, and the learning risk prediction effect is improved.

The invention provides a knowledge tracking method based on knowledge evaluation and learner state evaluation, and relates to the technical field of knowledge tracking, and the method comprises the steps: obtaining and processing an answer record and teaching resources, and obtaining historical answer features and teaching resource features; modeling is carried out on the multi-modal course knowledge point information through teaching resource features, and knowledge point proportion weights are obtained; initializing a state matrix of a learner through the teaching resource characteristics; updating a state matrix through knowledge point proportion weights and historical answer features; and predicting the correct answering probability of the learner through the state matrix after state updating, the knowledge point proportion weight and the teaching resource characteristics. According to the method, the knowledge and the state of the learner in the learning process can be effectively evaluated and modeled, and the answer condition of the learner can be accurately predicted.

The invention relates to a power prediction technology, in particular to a wind power prediction method, which comprises the following steps of: identifying a global optimal particle in a plurality of wind power characteristic particles; a power prediction learning model is constructed according to an IABFLPSO algorithm, whether the wind power characteristic particles are initialized and updated or not is determined according to the number of non-updating times, and the inertia weight and learning factors of the power prediction learning model are determined according to the Euclidean distance between each non-global optimal particle and the global optimal particle; decomposing the historical time sequence wind power data; constructing a fitness function of the xLSTM neural network according to a plurality of absolute error values between the final predicted values of the plurality of component data sets and the corresponding true values; and inputting the meteorological characteristic data of the target time period into the IABFLPSO-xLSTM-BP neural network to predict the wind power of the target time period. According to the invention, the accuracy and efficiency of the prediction result of the prediction model are improved.

The invention relates to an English text auxiliary teaching method and system based on AI vision. The method comprises the following steps: collecting a dynamic eye movement track, a mouth shape change video stream and micro-expression time sequence data when a student reads, and generating a dynamic behavior feature vector by using a convolutional neural network; and carrying out multi-dimensional matching on the vector and a preset pronunciation standard model, accurately positioning a pronunciation deviation region and an understanding difficulty point, and carrying out classification by virtue of a support vector machine to obtain a learning state label. And extracting a high-frequency deviation mode from the tag, constructing a comprehensive behavior matrix through association of a clustering algorithm and an eye movement backtracking trajectory, calculating a teaching level evaluation value, finally predicting a learning trend and determining a resource allocation weight by combining historical evaluation data and adopting a linear regression model, and optimizing and generating a personalized teaching plan. By adopting the method, pronunciation deviation and understanding disorder in English reading can be accurately positioned, so that the resource allocation weight is adaptive to the individual learning track of students, and a data-driven technical path is provided for English personalized teaching.

The invention belongs to the technical field of computers, and particularly discloses a programming knowledge tracking method based on code prediction enhancement, and the method comprises the steps: extracting embedded representations based on historical programming questions and corresponding historical programming answers, and obtaining historical question embedded representations and historical answer embedded representations; based on the embedded representation of the historical question, the embedded representation of the historical answer and the embedded representation of the target programming question, the embedded representation of a source code input by the tested object for the target programming question is predicted through a multi-head attention mechanism model, and the predicted embedded representation of the source code serves as a programming answer prediction result; on the basis of the embedded representation of the target programming question, the programming answer prediction result and the current knowledge state of the tested object, the correct answer probability of the tested object for the target programming question is predicted, and the current knowledge state is obtained by analyzing time sequence evolution of the knowledge state on the basis of historical programming answers of the tested object. Through the method, the programming performance of the learner can be accurately predicted.

The invention belongs to the technical field of deep learning and education data mining, discloses a student academic performance prediction method based on learning behaviors and reflection texts, and provides a representation framework CE-BERT for the reflection texts of learners to obtain reflection text representation. Through extraction and verification of learning behaviors and reflective text features, the study shows that the learning behaviors and the reflective text features both influence academic performance prediction of students. According to the research, after the two feature sets are integrated, the accuracy of predicting the academic performance of the student is further improved. The discoveries show that the combination of the learning behavior features, the reflective text features and the two feature sets is very important for predicting the academic records, and the method is an important method for predicting the academic records of the students in current mixed learning.

The application relates to a heat user abnormal behavior monitoring method and system. The method comprises the following steps: acquiring influence factors affecting heat consumption of a heat user, screening the influence factors, and determining a characteristic vector; preprocessing the characteristic vector to generate a preprocessed characteristic vector; dividing the preprocessed characteristic vector into a training set, a test set and a prediction set; constructing a variation prediction learning machine prediction model by using the training set; inputting the test set into the variation prediction learning machine prediction model to output optimal input layer weights and optimal hidden layer thresholds; inputting the prediction set, the optimal input layer weights and the optimal hidden layer thresholds into the variation prediction learning machine prediction model to determine a predicted value of the heat consumption of the heat user; acquiring an actual value of the heat consumption of the heat user, and determining a current operation state of the heat user according to a deviation between the predicted value of the heat consumption of the heat user and the actual value of the heat consumption of the heat user. The application can effectively improve prediction precision and improve monitoring accuracy.

A processor-implemented method with three-dimensional (3D) occupancy prediction learning includes extracting multi-scale image feature vectors from received two-dimensional (2D) image data, generating a local cluster feature vector by clustering the extracted multi-scale image feature vectors, mapping the local cluster feature vector to a 3D space through an attention operation using a learnable voxel query; decoding a 3D voxel query generated according to the mapping result, and predicting a 3D occupancy state and a semantic class for a space, based on the decoding result.

The present invention provides a method for predicting abnormal learning states of learners in online education, including: preprocessing high-dimensional online education platform log information and learner registration information and encoding them based on a self-supervised learning method to construct learner portrait features; constructing learner state features, then constructing a state feature sequence based on the generation time sequence of state features, and constructing a state feature graph based on the cosine similarity between state features; constructing a long short-term memory-graph attention deep network that is suitable for predicting the degree of learning difficulties in online education, determining the number of network layers, the number of neurons in each layer, and the input and output dimensions; constructing pseudo-labels based on noise labels to iteratively train the network; and using the trained network to predict the learner's abnormal learning state and its degree during the learning stage to be predicted. The present invention uses learner registration information and learner log information to predict the degree of learner state abnormality, providing a reference for teachers to provide targeted guidance and assistance to learners.

The purpose of a disclosed technology is to make employee resignation prediction for assisting in implementing an employee resignation prevention measure at an appropriate timing.　A generation unit (41) generates a feature vector including a plurality of feature quantities from personnel related information sets in a prescribed period of time for each active employee and each resigned employee. A prediction unit (42) predicts an employee resignation probability of an active employee subjected to prediction resigning at each of a plurality of time points in the future by using a feature vector of the active employee subjected to prediction and a prediction model 31 that is obtained by performing learning of feature vectors generated for a plurality of resigned employees as positive examples and feature vectors generated for a plurality of active employees as negative examples and that provides a prediction of, when the feature vector of the active employee is inputted, an employee resignation probability indicating the possibility of the active employee resigning at each of the plurality of time points in the future.

Embodiments are directed to the tracking of data in a generative adversarial network (GAN) model using a distributed ledger system, such as a blockchain. A learning platform implementing a classification model receives, from a third party, a set of data examples generated by a generator model. The set of data examples are processed by the classification model, which outputs a prediction for each data example indicating whether each data example is true or false. The distributed ledger keeps a record of data examples submitted to the learning platform, as well as of predictions determined by the classification model on the learning platform. The learning platform analyzes the records of the distributed ledger, and pairs the records corresponding to the submitted data examples and the generated predictions determined by the classification model, and determines if the predictions were correct. The classification model may then be updated based upon the prediction results.

The invention relates to the technical field of make-up waterproof performance evaluation, and discloses a make-up waterproof performance evaluation method and system based on deep learning, and the method comprises the steps: constructing a microstructure diagram; constructing and training a space-time diagram convolutional network; constructing a hierarchical reinforcement learning framework; constructing a structure sensitivity prediction module; implementing a test state self-adaptive conversion mechanism; the predictive learning ability is realized; the problems that a traditional static test method cannot reflect a real use scene, microscopic and macroscopic view angles are separated, the test efficiency is low, and the prediction capacity is insufficient are solved, and dynamic, efficient and accurate waterproof performance evaluation is achieved through the deep learning technology.

The invention discloses a ship hydrostatic performance prediction large model training, deployment and implementation method based on a large model technology. Training a ship hydrostatic performance prediction learning large model according to real sample data of various types, main scales, tonnages and other real ship completion technical data; on the basis of the trained ship hydrostatic performance prediction learning large model, a model structure is adjusted, and a ship hydrostatic performance prediction large model is constructed for deployment; and inputting ship design elements and variable prediction tasks into the ship hydrostatic performance prediction large model to predict ship hydrostatic performance indexes. According to the method, designers can be replaced to complete ship hydrostatic performance estimation work, the working efficiency and level are improved, and basic service is provided for a ship concept designer (intelligent agent) based on artificial intelligence.

The application provides an online course dropout prediction method based on context-aware timing deep network, and belongs to the technical field of computer-aided intelligent education. A prediction data set containing static features and dynamic behavior features is collected; global statistical features are extracted from the dynamic behavior features, and a dynamic behavior matrix is constructed according to the dynamic behavior features; an online course dropout prediction network is constructed; the online course dropout prediction network is trained by using a training set, a binary cross-entropy loss is used as a loss function, and an L2 regularization term is introduced; data of a learner to be predicted is collected and input into the network for dropout risk prediction. The application realizes learner-course context and timing behavior feature fusion based on early data to improve the dropout risk prediction effect.

The SHAP-XGBoost algorithm-based power system inertia short-term prediction method determines power system short-term inertia prediction input features, and constructs an XGBoost-based short-term inertia prediction learning model; based on the established short-term inertia prediction learning model, an explanatory learning algorithm based on SHAP-XGBoost is proposed, and deep learning of the short-term inertia prediction learning model is realized; a power system short-term inertia prediction framework is constructed, and online deployment and application of the prediction model are realized. The explanatory mechanism of the XGBoost machine learning model is fully utilized, the high accuracy of the power system inertia short-term prediction is ensured, the feature correlation in the model can be mastered at the same time, and it is beneficial for the power grid dispatching department to formulate corresponding control measures.

Techniques are disclosed for a multi-layer micro model analytics framework for analyzing or otherwise processing data. For example, a method comprises building two or more micro models respectively for two or more stages of a given process, wherein each micro model of the two or more micro models comprises a user interaction layer and a predictive learning layer that coordinate to train the micro model based on input to the user interaction layer and data accessible by the predictive learning layer for the corresponding stage of the two or more stages of the given process. The method then assembles the two or more micro models to perform analysis for the given process. In one non-limiting example, the given process is a new product introduction process and each micro model is built and trained to perform analytics for a specific lifecycle stage of the process.

This invention discloses a method and system for judging attention levels during the learning process based on multimodal alignment, belonging to the field of multimodal pattern recognition. The method includes the following steps: acquiring multimodal data: acquiring raw multimodal data of students' educational behaviors and preprocessing the raw multimodal data; feature extraction: constructing a multimodal data representation encoding model corresponding to each modality of data for corresponding representation extraction; model training: after representation fusion, training the attention level judgment model using the fused multimodal representations and the student's attention level label; attention level prediction: inputting the fused multimodal representation to be detected into the trained attention level judgment model, and outputting the predicted student's attention level. This invention fully utilizes the rich information of multimodal data and the diverse characteristics of learners, improving the accuracy and interpretability of representation learning, and enabling better prediction of learners' attention levels during the learning process.

The application relates to an image language fusion prediction learning method for cervical cytology unbalanced data, and relates to the technical field of computers. The method comprises the following steps: acquiring a cervical cytology whole slice image, and cutting the cervical cytology whole slice image into a plurality of patch images; detecting the plurality of patch images to screen a plurality of suspicious positive cell images from the plurality of patch images; acquiring clinical text corresponding to the cervical cytology whole slice image; extracting a plurality of second image features related to the clinical text from first image features of the plurality of suspicious positive cell images; fusing the plurality of second image features and text features of the clinical text to obtain image language fusion features, and predicting a cervical lesion category to which the cervical cytology whole slice image belongs based on the image language fusion features. The method can improve the prediction accuracy of the cervical lesion category.

The invention discloses a course recommendation method based on a large language model. The method comprises the following steps: 1, constructing a learner-course sequence database; 2, constructing a course sequence denoising module for filtering noise courses from the course sequence; 3, constructing a course dependency-enhanced learning interest module, which is used for constructing a course dependency graph and capturing dynamic learning interests; 4, constructing an interest-target comparison learning module for narrowing the potential course selection range of the learner; 5, constructing a prediction module used for obtaining the prediction probability of the course selected by the learner; and 6, training the course recommendation network to obtain an optimal course recommendation model which is used for predicting a course recommendation scheme for the learner to select the next course. According to the method, more accurate modeling of learning interests and targets of learners is facilitated, and the accuracy of course recommendation can be remarkably improved.

The present invention belongs to the field of intelligent learning evaluation technology and discloses a learner cognitive tracking method and system for learning process evaluation. The method collects data and extracts multidimensional features based on the test knowledge cognition tensor TKC to generate the original four-tuple of answer features; sets hyperparameters to process the original four-tuple into a data format of equal length, encodes the answer features of different dimensions separately and fuses and embeds them to obtain a hierarchical embedding representation; constructs a cognitive tracking unit based on four gate structures: Slip Gate, Guess Gate, Level Gate, and Output Gate, and builds a deep cognitive tracking model with a fusion gate structure; tracks and outputs the learner's cognitive level of each knowledge point at different times, and predicts the learner's future answer performance on the knowledge point. The present invention promotes personalized learning for learners and helps model the learner's cognitive level change process in both objective and subjective senses.

The invention relates to the technical field of virtual teaching, and discloses a data processing method and system for virtual physical education teaching, and the method comprises the steps: collecting and preprocessing the exercise data of a learner; analyzing the difference between the action posture of the learner and the standard action; predicting a skill forgetting rule of the learner; generating a personalized review strategy; executing a self-adaptive learning forgetting review closed-loop process; the motion recognition accuracy is improved through multi-source data fusion, skill attenuation is predicted through a personalized forgetting curve, the optimal review strategy is generated through reinforcement learning, the technical problems that in virtual physical education, the motion capture accuracy is insufficient, skill forgetting is difficult to predict, and the review strategy lacks personalization are solved, and the virtual physical education teaching experience is improved. The learning efficiency and the skill retention rate are improved.