57 results about "Predictive learning" patented technology

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 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 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 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.

A server of a health prediction system in one example includes a communication unit for performing wireless communication with a collection terminal for collecting user biometric information measured; an artificial intelligence unit which, when characteristic information about at least one measurement apparatus, generates a prediction model for a specific disease, and trains the prediction model on the basis of the collected user biometric information; and a control unit which, when the prediction model is trained, controls the artificial intelligence unit so that a prediction level related to the specific disease is calculated from the trained prediction model, determines a prediction result for the specific disease on the basis of the calculated prediction level and a preset threshold value, and transmits the determined prediction result to the collection terminal.

The invention discloses an anti-slagging control method for a coal slime circulating fluidized bed, and relates to the field of combustion of the coal slime circulating fluidized bed, which comprises the following steps: S1, data acquisition: monitoring the interior of the CFB by using a high-precision temperature sensor, a pressure sensor, an oxygen analyzer, a microwave material concentration sensor and a laser particle size analyzer; the method comprises the steps of S1, building a prediction model, S2, utilizing training and prediction learning of a vector machine (SVM) algorithm model machine to recognize potential slagging risks in advance, S3, optimizing combustion conditions, S4, carrying out a slag removal program, S5, carrying out data analysis, S6, carrying out equipment maintenance and S7, carrying out multi-system linkage coordination. According to the method, through data acquisition, prediction model establishment, combustion condition optimization, real-time monitoring and dynamic combustion parameter adjustment, the problem of unstable combustion caused by slagging is effectively avoided, it is ensured that the CFB can stably operate under various working conditions, the energy utilization efficiency is improved, and the frequency of slagging events in the CFB operation period can be greatly reduced.

The calculation thinking evaluation method based on the BP neural network and the multi-source data fusion in the application fuses multi-source calculation thinking evaluation data, constructs secondary indexes and primary indexes based on a clustering algorithm, constructs a BP neural network with self-feedback regulation by using the standardized calculation thinking evaluation data as input, is used for fusing the calculation thinking evaluation data collected in various ways, solves the calculation thinking ability evaluation problem under the unlabeled data set, can shield the multi-source heterogeneous characteristics of the evaluation data, and predicts the calculation thinking ability of learners.

This invention relates to the field of bioaerosol analyzer technology and discloses a method for predicting the mean time between failures (MTBF) of bioaerosol analyzers. The method involves integrating operational data from the operational logs of various bioaerosol analyzers; constructing time-series sequences characterizing the operational status of key components within the same bioaerosol analyzer; aligning these time-series sequences according to the actual runtime of their respective bioaerosol analyzers to construct a time-series matrix characterizing the operational status of different bioaerosol analyzers; extracting fault data features and fault timing features from the time-series matrix; calculating the MTBF of bioaerosol analyzers under the same operating environment parameter range based on the time-series matrix; and predicting the MTBF of a target bioaerosol analyzer using a predictive learning model. This invention is beneficial for optimizing the prediction strategy for the MTBF of bioaerosol analyzers.

The invention relates to the technical field of intelligent teaching, and particularly provides a multi-target exercise recommendation method based on learner knowledge state prediction, which specifically comprises the following steps: 1) predicting the knowledge concept mastering degree of a learner by using a knowledge tracking model; 2) predicting the learning progress of the learner by using the time sequence prediction model; 3) screening out a candidate exercise set from the exercise library through the predicted values of the first two models; 4) further optimizing and screening the candidate exercise set through an optimization algorithm to generate an exercise recommendation list; and 5) arranging the exercises in the exercise recommendation list according to the difficulty from small to large through an exercise difficulty function so as to meet the requirement of the learner for learning step by step. According to the invention, accurate prediction of the learning state of the learner is realized, the accuracy, diversity and novelty of the exercise recommendation system are improved, and personalized exercises are recommended to students from the multi-objective perspective.

The invention provides a method, device and equipment for determining a thickening CO2 flooding development scheme, a medium and a product, and relates to the technical field of oil and gas development. The method comprises the steps of obtaining a plurality of initial development schemes of a target oil reservoir, wherein the initial development schemes are obtained by permutation and combination of a plurality of preset values corresponding to a plurality of development characteristic parameters; inputting each initial development scheme into a pre-trained development dynamic prediction learning model to obtain development dynamic data corresponding to each initial development scheme; adopting a preset multi-target mathematical model to calculate a plurality of index values corresponding to each initial development scheme; determining a candidate development scheme set by adopting a multi-objective collaborative optimization algorithm according to the plurality of index values corresponding to each initial development scheme; and determining a thickening CO2 flooding target development scheme of the target oil reservoir based on the preset weight threshold values corresponding to the indexes. According to the method, through fusion of the preset multi-target mathematical model and the multi-target collaborative optimization algorithm, the optimization efficiency and precision of the thickening CO2 flooding development scheme are remarkably improved.