89 results about "Predictive learning" patented technology

The invention provides a GPU resource scheduling optimization method and device based on a hybrid model and a medium, and the method comprises the steps: receiving a plurality of new tasks, and obtaining a target resource demand and a target priority of any new task; obtaining various current performance indexes of the GPU resources, and storing the various performance indexes as target time sequence data; calling a CNN-LSTM hybrid model deployed in a production environment, and performing prediction learning on the target time sequence data to obtain a prediction result; selecting the most suitable target scheduling strategy according to the prediction result, various performance indexes and the target priority of any new task; according to the target scheduling strategy and the target resource demand of any new task, GPU resource allocation is carried out on any new task; the task execution condition and the GPU resource use condition are monitored in real time, and a monitoring result is fed back; and according to a monitoring result, dynamically adjusting a currently used target scheduling strategy so as to realize adaptive optimization of GPU resources.

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 learning path recommendation method fusing a hierarchical knowledge graph and deep knowledge tracking, and belongs to the technical field of data analysis, and the method comprises the following steps: constructing a KGDKT model, and constructing a deep knowledge tracking model capable of considering a logic relationship between knowledge points; searching a learning path, and generating all paths pointing from the starting point knowledge point to the target knowledge point based on the hierarchical course knowledge graph and the KGDKT; and optimal path selection: predicting the mastering degrees of the target knowledge points of the learner on different paths through KGDKT, and selecting one path with the best learning effect to be recommended to the learner. The path generated by the method accords with the logical relationship between the knowledge points, can adapt to the dynamically changing knowledge level of the learner, and improves the learning effect of the learner on the target knowledge point.

The invention relates to the technical field of examination and evaluation, in particular to a student examination and evaluation method and system based on an AI big data model. Comprising the following steps: S1, collecting historical examination and evaluation data, establishing an AI big data model, and then dividing the AI big data model into a learning distribution module, a training distribution module, an examination module and a comprehensive scoring module; s2, a learning distribution module analyzes the predicted learning duration of each student, performs learning course generation according to the predicted learning duration in combination with the student basic data, and divides the learning courses into online courses and offline courses; the AI big data model is used for analysis and prediction, students can be evaluated more accurately, a more reasonable learning plan can be made for the students by predicting the learning duration, meanwhile, a teacher can better arrange teaching resources, and the teaching efficiency is improved. The learning efficiency score and the examination score can accurately reflect the knowledge mastering degree and the application ability of the student.

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 invention discloses a predictive learning tracking control method and device for an autonomous vehicle. The method comprises the steps that a monorail bicycle model of the vehicle is established according to the transverse degree of freedom of the vehicle; establishing a vehicle dynamics model according to the monorail bicycle model and the vehicle parameters; an automobile augmentation model is obtained according to the automobile dynamics model, and an automobile input and output relation model is obtained according to the automobile augmentation model; constructing an automobile prediction model according to the automobile augmentation model and a preset control step length, and determining a target function according to the automobile prediction model; constructing an event-triggered data transmission mechanism according to event triggering conditions, and constructing an event-triggered controller updating mechanism according to the automobile input and output relation model; and constructing a trajectory tracking controller according to an iterative learning model predictive control algorithm, the vehicle dynamics model, a data transmission mechanism, a controller updating mechanism and a target function to realize tracking control of a vehicle trajectory.

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 discloses a wind shear risk prediction method and system in combination with a gating cycle and a whale algorithm. The method comprises the following steps: S1, collecting historical wind shear data of all take-off and landing transport aircrafts in a research area for several years according to a time sequence; segmenting into m time slices according to a natural year, sorting out a time slice risk value and time slice data for historical wind shear data according to each time slice, and carrying out associated coding storage; s2, the gating loop unit model takes the time slice risk value as a predicted value and takes the time slice data as data content to carry out prediction learning iteration training; s3, performing model hyper-parameter optimization processing on the gating circulation unit model by adopting a spiral strategy of an improved whale algorithm; and S4, collecting wind shear data of the current time slice, inputting the wind shear data into the gating circulation unit model, and outputting a risk prediction value. The whale optimization algorithm is utilized to optimize the hyper-parameter combination of the gating circulation unit model, and the wind shear risk prediction method shows remarkable performance advantages in a wind shear risk prediction task.

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 discloses a knowledge tracking method and device based on dual-channel difficulty perception. The method comprises the following steps: firstly, obtaining the statistical difficulty and model evaluation difficulty of a target topic; then, difficulty fusion is carried out on the statistical difficulty and the model evaluation difficulty based on a multi-head attention mechanism; and finally, predicting the accuracy of the target question in the current time step through the knowledge tracking model. According to the method, a multi-dimensional difficulty assessment system is constructed, the model assessment difficulty, the statistical difficulty and the knowledge state of the student are combined, the objectivity and reliability of difficulty assessment are improved, and the learning track and performance can be predicted more accurately. By integrating the dynamic change characteristics of the difficulty perception deviation sequence and the difficulty mastering ratio, the dynamic difficulty adaptability index can accurately describe the adaptability and learning characteristics of students facing different difficulty questions, and the knowledge tracking model can better capture the ability improvement and the adaptability change of the students in the learning process.

The invention relates to an interactive prediction learning and training method and device and computer equipment. The method comprises the following steps: acquiring a plurality of original feature points of a to-be-clamped object of a robot, and acquiring respective three-dimensional point cloud data of the plurality of original feature points; based on the respective three-dimensional point cloud data of the plurality of original feature points, screening out geometric feature points representing the geometric shape of the to-be-clamped object from the plurality of original feature points; determining the associated features of each geometric feature point, and predicting the clamping probability of the robot for each geometric feature point based on the associated features of each geometric feature point; and based on the clamping probability, a target clamping point is screened out from the geometric feature points, and the target clamping point is used for the robot to clamp the to-be-clamped object. By adopting the method, the interaction success probability between the robot and the to-be-clamped object can be improved.

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 invention provides a meta-intelligent model in an intelligent information network and an implementation method, and belongs to the technical field of data processing. The meta-intelligent model comprises a multi-domain sensing module, a learning analysis module, a network knowledge module and an optimization decision module; the multi-domain sensing module senses a multi-domain environment inside and outside the network to obtain multi-domain characteristic parameters, and pre-processes the multi-domain characteristic parameters; the learning analysis module performs data analysis and fusion on the preprocessed multi-domain feature parameters by adopting a machine learning mode, so that a trained multi-domain environment prediction learning model is obtained, and the multi-domain feature information and the trained multi-domain environment prediction learning model are stored in the network knowledge module; the network knowledge module stores network multi-dimensional polymorphic data information with the same representation form, including multi-domain situation information and a trained multi-domain environment prediction learning model; and the optimization decision-making module is used for matching a comprehensive decision-making scheme matched with the current network multi-domain environment from the network knowledge module.

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 provides a depth domain reservoir parameter prediction method and system, a storage medium and equipment, and belongs to the field of geophysical interpretation. According to the method, reservoir parameter prediction learning models are constructed, optimized sensitive body attribute data are used as input, seismic facies are used as constraints, reservoir parameter prediction is carried out by using corresponding learning models under different waveform characteristics, and a prediction result of a three-dimensional space depth domain parameter body is obtained. According to the method, the problem that depth domain wavelets are difficult to solve in a conventional depth domain parameter prediction method is avoided, the learning model is established through the optimized reservoir parameter sensitive attributes, direct parameter prediction is realized under the guidance of geologic features, and the parameter prediction precision is effectively improved.

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.