45 results about "Layered learning" patented technology

According to the long-tail human movement prediction method based on adaptive hierarchical learning, coarse-grained semantic grouping is introduced through a hierarchical tree structure based on the Maslow human motivation theory, and the optimization process is rebalanced in a framework-independent mode. Firstly, thinking chain cues are designed based on the Maslow human motivation theory, a hierarchical structure for city customization is constructed by applying a large language model, and knowledge in human mobile data is fully developed through hierarchical learning. Secondly, exploring hierarchical position prediction through Gumbel interference and adaptive weight, so as to fully capture complex space-time semantics; wherein Gumbel interference is used for rebalancing learning of head and tail positions, adaptive weight performs node-level adjustment on head categories and tail categories in each layer, and these components effectively promote exploration of mobility knowledge. The invention further comprises a method adaptive to the advanced mobile prediction algorithm, and the effectiveness and applicability of other methods in long-tail mobile data are improved by embedding the adaptive hierarchical learning loss into other mobile prediction methods.

The invention relates to the field of intelligent manufacturing, and provides a bio-pharmaceutical wastewater cooperative treatment method and system based on a graph neural network. The method comprises the following steps: monitoring a biopharmaceutical process in real time; performing dynamic aggregation on the real-time monitoring data to obtain molecular structure information, microbial community network information and a topological relation among process links in a pharmaceutical process to obtain a dynamic heterogeneous knowledge graph; analyzing interaction rules of different node types in the dynamic heterogeneous knowledge graph by adopting a hierarchical learning mechanism through a multi-modal graph neural network, and predicting migration paths of pollutants and / or pollutant detection indexes in the biopharmaceutical process according to the interaction rules; based on the key index change trend in the migration path, multi-objective optimization decision making is carried out, an optimization control strategy for the biopharmaceutical process is generated, intelligent source tracing and optimization treatment of biopharmaceutical wastewater are achieved, the wastewater treatment efficiency is improved, and the wastewater treatment effect is improved.

The invention relates to the technical field of big data systems, and particularly discloses an AI big data driven hierarchical learning condition diagnosis and intervention system, which comprises a data acquisition module, a multi-dimensional learning condition fusion analysis module, an AI hierarchical diagnosis module, a dynamic intervention strategy generation module, an effect tracking module and a system linkage interface, through technical innovation, complete data, accurate diagnosis, precise intervention and smooth cooperation are realized in a K12 learning condition diagnosis and intervention scene, the problem that teachers are difficult to consider individual differences in large-scale teaching can be solved, the defect that high-quality teachers are insufficient in rural schools can be overcome, and a landing technical scheme is provided for K12 personalized teaching.

The invention discloses a lithium ion battery SOC estimation method, system, medium and equipment based on electrochemical impedance spectroscopy and parameter fine tuning migration, and the method comprises the steps: collecting electrochemical impedance spectroscopy data of a lithium ion battery in different states of charge; screening out input features through correlation analysis; constructing a neural network model comprising an encoder for extracting an abstract representation of the input feature, a decoder for reconstructing the input feature, and a predictor for outputting a state of charge estimate; normalizing the input features of the source domain data, inputting the normalized input features into a neural network model, and carrying out unsupervised or self-supervised pre-training to adjust model parameters of an encoder and a decoder; and after the pre-training is completed, freezing at least a part of residual block layers in the encoder, setting the hierarchical learning rate of the encoder and the predictor, inputting the input features of the target domain data into the frozen model, and carrying out supervised fine tuning training to obtain a trained neural network model for SOC estimation.

The invention discloses a robot motion control method and system based on cerebellum reinforcement learning, and the method comprises the steps: obtaining a current environment state vector, and inputting the current environment state vector to a main strategy channel and a cerebellum compensation channel in parallel; the main strategy channel outputs a basic action based on a long-term task target, and the cerebellum compensation channel outputs a compensation action responding to real-time dynamic through an efficient query mechanism; synthesizing the basic action vector and the compensation action vector into a synthesized action vector driving robot; feeding back latest data after the robot drives the motion action vector, and determining a sensory prediction error based on the latest data; and updating the original parameters of the cerebellum compensation channel based on the sensory prediction error, and optimizing the original strategy parameters of the main strategy channel based on the latest data. According to the invention, by constructing a parallel double-channel architecture, functional decoupling of advanced decision and rapid adaptation is realized, and unification of rapid adaptation and continuous optimization is realized through a double-loop hierarchical learning system.

The invention discloses a mechanical fault diagnosis method based on dynamic wavelet optimization and hierarchical reinforcement learning, and belongs to the technical field of fault diagnosis, the method introduces an adaptive convolution wavelet transform (ACWT) method to perform feature extraction of an original vibration signal, the original vibration signal and an adaptive convolution kernel are subjected to convolution, and a fault diagnosis result is obtained. The wavelet basis can be dynamically adjusted according to the characteristics of the signal, so that the accurate extraction of the time-frequency characteristics of the signal is realized; q learning and hierarchical reinforcement learning are combined to obtain a hierarchical Q learning (HQL) optimization framework, and a Q value is optimized on multiple levels, so that efficient learning and decision making are realized in a structured environment; a hybrid deep learning model combining the HQL with a convolutional neural network (CNN) and a long short-term memory neural network (LSTM) is also designed, so that space and time information can be comprehensively extracted, and meanwhile, a fault diagnosis model is dynamically optimized; the combination of HQL and CNN-LSTM enhances the adaptability of the model to complex fault features, so that the model is more effective in fault diagnosis tasks.

Disclosed herein are embodiments of methods, devices and systems for device fingerprinting and automatic and dynamic software deployment to one or more endpoints on a computer network. The device fingerprinting systems and devices herein are configured to operate with limited data without sitting between network devices and the internet, without monitoring all network traffic, and without limited or no active scanning. The embodiments herein may passively collect information as distributed peers and may perform very limited active scans. In some embodiments, the information is used as an input to a custom hierarchical learning model to fingerprint devices on a network by identifying attributes of the devices such as the operating system family, operating system version, and device role. In some embodiments, a dynamic deployer selection process may be utilized to simply and efficiently deploy software. Some embodiments herein involve end-to-end encryption of credentials in a deployment process.

The invention relates to a high-precision land carbon flux reconstruction method based on four-dimensional space-time deep learning. The method comprises the steps of obtaining a multi-source heterogeneous data set and performing preprocessing; constructing a four-dimensional space-time deep learning model architecture which is sequentially connected with a multi-scale space-time feature extraction module, a space-time feature fusion module, a prediction layer and an uncertainty quantization layer from input to output, and taking the preprocessed multi-source heterogeneous data set as input and a carbon flux reconstruction result as output; constructing a loss function, adopting a hierarchical learning rate strategy and introducing a cosine annealing scheduler, and training a four-dimensional space-time deep learning model architecture based on the preprocessed multi-source heterogeneous data set to obtain a four-dimensional space-time deep learning model; and obtaining a carbon flux data reconstruction set, and inputting the four-dimensional space-time deep learning model to generate a carbon flux reconstruction result. By adopting the method, multi-source heterogeneous data can be effectively integrated, the complex spatial-temporal dynamic characteristics of the carbon flux are captured, and accurate reconstruction of the land carbon flux with high spatial-temporal resolution is realized.

The invention belongs to the technical field of power failure repair, and provides an intelligent generation method and system for a power failure repair scheme, and the method comprises the steps: constructing a multi-modal instruction template, obtaining the basic knowledge of the power industry, constructing a data set, training a general large language model through the data set, and employing a hierarchical learning rate strategy to achieve the intelligent generation of the power failure repair scheme. Obtaining a large language model after fine tuning of domain knowledge; obtaining a power failure first-aid repair case, constructing the power failure first-aid repair case as a sample according to a pre-constructed multi-modal instruction template, and optimizing the parameters of the large language model finely adjusted by the domain knowledge by using the sample by taking semantic loss, physical constraint loss and safety loss as a weighted mixed loss function; obtaining a large language model after multi-modal task fine tuning; and processing the target data by using the large language model subjected to multi-modal task fine tuning to obtain a final power failure repair scheme. According to the invention, the accuracy and intelligence of power fault first-aid repair scheme generation are improved.

The invention provides an edge computing network-oriented energy efficiency service deployment and delivery method and system, and relates to the technical field of edge computing, and the method comprises the steps: extracting multi-dimensional service features, carrying out the coding fusion of the extracted multi-dimensional service features, and generating a global context feature representation; in combination with a system load dynamic prediction result, adaptively adjusting an upper layer decision time scale based on the global context feature representation; under the adjusted upper-layer time scale, executing a service deployment decision and a base station dormancy / activation switching decision; under the fine-grained lower-layer time scale, executing a service delivery decision and a resource allocation decision based on the real-time service request; and coordinating the decision process through a double-time-scale hierarchical learning framework. According to the method provided by the invention, the long-term network overhead can be effectively reduced, the dynamic service request can be effectively responded, the processing capability of the time coupling relationship between deployment and delivery weeks is improved, and the system cost is reduced.

The invention relates to the technical field of groundwater reserve detection, in particular to a GRACE groundwater reserve change downscaling method based on hierarchical learning. The method comprises the following steps: acquiring multi-source satellite data, underground water actual measurement data and geological auxiliary data; constructing a double-branch feature encoder, and respectively extracting shallow underground water features and deep underground water features according to multi-source satellite data and underground water measured data; the shallow underground water features and the deep underground water features are fused, channel splicing is carried out on the fused features and geological auxiliary data with the same resolution, and spatial downscaling is carried out through a multi-layer perceptron (MLP) network; and setting a physical constraint for a high-resolution prediction result output by the multi-layer perceptron MLP network, and obtaining a high-resolution groundwater reserve change diagram through iterative optimization. And by means of hierarchical learning, cross-modal fusion and the like, the GRACE underground water reserve change downscaling precision and the model generalization ability are improved.

The invention relates to an event element information extraction method based on a GPlinker, and belongs to the technical field of artificial intelligence and information extraction. In order to solve the problems of unbalanced sample distribution, poor data labeling quality, data pollution and the like, data is cleaned by adopting a model hyper-parameter search method based on an event argument relationship and trigger word confidence; encoding the event text by using an open-source pre-training model, and performing event trigger word extraction and event classification by using a GPlinker model; a hierarchical learning rate strategy is adopted to carry out model training, and a model fusion method based on voting correction is adopted. According to the method, the event element information extraction capability of the model is enhanced, and the stability and accuracy of the model are improved.

The invention discloses an unmanned aerial vehicle data acquisition hierarchical learning method for a low-altitude wireless network. The method comprises the following steps: constructing a joint optimization problem by taking maximization of the total data volume collected from communication users during the flight of the unmanned aerial vehicle as a target; decomposing the joint optimization problem into a continuous unmanned aerial vehicle trajectory optimization sub-problem and a beam forming optimization sub-problem; for the unmanned aerial vehicle trajectory optimization sub-problem, a hybrid particle swarm optimization framework is adopted to search an optimal path; and for the beamforming optimization sub-problem, a deep learning model is used to obtain an unmanned aerial vehicle beamforming vector corresponding to each specified hovering position, and the input of the deep learning model is an estimated channel gain value between a communication user and an unmanned aerial vehicle. The method can effectively capture the coupling relation between the data collection demand and the motion of the unmanned aerial vehicle through the precise modeling data collection process and trajectory planning, and improves the overall collection efficiency and the resource utilization rate.

The invention discloses a task unloading and resource allocation method based on a mobile edge scene, which comprises the following steps of: a static decision-making stage: modeling a task unloading and resource allocation problem into a mixed integer nonlinear programming model, and aiming at minimizing the weighted sum of overall delay, overall energy consumption and individual delay; in the dynamic decision-making stage, a hierarchical learning framework is adopted, task unloading and resource allocation problems are decomposed, collaborative optimization is carried out through an upper-layer single agent and a lower-layer multi-agent, the upper-layer single agent is used for processing cloud data migration and resource allocation problems, and the lower-layer multi-agent is used for processing cloud data migration and resource allocation problems. And the lower-layer multi-agent is used for performing task unloading and base station selection on multiple users. The task unloading and resource allocation method based on hierarchical reinforcement learning is designed, the high-dimensional complex problem is more efficiently solved, and the task unloading and resource allocation optimization method is suitable for the task unloading and resource allocation optimization problem in a large-scale network.

The application discloses a kind of robot motion control method and system based on cerebellum reinforcement learning, it includes: obtaining current environment state vector, and parallel input to main strategy channel and cerebellum compensation channel;Main strategy channel outputs the basic action based on long-term task target, and cerebellum compensation channel then outputs compensation action by efficient query mechanism to cope with real-time dynamics;The basic action vector and compensation action vector are synthesized into synthesized action vector to drive robot;After robot drive movement action vector, feedback latest data, and determine sensory prediction error based on latest data;Based on sensory prediction error, the original parameters of cerebellum compensation channel are updated, and the original strategy parameters of main strategy channel are optimized based on latest data.The application realizes the functional decoupling of high-level decision and rapid adaptation by constructing parallel double-channel architecture, and the hierarchical learning system of double loop realizes the unity of rapid adaptation and continuous optimization.

This invention proposes a priority-based hierarchical learning method. For N tasks with different priorities, the evaluation function for task i is denoted as Q. i and maintain a prediction model π i For task i with priority, and all higher priority tasks j < i, prepare a predetermined threshold ε. ij and maintain a slack variable λ ij For any i > j, if Q j (π i )+ε ij <Q j (π j ), then represents π. i Performance on task j was better than π. j If the difference is too large, then increase λ. ij Conversely, λ decreases. ij But keep λ ij >0. With λ ij j < i is used as the weight to optimize π i Repeat the previous steps until convergence, and finally obtain π. N This is the desired model. In this invention, constraints are used to describe priorities, solving the problem of traditional multi-objective optimization methods lacking a priority order. Slack variables are introduced to automatically adjust the weights of each optimization objective. The dual variable is adaptively adjusted, resulting in zero duality with the primal problem, making it a convex optimization problem that can be solved quickly by existing solvers.

The invention relates to the field of computer technology application, and provides a flight delay prediction method and device based on space-time multi-modal fusion and a medium, and the method comprises the steps: firstly obtaining flight delay associated data and airport basic information of multiple airports in a preset time window, and then generating a node embedding matrix; and three spatial dependence matrixes are constructed based on route physical connection, a historical cooperative delay rate and a time-space relationship. And carrying out time feature mining on the preprocessed flight data through a time feature extraction module, and fusing node embedding and a multi-source spatial dependency matrix by utilizing a layer-by-layer graph learning module to realize hierarchical learning of spatial features. And finally, integrating time and space features through a multi-modal feature fusion module, and inputting the time and space features into a delay prediction module to obtain a flight delay time prediction result. According to the method, through joint modeling of multi-modal spatial-temporal characteristics, the spatial-temporal propagation rule of flight delay in an airport network can be effectively captured, and the flight delay prediction accuracy is improved.

The application relates to the field of computer technology, and provides a flight delay prediction method, equipment and medium based on space-time multi-modal fusion, which comprises the following steps: firstly, flight delay correlation data and airport basic information of multiple airports within a preset time window are acquired; then, a node embedding matrix is generated; and three space dependence matrices are constructed based on route physical connection, historical collaborative delay rate and space-time relationship. Time feature extraction is performed on the pretreated flight data through a time feature extraction module, and a layer-by-layer graph learning module is used to fuse the node embedding and the multi-source space dependence matrix, so that hierarchical learning of space features is realized. Finally, the time and space features are integrated through a multi-modal feature fusion module, and the flight delay time prediction result is obtained by inputting the delay prediction module. Through joint modeling of the multi-modal space-time features, the method can effectively capture the space-time propagation law of flight delays in the airport network, and improve the flight delay prediction accuracy.

A small sample image classification method based on hierarchical learning genetic programming algorithm, comprising the steps of: 1: constructing a small sample image classification system based on hierarchical learning genetic programming algorithm; 2: the image acquisition module acquires the image data set and divides it into a training set and a test set; 3: the PEGP module acquires the training set and performs image preprocessing and feature extraction operations on it to construct a feature storage table; 4: the DEGP module takes the features in the feature storage table as the terminal input, constructs an integrated solution, and optimizes the final classification effect through an integrated strategy based on individual difference values; 5: use the test set as the input of the image classification solution, output the predicted class label of the test set, and evaluate the performance of the image classification solution according to the actual label of the test set; 6: take the image data to be classified as the input of the image classification solution and output the image classification result. Effect: good classification effect can be achieved under the condition of limited sample quantity.

The application discloses a gesture recognition method based on sparse millimeter wave radar point cloud, and relates to the field of gesture recognition, and comprises the following steps: acquiring millimeter wave radar data of various gesture actions, and constructing a sparse space-time point cloud data set; constructing a space-time graph structure data comprising a space graph and a time graph; performing gesture recognition model training, combining a composite loss function and a hierarchical learning rate strategy to optimize model parameters; the gesture recognition model comprises a graph feature coding module, a space-time feature fusion module and a time series modeling and classification module connected in sequence; millimeter wave radar data of a gesture to be recognized is processed into space-time graph structure data, input into the trained gesture recognition model, and a corresponding gesture category recognition result is output. Through the space-time graph neural network modeling method and the training strategy specially designed for the sparse point cloud, the precision and robustness problem caused by the space-time sparsity of the point cloud in the rapid gesture recognition of the millimeter wave radar is effectively solved.

The invention relates to a partial discharge mode recognition method based on transfer learning. The method comprises the following steps: preprocessing original partial discharge pulse data into a PRPD spectrogram or a time-frequency image; the method comprises the following steps: constructing an EfficientNet-ECA model, embedding an ECA module into the model behind each convolution block of an EfficientNet-B0 network, dynamically carrying out channel dimension re-calibration on a feature map extracted by the EfficientNet-B0 network through the ECA module, and calculating an attention weight so as to re-scale the input feature map; migration training: adopting parameters of an ImageNet data set pre-training model to initialize network parameters of an OfficientNet-ECA model, and performing fine tuning through a hierarchical learning rate strategy; and inputting the PRPD spectrogram or the time-frequency image to be identified into the migrated and trained OfficientNet-ECA model, and outputting the partial discharge type to which the PRPD spectrogram or the time-frequency image belongs. Compared with the prior art, the method has the advantage of efficiently realizing accurate identification of the partial discharge mode.

The invention belongs to the field of behavior detection, and provides a skier falling detection method and system based on hierarchical learning, and the method comprises the steps: collecting multi-source motion data and personnel information of a skier, and carrying out the processing of the data, and obtaining time sequence feature data and personnel feature data; segmenting the time sequence characteristic data by using a window with a preset length, and marking according to a fall identifier to divide the time sequence characteristic data into fall data and non-fall data; screening non-tumble data by adopting a multi-level threshold method based on speed and attitude variation, and retaining data which are judged as high-risk behaviors; merging the falling data, the high-risk behavior data and data generated through data enhancement to form a model training data set; training the hierarchical learning network model by using the model training data set; and the trained hierarchical learning network model is utilized to identify real-time motion data, and skier falling detection is realized. The problems that in the prior art, generalization performance is poor, and individuation characteristics are not achieved are solved.

The application relates to a high-precision land carbon flux reconstruction method based on four-dimensional space-time deep learning, comprising the following steps: acquiring a multi-source heterogeneous data set and performing pretreatment; constructing a four-dimensional space-time deep learning model architecture connected in sequence from input to output, including a multi-scale space-time feature extraction module, a space-time feature fusion module, a prediction layer and an uncertainty quantification layer; taking the pretreated multi-source heterogeneous data set as input and taking a carbon flux reconstruction result as output; constructing a loss function, adopting a hierarchical learning rate strategy and introducing a cosine annealing scheduler, training the four-dimensional space-time deep learning model architecture based on the pretreated multi-source heterogeneous data set, and obtaining a four-dimensional space-time deep learning model; acquiring a carbon flux data reconstruction set and inputting the four-dimensional space-time deep learning model to generate a carbon flux reconstruction result. The method can effectively integrate multi-source heterogeneous data, capture the complex space-time dynamic characteristics of carbon flux, and realize accurate reconstruction of high-spatial and temporal resolution land carbon flux.

The invention provides a power distribution network equipment health state analysis method and device based on a multi-modal large model, and relates to the technical field of power grids. According to the method, multi-modal data such as text data, image data and audio data of power distribution network equipment are comprehensively analyzed, a model fine tuning data set is constructed in combination with the state type of the equipment, and parameter fine tuning is performed on a trained multi-modal large model. In the fine adjustment process, a hierarchical learning rate adjustment and mixed precision training mode is adopted, model parameters are finely adjusted in combination with an evaluation function, and prediction of the multi-modal evaluation model obtained through adjustment is more comprehensive and accurate. Compared with an evaluation model adopting a single data source, the multi-modal evaluation model can comprehensively and accurately analyze the health state of the power distribution network, various data sources do not need to be detected respectively, and the accuracy and efficiency of health state analysis of the power distribution network equipment are improved.

The invention relates to a railway work experience text high-precision entity recognition method and device and electronic equipment, and the method comprises the steps: constructing a special labeling system based on railway human resource domain characteristics, and defining entity tags, including work experience starting time, work experience ending time, units and departments, positions, levels and non-entities; obtaining a railway staff work experience text data set, and carrying out manual labeling according to a labeling system; a named entity recognition model is trained through the annotation data set, the named entity recognition model is based on a model composed of a BERT model, a BiLSTM network and a CRF network, model parameters are finely adjusted through the hierarchical learning rate, and an early stop mechanism is introduced for training; and inputting a to-be-recognized text into the named entity recognition model, and outputting a named entity labeling sequence. The method solves the problems that the railway field lacks a targeted labeling system and an adaptive model, and the existing method is insufficient in recognition accuracy and incomplete in entity coverage in a railway scene.

The application discloses a small sample target detection method based on structured filter learning, and comprises the following steps: preparing a small sample target detection data set and a base class target detection data set, and building a network structure; a backbone feature extraction network uses a CSPDarknet53, and a general feature enhancement module is introduced; a small sample target detection deep neural network based on structured filter learning is built; the network is trained using the base class data set; on the small sample target detection data set, a KSVD algorithm is used to hierarchically learn a dictionary of input feature maps of the structured filter, which is used as a weight parameter of the filter; after one forward propagation process, the initialization of the parameters of the structured filter is completed; the initialized network model is used to continue training on the small sample data set; and a trained neural network is used to complete a detection task. According to the method, a small sample data set can be used to train a target detection deep neural network with good generalization performance.

According to the microwave assembly heat source distribution optimization method based on the improved DNN algorithm, the improved DNN algorithm is applied to microwave assembly heat source distribution optimization, and a new optimization method is provided for microwave assembly heat source distribution optimization. According to the method, an improved DNN prediction model framework is constructed, residual connection, an attention mechanism and parallel CNN branches are fused in the framework, multiple groups of data are substituted into the framework, a hierarchical learning rate strategy is adopted for training, and model parameters are optimized by using a temperature constraint weighted loss function; according to the method, the structure of a model output layer is optimized, a dual-output form of'temperature mean value + prediction variance 'is adopted, and the uncertainty of prediction is quantified while the highest temperature prediction value is output; meanwhile, a composite loss function fusing a mean square error (MSE) and a temperature threshold penalty term is constructed, and for a prediction error exceeding a set safe temperature threshold, 2-3 times of weight is given to the loss function, so that the model preferentially guarantees the prediction precision of a high-temperature scene.

The present invention relates to an interactive recommendation system and method based on item-side fairness enhancement, and belongs to the field of big data artificial intelligence. The system is composed of two layers of intelligent agents, high and low, connected in series, and any intelligent agent is a reinforcement learning network of an Actor-Critic framework. The method adopts a hierarchical learning method, and divides the guidance process into two stages: macro-learning and micro-learning: in the macro-learning stage, the high-level intelligent agent formulates fairness-oriented goals based on multi-step feedback, while considering fairness in terms of items and user satisfaction, to guide the micro-learning stage; in the micro-learning stage, the low-level intelligent agent converts the goal into an executable operation customized for a single user, balances the goal and user satisfaction, and gradually shifts user preferences to the desired goal. The method of the present invention enhances the fairness of items in the recommendation system while minimizing interference with user satisfaction.