1911 results about "Multi-task learning" patented technology

The invention provides a method and system for analyzing defects in wafer manufacturing based on big data, and relates to the technical field of big data, and the method comprises the steps: collecting technological parameter information, defect scanning images and manufacturing process data information, and carrying out multi-scale feature pyramid network processing and fusion to obtain fusion features; the fusion features are analyzed through a deep learning network, the deep learning network adopts a multi-task learning framework to complete a defect classification task, a defect segmentation task and a defect evolution prediction task at the same time, consistency loss based on graph matching is introduced, and a defect type recognition result and a feature map of defect space distribution are obtained; based on the characteristic spectrum, when it is detected that the process parameters are abnormal, analyzing historical defect data by using a graph neural network containing a time sequence attention mechanism; technological parameter constraint conditions are set in combination with an expert knowledge base, and technological parameters are adaptively adjusted through a reinforcement learning method. According to the invention, the accuracy of defect detection and classification is improved.

The invention provides a home abnormal state signal detection method and system based on multi-modal sensing, and relates to the technical field of detection, and the method comprises the steps: collecting human body motion, acoustics and environment parameters through a millimeter wave radar, an acoustic sensor and an environment sensor, inputting the parameters into a deep fusion network, a spatio-temporal attention mechanism and a multi-scale convolutional neural network are used to extract a spatio-temporal feature sequence, and joint probability features are formed in combination with an acoustic feature spectrum analysis result. And inputting the joint probability features into a time sequence knowledge graph, and obtaining scene adaptive features after processing by a graph attention network and a double-flow auto-encoder network. And finally, the multi-task learning network is combined with the risk propagation neural network and the recursive neural network to carry out anomaly prediction and risk level evaluation, and is combined with historical early warning information to output an early warning strategy. According to the invention, multi-modal data can be effectively fused, the accuracy and reliability of home abnormal state detection are improved, and a more accurate risk assessment and early warning strategy is provided.

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

The invention discloses a photovoltaic module fault diagnosis system and method based on deep learning, which significantly improves the accuracy and comprehensiveness of fault diagnosis through multi-modal data fusion and advanced deep learning technology, and improves the system operation efficiency through the application of an adaptive sampling strategy and edge calculation. The system can simultaneously execute fault classification, severity evaluation and life prediction through a multi-task learning framework, the diagnosis depth and breadth are enhanced, a predictive maintenance decision-making mechanism based on reinforcement learning optimizes a maintenance strategy, the service life of equipment is prolonged, the operation and maintenance cost is reduced, and the maintenance efficiency is improved. The distributed learning framework ensures data privacy and continuous optimization of the model, improves the overall power generation efficiency and economic benefits of the photovoltaic power station, and provides powerful support for large-scale application of renewable energy sources.

The invention discloses a rolled metal surface defect automatic labeling method based on a multi-task self-adaptive model, which comprises the following steps: S1, collecting a rolled metal surface image, preprocessing and enhancing the rolled metal surface image, performing data labeling, and constructing a rolled metal surface defect data set; s2, constructing a multi-task adaptive model, and training the multi-task adaptive model by using the data set to obtain an automatic defect labeling model; and S3, inputting a to-be-labeled metal surface image into the defect automatic labeling model, and outputting a surface defect labeling result by executing the target detection task. According to the method, a multi-task learning framework is combined, joint training of target detection and instance segmentation tasks of metal surface defects in the rolling technology is achieved, the target detection task is focused in the reasoning stage, the labeling efficiency is improved, the labor cost is remarkably reduced, and the labeling efficiency is improved. The method can be widely applied to industrial defect detection and data automatic labeling scenes of rolling process metal, and is suitable for defect image automatic labeling tasks in a complex industrial environment.

The invention relates to the field of environment monitoring, and discloses an efficient and accurate pollution source monitoring quality control system and method, and the system comprises a multi-modal data collection module, a high-dimensional data modeling module, a time-space correlation analysis module, a time sequence prediction module, a multi-task learning module and an anomaly detection feedback module. The method comprises the following steps: collecting data of pollutants such as air, water quality and noise, performing high-dimensional modeling and optimization on the data, and capturing space-time propagation paths of the pollutants among different sensor nodes; the system predicts the short-term change and the long-term trend of the pollutant concentration in real time; the multi-task learning model is used for predicting concentration changes of multiple pollutants at the same time, multi-scale decomposition is carried out on a time sequence through wavelet transform, abnormal changes in data are detected, and a feedback control strategy is generated. According to the invention, the precision and real-time performance of pollution source monitoring are improved, the dynamic change of multiple pollutants can be effectively handled, and the high efficiency and reliability of environment quality monitoring are ensured.

The invention provides a textile defect positioning method, which comprises the following steps: according to an extracted defect feature map, fusing feature maps of different scales, extracting multi-scale defect features, and comprehensively depicting space distribution and combination information of defects; aiming at the condition that multiple flaws exist at the same time, a multi-task learning model is constructed, flaw detection, classification and positioning tasks are carried out at the same time, and a back propagation algorithm is adopted to optimize the multi-task learning model; positioning the defect area by adopting an image segmentation algorithm, obtaining the contour of the defect area according to the positioning result of the defect area and by adopting a Canny edge detection algorithm, and calculating the area and position information of the defect; and based on an evaluation result, a dynamic threshold adjustment algorithm is adopted, a defect judgment threshold is adjusted according to a production batch and a product type, and a threshold parameter is dynamically updated by analyzing historical data and a current production condition.

The present invention provides a method and intelligent agent for data collection and drug safety signal mining for PMS, which relates to the field of drug monitoring technology, including standardizing PMS data using a medical text pre-training model, and integrating drug molecular characteristics and clinical medication information to construct a multi-layer drug knowledge graph. A heterogeneous graph neural network with a time series feature extraction function is used to represent the graph, and the drug action mechanism rules are integrated into the training process to obtain a node vector representation of multi-dimensional information fusion. Based on the node vector representation, a multi-task learning framework is constructed to simultaneously complete the drug-adverse reaction association probability prediction and risk level prediction, and combine time series analysis to detect dynamic change characteristics to generate candidate signals. Finally, a hierarchical evaluation system is constructed to verify the candidate signal, and its reliability is confirmed in combination with clinical data, the target signal is output, the warning level is determined, and differentiated monitoring cycles and trigger thresholds are set to perform real-time risk warnings.

The invention discloses a method for correcting dialogue intention information of old people based on a deep neural network, and particularly relates to the technical field of dialogue optimization, and the method comprises the steps: collecting and cleaning multi-topic multi-round dialogue data, marking multi-round dialogue corpora, and unifying the format; performing feature extraction on the labeled corpus by using the multi-intention recognition sub-model to generate a multi-intention representation vector capable of representing each intention; screening context content related to the target intention in combination with a hierarchical attention mechanism of user context dynamic weight adjustment; processing context content related to the target intention through gating operation based on multiple rounds of interaction history, and reserving semantic fragments with reference value; performing parallel analysis on the reference semantic fragments based on a multi-task learning structure of a deep neural network, performing classification processing on different intentions, and calculating scores; and executing multi-intention fusion correction operation according to classification and scoring results, and outputting intention information capable of distinguishing each intention.

The invention discloses a smart power grid fault early warning and diagnosis system, and relates to the technical field of smart power grids. Comprising a multi-modal data acquisition and fusion module, a deep reinforcement learning early warning module, a fault diagnosis module based on a graph neural network, an adaptive diagnosis module integrated with multi-task learning, an edge calculation and cloud collaboration module and a system test and verification module, and comprises the following steps: step 1, acquiring multi-source data through a power sensor, an intelligent electric meter and a meteorological station; dimension reduction and feature fusion are carried out on the data by using a depth auto-encoder model; 2, importing the collected data into a deep reinforcement learning early warning module, and dynamically adjusting an early warning strategy through interaction with a power grid operation environment; 3, a fault diagnosis module based on the graph neural network analyzes the topological structure of the power grid and identifies a fault position; 4, learning a model and processing a fault type; 5, edge calculation and cloud cooperative work are combined; and 6, testing, verifying and optimizing the model by the system testing and verifying module.

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

The invention provides a multi-heat-source multi-material additive manufacturing optimization method and system based on machine learning, and relates to the field of additive manufacturing, and the method comprises the following steps: collecting process parameters, material physical properties and target response data in real time, and constructing a multi-source data set; on the basis, a multi-task learning model is trained, common features are extracted, target responses are synchronously predicted, a proxy target function is constructed through predicted values, if errors exceed a threshold value, experimental verification is conducted, and new data is fed back to update the model. And applying the weight coefficient to a multi-objective evolutionary algorithm, iteratively screening a Pareto candidate solution set, verifying a high-uncertainty solution, and updating the model to error convergence. And selecting and deploying an optimal parameter combination by using an entropy weight-TOPSIS method, and embedding an online learning mechanism to form closed-loop optimization. According to the method, multi-task learning and a self-adaptive evolutionary algorithm are combined, and efficient optimization of multi-heat-source multi-material additive manufacturing process parameters can be achieved.

The invention belongs to the technical field of intelligent perception and mode recognition, and particularly relates to a human body behavior recognition method based on millimeter wave radar and multi-task learning. The method comprises the steps that millimeter wave radar point cloud data and RGB video streams are synchronously collected, and a five-dimensional point cloud scene is generated through three-dimensional analysis and dynamic target extraction; a hierarchical Point Transform network is constructed to extract radar space-time features, and human body key point features are generated by using visual auxiliary attitude estimation; radar and key point features are dynamically fused through a cross-modal attention mechanism, a multi-task joint optimization strategy is combined, behavior classification serves as a main task, attitude estimation serves as an auxiliary task, and a behavior recognition result is output. According to the method, through multi-task cooperation and cross-modal feature interaction, on the premise of ensuring privacy security, the accuracy and robustness of human behavior recognition in a complex scene are remarkably improved, and efficient and reliable technical support is provided for the fields of intelligent monitoring, human-computer interaction and the like.

The invention discloses a water supply network sound leakage monitoring method based on multi-modal data fusion and multi-task learning, and the method comprises the steps: inputting an original sound signal, and carrying out the labeling of a label; converting the original sound signal to obtain a corresponding logarithmic Mel spectrogram; performing feature extraction on the original sound signal to construct a time-frequency feature set; forming a data set by the original sound signal, the logarithmic Mel spectrogram, the label and the time-frequency feature set; constructing a multistage task prediction network based on a residual convolutional neural network framework; performing multi-task training on the multi-level task prediction network by using the data set to obtain a water supply network sound leakage monitoring model; and inputting the original sound signals of the collection points into the water supply network sound leakage monitoring model to obtain a prediction result. The invention further provides a water supply network sound leakage monitoring device. According to the method provided by the invention, the leakage detection aspect is promoted to more comprehensive sound leakage monitoring application through a data-driven acoustic detection technology.

The invention relates to the technical field of distributed resource management and optimization, in particular to a distributed resource life cycle evaluation and dynamic updating method, which comprises the following steps: data acquisition: collecting multi-modal data of distributed resources, including resource state data, use behavior data, environment data and historical maintenance data; feature extraction: carrying out feature engineering processing on the multi-modal data, and extracting key indexes such as a resource health state, a degradation rate and environmental sensitivity; building a life cycle evaluation model, building the model by using the time sequence data of the resources based on a multi-task learning framework, and predicting the health state and the residual life of the resources; and optimizing a dynamic updating strategy, and monitoring and dynamically adjusting in real time. The method provided by the invention overcomes the problems of insufficient life cycle dynamics and rigid updating strategy in a traditional method, has the characteristics of high efficiency, intelligence and accuracy, and is suitable for intelligent management and optimization of new energy equipment, industrial assets and other distributed resources.

The invention belongs to the technical field of intelligent medical treatment, and particularly relates to a traditional Chinese medicine tongue diagnosis and prescription recommendation system based on multi-modal feature fusion. The system comprises a data preprocessing module used for preprocessing tongue picture image data and text data; the feature extraction module is used for extracting image features and text features from the preprocessed tongue picture image data and text data; the multi-modal feature fusion module is used for effectively fusing the extracted image features and text features by adopting a self-attention mechanism as a core fusion strategy to generate joint features; the multi-task learning module is used for completing parallel learning of a plurality of tasks on the basis of the representation of the joint features; and the model training and optimization module is used for designing a training method, a loss function and an optimization algorithm, so that the targets of disease name judgment, symptom judgment and prescription conditioning recommendation are completed after the multi-task neural network model processes the joint features.

A method based on a multi-task learning model comprises the following steps: acquiring and processing histopathological image data of gastritis through a data preparation and preprocessing step; feature extraction is performed by using a self-supervised learning pre-trained model, and image blocks are coded into high-dimensional feature vectors; and constructing a multi-task learning model, learning feature representation through a full connection layer module and an attention layer module, and outputting a classification result of each task. And carrying out model training and optimization by using an optimizer, adding multitask loss through a loss function, and dynamically adjusting the model performance. The trained model can automatically detect and grade gastritis, atrophy, acute activity, intestinal metaplasia and other pathological indexes, outputs a standardized evaluation result, and assists in pathological diagnosis. According to the method, a multi-task deep learning framework based on self-supervised learning pre-training is constructed, a traditional single-task modeling mode is broken through, deep learning framework design is driven through pathological index association, and accuracy and clinical practicability of non-neoplastic disease assessment of the stomach are remarkably improved.

The invention belongs to the technical field of medical image processing, and discloses a CT image segmentation and classification system based on segmentation feature guidance, and the specific technical scheme is as follows: the system adopts a shared encoder to extract general features, realizes collaborative optimization of segmentation and classification through a double decoding path, adopts a partial decoder in a segmentation path, and adopts a partial decoder in the segmentation path; in combination with a local feature attention module, through multi-scale feature fusion and a boundary perception mechanism, the region consistency of a global segmentation map is gradually optimized, edge detail information is supplemented, and a classification path generates a space attention weight through a segmentation feature guide module by utilizing segmentation prediction; the classification network is guided to focus on a focus area and suppress background interference, a self-adaptive loss weighting strategy based on multi-task learning is adopted, the double-task gradient flow is dynamically balanced, and the gradient competition problem in the multi-task learning is effectively relieved.

The invention belongs to the technical field of knowledge maps, and discloses a traditional Chinese medicine knowledge map fusion system based on semantic alignment. Comprising a multi-modal semantic embedding and entity alignment layer, a structure embedding and attribute fusion service presentation layer, an enhanced TransR model and knowledge graph construction layer, an intelligent rule engine and conflict resolution layer, an end-to-end automatic assembly line layer, a multi-task learning framework and incremental dynamic updating. Through innovative combination of a multi-level semantic alignment technology and an intelligent traditional Chinese medicine rule engine, end-to-end automatic fusion of traditional Chinese medicine knowledge maps is realized, and heterogeneous knowledge maps from a plurality of data sources are automatically converted into a standardized unified knowledge map through an assembly line. Compared with a traditional character string matching method, the accuracy rate of the method is about half, and the alignment accuracy rate is improved to 80-90%. More importantly, under the same data condition, the fusion time is greatly shortened, and the incremental updating efficiency is remarkably improved.

The invention discloses an intelligent fusion classification and grading advanced early warning method for major disasters of a coal mine. Firstly, coal mine safety monitoring data are collected to construct a data set; secondly, an Angle-Transform model and an E2GAN model are trained, abnormal values and missing values in the data are recognized and interpolated, and data preprocessing is achieved; and thirdly, a new multi-index fusion prediction model is introduced in combination with a BiLSTM and MMOE multi-task learning network, and the future trend of each index can be synchronously predicted after training optimization. And then, improving an AdaTT network to realize a multi-disaster risk fusion early warning model, and optimizing training times and an early warning threshold value by using an F1 index. And inputting real-time monitoring and fusion prediction data into the optimized early warning model to obtain the risk probability of each disaster. And finally, in combination with the optimal early warning threshold and the risk probability, providing a multi-disaster grading early warning method, and realizing grading identification of different disaster risks. According to the method, disaster risks such as gas, fire disasters, dust and roofs can be effectively warned, risk information is provided in advance, and the method is of great significance to advanced management and control of multiple disaster risks of a coal mine.

The invention discloses an intestinal cancer immune response graph neural network prediction method, a medium and equipment, and the method comprises the steps: collecting pathological image information, immunodetection information and basic clinical information, extracting a tissue space distribution characteristic spectrum through a deep convolutional network, and constructing a graph neural network model in combination with an immunomarker expression characteristic matrix; spatial interaction characteristics of a tumor microenvironment are modeled by adopting a graph attention mechanism, finally a treatment response probability, an optimal treatment opportunity and an adverse reaction risk are predicted through a multi-task learning framework, and a clinical decision report containing a prediction response curve, a risk early warning threshold and a treatment time window suggestion is output. According to the method, through multi-modal data fusion and spatial interaction modeling, accurate prediction of intestinal cancer immunotherapy response is realized, and a more comprehensive reference basis is provided for clinical decision making.

The invention relates to the technical field of power grid monitoring, in particular to a power operation risk identification method, system and device based on multi-modal data fusion and a storage medium. In order to solve the problems of multi-modal data splitting, topological constraint missing and the like in traditional power disturbance analysis, an improved BERT model is constructed, and electrical signal time-frequency features and text semantic information are mapped to a unified vector space through a multi-modal embedding mechanism; a time sequence attention mechanism is adopted to establish a time dependency relationship between signals and texts, and a graph attention network is combined to realize risk propagation modeling under power grid topology constraints; and collaborative optimization of disturbance classification, risk prediction and trend analysis is carried out through a multi-task learning framework. The technical problems that heterogeneous data fusion is difficult and risk identification precision is insufficient are effectively solved, accurate identification and intelligent early warning of electric power operation risks are achieved, and the safe operation level of a power grid is improved.

The invention provides an airfoil flow field prediction method based on multi-task learning, and the method comprises the steps: firstly building an airfoil flow field prediction model based on multi-task learning, which comprises an encoder, a backbone network and a multi-head decoder; then training the established model by using an airfoil flow field data set and adopting an optimization strategy based on multi-task learning; and finally, performing geometric parameterization and grid generation processing on an airfoil which actually needs flow field prediction to obtain standardized airfoil flow field data, and inputting the airfoil flow field data into the trained airfoil flow field prediction model for prediction to obtain full flow field physical quantity distribution and lift-drag coefficient information. According to the method, a multi-task loss optimization strategy is used in the prediction model training process, so that the problem of conflict between airfoil profile surface loss and volume loss optimization can be effectively solved, and then the construction of an airfoil profile proxy model and the accurate prediction of flow field information and aerodynamic parameters are realized.

The invention discloses a radar signal detection method and system based on multi-task learning of a time-frequency domain feature fusion network, and the method comprises the following steps: building a radar signal mathematical model, and obtaining various types of radar signal sequences through the mathematical model; processing the radar signal sequence to obtain a radar signal I sequence and a radar signal Q sequence; marking the radar signal I sequence and the radar signal Q sequence, and constructing a training data set; constructing a time-frequency domain feature fusion network, and training the time-frequency domain feature fusion network by using the training data set to obtain a signal detection and recognition model; and obtaining a to-be-detected radar signal, and completing radar signal detection by using the signal detection identification model. According to the method, combined modeling analysis is carried out on various radar signals, local features and global features of the signals are extracted by adopting a time-frequency domain feature fusion network from the angle of signal I / Q, and the influence of noise on signal identification is reduced by fusing the features of the frequency domain.

The invention relates to the technical field of phishing detection, in particular to a phishing detection method and system based on multi-modal fusion features, and the method comprises the following steps: S1, constructing a phishing sample based on multi-modal simulation and data generation; according to the method, the multi-modal simulation and data generation technology is utilized, phishing samples with high diversity and authenticity are created, a solid foundation is provided for the model, diversified interactive behavior data are generated through the user behavior simulator designed by the reinforcement learning model, the system can simulate and understand behaviors of different types of users, and the user experience is improved. The system improves the detection precision and efficiency through a non-traditionally designed multi-mode fusion deep detection model in combination with a multi-branch neural network and multi-task learning, realizes the real-time adaptive detection and defense capability through adversarial training and federated learning, greatly improves the accuracy and robustness of phishing detection, and improves the detection accuracy and efficiency. And comprehensive protection is provided for network security.

The invention discloses a nursing system and a monitoring method thereof, and the method comprises the following steps: collecting the physiological data of a patient through an intelligent sensor, forming a dynamic health data flow, carrying out the analysis in combination with historical health data, extracting key health features based on a time sequence attention mechanism, and carrying out the monitoring of the key health features. A machine learning model is adopted to predict the health state of a patient, a health mode between the patient and a similar patient is calculated to generate a personalized nursing scheme, and combined optimization is performed on health state prediction and a nursing recommendation scheme in combination with a time sequence collaborative multi-task learning framework, so that dynamic adjustment of the nursing scheme is realized; the system also includes an adaptive care intervention module that optimizes a care intervention strategy according to the latest health data of the patient and continuously iteratively optimizes the care scheme based on the care execution feedback. According to the invention, a complete nursing closed loop from data acquisition, analysis, prediction and recommendation to optimization and intervention is constructed, and accurate monitoring and personalized nursing management of the health state of the patient are realized.

The invention discloses an air pollution risk prediction and early warning method based on multi-task learning, and the method comprises the following steps: S1, collecting multi-source air pollution monitoring data, and carrying out the preprocessing of the data, and generating preprocessing data; s2, constructing a multi-task learning model, and generating a preliminary prediction result through the multi-task learning model based on an improved multi-head attention mechanism; s3, optimizing parameters of a shared feature extraction layer and a task specific prediction layer; s4, optimizing the preliminary prediction result, and generating an optimized prediction result; s5, generating a risk level, and generating a risk assessment report by integrating the risk information of time and space dimensions; and S6, designing a trigger condition according to the risk level, defining a joint early warning rule, judging and triggering early warning through a dynamic threshold value, and generating early warning information. According to the method, multi-task learning and an improved multi-head attention mechanism are combined, multi-pollutant concentration prediction and risk early warning are realized, and the method has the advantages of high timeliness, high prediction precision and comprehensive risk assessment.

The invention relates to an intelligent computing resource allocation method based on reinforcement learning. According to the technical scheme, the method comprises the steps that monitoring data from different sources including hardware monitoring, software logs and network bandwidths are fused, and a multi-dimensional time sequence state space is formed; carrying out dimensionality reduction and de-noising processing on the multi-modal data by using a depth auto-encoder; multi-task learning MTL is introduced into time sequence modeling, and resource requirements and state evolution of multiple tasks are predicted at the same time; generating a plurality of predictive resource scheduling strategies by using a GAN (Generative Adversarial Network), and dynamically selecting a strategy scheme when a load changes; each strategy is realized by an independent sub-network, and part of core knowledge is shared; through a strategy evolution mechanism, according to a historical feedback optimization strategy combination including task completion time and resource consumption, in a heterogeneous resource environment including a plurality of cloud computing platforms and edge computing nodes, based on difference of resource types, fine-grained scheduling of strategies is carried out; and scheduling the decision by using a distributed Q-learning mechanism in the reinforcement learning model.

The invention provides a personalized fitness system and method based on multi-modal data and an adaptive large model. According to the method, multi-modal data are integrated, and heterogeneous data collaborative analysis is realized by using a space-time alignment algorithm and a confidence coefficient weighting mechanism. A multi-task learning framework is adopted, physiological prediction, exercise risk early warning and psychological incentive strategy generation tasks are synchronously processed, a user stage target is adapted through a dynamic attention mechanism, and federal learning and transfer learning technologies are combined. A nonlinear periodic planning engine based on reinforcement learning dynamically adjusts training load and action difficulty according to real-time biomechanical simulation results and recovery state evaluation, and meanwhile, a lightweight model compression technology and end-side reverse dynamics calculation are adopted. Based on the scheme, the exercise performance prediction precision and the early warning timeliness are improved, and the exercise loss risk caused by overtraining is reduced; the cold start data bottleneck is broken through; low-delay virtual coach interaction and privacy protection are realized; and dynamically adjusting the training intensity and the incentive strategy.

The invention belongs to the field of voice noise reduction processing, and discloses an intelligent communication equipment voice noise reduction method and system based on artificial intelligence. Comprising intelligent communication equipment, and a microphone array is deployed on the intelligent communication equipment to record real-time noisy voice data of a user; inputting the recorded noisy voice data into the edge layer, and obtaining the voice data after noise reduction through real-time processing; the method comprises the following steps: pre-constructing a dialect voice database, adding a scene evolution layer, a voice change layer and a noise mixing layer in a conditional adversarial network cGAN to expand the dialect voice database, forming a data sample, and performing three-dimensional marking on each piece of voice data in the data sample; an input, output and multi-task learning mechanism is designed based on an end-to-end voice enhancement model, pre-training is carried out by using a data sample, and a shared encoder is arranged in the multi-task learning mechanism and is used for combining a task R1, a task R2 and a task R3 to realize voice noise reduction of intelligent communication equipment.