195 results about "Adaptive learning rate" patented technology

The invention relates to a supporting structure stress state monitoring method based on artificial intelligence, and belongs to the technical field of artificial intelligence and data processing. The method comprises the following steps: acquiring and marking strain data of a supporting structure; after abnormal values are removed, normalizing the multi-sensor data to generate a normalized strain sequence; a state monitoring model is constructed, a deep time sequence neural network architecture is adopted, and the state monitoring model comprises an input layer, a self-adaptive wavelet attention feature mapping layer, a time domain gating convolution module, a global maximum pooling layer, a dynamic feature importance reweighting layer and a full-connection classification layer; inputting a normalized data training model; optimizing a loss function through a quantile interval adaptive learning rate and a momentum updating strategy; after real-time monitoring data is processed, inputting the data into the training model according to time window slices, outputting four types of probabilities, and taking the maximum value as a prediction state; and if a plurality of continuous windows are early-warning and dangerous, triggering the terminal to give an alarm. The accuracy of monitoring the stress state of the supporting structure can be improved.

The invention discloses a complex terrain self-adaptive motion control method and system for a double-wheel-foot robot, and relates to the technical field of robot motion control. The method comprises the following steps: constructing a complex terrain model comprising a robot model, obstacles and environmental constraints; a motion decision controller is constructed, a reinforcement learning model is utilized to perform multi-target collaborative optimization training on the motion decision controller by adopting an asymmetric training strategy for different complex terrain models, and risk constraints are introduced during updating of the asymmetric training strategy to constrain behaviors of the strategy; strategy gradient back propagation is carried out according to a training result, and an asymmetric training strategy is optimized by using a self-adaptive learning rate adjustment method based on performance feedback. According to the method, an asymmetric training strategy and a segmented training mechanism are designed in the reinforcement learning process, and the efficient, robust and self-adaptive motion control problem of the double-wheel-foot robot in various complex terrain environments is solved.

The invention discloses an energy storage system health state assessment method and system, mainly relates to the technical field of health state assessment, and is used for solving the problems that in the prior art, key degradation signals on a frequency domain cannot be identified, an entropy change rule of non-stable change cannot be reflected, and a deep neural network is insufficient in perception of a degradation region. Comprising the following steps: acquiring original monitoring data of the energy storage system in real time, calculating to obtain normalized data after time domain and frequency domain processing, and calculating characteristics of the enhanced monitoring data; constructing a residual attention deep neural network, and autonomously adjusting the current adaptive learning rate; dynamic focus loss is obtained through calculation, and a total loss function is calculated; obtaining a trained residual attention deep neural network; and acquiring current monitoring data, calculating features of the enhanced current monitoring data, and further performing energy storage system health state assessment by using the trained residual attention deep neural network.

The invention belongs to the technical field of asynchronous federated learning, and discloses a data isomerism-oriented knowledge alignment asynchronous federated learning method. According to the method, a data quality perception aggregation strategy is introduced, and a knowledge distillation mechanism based on the old degree is combined, so that a global model is subjected to balanced training on heterogeneous data of different devices, and the generalization ability of the model is improved. Meanwhile, a self-adaptive learning rate adjustment mechanism based on aggregation frequency and weight is designed, and it is ensured that contribution of different devices to the global model is more fair. According to the method, the training deviation in asynchronous federated learning is effectively relieved, the accuracy and stability of a global model are improved, and the method has a considerable application value for a real federated environment.

The invention provides a network protocol fuzz testing method based on strategy gradient reinforcement learning, and relates to the technical field of fuzz testing, and the method comprises the steps: building a test corpus through obtaining a to-be-tested protocol data packet format; establishing an action space containing a mutation operation set and a state space of a benchmark test data packet feature vector; determining a reward value based on the test result; calculating a strategy gradient by using a reward value, and updating strategy network parameters in combination with an adaptive learning rate; and performing fuzzy testing by using the trained model. According to the invention, the test efficiency is improved, the variation strategy selection is optimized, and the effectiveness of the network protocol security test is enhanced.

The invention discloses a packaging printed matter printing quality detection method. The method comprises the following steps: S1, image acquisition; s2, image preprocessing; s3, feature extraction; s4, defect identification and classification; and S5, quality evaluation and report generation. According to the method, single detection limitation is broken through, spectrum, audio, touch and image multi-modal data are synchronously acquired, deep fusion is performed by means of a multi-modal fusion algorithm, feature extraction is optimized for different package types, detection comprehensiveness and accuracy are greatly improved, and model construction is more accurate. Structures and training strategies are customized for conventional paper cigarette cases and special-shaped paper cigarette cases, the adaptive learning rate and data enhancement are adopted, the ability of model generalization and complex feature learning is enhanced, in the defect recognition link, the process is optimized, and through multi-mode cooperation, various defects can be accurately positioned, new and special defect types can be recognized, and the defect recognition efficiency is improved. And the defect classification dimension is enriched.

The invention provides an electromechanical equipment fault prediction method and system based on multi-source information fusion, and the method comprises the steps: collecting operation data, including vibration data, temperature data and current data, during the operation of electromechanical equipment; performing feature extraction on the operation data based on a principal component analysis algorithm to obtain a fusion feature vector; inputting the fusion feature vector into a fault prediction model based on a deep belief network, and outputting a prediction result; wherein the deep belief network adopts a small-batch stochastic gradient descent algorithm combined with an adaptive learning rate adjustment strategy during training; and judging whether the electromechanical equipment has a fault hidden danger or not according to the prediction result. According to the method, the relevance between different types of data is mined, and the defect of low prediction precision is overcome.

The invention discloses a generative adversarial network architecture search method and system and an image generation method, and belongs to the technical field of network architecture search. The searching method comprises the following steps: performing single-path sampling on a pre-constructed generator super network according to a parameter quantity constraint range to obtain an effective subnetwork; training the generator super-net by adopting a complexity adaptive learning rate optimization strategy to obtain a pre-trained generator super-net; adversarial training is carried out on the generator hypernet and the discriminator to obtain a pre-trained discriminator; generating a generator super-network candidate architecture through a genetic algorithm in the early stage of the evolution stage and through a covariance matrix self-adaptive evolution strategy in the later stage of the evolution stage; and performing multi-target non-dominated sorting on the generator super-network candidate architecture, updating an effective sub-network and keeping a Pareto optimal solution to obtain a searched optimal generator architecture and further obtain a searched optimal generative adversarial network architecture. The method not only ensures the search quality, but also improves the calculation efficiency.

The invention discloses a power transmission line risk monitoring method and device based on a learning rate strategy, electronic equipment and a storage medium, and belongs to the technical field of power transmission line risk monitoring. The method comprises the following steps: acquiring system operation information of a power system, wherein the system operation information comprises real-time meteorological data and line operation data; extracting real-time meteorological characteristics from the real-time meteorological data, extracting line operation characteristics from the line operation data, and constructing vector representation to be measured based on the real-time meteorological characteristics and the line operation characteristics; inputting the to-be-measured vector representation into a risk assessment model to obtain a risk assessment result output by the risk assessment model; the risk assessment model is obtained by training a logistic regression model based on a plurality of training samples and updating a self-adaptive learning rate, and the training samples comprise sample vector representations and risk assessment labels corresponding to the sample vector representations. The method can improve the assessment precision of the power transmission line risk, and guarantees the safe and stable operation of a power system.

The invention discloses an automatic history fitting method based on multiple data assimilation of an improved set smoother, and relates to the technical field of petroleum engineering. The method comprises the following steps: firstly, constructing a plurality of oil reservoir models, setting a prior model, a real model and an initial expansion factor, obtaining the yield of each oil reservoir model by utilizing oil reservoir model numerical simulation, and calculating a residual error; based on corrected data covariance matrix singular value decomposition, production observation data disturbance enhancement, adaptive learning rate matrix scaling and geological boundary constraint, improving a set smoother, updating the permeability of each oil reservoir model, performing numerical simulation again, updating an expansion factor, judging whether the expansion factor meets a preset condition or not, continuing iteration if the expansion factor meets the preset condition, and if the expansion factor does not meet the preset condition, continuing iteration until the expansion factor meets the preset condition; and if not, updating the expansion factor of the iteration, ending the iteration, and outputting the permeability field of each updated oil reservoir model, thereby solving the problems of parameter overshoot, covariance statistical deviation and low calculation efficiency in oil reservoir history fitting, and facilitating history fitting of complex oil reservoir production parameters.

The invention discloses an internet advertisement accurate putting method and system based on big data, relates to the technical field of internet advertisements, and is used for solving the problem that the accuracy and dynamic adaptability of interest prediction are reduced due to the fact that advanced algorithms such as deep learning or reinforcement learning are not fully adopted by a model. User interaction features are captured in real time through a dynamic behavior analysis module, multi-dimensional data modeling is performed in combination with deep learning, and user interest prediction and real-time adjustment of an advertisement recommendation strategy are achieved. The dynamic behavior feature vector is fused with the time sequence and the spatial distribution feature, so that the prediction accuracy is improved; a multi-layer perceptron and a self-adaptive learning rate optimization algorithm are adopted to train a model, and generalization and response capabilities are enhanced; an advertisement adaptation degree score is calculated in combination with semantic similarity, and recommendation correlation is ensured; rapid feedback and strategy optimization are achieved through incremental learning, the defects of the prior art in the aspects of real-time performance and automation are overcome, and the advertisement putting efficiency and accuracy are improved.

The invention relates to the technical field of power equipment state operation early warning, and discloses a power equipment state operation time sequence early warning system and method, and the system comprises a data collection module which collects the multi-source operation data of power equipment through infrared, ultrasonic TEV, high-frequency, ultrahigh-frequency and vibration sensors; the data preprocessing module is used for carrying out normalization processing on the collected multi-source operation data by adopting an extreme value method or an averaging method and mapping the data to a [-1, 1] interval; and the time sequence analysis modeling module is used for constructing a Prophet time sequence model for the normalized data, and decomposing a trend item, a season item and a holiday item. A dynamic factor is added in weight updating through a dynamic parameter adjustment method, network training is effectively prevented from falling into a local minimum point, fluctuation in the learning period is reduced, the learning rate is flexibly adjusted according to error changes in combination with an adaptive learning rate algorithm, and slow convergence caused by the too low learning rate is avoided.

The present invention discloses a tightening quality assessment method based on deep learning, which relates to the technical field of tightening quality assessment. The present invention collects torque, preload, lubrication status and environmental data in real time during the tightening process, uses an LSTM dynamic model to capture the nonlinear fluctuation of the friction factor during repeated tightening, and promptly corrects the torque-preload conversion relationship. Adaptive learning rate and fully connected layer mapping are adopted to enable the model to automatically update parameters under different environmental conditions, dynamically adjust the tightening speed, preset torque and lubrication strategy. The environmental integration module quantifies changes in temperature, humidity and salt concentration, corrects process parameters in real time, and effectively suppresses instantaneous abnormal fluctuations.

The invention discloses a point cloud single tree segmentation method and system. The method comprises the steps of obtaining point cloud data of a target tree and an environment in a power transmission corridor area; constructing a segmentation network model, and performing model training through the optimized loss function and adjustment of the adaptive learning rate to obtain an optimized segmentation network model; inputting the preprocessed point cloud data into the optimized segmentation network model, calculating the category of each point through an activation function, and outputting the category prediction of each point; and point cloud segmentation is carried out based on the category, and a segmentation result is evaluated. According to the invention, point cloud data segmentation is carried out through the segmentation network architecture, the learning rate and the loss function in the architecture are optimized, and the recognition and segmentation precision of the tree monomers is improved in combination with the probability distribution of deep learning; the robustness in a complex environment is enhanced, the problems of noise, sparse point clouds and data imbalance can be effectively solved, and the method is suitable for tree monitoring, accurate positioning and safety evaluation of the transmission line corridor.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to an online incremental learning AI chat robot response generation system which comprises a cache reweighting module, a semantic offset calculation module, an adjusting module, an updating module, a response generation module and a feedback module. According to the method, cache reweighting based on semantic similarity and time decay, a self-adaptive learning rate based on semantic offset and rollback protection are introduced, low-rank increment updating of LoRA and a weight adjustment closed loop of multi-target weighted decoding and feedback driving are adopted, so that the system can effectively improve the real-time performance of the system while preferentially utilizing aging and related historical contexts, and the real-time performance of the system is improved. And the updating strength is dynamically controlled, and stable parameters are recovered during abnormal offset, so that the problem that the response reliability is reduced due to model updating delay or parameter offset during high-frequency interaction or semantic drift caused by conflict between speed and stability in online incremental updating is effectively solved.

The invention relates to the technical field of knowledge graph updating, discloses an end-side knowledge graph dynamic updating method based on an incremental learning neural network, and solves the problems that in the prior art, when facing a large-scale knowledge graph, the calculation amount in the updating process is huge, and the performance bottleneck of end-side equipment is possibly caused. According to the method, the sub-graphs are constructed by newly-added data and the semantic similarity is calculated, and then the sub-graphs are merged by using the lightweight graph neural network, so that knowledge updating can be efficiently realized, the problem of high time consumption of traditional cloud retraining is avoided, the end-side real-time requirement is met, sparse storage compression and incremental pruning optimization are adopted, the storage overhead and the computing resource consumption are reduced, and the system performance is improved. The performance bottleneck of end-side equipment is relieved, a self-adaptive learning rate adjustment strategy enables model training to be more flexible, the learning effect is improved, all the modules work cooperatively, and a complete, efficient and low-consumption solution is provided for dynamic updating of the end-side knowledge graph.

The invention provides an integrated energy system fault rapid identification method, system and device and a medium. The method comprises the steps of collecting measurement data of an integrated energy system; based on the measurement data, a time-frequency analysis method is combined with a principal component analysis algorithm to carry out feature extraction and selection, and a measurement data feature set is screened out; based on the measurement data feature set, fault identification is carried out through a pre-constructed fault diagnosis model, and a fault diagnosis result of the integrated energy system is obtained; the fault diagnosis model is constructed by training a fault diagnosis model architecture in combination with an adaptive learning rate adjustment method, and the adaptive learning rate adjustment method can automatically adjust the parameters of the fault diagnosis model according to the change of the operation state of the system so as to better adapt to the complex and changeable working conditions of the integrated energy system; according to the invention, the fault diagnosis model is used for fault diagnosis, and the fault diagnosis is comprehensively judged, so that the accuracy of the fault diagnosis result is improved.

The invention discloses an anti-multipath interference UWB radar signal denoising and positioning enhancement method and system, and the method comprises the steps: deploying a plurality of groups of UWB radar equipment, collecting original signals in a multipath interference scene, marking the original signals, and constructing a UWB radar signal data set containing multipath interference features, target features and marking information; preprocessing the original signal and extracting multi-domain features; a deep learning model containing a denoising model and a positioning enhancement model is constructed, and joint training is carried out through a joint loss function; training the model by adopting a training strategy of dynamic regularization and a self-adaptive learning rate; and finally deploying equipment acquisition signals, inputting the signals into the trained system for processing, and outputting a positioning result. The method can effectively suppress multipath interference, improves the UWB radar positioning precision and generalization capability, and is suitable for indoor navigation, automatic driving and other scenes.

The invention provides a learning rate adaptive bearingless magnetic flux switching motor neural network PID suspension method, which is used for PID magnetic suspension control of a motor PID controller on a rotor, and adjusts a neural network weight coefficient in real time according to a rotor radial displacement control error so as to realize real-time adjustment of parameters of the neural network PID controller. Establishing a neural network PID parameter learning rate value range according to a neural network PID closed-loop control stability requirement; within a learning rate value range, designing a self-adaptive learning rate adjustment algorithm based on fuzzy reasoning for high-steady-state control precision and high-dynamic response of the dynamic eccentric magnetic suspension of the rotor in a wide range; the method can meet the requirements of magnetic suspension high-steady-state control precision and high dynamic response of wide rotor dynamic eccentricity.

The invention relates to the technical field of refrigeration system fault detection, and discloses a deep separable convolution-BiLSTM multi-head attention refrigerant leakage detection method. The method comprises the following steps: acquiring historical data of a unit and performing standardized preprocessing; constructing a time sequence sample by adopting a sliding window method; building a parallel hybrid model of deep separable convolution and BiLSTM (Bidirectional Long Short-Term Memory Network), and fusing local space features and global time sequence features through channel splicing; a multi-head attention mechanism is introduced to dynamically weight key time steps, and a dynamic category weight compensation strategy is adopted to optimize a loss function; and training the model by using an adaptive learning rate and an early stop strategy, and finally outputting a high-precision fault diagnosis result. According to the method, the accuracy rate of a test set reaches 98%, the AUC fraction reaches 0.99 and is improved by 3%-15% compared with a traditional mixed model, the detection efficiency and robustness of the refrigerant leakage fault of the refrigeration system are remarkably improved, and the method is suitable for accurate maintenance in a high-precision industrial scene.

The invention relates to the technical field of unmanned aerial vehicle aerial photography and computer vision, in particular to an unmanned aerial vehicle aerial photography long-term target tracking method based on matching loss confidence. Comprising the steps of performing multi-feature fusion extraction on a tracking target object, calculating the similarity between a target template and a candidate region, performing target search matching by using an improved pelican search algorithm, realizing target scale adaptive updating through a scale pyramid, and dynamically updating the target template based on an adaptive learning rate; and designing a long-term tracker combined with the matching loss confidence, wherein the long-term tracker comprises the steps of judging a target tracking state based on the matching loss confidence and executing rapid positioning redetection when the target is lost. Through multi-feature fusion, an improved pelican search algorithm and a matching loss confidence judgment mechanism, the long-term stable tracking of the target in the aerial photographing scene of the unmanned aerial vehicle is realized, the problems of shielding, scale change, target loss and the like are effectively solved, and the tracking precision, the success rate and the real-time performance are considered.

According to the power transmission line geological disaster time sequence InSAR prediction method based on the improved LSTM model, an improved long short-term memory network LSTM model is constructed, and a basic data set is established for LSTM model training by using obtained power transmission line monitoring area time sequence InSAR deformation result data. And a regularization technology, an attention mechanism and an adaptive learning rate adjustment strategy are introduced in a model training process, so that an overfitting risk is reduced, the generalization ability of the model is improved, and a high-precision deformation prediction model for time sequence InSAR monitoring of the power transmission line is obtained. The method is of great significance to early warning and deformation trend analysis of geological disasters around the power transmission line, and can provide scientific basis and decision support for safe operation of power transmission facilities, thereby effectively reducing potential hazards of the geological disasters to a power system.

The invention discloses an industrial robot health state monitoring method and system based on digital twinning, and the method comprises the steps: building the digital twinning of an industrial robot, collecting multi-dimensional health state parameters, carrying out the feature extraction through a deep Q network, carrying out the modeling of a feature vector time sequence through an improved hidden Markov model, and calculating the posterior probability of the health state. The health state is evaluated in a grading mode by combining a preset rule, a maintenance strategy is generated based on reinforcement learning when early warning or faults occur, and the digital twinborn state is fed back and updated. The monitoring system comprises a data acquisition and preprocessing unit, a deep Q network feature extraction unit and other seven units, and all the units work cooperatively. According to the method, models such as adaptive learning rate adjustment and environmental influence factors are introduced, the problems that a traditional method is weak in data processing capacity and poor in environmental adaptability are solved, and dynamic accurate monitoring and intelligent maintenance guidance of the health state of the industrial robot are achieved.

The invention discloses a YOLOv11 road disease detection method and system based on multi-scale feature enhancement. The method comprises the steps of road image acquisition and marking, adaptive learning rate adjustment and data enhancement, construction of a YOLOv11 model embedded with a sub-pixel level edge enhancement module and a wavelet transform feature extraction module, staged training, model evaluation, disease detection and the like. Wherein the sub-pixel-level edge enhancement module is used for enhancing fine crack features through multi-operator fusion and a sub-pixel convolution technology; the wavelet transformation module improves the multi-scale feature perception capability through adaptive wavelet basis selection and a multi-stage decomposition and reconstruction mechanism. The system correspondingly comprises a data preprocessing module, a model building module, a training optimization module and an evaluation deployment module. On the basis of keeping the real-time performance of the YOLOv11, the detection precision and robustness of multi-scale diseases in low-contrast, sub-pixel-level cracks and complex environments are remarkably improved, and the method is suitable for road maintenance and safety monitoring scenes.

The invention provides a deep learning adaptive learning rate optimization method based on gradient variance and time sequence attenuation, and the method comprises the steps: collecting single sample gradients of all samples, forming a gradient set in a batch, and calculating a batch gradient global variance signal in the batch based on the single sample gradients; performing random disturbance reasoning on the deep learning model to obtain prediction output; on the basis of the confidence trajectory unit and the historical trajectory thereof, constructing a composite dynamic feature containing historical time step information; jointly inputting the composite dynamic features and the confidence calibration variance, and inputting the combined input to a scheduler to generate a dynamic learning rate; and updating the deep learning model according to the dynamic learning rate, generating new model parameters, completing model parameter updating in combination with a gradient calculated on the current batch of training data, and transmitting training dynamic information to the next round of signal extraction process through a state feedback mechanism to perform closed-loop optimization.

The invention discloses a coal-rock mass stress field inversion method and system driven by a mechanical mechanism. The inversion method comprises the following steps: obtaining detection monitoring data; constructing a neural network model of a multi-layer perceptron through a loss function, taking multi-point stress monitoring number stress as an initial value condition, combining a boundary condition and a mechanical equation, and performing iterative solution to obtain global stress field distribution data; constructing an acoustic emission signal as input, global stress field distribution as output, a long-short-term memory network as a main structure, and combining a full connection layer to perform feature extraction and mapping; constructing a loss function with a self-training weight according to the properties of the coal and rock mass, the mechanical balance and the constitutive model; and optimizing the deep neural network according to a stochastic gradient descent method and an adaptive learning rate, and training the deep neural network by using back propagation according to a constructed loss function with a self-training weight to obtain an inversion model.

The invention provides a species detection and identification method based on a hybrid scaling strategy and a cascade architecture, which belongs to the technical field of image identification, integrates multiple technologies such as image preprocessing, neural network model construction and expansion, model transfer learning and fine tuning, optimizes the network structure by means of the hybrid scaling strategy, and improves the identification accuracy. A detection network and a recognition network are organically combined, species recognition tasks are concentrated in a region where detection is meaningful, fine-grained detection and accurate recognition of species are achieved, and the accuracy and reliability of species recognition are greatly improved. The hybrid scaling strategy realizes feature extraction optimization of targets with different scales by dynamically adjusting the depth, width and input resolution of the network; the cascade architecture reduces the influence of background interference on a classification task through progressive processing of positioning and then classification. In addition, an adaptive learning rate adjustment mechanism is introduced, and a verification set early stop strategy is combined, so that the convergence efficiency and generalization performance of the model are further improved.

The invention discloses an umbrella ladder system modeling optimization method and system based on mechanics and parameter optimization, and relates to the technical field of wind power generation modeling. The efficient umbrella ladder system modeling and optimizing method is provided by combining mechanical analysis and a parameter optimization algorithm, and the performance and reliability of a high-altitude wind energy capturing system are remarkably improved; a CPO optimization algorithm is adopted to carry out dynamic parameter optimization on a floating system and an acting system under the multi-constraint condition, and the output power is maximized on the premise that buoyancy balance, rope speed limitation and parachute area constraint are met. Through dynamic updating of an adaptive learning rate adjustment mechanism and a trust region radius, the algorithm can rapidly converge to an optimal solution, the common problem of local optimum or constraint violation in a traditional optimization method is avoided, the method can adapt to changes of wind conditions of different heights, the system can be effectively prevented from overturning, and long-term stable operation is guaranteed.

The invention relates to the technical field of DAS data enhancement processing, in particular to a DAS data enhancement method based on an improved DRAGAN, and the method comprises the following steps: S1, constructing a DAS perimeter security system, and collecting and preprocessing data; s2, a DRAGAN data enhancement model is improved, an EnhancedMCAM module is introduced into a generator, a residual block and a CBAM module are introduced into a discriminator, and an MCDRAGAN data enhancement model is formed; s3, training a data enhancement model, and adopting an adaptive learning rate adjustment strategy; s4, inputting the preprocessed data into an MCDRAGAN data enhancement model to obtain a generated sample; s5, evaluating the MCDRAGAN data enhancement model by adopting an AlexNet classification model, and verifying the validity of the data enhancement model; according to the method, the improved MCDRAGAN data enhancement model is used to generate the new data sample, the distribution characteristics of the data are well simulated, the diversity of the data set is expanded, and better identification accuracy can be obtained.