666 results about "Extreme learning machine" patented technology

The invention relates to the technical field of audio processing, and discloses an audio processing method and system for speech spectrum reconstruction, and the method comprises the steps: carrying out the preprocessing of an original speech signal collected by a vehicle-mounted microphone, and obtaining a speech signal time-frequency matrix; performing singular value decomposition on the voice signal time-frequency matrix to obtain a sparse matrix representing voice components and a low-rank matrix representing noise components, and establishing a frequency band characteristic incidence matrix through a mutual information entropy calculation method; inputting the sparse matrix into a selection forgetting extreme learning machine for voice feature prediction to obtain predicted voice feature data; and based on the predicted voice feature data and the frequency band feature incidence matrix, performing voice probability calculation and feature interpolation on each time frequency point by adopting a logarithmic frequency spectrum amplitude calculation method to obtain reconstructed frequency spectrum data, thereby improving the accuracy of voice recognition and meeting the requirement of real-time processing in a vehicle-mounted environment at the same time.

The invention relates to the technical field of data processing, in particular to an earthwork balancing method based on discrete elevation points and a free-form surface creation technology, and the method comprises the steps: generating topographic feature partition tags through an improved DBSCAN density clustering algorithm, and constructing a regularized elevation field in combination with octree and R * tree mixed indexes; generating an elevation standard deviation thermodynamic diagram based on Gaussian process regression, and outputting a multi-target digging and filling scheme set in combination with an FEM-DEM coupling model; a multi-layer graph model fused with soil bearing capacity is constructed, a collision-free construction path is optimized through an improved D * Lite algorithm, a positioning track and a planning deviation are fused by using Kalman filtering, and an online sequence extreme learning machine is triggered to realize dynamic updating of the model. According to the method, the complex terrain modeling efficiency and the earthwork volume calculation precision are improved, the resource allocation path is optimized, the construction cost is reduced, and the problem of excavation and filling imbalance caused by data discreteness and modeling staticization in a traditional method is solved.

The invention discloses a transformer fault diagnosis method based on multi-sensor multi-domain feature fusion, and the method comprises the following steps: 1, obtaining a vibration signal data set of a transformer box, and carrying out the decentralization of a collected signal; 2, decomposing the decentralized transformer vibration signal by adopting a dung beetle optimized ICEEMD method, calculating a Pearson correlation coefficient between an IMF component and an original signal, and selecting a strongly correlated component to reconstruct the transformer vibration signal, so as to filter and denoise the transformer vibration signal; step 3, extracting time domain, frequency domain and space domain features in the transformer vibration signals by adopting an MVAR model, VMD-BIS decomposition and Riemannian manifold, and constructing a joint feature vector; 4, performing fusion dimension reduction by adopting a kernel principal component analysis algorithm to obtain a fusion dimension reduction feature vector; and a fifth step of constructing a kernel extreme learning machine model, training parameters of the kernel extreme learning machine model in combination with the time-frequency-space fusion features, and optimizing hyper-parameters of the kernel extreme learning machine model in combination with a Harris eagle optimization algorithm so as to obtain an optimal kernel extreme learning machine classification model. According to the method, the problems of misjudgment, missed judgment and low diagnosis rate caused by unbalanced distribution due to nonlinear characteristics of vibration signals in transformer fault diagnosis are effectively solved.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

The invention discloses a lithium battery diagnosis method and system based on multivariable and infrared point cloud. The method comprises the steps that voltage, current, temperature, voiceprint and infrared three-dimensional point cloud data of a lithium battery are collected through multiple sensors; performing mode decomposition on the electric signal by adopting symplectic geometric mode decomposition SGMD, and constructing a two-dimensional input matrix in combination with an original signal; an electric signal is processed by using a Tokenformer model optimized by CBAM, and image data is analyzed by using a YOLOv9 model improved by ECA; integrating the electric signals and the image features through a PTP multi-mode fusion method; and finally, an improved extreme learning machine IELM is adopted to realize fault diagnosis. According to the method, through a multi-modal coupling structure and intelligent algorithm optimization, the diagnosis precision and the calculation efficiency are remarkably improved, meanwhile, the dependence on labeled data is reduced, and the generalization ability and the adaptability of the model are enhanced.

The invention relates to the technical field of artificial intelligence, and discloses a geological mineral resource prediction model training method based on artificial intelligence, and the method comprises the steps: carrying out the expansion of geological mineral resource data through a data expansion model, and obtaining a training data set; building a prediction model through an extreme learning machine based on conjugate gradient, and inputting the geological mineral resource data in the training data set into the prediction model for training; randomly initializing hidden layer parameters of the extreme learning machine, and calculating a hidden layer output matrix; calculating an output layer weight of the extreme learning machine through a conjugate gradient method; and updating the hidden layer output matrix. According to the method, a belief propagation mechanism is adopted, the internal relevance of geological mineral resource data can be simulated, and samples are adjusted and generated through a covariance matrix, so that the method is more in line with the distribution characteristics of actual geological mineral resources; a dynamic constraint game strategy is adopted, and strategies of a generator and a discriminator are dynamically adjusted, so that adaptability to complex data is enhanced.

The invention discloses a motor fault intelligent diagnosis method based on digital twinning, and belongs to the field of motor fault diagnosis. According to the method, in a model training stage, characteristic quantities are selected through a random forest method to train an extreme learning machine model with an autoregressive cycle function as a digital twin motor. According to the method, in an open-circuit fault diagnosis stage, a physical entity motor characteristic quantity is collected and input into a digital twin virtual motor to obtain a digital twin current, the difference between actual winding current data and the digital twin winding current is obtained to obtain a current residual error, and the difference value of the current residual error is used as a threshold value to carry out open-circuit fault diagnosis. Fault positioning is carried out by adopting a moving average window method; in the turn-to-turn short-circuit fault diagnosis stage, a current symmetric mean absolute error is used as a judgment standard to diagnose a short-circuit fault. According to the method, accurate design parameters and fault data of the motor are not needed, the open-circuit fault and the turn-to-turn short-circuit fault of the motor can be quickly diagnosed in real time only through the data during normal operation of the motor, and the robustness is very high.

The invention discloses a DC power supply health state prediction model optimization method based on an extreme learning machine, and relates to the technical field of power supply intelligent management. According to the method, an extreme learning machine basic model is improved, multi-layer ELM-AE is adopted to carry out greedy unsupervised learning on input data layer by layer, advanced features of an original data set are extracted, a mixed kernel function is adopted as a regression layer of a supervised learning stage, the complex nonlinear problem is solved, and the method has the advantages of being high in robustness and high in robustness. The adaptability of the prediction model to diversified characteristics under different battery working conditions is improved, the generalization ability and prediction precision of the model are improved, and the convergence speed and prediction precision of the model are remarkably improved by optimizing the model parameters through the improved black-wing calculation algorithm.

The invention relates to the field of electric digital data processing, in particular to a building digital twin data processing method based on artificial intelligence, which comprises the following steps of: acquiring real building digital twin data, training a data expansion model by adopting a generative adversarial network algorithm based on topology dual optimization to obtain training data, and calculating the real building digital twin data according to the training data. Obtaining a central building energy consumption evaluation model and a local building energy consumption evaluation model by adopting a distributed federated learning method, training the local building energy consumption evaluation model of each building by adopting a self-adaptive constraint-based extreme learning machine algorithm, continuously iterating the local building energy consumption evaluation model, and evaluating the energy consumption of each building. And evaluating new real building digital twin data by using the trained local building energy consumption evaluation model. An existing building digital twin data processing method has the problem that energy consumption grade evaluation is not accurate, and the method is accurate in energy consumption grade evaluation.

The invention provides a distribution network automation equipment group fault diagnosis system and method, and relates to the technical field of power distribution networks, and the method comprises the following steps: carrying out the feature extraction of fault data through employing fractional order Boltzmann-Shannon interaction entropy; the rational activation function is combined with an extreme learning machine algorithm, a rational extreme learning machine is constructed, and faults are diagnosed; the non-smooth optimization problem of the rational extreme learning machine is solved by adopting a novel umbrella-exponent optimization algorithm; according to the method, the fractional order Boltzmann-Shannon interaction entropy is adopted to carry out feature extraction on the fault data, the data does not need to be decomposed or transformed, and the complexity of the data is directly measured, so that the defect that hidden fault features cannot be effectively extracted by a traditional time-domain and frequency-domain feature extraction method is overcome; the parameter-free measurement can be accurately estimated in a calculation-efficient mode, fractional order information is further considered, and the dynamic change of a fractional order time sequence can be measured.

The invention discloses a physical sign data monitoring model construction method and device, a medium and equipment, and relates to the technical field of health monitoring. The method comprises the steps of performing feature extraction of physiological sign data by adopting a fractional order reverse scatter entropy method, replacing a Sigmoid activation function in a traditional extreme learning machine with a DiTAC activation function, modifying an L2 norm of an output layer weight coefficient in a loss function in the extreme learning machine into an L1 norm, establishing an optimization model based on the improved extreme learning machine, and performing feature extraction of the physiological sign data based on the optimized model. And solving the optimization model by using an Aribus algorithm and a forty-thief algorithm which introduce discrete fractional order Caputo, so as to obtain a physical sign data monitoring model. The extreme learning machine is constrained by the trainable parameters introduced by the DiTAC activation function and the L1 norm of the output layer weight coefficient in the training process, so that the trained extreme learning machine can be more suitable for physical sign data monitoring, and a more accurate monitoring result is obtained.

The invention discloses a wind turbine generator fault prediction method based on a multi-strategy improved hybrid optimization algorithm, and belongs to the technical field of wind turbine generator fault diagnosis. The method comprises the following steps: collecting vibration signals of a wind turbine generator in different states; optimizing the initial population quality of a sparrow algorithm by using dynamic reverse learning, fusing an exploration strategy of a northern eagle algorithm in a search stage of the sparrow algorithm, introducing a Gaussian random walk strategy to update the position of the sparrow, obtaining an improved hybrid optimization algorithm, and determining the number k of modal decomposition and a penalty factor alpha; decomposing the vibration signal by using a modal decomposition method to obtain an IMF component; calculating to obtain a multi-dimensional feature vector; and introducing the mixed kernel function into an extreme learning machine to obtain a mixed kernel learning machine, and performing state recognition on the multi-dimensional feature vector. Under a test data set, the accuracy rate of the fault diagnosis method is as high as 100%, and in actual fault early warning classification, the accuracy rate is as high as 97.5%, and an effective solution is provided for fault early warning of the wind turbine generator.

The invention relates to a gyroscope-oriented temperature compensation method, in particular to an MEMS gyroscope-oriented temperature compensation method, which comprises the following steps: S1, after specific noise is added, decomposing an output signal of a gyroscope into a series of intrinsic mode functions (IMF); s2, dividing the decomposed intrinsic mode function IMF into a noise segment, a mixed segment and a feature segment by using a sample entropy SE; s3, de-noising processing is carried out on the noise section by using long-window time-frequency peak filtering TFPF, and useful signals are reserved while de-noising is carried out on the mixed section by using short-window time-frequency peak filtering TFPF; s4, using an extreme learning machine ELM optimized by a non-dominated sorting genetic algorithm NSGA II to perform temperature compensation on the feature segment containing the temperature drift term; and S5, combining the de-noised noise section, the de-noised mixed section and the compensated feature section, and reconstructing a final gyroscope output signal. According to the temperature compensation method for the gyroscope, interference of noise can be well reduced, and the temperature compensation effect of the gyroscope is greatly improved.

The invention discloses a strain culture monitoring method and system based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the following steps: collecting multi-dimensional original data in a fermentation process, and carrying out the preprocessing to construct a multi-dimensional feature data set; training a bidirectional LSTM model through the multi-dimensional feature data to obtain a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector through a sparse self-encoding technology to obtain a low-dimensional feature vector, and processing the low-dimensional feature vector through phase mapping and an extreme learning machine to obtain a target classification result; and constructing a Markov decision model based on the low-dimensional feature vector and a target classification result, and solving the Markov decision model through a reinforcement learning algorithm to obtain an optimal adjustment strategy. According to the scheme, intelligent monitoring and precise regulation and control of strain culture based on artificial intelligence are achieved, the defects of a traditional method in data utilization, stage adaptability and model generalization ability are overcome, and the intelligent level and production efficiency of strain culture are improved.

The invention discloses a lithium ion battery energy storage system state monitoring method and device. The method comprises the following steps: collecting operation parameter historical data under each state type in a lithium ion battery energy storage system; preprocessing the collected historical data to obtain an expanded training data set; performing feature extraction on the expanded training data set to obtain a training data set after feature extraction; performing feature dimension reduction on the training data set after feature extraction to obtain a training data set after dimension reduction; performing optimization training on the constructed classifier model by combining an extreme learning machine algorithm based on quantum information entanglement measurement on the basis of the dimensionality-reduced training data set; and performing feature extraction and feature dimension reduction on the obtained real-time data of each operation parameter, inputting the data into the trained classifier model for processing, and outputting a state monitoring result of the lithium ion battery energy storage system. According to the invention, the operation state type of the lithium ion battery energy storage system can be timely and accurately monitored, so that corresponding measures can be taken as soon as possible.

The invention discloses a physical field solving method based on a Bayesian physical information extreme learning machine, and the method comprises the steps: constructing a single-layer full-connection neural network, carrying out the random initialization, and fixing the weight of an input layer; based on a partial differential equation of a physical system and boundary conditions thereof, defining a training loss item containing physical information; a physical system solving problem is converted into a linear least square problem, and a linear equation set is constructed; supposing that an output layer weight parameter obeys Gaussian prior distribution with the mean value being zero, and controlling a covariance matrix by an adjustable hyper-parameter; constructing a Gaussian likelihood function based on the observation data, and calculating posterior distribution of the output weight in combination with the prior distribution; carrying out iterative optimization on the hyper-parameter by adopting an evidence maximization method to obtain a mean value and a covariance of posterior distribution; based on posterior distribution, adopting a Monte Carlo integral method to generate prediction output of the physical system; and performing uncertainty quantization based on the variance of prediction output, and outputting a prediction value and a confidence interval thereof.

The invention discloses a classroom student attention assessment method and system based on multi-source feature fusion. According to the invention, face recognition is carried out through the face detection unit of the system, occurrence frequencies of different emotional states of students are obtained through a trained student expression recognition neural network model containing four expression discrimination blocks in the expression recognition unit, and fatigue times of the students are obtained through the fatigue detection unit. The number of times of abnormal head postures of the students is obtained through the head posture estimation unit, finally attention evaluation parameters of the students are obtained through an extreme learning machine model of the attention evaluation unit, and then the attention states of the students in the class are evaluated and displayed. According to the method, a more efficient dynamic student expression recognition method can be provided, real-time and accurate classroom feedback is provided for teachers, optimization and implementation of personalized teaching strategies are supported, longitudinal extension of teaching of the teachers and transverse extension of learning modes of the students are facilitated, and new talents are expanded to cultivate a new normal form.

In order to solve the problems of high energy consumption and unbalanced comfort of a traditional static control strategy of the air conditioning system during environment change, a building three-dimensional model is constructed, and sensor data and energy consumption simulation software are fused to construct a joint simulation environment. The improved extreme learning machine neural network is used for predicting the air conditioner energy consumption and the personnel thermal comfort degree, the network structure and parameters are optimized through the rotary particle swarm optimization, and the prediction precision and generalization ability are improved. And then, in combination with a Newton-Raphson multi-objective optimization algorithm, solving an air conditioner temperature set value optimal dynamic sequence in a rolling manner, and balancing energy consumption and comfort through a Pareto frontier solution set. Through dynamic simulation and data driving optimization, precise regulation and control of the air conditioner are achieved, and the thermal comfort degree is improved.

The invention discloses a lithium battery remaining service life prediction method based on Stacking fusion, which comprises the following steps: firstly decomposing lithium battery capacity data through robust local mean decomposition, and then reducing the number of sequences through fuzzy entropy aggregation; carrying out secondary decomposition by utilizing a modal component with the highest entropy value of time-varying filtering empirical mode decomposition, inputting the processed data into a lightweight time sequence analysis model and a non-inverse extreme learning machine for prediction, and carrying out parameter optimization on an FITS model and an IFELM model by adopting an improved wave search algorithm; the prediction values of the two models are fused by using a Stacking algorithm, then error correction is carried out, finally, the fused prediction result and the error prediction result are superposed to obtain a final lithium battery capacity prediction result, and the prediction result is obtained according to the relationship between the capacity data and the residual life. According to the method, the prediction model precision of the residual life of the lithium battery can be effectively improved, and a reliable basis is provided for safety judgment of the lithium battery.

The invention provides a mountain area tunnel structure safety evaluation method, device and equipment and a readable storage medium, and relates to the technical field of tunnel structure safety evaluation analysis, and the method comprises the steps: obtaining a safety evaluation index of a tunnel structure; randomly generating a plurality of samples in a preset security level according to the security evaluation index to obtain a constructed sample set; combining the most unfavorable reference values of the safety evaluation indexes under the safety level to obtain a limit sample set; calculating the combined weight of each safety evaluation index based on a group decision, an AHP algorithm, an entropy weight method, a CRITIC method and a game theory; based on a TOPSIS decision-making method and the combined weight, determining a quantization interval where each second proximity value is located, and obtaining a training sample set; and training a preset extreme learning machine according to the training sample set to obtain a safety evaluation model, and inputting sample data of the to-be-evaluated tunnel structure into the safety evaluation model for evaluation to obtain a safety evaluation result. According to the invention, the accuracy of evaluation index weight calculation is improved.

The invention discloses a computer network security data processing method and system based on artificial intelligence, and the method comprises the steps: building a multi-channel deep learning fusion model, extracting spatial local features in a traffic sequence through employing a 1D-CNN one-dimensional convolutional neural network, capturing a long-range time sequence dependence relation between log events, and carrying out the recognition of the long-range time sequence dependence relation between log events; modeling the user operation behavior sequence based on an LSTM (Long Short-Term Memory) network, and fusing the feature weight by using an attention mechanism to obtain a fused feature vector; inputting the fusion feature vector into a classifier established based on an OS-ELM online sequence extreme learning machine to perform real-time threat assessment, and outputting a probability index of network attacks occurring in a short time in the future; and generating a cooperative defense decision according to the network attack probability index, and sending the cooperative defense decision to security equipment for execution. Excessive defense or insufficient protection is avoided, and the cooperation efficiency of safety equipment is remarkably improved.

The invention provides a pipeline defect assessment method and system based on multi-source data fusion, and the method comprises the following steps: collecting the data of the operation state of a pipeline body through a related collection device, and marking the state and fault type of the pipeline, so as to construct a training set and a test set; and preprocessing the static data of the pipeline, wherein the preprocessing comprises data cleaning, standardization and feature extraction. And optimizing the extreme learning machine by using a bird flock algorithm, analyzing static data and evaluating pipeline defects. Preprocessing the image data of the pipeline body, wherein the preprocessing comprises image conversion, enhancement and denoising; and evaluating the circumferential weld defect based on the pre-trained VGG16 model. And performing decision-level fusion on a defect evaluation conclusion obtained by static data analysis and circumferential weld image analysis by using a D-S evidence reasoning algorithm to obtain a final evaluation result. The method effectively improves the accuracy and reliability of pipeline defect evaluation, and has wide application prospects and important practical value.

The invention discloses a self-adaptive ultrasonic measurement method and system based on multi-channel cooperation, and relates to the technical field of ultrasonic measurement. The method comprises the following steps: collecting original flight time signals of each channel; meanwhile, environment data are collected in real time; original flight time signals are processed, effective signal arrival pre-flight time is extracted, and a fusion data set is constructed; constructing a two-dimensional sound channel spectrogram based on the multi-sound channel time sequence of the pre-flight time, and performing feature extraction based on a lightweight convolutional neural network to generate an abnormal confidence vector; analyzing the sound channel state based on the abnormal confidence vector; constructing a physical information neural network, and analyzing the corrected sound velocity value of each sound channel and the two-dimensional sound velocity field distribution on the section of the whole pipeline; training an online sequence extreme learning machine model in combination with historical measurement data; and based on the final fusion weight of each sound channel and the corresponding sound channel flow velocity, carrying out weighted fusion to generate a flow velocity optimal estimation value. And the measurement precision and robustness are improved.

The invention relates to the field of power optical cable facilities, in particular to a power optical cable fault detecting and positioning method based on an extreme learning machine. The OTDR signal is subjected to multi-scale decomposition and de-noising processing based on wavelet packet analysis, and high-frequency noise is effectively suppressed and key event point features are reserved by refining frequency band division, so that the signal quality is improved. And constructing a fault classification model based on an extreme learning machine, and realizing efficient classification and fault mode identification of de-noised OTDR data. And in combination with a white whale optimization algorithm (BWO), carrying out optimization search on a fault detection result output by the classification model so as to improve the detection precision and algorithm robustness in a complex optical cable environment. And an improved Dijkstra algorithm is introduced to realize accurate positioning and path tracking, so that the accuracy and response speed of fault positioning are remarkably improved. Through multi-level algorithm fusion, the problems of signal noise interference, insufficient pattern recognition accuracy and low positioning precision in a complex network environment in optical cable fault detection are solved.

The invention provides an intelligent machine tool cutter wear state recognition and machining parameter adjustment method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting the working condition data of a machine tool machining process, carrying out the sliding window segmentation, denoising and normalization processing, extracting a time sequence feature sequence through a bidirectional gating circulation unit, and carrying out the recognition of a machining parameter; the method comprises the following steps: acquiring an intrinsic mode component by combining Hilbert-Huang transform decomposition, extracting a frequency domain feature sequence by utilizing four-layer spatial pyramid pooling, determining a fusion weight based on mutual information entropy to obtain a tool wear state feature vector, and inputting the feature vector into a pre-training extreme learning machine to predict the wear loss and the residual life. And determining an initial adjustment scheme in combination with observation sharing information of a parallel agent structure, predicting a processing quality score through a capsule network and feeding back the score as a reward signal, and obtaining an optimization correction strategy for adjusting working parameters of a machine tool.

The invention discloses a multi-scale width ELM lithium battery RUL prediction model and method based on an elastic network. The prediction model comprises a CEEMDAN multi-mode decomposition module, a multi-extreme learning machine module and an elastic network regularization width neural network module; a CEEMDAN multi-mode decomposition module decomposes battery recession data into a plurality of intrinsic mode IMFs, extracts multi-scale feature information through a multi-scale feature extraction module, inputs the extracted multi-scale feature information into an ELM module for training, selects a plurality of better ELMs based on a plurality of performance indexes, and finally, takes prediction results of the plurality of ELMs as new features, so that the performance of the battery is improved. And inputting the two features into an elastic network regularization width neural network module through enhancement features of width expansion to obtain a final prediction result. According to the method, the training data volume can be effectively reduced, the calculation complexity can be remarkably reduced while the prediction precision is improved, and the real-time application of the system is ensured.

The invention discloses an Alzheimer disease risk prediction method based on feature fusion, a medium and equipment. The method comprises the following steps: firstly, acquiring an sMRI image, an FDG-PET image, a biomarker and intelligence score data of a target subject; then, sMRI and FDG-PET image data are respectively input into a three-dimensional convolutional neural network module, and a corresponding first image feature map and a corresponding second image feature map are extracted; secondly, inputting the two feature maps into a cross attention fusion module to obtain image fusion features; and inputting a U-shaped up-sampling network to segment a hippocampus region to obtain hippocampus features. And then, merging the image fusion features, the hippocampus features, the biomarker data and the intelligence score data, and inputting the merged data into an extreme learning machine for calculation, thereby obtaining an onset risk prediction result of the Alzheimer's disease. According to the method, by means of cross-modal data fusion, multi-modal data complementary information is extracted, and the accuracy of onset risk prediction can be effectively improved.

The invention discloses a distributed photovoltaic power prediction method and system, belongs to the technical field of renewable energy prediction, and solves the prediction precision and efficiency bottlenecks of a traditional model under complex meteorological conditions through fusion of deep feature learning and an adaptive optimization mechanism. Firstly, a time sequence sample set is constructed based on a sliding window mechanism, and non-stationary fluctuation characteristics of a power sequence are dynamically captured; eliminating the dimensional difference between the input features and the tags through minimum-maximum normalization; constructing a four-level restricted Boltzmann machine stacking structure, and extracting time-space coupling characteristics of power data by layer-by-layer unsupervised pre-training; and designing an improved extreme learning machine dynamic analysis architecture, optimizing the number of neurons in a hidden layer in combination with grid search, and realizing global optimal balance between model complexity and prediction precision. According to the method, an effective dynamic mode and noise interference are distinguished by using hierarchical feature abstraction capability of the RBM, prediction robustness in strong fluctuation scenes such as cloudy and rainy scenes is remarkably improved through analytical solution and parameter adaptive adjustment of the ELM, and efficient technical support is provided for intelligent scheduling and energy storage optimization in a high-proportion photovoltaic grid-connected background.

The invention discloses an optimal power flow solving method and device based on a graph neural network and an extreme learning machine, and relates to the field of power grid power flow calculation, and the method comprises the steps: obtaining the node topology of a to-be-solved power system and the historical operation data of each power node, and constructing a system topological graph according to the node topology and the historical operation data; extracting features of a system topological graph based on a multilayer graph convolutional neural network to obtain first node features; taking the minimum total output of the system as a target, screening first node features based on regularization, and fusing the first node features to obtain second node features; performing multiple times of iterative training on the initial extreme learning machine model according to the second node features, optimizing model parameters based on mean square error loss during iteration, completing training until the model parameters meet preset values, and obtaining a first extreme learning machine model; and obtaining real-time operation data of the to-be-solved power system, and obtaining an optimal power flow solving result of the to-be-solved power system according to the real-time operation data and the first extreme learning machine model.

The invention discloses a tool fracture and fatigue intelligent detection method based on vibration signal analysis, and the method comprises the following steps: S1, installing a vibration sensor, and collecting the vibration signal of a tool in real time; s2, the collected tool vibration signals are preprocessed, and noise in the signals is removed; s3, performing time-frequency analysis on the preprocessed vibration signals, and extracting time-frequency features in the signals; s4, performing deep feature learning on the extracted time-frequency features to form deep features; s5, the depth features are classified and analyzed, and the health state of the cutter is output; s6, according to the health state optimization feature extraction and prediction result of the cutter, generating learning output; s7, evaluating the health state of the cutter in real time according to the learning output, and pushing alarm information; and S8, according to the alarm information, predicting the service life of the cutter and optimizing a cutter replacement and maintenance strategy. According to the method, short-time Fourier transform and Hough transform are combined, and the extreme learning machine is applied, so that intelligent detection on the fracture and fatigue of the cutter is realized.