87 results about "Kernel extreme learning machine" patented technology

The invention discloses a satellite remote sensing target data identification method based on artificial intelligence, which belongs to the technical field of data processing, and realizes distributed model training and data mining of a multi-node joint model by performing satellite remote sensing target identification on an acquired spectral vector and adopting a model training architecture of distributed federated learning; carrying out manual annotation on the acquired spectral vector data; a generative adversarial network based on multi-scale spectral decomposition is adopted for sample generation, and then data expansion is achieved; and performing data identification by adopting a satellite remote sensing target data identification model. According to the method, the generative adversarial network based on multi-scale spectral decomposition is adopted for sample generation, and the data set is expanded. A self-encoder algorithm is used for carrying out feature dimension reduction, feature independence is enhanced through a nonlinear conversion layer, and redundant information between features is reduced. A kernel extreme learning machine algorithm based on conjugate gradient compensation is adopted, a kernel function is mapped to a high-dimensional space, and the classification accuracy and efficiency are improved.

The invention relates to the technical field of synchronous reluctance motor structure optimization, in particular to a permanent magnet auxiliary synchronous reluctance motor structure parameter multi-objective optimization method. The method comprises the steps that motor structure optimization parameters are selected, and the change range of the parameters is determined; carrying out comprehensive sensitivity analysis according to the selected optimization target; performing hierarchical processing on the optimization variables according to the comprehensive sensitivity index, dividing the optimization variables into a strong sensitive layer and a weak sensitive layer, and taking parameters in the strong sensitive layer as to-be-optimized parameters of the next algorithm; comparing the prediction precision of the back propagation neural network, the radial basis, the extreme learning machine, the support vector machine and the kernel extreme learning machine, and selecting an agent model with an optimal prediction effect; combining the established high-precision agent model with a fast non-dominated sorting genetic algorithm, and searching an optimal combination of to-be-optimized parameters; and finally, carrying out single parameterization scanning determination on parameters in the weak sensitive layer by utilizing finite element simulation so as to obtain optimal structural parameters of the permanent magnet auxiliary synchronous reluctance motor.

The invention relates to a transformer fault diagnosis method. The invention discloses an oil-immersed transformer fault diagnosis method fusing a digital twinborn model, and the method specifically comprises the four steps: 1, achieving the equalization of a fault sample through employing the I NNOS; 2, extracting 9-dimensional candidate ratio signs, and performing fusion and dimension reduction on the ratio signs by using sparse principal component analysis (SPCA); 3, a kernel extreme learning machine (KELM) model is built, an AO algorithm is introduced to optimize parameters of the KELM model, and an AO-KELM optimal diagnosis model is built; and 4, establishing a digital twinborn five-dimensional model to achieve real-time interactive mapping between a physical entity and a virtual space and realize online diagnosis of the transformer fault. The transformer operation state can be simulated in real time, the fault diagnosis precision is improved, and intelligent application of transformer digital twinning is achieved.

The invention discloses a transformer fault diagnosis research method and system, and relates to the technical field of transformer fault diagnosis, and the method comprises the steps: introducing an SHAP value to carry out the feature optimization of transformer fault data based on a decision tree and an integration method thereof; dividing the optimized data into a training set and a test set, and performing data cleaning and oversampling processing on the training set; and optimizing the deep mixed kernel extreme learning machine based on the improved dung beetle algorithm, constructing a transformer fault model, and performing fault diagnosis by using the transformer fault model. According to the feature system provided by the invention, the input features of the transformer fault diagnosis model can be effectively optimized, transformer fault data can be effectively utilized, and the influence of redundant features on the diagnosis model is reduced. Compared with other heuristic artificial intelligence algorithms, the improved dung beetle algorithm provided by the invention has more excellent optimization ability, and can find more excellent parameters of the deep mixed kernel extreme learning machine in the same number of iterations and range.

The invention discloses a motor imagery electroencephalogram signal classification method combining function type data analysis and a kernel extreme learning machine. The method is an MI-EEG signal four-classification problem identification method combining function type data analysis and a sparrow search algorithm optimization kernel extreme learning machine (SSAKELM). Firstly, collected MI-EEG signals are preprocessed, data samples are established according to the duration of single motor imagery, and then a multi-channel and multi-sample alpha-waveband power spectral density (PSD) and frequency relation curve is obtained through FIR filtering. According to the basic theory of functional data analysis (FDA), functional smoothing processing is carried out on all PSD-frequency curves one by one according to brain electrode channels by utilizing a B spline, then characteristic parameters of each sample are obtained by utilizing functional principal component analysis (FPCA) and principal differential analysis (PDA), and then the characteristic parameters of all the samples are merged for normalization processing. The method comprises the following steps: selecting a training sample proportion, randomly selecting a training sample, carrying out clustering training by using SSAHELM, and then carrying out category prediction on a test sample. The MI-EEG signal multivariate characteristic parameter extraction method provided by the invention has clear mathematical and physical significance, mainly relates to matrix operation, is small in calculation scale, and is easy to embed into a chip.

The invention belongs to the technical field of electric power information physical system security, and discloses an urban power grid information physical system security situation early warning method based on cross-space risk propagation. A power grid physical layer and information layer coupling model and a cellular space false data injection attack model are constructed, and a fault cross-space propagation mechanism is simulated through an event-driven cellular automaton theory; an integrated kernel extreme learning machine model is adopted to predict the operation state of the power grid, multi-dimensional data fusion is realized through a radial basis kernel function, and an integrated structure is adopted to fuse a plurality of model prediction results so as to improve the model prediction precision; and establishing an early warning system including voltage out-of-limit, line overload and load loss indexes, and dynamically distributing weights and dividing early warning grades in combination with an entropy weight method. According to the method, the problem of low precision of urban power grid security situation early warning under multivariate disturbance is solved, and the accuracy and robustness of security situation early warning can be effectively improved.

The invention provides a gear fault diagnosis method based on feature fusion and GSWOA-KELM. The method comprises the following steps that an original gear fault vibration signal data set is acquired and preprocessed; extracting time domain and frequency domain features in a gear fault state, and performing feature fusion on the time domain and frequency domain features to construct a fault feature vector; optimizing a regularization coefficient C and a kernel function parameter gamma of a kernel extreme learning machine (KELM) by using an improved whale optimization algorithm (GSWOA), and constructing a GSWOA-KELM fault diagnosis model; respectively inputting the time domain, the frequency domain and the fusion feature vector into a GSWOA-KELM model to realize intelligent diagnosis and identification of gear faults; and establishing a KELM model, an SSA-KELM model and a WOA-KELM model, inputting time and frequency domain fusion features to carry out fault diagnosis, and carrying out comparison verification on a diagnosis result and a fault diagnosis result of the GSWOA-KELM model. Experimental verification shows that the gear fault diagnosis method has higher gear fault diagnosis accuracy, faster convergence speed and stronger global search capability.

The invention relates to the technical field of positioning and navigation, in particular to a multi-modal recognition pedestrian positioning and navigation method. The method comprises the following steps: firstly, respectively acquiring data in six motion modes of walking reading, walking telephone calling, walking swinging, walking pocket, jogging telephone calling and jogging pocket, and extracting 30-dimensional motion features; thirdly, constructing a multi-mode recognition model of a PCA-GWO-KELM (principal component analysis and grey wolf algorithm optimization kernel extreme learning machine) based on the PCA-GWO-KELM; inputting the motion features into a multi-mode recognition model for training to obtain recognition accuracy rates under different motion modes; then, step number detection is carried out by combining a peak value detection method with a self-adaptive acceleration upper threshold value and a self-adaptive dynamic time threshold value, the step length is calculated by adopting a multi-feature-based self-adaptive step length estimation algorithm, and the course is calculated by adopting a method of fusing direction sensor data and gyroscope data; finally, continuous dead reckoning is carried out on pedestrian displacement, and low-cost and high-robustness pedestrian navigation positioning is achieved.

The invention relates to the technical field of power grids, and discloses a power prediction method and device for photovoltaic power generation, equipment, a medium and a product. The method comprises the steps of obtaining a multi-modal historical data set corresponding to a photovoltaic station, and performing modal decomposition on the multi-modal historical data set by adopting a multivariate variational modal decomposition method to obtain a plurality of first modal components; obtaining a modal prediction result corresponding to each first modal component according to the plurality of first modal components through a kernel extreme learning machine prediction model optimized by a multi-target white shark optimization algorithm; and searching a prediction weight value corresponding to each first modal component, and obtaining a generation power prediction result corresponding to the photovoltaic station according to the modal prediction result and the prediction weight value corresponding to each first modal component. According to the scheme of the embodiment, multi-modal data feature mining and efficient prediction of the photovoltaic station can be realized, and the accuracy of power prediction of photovoltaic power generation can be improved.

The invention discloses an image recognition method for confrontation domain self-adaption and a conjugate gradient kernel extreme learning machine. The method is characterized by comprising the following steps: firstly, constructing a feature extractor to obtain initial features of a source domain and a target domain; and then constructing an adversarial domain adaptive network to optimize two domain features, enabling a source domain feature extractor and a target domain feature extractor to play a game with a discriminator so as to reduce inter-domain differences between a target domain and a source domain, projecting the inter-domain differences to a common feature space, and finding an optimal target domain feature. And finally, constructing a classifier to recognize the image, and constructing a conjugate gradient kernel extreme learning machine as a classifier to classify the optimal target domain features to obtain a classification result. The invention aims to provide a lightweight network, namely an adversarial domain adaptive and conjugate gradient kernel extreme learning machine, which solves the problem of dependence of existing deep learning on a large amount of annotation data, reduces sample annotation cost in image recognition, reduces hardware implementation cost, realizes cross-domain knowledge migration, and completes unsupervised and high-precision image recognition of a target domain.

The invention discloses a discharged material water content prediction method based on TA-MMKELA-BiGRU, and the method comprises the steps: sequentially carrying out the preprocessing and noise reduction of collected flow production line time sequence working condition data, and obtaining a denoised sample set; constructing a time sequence attention mechanism, and taking the de-noised sample set as the input of the time sequence attention mechanism; constructing a multi-level multi-kernel extreme learning machine encoder, and taking the output of the time sequence attention mechanism as the input of the multi-level multi-kernel extreme learning machine encoder; constructing a two-way gating cycle unit model, and taking the output of the multi-level multi-kernel extreme learning machine encoder as the input of the two-way gating cycle unit model; training a TA-MMKELA-BiGRU prediction model constructed by the time sequence attention mechanism, a multi-level multi-kernel extreme learning machine encoder and a bidirectional gating cycle unit model; and inputting the trained TA-MMKELA-BiGRU prediction model into the process parameter characteristic data of the flow production line for prediction, and outputting a prediction result. According to the invention, the prediction precision of the quality index of the flow production line is improved.

The invention relates to the technical field of power prediction, in particular to a photovoltaic power generation power prediction method, device and equipment and a computer storage medium. According to the photovoltaic power generation power prediction method provided by the invention, in order to make up the defects of a heuristic algorithm, a VMD-KELM photovoltaic power generation power prediction model based on BWO optimization is provided; the model combines the advantages of VMD (variational mode decomposition), KELM (kernel extreme learning machine) and BWO (white whale optimization algorithm), and the photovoltaic power environment can be quickly and accurately predicted according to the nonlinear data mining requirement and the high-uncertainty photovoltaic power environment.

The invention discloses a lithium battery health state estimation method and system. Belongs to the technical field of lithium batteries, and comprises the following steps: firstly, collecting single lithium battery data under the condition of continuous charging and discharging, then introducing an adaptive factor to improve Kalman filtering to obtain adaptive Kalman filtering, and using the improved adaptive Kalman filtering to carry out missing value filling on the collected data to improve data integrity. Secondly, improving an information acquisition optimization algorithm by using a quadratic interpolation method and an elite random reverse learning strategy to obtain an IIAO, optimizing hyper-parameters of a time sequence analysis model LightTS model by using the IIAO, inputting the processed lithium battery data into the optimized LightTS model for prediction to obtain a preliminary prediction result, and carrying out prediction on the preliminary prediction result; and finally, performing error correction on an error between the preliminary prediction result and the true value by using an online sequential reduced order kernel extreme learning machine (OS-RKELM) to obtain an error prediction value, and finally, superposing the preliminary prediction value and the error prediction value to obtain a final lithium battery health state prediction value.

The invention discloses a circuit breaker fault diagnosis method based on global-local feature extraction. Comprising the following steps: 1, collecting vibration signals of a circuit breaker in a normal working condition and a fault state as original data; 2, extracting original time domains and frequency domains of the vibration signals of the circuit breaker operating mechanism in different states and time-frequency characteristics of a generalized S transformation matrix, and regarding the original time domains and frequency domains as global characteristics; 3, extracting local features of the vibration signals by using a texture feature extraction mode of matching a local binary pattern with a gray-level co-occurrence matrix; 4, further screening the features by using maximum correlation and minimum redundancy to obtain a feature set; and 5, in combination with a kernel extreme learning machine classifier and GL-mRMR feature extraction, obtaining a GL-mRMR-KELM model applied to circuit breaker vibration data classification. The method is used for fault diagnosis of a high-voltage circuit breaker, and solves the technical problems of low universality and poor accuracy under small samples in a current operating mechanism state diagnosis algorithm.

The invention discloses a battery health state estimation method and system, and the method comprises the steps: extracting the strong correlation features of a battery health state through fusing a physical model and relaxation time based on electrochemical impedance spectroscopy data, and carrying out the feature screening and reconstruction through a random forest algorithm, and then optimizing network parameters of the multi-scale kernel extreme learning machine by using a sparrow algorithm so as to estimate the state of health of the battery. The estimation method provided by the invention can capture data features from multiple angles, has good generalization ability, and has significant advantages in the aspects of precision and efficiency compared with an existing estimation method.

The invention belongs to the technical field of power transmission line monitoring and dynamic capacity increasing. A power transmission line dynamic current-carrying capacity prediction and capacity increase method based on double-model adaptive fusion comprises the following steps: data acquisition and preprocessing: acquiring historical weather and current-carrying capacity data, and cleaning and standardizing the historical weather and current-carrying capacity data; weather-driven prediction: optimizing a kernel extreme learning machine through an artificial egret optimization algorithm to predict future meteorological parameters, and substituting the future meteorological parameters into a heat balance equation to calculate a first current-carrying capacity predicted value; performing data-driven prediction, optimizing variational mode decomposition by adopting a dynamic perception and accurate capture algorithm, and directly predicting a second current-carrying capacity predicted value in combination with a long-short-term memory network; in the fusion step, weights are dynamically distributed according to errors of the two models on the verification set, and self-adaptive weighted fusion is carried out to obtain a final point prediction value; and a probability modeling step: fitting probability distribution based on historical errors, and constructing a confidence interval. According to the invention, the precision and reliability of current-carrying capacity prediction are improved, and dynamic capacity increasing and safe operation of the power transmission line are realized.

The invention discloses a power distribution network bearing capacity evaluation method and system for large-scale automobile charging loads, and the method comprises the steps: firstly, achieving the efficient expansion of data through combining a variational auto-encoder and a polynomial fitting method, effectively increasing the number of training samples, reducing the risk of over-fitting, and improving the reliability of the power distribution network bearing capacity evaluation. Then, the stability in a complex training environment is improved by adopting a self-adaptive biological influence optimization method, the accuracy of feature extraction and the training efficiency are improved, then, data are carefully captured and reconstructed by adopting a self-encoding neural network with self-adaptive refined features, the information flow is optimized, the dimensionality is reduced, important information is reserved, and the accuracy of feature extraction is improved. Then, an adaptive error compensation mechanism of a kernel extreme learning machine is adopted to adjust an error learning rate and weight update in real time, and the accuracy and robustness of the model are improved; through the above optimization training, the model can fully master the characteristics of the charging load data, and achieves the precise evaluation of the bearing capacity of the power distribution network through the output final category. Therefore, the method is high in accuracy.

The invention discloses a fault diagnosis and health management prediction method based on a laser gyroscope, and belongs to the technical field of inertial navigation equipment health management. The method comprises the following steps: acquiring and preprocessing a time sequence data set of operation core parameters of the laser gyroscope; performing wavelet packet decomposition on the time sequence data to extract high-frequency fault features, extracting time sequence statistical features by a sliding window method, and combining principal component analysis to perform dimensionality reduction and fusion to obtain a multi-dimensional feature vector; inputting the feature vectors into an improved kernel extreme learning machine model, optimizing parameters through a particle swarm optimization algorithm, and then outputting fault types and grades; on the basis of a fault diagnosis result, an analytic hierarchy process is adopted to endow parameter weights, and a health factor calculation model is constructed to quantify a health state; taking the health factor sequential sequence and the key parameter degradation trend as input, and combining a bidirectional long-short-term memory neural network with health factor sequence constraint to predict residual life and a confidence interval; new data are regularly brought in, and model parameters are updated through transfer learning to realize dynamic iteration. The method solves the problems of early fault recognition lag and low life prediction precision of a traditional method, is suitable for the fields of aerospace, precision navigation and the like, and has remarkable engineering application value.

The invention discloses an SVG voltage ride-through control parameter identification method based on a kernel extreme learning machine in a double-solution space, and the method comprises the following steps: S1, dividing SVG fault stages, building an SVG steady-state control model and a fault control model, and according to the difference of the influence of control parameters on steady-state characteristics and fault characteristics, carrying out the recognition of SVG voltage ride-through control parameters; dividing a solution space of SVG control parameters into a double-solution space composed of a steady-state parameter solution space and a dynamic parameter solution space; s2, extracting a corresponding observed quantity characteristic value, constructing an input-output sample, and establishing a kernel extreme learning machine model in a double-solution space; s3, training a kernel extreme learning machine by using the input-output samples, and inputting RT-LAB hardware in-loop test data into the trained kernel extreme learning machine to identify SVG control parameters; according to the method, the adaptability and the accuracy of identification are improved, so that accurate modeling of the SVG is realized.

The invention provides a resin composition for a halogen-free flame-retardant cable sheath and a preparation method of the resin composition, and belongs to the technical field of polymer composites for cables, the preparation method comprises the following steps: S1, raw material grading pretreatment and core parameter accurate determination; s2, gradient pre-dispersion and uniformity feedback regulation and control; s3, step-by-step grafting compatibility and grafting rate feedback regulation and control; s4, carrying out dynamic flame-retardant regulation and control melt blending; and S5, gradient cooling granulation and finished product performance verification. In two key links of gradient pre-dispersion and dynamic melt blending, an improved fruit fly optimization algorithm-adaptive fuzzy neural network algorithm and an improved sparrow search algorithm-kernel extreme learning machine algorithm are respectively embedded, and the two algorithms are bidirectionally interactively fused, so that the problems that traditional process parameters are set according to experience and the performance fluctuation is large are solved, and the dynamic melt blending method is suitable for industrial production. The synergistic improvement of the uniformity, the flame retardant property and the mechanical property of the resin composition is realized.

The invention discloses a lithium battery health state estimation method and system, electronic equipment and a storage medium, and belongs to the field of battery state prediction.The method comprises the steps that probability modeling is conducted on a small amount of lithium battery charging and discharging time sequence data through a variational self-encoding network, an expansion sample with degradation consistency is generated, and an expansion data set is formed; key health indexes reflecting battery aging characteristics in the extended data set are constructed into a characteristic matrix as input; a kernel extreme learning machine is adopted as a nonlinear regression prediction model, global optimization is carried out on kernel function parameters and regularization coefficients of the nonlinear regression prediction model through an artificial bee colony optimization algorithm, and finally an SOH prediction model which is obtained through training and represents the relation between the battery health state and the characteristics is obtained; and outputting a battery health state estimation result based on the SOH prediction model. According to the method, the health state prediction precision and efficiency can be improved.

The present invention provides a method and a device for training a bearing fault diagnosis model. The training method includes the following steps: performing data segmentation on the fault vibration signals of different types of bearing faults to obtain a training set, where the training set includes a number of training samples; calculating the silhouette coefficients of the training samples in the training set under different parameters to select the optimal parameters of the enhanced composite multi-scale slope entropy; using the enhanced composite multi-scale slope entropy after parameter selection to extract fault features to obtain a training feature set; and using the training feature set to train the honey badger algorithm optimized kernel extreme learning machine to obtain a trained composite multi-scale fault diagnosis model. The present invention can effectively identify bearing faults of different types and damage degrees, and the average recognition rate reaches 99.9%.

This application provides a method, apparatus, and electronic device for detecting outliers in satellite telemetry data. The outlier detection model for telemetry data is trained using a kernel extreme learning machine, which can effectively detect outliers in satellite telemetry data, greatly improving the efficiency and accuracy of outlier detection.

The application relates to a fan blade icing fault diagnosis method based on a GWO-REKELM, and the method comprises the following steps: 1) pre-processing and normalizing all fan operation data; 2) using a random forest to perform feature screening on all fan operation data; 3) using an oversampling technology to balance the data set of the screened fan operation data; and 4) using a grey wolf optimization kernel extreme learning machine model with an improved objective function to train and test the data set, and completing fan blade icing fault diagnosis. Compared with the prior art, the application has the advantages of improving the correctness of fan blade icing fault diagnosis and the like.

The invention provides a pumped storage unit vibration trend prediction method and system based on a combined model, and belongs to the technical field of pumped storage unit detection. Comprising the following steps: acquiring an original vibration signal and performing variational mode decomposition by adopting a self-adaptive signal decomposition method to generate a plurality of subsequences; a singular spectrum analysis method is adopted to extract dominant components of all the subsequences, and superposition reconstruction is carried out on residual components; the processed subsequences are converted into a training set and a test set of a prediction model through phase-space reconstruction, and the training set and the test set serve as prediction model input for model training; and on the basis of a kernel extreme learning machine model and an IBiGRU-KAN model, predicting the high-frequency sub-sequence and the low-frequency sub-sequence in each sub-sequence, and superposing the prediction results of each sub-sequence to determine the vibration trend of the pumped storage unit. According to the method, complex data processing and model calculation tasks can be efficiently executed, and the vibration trend of the pumped storage unit is accurately predicted.

The invention relates to a tobacco shred moisture early warning method, a tobacco shred moisture early warning device and a storage medium, and the tobacco shred moisture early warning method comprises the following steps: obtaining a to-be-detected tobacco shred sample; inputting the to-be-detected tobacco shred sample into a pre-trained tobacco shred moisture prediction model to obtain a tobacco shred moisture prediction result; wherein the pre-trained cut tobacco moisture prediction model is obtained by training a preset kernel extreme learning machine prediction model by adopting an improved hunter prey optimization algorithm; judging whether the tobacco shred moisture prediction result exceeds a preset moisture threshold value or not; and if yes, sending out an early warning prompt. According to the invention, the problem of inaccurate moisture early warning in the tobacco shred production process is solved, and the accuracy of moisture early warning in the tobacco shred production process is improved.

The present application relates to a kind of short-term load prediction method based on ISSA-HKELM, the method includes: first, in view of the defect of kernel extreme learning machine KELM, combined with Gaussian kernel function and polynomial kernel function, construct the hybrid kernel extreme learning machine HKELM with stronger generalization ability;Second, in view of the problem that sparrow search algorithm is easy to fall into local extremum, adaptive t distribution strategy and dynamic adaptive weight are introduced to improve sparrow search algorithm;Third, the improved sparrow search algorithm ISSA is used to optimize the parameter of hybrid kernel extreme learning machine HKELM and establish ISSA-HKELM prediction model;Finally, short-term load prediction is carried out using the established ISSA-HKELM model.Compared with prior art, the present application has good prediction accuracy and robustness and the like advantages.

The present invention discloses a wind power prediction method based on a sparrow search algorithm optimized kernel extreme learning machine, which specifically includes the following steps: step 1, determining the dominant influencing factors affecting wind power; step 2, using the Thompson tau-quartile method to process the dominant influencing factors determined in step 1; step 3, constructing a sparrow search algorithm optimized kernel extreme learning machine prediction model, and predicting wind power through the model. The present invention solves the current problem that the wind power prediction accuracy is low and the prediction performance is greatly affected by its own parameters.

The invention provides a bearing fault diagnosis method of a self-optimization deep mixed kernel extreme learning machine, and belongs to the technical field of bearing fault diagnosis. The technical problems that the bearing fault diagnosis accuracy is low, and fault classification is prone to errors are solved. According to the technical scheme, the method comprises the following steps of S1, dividing a data set after fault information extraction into a training set and a test set; s2, setting the number of hidden layers and an activation function, and constructing a mixed kernel extreme learning machine model; s3, obtaining various parameters of the model by using an improved hawk algorithm; and S4, judging whether the bearing has a fault or not in the operation process and determining the fault type. The method has the advantages that the advantages of the improved optimization algorithm and the deep mixed kernel extreme learning machine are fused, the fault type of the bearing can be accurately judged, and the fault features of various faults can be effectively obtained for subsequent state judgment and fault type classification.

The present invention discloses a temperature extraction method for a Brillouin optical time-domain analysis system (BOTDA) based on a nuclear extreme learning machine, comprising: using the Brillouin optical time-domain analysis system to collect Brillouin gain spectrum parameters of a test optical fiber; using the nuclear extreme learning machine to analyze the parameters collected by the Brillouin optical time-domain analysis system; using the obtained real matrix as training data for the nuclear extreme learning machine; training the nuclear extreme learning machine using the training data to extract accurate temperature information; and utilizing a faster processing speed to improve system performance. The present invention introduces a nuclear extreme learning machine algorithm to improve the temperature extraction accuracy and efficiency of the Brillouin optical time-domain analysis system, which is beneficial to the application of the Brillouin optical time-domain analysis system in actual detection.