96 results about "Hypersphere" patented technology

The invention discloses a hyperspectral image open set classification method based on prototype similarity distribution modeling. The method comprises the following steps: acquiring a training sample set and a test sample set; initializing a category prototype on the unit hyper-sphere and forming uniformly distributed prototype spaces through iterative optimization; training a deep learning network based on a joint loss function, guiding the samples to form high-discrimination similarity distribution in a prototype space, and calculating similarity vectors of the samples and prototypes of each category; training the OCSVM by using the similarity vector of the known class sample to learn the distribution boundary of the known class in the prototype space; in the test stage, a test sample is input into the deep learning network, the similarity vector of the test sample is calculated, and whether the test sample belongs to a known class or not is judged by the OCSVM; and if the class is a known class, determining the fine-grained classification according to the class prototype of the maximum similarity component in the similarity vector. According to the method, the interpretability and generalization of the open set classification model are enhanced, and the misrecognition risk between similar categories is reduced.

The invention provides a large language model generation code detection method and system, which is applied to the technical field of artificial intelligence, and comprises the following steps: obtaining a to-be-detected code; a to-be-detected code is input to a trained shared encoder, a code feature vector is obtained, the shared encoder is obtained through multi-target joint training, and the multi-target joint training is used for optimizing classification loss, domain confrontation loss, comparison loss and difficult sample loss at the same time; l2 normalization is carried out on the code feature vector, and the code feature vector is mapped to a hyperspherical space to obtain spherical embedding; the sphere is embedded and input into a sphere category classifier based on sphere logistic regression for classification processing, a detection result of the to-be-detected code output by the sphere category classifier is obtained, the detection result comprises AI generation and human writing, and a decision boundary of the sphere category classifier is an intersection line of a hyperplane and a hypersphere for classification. According to the invention, the AI generation code and the human compiled code can be accurately distinguished.

The invention discloses a single-classification industrial control system anomaly detection method based on double-view information bottleneck fusion, which belongs to the technical field of industrial control system anomaly detection and comprises the following steps of: in a training stage, simultaneously constructing a time sequence view and a pseudo image view; respectively extracting characterization through a time dynamic encoder and a pre-trained visual backbone; performing cross-view compression in the variational information bottleneck fusion module and establishing projection branches only depending on sequence representation; a normal subspace is learned around a center vector, an accurate hypersphere is established to describe normal data, and a center distance is used as an abnormal score and a quantile threshold is used to complete discrimination; in the detection stage, online recognition can be performed only by inputting a time sequence view. According to the method, dual-view prior and variational information bottleneck are fused, end-to-end collaborative optimization is carried out on the dual-view prior and the variational information bottleneck and deep support vector data description, an accurate decision boundary can be established under the condition of a small number of samples, and a solution for industrial control system anomaly detection under the condition that the samples are limited is provided.

The invention provides an incomplete vibration data damage identification method and system fusing comparative learning and self-supervised learning, and aims to solve the problems of data scarcity and unbalanced and incomplete data sets in civil engineering structure damage identification, structure acceleration data is acquired through a vibration sensor, data enhancement is performed after preprocessing, and the structural acceleration data is acquired through a vibration sensor. Comprising random mask enhancement and white noise enhancement, a neural network model based on an auto-encoder is constructed, and the neural network model comprises an encoder, a reconstructor, a classifier, a predictor and a dimension rising device. Low-dimensional features are mined through self-supervised learning, feature distribution is optimized in combination with comparative learning, the similarity of similar features and the distinction degree of different types of features are maximized, and finally the health state, known damage, unknown damage and fuzzy damage states of the structure are judged through the hyper-sphere boundary of a high-dimensional decision space. According to the method, the accuracy and robustness of damage identification are remarkably improved, and particularly, the method has excellent performance in the aspect of slight damage identification.

The invention discloses a rapid yield analysis method based on xgboost proxy model truncation sampling, and the method comprises the following steps: generating a first random sample point set, and obtaining a training set through simulation; constructing a proxy simulator, and training the proxy simulator by using the training set; generating a second random sample point set, and inputting the second random sample point set into the proxy simulator to obtain a pseudo data set; establishing a filter, determining two hyperspherical surfaces based on the pseudo data set, dividing a process parameter space, evaluating a safety coefficient and the probability that a sample point enters a real simulator, and simulating the sample point of the process parameter space according to the probability to obtain a real sample set; iteratively optimizing the hyper-spherical surface and the proxy simulator by using the real sample set until convergence to obtain a final safety coefficient; and generating a third random sample point set, carrying out truncation sampling and failure discrimination by combining a real simulator and an agent simulator, and carrying out statistics on the final yield. According to the method, the xgboost network is adopted as an agent model to reduce the simulation cost, the sample size is reduced through truncation sampling, and rapid and accurate prediction of the yield of the integrated circuit is realized.

The invention belongs to the technical field of microservice system anomaly detection, and discloses a Trace anomaly detection method under a microservice architecture based on a graph attention network. A service operation graph (SOG) is constructed to serve as a mesoscopic layer trace representation method, and the graph representation granularity and efficiency of an existing method are balanced. Meanwhile, a group of delay-related features and error propagation modes are extracted as multi-dimensional edge features to be integrated into the SOG, and interaction information between services is fully utilized to improve the accuracy of anomaly detection. In order to capture various index data generated in the service and a calling relation between the services, the characteristics of nodes and edges of SOG are learned through GAT, and a graph embedding vector of a trace is obtained. And a hypersphere loss function training model is utilized to avoid dependence on labels, normal traces are gathered to a hypersphere center, and traces far away from the hypersphere center are regarded as anomalies.

The invention discloses an LSTM-SVDD anomaly detection method and system based on rule guidance, belongs to the technical field of industrial nondestructive quality inspection, and aims to solve the technical problems that noise samples are sensitive and excessively depend on annotation of abnormal data, and expert experience and knowledge cannot be effectively introduced. Constructing a feature extraction model based on a bidirectional LSTM network, a rule template engine and a feature projection fusion layer; calculating a hypersphere radius through a quantile method, constructing an SVDD loss function as a basic loss function, introducing a rule penalty term to construct a rule loss function, carrying out weighted summation on the basic loss function and the rule loss function to construct a total loss function, and obtaining a trained feature extraction model and an optimized hypersphere radius through minimizing the total loss function; and inputting a to-be-detected sample into the trained feature extraction model, calculating an Euclidean distance between a fusion feature vector and the center of the hypersphere, and comparing the Euclidean distance with a dynamic detection threshold value.

The invention discloses a unified unsupervised depth forgery detection method based on prototype guidance and double hyperspheres, and the method comprises the following steps: S1, extracting visual artifact features generated by depth forgery, and achieving the generation of pseudo labels through the clustering of a Gaussian mixture model; s2, performing comparative learning through a category prototype of momentum updating; and S3, realizing effective fusion of a feature space and a geometric decision by respectively constructing independent hyper-spheres for real and forged samples, and constructing a dual-depth support vector data description framework. The system has the beneficial effects that the system is composed of three core modules: a visual artifact feature-based pseudo label generator provides a reliable supervision signal, and the visual artifact feature-based pseudo label generator provides a visual artifact feature-based pseudo label description framework; a prototype guided contrast learning (PGCL) module enhances the discrimination capability through a prototype of momentum update, and a dual-depth support vector data description (Dual-DeepSVDD) module constructs a dual-hyperspherical decision boundary of true and false samples, thereby realizing effective integration of feature learning and geometric decision.

The invention discloses a robot virtual-real cooperative training decision optimization system and method based on digital twinning, and the method comprises the following steps: collecting state data and disturbance data of an entity robot in a real environment, and carrying out the preprocessing of the data to generate standardized input; joint coding and state perturbation mapping are carried out on the standardized data, and a feature vector sequence embedded in a hyperspherical manifold space is generated; inputting to a virtual twin control body based on a hypersurface neural element structure, executing disturbance direction sensitive activation, and outputting an activated state vector sequence; virtual and entity control action prediction sequences are generated respectively, an embedded space difference vector is calculated, and control body parameters are updated based on a consistency optimization criterion; after convergence, the control body executes reasoning to generate a target control action sequence, the entity robot is driven to complete action execution, and control strategy optimization is achieved. According to the method, high-precision migration and rapid convergence of a robot control strategy are realized, and the execution stability in a complex disturbance environment is improved.

The invention discloses a method and a system for monitoring pressure abnormity of a fuel oil common rail pipe of a marine main engine. The method comprises the following steps: acquiring historical operation parameter data and constructing a data set; the historical operation parameter data comprises historical host load data, historical host rotating speed data and historical fuel oil common rail pressure data; historical operation parameter data are preprocessed, an SVDD model is constructed and trained based on the historical operation parameter data, the model obtains a historical normal data hyper-sphere in a three-dimensional feature space, and the center and the radius of the hyper-sphere are obtained; similarly, real-time operation parameter data collected in real time are preprocessed and then input into the model, the distance between the real-time operation parameter data and the center of the hypersphere is obtained, and the value of the distance is compared with the value of the radius of the hypersphere in historical normal data; and when the numerical value of the distance is large, it is judged that the real-time operation parameter data is within an abnormal range, the fuel oil common rail pressure of the ship main engine is in an abnormal state, and an alarm signal is triggered. The method has the effect of low error rate.

The present application relates to a kind of single class image recognition method based on multiple hypersphere space division, belong to image recognition and machine learning technical field.First, sample is mapped to a latent space by feature extraction network, then according to the similarity of training sample and test sample in space, and the classification identification of test image sample is carried out.The way of introducing threshold radius is used to judge whether the distance of test sample from its nearest neighbor training sample is close enough.The representation of target category in feature space is the space formed by the superposition of multiple hypersphere regions, with threshold as the radius of each hypersphere, if the feature of a test sample is within the space range formed by the multiple hyperspheres, it will be predicted as positive sample, otherwise it will be predicted as negative sample.The prediction accuracy of the present application for test image sample is significantly improved, and the prediction of positive sample and negative sample in test image sample is more balanced.

The application discloses an iris recognition method and device, system and storage medium, and performs preprocessing such as polar coordinate unfolding on an input original iris image in sequence; multi-level image features are extracted through an inverse residual module; subsequently, a global depth convolution GDConv operator is introduced to replace a global average pooling layer, so that spatial structure distribution features of iris textures are retained under the premise of extremely low calculation complexity; finally, an ArcFace loss function is used to optimize the distribution of features in a hyperspherical space, and the class separability of the features is enhanced by introducing an additive angle interval. By using the technical scheme of the application, the technical problems that spatial topological information is lost due to a global average pooling GAP operation in iris feature extraction by using an existing lightweight convolutional neural network, and the feature discrimination is reduced due to network lightweight are solved.

This invention discloses a continuous vector discretization representation method, system, and application, comprising: constructing an end-to-end unified architecture including an attention encoder, a binary spherical quantization module, and an attention decoder; utilizing the attention encoder combined with a block causal masking mechanism to uniformly extract high-dimensional feature vectors from single-frame images or multi-frame videos; performing dimensionality reduction, spherical normalization, and binary quantization based on a learnable hyperplane on the high-dimensional feature vectors through the BSQ module, mapping the features to a unit hypersphere and generating binary discrete codes without an explicit codebook; and using the attention decoder combined with spatiotemporal position coding to perform high-fidelity reconstruction of the discrete codes. This invention solves the problems of low parameter efficiency, incompatibility with image and video processing, imbalance between reconstruction quality and computational efficiency, and unstable training caused by the reliance on explicit codebooks in existing technologies. It achieves lightweight, high-fidelity, wide compatibility, and easy training, making it particularly suitable for efficient storage, transmission, and accurate reconstruction of multimodal medical images.

This application belongs to the interdisciplinary field of artificial intelligence, computer vision, and network information security. It discloses a deepfake detection method and system based on orthogonal subspace decomposition and hyperspherical metric. By applying singular value decomposition to the weight matrix of a pre-trained visual model, it explicitly constructs a frozen principal subspace that preserves general semantic knowledge and a trainable orthogonal residual subspace that captures specific forgery traces, achieving orthogonal isolation of the parameter space. Simultaneously, hyperspherical metric learning is introduced into the feature space, performing L2 normalization on the features and applying alignment and uniformity losses. Combined with spherical linear interpolation, latent space data augmentation is performed while preserving the Riemannian geometric structure. Through the synergistic constraints of the parameter and feature spaces, this application can reduce the interference of fine-tuning on pre-trained general visual knowledge and improve the feature discrimination stability and cross-forgery generalization ability in deepfake detection tasks.

The invention provides an industrial anomaly detection method based on an improved coupling hypersphere feature adaptive model. The method comprises the following steps: acquiring an original industrial image data set; constructing an improved coupling hypersphere feature adaptive model, wherein the improved coupling hypersphere feature adaptive model combines partial convolution and dynamic upsampling to enhance the multi-scale feature fusion capability; taking a training set as input, performing iterative alignment increment memory bank optimization on a memory bank in the improved coupling hypersphere feature adaptive model, and constructing normal feature representation; taking the training set as input, and training the improved coupling hypersphere feature adaptive model based on the constructed normal feature representation; and taking the test set as input, and carrying out anomaly detection and positioning by adopting the trained improved coupling hypersphere feature adaptive model. According to the method, the accuracy of unsupervised industrial anomaly detection and the pixel-level positioning precision are improved by optimizing the feature up-sampling precision and the memory bank characterization capability.

The invention belongs to the technical field of aero-engine gas path fault diagnosis, and discloses a gas path fault hyper-spherical clustering method based on angle interval constraint, comprising the following steps: S1, acquiring a gas path fault data sample and a fault-free data sample; s2, extracting features in the gas circuit fault data sample and the fault-free data sample, and obtaining sample features; s3, calculating a characteristic residual error according to the sample characteristics, and calculating a residual error difference according to the characteristic residual error; s4, constructing an angle interval constraint loss function, an L2 norm constraint loss function and a class center aggregation function; s5, constructing a total loss function; and S6, inputting the sample features, the feature residuals and the difference of the residuals into a total loss function to obtain a hyperspherical clustering result. The accuracy and robustness of gas path fault diagnosis are improved, and assistance is provided for health management and gas path fault analysis of an engine.

The present disclosure relates to a user financial risk identification model generation method and device, electronic equipment and computer readable medium. The method comprises: obtaining financial data of a plurality of historical users, wherein the financial data comprises a default state; selecting a plurality of positive sample users from the plurality of historical users according to the default state; generating a hypersphere equation set through the financial data of the plurality of positive sample users and an initial hypersphere equation; solving the hypersphere equation to obtain an optimal solution; and generating a user financial risk identification model based on the optimal solution, wherein the user financial risk identification model is used to determine the financial default risk probability of a user. The user financial risk identification model generation method and device, electronic equipment and computer readable medium disclosed by the present disclosure can adapt to the machine learning model training process when the number of samples is unbalanced, and can also generate an accurate and efficient user financial risk identification model using unbalanced sample data.

The application belongs to the technical field of wind farm stability domain construction, and specifically discloses a wind farm real-time stability domain construction method, system, terminal and medium. The method comprises the following steps: collecting power grid dynamic characteristic data to construct a dynamic frequency related node admittance matrix; collecting wind farm operation signals and introducing a dynamic error boundary to construct a wind farm measured impedance model containing error variables; combining time-varying working conditions to calculate a geodesic line stability margin, and discretizing the camera to generate a multi-parameter sample set; based on the multi-parameter sample set and a high-dimensional mapping algorithm, a multi-parameter stability domain hypersphere boundary is constructed in real time, and the absolute distance of the stability margin is output; when the distance is lower than a threshold value, a sensitivity gradient vector is solved, and an active control instruction is issued. The application comprehensively considers the measured model error and the wideband dynamic characteristics of the power grid, can accurately quantify the multi-parameter grid-connected stability boundary, and significantly improves the safety and survival ability of the new energy grid-connected system.

The application discloses a sea surface weak target detection method based on a residual network and a hypersphere constraint. The method can solve the problems of training underfitting or overfitting caused by the fact that the target sample is much smaller than the sea clutter sample under the sea clutter background, and the specific steps include: 1, performing short-time Fourier transform on each piece of data obtained after dividing the radar echo signal sample to obtain a time-frequency spectrum; 2, constructing an anomaly detection network model combined with a support vector data description based on a variational autoencoder of a residual network; 3, inputting the time-frequency spectrum into the anomaly detection network model for end-to-end training; 4, performing online detection of the target based on the trained model; and 5, evaluating the performance of the application from the constructed multi-dimensional index. The application can effectively enhance the separability between the target and the sea clutter, and improve the weak target detection precision under the condition of a real small sample.

This application provides a learning embedding method, apparatus, electronic device, and storage medium for a dual-view knowledge graph. The method includes: performing a global mapping transformation on the entity embedding features of the triples to obtain global entity features; performing a local mapping transformation on the global entity features to obtain hypersphere features, hyperbolic features, and Euclidean features; inputting the hypersphere features, hyperbolic features, and Euclidean features into an attention fusion model to obtain entity fusion features; obtaining a score value for the triples based on the global entity features and the entity fusion features; and updating the relationships between entities in the initial knowledge graph based on the score value to obtain a target knowledge graph. This application can model entity embedding from both global and local perspectives, improving the accuracy of learning.

The invention discloses a Bayesian deep active learning guided energy field prediction test design method, and belongs to the field of spacecraft manufacturing and application. The implementation method comprises the following steps: dividing energy field analysis models with different precisions according to grid density in finite element analysis, constructing a Bayesian multi-credibility neural network model for energy field prediction, deeply mining deep features of training data with different precisions by using the network model, constructing a deep feature explicit transfer link based on meta learning, and predicting the energy field according to the explicit transfer link. Step-by-step learning of deep features of the energy field is realized; a high-dimensional space hypersphere with mutual exclusion attributes is constructed, sample stacking is avoided, and active selection and collection of new samples are completed; constructing a multi-credibility model screening method with the maximum cost-effectiveness ratio, selecting an analysis model with the cost performance as high as possible at a newly-added sample, calling the selected analysis model to generate a result, obtaining new training data, and updating part of parameters of the Bayesian multi-credibility neural network until a termination condition is met, so as to realize energy field prediction.

The invention provides a surge management system of a fluid machine, an early warning method, a model training method, a computer readable storage medium and a computer program product. The surge management system comprises a computing device which is configured to operate a surge early warning model; the surge early warning model comprises a deep support vector convolutional neural network, and the deep support vector convolutional neural network has a deep separable convolutional network structure and is used for mapping input data reflecting the operation state of the fluid machine to a feature space to obtain corresponding feature points; in addition, the surge early warning model predefines a hypersphere in a feature space; and the calculation device is configured to trigger surge early warning when it is judged that the feature points are located outside the hypersphere. The surge early warning model adopted by the system can complete reliable development training under the conditions of scarcity of surge samples and imbalance of data sets, and light weight of a deep learning model is realized.

The application provides a log anomaly detection method based on space-time feature fusion, and relates to the technical field of intelligent operation and maintenance of computer software systems. The method first constructs a log original sample data sequence set and forms a corresponding log template data sequence; then extracts features in the time dimension and the space dimension from the log template sequence data, and fuses the two kinds of features to obtain the final feature representation of the log template data sequence; finally, a Deep SVDD algorithm is used to train an anomaly detection model based on log space-time feature fusion, to learn and optimize a hypersphere associated with the log template sequence representation vector, and to realize log anomaly detection. The method of the application mines the time and space correlation in the log original data sequence, and further gives the system good anomaly detection accuracy, which helps to accurately capture anomalies in the software system operation mode and improve the stability of the system.

This invention discloses a TimeGAN-based intelligent detection method for abnormal data in multidimensional signals of electromechanical servo systems. The method includes collecting time series data from electromechanical servo system sensors and performing data preprocessing; inputting the data into a TimeGAN model for training to generate a synthetic data set of a certain size, which is then merged with the original samples into an extended data set; initializing an anomaly detection model based on LSTM-Deep SVDD; inputting the extended data set into an anomaly detection model based on LSTM-Deep SVDD for training to optimize the hypersphere radius; inputting test data into the trained anomaly detection model to calculate a judgment criterion; and inputting the data to be detected into the trained anomaly detection model to calculate a judgment threshold and determine whether the data is abnormal. The method can be used for detecting abnormal data in multidimensional signals when the original data of the electromechanical system is insufficient, and can improve the accuracy and precision of the detection model.

The application relates to a flight trajectory anomaly detection method based on an LSTM-GBSVDD model. In order to cope with the length variation problem of flight trajectory data, the application uses LSTM to extract key features in a time sequence, converts variable-length trajectory data into fixed-length representation, enables anomaly detection to be carried out in a fixed-length feature space, and improves the universality of the model. On the basis of feature extraction, the SVDD algorithm is introduced to construct a multi-dimensional hypersphere classifier to model normal flight trajectories. Through the model, potential abnormal trajectories can be identified in an unsupervised framework, the dependence on data labels is avoided, and the bottleneck that abnormality cannot be identified in an unsupervised environment is solved. The application first realizes the joint optimization of the LSTM network parameters and the SVDD scoring function, and proposes a gradient-based training method. The method significantly improves the accuracy and calculation efficiency of anomaly detection and improves the detection performance.

The invention discloses a federated learning model optimization method and system and an image recognition method and system, and relates to the technical field of machine learning, and the method comprises the steps: constructing a sample data set; initializing a federated learning model; the federal learning model comprises a global model and a plurality of local models; each of the global model and the local model comprises a feature extractor and a classifier; and based on the sample data set, optimizing the federated learning model by adopting a hyperspherical decoupling training method and a negative label distillation method to obtain an optimized federated learning model. According to the invention, the precision and efficiency of federal learning model recognition can be improved, and the precision and efficiency of image recognition can be improved.

The present application discloses a kind of soft rock zone high spectral remote sensing identification method for railway engineering geological survey, comprising: obtaining railway survey area hyperspectral image and constructing data set;Subsample set is constructed by random sampling, and the space division of multiple groups of isolated hyperspheres and residual space is generated based on the distance of pixel and its nearest neighbor;According to the position of pixel falling into each space division, generate isolated kernel feature vector;With all or part of pixel as background pixel set, calculate the average value of its isolated kernel feature vector as isolated distribution kernel mean model;The inner product of the isolated kernel feature vector of the pixel to be measured and the mean model is calculated to obtain the initial abnormal similarity, and the contiguous area is extracted as soft rock zone after spatial neighborhood regularization.The present application reduces the computational complexity from quadratic to linear, has data density adaptive ability, and can efficiently and accurately identify soft rock zone under complex geological background along railway by fusing spatial continuity constraint.

The invention discloses a high-voltage cable partial discharge fault accurate positioning method and system based on feature space topology mapping. The method comprises the following steps: synchronously acquiring three-phase high-frequency current signals through a high-frequency current sensor array; filtering and de-noising the signal; extracting a four-dimensional feature vector formed by the amplitude ratio, the polarity combination, the phase difference and the energy distribution; mapping the feature vector to a four-dimensional hyperspherical feature space and constructing a topological manifold; calculating a surrounding number as a topology invariant, and establishing a bijection mapping relation between the surrounding number and a physical position to realize accurate positioning; when the matching degree is insufficient, starting an auxiliary criterion based on time delay and attenuation characteristics to carry out secondary distinguishing; and the fault position is visualized through three-dimensional projection. According to the invention, the high-precision positioning of the partial discharge position of the high-voltage cable can be realized, and the robustness and diagnosis efficiency in a complex scene are effectively improved.

Embodiments of the present application disclose a layout analysis model determination method and device, electronic equipment and a medium. Based on a pre-trained layout analysis model, at least one pseudo label of an unmarked layout sample is determined to obtain a candidate pseudo label sample with the pseudo label; for each kind of pseudo label, based on an initial feature extraction model of the kind of pseudo label, a hypersphere center corresponding to the kind of pseudo label is determined, and according to the hypersphere center and a marked layout sample corresponding to the kind of pseudo label, a target hypersphere radius of the pseudo label is determined; according to the target hypersphere radius of the kind of pseudo label, a target pseudo label sample is selected from the candidate pseudo label sample corresponding to the kind of pseudo label; and according to the target pseudo label sample of each kind of pseudo label, the pre-trained layout analysis model is trained to obtain a target layout analysis model. The embodiments of the present application reduce the dependence on label samples and improve the accuracy of layout analysis.

The present invention relates to the technical field of reliability analysis of complex structures, and specifically to a method, system, device and medium for analyzing the small failure probability of turbine shaft fatigue life based on a hierarchical surrogate model. The method comprises the following steps: S1, constructing a large-capacity candidate sample pool; S2, selecting a small number of samples to construct a Kriging surrogate model; using hypersphere segmentation to generate several small-scale candidate sample pools; S3, training the Kriging model in each small-scale candidate sample pool in turn to determine the number of failure samples in each small-scale candidate sample pool; S4, accumulating the number of failure samples in all small-scale candidate sample pools to obtain the total number of failure samples. N F , and then calculates an estimated value for the turbine blade fatigue life failure probability. This invention uses a large-capacity sample pool segmented by hyperspheres and a Kriging surrogate model to sequentially identify and accumulate failure samples, addressing the problem of poor or even non-convergence in existing surrogate model methods when solving problems with small failure probabilities.