498 results about "Initial sample" patented technology

The invention provides a training data synthesis method and device based on error extrapolation and inference chain analysis, a medium and a program product. The method comprises the steps of obtaining an initial sample set; performing multiple sampling reasoning on the problem of each task sample by using a small language model to generate a plurality of reasoning chains; calculating an overall error score of each reasoning chain based on a preset error evaluation rule, and determining a to-be-corrected reasoning chain; the inference chain to be corrected and the corresponding question are input into the large language model together, and a corrected answer is generated; forming a new task sample by the question and the corrected answer, and finely adjusting partial parameters of the small language model; repeatedly executing the process until the performance index change rate of the model on the task evaluation set is lower than a preset threshold value, and outputting a final task sample; and forming a training sample set by a plurality of final task samples, and performing all-parameter fine tuning on the small language model. According to the method, the training data self-optimization path is constructed by taking the model error as guidance, so that the semantic consistency and the data validity are improved.

The invention discloses an optimization design method for a control surface of a wide-speed-range large-attack-angle reusable carrier, and relates to optimization design of aerodynamic configuration of an aircraft. The method comprises the following steps: S1, setting a control surface aerodynamic configuration design variable range, and generating an initial sample library by adopting Latin hypercube sampling; s2, aerodynamic parameters are obtained through CFD simulation, and a Kriging proxy model is trained; s3, evaluating the precision of the proxy model by taking a U learning function as a criterion, and stopping adding points when the minimum U function value is smaller than a threshold value; s4, constructing an optimization model which takes maximization of the lift-drag ratio and the static stability margin under the hypersonic speed as a target and takes the condition that the aerodynamic parameters of the supersonic speed / subsonic speed are not lower than a reference value and the hinge moment as constraints; and S5, performing iterative optimization by adopting an improved multi-target particle swarm algorithm based on genetic algorithm crossover mutation operation, updating the proxy model after the optimal solution of each generation is subjected to CFD verification, and outputting an optimal solution set. The problem of wide-speed-range aerodynamic configuration contradictions is solved, and the comprehensive performance of the carrier is remarkably improved.

The invention discloses a small sample self-adaptive mixed gas sensor dynamic response modeling method, which belongs to the technical field of gas sensors, and specifically comprises the following steps: testing mixed gas through a gas sensor array, and generating a response signal; pulse signals representing gas concentration mutation characteristics are generated, the pulse signals and response signals form an initial sample set, short-time-sequence fragments are obtained through truncation separation, initial state signals are generated at a time step before a response rising point, and a short-time-sequence fragment sample set is obtained; forming an enhanced data set through non-playback extraction and carrying out normalization processing on the enhanced data set; and constructing and training to obtain gas sensor dynamic response models of different gas sensors. By simulating the complex dynamic response of the gas sensor, the data sample enhancement of the mixed gas is completed, the difficulty of training a mixed gas component identification model by using a small sample is reduced, and the model can quickly adapt to environmental change and performance difference of different sensors; and reliable and effective data enhancement support is provided for mixed gas component identification.

The invention discloses a carbon dioxide oil displacement burying multi-objective optimization method based on a self-adaptive agent model, and relates to the technical field of oil reservoir injection and production optimization. The method comprises the following steps: firstly, constructing a carbon dioxide flooding embedding injection-production optimization model, then acquiring a plurality of initial samples by adopting Latin hypercube sampling to construct a database, preferably selecting each target agent model based on the database, and then generating a Pareto leading edge by utilizing a dominating class search strategy, a decomposing class search strategy and an index class search strategy; and in the optimization stage, a preferred potential solution is searched according to a hypervolume improvement maximum strategy, numerical simulation is carried out, the database is updated until a preset number of times is reached, each iteration optimization scheme, an oil reservoir net present value and a carbon dioxide burying amount are output, and multi-target optimization of carbon dioxide flooding burying is completed. According to the method, the carbon dioxide flooding multi-objective optimization efficiency is improved, meanwhile, the search direction is dynamically adjusted through the hyper-volume evaluation index, the Pareto frontier is accelerated, and accurate prediction of the carbon dioxide flooding development scheme is achieved.

The invention discloses a wind field rapid prediction method and system based on optimized Latin hypercube sampling and POD-BPNN, and the method comprises the steps: carrying out Latin hypercube sampling to generate an initial sample point set, introducing a sensitivity weight, and carrying out the iterative optimization of sample distribution through a greedy strategy; assembling the CFD numerical simulation flow field data of all sample points in the sample point set into a flow field snapshot matrix, and determining a dynamic multi-target truncation order and a corresponding POD mode and coefficient; building and training a BPNN model, packaging the trained BPNN model, the POD modal matrix, the mean field and the flow field reconstruction logic into an FMU module, obtaining the FMU module which can be called in a cross-platform manner, and achieving the real-time prediction of a wind field. The invention relates to the technical field of wind field prediction, significantly improves the precision and calculation efficiency of wind field prediction, and provides an efficient and accurate solution for the rapid prediction of a wind field.

The invention discloses a post-pumping air tank robustness collaborative design method considering hydraulic parameter time-varying characteristics, which comprises the following steps: defining a hydraulic parameter uncertainty fluctuation interval of a water delivery system in a full life cycle, and setting a body structure decision variable range of a post-pumping air tank; performing combined sampling in the water conservancy parameter uncertainty fluctuation interval and the body structure decision variable range by using a test design method to generate an initial sample set; performing steady-state and transient-state coupling simulation on the initial sample set, constructing a constant-flow operation condition of the water delivery system by using a hydraulic equation, updating a water pump working point and pipeline pressure distribution, performing transient simulation by using a characteristic line method to obtain a hydraulic response index, and training to obtain a water hammer response agent model; constructing a robustness optimization objective function based on failure probability constraint; and performing global optimization on the target function by using an intelligent optimization algorithm, calling the water hammer response agent model to perform random simulation, evaluating a failure probability, and outputting a target design scheme.

The invention relates to a limestone granularity detection method based on YOLO-ADM, which comprises the following steps: firstly, collecting a raw material crushing section limestone data set, then carrying out data enhancement on an initial sample limestone data set, and dividing the initial sample limestone data set into a training set, a verification set and a test set; a deformable attention mechanism DAttention, a multi-scale sequence fusion module SSFF and a triple feature decoding module TFE are integrated in the YOLOv8 framework, and a YOLO-ADM model is obtained; on this basis, a training loss function is improved, and a loss function MioU based on the auxiliary frame and the minimum point distance is designed; and training the YOLO-ADM model through the training set and testing the performance of the network model through the test set. According to the method, the deformable attention DAttention is introduced into the YOLOv8 network, the YOLOv8 neck network is redesigned based on the multi-scale sequence fusion module SSFF and the triple feature decoding module TFE, the designed loss function MioU is combined, the excellent performance is shown in limestone detection, and the generalization performance is good.

The invention provides a structure reliability analysis method based on an adaptive variable fidelity model, and the method comprises the steps: generating an initial sample point set which is uniformly distributed and has representativeness through an improved random sampling method KMODMC, enabling the initial sample point set to comprise a low-fidelity sample set and a high-fidelity sample set, training a BP neural network through employing the low-fidelity sample set, and carrying out the training of the BP neural network through employing the high-fidelity sample set; a low-fidelity BP neural network model is obtained; meanwhile, based on an error training Kriging model of a high-fidelity sample set and a low-fidelity model predicted value, an error correction Kriging model is constructed, the low-fidelity BP neural network model and the error correction Kriging model are combined to form a multi-fidelity mixed agent model, and adaptive iterative optimization is performed through a double-model alternate point adding sampling strategy. And finally obtaining a high-precision multi-fidelity hybrid agent model for structural reliability evaluation. According to the method, the problems of high cost of high-fidelity simulation calculation and insufficient precision of a low-fidelity model are solved, and the adaptive capacity and prediction precision of the model in a complex nonlinear problem are effectively improved.

The invention provides a multi-objective optimization method based on an adaptive point adding criterion and an agent model, and relates to the technical field of multi-objective optimizing.The method comprises the steps that a Latin hypercube sampling method is used for sampling an optimization algorithm, and initial sample points are obtained; constructing an initial agent model by using the initial sample points; based on an error standard and a self-adaptive point adding criterion, updating the initial agent model to obtain an updated agent model; and calculating the optimization algorithm by using the updated agent model to obtain a multi-target optimization result, and completing multi-target optimization. According to the method, the problem that multi-objective optimization is difficult to balance precision and convergence speed is solved.

The invention provides a photovoltaic power prediction method and system based on a double-current network and multiple attention, and the method comprises the steps: obtaining historical photovoltaic generating capacity and meteorological information, and obtaining initial sample data; performing variable factor influence analysis on the initial sample data to obtain a simplified initial sample data set; performing similar daily clustering processing on the simplified initial sample data set to obtain each scene data set after clustering; providing a photovoltaic power prediction initial model based on a double-current network and multiple attention, and training the photovoltaic power prediction initial model by using each clustered scene data set to obtain a photovoltaic power prediction model; and taking the historical generating capacity and meteorological data of the target photovoltaic power station in the set time period as input data of the photovoltaic power prediction model, and outputting a photovoltaic power prediction result. According to the invention, through the clustering integration method, the clustering robustness and accuracy are improved; and through multi-dimensional feature extraction and feature fusion, the prediction accuracy is improved.

Provided is a model processing method, a voice interaction method, an electronic device and a storage medium, relating to fields of artificial intelligence, big data and voice technologies. The model processing method includes: obtaining a candidate question set of each initial sample data in M initial sample data, wherein the initial sample data includes m rounds of question-and-answer between an object and an agent, the candidate question set includes a next round of question set corresponding to the mth round of question in the m rounds of question-and-answer; obtaining M training sample data based on the M initial sample data, the candidate question set and label data of each initial sample data, wherein the label data includes a target question to be generated by the agent in the (m+1)th round; and training a model to be trained by using the M training sample data to obtain a target model.

The invention discloses an electromagnetic inversion uncertainty evaluation method based on ensemble Kalman filtering, and belongs to the technical field of geophysical electromagnetic inversion, and the method comprises the steps: carrying out the time recursion of an initial sample set through the prediction step of ensemble Kalman filtering, and obtaining a prediction state variable; an observation variable of a prediction state variable is calculated through an updating step of ensemble Kalman filtering, a cross-covariance matrix is constructed based on the prediction state variable and the observation variable, and localization operation is performed on the cross-covariance matrix by using a localization weight matrix so as to correct estimation deviation of the cross-covariance matrix. A Kalman gain matrix is constructed based on the corrected cross covariance matrix, and prediction state variables are updated in combination with actual observation data; and performing electromagnetic inversion uncertainty evaluation based on the updated state variable. According to the method, the electromagnetic inversion precision and stability can be effectively improved, and uncertainty evaluation is provided for the magnetotelluric two-dimensional inversion result.

The invention discloses a variant reconstruction AUV (Autonomous Underwater Vehicle) shape optimization method based on proxy-assisted multi-starting-point space reduction. The variant reconstruction AUV shape optimization method comprises the following steps: establishing a parameterized model of a variant reconstruction AUV; constructing a variant reconstruction AUV shape modeling-grid division-performance simulation automation framework, realizing input of design variables, and automatically performing variant reconstruction AUV shape modeling and watershed grid division; a variant reconstruction AUV shape hydrodynamic force simulation calculation framework is built, and assessment of variant reconstruction AUV hydrodynamic force performance is achieved; generating a plurality of groups of initial samples for a design space of design variables, calculating target function values corresponding to the samples by utilizing the simulation automation framework and the simulation calculation framework, and importing the samples and corresponding sailing resistance into a sample library; and performing optimization in the design space of the design variables by utilizing an agent-assisted multi-starting-point space reduction method to obtain an optimal solution of the variant reconstruction AUV shape design problem. According to the method, the reliability of an optimization result and the design efficiency can be improved, and the optimization time is shortened.

The invention provides a method and a device for performing large-model self-distillation absorption depth reasoning by an intelligent computing center cloud platform through computing power, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the following steps: S1, obtaining multiple groups of initial sample data; s2, constructing multiple groups of first sample data and multiple groups of second sample data based on the multiple groups of initial sample data; and S3, training the initial large model based on the multiple groups of first sample data and the multiple groups of second sample data to obtain a first large model. According to the method, the displayed natural language deep reasoning step is distilled to a continuous hidden space, and the first model can still ensure complete and excellent reasoning ability under the condition of not displaying and generating the middle deep reasoning step, so that resources occupied by a large model can be reduced; therefore, the large model service provided in the intelligent computing center cloud platform is greatly increased.

The invention discloses a proxy model auxiliary evolution method based on two-stage adaptive switching and Voronoi niche. The method comprises the steps of generating an initial sample by adopting optimal Latin hypercube sampling, obtaining a real fitness value through simulation, constructing an initial training data set, and setting an initial iteration counter k and an evaluation budget threshold value; training a proxy model based on the current data set, and executing evolutionary algorithm optimization by using the proxy model to obtain a current optimal individual; judging two stages according to an iteration counter k and a distance threshold value; the development execution stage comprises the following steps: evaluating the real fitness and updating a data set; the exploration execution stage comprises the steps of multi-modal approximate detection, Voronoi niche division, multi-modal collaborative search, real fitness evaluation and data set updating; judging a termination condition and outputting a result; according to the method, three core technologies of an adaptive stage switching mechanism, multi-modal approximate detection and Voronoi niche division and multi-extremum collaborative filling are creatively integrated, so that the defects of insufficient global exploration, low local development precision and poor convergence efficiency of a traditional method in a high-dimensional multi-modal expensive optimization problem are effectively overcome; and the solving efficiency and reliability of a complex and expensive optimization problem are remarkably improved.

Active machine learning systems for anomalous event detection and classification. Initial samples from an industrial environment may be received and labeled. Initially, a training pool of audio samples may be labeled. These labeled samples may be used to train an audio event classifier to detect and categorize sounds. Environment states may be calculated using outputs from the classifier. A batch of audio samples may then selected from an unlabeled pool for annotation, guided by a reinforcement learning agent. These selected samples may be annotated and added to the labeled training pool. The classifier may be retrained with this updated pool. Rewards may be calculated for each of the annotated samples based on their annotations. The environment states may be updated using the retrained classifier, and the exploration-exploitation parameter of the reinforcement learning agent may be adjusted. The reinforcement learning agent may be retrained using the updated environment states and rewards.

The invention discloses a Kriging agent model-based engineering structure reliability analysis method, which comprises the following steps of: sampling from random variables and probability distribution of an object to be analyzed, and respectively obtaining a candidate sample set and an initial sample set; obtaining an initial experimental design set based on the initial sample set and the corresponding real response value, and constructing an initial Kriging agent model about the to-be-analyzed object; obtaining an optimal sample from the candidate sample set through a random weight learning function, and expanding the optimal sample and a corresponding real response value to an initial experimental design set; before the initial experimental design set enters the next round of data expansion, deleting an optimal sample in the current round and sample data in a preset redundancy distance range of the optimal sample from the candidate sample set; updating and iterating the initial Kriging agent model about the to-be-analyzed object through the experimental design set; and calculating the reliability index of the to-be-analyzed object according to the failure probability calculated by the current Kriging agent model.

The invention discloses a depth map super-resolution method based on blind degradation. The method comprises the following steps: forming an initial RGB-D sample pair by using a high-resolution RGB image and a corresponding high-resolution synthetic depth map; screening out a high-quality RGB-D sample pair from the initial RGB-D sample pair; inputting the high-quality RGB-D sample pair into a learnable degradation network, converting the high-quality RGB-D sample pair into a low-resolution training sample, and constructing an RGB-D training data set by using the low-resolution training sample and the high-resolution RGB image corresponding to the low-resolution training sample; using the RGB-D training data set to train the multi-modal super-resolution reconstruction network to obtain a depth map super-resolution model; inputting the low-resolution depth map and the corresponding high-resolution RGB image into a depth map super-resolution model to output a high-resolution depth map; the method solves the problems that an existing depth map super-resolution method mostly depends on a fixed degradation hypothesis, a real sensor and complex degradation distribution of compression transmission are ignored, and accordingly generalization ability is insufficient, edges are fuzzy and artifacts are increased in an actual scene.

The invention discloses a visual identification method and system fusing a large model and a visual model, a medium and a product, and relates to the technical field of visual identification. The method comprises the steps of obtaining a preset number of initial sample images of a target recognition category, inputting the initial sample images into a target multi-modal large model, generating fine-grained structured text description corresponding to each initial sample image, and constructing an enhanced data set; training by using the enhanced data set to obtain a target visual model; and in response to a received new input image, obtaining a first category probability distribution output by the target visual model and a second category probability distribution output by the target multi-modal large model based on semantic similarity matching in parallel, and determining a final recognition result according to the first category probability distribution and the second category probability distribution. By implementing the technical scheme, the recognition precision in a special visual recognition scene can be improved in an unstructured visual recognition scene with few samples.

The invention discloses an adaptive fidelity model scheduling method and system for circuit parameter optimization. The method comprises the following steps: selecting initial sample points to train a multi-precision agent model cluster; according to the parameter distribution characteristics of the to-be-evaluated individual, determining an adaptive target agent model from the multi-precision agent model cluster to perform performance evaluation on the to-be-evaluated individual; based on the evaluation error of the target agent model and the convergence state of the optimization process, judging whether a trigger threshold value of the high-precision circuit simulator is reached or not; if the triggering threshold value is reached, calling a high-precision circuit simulator to carry out performance verification on the to-be-evaluated individual, and obtaining generated verification data; and if the triggering threshold is not reached, obtaining an evaluation result output by the target agent model. According to the method, calling of a high-precision simulator is reduced from each individual to a key individual, conversion from whole-process high-precision to on-demand high-precision dynamic adaptation is achieved, and the large-scale circuit multi-target parameter optimization requirement under the advanced technology can be met.

The invention relates to a test tool and method for testing the shear strength of a material interface. The tool comprises a chuck, a fixing block, a base and a sliding block, a guide rail groove is formed in the base, one side of the guide rail groove is connected with the sliding block, and the other side of the guide rail groove is connected with the fixing block; the fixing block is provided with a fixing hole and a fixing block threaded hole, a pin penetrates through the fixing hole, the sample hole and the sliding block hole to fix the sample, and a bolt is fixed to the base through the fixing block threaded hole. The sliding block is provided with a sliding block sliding rail, a sliding block threaded hole and a sliding block hole, and the sliding block sliding rail is arranged in the guide rail groove of the base and fixed through a bolt; the chuck is located on the lower side of the base and serves as a device for connecting the base and the stretcher. According to the invention, the interface shear strength performance test can be carried out on the sample with any thickness, the bonding surface of the sample is always kept parallel to the axial force line in the initial sample mounting process and the tensile test process, and the bonding surface of the sample base material and the lap joint material in the initial test state is prevented from being abnormally damaged.

The invention provides an efficient and high-precision prediction method for the minimum failure probability of an aviation structure system based on an exponential penalty learning mechanism, and relates to the technical field of structural reliability analysis. Respectively generating an initial sample and a candidate sample according to the probability density function of the random variable of the performance function to be analyzed; real performance function responses corresponding to the initial samples are calculated to form an initial training sample set, and an initial Kriging agent model is constructed; selecting an optimal sample point from the candidate sample set through the proposed EPAL function; merging the optimal sample and the real response thereof into the initial training sample set, and iteratively updating the Kriging model until an error-based stopping criterion is met; judging whether the failure probability variation coefficient meets the requirement or not; and finally, calculating the failure probability of the structure based on a trained Kriging model and a Monte Carlo method.

The invention discloses a time-space multi-scale feature fusion deep learning landslide susceptibility evaluation method based on number object dual drive, and the method comprises the steps: constructing a time sequence branch + Swindow-Transformer (upper branch) + CNN (lower branch) time-space fusion DL-LSA model, combining an AFF multi-scale space fusion module and a TSF time-space fusion module, and carrying out the deep learning of landslide susceptibility. A complete technical scheme of high-quality sample screening, deep spatial-temporal feature extraction, multi-scale adaptive fusion and dynamic probability prediction is formed, and normal form upgrading of geological disaster risk management from single-drive vector object double-drive and from static evaluation to dynamic early warning is promoted. Comprising the following steps: 1, acquiring influence factors (X); 2, constructing an initial sample set (X-Y pairs); 3, preprocessing the influence factor (X); and 4, influence factor coding (generating a standardized feature X '). And 5, providing physical constraints (optimizing a sample set X '-Y) based on the P-LSA model. And 6, constructing a space-time fusion DL-LSA model. And 7, designing a feature fusion module. And 8, outputting a landslide susceptibility evaluation result.

A power system transient voltage stability evaluation method based on a gain-swin transformer comprises the following steps: step 1, collecting historical online data and analogue simulation data of a power system, and constructing an initial sample set; converting the multivariate time series data into a GAF transient state integrated image with reserved time characteristics; generating an unstable or stable label for each sample image, and randomly dividing a training set and a test set according to a certain proportion; 2, a feature extraction module of the Swin Transform model is improved, and multi-scale spatial-temporal features are extracted through a shift window attention mechanism and a cross-window attention mechanism; and in the offline training process, an optimal transient voltage stability evaluation model is obtained. And step 3, deploying the optimal model in an online evaluation system, monitoring transient voltage data in real time and performing stability evaluation. When the power system is unstable, the model can also position a key bus. Compared with a traditional method, the method can achieve efficient and accurate voltage stability evaluation in a large-scale power system.

The invention discloses an energy storage system parameter fitting method based on an active learning Kriging model. The method comprises the following steps: extracting a plurality of sample points to construct an initial training set, and normalizing the training set; constructing a Kriging model taking the initial sample set or the added sample set as a training set; utilizing a predictor function to obtain a prediction result and an estimation variance of the Kriging model on the sample points of the verification set; respectively substituting the prediction result and the variance into the U active learning function for calculation to obtain a function value of the corresponding learning function; repeating according to a convergence condition to obtain an active learning Kriging model; according to the method, the Kriging model is utilized to establish the energy storage system parameter agent model, the energy storage system model can be dynamically fitted on the basis of limited experimental data, the energy storage system parameters are updated in real time, and the precision and adaptability of an energy storage system management system are improved.

The invention discloses a data label calibration method based on a graph neural network and confidence reasoning, and the method comprises the steps: S1, collecting sample data to form an initial sample set, and constructing a label structure chart; s2, structural feature coding is executed on each node in the label structure chart through an improved Grapher network, and a node embedding vector is generated; s3, constructing a double-domain confidence scoring network, jointly evaluating a node embedding vector, and outputting a label confidence score; s4, establishing a confidence-density combined screening strategy, and generating a to-be-calibrated node set; s5, based on the to-be-calibrated node set, generating a calibration label set by adopting a similarity voting mechanism; s6, performing label consistency comparison and updating processing on the calibration label set and the initial sample set, and outputting a label calibration graph; and S7, constructing an end-to-end training process, and jointly optimizing the improved Grapher network and the double-domain confidence scoring network. According to the method, the label correction precision, the structure perception capability and the calibration robustness are improved.

The invention discloses a sparse magnetic field data reconstruction method, system and device and a storage medium, and relates to the technical field of magnetic field imaging and neural network image processing, and the method comprises the steps: obtaining initial sampling sparse magnetic field data of a target detection region through a data collection part; inputting the initial sampling sparse magnetic field data into a sampling strategy part, generating a sampling probability distribution diagram, and obtaining active sampling sparse magnetic field data according to the sampling probability distribution diagram; inputting the active sampling sparse magnetic field data and the initial sampling sparse magnetic field data into a space superposition part to obtain final sampling sparse magnetic field data; and inputting the final sampling sparse magnetic field data into a sparse data reconstruction module, and outputting high-resolution reconstruction magnetic field data. The method provided by the invention achieves better effects in the aspects of improving the sampling efficiency, optimizing the reconstruction quality, reducing redundant data and enhancing the reconstruction stability.

The invention relates to the technical field of image recognition, and discloses a corn seedling recognition method and system based on a remote sensing image and related equipment, and the method comprises the steps: obtaining the remote sensing image; performing scene filling on the remote sensing image not containing the preset scene by using the remote sensing image containing the preset scene, and determining an initial sample data set; labeling a preset scene of each remote sensing image in the initial sample data set to obtain a target sample data set; introducing the target sample data set into a target detection model for training, and determining a corn seedling identification model; and identifying the corn seedlings through the corn seedling identification model. According to the method, the remote sensing data not containing the preset scene is filled through the remote sensing image containing the preset scene, so that the number of the remote sensing images containing the preset scene and the number of the remote sensing images not containing the preset scene are balanced, the sample imbalance phenomenon is relieved, the data set quality is improved, and the corn seedling recognition precision is improved.

The invention relates to the field of fault diagnosis, in particular to a wind turbine gearbox fault diagnosis method based on accelerated back diffusion. According to the method, efficient fault identification and classification are realized under the condition of limited samples by using the enhanced short-time Fourier transform and the accelerated back diffusion model. According to the enhanced short-time Fourier transform, the attention mechanism is guided through content, the limitation of the traditional short-time Fourier transform in processing non-stationary signals is effectively solved, the time-frequency characteristic difference between different fault categories is enhanced, and the distinguishability of fault diagnosis is improved. The accelerated back diffusion model optimizes the back diffusion process through implicit distribution of learning data, and uses the convolutional generative adversarial network to truncate a part of the reverse structure to generate an initial sample with higher quality, thereby improving the sample generation efficiency and diagnosis accuracy.