193 results about "Data diversity" patented technology

The invention relates to the field of Raman spectrums, and discloses a Raman spectrum nonlinear generation method driven by mixed machine learning, which is used for meeting the requirements of a modern Raman spectrum analysis technology on mixture spectrum data with wide coverage, complete types and reliable quality. Comprising the following steps: performing weighted linear combination on pure component spectrums based on a concentration ratio to generate a primary mixture spectrum; the data diversity is enhanced; generating a prediction spectrum; generator parameters are optimized through multi-level feature comparison and adversarial training of the discriminator; matching and deleting false peaks, and reconstructing physical reasonable characteristic peaks; and dynamically adjusting the intensity of the characteristic peak, and outputting high-fidelity spectrum data. According to the method, by fusing physical constraint and deep learning, the peak position precision, intensity consistency and noise robustness of the generated spectrum are remarkably improved, quantitative analysis of a complex mixture in a wide concentration range is supported, and an efficient solution is provided for Raman spectrum database construction and chemical detection.

Aiming at the problems of insufficient data diversity, data sparsity and model generalization ability in an industrial time sequence prediction model, the invention provides a method for establishing a time sequence prediction model oriented to industrial multi-modal data, which comprises the following steps: acquiring multi-source heterogeneous industrial time sequence data, and cleaning the acquired data; performing data enhancement on the cleaned data to generate an enhanced time sequence data set; fusing the multi-source heterogeneous time series data in the obtained enhanced time series data set by using a heterogeneous data fusion sensing method of multi-dimensional space-time mapping; using the fused data to construct a prediction model oriented to the multi-modal industrial time series data based on an AI large model, and performing model training; and according to the trained prediction model oriented to the multi-modal industrial time series data, predicting the change trend of the key indexes in the industrial process in a period of time in the future.

The invention discloses a wheat organ three-dimensional point cloud segmentation method based on dynamic voxel and category perception. Three-dimensional reconstruction is carried out on a wheat sample based on a three-dimensional Gaussian splash method, and dense point cloud data are obtained; performing data amplification on the dense point cloud data by adopting a dynamic voxel rasterization random sampling method combining dynamic voxel division and a random farthest point sampling strategy, and performing standardized point cloud quantity processing to obtain a standard point cloud data set; and based on the standard point cloud data set, carrying out key organ segmentation by adopting a category perception segmentation network. According to the data enhancement method, key geometric features are maintained while data diversity is expanded, a feature extraction module of the category perception segmentation network solves the problem of ignoring important points during sampling by fusing DCA and a weighted farthest point sampling strategy, and an improved loss function combining a point cloud space structure and information is adopted to improve the accuracy of data enhancement. And the segmentation precision of the wheat complex point cloud data is effectively improved.

The invention discloses a wolfberry screening method and system based on image analysis, and particularly relates to the technical field of wolfberry screening. Lycium barbarum samples with different growth environments, maturity, varieties and drying degrees are selected, images are shot under various illumination conditions, and data diversity is ensured; the image is preprocessed, surface texture roughness features and color distribution uniformity features are extracted, and support is provided for accurate classification; constructing a data prediction model based on the extracted features, evaluating the accuracy of the algorithm for identifying the appearance difference of the Chinese wolfberry fruits, and if the algorithm is high in accuracy, directly applying to a production line to realize automatic sorting of qualified and unqualified Chinese wolfberry fruits; and if the accuracy is low, the problem of misjudgment caused by natural differences of the Chinese wolfberry fruits is effectively solved by predicting the abnormal degree of the accuracy, dynamically adjusting the identification strategy and improving the screening precision, the accuracy and the automation level of Chinese wolfberry fruit screening are improved, the waste of high-quality Chinese wolfberry fruits is reduced, and the enterprise income and the brand reputation are remarkably improved.

The invention provides a ship noise multi-feature classifier data enhancement method and system based on a multi-fine-grained conditional diffusion model. And compressing a waveform to a potential space through VQ-VAE, extracting a ship type / ship name cross semantic vector by using ResNet, and optimizing clustering in combination with a loss function. And a one-dimensional U-Net conditional diffusion model is constructed, unconditional / conditional model output is dynamically weighted and fused, and the weight is adaptively adjusted according to training loss. In the generation stage, a semantic prototype is constructed by using a high-fine-granularity label, parameters are determined by using low / medium-granularity mean value sampling and Bayesian optimization, and fine-granularity controllable waveform generation is realized. After the generated data is converted into multiple features such as MFCC and Lofar, the generated data and original data are combined to train a classifier, and a virtual class strategy relieves class imbalance. Experiments show that the MSE of generated data and real data is reduced, the classification accuracy is improved, the data diversity and the model generalization ability are remarkably enhanced, and the method is suitable for scenes such as underwater target recognition.

The invention provides a bearing small sample data expansion method and system based on a variational auto-encoder, and belongs to the field of deep learning and data enhancement. The problems that a traditional generation model has limitation in bearing small sample data, feature fuzziness and distortion are prone to occurring, and the data set quality is poor are solved. According to the method, a deep VAE framework is constructed, and a dimension reduction module, a data expansion module and a dimension raising module are used in a potential space; dimensionality reduction is performed on high-dimensional data by adopting a UMAP algorithm, so that the topological structure of the data is effectively reserved, and the extraction efficiency and quality of data features are improved; a Gaussian mixture model combining regularization and particle swarm optimization optimization is used for fitting distribution of scattered small sample data, new data with fusion features are expanded through sampling, and data diversity is increased; a radial basis function is used for nonlinear data dimension raising, new data can be ensured to be accurately mapped back to a high-dimensional space, meanwhile, the relation between features is reserved, and defect data with fusion features is reconstructed through a decoder.

The invention relates to the technical field of advertisement marketing analysis, in particular to a data diversity identification advertisement marketing system, which comprises a data diversity identification module, a double-loop control module and a cross-layer information sharing channel module, and is characterized in that the data diversity identification module is used for calculating dynamic diversity indexes of multi-source heterogeneous data in real time; the double-circulation control module comprises an outer circulation strategy optimization layer unit and an inner circulation real-time control layer unit, the outer circulation strategy optimization layer unit operates at an hour-level time scale, a reinforcement learning algorithm is adopted to carry out long-term advertisement marketing strategy optimization, and an optimization objective function of the outer circulation strategy optimization layer unit is fused with a dynamic diversity index; the internal circulation real-time control layer unit operates at a second-level time scale, and adopts a parameter self-adaptive control algorithm to adjust an advertisement putting strategy in real time; and the cross-layer information sharing channel module is used for realizing strategy parameter transmission between the outer circulation and the inner circulation and applying strategy consistency constraint conditions so as to collaboratively optimize an advertisement marketing strategy.

The invention discloses a big language model knowledge extraction method and device based on difficulty perception, and aims to solve the problems that an existing knowledge extraction method is high in training cost and lack of pertinence in sample selection. According to the method, the difficulty of a distillation difficulty score evaluation sample is introduced, a difficult sample with learning value for a student model is identified, a distillation data set is dynamically adjusted in combination with a hierarchical data updating strategy, the difficult sample is preferentially reserved, a simple sample is removed, and meanwhile, data diversity is kept. Besides, a bidirectional difference loss function is provided, KL divergence and inverse KL divergence are combined, the optimization process is stabilized, more attention is paid to difficult samples, and gradient explosion or disappearance is avoided. Experimental results show that the performance of a student model is effectively improved, the method even exceeds a teacher model under some conditions, meanwhile, the training cost is remarkably reduced, and the method is suitable for task-independent instruction following and specific tasks and has wide application prospects.

The invention relates to the technical field of image processing, in particular to a multi-modal image processing method and system based on deep learning, and the system comprises a self-adaptive perception and generation module which is used for exploring the new characteristics of image data, enhancing the perception capability through a generation and simulation technology, and discovering that details are difficult to capture in an existing processing mode. According to the invention, through reverse modal generation, cross-modal characteristic unification, dynamic modal reconstruction and data simulation experiments, data diversity expansion and accurate characteristic analysis are realized; through causal reasoning, knowledge migration and adversarial learning, inter-modal characteristic consistency and analysis performance are optimized; according to the multi-modal image analysis method and system, the multi-modal image analysis efficiency and accuracy are remarkably improved by combining the task-driven model switching and federated learning technology and supporting distributed model training of dynamic adaptation and privacy protection, and meanwhile, the application range of medical image analysis in a resource-constrained environment is widened.

The invention discloses a face privacy protection-oriented generated image detection method and a face privacy protection-oriented generated image detection system. The method comprises the following steps of: firstly, preparing face and text pairing data, and finely adjusting a diffusion model; secondly, on the basis of the diffusion model after fine tuning, potential vectors are extracted and clustered, and text prompts and center vectors obtained through clustering form a dictionary; and finally, based on the obtained dictionary, obtaining a pseudo image and a label through the fine-tuned diffusion model, and outputting a detection result through a classifier. According to the method, potential spatial clustering and conditional diffusion generation are combined, privacy protection and data diversity are taken into consideration, and the security and generalization ability of forged face image detection are remarkably improved.

The invention discloses a hospital terminal threat behavior recognition method based on interpretable deep learning, and particularly relates to the technical field of network security, threat intelligence analysis and interpretable artificial intelligence. According to the method, a multi-modal hospital terminal operation data set fusing time sequence images, logs and other data is constructed, and different modal data are effectively integrated by adopting a multi-modal data fusion method; integrating a dynamic attention mechanism into a long short-term memory network model for adaptively extracting key features in a terminal user behavior sequence; and a double-layer interpretable framework based on gradient weighted class activation mapping and a local interpretable model irrelevant interpretation technology is introduced, so that high-precision recognition and interpretable analysis of the threat behavior of the hospital terminal user are realized. According to the method, complex threat behaviors in the hospital terminal can be efficiently and accurately identified, the limitation of a traditional method in the aspects of data diversity, interpretability and hidden threat detection is overcome, and the transparency and efficiency of threat analysis are enhanced.

The invention belongs to the technical field of data processing, particularly discloses a bridge crack data set construction method based on multi-modal data enhancement and a GAN network, and aims to solve the key problems of small sample learning, insufficient data diversity, high-resolution image generation and the like in a bridge crack detection task. A solution integrating multi-modal data enhancement and deep symmetry GAN is provided, real scene interference is simulated through a combined enhancement strategy, 98% of interference types of a real scene are covered, the problem that a traditional enhancement technology cannot simulate complex shooting conditions is solved, a deep symmetry GAN network is designed, and the real scene interference can be simulated through the deep symmetry GAN network. According to the method, the chessboard effect of the generated image is eliminated, the high-frequency detail retention capability is improved, a 640 * 640-resolution image is generated, a high-quality bridge crack data set is constructed, bridge crack data is expanded, crack images in different forms are generated, and the problems of scarcity of the crack image data set and the like in bridge crack detection under a complex background are solved.

The invention discloses a large-scale language model fine tuning method, device and equipment and a storage medium, and relates to the technical field of large-scale language model fine tuning. According to the method, a preset check point is firstly subjected to large-scale language model fine tuning based on training loss performance, sample embedding space distribution and sample reply score performance of a current model; and performing multi-dimensional self-reference diagnosis on the training data set, identifying a suboptimal sample which is not matched with the capability of the current model, and then processing the suboptimal sample through an adaptive optimization engine to complete dynamic evolution of the training data set. Therefore, dynamic evolution of the data set adaptation model can be realized, the problem of redundancy or insufficient adaptation of static data is avoided, and the training efficiency is improved; meanwhile, data waste is reduced, data diversity is reserved, the model generalization ability is promoted, manual intervention is not needed, and the data optimization cost is reduced.

The invention relates to the field of data analysis, in particular to a database information acquisition and analysis method for pharmacological analysis of heat stroke, which comprises the following steps: determining a data state according to data diversity and data stability of rat input data, and determining a data processing mode according to the data state; in analog data supplementation, determining a supplementation mode according to a comparison result of the effective standard index proportion and a preset effective standard index proportion; in the keyword analysis, a use state is determined according to the matching frequency and the spacing distance of the effective keywords, and a corresponding search mode is determined according to the use state; in the effective keyword search process, determining a page distribution state according to the page similarity and the information relevancy of the target website, and determining an acquisition adjustment mode as track simulation adjustment or search simulation adjustment according to the page distribution state; the searching and matching efficiency of database information acquisition and analysis in pharmacological analysis related to heat stroke is improved.

The invention is suitable for the technical field of credit risk assessment, and provides a federated learning-based credit risk assessment method, which comprises the following steps of: obtaining a local model accumulation gradient and a global model gradient, and constructing a graph relationship between clients according to the similarity between the local model and a central server-side global model obtained by the local model accumulation gradient and the global model gradient; obtaining the difference of clients based on the graph relationship between the clients, calculating an aggregation weight, and forming global model update; and the central server side sends the aggregated global model update to each client side, and the client side continues the next round of local training until the model reaches the expected performance. The method has the effect of improving the global model generalization ability, and can be effectively applied to financial credit risk assessment. Through cooperative training, the problems of reduced accuracy, poor robustness and the like of the model effect due to insufficient data volume, data category or data diversity can be avoided, so that a credit risk assessment model with higher generalization ability is constructed.

The invention discloses an improved YOLOv12-based aircraft surface damage detection algorithm and system, and relates to the technical field of aircraft surface damage, and the method comprises the following steps: S1, data collection and preprocessing: employing an unmanned plane and an unmanned vehicle to collect the related data of the aircraft surface damage, precisely marking the aircraft surface image, and increasing the data diversity; s2, model improvement: improving a YOLOv12 aircraft surface damage detection model according to an aircraft surface detection task; a high resolution input size is employed to capture small defect features. According to the method, the YOLOv12 aircraft surface damage detection model is improved, the unmanned aerial vehicle and the unmanned vehicle can comprehensively record key structural components on the surface of the aircraft under various illumination conditions, the data integrity is guaranteed, and through the CLAHE algorithm, diversified data and the imaging effect in various environments, the detection accuracy of the aircraft surface damage detection model is improved, and the detection accuracy of the aircraft surface damage detection model is improved. And the robustness of subsequent model training can be obviously improved, so that the detection algorithm has higher environmental adaptability.

The invention discloses a classification method and system for data enhancement and hybrid expert mechanism feature selection. The system comprises an automatic speech recognition (ASR) module, a text-to-speech synthesis (TTS) module, a multi-modal feature extraction module, a hybrid expert mechanism (MoE) module, a common attention mechanism module, a feature fusion module and a classification module. According to the invention, a voice data enhancement module based on a voice-to-text (TTS) technology is utilized to improve data diversity and model generalization ability; by means of multi-level acoustic and text feature extraction, language changes are represented more comprehensively; a hybrid expert mechanism (MoE) is utilized to realize dynamic selection of multi-modal features, and the feature utilization efficiency is improved; according to the method, the fusion mode between different modal features is optimized by using a co-attention mechanism, the interaction expression ability between the features is enhanced, the recognition precision and the robustness of the system in a multi-modal environment are remarkably improved, and the defects are overcome.

The application provides a bearing data expansion method and system based on a Gaussian mixture model and a particle swarm optimization, and belongs to the field of data expansion. In order to solve the problem of how to effectively expand the data set, increase the data diversity, alleviate the data scarcity, and improve the generalization ability and detection performance of the model in the existing small sample bearing defect detection, the application adopts a Gaussian mixture model (GMM) to model the original data, optimizes the parameters of the GMM through a particle swarm optimization (PSO) algorithm, generates new data points, and expands the data set. The method can effectively increase the data diversity, alleviate the data scarcity problem, and improve the generalization ability and detection performance of the model.

The invention discloses a UWB radar multi-target sensing and tracking method and system. The method comprises the steps that multi-scene original echo signals are collected through a UWB radar, a synchronous optical motion capture system obtains real position information of a target, data diversity is enhanced through noise injection and time-frequency transformation, and a multi-modal data set is constructed; de-noising and signal enhancement are carried out by adopting a three-stage cascade processing flow, and features are extracted by utilizing a CNN and Transform hybrid encoder; based on a graph neural network and a dynamic filtering theory, constructing a model of an improved PointPill detection head and a GAT tracking head to carry out target detection and track association; and finally deploying to edge equipment to realize real-time multi-target sensing and tracking by combining a multi-task joint loss function with a strategy optimization model such as adaptive loss balance. According to the method, the traditional multi-sensor dependence is broken through, the track continuity in a complex scene is improved in a target shielding scene, the probability of wrong tracking and missing tracking is reduced, and high-precision and high-real-time multi-target sensing and tracking are realized in the complex scene.

The present invention relates to the technical field of engine condition monitoring, and particularly relates to an engine performance prediction method using sample adaptive weighting, which comprises the following steps: establishing a first data set and a second data set, wherein the first data set is the already-operated data set of the current engine, and the second data set includes the operation data sets of N other engines of the same model as the current engine; clustering the first data set and the second data set according to the flight envelope distribution to obtain M clustering centers; establishing sub-models such that each of the M clustering centers includes N sub-models; initializing a prediction weight vector W for each of the clustering centers, and performing weighted averaging on the N sub-models included therein to construct M linear weighted prediction models, which are used to predict engine performance; and optimizing the prediction weight vector W of the clustering centers. This method gives full play to data diversity and meets the requirements for accurate performance prediction of aero-engines.

The invention belongs to the technical field of image processing, and particularly relates to a graphite ore image segmentation method based on an improved YOLO11-seg model, which adopts a balanced design of precision and light weight, optimizes redundancy in a network structure, introduces GSConv and C3k2-Faster modules, effectively reduces calculation overhead and memory occupation on the premise of keeping strong feature representation capability, and improves the image segmentation efficiency. The real-time reasoning efficiency of the model is obviously improved; a Mosai c data enhancement method is fully utilized, the data diversity is improved while the number of samples is increased, and the robustness and generalization ability of the model in a complex environment are enhanced; the trained optimization model can be deployed on intelligent edge equipment of graphite ores, supports real-time and accurate grade estimation on site, remarkably improves the mineral separation efficiency, and provides an efficient and reliable computer vision solution for intelligent mining scenes.

The invention discloses a domain generalization personnel re-identification method and system based on multi-modal fusion and structure perception enhancement. According to the method, a grey-scale map mode is innovatively introduced to extract biological characteristic information irrelevant to dressing, so that the identification limitation of a visible light image under the conditions of uniform shielding and severe illumination is effectively made up; meanwhile, a structure perception data enhancement strategy is provided, a key identification area is protected through a semantic segmentation technology, and damage to effective features is avoided while data diversity is improved; besides, by introducing targeted alignment loss, uniformity loss and intra-domain uniformity loss, the distribution characteristics of the feature space are directly optimized and regularized, and the generalization performance in an unknown substation scene is significantly improved. According to the method, superior detection performance can be realized under the condition of limited annotation data, the dependence on large-scale annotation data is effectively reduced, and the deployment cost is reduced.

The invention provides a multi-task space-time fusion Wi-Fi fingerprint positioning system aiming at the problems that Wi-Fi signals are easily interfered in a complex indoor environment and the generalization ability of a traditional method is weak. The system takes spatio-temporal feature fusion as a core, deep features are extracted from space and time dimensions, building classification, floor classification and coordinate regression targets are optimized through weight collaboration, and the generalization and discrimination performance of multi-scale positioning is remarkably improved. In order to further enhance the robustness of the model, channel attention, a generative adversarial network and a de-noising auto-encoder are introduced to enhance feature expression and data diversity. Experiments on a UJIIndoorLoc data set show that the method is superior to some traditional methods and existing neural network models in the aspects of building and floor recognition accuracy and average positioning error, and the effectiveness of the method in a complex indoor environment is verified.

A ship pipe fitting recognition system based on multi-source data enhancement and intelligent recommendation comprises a multi-source data enhancement module, a model training module, a recommendation decision module, a federated learning platform and a block chain evidence storage module, the system is of a'cloud + side 'double-layer architecture, the cloud is used for intelligent evolution, distributed knowledge is integrated through federated learning, and the block chain evidence storage module is used for storing the distributed knowledge. The global model is continuously updated and optimized, the edge end is used for edge real-time response, and efficient pipe fitting feature detection and pipe fitting number recommendation are carried out at the equipment end. According to the method, the detection precision and the non-standard pipe fitting recognition capability in complex illumination and shielding scenes can be remarkably improved, and the misjudgment rate is reduced; the decision-making problem of similar part hybrid scenes is solved, and the assembly efficiency and accuracy are improved; meanwhile, the data privacy security is ensured; the technical blank in the aspects of data diversity, algorithm robustness and industrial adaptability in the field of ship pipe fitting recognition is filled, the efficient requirement of a ship assembly scene is met, and technical support is provided for ship manufacturing intelligence.

The steam generator state real-time monitoring method based on artificial intelligence belongs to the field of artificial intelligence and comprises the following steps: S1, data acquisition and labeling; S2, sample generation is performed by using a quantum generative adversarial network based on random projection embedding to realize data expansion; S3, the expanded data is input into a feature extraction model to perform training of the feature extraction model, and a five-layer fully connected neural network is used for feature extraction; S4, the feature-extracted data is input into a feature dimension reduction model to perform training of the feature dimension reduction model, and a self-encoding neural network algorithm based on local preserving projection is used to realize feature dimension reduction; S5, the dimension-reduced data is input into a classifier to perform training of the classifier model; and S6, steam generator state recognition and monitoring are performed.The steam generator state real-time monitoring method based on artificial intelligence can solve the problems of insufficient sample quantity and lack of data diversity and enhances the robustness of the model when the model has noise or fuzzy classification boundary data.

The invention discloses a Chinese network violent event data set construction method based on man-machine collaboration, which comprises the following steps: 1) extracting comment data related to network violent events from a plurality of Chinese social media platforms, and ensuring data diversity and complexity; 2) adopting three network violence detection methods based on paraphrasing, thinking chain and multiple agents to generate pseudo labels and corresponding interpretation contents, and combining results of the three detection methods through an integration method; 3) performing manual labeling by a plurality of Chinese native language persons according to the generated pseudo labels and explanations to ensure the labeling accuracy; and 4) constructing an event-based Chinese network violent detection data set CHNCI according to a labeling result, and carrying out statistics on basic information of the data set. According to the method, the Chinese network violent detection data set based on the events is constructed in a mode of combining machine generation of pseudo labels and manual annotation, the data annotation cost is remarkably reduced, and meanwhile the coverage range and quality of the data set are improved.

The invention discloses an online granularity detection device for ultrasonic cleaning equipment, and relates to the technical field of ultrasonic cleaning equipment.The online granularity detection device comprises an ultrasonic cleaning machine body, a detection machine body, a displayer and a machine cover, connecting holes are formed in the corners of the periphery of the top end of the ultrasonic cleaning machine body, and the machine cover is located at the bottom end of the ultrasonic cleaning machine body; an online particle detection mechanism is fixedly connected to the corner of one side of the top end of the machine cover, the bottom end of the online particle detection mechanism penetrates through the machine cover and extends into one connecting hole, and the detection machine body is fixedly connected to the side, close to the online particle detection mechanism, of the ultrasonic cleaning machine body. Sampling work of different water layers is facilitated, data diversity and accuracy are improved, it is guaranteed that the flow speed of cleaning liquid and the particle sensor rotate at the same speed, it is avoided that the flow speed of the cleaning liquid is too high to affect data and cause blurred shot pictures, the metal content of the cleaning liquid is detected in real time, and it is avoided that the metal content exceeds the standard.

The invention relates to the field of image processing, in particular to a fundus disease image segmentation method and system based on multi-mode focus simulation. According to the method, for a simulation focus image of an OCT mode, image denoising, OCT layer information detection, layering abnormity judgment, RPE layer inclination angle determination, to-be-implanted OCT focus-free eye fundus image and focus implantation position determination, simulation focus size adjustment and simulation focus pixel value reconstruction are carried out in sequence, and an OCT multi-class synthetic eye fundus disease image and a segmentation label thereof are generated; and for the simulation focus image of the CFP mode, a CFP multi-class synthetic fundus disease image and a segmentation label thereof are generated by selecting a proper background image, adjusting the brightness of the background image, cutting off the edge of the simulation focus and reconstructing the pixel value of the simulation focus in sequence. Clinical pathology constraints are followed, fundus disease image data diversity is increased, meta-task enhancement is realized, and segmentation precision of a focus segmentation network is improved.

The invention discloses a voiceprint detection method for internal abnormity of a wind driven generator blade, and relates to the technical field of electrical equipment fault detection. According to the voiceprint detection method for the internal abnormity of the wind driven generator blade, the problems of data diversity and fault sample scarcity are solved through a unique mode of implementing corresponding diagnosis for different working condition information in the wind driven generator and an abnormity diagnosis mode based on an auto-encoder. The voiceprint detection method for the internal abnormity of the wind driven generator blade is used for efficiently and accurately detecting the internal abnormity of the wind driven generator blade.

The embodiment of the invention discloses a query statement processing method and device, a storage medium and a program product, the accuracy of equivalence verification is improved, and the method and device are widely applied to scenes with data diversity. The method comprises the following steps: obtaining a to-be-verified statement pair comprising a first query statement and a second query statement, a plurality of first examples and a plurality of second examples; generating first prompt information based on the first query statement and the plurality of first examples, generating second prompt information based on the second query statement and the plurality of second examples, and calling a large language model to perform semantic analysis processing on the first prompt information and the second prompt information, semantic analysis information of the first query statement and semantic analysis information of the second query statement are generated; and determining a plurality of third examples based on the semantic analysis information of the two query statements, and generating third prompt information in combination with the to-be-verified statement and the semantic analysis information of the two query statements, thereby performing equivalence verification processing through a large language model, and generating a target analysis result.