1025 results about "Gaussian noise" patented technology

A conditional temporal diffusion model-based method and apparatus for generating a time series of an industrial device, including: acquiring parameter indicator data for the time series of the industrial device; using a noise at a target time instant in a target Gaussian noise distribution as an initial variable of the time series; inputting the parameter indicator data and the initial variable into a noise prediction model constructed based on a conditional temporal diffusion model, to obtain a predictive noise output by the noise prediction model; denoising the predictive noise according to the initial variable, to obtain a target variable of the time series located at a previous time instant of the target time instant; and inputting the target variable and the parameter indicator data into the noise prediction model for an iteration, to generate the time series of the industrial device.

An image-guided diffusion network has two Convolution Neural Networks (CNNs). A RGB image and an IR image are concatenated with a Gaussian noise image and input to a denoising neural network that merges information from the RGB and IR images as noise is removed over many iterations. Then an enhancement neural network up-samples for Super Resolution (SR) and convolutes to generate a condition vector that controls Global Feature Modulation (GFM) at three convolution layers to generate a SRGFM enhanced fusion image. Timesteps are embedded using adaptive group normalization blocks within Adaptive Bottleneck Residual (ABR) blocks in the denoising network, which is a UNet having many levels of ABRs, and in the enhancement network before feature modulation. Global image features are detected by triple convoluting the image input to the enhancement network to generate the condition vector that controls feature modulation blocks at three layers of convolution.

The invention relates to the technical field of data protection, and discloses a privacy protection type data joint modeling method based on federated learning, which comprises the following steps: acquiring local data to perform meta-feature extraction, calculating key statistics to characterize data characteristics, collecting meta-features, grouping the meta-features into similar feature clusters through spectral clusters, and carrying out feature clustering on the similar feature clusters; dynamically allocating and calculating resource weights according to the similar characteristic cluster scale and the equipment computing power; distributing a basic privacy budget according to the client type, calculating a local model accuracy rate and an intra-cluster level difference, dynamically adjusting the privacy budget, adding adaptive Gaussian noise based on the privacy budget, and adjusting gradient sensitivity of gradient calculation; verifying gradient compliance through zero knowledge, carrying out safe aggregation on gradients passing verification, optimizing a meta-model through a knowledge distillation loss function, and generating confrontation sample analysis to obtain a leakage risk value to identify knowledge leakage risks; sensitive neurons in the neuron sensitivity positioning element model are analyzed and calculated, directional noise is injected, and initial parameters are adjusted for initialization training.

The invention relates to the technical field of medical information processing, in particular to a health data processing method and system based on a distributed ledger library, and the method comprises the steps: a node firstly links a health data abstract to obtain a differential privacy budget token and a reversible tensor hash key; then, carrying out encryption training on local model parameters by using the key, injecting Gaussian noise according to budget to generate a differential protection gradient, and carrying out uplink together with zero-knowledge proof; the account book end decrypts the threshold value, uses a structural equation model to deduce a causal correction matrix according to the reputation weight aggregation gradient, and automatically adds a budget and turns the key when the concept drifts and the budget is insufficient; the operation mechanism splices the aggregation parameters and local parameters, generates a gating vector in combination with a causal correction matrix and a reputation weight, outputs disease risk prediction, and only uploads prediction hash and error information; chain-level audible privacy protection, dynamic budget management and hybrid deviation suppression are realized, and the safety and accuracy of a cross-institution medical model are improved.

The invention discloses a power distribution network planning method and system considering distributed energy uncertainty, and relates to the technical field of power grid planning, and the method comprises the steps: collecting distributed energy node data, compensating space-time migration in combination with meteorological data, and carrying out the time sequence alignment through a dynamic time warping algorithm; constructing an improved Wasserstein generative adversarial network to generate a conventional scene, and injecting Gaussian noise through potential spatial disturbance to generate an extreme scene deviating from training distribution; inputting the mixed scene set into a mixed integer nonlinear programming model, and adopting a graph neural network to establish a topology-power flow agent model to accelerate solution; updating line impedance parameters through a Kalman filter, and collecting and checking actual output; and decomposing the corrected planning scheme into cloud global optimization and edge local control, and carrying out cloud-edge collaboration. According to the method, the adaptability of a power distribution network planning scheme in a complex and uncertain environment is improved by combining spatial-temporal feature alignment, adversarial network scene enhancement, a graph neural network and cloud edge collaborative optimization.

The invention discloses a charging and moisture regaining equipment temperature prediction method based on a time sequence fusion network model, and relates to the field of production process control, and the method comprises the steps: collecting time sequence data in a charging and moisture regaining equipment production environment, and carrying out the preprocessing; dividing the data set into a training set, a verification set and a test set, and injecting Gaussian noise into the training set; a prediction model for predicting the outlet temperature is constructed, and the prediction model is a time sequence fusion network model and comprises a residual TCN time sequence convolutional network, an SK-Net multi-scale attention network and a BiLSTM bidirectional circulation network; pre-training the prediction model by using the training set; utilizing the trained prediction model to predict the outlet temperature of the feeding and moisture regaining equipment; and evaluating a prediction result, and if an evaluation index is greater than a threshold value, starting an incremental training process to re-train the prediction model. According to the invention, through a multi-module combined deep learning model, the prediction accuracy of the outlet temperature of the charging and moisture regaining equipment can be improved in a complex and changeable industrial environment.

The invention discloses an industrial defect detection method based on self-supervised fine tuning, and solves the problems of scarcity of industrial scene defect samples and weak model generalization ability. The method comprises the steps that a data set is divided and preprocessed, and the data robustness is improved through size scaling, random luminosity transformation, geometric enhancement and the like; extracting a foreground mask by using a saliency model, synthesizing a Perlin Noise and DTD texture fused pseudo-abnormal image, carrying out self-supervised fine tuning on the ImageNet pre-trained WideResNet-50, and enhancing the industrial data feature extraction capability; a model containing a visual trunk, feature aggregation mapping, noise feature adaptation and a discriminator is established, local neighborhood features are fused through Unfold operation, Gaussian noise is superposed to generate pseudo-abnormal features, and an abnormal score is output by the discriminator after multi-scale fusion. And the training adopts binary cross entropy and focus loss optimization parameters. The innovation points of the method are that self-supervised fine tuning adapts to industrial data distribution, feature aggregation improves fine-grained detection, and multi-scale fusion considers different defects.

The invention relates to the technical field of ocean data interpolation filling, in particular to a space-time deficiency filling method and system based on context association and physical guidance. The method comprises the following steps: acquiring seawater dissolved oxygen data and context data; multivariable space-time dependence extraction is carried out based on the obtained seawater dissolved oxygen data and context data; gaussian noise diffusion is carried out based on the obtained seawater dissolved oxygen data; noise prediction is carried out based on double-view space-time correlation; and the prediction error is constrained based on the joint loss function. According to the method, a physical consistency constraint mechanism based on a partial differential equation is introduced in a model training process, so that model output better conforms to a physical coupling rule among variables in a marine environment. The constraint effectively inhibits non-physical fluctuation possibly occurring in the interpolation result, enhances the physical credibility and interpretability of the result, and provides a more reliable data basis for subsequent scientific analysis and process modeling.

The invention discloses a mechanical arm path planning method based on Transform and a diffusion model, and belongs to the technical field of robots, and the method comprises the following steps: collecting environment information to generate a reference path, and constructing a training data set; a conditional diffusion Transform prediction network is constructed, features are extracted, and multi-modal path prediction is realized; gaussian noise is applied to the reference path, and denoising training is carried out on the conditional diffusion Transform prediction network; in combination with a diffusion model and a cost guidance mechanism, optimizing a noise path sampled from Gaussian distribution until a smooth collision-free path is generated; the candidate paths are evaluated, and a mechanical arm joint control instruction is generated; and the mechanical arm executes the planning track and carries out real-time sensing and online re-planning. According to the method, environment perception, Transform coding and diffusion generation are organically combined, a plurality of feasible tracks which are smooth and capable of avoiding obstacles are rapidly generated in a complex obstacle scene, and the method has good generalization ability and can adapt to different scenes and dimension changes.

The invention relates to the technical field of internet of things federated learning, in particular to a federated learning method oriented to internet of things privacy and heterogeneous data and an internet of vehicles privacy data protection method and system, differential privacy is introduced into federated learning, Gaussian noise meeting the differential privacy is added to a client to be uploaded to local parameters of a server, and meanwhile, the local parameters of the client are added to the internet of things privacy and heterogeneous data. In order to prevent excessive accumulation of privacy cost in model iteration training, noise disturbance is added to parameters uploaded by a local model through dynamic differential privacy, and the privacy and availability of the model are balanced to the maximum extent by using an exponential attenuation mechanism of Gaussian noise; the Wasserstein distance between the local parameter and the global parameter is calculated to serve as a regularization item to update the local model, the generalization ability of the model to Non-IID data is improved, and the accuracy of the global model is improved; and when high-dimensional data is processed, dimension reduction processing is performed on a high-dimensional data set by combining PCA with federated learning, so that the data set consistency of each client is improved while the local model training speed is improved.

The invention discloses a hydroelectric generating set operation fault data enhancement method and device, and the method comprises the steps: firstly collecting a set vibration signal, selecting a time-frequency transformation method to convert a one-dimensional vibration signal into a two-dimensional time-frequency image, enhancing the feature dimension of the signal, constructing a diffusion feature migration model, gradually disturbing the data distribution to Gaussian noise through forward diffusion, and carrying out the recognition of the Gaussian noise. The method comprises the following steps of: performing inverse denoising to generate simulation data highly similar to a real fault sample, realizing relevance learning and migration sharing of fault features among different working conditions in combination with an adversarial feature migration architecture, and finally evaluating an enhancement effect by calculating similarity among samples, and inputting enhanced data into a fault diagnosis model to verify precision improvement. Through the combination of time-frequency transformation and a diffusion model, sample scarcity and working condition barriers are broken through, a remarkable effect is shown in the aspects of expanding the fault sample scale and enriching the sample dimension, the similarity of generated data and a real sample is improved, the diagnosis precision is improved, and the model generalization ability is remarkably enhanced.

The invention discloses a graph neural network-based relevance credit default early warning method. The method comprises the following steps of S1, performing multi-source cross-mechanism data fusion and dynamic graph construction; s2, designing a space-time diagram neural network model to output a final risk score; s3, a federal learning framework: local training: locally training a sub-graph model by each participation mechanism, and retaining sensitive data; parameter aggregation: aggregating gradient information by the central server, and adding Gaussian noise by using differential privacy; model updating: the space-time diagram neural network model supports a heterogeneous graph structure through weighted average updating parameters; and S4, carrying out risk early warning and interpretability output. The method at least has the following beneficial effects: comprehensive risk coverage: dynamic heterogeneous graph construction: integrating multi-source heterogeneous data and dominant / implicit relationships, constructing a dynamic graph comprising enterprises, individuals and geographic nodes, dynamically adjusting edge weights through a time decay function, and quantifying timeliness of association strength;

The invention relates to a federated learning method, system and device for personalized differential privacy protection and a medium. The method comprises the steps that a central server initializes global model parameters and issues the global model parameters to clients; each client sets an initial value and an extreme value of a personalized privacy budget based on data characteristics of the client; the client performs local training, cuts the gradient in the training process, and adds corresponding Gaussian noise processing based on the current personalized privacy budget; the central server performs weighted aggregation on the model parameters uploaded by the clients to update a global model, and issues the updated global model parameters to the clients for a new round of local training; and the central server dynamically adjusts the personalized privacy budget of each client based on the reward factor, and then allocates the personalized privacy budget to each client for local training again until a global model meeting a preset requirement is obtained. The method can be widely applied to the field of distributed machine learning data security.

The invention relates to the field of signal processing, and discloses an FPGA-based dynamic multi-modal signal fusion algorithm and an adaptive noise suppression system, and the algorithm comprises a signal collection module which is used for collecting signals of different modals through a multi-modal sensor, adding a timestamp and a sensor identifier to each modal signal, and obtaining an original signal; the space-time calibration module is used for performing time synchronization and space external parameter calibration on the original signal and outputting a space-time aligned single-mode signal; and the noise suppression module is used for mapping high-dimensional signals to low-dimensional Riemannian manifolds through a manifold embedding algorithm for each single-mode signal aligned in time and space, and suppressing non-Gaussian noise by using a manifold thermonuclear filtering algorithm. Space-time labels are given to multi-mode signals by means of a signal acquisition module, time deviation and space position difference between sensors are eliminated through a space-time calibration module, and non-Gaussian noise is filtered out through a noise suppression module based on manifold learning and intrinsic characteristics of the signals are reserved.

The invention provides a vibration signal space-time reconstruction method based on a multi-modal condition diffusion model, and relates to the technical field of vibration signal reconstruction, and the method comprises the steps: firstly collecting structure vibration response through multiple sensors, constructing a multi-dimensional vibration signal matrix, and automatically recognizing a space continuous missing region and a time random missing region; performing coarse reconstruction on the missing region by adopting self-adaptive multi-scale interpolation so as to recover the basic trend and frequency band characteristics of the signal; a pseudo-missing mask is further applied to complete data, a training sample is constructed through a self-supervision strategy, and the model is guided to learn spatio-temporal correlation features and missing modes; in a training stage, a diffusion model is used as a generation framework, Gaussian noise disturbance is applied to a missing region, four types of condition embedding of time, space, trend and frequency domain are introduced in a denoising inversion process, signal periodicity, multi-sensor space coupling, low-frequency change and a physical frequency spectrum structure are respectively described, and the noise is reduced; and high-fidelity signal reconstruction under multi-modal information joint constraint is realized.

The invention discloses a causal association privacy protection cyberspace surveying and mapping threat detection method and system, and relates to the technical field of information security and cyberspace surveying and mapping. The method specifically comprises the following steps: constructing a causal incidence matrix based on a multi-modal network space surveying and mapping data dynamic fusion method of multi-scale convolution and causal reasoning to realize cross-modal surveying and mapping data feature fusion; according to the dynamic Gaussian noise privacy protection method based on GCN importance evaluation, privacy budget is dynamically allocated by utilizing causal association importance, and fusion feature privacy is protected; according to the self-adaptive pre-training model gradient protection method based on the self-supervised dual disturbance mechanism, the privacy and availability of the model are dynamically protected; according to the network space surveying and mapping threat detection method based on the stacked LSTM model, abnormal modes in multi-scale traffic are learned, and high-precision threat detection is achieved. According to the invention, through causal association modeling and dynamic privacy protection, the threat detection capability in a complex network environment is improved while the data security is guaranteed.

The invention provides a self-adaptive bearing fault diagnosis method based on multi-base wavelet fusion. The objective of the invention is to solve the problems of noise reduction, insufficient feature extraction and low diagnosis precision under noise conditions. A Kaisixi University bearing public data set is used as original data, and Gaussian noise with different SNRs is superposed to simulate various noise intensities. And uniformly carrying out length alignment, down-sampling, equal-length segmentation, division and normalization preprocessing. Then, wavelet bases such as sym4, db4, coif5 and the like are adopted for parallel multi-scale decomposition and reconstruction; and adaptively determining the number of decomposition layers and a threshold strategy according to the noise level, and generating a de-noising branch. And performing weighted fusion on the denoising results of the branches, and performing iterative denoising on the residual error. Signals subjected to noise reduction processing are sent to a double-branch convolution-cycle-attention network, a convolution layer extracts features, an LSTM and a self-attention module capture time sequence changes, and accurate recognition of various bearing faults is achieved. The training adopts a segmented attenuation learning rate and an early stop strategy, and the robustness and generalization ability of different SNR working conditions are improved.

The invention provides a wheat stripe rust remote sensing change information-driven early disease prediction method and system, and the system comprises a multi-source data progressive fusion module which enables a natural image and a multispectral image to be spliced step by step into corresponding hierarchical features, and carries out the fusion and outputting of deep fusion features; the frequency domain decoupling-based change detection module is used for outputting a change probability graph of adjacent moments; constructing a plurality of groups of training samples and inputting the training samples into a conditional diffusion prediction model for training; to-be-predicted wheat remote sensing image data and corresponding meteorological data are collected, a change probability graph is obtained, the change probability graph, the corresponding meteorological data and diffusion mode prior data are fused to serve as a condition vector, the condition vector and random Gaussian noise are input into the trained condition diffusion prediction model together for denoising, and prediction denoising data are obtained; then a prediction change probability graph is obtained through a visual decoder; and on the basis, obtaining a prediction result of the severity and distribution range of the wheat stripe rust disease on the d-th day. The method can be used for predicting the early wheat stripe rust.

The invention provides a training method of a generative denoising device and an image controllable generation and restoration method. The training method comprises the following steps: taking a pre-trained diffusion model as a teacher diffusion model; initializing a generative denoising device, a score model and a discriminator; obtaining a noiseless signal, and obtaining a noisy signal and Gaussian noise in combination with a generative denoising device; obtaining effective noise intensity and effective noise signals according to the noisy signals and the Gaussian noise; estimating a data score by using a teacher diffusion model according to the effective noise intensity, the effective noise signal and the noisy signal; according to the noisy signal, estimating a model score by using a score model; calculating and optimizing the gradient of learnable parameters of the generative denoising device; and according to the noisy signal, the data score, the model score and the optimized generative de-noising device, calculating de-noising score matching of the score model and adversarial loss of the discriminator, and optimizing the score model and the discriminator. And realizing image controllable generation and image restoration based on the generative de-noising device.

The invention provides a federated learning differential privacy method based on Rayleigh divergence and adaptive noise adjustment, and aims to balance data privacy protection and model training performance and improve model accuracy and convergence speed of federated learning on the premise of protecting user data privacy. And the contradiction between privacy protection and model performance in the existing federated learning is solved. The method comprises the following steps: step 1, constructing a privacy loss quantification model based on Rayleigh divergence; 2, deducing a tight upper bound of a Gaussian noise standard deviation; 3, initializing noise parameters of the federated learning system; 4, the client side executes local model training and noise adding; 5, updating the aggregation model of the central server and evaluating the performance; step 6, implementing a self-adaptive noise adjustment decision based on model performance; and step 7, iterating federal learning training until convergence or completion.

The invention discloses a privacy information desensitization method and system for a voice generation type large model, and relates to the technical field of artificial intelligence. Input voice data is discretized, Gaussian noise disturbance sensitive features are injected, and a cross-modal voice generation model is constructed in combination with three-stage training; and meanwhile, a cross-modal privacy enhancement mechanism is applied to detect and fuzzify sensitive information in real time in an output stage. According to the invention, the adaptability of a large-scale voice generation model in a privacy protection scene is improved, and comprehensive protection of user privacy is realized. And moreover, the capability of extracting user privacy information by an adversarial attacker is effectively limited, and the risk of sensitive information leakage in the data transmission, storage and generation process of the voice generation model is reduced. On the premise that privacy is ensured, the voice generation model can still keep high-quality generation performance, generated voice output has high naturalness and accuracy, and actual application requirements are met.

The invention provides a federated learning backdoor defense method based on singular value decomposition and model weight amplification, and the method comprises the steps: carrying out the normalization processing of a model updating parameter locally trained by a client, and obtaining a normalized model updating parameter; on the basis of the normalized model updating parameters, model updating parameters after dimension reduction are obtained; performing clustering algorithm processing on the model updating parameters after dimension reduction to obtain clustered clusters; obtaining cluster model parameters based on the clustered clusters; combining the cluster model parameters into a cluster model parameter matrix; performing singular value decomposition on the cluster model parameter matrix to obtain a singular vector; obtaining a trust score through the singular vector; obtaining global model update parameters based on the trust score; calculating by utilizing the global model updating parameters to obtain a global model; and adding Gaussian noise to the global model through a differential privacy mechanism to obtain a final global model. According to the method, backdoor attacks can still be effectively resisted under the scene that the client data sets are non-independent and identically distributed.

The invention discloses an infrared small target detection method based on a conditional diffusion model. Specifically, the conditional diffusion model comprises a conditional perception coding module, a conditional guidance feature fusion coding module and a feature decoding module. According to the method, multi-scale feature extraction and fusion are carried out on an infrared image through a condition-guided condition perception coding module to generate condition features, feature fusion is carried out on the condition features and a noise mask of a time step t, and a model is guided to directionally optimize a target mask. In the training process of the conditional diffusion model, a mean square error loss function based on a time step t is adopted to supervise the accuracy of mask generation, and model parameters are dynamically adjusted through an Adam optimizer to minimize noise prediction errors. In the target detection stage, denoising is carried out step by step from the initial state of Gaussian noise through Markov chain iteration in the reverse diffusion process, and finally a high-precision target mask is generated.

The invention discloses a personalized federated learning method based on a kernel distance between clients, a framework and an application, and belongs to federated learning and an application technology thereof. The method aims at double challenges of data isomerism and privacy protection, and is based on a federated learning framework of a conditional policy network and differential privacy. The CPN adaptively balances the conflict between the global consistency and the local personalized characteristics by dynamically generating the weights of the personalized characteristics and the global characteristics, so that the influence caused by data isomerism is effectively relieved. Meanwhile, the differential privacy technology protects the sensitive data characteristics of the client and reduces the risk of privacy disclosure by injecting Gaussian noise in gradient updating. The personalized federal learning method provided by the invention not only is superior to the existing mainstream method in global model performance and fairness between clients, but also realizes good balance between privacy protection and model performance.

The invention relates to the field of knowledge maps, and discloses a multi-modal entity alignment method and device and a medium, and the method comprises the steps: obtaining the data of two multi-modal knowledge maps, preprocessing the corresponding data, and obtaining the preprocessed data; constructing a multi-modal encoder containing a modal adaptive noise enhancement mechanism, enhancing the expression ability of multi-modal data through Gaussian noise injection, and inputting the preprocessed data to the multi-modal encoder to obtain enhanced multi-modal embedding features; calculating a single-modal confidence coefficient and a joint confidence coefficient for the enhanced multi-modal embedded feature to obtain a modal weight; through a relative calibration strategy, the weight of a fusion mode is adjusted according to the uncertainty of the mode, and the upper bound of a generalization error is reduced; multi-modal joint embedding is obtained, and entity alignment is carried out by combining intra-modal and inter-modal comparison loss; according to the method and the device, the technical problem of wrong alignment caused by modal quality difference, noise and inconsistency and isomerism among modals in a multi-modal entity alignment method is solved.

The invention belongs to the field of image processing, and relates to a method for embedding a robust watermark in a diffusion model generated image, which comprises the following steps of: acquiring cue words, initial Gaussian noise and watermark information, and inputting the cue words, the initial Gaussian noise and the watermark information into a trained diffusion model based on watermark embedding to obtain a watermark-embedded image; the training process of the diffusion model comprises the following steps: acquiring cue words, initial Gaussian noise and watermark information, and inputting the cue words, the initial Gaussian noise and the watermark information into an encoder to obtain potential vectors; inputting the potential vector into a self-attention module to obtain an embedded position vector; embedding the watermark information into the potential vector according to the embedding position vector; inputting the potential vector embedded with the watermark information into a decoder to obtain an image embedded with the watermark; extracting watermark information from the image embedded with the watermark; updating parameters of a diffusion model according to the image embedded with the watermark and the extracted watermark information until a trained diffusion model is obtained; according to the method, the embedding position is selected by combining the potential of the diffusion model and the accuracy of the self-attention mechanism, so that efficient watermark embedding and extraction are realized.

The invention discloses a 12-lead electrocardiosignal generation method based on a medical text and related equipment. The method comprises the steps that text information is acquired; inputting the text information into a pre-trained TTE model to generate a 12-lead electrocardiogram signal; wherein the TTE model comprises an encoder, a noise predictor and a decoder; in an electrocardiogram generation stage, a noise predictor takes a text semantic condition as input, starts from initial random Gaussian noise zT, gradually recovers a potential feature vector # imgabs0 # pretrained decoder meeting conditional constraints through iterative denoising, reflects a potential feature vector # imgabs1 # to a high-dimensional original signal space, and outputs the potential feature vector # imgabs1 # pretrained decoder to a high-dimensional original signal space. According to the method, medical text description is used as condition input, text semantic constraints are embedded in a potential diffusion model framework, 12-lead simulated electrocardiosignals conforming to specific pathological features are generated, and a feasible alternative scheme is provided for shortage of current labeled ECG data sets.

The invention discloses an intelligent creative design system based on a diffusion model. The system comprises sketch generation, sketch optimization, style migration and high-quality rendering. According to the method, the design efficiency and innovativeness are improved through an intelligent technology, and meanwhile, the personalized requirements of users are met. According to the system, firstly, diversified design sketches are generated through a diffusion model, an initial sketch is generated through Gaussian noise step-by-step iteration, and keywords or style labels input by a user are introduced in combination with a conditional diffusion model so as to control the generation direction; performing feature extraction on the sketch by using a convolutional neural network, and optimizing lines and structures of the sketch through a multi-layer perceptron; then, in combination with a style migration technology, migrating an artistic style specified by a user into the sketch, and meanwhile, introducing an attention mechanism to ensure local detail consistency of style migration; and finally, performing high-quality rendering on the design by using a GAN, optimizing detail performance through adversarial training of a discriminator and a generator, and improving the image resolution by using a super-resolution technology.

The invention belongs to the technical field of seismic exploration, and discloses a robust full-waveform inversion method, system and device based on generative modeling, and a medium, and the method comprises the steps: obtaining seismic observation data; the method comprises the following steps: starting from random Gaussian noise, establishing a model space comprising a plurality of initial velocity models through an unconditional score model, and determining a global optimal initial velocity model of the model space by adopting a strategy search algorithm; de-noising is carried out on the global optimal initial velocity model based on back diffusion, seismological observation data are introduced as observation constraints, the velocity model is updated by calculating the mismatch gradient of forward modeling data of the current velocity model and the seismological observation data, and an implicit condition sample is obtained; performing forward diffusion processing on the implicit condition sample to obtain a speed model for a subsequent annealing time step; and when the annealing process reaches a preset condition, outputting a final speed model. According to the method, the robustness and accuracy of full-waveform inversion are improved, and the bottleneck problem of traditional full-waveform inversion is solved.

The invention relates to the technical field of identity authentication, in particular to a federated identity authentication method fusing mouse behavior modeling and self-adaptive differential privacy, which comprises the following steps: building a federated learning framework; an authentication problem is converted into a time sequence behavior modeling task; updating model parameters by each client, and calculating local gradient information of the current round; cutting the local gradient information; the privacy budget is dynamically distributed; gaussian noise is added to the cut local gradient information; the central server aggregates the disturbed local model parameters to obtain new global model parameters; when the model reaches the preset convergence standard or the maximum round limit after multiple communication rounds, training is ended, a global model terminal is deployed, mouse behavior modeling and a federal learning mechanism are combined, the collection requirement for original user data is avoided, the leakage risk of sensitive data in the transmission and storage process is fundamentally reduced, and the user experience is improved. And the data security and the privacy protection capability are improved.