1257 results about "Gaussian noise" patented technology

The invention relates to the technical field of lithium battery charging and discharging management, and discloses a lithium battery charging and discharging detection and management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of the charging and discharging process of a lithium battery pack, applying consistent Gaussian noise, and obtaining an electrode-level expansion data set and a battery-level expansion data set; performing feature mapping and feature mixing by minimizing the maximum average difference loss function to obtain a mapping feature vector; estimating the state of charge and the state of health of the battery through a fusion model of a recurrent neural network and a physical information hybrid neural network to obtain a state of charge estimated value and a state of health estimated value; and constructing a battery pack electric heating model of the lithium battery pack, and generating a corresponding charging and discharging control instruction sequence. Self-adaptive adjustment can be carried out according to the real-time state of the battery, potential safety hazards such as over-charging and over-discharging and out-of-control temperature are effectively avoided, and the use safety of the battery pack is remarkably improved.

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.

The invention relates to the technical field of image classification, in particular to a fetal heart monitoring image classification method and system based on a neural network. The method comprises the following steps: acquiring a fetal heart monitoring image, generating a standard monitoring image through Gaussian noise elimination and spatial domain normalization, then performing multi-channel convolution on the standard image to extract image convolution features, obtaining standard fetal heart features through compression regular optimization, performing morphological reconstruction to generate a fetal heart dynamic model, and finally obtaining the fetal heart dynamic model. Baseline features are identified based on historical data, then periodic variation quantification and safe floating range derivation are performed on fusion features, and finally a multi-scale discrimination framework is constructed to realize intelligent classification of fetal heart monitoring images. The fetal heart monitoring image classification method is more stable and more accurate.

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 relates to an underwater image enhancement method and system. The method comprises the following steps: acquiring a basic underwater image data set and a corresponding reference enhancement image set; forward diffusion and reverse denoising are carried out on the underwater images in the basic underwater image data set and the corresponding reference enhanced image set through a diffusion model; gaussian noise is gradually added to the underwater image in the forward diffusion process, and the degradation process of the underwater image is simulated; stepwise iterative denoising is carried out on the data added with the Gaussian noise in the reverse denoising process; a color correction module is introduced, and color correction and detail enhancement are carried out on the image in the reverse denoising processing process; and constructing a denoising network based on a Transform model, removing noise in the underwater image by combining forward diffusion and reverse denoising processes of the diffusion model, and outputting a high-quality enhanced underwater image.

The invention discloses a power system load prediction method based on a conditional guidance diffusion process, and belongs to the field of power system time sequence prediction. According to the method, a multi-stage progressive guide denoising process is designed, and the problem that the denoising process is uncontrollable is solved; mode features are effectively mined through a similar period load curve mining algorithm and multi-resolution trend features, and a mixed feature condition generation network is utilized to guide a multi-stage denoising process; and finally, designing a deep denoising network based on a Transform architecture, and improving the capability of restoring a load curve from Gaussian noise by the model through embedding a deep frequency domain decomposition module. According to the method, the denoising process is controllable, the fineness and accuracy of load curve generation are ensured, the interpretability of the model is enhanced, the precision of day-ahead load point prediction and deterministic prediction is remarkably improved, and more accurate prediction information can be provided for power system control and optimized operation.

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 a face image super-resolution method and device of a diffusion model based on residual prediction, and relates to the field of image super-resolution, and the method comprises the steps: obtaining a to-be-reconstructed low-resolution image; a diffusion model based on residual prediction and a conditional noise predictor are constructed and trained, and a trained diffusion model and a trained conditional noise predictor are obtained; gaussian noise is sampled from standard Gaussian distribution to serve as residual noise images, a to-be-reconstructed low-resolution image, the residual noise image of each step and the corresponding step length are input into a trained conditional noise predictor, prediction noise of each step is obtained through prediction, and the low-resolution image is reconstructed. Iteratively executing a de-noising process of T steps through a reverse de-noising network in the trained diffusion model to obtain a restored residual image; and adding the restored residual image and the up-sampled low-resolution image to be reconstructed to obtain a restored high-resolution image. The image super-resolution method solves the problems that an existing image super-resolution method is complex in generation process and limited in detail recovery.

The invention discloses an image generation method and system based on diffusion model adaptive reasoning. The method comprises the following steps: constructing a next noise intensity prediction module TPM; improving an original diffusion model, and adding a TPM; the improved diffusion model is trained by using a reinforcement learning method, the whole denoising process is expanded for multiple times, the next time step is predicted in each iteration, an award is calculated according to the quality of a finally generated image and the number of steps, and TPM parameters are updated by using a PPO loss function; in the actual reasoning process, noise is randomly sampled from Gaussian noise and input into the trained diffusion model, the diffusion model is used for predicting a denoising result, meanwhile, the TPM is used for outputting distribution of time steps based on next denoising, and a distribution mode is used as the next time step used in actual reasoning; and when the predicted time step is smaller than a set threshold value, a final image generation result is obtained based on model output. According to the method, the image generation process can be dynamically adjusted, and a reasoning result with both efficiency and quality is realized.

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 discloses a human face sensitive data protection method based on an elastic anti-interference privacy protection mechanism, belongs to the field of information processing, and provides a dynamic self-adaptive sampling strategy based on information entropy in a data preprocessing stage, and the sampling rate is adjusted according to the distribution characteristics of a human face data set and the weight of sensitive information. And the model performance is maximized on the premise of ensuring user privacy. In order to obtain a better privacy protection effect, an entropy adjustment differential privacy mechanism is designed, and Gaussian noise of different degrees is adaptively added according to an information entropy value of face data after a face data set is dynamically and adaptively sampled, so that refined privacy protection of face sensitive data under differential privacy constraint is realized. In a model training stage, an elastic anti-interference training method is provided, diversified adversarial samples are dynamically generated in different stages of model training, and the over-fitting risk of the model is reduced while the robustness of the model to various types of adversarial attacks is enhanced.

The invention relates to artificial intelligence, and discloses a speech synthesis method and device with a conditional matching stream, equipment and a medium, and the method comprises the steps: receiving text information, coding the text information into a high-dimensional implicit vector, and obtaining a text information vector; label information is formed according to the alignment relation between the text information and the voice, and the relation between the acoustic features of the text information and the voice is predicted; performing matching in the voice data set according to the text information vector to obtain corresponding text-related voice, generating intermediate-state voice according to Gaussian noise, the text-related voice and the time step, performing noise prediction according to the intermediate-state voice and the time step by adopting a conditional matching flow model to obtain predicted noise, and outputting the predicted noise. Calculating the loss between the input Gaussian noise and the prediction noise, and combining the prediction noise with the intermediate state voice to extract and obtain the acoustic features of the text related voice; and generating a decoded speech waveform according to the obtained acoustic characteristics of the text-related speech by adopting an acoustic decoder. And the speech synthesis quality is improved.

An earth and rockfill dam termite nest detection method based on MSCCEAUNet and physical constraint combined driving comprises the following steps that a miscellaneous geologic model of an earth and rockfill dam structure with a termite nest is simulated, and corresponding b-scan data and underground dielectric constant data are generated through a finite difference method FDTD to serve as a simulation data set; 2, constructing an MSCCEAUNet network with multi-scale cascade convolution (MSC) and an efficient channel attention mechanism (ECA), and inputting the complex geologic model data generated by simulation in the step 1 into the MSCCEAUNet network for training; according to the method, the MSCCEAUNet is trained again by adding Gaussian noise and random medium disturbance data with different signal-to-noise ratios, the structure of the MSCCEAUNet is optimized according to the training condition of the model, and the anti-jamming capability and robustness of the model are improved; a physical residual equation meeting physical information constraints of Maxwell equations of the ground penetrating radar is used as a physical loss function to be fused into an MSCCEAUNet framework, and data and physics combined drive inversion is achieved.

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 relates to a spacecraft thermal control film coating defect identification method based on a multi-dimensional attention mechanism. According to the method, a correlation mapping model based on monitoring data-detection image sensitive features-coating defects is constructed, and accurate feature information extraction of the multi-type defects of the coating on the surface of the spacecraft by a convolution kernel is realized by constructing a channel-space attention mechanism convolution neural network; through modes of random rotation, mirroring, symmetry, blurring, brightness change, contrast adjustment, saturation change, Gaussian noise addition and the like, data expansion is performed on variety defect categories with relatively small sample sizes, a data set is enriched, and the problem that the sample sizes of multiple types of defects are unbalanced is solved; a lightweight module (LM-Block) and a feature enhancement module (FEM-block) based on an attention mechanism are designed on the basis of a GhostConv self-adaptive ghost convolution module, and a coating defect detection model architecture based on efficient self-adaptive convolution and channel-spatial pyramid pooling is proposed on the basis of the modules to realize accurate recognition of defects.

The invention relates to an ECG anomaly detection method based on AE-GAN, and belongs to the field of artificial intelligence. According to the method, AE-GAN is constructed and trained for ECG anomaly detection, and a training network model is divided into an encoder training stage, the AE-GAN is only trained on a normal ECG sample, and an encoder, a generator and a discriminator are optimized by using confrontation loss and reconstruction loss; the encoder includes a channel attention module and a memory module. In a classifier training and detection stage, the AE-GAN extracts low-dimensional features from a real sample by using a trained encoder, adds weak random Gaussian noise to generate a pseudo normal sample, and adds strong random Gaussian noise to generate a pseudo abnormal sample; the classifier is trained by using a binary cross entropy loss function so as to distinguish normal samples from pseudo abnormal samples; during testing, the trained classifier uses features extracted by the encoder to detect whether data is abnormal or not.

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 semantic transmission method for a visible light and infrared fusion image of a night vehicle road environment, and the method comprises the steps: collecting visible light and infrared images in a vehicle driving process, and improving the visibility of the images under a low-light condition through a visible light image enhancement network; secondly, fusing the enhanced visible light image and the infrared image; thirdly, performing semantic guidance processing on the fused image, identifying target semantic information, extracting semantic features, and evaluating the importance of feature vectors so as to generate a semantic feature group; and finally, the sending end encodes the fused image into a potential space representation, the fused image is encoded and compressed into a conditional signal through a channel, the conditional signal is transmitted to a receiving end through an additive white Gaussian noise channel, and the receiving end dynamically adjusts the weight of a denoising device based on a potential diffusion model by using a conditional perception neural network and controls a generation process. And finally, generating a restored image through a semantic decoder. The method is of great significance in improving the reliability and safety of the automatic driving system.

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 relates to an SAR (Synthetic Aperture Radar) image target detection method and device based on the combination of a Mangbar and a diffusion model, and provides a target detection model taking a Resnet50-FPN network as a framework, the Resnet50 network is utilized to extract a plurality of resolution features of an SAR sample image, the extracted features are processed by a Mangbar SAR unit to obtain complete semantic features, and the complete semantic features are extracted by a Mangbar SAR unit. Then, a feature pyramid is constructed from top to bottom through the processed features by adopting an FPN network, feature images of multiple scales are obtained, Gaussian noise controlled by variance scheduling is added into bounding box truth value data through a diffusion model, regions of interest are cut out from the feature images of the multiple scales through the obtained noise boxes, and the feature images of the multiple scales are obtained; and the dynamic detection head obtains prediction data of each target in the SAR sample image according to the feature image of the region of interest, then the true value data and the prediction data are adopted to calculate a loss function, and learnable parameters are adjusted according to a calculation result until convergence. When the method is used for target detection, the accuracy of a detection result can be effectively improved.

The invention provides a method and a device for generating a multiphase flow-containing solid rock three-dimensional model based on a diffusion model, and a medium, and can solve the problems that a multiphase pore and a rock skeleton structure cannot be generated at the same time and only a single-phase structure or a pure skeleton image can be reconstructed in the prior art. According to the method, the watershed algorithm is introduced to accurately segment the multiphase features, Gaussian noise is used as diffusion model input, and the model gradually learns' de-noising 'from the noise to obtain a real and complex multiphase rock image in training, so that a high-fidelity multiphase distributed rock internal image can be obtained only by inputting random noise in an inference stage.