300 results about "Noisy data" patented technology

The invention relates to the technical field of engine cylinder surface defect detection, and discloses an engine cylinder surface defect detection method and system. The method comprises the following steps: acquiring three-dimensional height information of cylinder surface defect points based on a laser displacement sensor, identifying surface defects by combining an edge detection algorithm with a standard template library, calculating geometrical characteristics of the defects by fitting a polynomial and a least square method, and finally generating a defect report and feeding back the defect report to a central control system. Compared with a detection method in the prior art that fine defects on a complex curved surface are difficult to accurately identify, and especially in a data environment with large noise, accurate detection of micron-sized defects cannot be realized, the detection method has the advantages that noise is filtered through a high-precision laser displacement sensor and Fourier transform, so that the detection accuracy is improved, and the detection efficiency is improved. Precise detection and identification of surface fine defects are realized, so that the problems of missing detection and false detection in the prior art are avoided, and the detection precision and the production efficiency are improved.

The invention discloses an underwater acoustic signal denoising method based on a time-frequency adaptive dual-path Conformer network, and the method comprises the steps: carrying out the preprocessing of real marine environment noise and underwater acoustic target signals, and generating a multi-signal-to-noise-ratio noisy data set; performing short-time Fourier transform on the noisy data, and extracting real part and imaginary part features to form a feature tensor; extracting features by using a feature encoder, and generating intermediate feature representation; respectively extracting a time path feature and a frequency path feature through a time-frequency adaptive dual-path Conformer network; performing multi-scale convolution processing and weighted fusion on the extracted time-frequency features by adopting a multi-scale fusion dynamic gating network; performing nonlinear mapping on the fused features by using a feature decoder to generate a mask matrix; and restoring the complex frequency spectrum based on the mask matrix, and restoring the denoised underwater acoustic time domain signal. According to the method, time-frequency information is fully mined in combination with underwater sound noise characteristics, and the underwater sound signal denoising effect is improved.

The invention discloses a physics-based method for discovering a control equation from scarce and noise data. According to the method, a physical information neural network and a sparse regression method are combined, and a partial differential control equation of a dynamic system is found from scarce and noisy data. Firstly, the size of a candidate function library is effectively reduced through a dimension verification method. And then modeling a physical system and calculating a candidate function by using the strong nonlinear fitting capability and automatic differential characteristic of the deep neural network. And finally, the form of a control equation and the coefficient of the equation can be obtained through sparse regression, and the coefficient is finely adjusted through DNN. The method not only can discover the control equation from the data, but also can obtain the network model to realize the response prediction of the dynamic system. The method is easy to implement, efficient, high in precision and high in universality. The method can be widely applied to physical knowledge mining, modeling and reasoning of a complex dynamic system.

A system for stable and streamlined transaction approval includes a rule management server and an authorizer server. The rule management server is configured to receive a transaction rule that includes a list of named entities specified by a client. The rule management server stores past transaction data and analyzes patterns in the past data to determine a named entity identification rule. The authorizer server receives a transaction data payload related to a pending transaction associated with the client and identifies a noisy data field in the transaction data payload. The noisy data field includes a representation of a particular named entity and one or more irrelevant strings. The authorizer server parses the particular named entity based on the named entity identification rule and determines whether the particular named entity is one of the named entities in the list. The authorizer server conducts a transaction evaluation using the result.

The invention discloses an image generation method. The method comprises the steps of obtaining a reference face image, a text cue word and source noisy data ZT; the text cue word is used for describing features of the customized image, and ZT is noisy data obtained after Gaussian noise of T time steps is gradually added to the reference face image through a diffusion model; generating a lexical element embedding vector based on the reference face image and the text cue word, and inputting the lexical element embedding vector into a de-noising device of the diffusion model to obtain a de-noising device taking the lexical element embedding vector as a condition; inputting the Zi into a de-noising device under the condition of lexical element embedding vectors for noise estimation to obtain first noise data belonging to i; according to the set i, the Zi and the reference face image, second noise data # imgabs0 # are generated, noisy data Zi-1 of the (i-1) th time step are generated according to # imgabs1 # and Zi, the initial value of i is T, the step is executed circularly after i is subtracted by 1 accumulatively until i is equal to 0, and noise-free data Z0 are obtained; and generating a customized image corresponding to the reference face image based on the Z0.

The invention discloses a data security sharing method and system, and relates to the technical field of data sharing. The method comprises the steps that a data provider uploads original data, a privacy budget value is calculated through a differential privacy algorithm, noise is added to obtain noisy data, and the noisy data is processed through an anonymous privacy protection algorithm based on maximum dissimilarity degree clustering to obtain desensitized data; encrypting the desensitized data by adopting a block chain decentralization-based ciphertext policy attribute-based encryption method, obtaining an encrypted data ciphertext, uploading the encrypted data ciphertext to a cloud server, and obtaining a content addressing hash value; encrypting the hash value through an elliptic curve encryption algorithm, and storing the encrypted hash value to a block chain account book; and when an access request of a data requester is received, the cloud server verifies the authority by using a non-interactive zero-knowledge proof protocol and returns an encrypted hash value after passing the verification, and the requester decrypts to obtain the hash value and decrypts the encrypted ciphertext to obtain desensitized data, thereby realizing data sharing.

The invention discloses a seismic denoising method and system based on an improved generative adversarial network, is used for seismic denoising, belongs to the field of oil-gas exploration and seismic data processing, and solves the problems that the existing generative adversarial network is slow in operation speed and cannot fully extract seismic record features when performing seismic denoising. The method comprises the following steps: slicing noise-free seismic data and adding Gaussian noise to make a data set; inputting the noisy data into a generator, extracting features of the noisy data, and generating denoised seismic data; and inputting real data into the discriminator, enabling the discriminator to judge the similarity between the de-noised seismic data generated by the generator and the noise-free seismic data, then returning a probability value to indicate whether the seismic data is the real seismic data, returning a discrimination result to the generator by the discriminator, optimizing the generator, and carrying out confrontation training in this way. Model weights are saved after training is completed; and using the trained model to perform de-noising processing on input noisy seismic data, and outputting the de-noised seismic data. The method is used for seismic data noise removal.

The invention discloses a vehicle lane changing track high-precision redrawing and key parameter accelerated extrapolation method. The method comprises the following steps: acquiring a sampling data set of a real-time dynamic driving scene; screening a complete lane changing scene of multi-vehicle interaction; filtering preprocessing is carried out on the original noisy data; a lane changing track model is constructed based on a midpoint step-by-step lane changing hypothesis, a lane changing process is modeled in a segmented manner by taking a lane line crossing moment as a node, track characteristic parameter identification is carried out based on the lane changing track model of transverse and longitudinal decoupling, a track characteristic parameter distribution rule is mined, and lane changing scene parameter distribution characteristics are condensed; extracting parameter distribution characteristics of the lane changing scene based on a Gaussian mixture model, and proposing a joint probability model of scene multi-dimensional parameters; extracting lane changing scene distribution gradient information; carrying out Hamilton Monte Carlo sampling; and forming a lane changing acceleration test scene library. According to the invention, dimensionality reduction and high-precision redrawing of the lane changing track are realized, and extrapolation of key scene parameters and construction of a high-precision and high-efficiency lane changing acceleration test scene library are completed.

The invention discloses a training method and system for a fraud-related APP multi-modal feature vector embedder, and relates to the technical field of computer security, and the method comprises the steps: collecting basic information of an application and user behavior data after a user provides a protection authority; calculating a current fraud-related risk value, setting an initial fraud-related risk threshold, running a double-layer training embedding strategy when the current fraud-related risk value is greater than the initial fraud-related risk threshold, and forming a fraud-related APP judgment model by combining a shallow feedforward neural network model and a deep fraud-related APP feature classification model; when the current fraud-related risk value is smaller than or equal to the initial fraud-related risk threshold value, an adaptive data remigration strategy is directly operated, and a remigration period is obtained according to the APP active state and the fraud-related risk value; according to the method, the model training efficiency and the real-time response capability are balanced, the system can flexibly adapt to different data qualities, and the model performance is prevented from being influenced by noise data or low-quality data.

The invention provides an industrial time sequence generation method based on a time sequence decomposition conditional diffusion model. The method comprises the steps that industrial time sequence data are collected and preprocessed to obtain a data set; introducing conditional variables to construct a conditional diffusion model, and injecting noise through forward diffusion; designing a time sequence decomposition and reconstruction UNet module, performing feature extraction on noisy data and conditional variables to obtain an intermediate state, performing time sequence decomposition on the intermediate state, and performing feature reconstruction by using a decoder; the Sinkhorn distance is used as a regularization term to be fused into conditional noise prediction loss to construct a loss function for training; and randomly generating pure Gaussian noise, inputting the pure Gaussian noise into the trained TDA-CDM model, obtaining predicted noise of the current time step, calculating noisy data of the next time step, performing cyclic operation until a time sequence without noise is obtained, and accelerating sampling by using a denoising diffusion implicit model in circulation. According to the method, coexisting multi-scale dynamic features in the complex industrial MTS can be carefully and effectively captured and restored, and the comprehensive quality of generated data is remarkably improved.

The invention discloses a method for suppressing magnetotelluric mixed noise in a shallow water area. The method comprises the following steps: reading a noisy ocean MT time sequence; setting phase space reconstruction parameters; constructing a phase-space vector to obtain a noisy data matrix; noise priori information is obtained through noise pre-estimation; constructing an original noisy data matrix after noise whitening; performing singular value decomposition to obtain a feature vector and a feature value matrix of a data covariance matrix after noise whitening; selecting a low-order principal component after noise adjustment principal component transformation to reconstruct a denoised data matrix; recovering the denoised data matrix into a time sequence; carrying out Fourier transform to obtain an ocean MT magnetic field component amplitude spectrum, and combining to obtain an ocean MT four-component amplitude spectrum; detecting the frequency point of the pulse in the four-component amplitude spectrum; and setting all amplitudes corresponding to pulse frequency points in the four-component amplitude spectrum to be 0, and performing inverse Fourier transform to obtain a processed four-component time sequence. According to the invention, the ocean magnetotelluric signals can be better processed, and the data quality is improved.

The invention discloses a rainfall prediction method based on LightGBM and a variable attention mechanism, and belongs to the technical field of weather prediction. The objective of the invention is to solve the problem of poor prediction effect of an existing model when noise or missing exists in data. According to the method, the LightGBM and the variable attention mechanism are combined, the variable attention mechanism is used for accurately capturing cross-variable dynamic association and multi-scale time dependency in meteorological data, the model can flexibly adapt to the relative dependency between different time steps through relative position coding, and the modeling capacity for long-time-sequence data is improved; by adopting a split weighting mechanism based on LightGBM, different types of exogenous variable data can be weighted, large-scale data can be effectively processed, and feature selection can be carried out, so that the model is helped to pay more attention to variables having great influence on meteorological prediction in the training process, and the robustness of the model to noise data and missing data is enhanced. The method can be applied to rainfall prediction.

The invention belongs to the technical field of nuclear magnetic resonance sounding, and relates to a magnetic resonance random noise intelligent suppression method and system based on dynamic weighting, and the method comprises the steps: employing a forward conversion path to enable a Y-domain noisy signal to generate a pseudo-X-domain pure signal, or enable a pseudo-Y-domain noisy signal to generate a reconstructed X-domain pure signal; generating the pure signal of the pseudo X domain into a reconstructed noisy signal of the Y domain by adopting a reverse conversion path, or generating the pure signal of the X domain into a noisy signal of the pseudo Y domain; performing forward feature authenticity evaluation calculation on the pure signal of the pseudo X domain; performing reverse feature authenticity evaluation calculation on the noisy signal of the pseudo Y domain; alternately optimizing the two conversion paths; and de-noising processing is carried out on the actually measured noisy data by adopting the optimized forward conversion path. The suppression effect on random interference in actual field detection signals with more complex noise conditions and lower signal-to-noise ratios is enhanced.

Embodiments described herein provide systems and methods for noise-robust contrastive learning. In view of the need for a noise-robust learning system, embodiments described herein provides a contrastive learning mechanism that combats noise by learning robust representations of the noisy data samples. Specifically, the training images are projected into a low-dimensional subspace, and the geometric structure of the subspace is regularized with: (1) a consistency contrastive loss that enforces images with perturbations to have similar embeddings; and (2) a prototypical contrastive loss augmented with a predetermined learning principle, which encourages the embedding for a linearly-interpolated input to have the same linear relationship with respect to the class prototypes. The low-dimensional embeddings are also trained to reconstruct the high-dimensional features, which preserves the learned information and regularizes the classifier.

The invention relates to the technical field of numerical calculation and data analysis, in particular to a data interpolation and fitting analysis system which comprises a data processing module, a depth generation interpolation module, an uncertainty quantification module, an interpretability optimization module, a multi-source heterogeneous data processing module and a hybrid calculation acceleration module. In the prior art, a traditional interpolation method is easy to generate over-fitting or under-fitting in a complex data distribution and high noise scene, and a single deep learning model is insufficient in generalization ability in a small sample or data sparse region; through the dynamic fusion architecture of the depth generation interpolation module, the advantages of a traditional numerical method and deep learning are combined, the weight is automatically adjusted based on data characteristics, the prediction error under noise data is remarkably reduced, the adaptability to complex distribution is improved, and the stability of a data sparse region is enhanced; the problems of insufficient precision and weak generalization ability of a single model of a traditional method are effectively solved.

The invention discloses a seismic strong background noise removal method based on a two-channel network, and relates to the field of geophysical exploration data processing, and the method comprises the steps: selecting a noiseless signal and background noise when the noiseless signal and the background noise are not excited from a single-channel seismic record after a seismic source is excited, carrying out the superposition, generating noisy data, and carrying out the normalization processing; constructing a dual-channel constraint denoising network composed of a global constraint sub-network and a denoising sub-network; optimizing network parameters through the training sample set; and performing global constraint denoising and slice local denoising on the noisy seismic data by using the trained model, and finally splicing to obtain a complete denoising result. According to the method, the splicing effect problem caused by traditional blocking processing is effectively solved, the strong background noise removal effect is remarkably improved, and seamless high-quality denoising with the complete edge is achieved.

The invention belongs to the field of speech synthesis, and relates to a noise robust speech synthesis method based on a diffusion auto-encoder. Comprising the following steps: constructing a noise data set, and extracting Mel spectrum features from noise audios and clean audios as a training set and a test set; performing diffusion auto-encoder training, and training a diffusion auto-encoder model by using noisy Mel spectrum features as input and clean Mel spectrum features as output so as to generate noise robust hidden features; performing text-to-feature model training, training a model by using a text data set and hidden features extracted by a diffusion auto-encoder, the model being obtained by further processing based on a FastSpeech2 model; reasoning a speech synthesis model, inputting a test text, generating features from the trained text to a feature model, generating a Mel spectrum through a conditional diffusion model, and finally restoring the Mel spectrum into a speech waveform by using a vocoder. According to the method, the low-quality noisy data is utilized to synthesize the voice with high naturalness and high quality.

The invention discloses a face recognition method based on adaptive sine angle robust principal component analysis, and belongs to the technical field of face recognition, and the method comprises the steps: obtaining a face image, and carrying out the preprocessing of the face image; constructing a robust principal component analysis model based on an adaptive sine angle, and training the robust principal component analysis model by using the training set; solving an optimal projection matrix by using a non-greedy iterative algorithm; mapping the original face features to a discrimination space by using the optimal projection matrix; and performing face recognition by using a nearest neighbor classifier, and performing robustness evaluation on a recognition result. According to the method, the adaptive sine angle is added, abnormal values can be effectively suppressed in the face of noise data, the influence of the abnormal values on the projection direction is reduced, and the method plays a key role in improving robustness.

The invention relates to the field of field groundwater magnetic resonance detection methods, in particular to an augmentation system and method for field groundwater magnetic resonance detection small sample data. Comprising a generation network configured to receive random noise and generate augmented data based on the random noise; the discrimination network comprises a time domain discrimination network and a frequency domain discrimination network; the generative network and the time domain discrimination network form a first discrimination channel, the generative network and the frequency domain discrimination network form a second discrimination channel, and in the second discrimination channel, augmented data generated by the generative network and the noisy data are jointly input into the frequency domain discrimination network; the frequency domain discrimination network is used for discriminating whether the source of input data is a generation network or noisy data in a frequency domain. According to the method, a magnetic resonance detection small sample noisy data set is established, and multi-scale features in an amplitude spectrum and a phase spectrum are captured through the frequency domain discrimination network; the collaborative driving generation network generates a high-quality sample which is close to small sample noisy data in waveform form and spectral characteristics.

The invention discloses a satellite remote sensing image random stripe noise suppression method and system based on low-rank tensor approximation. The method specifically comprises the following steps: establishing a random stripe mixed noise image model; then converting a core task of satellite image denoising into an unconstrained optimization problem, and determining a target function; lRA operation is carried out on a noisy data matrix / tensor for random noise in a satellite image to realize random noise suppression, and stripe noise separation is realized through direction selective regularization by using a stripe noise removal model based on one-way total variation UTV for stripe noise in the satellite image; a UTV-LRTA mixed denoising model is established, and the mixed stripe noise in the satellite image is effectively suppressed through combination of low-tube-rank tensor constraint and one-way total variation regularization. According to the method, the random stripe mixed noise is effectively suppressed, the image quality is improved, and the visual effect is improved, so that the accuracy of information identification and analysis is improved.

The invention discloses a driving decision model optimization method and electronic equipment, and relates to the technical field of automatic driving, and the method comprises the steps: carrying out the denoising processing through a training diffusion model according to first driving perception data and first noisy data, so as to generate a first driving path point sequence of the first driving perception data at a future moment; therefore, abundant and diversified driving tracks are generated by utilizing the strong complex multi-modal distribution modeling capability of the diffusion model; using a first diffusion model to generate a plurality of second driving path point sequences at future moments according to the second driving perception data so as to construct an evaluator training sample, and training an evaluator model to select an optimal driving path point sequence from the plurality of second driving path point sequences, according to the method, the diffusion model and the evaluator model obtained through training are utilized to construct the driving decision-making model, through joint training of the evaluator model and the diffusion model, the decision-making ability of the driving decision-making model for generating candidate driving tracks and selecting the optimal driving track is enhanced, and the safety of automatic driving is improved.

The invention relates to a high-robustness three-dimensional target detection method based on Leiyu fusion, and belongs to the field of target detection. The method comprises the steps that laser point cloud data and image data are collected and preprocessed; extracting features of the preprocessed point cloud data and image data; converting the extracted point cloud features and image features into BEV features, and fusing the BEV features of the two features; and inputting the fusion features into a trained three-dimensional detection network to generate a high-robustness 3D target detection frame. According to the method, the complementary characteristics of two modes of the laser radar and the camera are combined, the synergistic effect of accurate and comprehensive three-dimensional object information provided by the laser radar and rich semantic information provided by the camera is fully played, better interference resistance is achieved on a noisy data set, the robustness of the system in a variable scene is remarkably improved, and the robustness of the system is improved. And it is ensured that the three-dimensional target detection task can still be efficiently and stably completed under the complex conditions of low light, severe weather, shielding and the like.

The invention discloses a multi-mode chemical reaction yield prediction method based on adaptive data screening, and belongs to the technical field of chemical synthesis and machine learning crossing. According to the method, a multi-modal input system containing one-dimensional chemical attribute data, a two-dimensional molecular structure map and a three-dimensional spatial configuration is constructed, so that multi-dimensional analysis of molecular interaction is realized; and meanwhile, a multi-stage training process is adopted to simulate a human cognitive rule, so that the model progressively learns layer by layer from a basic reaction feature to a complex reaction mechanism. According to the method, the screening threshold is dynamically adjusted, noise interference is effectively suppressed, and the adaptability to long-tail distribution data is improved. Compared with a traditional single-mode prediction model, the method has the advantages that the prediction accuracy on a noisy data set is improved, the trial and error cost of chemical synthesis experiments is remarkably reduced, and reliable technical support is provided for efficiently screening reaction conditions and accelerating research and development of new compounds.

The invention provides an aeromagnetic data noise reduction method of a noise reduction auto-encoder based on adversarial regularization, and the method comprises the steps: constructing a simulation target signal library, and designing an adversarial regularization noise reduction auto-encoder model structure composed of a noise simulator-encoder-decoder-discriminator structure; inputting the clean signal into a noise simulator to generate noisy data, inputting the noisy data into a decoder-encoder, and outputting a de-noised signal; secondly, the de-noised signal is input into a discriminator, and the discriminator generates a score for the de-noised signal; the reconstruction loss and the adversarial loss form an adversarial loss function; then, the model is trained; and finally, inputting an actually measured signal into the trained model, and carrying out aeromagnetic signal denoising. According to the technical scheme, the technical problems that in the prior art, when an aeromagnetic interference compensation method is used for processing non-linear, time-varying and atypical interference, the compensation precision is low, manual modeling is depended on, and the generalization ability is weak are solved.

The invention discloses a robust target recognition method and device based on noise analysis and a storage medium thereof. The method comprises the following steps: acquiring original noisy data to establish an original data set, screening the original data set based on a K-nearest neighbor method in cooperation with Jensen-Shannon divergence, and respectively establishing a net data subset and a noise data subset; corresponding weight parameters and gradient parameters are obtained in the training process of the network, responses of the net data subset and the noise data subset are calculated, and corresponding sensitivity and contribution degree are obtained; the sensitivity and the contribution degree are fused, and the overall importance score of the parameters is obtained through integration; and improving a loss function in a pruning criterion of Taylor expansion through the overall importance score, and building a pruning model. According to the method, information in the feature space and information in the prediction space are fused, clean samples and noise samples in an original data set are accurately divided, and the reliability of weight parameter evaluation samples in the pruning process of the pruning model is improved.

The invention provides a soil moisture sensor data quality inspection and interpolation method, which comprises the following steps: screening multivariable soil moisture time sequence data based on a preset core physical feature list, carrying out abnormal value detection through physical rule constraint and an isolation forest algorithm, and carrying out data labeling by creating a complete time axis; generating a training sample from the preprocessed data through sliding window sampling, and performing deep feature learning by using a denoising network based on a space-time diffusion probability model; performing interpolation on missing values in the original data by using the trained space-time diffusion probability model, generating a noisy data sample through a forward noise adding process, and performing conditional data interpolation based on a known observation value and a mask matrix in a reverse denoising process to generate a preliminary interpolation result; and performing post-processing correction on the preliminary interpolation result, wherein the post-processing correction comprises clamping correction based on a monthly historical range and correction based on interlayer physical logic.

The invention provides a U-shaped expansion convolutional neural network microseismic signal noise reduction method and system fused with a residual space attention mechanism, and belongs to the field of microseismic signal noise reduction. The problem that an existing noise reduction network model is poor in noise reduction effect and causes effective signal loss is solved. The method comprises the following steps: synthesizing noise-containing microseismic data and clean data, and constructing a training data set; inputting noisy data into the network model, and performing feature extraction through convolution operation in down-sampling; effective signal features are highlighted through a space attention mechanism, and interference noise is suppressed; in up-sampling, restoring the original size of the feature map through deconvolution operation, and fusing deep information and shallow information through a jump connection structure; residual learning is utilized to obtain estimation of a clean micro-seismic signal; and the trained network model is used for noise reduction of actual microseismic signals. According to the method, more effective information of the microseismic data can be reserved, and the noise reduction effect is effectively improved.

The embodiment of the invention discloses a key generation method and device based on a diffusion model, electronic equipment and a storage medium. The method comprises the following steps: constructing an initial noise distribution and diffusion model; inputting secret information and the initial noise distribution into the diffusion model to obtain noisy data, and performing denoising processing on the noisy data by adopting the diffusion model to obtain target distribution; determining a target random sample from the target distribution, and taking the target random sample as an initial key; and performing multi-level verification and optimization on the initial key to generate a target key. According to the technical scheme provided by the embodiment of the invention, the high-randomness and high-security key is generated by using the diffusion model, so that the attack of quantum computing is effectively resisted, and the security of key generation is improved; the flexibility and expandability of the diffusion model enable the diffusion model to adapt to the requirements of different application scenes, and the method has high application value and wide application prospects.

The invention provides a lithium ion battery remaining service life prediction method based on DAE-ResBiLSTM-MHA. The lithium ion battery remaining service life prediction method comprises the following steps: step 1, constructing a lithium ion battery data set; step 2, carrying out normalization processing on the battery capacity value in the new data set; step 3, using a de-noising auto-encoder to remove noise introduced by noise interference and capacity regeneration in the acquisition process in the battery data set; 4, on the basis of the BiLSTM model, a ResBiLSTM-MHA model is built in combination with residual connection and a multi-head attention mechanism; 5, dividing a data set by adopting a leave-one-out method cross validation, namely randomly selecting one battery sample as a test set each time, and using the rest samples as a training set and a verification set; training the ResBiLSTM-MHA model by using the training set, and preliminarily evaluating the predictive ability of the model through the verification set; step 6, the RUL prediction performance of the DAE-ResBiLSTM-MHA model is evaluated; by applying the technical scheme, the robustness and accuracy of the model facing noise data and capacity regeneration phenomena can be improved.

The invention provides artificial intelligence-based characteristic guidance method and system for enhancing quality of a diffusion model in generating a data from a noisy data based on a condition information. The method comprises: generating a nonlinear precorrection vector; performing, by a regularization module, a context regularization iteration to obtain an updated nonlinear correction vector and a nonlinear correction gradient; checking if a convergence criterion is met; and denoising the noisy data based on the updated nonlinear correction vector to generate the data. By using the regularization module, the provided characteristic guidance method not only greatly improves the stability of data generation by the diffusion model, but also provides enhanced control over context through two context modes: the detail enhancement mode and the context enhancement mode. The present invention can enhance the semantic characteristics of prompts and mitigate irregularities in image generation.