95results about How to "Suppress noise interference" patented technology

The invention relates to the technical field of remote sensing data processing and earth surface parameter inversion, and discloses an earth surface matrix parameter hyperspectral data inversion method and system based on machine learning. Comprising the following steps: constructing a multi-source heterogeneous hyperspectral data set; performing feature screening on the preprocessed hyperspectral data set based on an adaptive band selection algorithm, constructing a dynamic weight matrix by calculating mutual information entropy and inter-class distance measurement between spectral bands to realize intelligent screening of key feature bands, and combining spectral derivative conversion and spectral index calculation to generate an enhanced feature vector; and a multi-task transfer learning neural network model is constructed, and an output layer realizes multi-parameter collaborative inversion based on a multi-task learning architecture. And performing preprocessing and feature enhancement operation which is the same as that of the training data on the hyperspectral image data of the to-be-inverted region, inputting the trained neural network model, and outputting a surface matrix parameter inversion result.

The invention discloses a hyperspectral image classification method based on frequency domain denoising and element gradient correction, and the method comprises the following steps: carrying out the preprocessing of all hyperspectral image data, and dividing an overall training sample set formed by the processed hyperspectral images into a training set and a verification set; constructing a sample weighting model based on frequency domain denoising and element gradient correction; in the training process, a parameterization frequency spectrum gating sensing transformation module is utilized to map features to a frequency domain through discrete Fourier transform, a learnable frequency spectrum response function is utilized to adaptively suppress spectrum jitter noise, and finally pure features are reconstructed. And automatically constructing a high-confidence pseudo-clean verification set based on a Gaussian mixture model and time domain consistency. According to the method, a time domain momentum updating mechanism is introduced, the variance of statistical estimation is effectively smoothed, random interference caused by training fluctuation is resisted, and the accuracy of pseudo clean set construction and the convergence stability of overall model training are further improved.

The invention relates to a structured light illumination confocal super-resolution measurement system and method, and relates to the technical field of optical measurement. The system comprises a digital micromirror device (DMD), a sample platform, an electric displacement platform, a microscopic imaging assembly, a complementary metal oxide semiconductor (CMOS) imaging assembly and computer equipment, and the DMD is used for adjusting the point light source array and the structured light field. According to the DMD-based confocal super-resolution measurement method, a structured light field and a signal-noise separation technology are combined, and high-precision and high-resolution three-dimensional surface reconstruction can be realized. By using the flexibility of the DMD and the advantages of the super-resolution technology, the spatial resolution and speed of confocal imaging are effectively improved, so that the measurement process is more accurate. In addition, low-rank decomposition and self-supervised deep learning methods are fused, the image reconstruction process is optimized, noise interference is suppressed, and the overall performance of the system is further improved.

The invention relates to the technical field of intelligent manufacturing, and particularly discloses a noise robust multivariable time sequence anomaly detection method for the field of intelligent manufacturing, which comprises the following steps: acquiring multivariable time sequence data; mapping the input data to a low-dimensional potential space, and restoring the input data to an original feature space to obtain reconstructed data; calculating a reconstruction error matrix of the original data and the reconstruction data, calculating a gradient matrix of the loss function relative to the input data, averaging and normalizing the gradient matrix along the time dimension to obtain a dimension importance score, and fusing the dimension importance score and the reconstruction error matrix to generate a weighted reconstruction error matrix; distinguishing the original data from the reconstructed data through a discriminator, and alternately optimizing parameters of the generator and the discriminator; and calculating an abnormal score based on the weighted reconstruction error matrix, introducing an abnormal diffusion mechanism to ensure the time sequence consistency, and judging that the sample with the abnormal score exceeding a threshold value is abnormal.

The application belongs to the field of integrated circuits, and specifically provides a low-power consumption enhanced reading type 11T CNFET SRAM cell circuit to solve the problems of slow speed, low stability, high power consumption and the like in the prior art; the application comprises N-type carbon nanometer field effect transistors MN1-MN9 and P-type carbon nanometer field effect transistors MP1-2, on the basis of a conventional 6T SRAM cell circuit, a dynamic load structure composed of transistors MN7, MN8 and MN9, and a high-resistance differential readout circuit composed of transistors MN3, MN4, MN5 and MN6; the adoption of P-type transistors realizes a bistable latch structure to reduce holding and writing power consumption, eliminate path competition phenomena caused by data flipping during writing, and improve writing speed; during reading, a resistive load tube is turned on to eliminate charge accumulation and ensure data storage stability; the high-resistance differential readout circuit reduces voltage disturbance of a storage node caused by bit line load; meanwhile, the differential writing / reading circuit can suppress common-mode noise and external interference on the bit line, reduce the probability of error sampling, and enhance overall anti-interference capability.

The application relates to the field of fault detection, and discloses a device fault detection method based on small sample learning and acoustic feature migration, a device and a medium, the method comprising the following steps: acquiring an acoustic signal of a target device; extracting a fusion feature vector; generating device-independent features based on a DANN network; generating a synthesized fault acoustic signal by using a Mel-CGAN; mixing the synthesized fault acoustic signal with an actual acoustic signal, and then training the DANN; and obtaining a fault detection result by using the trained DANN. The application can significantly reduce the dependence of fault diagnosis on labeled data, and can reduce the accuracy in a cross-device scene. The application can be widely applied in the fields of wind power and intelligent manufacturing.

The application provides a steam delivery pipeline leakage detection system, which comprises a metal conducting rod fixedly connected to the outer wall of a steam delivery pipeline, a sensor connected to the metal conducting rod, a signal analyzer electrically connected to the sensor, and a DCS control center electrically connected to the signal analyzer, and the sensor is used for collecting the vibration sound signal of the metal conducting rod. The steam delivery pipeline leakage detection system of the application can accurately and timely determine the leakage and the position of the leakage point by using the metal conducting rod connected to the steam delivery pipeline to transmit the vibration sound generated by the intense friction between the leakage medium and the pipeline wall, and the sound signal is not obviously attenuated in the transmission process due to the solid medium transmission, the transmission distance is long, and the sound signal is not easily interfered by other signals, the false positive rate of the leakage detection is effectively reduced, the operation and maintenance cost of the steam delivery pipeline is significantly reduced, and the industrial safety is improved.

The invention provides a cable joint partial discharge fiber bragg grating sound wave intelligent sensing and detection method, and relates to the technical field of cable partial discharge detection, and the method comprises the steps: obtaining multi-channel time-domain spectral response data of a detected cable joint through a fiber bragg grating sensing array, and demodulating the data to obtain a multi-dimensional feature vector group; decomposing the waveform signal into narrowband intrinsic mode components, extracting energy density through Hilbert transform and executing singular value decomposition, and screening dominant mode components to reconstruct the waveform signal to obtain stress wave time sequence characteristics; calculating a waveform high-order cross-correlation tensor as a hyperedge weight, extracting a time-frequency amplitude feature vector as an initial node feature, constructing a space-time hypergraph, and generating a node embedding vector through graph convolution; and initializing a Gaussian noise field based on a node embedding vector and setting condition information, executing reverse sampling denoising to generate a three-dimensional space probability density field to identify an initial positioning coordinate, and iteratively correcting the positioning coordinate and a wave velocity value until a residual error converges.

This application belongs to the field of visual compensation technology and provides a visual compensation method under starlight conditions. Through pre-sampling and grayscale variance statistics, it achieves the determination of starlight compensation intervals and the dynamic division of target and background regions in the imaging plane. It adopts a sampling method with different exposure time series for the target and background regions to obtain multiple frames of original sampled data and pixel integration time. Based on the pixel integration time, it constructs a spatially variable gain matrix and completes inter-frame registration and gain normalization processing to separate signal and noise components in the image. The signal component is used as a sparse sampling stream, and the pixel variance distribution of the noise component is used as a hyperparameter of the variational inference algorithm. Image reconstruction is completed through probability density gradient estimation, making the variational inference process match the actual noise distribution characteristics under starlight conditions, thereby improving the quality and reliability of visual imaging under starlight conditions.

This invention relates to the field of energy forecasting technology, and in particular to a method and system for short-term energy load forecasting based on an improved Transformer using SE-Block. The method includes reversible normalization preprocessing of acquired multivariate load sequence data; feature extraction and fusion of the preprocessed data using improved cross-scale interactive patching, including multi-scale feature extraction, cross-scale interactive alignment, residual correction, and dynamic fusion; and feature filtering of the fused features based on a channel attention mechanism, including feature response based on improved SE-Block and nonlinear interaction of context vectors. This invention addresses the non-stationarity of actual load caused by meteorological conditions and user behavior. By automatically eliminating noise interference among multiple variables, the model accurately depicts the fluctuation details of the load curve, demonstrating its robustness in multivariate load forecasting for integrated energy systems.

The invention discloses a total variation regularization depth expansion network method for hyperspectral image unmixing. The method comprises the following steps: inputting hyperspectral image data and an end member spectrum library; constructing a hyperspectral data unmixing model fusing a sparse constraint term and a total variation regularization term and an optimization objective function of the hyperspectral data unmixing model; converting the optimization objective function into an equivalent augmented Lagrangian form, and performing iterative solution on each variable; expanding an iteration process into a deep learnable network structure with a plurality of layers, and parameterizing hyper-parameters and a plurality of approximate operators in iteration into learnable network parameters; constructing a comprehensive loss function, and continuously optimizing network parameters by using back propagation and gradient descent methods; according to the method, total variation regularization prior is fused into a sparse unmixing model, the sparse unmixing model is expanded into a learnable end-to-end neural network through iterative solution, and the method has excellent unmixing precision and robustness for noise-containing data.

The application provides a temperature control system and an air conditioner; the temperature control system comprises a control unit, a power supply unit, an ambient temperature detection unit and an evaporator coil temperature detection unit; the ambient temperature detection unit comprises a ring temperature sensor assembly and a first inductor, the evaporator coil temperature detection unit comprises an inner coil sensor assembly and a second inductor, the control unit is used for acquiring the ambient temperature collected by the ring temperature sensor assembly and the evaporator coil temperature collected by the inner coil sensor assembly, and controlling the temperature of the air conditioner according to the ambient temperature and the evaporator coil temperature; the system structure not only realizes the temperature control of the air conditioner, but also plays a role in inhibiting common mode and differential mode noise interference by arranging the inductors in the ambient temperature detection unit and the evaporator coil temperature detection unit respectively, improves the EMC test qualified rate of the air conditioner, and has a simple structure and good practical value.

The application discloses a kind of based on acoustic wave FMCW's respiratory frequency detection method, belong to acoustic wave signal processing technical field;Method is: utilize mobile phone loudspeaker to generate a segment frequency continuous change acoustic wave signal, form continuous acoustic wave detection sequence;Mobile phone microphone receives the acoustic wave signal that is reflected back synchronously;To the acoustic wave signal that is reflected back received filtering processing;With reflected signal, the distance change curve of thoracic cavity with breathing movement is generated by autocorrelation operation to transmission signal;Distance change curve is carried out smoothing processing;The main frequency frequency is obtained by fast fourier transform, and respiratory frequency is calculated.The present application effectively suppresses environmental noise interference by using the advantages of FMCW technology, analyzes the autocorrelation coefficient of reflected signal, accurately extracts the thoracic movement information related to respiration, and realizes the accurate detection of human respiratory frequency;Using autocorrelation function is easier to find peak, more easily tracked nature, greatly increase the convenience of user.

This invention provides an enzyme turnover rate prediction method based on a dual-route hybrid expert mechanism, belonging to the field of bioinformatics technology. It solves the problems of low data utilization, insufficient information mining, and poor robustness in existing prediction methods due to missing values ​​and temperatures. The technical solution includes the following steps: S1: Constructing a unified standard enzyme turnover rate dataset; S2: Extracting multimodal embedding features; S3: Constructing intra- / inter-modal encoders; S4: Combining modalities into an expert hybrid module; S5: Designing an attention fusion mechanism. This invention can achieve high-precision prediction under complex in vitro environmental conditions.

This application provides a multimodal ultrasound intelligent identification method and precision diagnosis system for thyroid lesions. The method determines image data blocks with multimodal spatial registration and grayscale-contrast temporal alignment. Based on the grayscale ultrasound image sequence in the image data blocks, a first depth feature map of the target user's thyroid region is determined. A second depth feature map of the target user's thyroid region is determined based on the elastography map in the image data blocks. The hemodynamic spatial distribution characteristics of the parametric map in the image data blocks are captured to determine a third depth feature map of the target user's thyroid region. Cross-modal fusion is performed on the first, second, and third depth feature maps, and then the lesion segmentation mask, benign / malignant probability value, and risk classification of the target user's thyroid region are inferred and output in parallel. Using the scheme of this application, high-precision spatiotemporal alignment and cross-modal depth feature fusion of multimodal ultrasound images can be achieved to complete end-to-end intelligent identification of thyroid lesions.

The application discloses a color video restoration method based on a low-rank quaternion tensor and a space-time SVTV constraint, and comprises the following steps: acquiring a color video sequence to be restored, judging a video format and completing format analysis, converting YUV format into RGB format, constructing a corresponding pure quaternion tensor representation, and generating damaged video data; building a neural network parameterized low-rank quaternion tensor continuous function model, constructing a neural representation driven space-time SVTV regular term, and restraining a saturation gradient and a brightness gradient in different dimensions; fusing data consistency constraints and regular constraints, constructing a finite-dimensional solvable joint optimization model, and completing solvability verification; taking the damaged video as input, iteratively solving an optimal prediction tensor through a gradient descent algorithm or an Adam optimization algorithm, fusing a predicted pixel and an original known pixel, and outputting a complete restored video after data restoration and format adaptation. The application can realize high-precision, high-robustness and high-stability restoration of a missing area of a video, and is suitable for various video damage scenes.

The application discloses a fault detection method and device based on graph structure difference and electronic equipment, and relates to the technical field of intelligent monitoring. The method comprises the following steps: acquiring an observation data vector of a physical system to be monitored at time t, a linear mapping matrix and an upper state matrix vector, the observation data vector being determined by collecting sensing data of the physical system to be monitored in real time, and the linear mapping matrix and the upper state matrix vector being constructed based on a basic structure model, operating parameters and predictive input of the physical system to be monitored; constructing an optimization problem objective function at time t based on the observation data vector, the linear mapping matrix and the upper state matrix vector at time t; obtaining a sparse difference vector at time t by solving the optimization problem objective function at time t; comparing the sparse difference vector at time t with a threshold value to obtain a fault detection result, and sending a control instruction to a system controller. The method can improve the accuracy of fault diagnosis.

This invention discloses a method and apparatus for audio line status identification in communication equipment rooms. The method includes audio data acquisition, acoustic physical constraint modeling, multi-region acoustic feature importance classification, audio line status identification model design, constraint loss dynamic balance optimization, acoustic feature loss function construction, and audio line status identification. This invention belongs to the field of data processing technology, specifically referring to a method and apparatus for audio line status identification in communication equipment rooms. This scheme models the sound wave propagation in the equipment room based on a three-dimensional acoustic wave equation to suppress non-fault noise interference; it divides the equipment room into zones, quantitatively allocates the number of monitoring points and regional loss weights, focuses on core areas, and significantly improves the fault detection rate of critical lines; it introduces the measured background noise sound pressure level of the equipment room to construct a noise correction coefficient, significantly improving the robustness of fault identification; and it designs an exponential dynamic relaxation coefficient, combined with benchmark weight allocation, to further enhance the early fault detection capability in critical areas.

This invention discloses a distributed intelligent decision-making and collaborative control system for unmanned aerial vehicle (UAV) swarms, relating to the field of UAV technology. The invention includes a data acquisition module, a situational awareness construction module, a distributed strategy negotiation module, a motion planning module, and a consistency control module. UAVs interact with neighboring nodes only to exchange decision parameters, reducing communication load and bandwidth requirements, avoiding the risk of single-node failure, and improving the robustness and operational reliability of the swarm system in complex maritime communication environments. It filters environmental information strongly correlated with its own motion, suppresses noise interference, and provides complete and reliable situational awareness data for swarm decision-making, improving the accuracy and anti-interference capability of maritime environmental situational awareness. Control parameters are adapted in real-time based on flight speed, attitude changes, and remaining battery power, balancing high-speed response accuracy and low-speed control stability, reducing low-power energy consumption, and enhancing the stability, endurance, and adaptability of swarm collaborative control to maritime operations.

This invention discloses a method and system for local differential privacy aggregate value data aggregation based on sketches and sampling, belonging to the field of privacy computing and big data analysis technology. The method includes: initializing and disclosing sketch structure parameters on the server side; constructing aggregate value data on the user side, randomly selecting counter indices and determining the hash collision set; randomizing the hash collision set elements on the user side according to the aggregation function type, calculating the hit rate and performing adaptive pruning; reporting triples on the user side; and accumulating and updating the sketch counter on the server side, calculating the aggregated statistics through median estimation. This invention replaces traditional multi-element perturbation with sketch sampling and lightweight reporting, without requiring additional privacy budget consumption, effectively solving the noise accumulation problem, significantly reducing computational and communication overhead, and achieving a balance between privacy protection and statistical utility.

The application discloses a kind of remote sensing time series data analysis method and system considering the spatio-temporal correlation of geographical object, belong to data analysis technical field.The method includes: obtaining multi-source multi-temporal remote sensing image and auxiliary data and pre-processing, generate consistent time series data stack;In three-dimensional space-time domain, image is divided into voxel and is adjacent tracking, identify geographical process object with evolution behavior and record its attribute;Determine the spatio-temporal topological relationship between geographical process object, generate unified spatio-temporal relationship table by composite reasoning;With geographical process object as node, spatio-temporal relationship as edge constructs geographical process object spatio-temporal graph model, extracts and standardizes the attribute characteristics of node and edge;Importance index of each node is calculated by using graph convolution network and topological dynamic mechanism joint modeling.The application realizes closed-loop analysis from data processing, relationship modeling, importance evaluation to decision support, improves the interpretability and decision effectiveness of remote sensing time series change detection.

The invention relates to the technical field of chronic disease information intelligent management, and discloses an AI-based multi-disease chronic disease information intelligent management system, and the system comprises a video collection module which collects original frames, screens out invalid frames according to the definition, and constructs an effective sequence; the space-time registration module is used for constructing a Lagrange view angle and removing noise in combination with spectrum gating; the signal decoupling module is used for separating hemoglobin and constructing an enhanced blood flow signal according to texture weighting; the phase imaging module is used for calculating a lag amount and generating a microcirculation phase thermodynamic diagram; the standardized mapping module is used for identifying the mark points and generating a standardized phase diagram without rigid mapping; and the risk assessment module is used for executing historical difference, analyzing disease course evolution, outputting risks and prompting. According to the method, the problem of detection distortion caused by common limb tremor or inautonomous movement of the chronic disease patient is effectively solved by constructing the Lagrange view angle video stream and combining the space-time spectrum gating technology.

The invention relates to a coral reef distribution identification method and system based on a remote sensing image. The method comprises the steps that an original remote sensing image is acquired and preprocessed to obtain preprocessed data, and a coral reef sample data set is constructed according to the preprocessed data; constructing a coral reef distribution identification model, wherein the coral reef distribution identification model is constructed by introducing a cavity space pyramid pooling module and a double attention mechanism into a Multi-Scale Attention UNet model; training a coral reef distribution identification model according to the coral reef sample data set to obtain a trained coral reef distribution identification model; and obtaining a to-be-detected image and realizing coral reef distribution identification according to the trained coral reef distribution identification model. According to the method, the multi-scale features are effectively captured by using the cavity space pyramid pooling module, and the IoU is remarkably improved. And noise interference caused by sea waves, uneven illumination and different water depths is effectively inhibited through pretreatment. And the identification result is more accurate.

The invention provides a limb movement assessment method based on a causal perception multi-granularity graph network. At present, although a video and graph convolutional network (GCN)-based method has more accessibility, the method is still easily interfered by irrelevant actions of non-evaluated parts, and a traditional graph structure only can model a binary relationship between joints and is difficult to capture high-order dynamic dependence generated by multi-joint collaboration. Aiming at the problems, the method comprises the following steps: firstly, identifying and focusing on key joints related to score causality, and eliminating interference nodes; and multi-level dynamic association of the key joints from pairwise interaction to high-order dependence is deeply excavated, so that comprehensive understanding and accurate scoring of action modes are realized. The result shows that the method can effectively inhibit irrelevant action interference, more accurately describes the multi-joint cooperation mode, and has higher scoring accuracy compared with a traditional method.

This application provides a method, apparatus, and device for measuring mass flow rate based on a mass flow meter. The method includes: acquiring a first measurement signal from a measurement sensor based on a first time period; measuring the mass flow rate parameters of a fluid to be detected within the mass flow meter based on the first measurement signal; if the fluid to be detected is determined to include a gas-liquid two-phase fluid based on the first measurement signal, updating the current operating mode of the mass flow meter to a dual-frequency excitation operating mode; acquiring a second measurement signal from the measurement sensor based on a second time period; if the current operating mode is the dual-frequency excitation operating mode, generating a high-frequency auxiliary excitation signal and a low-frequency auxiliary excitation signal based on the second measurement signal, and generating a first control signal based on the high-frequency auxiliary excitation signal and the low-frequency auxiliary excitation signal; and controlling a vibration sensor to vibrate the mass flow meter through the first control signal. With this solution, in the dual-frequency excitation operating mode, bubble noise interference can be suppressed, and mass flow rate can be accurately measured.

The application discloses a kind of multi-parameter integrated casing damage detection method and system, it is related to casing detection technical field.The method is by downhole detection system synchronous acquisition vision, deformation, residual magnetism eddy current and positioning data, after pre-processing and multiplex transmission to ground;Ground system completes data restoration, spatial position alignment and feature level fusion, intelligently identifies defect and generates multidimensional defect distribution atlas;Again according to defect comprehensive density and severity grade self-adaptive control downhole instrument speed, finally output integrated detection report.Downhole detection system uses integrated downhole instrument string, integrates multiple detection modules and FPGA+ARM preprocessing unit, cooperates with ground closed-loop control system.The application realizes multi-parameter synchronous detection, multi-source data accurate fusion, defect intelligent identification and adaptive speed regulation, greatly improves detection precision and efficiency, reduces operation cost, and is suitable for integrated efficient detection of oil and gas well casing damage.

This invention belongs to the field of battery health management technology, specifically relating to a battery life prediction method and device based on the fusion of time-series and multimodal models. The method collects electrochemical performance data, textual semantic data, and visual image data of the battery under test, and extracts corresponding time-series features, textual features, and image features. By dynamically adjusting the contribution weights of each modality through learnable gating coefficients, the life degradation task-driven correlation coefficient matrix and structure-preserving mask matrix are determined. A cross-modal multi-head attention mechanism is used to obtain enhanced features for each modality. Semantic enhancement and decoupling fusion of the enhanced features yield multimodal fused features, which are finally input into a regression predictor to output the battery's remaining lifespan prediction result. This disclosure adaptively adjusts the contributions of each modality through a dynamic weighting mechanism, achieves deep cross-modal feature interaction through a physically guided attention mechanism, and suppresses redundant information through decoupling fusion, thus realizing the full fusion and utilization of multi-source heterogeneous data.

The application discloses a complex road surface mechanical response monitoring device and method, relates to the technical field of road surface detection, and can solve the technical problem that internal structure damage caused by two different mechanisms of aggregate bonding failure and pore filler increase cannot be effectively distinguished, and comprises the following steps: acquiring a source end impact response event set and a receiving end impact response event set; generating a potential propagation path parameter set according to the source end impact response event set and the receiving end impact response event set, and screening a candidate propagation path set from the potential propagation path parameter set according to initial propagation statistics; determining refined propagation statistics according to the candidate propagation path set, and determining an identified propagation path set from the potential propagation path parameter set according to the refined propagation statistics; determining a current state coordinate according to the identified propagation path set; and diagnosing a road surface disease type according to the direction of a displacement vector between the current state coordinate and a road surface reference coordinate.

The application provides a kind of image recognition method of robot for home environment, belong to image processing technical field, specifically include: S1: the RGB-D camera of robot shoots the image under home environment, will RGB-D camera registration;S2: the image of registration in S1 is preprocessed, and improved image is obtained H ( x, y );S3: the improved image H ( x, y ) format conversion, is converted into normalized image;S4: neural network improvement;S5: the normalized image is input into improved neural network, and the object is identified by improved neural network.The application can realize high-precision identification and positioning of characters, medicines, furniture and obstacles and other targets in complex home environment, improve the environmental perception and autonomous decision-making ability of robot in indoor scene.

The invention discloses an equipment part fault diagnosis method based on a noise residual fusion strategy, and relates to the technical field of fault diagnosis, and the method comprises the steps: obtaining noise-containing state monitoring data collected when mechanical equipment operates in a noise scene, and carrying out the standardized preprocessing of the noise-containing state monitoring data to unify the data distribution and eliminate the dimensional difference; and inputting the preprocessed noise-containing data into the trained denoising-diagnosis combined model, filtering noise through the signal denoising model to obtain denoised data, integrating noise residual errors and the denoised data into comprehensive fusion features through the fusion module, and completing fault prediction through the diagnosis model. Noise interference is effectively suppressed through the signal denoising model, the fusion module avoids loss of important fault information, the joint model realizes denoising and diagnosis collaborative optimization through cascade logic, the problems of poor collaborative effect and easy information loss in the prior art are solved, the robustness and accuracy of fault diagnosis in a noise scene are improved, and the fault diagnosis efficiency is improved. The method is suitable for fault monitoring and diagnosis of various industrial devices.