321 results about "Filter noise" patented technology

The invention relates to the technical field of urban infrastructure intelligent detection, and discloses an urban drainage pipe network damage detection system based on intelligent analysis. A multi-source data acquisition module acquires pipe network static structure parameters and real-time operation monitoring data through a distributed sensor network; generating a topological characteristic value, a dynamic operation state characteristic value and a dynamic detection threshold set of each damage type; the data preprocessing module filters noise of the monitoring data, eliminates abnormal values and extracts time domain and frequency domain feature vectors; the feature fusion module fuses and generates a multi-dimensional fusion feature matrix based on the topology and operation feature values; the intelligent analysis engine calculates and outputs a damage type diagnosis result through mode matching; the spatial positioning module generates three-dimensional coordinate positioning data of the damaged area in combination with the topological characteristic value; and the dynamic learning module optimizes the detection rule according to the maintenance data. The system realizes intelligent damage detection and accurate positioning, improves the detection accuracy and efficiency, adapts to a complex environment, and is high in intelligent level.

The invention relates to the technical field of computer vision, and provides a DETR-based double-domain pseudo-tag generation cross-domain target detection method, a multi-scale decoding query clustering module performs multi-scale clustering on queries output by a decoder, and can accurately capture feature information of targets of different scales, so that the confidence coefficient of pseudo tags is effectively evaluated. The method has a strong cross-domain migration capability, can generate a pseudo label with higher quality between the style data of the target domain and the style data of the source domain, and optimizes the confidence of the pseudo label, thereby improving the accuracy and robustness of target detection, and remarkably reducing the problems of missing detection and false detection of pseudo labels with different scales. The double-domain cooperation and double-domain verification module enhances the reliability of pseudo label generation by combining pseudo labels under different confidence levels of style images of a source domain and a target domain, and effectively filters noise. The adaptability of cross-domain data is improved, it is ensured that the model can smoothly migrate among different fields, and the detection precision and generalization ability are further improved.

The invention provides a cross-time-space conversion method with a Fourier filtering operator for multivariable time sequence classification. The cross-time-space conversion method comprises the following steps: mapping an input time sequence to a target variable; mapping a time sequence data set to a group of C category labels Y; after training, the CSTFormer model outputs a vector containing C values so as to estimate the probability that the time sequence belongs to each category; in the CSTFormer model, data input by a multivariable time sequence firstly passes through a LayerNorm layer and then enters a global adaptive Fourier neural operator module, noise is adaptively filtered, and important frequency information is enhanced; the output passes through a LayerNorm layer and then enters a time-space consistency learning module; the time-space consistency learning module is composed of three sub-modules, namely a dynamic multi-scale time attention module, a dynamic space attention module and a dynamic cross-distribution fusion module; respectively learning a time context and a space context, and fusing to obtain a learned time-space consistency context; and finally, outputting a classification result by the classification head.

The invention discloses a method for detecting the quality of a functional layer of a building outer wall by an unmanned aerial vehicle, and particularly relates to the technical field of building outer wall detection.The method comprises the steps that firstly, an infrared image of a building outer wall facing object is collected through the unmanned aerial vehicle, pixel points and remaining pixel points of a serious hollowing area are determined, and the abnormal degree of the remaining pixel points is calculated; clustering is carried out by adopting a defect probability-based region growing method; based on seed point screening of morphological preprocessing, structural elements of different sizes are comprehensively determined to be used through weighted summation calculation according to the jitter frequency of the unmanned aerial vehicle and the image resolution, and noise is filtered step by step. Generating a multi-scale pyramid for the original image, and detecting candidate seed points at each level; sorting and preferentially selecting the candidate seed points based on the morphological closure degree and the shape regularity of each candidate seed point; pixel points in the neighborhood of the growth seed points are merged to obtain all hollow defect connected domains; and judging the quality condition of the building outer wall functional layer according to the area of the hollowing defect connected domain.

The invention provides a casting industry abnormal sound detection and grading response method based on voiceprint recognition, and belongs to the technical field of casting industry detection. A high-temperature-resistant microphone array is arranged at an easy-to-leak part of cast aluminum equipment, and three-stage filtering noise reduction and amplitude normalization preprocessing are adopted, so that the problem of poor signal quality caused by noise interference in a complex environment is effectively solved; the characteristics of the molten aluminum leakage sound in different frequency bands and different stages can be captured through variable window long-short time Fourier transform and extended Mel frequency cepstrum coefficient in combination with extraction of an energy change rate and a frequency spectrum gravity center; a Transform-CNN hybrid deep learning model based on an attention mechanism is constructed, and feature screening is optimized through principal component analysis and recursive feature elimination, so that the recognition and generalization ability of the model to the abnormal sound in the casting industry is significantly improved; and meanwhile, graded response measures are made based on the detection result, so that the abnormal conditions of molten aluminum leakage with different severity degrees are processed.

The invention provides a Mama-based multi-modal dialogue emotion recognition method and system, and belongs to the field of dialogue emotion recognition. The problems that an existing multi-mode emotion recognition method is limited in memory ability, information is not fully utilized, and noise accumulation exists in long sequence fusion are solved. Comprising the following steps: processing dialogue data in different modes by adopting different preprocessing modes to obtain corresponding feature codes; performing convolution and information embedding on different feature codes; external attention is adopted to capture semantic information in each modal; interaction among different modes is realized by using a cross fusion mechanism; filtering noise in each mode by adopting Kalman filtering and establishing a relation between the modes; fusing the semantic information of different modals by using grouping pooling and cross attention, and then fusing the fused semantic information with a Kalman filtering result to obtain a fusion result; mapping the fusion result into a predefined emotion category; the method and the device are applied to multi-mode dialogue emotion recognition.

The invention discloses a vehicle detection and identification method and system based on laser sensing, and relates to the field of vehicle detection.The method comprises the steps that firstly, obstacle reflection signals are obtained through a multi-band laser array, preliminary classification is conducted through wavelength, time difference and intensity difference, and point cloud features are constructed; a lightweight network is adopted to filter noise points, and point cloud holes and shielding areas are intelligently complemented; then, performing hierarchical voxelization processing according to the obstacle distance, performing coarse-grained downsampling on a long-distance target, and keeping details on a short-distance target fine-grained; and finally, through a double-stage verification mechanism of a lightweight PointPill network and a three-dimensional convolutional network, precise grading detection and target reliability rechecking of far and near targets are realized, real-time performance and accuracy are considered, the problems of poor adaptability, low precision and insufficient dynamic scene adaptability of an existing laser radar detection technology are solved, and the method has the advantages of high detection precision and high reliability. According to the invention, high-precision and high-real-time vehicle detection and identification can be realized.

The invention discloses a cross-modal remote sensing image unsupervised adaptive method based on unreliable pseudo tag guidance. The method comprises the following steps: step 1, acquiring data of a source domain and a target domain and preprocessing the data; 2, constructing a teacher-student self-training framework, generating a pseudo tag for a target domain by a teacher network, and filtering noise through confidence evaluation; 3, dividing the image into reliable pixels and unreliable pixels according to the confidence coefficient, and generating a mask; 4, constructing positive samples, negative samples and anchor point features; 5, designing an unreliable sample guide pixel contrast loss function; 6, optimizing the loss function training model until the optimal performance is achieved; and 7, predicting test set data by adopting the trained model to obtain a semantic segmentation result. According to the method, the potential of pseudo labels of which modals are difficult to label is fully mined, the problem that unreliable pixels are insufficient in utilization during training is solved, and finally more effective cross-modal domain alignment and better cross-modal unsupervised domain adaptive semantic segmentation precision are realized.

The invention relates to the technical field of signal processing, and provides an electromyogram data-based muscle movement analysis method, device and equipment and a storage medium, the electromyogram data-based muscle movement analysis method comprises the following steps: using an adaptive filter to pre-process original electromyogram signals, and dynamically filtering noise signals; segmenting the electromyographic signal after the noise signal is filtered by using a sliding window, calculating the energy of the signal in each window, and detecting the zero-crossing rate of the signal in each window; performing wavelet threshold denoising on the original electromyographic signals to obtain wavelet domain features; fusing the signal energy, the zero-crossing rate and the wavelet domain features to form a feature vector; and inputting the feature vector into a pre-trained classification model for analysis to obtain a muscle motion state. The signal energy, the zero-crossing rate and the wavelet are fused with the features for analysis, real-time effective analysis of the muscle motion state is achieved, and the accuracy of muscle motion state analysis under the dynamic condition is improved.

The invention discloses a power transmission line fault identification method and device based on artificial intelligence, and relates to the technical field of power transmission line fault identification. Determining line data of the target area, performing data acquisition according to the line data to obtain an original traveling wave signal, and filtering the original traveling wave signal to obtain a time domain signal; performing adaptive variational mode decomposition on the time domain signal to obtain a mode component, and performing calculation according to the mode component to obtain a fault point; and determining an inspection scheme according to the fault point, executing the inspection scheme to obtain a fault point image, and performing alarm judgment on the fault point image through artificial intelligence. Line data and traveling wave signals are accurately collected, noise is filtered by adopting an advanced filtering technology, and the signal quality is improved; adaptive variational mode decomposition is utilized to accurately extract a mode component, and the fault point positioning precision is enhanced; faults are quickly and accurately judged by combining artificial intelligence image recognition, automatic operation and maintenance are realized, and the operation and maintenance efficiency of the power transmission line and the stability of the power system are improved.

The invention discloses a multi-sampling and cross-domain matching-combined roadside multi-laser radar point cloud splicing method, which comprises the following steps of: firstly, inputting roadside point cloud data at the same moment and at different visual angles, and adaptively adjusting a sampling proportion by analyzing point cloud density and noise level so as to enhance effective information and filter noise; then, geometric and shape features are extracted based on neural networks such as DGCNN and Point Transform, and preliminary cross-domain feature matching and coarse registration are carried out through algorithms such as GeoTransform, so that an initial transformation matrix is obtained; and carrying out fine point pair matching and rigid body transformation optimization in combination with RANSAC and ICP algorithms to realize high-precision point cloud registration. And finally, finishing point cloud merging by using the final transformation matrix, removing residual noise through bilateral filtering, and outputting a spliced high-quality point cloud image. According to the process, the splicing precision and robustness of the heterogenous laser radar point cloud are effectively improved.

The invention provides an underground ore body positioning identification method and system based on a ground penetrating radar, and relates to the technical field of ore body detection, and the method comprises the steps: inputting a corrected multi-dimensional point cloud feature set into a pre-trained closed-loop convolutional neural network, optimizing the network parameters through transfer learning, and obtaining a multi-dimensional point cloud feature set; outputting the category confidence and the spatial position probability distribution point cloud of the ore body target; on the basis of the spatial position probability distribution point cloud, generating ore body depth and horizontal positioning point cloud through a reverse projection algorithm in combination with an electromagnetic wave two-way travel time calculation rule and a Kanay resistivity measurement result; and performing multi-scale two-dimensional empirical mode decomposition on the reverse projection positioning point cloud, and filtering noise components to extract a high-confidence target point cloud. According to the invention, the accuracy and stability of ore body identification are improved.

The invention discloses an electromagnetic spectrum prediction method and system based on fractional Fourier transform. The method comprises the following steps: constructing historical spectrum observation data in a target area; mapping the collected spectrum observation data to a corresponding fractional order Fourier domain by using adaptive fractional order Fourier transform, and adaptively adjusting fractional order parameters of a fractional order Fourier transform layer to output a fractional order Fourier domain containing predictable spectrum components; in the mapped fractional order Fourier domain, non-predictive noise in the converted data is filtered out; performing feature extraction and prediction on the frequency spectrum data of the fractional order Fourier domain after noise filtering by using a complex valued linear network to obtain a prediction result of the fractional order Fourier domain; and mapping the prediction result of the fractional Fourier domain back to the original time domain by adopting inverse fractional Fourier transform, and generating final frequency spectrum prediction output. According to the method, the accuracy of weak periodic spectrum prediction is effectively improved through fractional Fourier transform and a filtering strategy.

The invention relates to the technical field of radar echo denoising, in particular to a radar echo signal filtering noise self-adaptive suppression method and a radar echo signal filtering noise self-adaptive suppression system. Acquiring radar station position and beam pointing data, and synchronously acquiring forest vegetation data and environment interference data of a beam pointing target area to form radar environment data; the radar environment data are identified through the multi-band decision model, and multi-modal waveform parameters are obtained; based on the multi-modal waveform parameters, radar equipment is controlled to perform periodic detection on the target area, and radar echo data are obtained; the method comprises the steps of identifying radar echo data and radar environment data, extracting dynamic spectrum characteristics in the radar echo data, performing clutter suppression processing on the dynamic spectrum characteristics by using the radar environment data, calculating the confidence probability that an early fire exists in a target area, and executing space-time consistency joint verification; according to the invention, fire early warning is carried out through the confidence probability of the fire.

The invention relates to the technical field of fault positioning, and particularly discloses a fiber grating sensor fault positioning method and system, and the method comprises the steps: firstly obtaining first signal data of a fiber grating sensor, and carrying out the empirical mode decomposition of the first signal data, further introducing a signal processing technology based on deep learning to carry out feature extraction on each sensor signal component, and carrying out feature selection based on feature distribution guidance of each sensor signal component so as to filter noise and redundant information and realize high-efficiency expression of signal key features; and on the basis, intelligent identification of the fault type of the fiber grating sensor is carried out. In this way, noise interference can be effectively avoided, and therefore the accuracy and stability of fault positioning of the fiber grating sensor are improved.

The invention discloses a hysteresis compensation system and method for low-frequency compensation of a magnetoelectric vibration sensor. The compensation system comprises a difference to single-end circuit, a second-order lag compensation circuit, a single-chip microcomputer, an ADC conversion module, a first-order high-pass filtering module and a second-order low-pass filtering module which are sequentially connected with the magnetoelectric vibration sensor. The differential-to-single-end conversion circuit receives voltage signals output by the magnetoelectric vibration sensor and converts the voltage signals into single-end electric signals, the second-order lag compensation circuit receives the single-end electric signals of the differential-to-single-end conversion circuit for low-frequency compensation, the single-chip microcomputer receives low-frequency compensation signals, the ADC conversion module converts the low-frequency compensation electric signals into digital signals, and the digital signals are transmitted to the magnetoelectric vibration sensor. The first-order high-pass filtering module filters noise with the frequency lower than f1 out of the digital signals, and the second-order low-pass filtering module filters noise with the frequency higher than f2 out of the digital signals output by the first-order high-pass filtering module. According to the invention, the amplitude response of the magnetoelectric vibration sensor during low-frequency signal measurement can be improved.

The invention provides an intelligent anomaly detection method and system based on a convolutional flow adversarial network, and relates to the technical field of anomaly detection, and the method comprises the steps: collecting a full-life-cycle vibration signal of equipment, processing the full-life-cycle vibration signal into a time-frequency diagram, and dividing the time-frequency diagram into a normal training set and a full-cycle test set; constructing a convolutional flow adversarial network comprising a generator and a discriminator; pre-training the generator, and alternately optimizing the discriminator and the generator; and constructing an abnormal state detection model by using the trained generator to realize full-cycle test set online detection. The method can accurately capture normal features, is sensitive to early abnormality, reduces the risk of mode collapse, is high in stability, provides a probability reference to reflect fault evolution, can filter noise, and is good in robustness.

The invention provides an image video retrieval method based on a domain fine-tuning large language model, which comprises the following steps: performing fine-tuning on a pre-training model to obtain a fine-tuning pre-training model for intention classification and keyword extraction; performing dynamic iteration screening on an optimal prompt template through Monte Carlo tree search in combination with a hidden Markov model (HMM); performing noise filtering on the keyword list, and predicting category labels of the filtered keywords through a conditional random field model to obtain a keyword enhancement set; combining with the user intention to generate a query condition, and obtaining a candidate resource set; and according to the similarity between the user query text and the candidate resource set, and in combination with the optimal prompt template, obtaining the resource path with the highest matching score between the user query and the candidate resource, and obtaining the retrieved image or video, so that the identification deviation possibly occurring when a general model processes proper nouns and terminologies can be effectively solved, and the user experience is improved. And the retrieval accuracy and response speed are improved, so that the retrieval accuracy and professional adaptability are improved.

The invention discloses a station yard state evaluation and early warning method and system based on an algorithm fusion technology, and relates to the technical field of station yard monitoring and early warning, and the method comprises the steps: carrying out the noise filtering and abnormal value detection of large-scale real-time station yard data collected from station yard equipment according to a frequency range and a noise type; performing rapid feature extraction and dimension reduction processing on the large-scale real-time station yard data subjected to noise filtering and abnormal value detection by using a quantum support vector machine to generate a low-dimensional feature vector; the method comprises the following steps: constructing an information weighted graph by utilizing physical connection, functional dependence and historical fault records among station equipment, representing each equipment as a node in the graph, and representing a data flow relationship, an energy transmission relationship and a physical connection relationship among the station equipment as edges; performing multi-round iterative training on the graph neural network by using the information weighted graph to obtain an equipment relation graph neural network model; and generating a final equipment state prediction vector in combination with the equipment relation graph neural network model and the low-dimensional feature vector.

The invention relates to a system and a method for generating a chest X-ray image based on a text of a diffusion model, and the system comprises a hidden space noise filtering module, a self-adaptive visual perception text learning module and a medical knowledge injection module for the diffusion model, performing hidden space mapping, manifold modeling and noise discrimination processing through a pre-trained image coding unit to obtain a denoised CXR image, and then obtaining a corresponding text according to a corresponding relationship between the CXR image and the text; the adaptive visual perception text learning module uses the noise filtered CXR image and text to train a large visual language model; a medical knowledge injection module aiming at the diffusion model is used for gradually injecting template text coding information generated according to the trained large visual language model into the diffusion model in a training state through a trainable control adapter, so that the perception intensity of the diffusion model on the text information in the training process is stably improved; therefore, the CXR image generation quality of the model is improved. According to the method, through an innovative method combining three stages of hidden space noise filtering, adaptive visual perception text learning and a medical knowledge injection module, the model is endowed with the capability of fully understanding characters and CXR images and generating high-quality CXR images of a specified type.

The invention discloses a frequency modulation continuous wave radar multi-target dynamic separation and anti-interference method and system based on synchronous extraction transformation, and the method comprises the steps: S1, generating a linear frequency modulation continuous wave, and transmitting a high-frequency signal through a radio frequency front end; s2, receiving a target reflection signal, mixing the target reflection signal with the emission signal through a mixer, extracting an intermediate frequency beat signal, and performing denoising; s3, performing time-frequency decomposition on the signal by adopting synchronous extraction transformation to generate a high-resolution time-frequency distribution matrix; s4, separating overlapped targets based on the time-frequency matrix by adopting a density clustering algorithm, eliminating a distance-speed coupling error in combination with a dynamic decoupling compensation algorithm, and outputting a time-frequency track of an independent target; constructing a dynamic time-frequency domain mask filter to filter noise, and optimizing the detection sensitivity and performing false alarm control through an adaptive threshold detection mechanism; according to the method, dense targets are accurately separated through high-resolution time-frequency analysis, and the anti-interference capability and the dynamic target tracking precision are improved in combination with a dynamic Doppler decoupling algorithm.

The invention provides a motor stator fault detection system and method, and relates to the technical field of data processing, and the method comprises the steps: synchronously collecting a three-phase current signal and a stator winding temperature signal during the operation of a motor in real time through a current sensor and a temperature sensor; performing time-frequency domain analysis and spectrum feature extraction on the three-phase current signal to obtain a current unbalance degree and a current harmonic distortion rate, and forming a current feature parameter set; performing filtering noise reduction and trend elimination processing on the stator winding temperature signal to obtain a processed signal; on the basis of the processed signals, temperature abnormal points are recognized through a k-nearest neighbor clustering algorithm of the three-dimensional Euclidean distance, the temperature instantaneous change rate is calculated according to the temperature abnormal points, and a temperature characteristic parameter set containing the abnormal point density and the temperature rise rate is obtained. According to the invention, stator fault detection and differential early warning are realized.

The invention discloses an electronic circuit board defect detection intelligent method based on an RMT deep learning model, and relates to the technical field of electronic circuit board detection, and the method comprises the following steps: carrying out the image collection of an electronic circuit board through high-resolution camera shooting, carrying out the de-noising processing of an image, reducing the noise influence caused by environmental factors and equipment errors, and improving the detection precision of the electronic circuit board. And a clean input image is provided for subsequent analysis. According to the method, the convolutional neural network and the random matrix theory are combined, so that the accuracy and robustness of defect detection of the electronic circuit board are remarkably improved. The CNN accurately extracts image features, the RMT effectively filters noise, and the model stability is enhanced. The self-adaptive algorithm and the multi-task learning optimization training process reduce false detection and missing detection and improve the detection efficiency, thereby improving the automation level and the detection precision of the production line.

The invention provides an analog correlation circuit of a synthetic aperture radiometer receiver, and relates to the technical field of weak signal detection and processing. The circuit comprises a high-stability power supply reference unit, a high-performance broadband multiplication unit, a low-noise amplification unit and a high-linearity differential active filtering unit, wherein a second-order differential active filter in the high-linearity differential active filtering unit is used for carrying out noise filtering and low-noise amplification on a low-noise amplifier output signal in a differential form, and a differential signal of which the bandwidth is matched with the variation range of the amplitude of a target radiation brightness temperature signal and the amplitude meets the subsequent analog-to-digital conversion requirement is output. The high-linearity differential active filtering unit designed by the invention is realized by a second-order differential active filter formed by connecting two differential active filters in series, a low-pass filter with the cut-off frequency as low as possible filters noise in a link to the maximum extent, only the bandwidth matched with signal change is reserved, the amplitude which is two times of that of a single-ended signal can be provided, and the filtering efficiency is improved. And the linearity and the signal-to-noise ratio performance are better.

A method for characterizing oxide defects in semiconductor devices is described. Based on time-resolved SILC data, a total number of active oxide defects and each defect's characteristics can be determined for a deeply-scaled FET device. The discrete changes in leakage current correspond to switching of a single defect. A Bayesian-inspired algorithm is utilized to extract distinct current levels and experimental data is quantized into these extracted current levels by filtering noise. The evolution of current levels corresponds to a Markov chain. The defect currents are extracted by clustering transition probabilities and absolute differences in current levels using an affinity propagation algorithm. A maximum likelihood estimator is developed to extract a base leakage current. Lastly, defect currents are used to reconstruct the experimental data and its deconvolution into activity of individual defects provides the respective time constants.

The invention relates to the technical field of digital signal processing, in particular to a digital wireless audio signal noise reduction optimization method based on Matlab, and the method comprises the steps: S1, obtaining the real-time transmission data of a digital wireless audio signal; s2, performing noise identification processing according to the real-time transmission data of the digital wireless audio signal to obtain noise type data of the digital wireless audio signal; s3, performing feature analysis processing based on Matlab according to the real-time transmission data and the noise type data of the digital wireless audio signal to establish a noise feature database of the digital wireless audio signal; s4, establishing a noise reduction model of the digital wireless audio signal based on SVM-LSTM by using the real-time transmission data of the digital wireless audio signal and the noise feature database; s5, utilizing the noise reduction model of the digital wireless audio signal to obtain a noise reduction optimization result of the digital wireless audio signal; compared with the prior art, the limitation of the existing filtering noise reduction technology is solved, and the noise reduction accuracy and processing efficiency are improved.

The invention discloses an ultrasonic phased array weld defect intelligent identification method based on adaptive filtering, and belongs to industrial nondestructive detection image identification methods, the technical scheme is that the method comprises the following steps: sampling and sorting pipeline weld PAUT detection images; dividing the collected image samples into a training set and a test set; pixel classification and filtering noise reduction are carried out on the training set images through a hybrid adaptive filter; performing abnormal signal labeling on the preprocessed training set image and the unprocessed test set image respectively; training a target recognition network through the preprocessed training set; and collecting a new fan-shaped scanning image of the pipeline weld defect as network input, and identifying the weld defect. The ultrasonic phased array weld defect intelligent identification method based on self-adaptive filtering has the beneficial effect that the ultrasonic phased array weld defect intelligent identification method based on self-adaptive filtering is provided.

The invention discloses an electrocardio gating signal processing device and method used under nuclear magnetic resonance, and belongs to the technical field of nuclear magnetic resonance electrocardio gating processing.The method comprises the steps that an integrated circuit board of physical magnetic shielding collects superposed signals of electrocardio signals and nuclear magnetic noise, and the superposed signals serve as original electrocardio signals after noise filtering and amplification are conducted on the superposed signals in sequence; the method comprises the following steps: performing real-time frequency analysis on an original electrocardiosignal, extracting various electrocardiosignal characteristics, reconstructing a mask vector to filter noise and reconstruct a time-domain R-wave signal, reconstructing an electrocardiosignal based on the reconstructed time-domain R-wave signal and the various electrocardiosignal characteristics, detecting an R-wave trigger point based on the reconstructed electrocardiosignal and generating a gating signal, missed triggering and false triggering detection is carried out on R-wave triggering points in real time; and the reconstructed electrocardiosignal, the gating signal and various electrocardiosignal characteristics are output to a nuclear magnetic resonance system, and matching of different gating trigger conditions based on a magnetic resonance scanning sequence is supported, so that accurate positioning of R waves can be realized.

The invention relates to the technical field of anti-deception navigation, and discloses a dynamic anti-deception navigation method and system for an unmanned aerial vehicle, and the method comprises the steps: obtaining a signal of a satellite navigation system in the flight process of the unmanned aerial vehicle, and detecting and identifying a potential interference signal; adjusting filter parameters in real time according to the motion state of the unmanned aerial vehicle, and filtering noise and jump signals; when the signal of the satellite navigation system is seriously interfered, the signal of the satellite navigation system and the data of the inertial measurement unit are subjected to data fusion, short-time navigation of the unmanned aerial vehicle is maintained, and dynamic anti-interference deception of the unmanned aerial vehicle is realized. According to the method, interference and deception signals can be accurately identified and filtered by adopting multi-dimensional signal detection and analysis of multi-frequency multi-antenna receiving, time propagation error, power estimation, pseudo-range consistency and the like. When satellite navigation signals are seriously interfered, short-time navigation is carried out through IMU data, and it is ensured that the unmanned aerial vehicle keeps flight stability and navigation continuity in a complex environment.

The invention discloses a root cause alarm analysis method and system based on time sequence association and multi-modal fusion, and the method comprises the steps: taking digital twin dynamic threshold cleaning as a core, fusing multi-source data time sequence alignment verification, and forming a multi-source data set of time sequence alignment and preliminary restoration through exception handling and cross-system time consistency verification; a cleaning threshold is optimized in combination with a working condition to filter noise, a label confidence weighting method is fused, confidence is given to a label through twin simulation, and a multi-modal data set with a multi-dimensional label is constructed. A learning model is constructed and trained, exclusive feature extraction branches are designed for multi-source data, a twin working condition adaptation enhancement module is introduced to strengthen core modal extraction, and the proportion of abnormal modals in fusion is reduced by means of a dynamic attention filtering mechanism. Based on the model and a twinning system, root cause penetration type positioning is completed through dynamic sequencing of root cause confidence in combination with a twinning reduction time sequence. And the root cause analysis efficiency and accuracy are improved.