72 results about "Singular spectrum analysis" patented technology

The invention discloses an ultrasonic guided wave signal noise reduction method based on HHO-SVMD and singular spectrum analysis, and the method comprises the steps: obtaining a to-be-processed ultrasonic guided wave signal, optimizing a penalty factor of variation mode decomposition (SVMD) through employing an improved Harris eagle algorithm, initializing a population through Circle chaotic mapping, introducing chaotic disturbance and weight, and carrying out the noise reduction of the to-be-processed ultrasonic guided wave signal. Determining an optimal alpha value and decomposing the signal into an optimal modal component; calculating a kurtosis value of each modal component, and screening effective components containing damage information according to a threshold value; singular spectrum analysis denoising is carried out on the effective components, and a self-adaptive window mechanism is introduced to dynamically adjust the window length and the truncation strength; reconstructing the de-noised effective component to obtain a de-noised signal; according to the method, the parameter optimization precision and efficiency are improved, damage characteristics and noise are effectively separated, different dominant frequency signals are adapted, the damage characteristics can still be reserved in a low-signal-to-noise-ratio environment, the noise reduction effect of ultrasonic guided wave signals and the damage detection reliability are improved, and the method is suitable for nondestructive detection of components such as ultra-long small-diameter heat absorption pipes.

The invention discloses a space target orbit transaction behavior intelligent identification method based on satellite-borne passive measurement, and the method comprises the steps: carrying out the data preprocessing through the feature extraction capability of multi-dimensional singular spectrum analysis, putting forward a two-dimensional fusion feature quantity sequence as an input value, taking the maneuvering state quantity of a target satellite as an output value, and carrying out the recognition of the target orbit transaction behavior. And training the long-short-term memory neural network offline, establishing a maneuvering detection dichotomy model and deploying the maneuvering detection dichotomy model on a satellite for online use, thereby realizing maneuvering detection of any target on a GEO type orbit. The space-based non-cooperative target maneuvering detection method does not need other prior information and hypothesis conditions, can perform maneuvering detection judgment on the space-based non-cooperative target only by using the two-dimensional angle measurement sequence output by the space-based angle measurement system, takes the periodic feature component extracted by MSSA preprocessing as the input of the LSTM, eliminates high-frequency noise through orthogonalization decomposition, and improves the detection accuracy of the space-based non-cooperative target. The LSTM focuses on maneuver-related time sequence mode learning, and the problem of misjudgment caused by frequency band overlapping of noise and maneuver signals in a traditional method is solved.

The invention belongs to the technical field of mixing equipment, and particularly relates to a non-intrusive homogenizer operation process abnormity monitoring method and system, and the method comprises the steps: calculating the mechanical angular acceleration according to the real-time rotating speed of a motor shaft end; on the basis of a pre-constructed inverse dynamic model, according to the motor orthogonal axis current, the motor shaft end real-time rotating speed and the mechanical angular acceleration, a rheological resistance characteristic index is calculated; processing the rheological resistance characteristic index by using a singular spectrum analysis algorithm, and extracting a trend line of the rheological resistance characteristic index through construction of a trajectory matrix, singular value decomposition and diagonal averaging operation; the information entropy of the first-order difference sequence of the trend line is calculated to serve as the mixing stability entropy, and the mixing end point of the operation process of the homogenizer is judged according to the convergence condition of the mixing stability entropy relative to the reference entropy, so that anomaly monitoring of the operation process of the non-intrusive homogenizer is achieved. According to the invention, abnormity monitoring and end point determination of the mixing process of the homogenizer are realized.

The invention provides a CEEMDAN-SSA-based power distribution network fault identification method, and the method comprises the steps: receiving a traveling wave signal of a power distribution network, and carrying out the preprocessing of the traveling wave signal; decomposing the preprocessed traveling wave signal by adopting CEEMDAN to obtain an intrinsic mode function component set, and selecting a high-frequency intrinsic mode function component from the intrinsic mode function component set; performing noise reduction on the high-frequency intrinsic mode function component by using singular spectrum analysis to obtain a pure fault feature signal; analyzing and reconstructing the frequency spectrum characteristics of the pure fault characteristic signal by using fast Fourier transform, and calculating the average frequency spectrum content of the low-frequency-band signal and the amplitude ratio of the high-frequency-band signal according to the frequency spectrum characteristics; and generating a fault identification result of the power distribution network by taking the average frequency spectrum content of the low-frequency-band signal as a main criterion and taking the amplitude proportion of the high-frequency-band signal as an auxiliary criterion according to the fault traveling wave transmission characteristics. Therefore, high-precision power distribution network fault identification can be realized in a complex working condition and a high-noise environment.

The invention discloses a fault monitoring and self-adaptive regulation and control method and system for a ship desulfurization system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through a distributed sensor network, and carrying out the preprocessing; extracting multi-dimensional statistical features, performing weak fault feature enhancement by using an improved singular spectrum analysis algorithm, training a multi-channel attention mechanism LSTM network by using a high-discrimination feature sequence, learning a dynamic time sequence rule of a normal mode and a plurality of early fault modes, and performing early probability prediction of faults; identifying a complex fault source caused by coupling of a plurality of potential factors; a detection threshold value is dynamically adjusted by using an EWMA model driven by reinforcement learning, and the sensitivity and specificity of fault monitoring are dynamically optimized; and a fuzzy logic model is used for calculating a severity index, evaluating the severity of a fault, generating hierarchical early warning and iteratively optimizing a self-adaptive regulation and control strategy, so that the robustness and reliability of the system under strong noise and multivariable coupling are improved.

The invention discloses an intelligent prediction method for line loss of a regional power transmission line. The method comprises the following steps: sequentially carrying out data preprocessing of cleaning, preprocessing, clustering analysis and coding on acquired data; an EMD method is adopted to decompose a non-stationary line loss time sequence in the data set, IMF components are reserved, an automatic singular spectrum analysis Sliding SSA method is adopted to decompose a stationary line loss time sequence in the data set, and main trend components are reserved; the discrete IMF component and the discrete main trend component in the line loss data are subjected to CountEncoder feature coding; analyzing the training set through the improved LSTM model to obtain a final model trained by the improved LSTM model; evaluating the performance of the model in the power transmission line loss prediction according to the prediction value and the true value error; according to the method, signal decomposition is carried out through EMD and automatic singular spectrum analysis Sliding SSA, the accuracy of preprocessing the line loss data of the regional power transmission line is improved, and the accuracy of model prediction is improved through stacking improvement of the LSTM model and introduction of an attention mechanism.

The invention discloses a hydraulic control optimization system and method based on differential pressure direction change pattern recognition, and the method comprises the steps: carrying out the phase-space topology reconstruction and singular spectrum analysis according to the pressure signals of an oil inlet cavity and an oil return cavity of a hydraulic cylinder, and determining the topology mapping characteristics of a differential pressure direction; based on the topological mapping features, spatial features are extracted through a multi-scale convolutional network, and mode reference features are determined in combination with an attention mechanism; carrying out real-time differential pressure load mode identification by utilizing a manifold mapping space; constructing a mode comparison learning mechanism based on a historical load mode, predicting a mode trend and determining hydraulic control target parameters; and analyzing errors between displacement feedback of the piston rod of the hydraulic cylinder and control target parameters through the disturbance observer, and dynamically updating mode reference characteristics. Accurate real-time recognition and dynamic optimization control of the differential pressure fluctuation mode of the hydraulic system are achieved, and the operation stability and control precision of the system are remarkably improved.

The invention provides a lithium battery cargo abnormal temperature rise identification method, and belongs to the technical field of lithium battery customs detection.The lithium battery cargo abnormal temperature rise identification method comprises the steps that a temperature sensor array is arranged on the surface of a lithium battery cargo stacking body to collect multi-point temperature time sequence data, and trend characteristics are extracted through wavelet packet decomposition denoising and singular spectrum analysis; establishing a state space model based on a heat conduction partial differential equation, estimating an internal temperature field state by using Kalman filtering, solving a heat conduction inverse problem by using a conjugate gradient regularization iterative algorithm to invert an internal three-dimensional temperature distribution field, and inputting an inversion result and statistical characteristics into a thermal anomaly identification model fused with manifold learning. Dimensionality reduction is carried out through a local linear embedding algorithm, a mahalanobis distance is calculated in a low-dimensional manifold space, an abnormal temperature rise risk score is output, when the score exceeds a preset threshold value, early warning is triggered, and the technical problem that the abnormal temperature rise in the lithium battery cargo stacking body is difficult to accurately recognize through surface temperature measurement is solved.

The application provides a kind of unsupervised hyperspectral image classification method based on mixed space spectrum information, comprising the following steps: S1, obtains binary segmentation graph by entropy rate superpixel segmentation algorithm, applies binary segmentation graph on original hyperspectral image to obtain segmented superpixel block, converts input hyperspectral image into multiple homogeneous regions based on superpixel segmentation, removes redundant information and guides data purification;S2, optimize the redundant information in principal component domain by two-dimensional singular spectrum analysis method, enhance spatial spectral feature;S3, realize the unsupervised classification of large-scale hyperspectral image by anchor point graph clustering unsupervised classification method.The application is closer to actual engineering application compared with existing supervised classification method, can process larger image scale compared with existing unsupervised classification method, has the advantages of not needing prior information reference, high classification precision, fast classification speed and the like.

The invention discloses an industrial process oscillation detection method based on adaptive cyclic singular spectrum analysis, and the method comprises the steps: firstly, carrying out the data normalization processing of a PV signal, secondly, determining the optimal window length according to the frequency information of the signal, decomposing the optimal window length into a series of RCs through employing an SCISSA method, and carrying out the analysis of the RCs. And based on the energy contribution degree and the Euclidean distance, obtaining a dominant component of a significant periodic characteristic in the PV signal, and finally calculating an energy concentration index (EI) for the signal to realize oscillation detection. According to the method, priori knowledge of an industrial process is not needed, the method has high robustness for non-stationary trend terms and noise in actually collected PV signals, the PV signals can be divided into different frequency components, oscillation RCs with practical significance can be extracted, and therefore high-precision oscillation detection is achieved.

The invention discloses an industrial sewage water quality soft measurement method. The method comprises the following steps: collecting sewage water quality data of a sewage plant; carrying out feature extraction on the input data by adopting an AHSICLasso method; decomposing the variable signal into a plurality of symplectic geometric components by using symplectic geometric mode decomposition (SGMD), and then performing secondary decomposition on the decomposed high-frequency nonlinear components through singular spectrum analysis (SSA); carrying out complexity quantification on the decomposed multi-mode component by utilizing approximate entropy so as to evaluate the dynamic characteristics of the multi-mode component; according to a quantization result, reconstructing the multi-mode component; using a time step adaptive dynamic selection mechanism and approximate entropy to screen components at past moments, and using CBO to optimize component weights; the AHSICLasso feature extraction data, the screened components at the past moment and the components obtained after secondary decomposition are input into an OfficANet model; the method comprises the following steps: optimizing hyper-parameters of an EfficANet model by using a CBO collider, introducing an adaptive attention weight mechanism into a GTVA module of the EfficANet model for improvement, and learning and predicting a reconstructed multi-modal component to realize soft measurement of total nitrogen in industrial sewage; according to the invention, high-precision and real-time prediction of the total nitrogen concentration of the industrial sewage is realized.

The invention discloses a method for extracting characteristic parameters of a low-frequency oscillation mode of a power system, and in the power system, a time sequence signal generally refers to records of parameters such as voltage, frequency and the like in the system along with time change. In order to effectively analyze the signals, singular spectrum analysis is firstly carried out, the singular spectrum analysis comprises the steps of converting the time sequence signals into a track matrix, then carrying out singular value decomposition on the track matrix, and reconstructing a time empirical orthogonal function obtained after singular value matrix decomposition, and at the moment, a denoised reconstructed signal is obtained; and performing matrix pencil analysis on the reconstructed signal, and extracting characteristic parameters of the low-frequency oscillation mode. In this way, the characteristic parameters of the low-frequency oscillation mode can be accurately extracted under the condition that the identification integrity is guaranteed.

The invention relates to a transient electromagnetic signal denoising method based on self-adaptive fuzzy optimization singular spectrum analysis (SSA), which aims at denoising transient electromagnetic signals, and comprises the following specific steps of: 1, decomposing a noisy signal into a plurality of modes through a complementary ensemble empirical mode decomposition (CEEMD) method; and according to the sample entropy, classifying the modals into a signal-dominant category and a noise-dominant category. 2, taking a signal fuzzy entropy (FE) as a target function of each modal optimization, obtaining a strict boundary of each modal SSA reconstruction order by using a particle swarm optimization (PSO) algorithm, and taking a singular value in the strict boundary as a signal component; thirdly, solving loose boundaries of each modal reconstruction order through a singular value spectrum and a singular value difference spectrum, and generating a fuzzy interval by combining the loose boundaries with the strict boundaries; and 4, obtaining the membership degree of the singular value belonging to the signal or noise component in the fuzzy interval through a weighted fuzzy noise clustering algorithm (WFNC), and carrying out signal component reconstruction by taking the membership degree as a weight. And finally, adaptive reconstruction of each mode is carried out on the signal components by using an SSA method, and a de-noised signal is obtained through mode summation. According to the method, a low-signal-to-noise-ratio adaptive singular spectrum analysis denoising method is provided, the problem of wrong singular value selection in the reconstruction process is avoided, and the signal-to-noise ratio is effectively improved.

The invention provides a traffic flow data completion method and system based on a frequency domain diffusion model, and the method comprises the steps: carrying out the singular spectrum analysis of traffic flow data, obtaining an accumulated feature value contribution rate and an accumulated contribution rate threshold value, and obtaining a global reserved component number according to the accumulated contribution rate threshold value and the accumulated feature value contribution rate; performing masking processing on missing values in the traffic flow data, performing singular spectrum analysis on the masked traffic flow data according to the number of global reserved components to obtain frequency domain data, performing gradual noise adding and gradual noise reduction on the frequency domain data according to spatial association of the traffic flow data, and generating frequency domain recovery data by using reverse singular spectrum analysis; and fusing the frequency domain recovery data and the time domain recovery data after time sequence diffusion processing, and performing feature extraction on a fusion result through a graph deep learning model to obtain a final traffic flow data completion result. According to the technical scheme, the traffic flow data complementation precision and robustness can be improved.

The invention provides a pumped storage unit vibration trend prediction method and system based on a combined model, and belongs to the technical field of pumped storage unit detection. Comprising the following steps: acquiring an original vibration signal and performing variational mode decomposition by adopting a self-adaptive signal decomposition method to generate a plurality of subsequences; a singular spectrum analysis method is adopted to extract dominant components of all the subsequences, and superposition reconstruction is carried out on residual components; the processed subsequences are converted into a training set and a test set of a prediction model through phase-space reconstruction, and the training set and the test set serve as prediction model input for model training; and on the basis of a kernel extreme learning machine model and an IBiGRU-KAN model, predicting the high-frequency sub-sequence and the low-frequency sub-sequence in each sub-sequence, and superposing the prediction results of each sub-sequence to determine the vibration trend of the pumped storage unit. According to the method, complex data processing and model calculation tasks can be efficiently executed, and the vibration trend of the pumped storage unit is accurately predicted.

The application discloses application of a tensor ELM based on LDA and SSA in HSI classification, and comprises the following steps: step 1: inputting test tensor samples, training tensor samples, label samples and parameters; step 2: data processing part; step 4: obtaining tensor sample output of a hidden layer; step 5: initialization process, for each mode i; step 8: obtaining initial value of W (i) Step 16: obtaining final optimized classification result of a prediction test sample: test tensor sample A i After singular spectrum analysis denoising and a hidden layer of an extreme learning machine, output tensor B i of the hidden layer is obtained, and then, Γ i ∈R C×1 is obtained, and the serial number of the maximum element in Γ i is the classification result of the test tensor sample A i . The application can effectively utilize spatial sequence information of a hyperspectral image, can fuse spatial and spectral information into a tensor, can optimize the spatial and spectral information together, and can propose a tensor ELM model, so that the classification precision is higher and the calculation speed is faster in actual classification and other applications.

The application belongs to the technical field of microseismic signal processing, and in particular to a low signal-to-noise ratio microseismic signal denoising method and system. The method comprises the following steps: collecting multiple seismic time sequence signals; reconstructing the multiple seismic time sequence signals by using multivariate singular spectrum analysis to obtain reconstructed signals; splicing the reconstructed signals and the original multiple seismic time sequence signals to obtain a two-channel input data set; and inputting the two-channel input data set into a U-Net network for end-to-end recovery. The application significantly improves the recovery capability and phase preservation of weak seismic signals in an ultra-low signal-to-noise ratio environment.

The invention discloses a photovoltaic array short-term power prediction method, and relates to the field of photovoltaic power generation prediction and energy management. The method includes the steps that current and voltage values of a photovoltaic array power generation loop are collected in real time through a current and voltage sensor, power generation power data are obtained through calculation and serve as an original data set, and the original data set is divided into a training set, a verification set and a test set; the method comprises the following steps of: preprocessing an original data set by adopting a method of combining variational mode decomposition and singular spectrum analysis noise reduction, and optimizing parameters by using a spider bee optimization algorithm to obtain noise-reduced generation power data; by introducing a fractional order norm and energy entropy secondary screening mechanism, the screening precision of a sparse probability attention module on a key query sequence is optimized, a DESform model is constructed, the sparsity and the information concentration degree of key query features are quantified, and then the weight of the key features is calculated. According to the method, the problems that the photovoltaic power trend is complex, fluctuation is random and the like are difficult to predict are effectively solved.

The present application relates to remote sensing image processing technical field, specifically a kind of tensor singular spectrum analysis method for hyperspectral image three-dimensional feature extraction, including based on spatial self-similarity adaptive embedding, decomposition and low rank representation based on t-SVD, feature image and classification, compared with prior art, through adaptive embedding and t-SVD process, a new adaptive embedding operation, utilize the spatial similarity feature of HSI, jointly utilize target pixel and non-local similar pixel, combine with re-projection operation, keep target inter-class difference while enhanced intra-class similarity, by designing a trajectory tensor, combined with t-SVD, jointly represent the global low rank feature of HSI, the arrangement of similar pixel in trajectory tensor makes it have low rank feature, and further extract low rank feature by truncated t-SVD, realize the feature extraction of three-dimensional hyperspectral image, and then improve the class separability in hyperspectral image classification.

The present application provides a kind of singular spectrum analysis and orthogonal base method combined magnetic anomaly target detection method, device, equipment and storage medium, the method comprises: obtaining the magnetic measurement data to be detected, the magnetic measurement data to be detected is preprocessed;Based on singular spectrum analysis method, the preprocessed magnetic measurement data to be detected is denoised, and denoised signal data is obtained;Based on orthogonal base detection method, the denoised signal data is abnormally signal detected.The present application uses singular spectrum analysis method to first transform one-dimensional measurement data into two-dimensional data to construct trajectory matrix, then singular value decomposition is carried out to data, by selecting appropriate data component, reconstruct high signal-to-noise ratio signal, then using orthogonal base detection algorithm judges whether the target abnormal signal is contained in the data to be measured;So as to greatly improve the magnetic anomaly signal detection ability under low signal-to-noise ratio.

The application discloses a fault monitoring and self-adaptive regulation method and system for a ship desulfurization system, and the method comprises the following steps: collecting multi-source heterogeneous data in real time through a distributed sensor network, and performing pretreatment; extracting multi-dimensional statistical features, enhancing weak fault features by using an improved singular spectrum analysis algorithm, training a multi-channel attention mechanism LSTM network by using high-discrimination feature sequences, learning dynamic time sequence rules of normal modes and multiple early fault modes, and performing early probability prediction of faults; identifying complex fault sources caused by coupling of multiple potential factors; dynamically adjusting a detection threshold by using an EWMA model driven by reinforcement learning, and dynamically optimizing sensitivity and specificity of fault monitoring; calculating a severity index by using a fuzzy logic model, evaluating the severity of faults, generating graded early warnings, and iteratively optimizing self-adaptive regulation strategies, and the application improves the robustness and reliability of the system under strong noise and multivariate coupling.

The present application relates to the technical field of multi-element load prediction, and provides a comprehensive energy system multi-element load hybrid prediction method, system, device and medium, which comprises splicing the to-be-analyzed multi-element load sequence and the influence factor sequence generated by processing the multi-element load sequence and the influence factor sequence to obtain a first multi-element load characteristic sequence; analyzing the first multi-element load characteristic sequence based on a correlation evaluation model to generate a correlated load group and an independent load group; splicing the reconstructed multi-element load sequence obtained by singular spectrum analysis of the to-be-analyzed multi-element load sequence with the to-be-analyzed multi-element load sequence and the influence factor sequence to obtain a second multi-element load characteristic sequence; and based on the correlated load group and the independent load group, performing multi-element load hybrid prediction on the multi-element load prediction input characteristics obtained according to a double-attention mechanism to obtain a multi-element load prediction result. The present application can fully capture the complex dynamic coupling characteristics among multi-element loads, and improve the accuracy and robustness of load prediction.

The present invention discloses a method for filling continuously missing raw data for bottom-mounted acoustic wave observation, which relates to the technical field of bottom-mounted acoustic wave observation in the ocean. The method comprises decomposing the original time series into different characteristic subsequences through a singular spectrum analysis algorithm, extracting trend components and transient change components; filling missing data of the trend component by generating overall trend component data based on existing time series data through a BP neural network fitting; and filling missing data of the transient change component by using a GRU-DTW neural network model based on the complete time series data in the forward and backward directions of the period when the transient component data is missing. After the data filling is completed, the complete raw data is reconstructed for statistical calculation of wave eigenvalues. The present invention efficiently restores the original state of different amounts of continuously missing data, thereby improving the measurement accuracy of wave height and wave period.

The invention discloses a multi-model fusion polar shift prediction method and system, and relates to the technical field of polar shift prediction. The method comprises the steps of firstly obtaining a polar shift historical time sequence; constructing a linear trend model for representing a polar shift linear change trend in the polar shift historical time sequence, a multi-channel singular spectrum analysis model for representing a polar shift change trend in a primary residual sequence, and a trained XGBoost model for representing a polar shift change trend in a secondary residual sequence; and performing polar shift prediction by using the linear trend model, the multi-channel singular spectrum analysis model and the trained XGBoost model. According to the method, the linear model, the multi-channel singular spectrum analysis model and the XGBoost model are fused, polar shift prediction is realized, and the precision and the stability of polar shift prediction are improved.

This invention provides a noise reduction method for ground-penetrating radar (GPR) data based on grouped singular spectra. It employs zero-bias mode GPR and, based on singular spectrum analysis, decomposes the spectral matrix of GPR data into an orthogonal matrix and a singular value matrix. The singular values ​​are grouped using the k-means clustering method, and the singular values ​​of the spectral matrix are reweighted using a grouping weighting function, thereby reducing noise interference in the GPR data.

This invention discloses a plate motion analysis method based on GNSS and SLR stations of the Eurasian Plate, belonging to the field of geodesy and surveying engineering technology. The method includes: acquiring raw coordinate time-series data from GNSS and SLR stations and preprocessing them; decomposing the preprocessed sequence using singular spectrum analysis (SSA) to reconstruct and separate trend, periodic, and noise terms; performing Fast Fourier Transform (FFT) on the reconstructed signal to extract frequency domain features; establishing a comprehensive model of linear velocity plus periodic correction by combining time and frequency domain features; and analyzing the motion characteristics of the Eurasian Plate based on this model to achieve motion trend prediction and anomaly detection. This invention, through the combination of SSA and FFT, significantly improves the accuracy of motion parameter estimation and the ability to identify weak signals, providing a reliable method for plate motion analysis and earthquake prediction.

The application belongs to the technical field of pet management, and particularly relates to a chip positioning method and system for urban pet dog management, which comprises the following steps: S1, acquiring real-time motion data of a pet dog, constructing motion entropy according to an acceleration change rate in the motion data, and calculating an adaptive data interception window length at a current time by using the motion entropy; S2, intercepting an acceleration modulus sequence in the adaptive data interception window length, decomposing and reconstructing the acceleration modulus sequence by using singular spectrum analysis technology, and obtaining a behavior sequence after removing noise; and S3, constructing a multi-dimensional feature sequence containing acceleration and angular velocity, and calculating the curvature of each point in a standard abnormal behavior template. The application effectively distinguishes playing from attacking through statistical transformation of physical characteristics, and reduces the false positive rate while taking into account the calculation energy efficiency and detection sensitivity.

The invention provides a blind source separation method and system for a single-channel extremely-low-frequency signal, and the method comprises the steps: firstly carrying out the preprocessing of a collected signal through a Hanpur filter, and eliminating the pulse noise, aiming at a large amount of pulse noise generated by a natural environment in an extremely-low-frequency signal channel; secondly, decomposing the single-channel signal sequence into a plurality of independent components through singular spectrum analysis and a kmeans clustering algorithm to construct virtual multiple channels; and finally, separating a source signal with a high interference-to-signal ratio by using a feature matrix-based joint approximate diagonalization algorithm. According to the method, the accuracy of source signal estimation is improved, any probability distribution does not need to be assumed, parameters do not need to be manually selected, and good robustness and universality are achieved.

The invention belongs to the technical field of microseismic signal processing, and particularly relates to a low-signal-to-noise-ratio microseismic signal denoising method and system, and the method comprises the steps: collecting a plurality of seismic time sequence signals; multi-variable singular spectrum analysis is adopted to reconstruct the multi-channel seismic time sequence signals, and reconstructed signals are obtained; splicing the reconstructed signal and the original multi-channel seismic time sequence signal to serve as a dual-channel input data set; and inputting the dual-channel input data set into the U-Net network for end-to-end recovery. According to the invention, the recovery capability and phase reservation of weak seismic signals in an ultra-low signal-to-noise ratio environment are significantly improved.

The present invention discloses an ERP short-term prediction method that integrates SSA and cascaded LSTM, and relates to the field of satellite navigation and positioning technology. The method comprises the following steps: preprocessing ERP data; performing signal decomposition using a singular spectrum analysis method, performing spectrum analysis on the obtained signal components, and determining the periodic terms of the components; eliminating signal components with periodic terms less than n; reconstructing the remaining signal components into new ERP time series data after denoising and optimization; constructing a cascaded LSTM time series prediction model to calculate ERP short-term forecast data for the next m days; and adding correction terms to the ERP short-term forecast data to obtain a final ERP forecast sequence. The present invention achieves high-precision reconstruction of the ERP time series by denoising and optimizing the ERP time series signal. A cascaded LSTM prediction model is constructed in which multiple sub-models are interconnected and transmitted step by step, significantly suppressing the propagation of forecast errors and achieving high-precision and high-reliability forecasts of ERP data.