124 results about "Singular spectrum analysis" patented technology

The invention discloses a non-stationary industrial process early fault diagnosis method based on a common trend model, and belongs to the field of industrial process monitoring and fault diagnosis. The method comprises the following steps: collecting training data of a non-stationary industrial process under a normal working condition, performing denoising by using singular spectrum analysis, and obtaining a stationary projection matrix and a stationary data matrix by using a common trend model; orthogonal conversion is carried out on the stationary data matrix, a conversion projection matrix is calculated, and an orthogonal conversion data matrix and a statistic data matrix thereof are obtained; calculating a mean value and a covariance matrix of the statistic data matrix to obtain a control limit; the method comprises the following steps: acquiring test data under a real-time working condition of an industrial process, processing to obtain statistic data, calculating a mahalanobis distance index, and comparing with a control limit to judge whether a fault occurs; and if a fault is detected, realizing fault separation according to fault reconstruction and a mapping relation. According to the method, the interference of the non-stationary characteristic on the fault diagnosis model can be reduced, and fault detection and separation in the non-stationary process are realized.

The invention discloses a wind speed prediction method based on improved ensemble empirical mode decomposition and LSTM model series connection, and the method comprises the steps: adding grid point historical meteorological data into adaptive noise through singular spectrum analysis, carrying out the ensemble empirical mode decomposition, and carrying out the processing of an intrinsic mode function component and a residual error obtained through the decomposition; performing standardization processing on the topographic data, and extracting topographic spatial features through different spatial feature extraction schemes; and constructing the fused features into time sequence format data, inputting the time sequence format data into an LSTM model for prediction, obtaining a predicted intrinsic mode function component and a residual error, adding the intrinsic mode function component and the residual error to generate a short-time wind speed prediction result, comparing the short-time wind speed prediction result with the real-time wind speed data to calculate a model loss function, and training the LSTM model by adopting back propagation iteration to obtain a short-time wind speed prediction result. And performing rolling prediction to obtain a future wind speed prediction result. The prediction result of the method is highly consistent with the observation value in numerical value, and the method also shows high accuracy in trend prediction.

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 present application discloses a GPR signal denoising method based on variational mode decomposition and singular spectrum analysis. First, the optimal mode number K is determined based on the minimum energy loss ratio method, the original signal is decomposed by VMD and K components are obtained, and then the effective signal component is obtained by the Pearson correlation coefficient method. For the small amount of noise that still exists in the effective signal component, the singular spectrum analysis is introduced for secondary filtering to further improve the signal-to-noise ratio and reconstruct the denoised signal. Simulation experiments, numerical simulations and measured data have verified the effectiveness of the above method. The method of the present application can effectively suppress background noise and clutter interference, making the signal characteristics of the abnormal body more prominent, further confirming the effectiveness and applicability of the method for processing GPR signals, and providing a valuable reference for the data processing of GPR actual data and the secondary utilization of effective signals.

The invention discloses a singular spectrum modal synthesis algorithm for separating unsteady state components in a spectrum aliasing environment, which comprises the following steps of: reconstructing all elements in an original signal x into a high-rank Hankel matrix X, performing singular value decomposition on the high-rank Hankel matrix X to obtain a left matrix, a singular value matrix and a right matrix, and aiming at the left matrix, the singular value matrix and the right matrix, calculating a singular value of the left matrix, the singular value matrix and the right matrix; according to the singular spectrum modal comprehensive algorithm, fast singular spectrum decomposition is carried out, m basic components yi are divided into # imgabs0 # groups, yi divided into the same group is summed, and an intrinsic mode function component of # imgabs1 # original signals x. According to the singular spectrum modal comprehensive algorithm, fast Fourier transform is utilized for fast singular spectrum decomposition, a large amount of matrix calculation in singular spectrum analysis is replaced, the calculation time is greatly shortened, and the calculation efficiency is improved. While the calculation speed is ensured, non-steady-state intrinsic mode function components overlapped in a frequency domain can be separated, and non-steady-state components in voice and mechanical vibration signals can be separated.

The invention belongs to the technical field of wind power prediction, and particularly discloses an ultra-short-term wind power prediction method, which comprises the following steps: S1, obtaining an original wind power sequence; s2, decomposing the wind power signal into a series of sub-signals by adopting a CEEMDAN signal decomposition algorithm; s3, denoising and reconstructing the IMF1 component by adopting an improved singular spectrum analysis (SSA) algorithm; s4, the permutation entropy of each component is calculated, the components are divided into high frequency, medium frequency and low frequency according to the permutation entropy, the high frequency is combined together, and the medium frequency and the low frequency are combined together; and S5, sending the data into the established TCN-GRU prediction network, and superposing the results of the two prediction networks to obtain a final prediction result. According to the ultra-short-term wind power prediction method, the prediction performance of the model is better than that of a traditional model and other models based on TCN, high-precision and high-stability prediction of the wind power plant power is achieved, and decision support is provided for operation management of the wind power plant.

Disclosed are rotor crack fault diagnosis and model construction methods and apparatus. The rotor crack fault diagnosis method comprises: collecting a vibration signal to be diagnosed of a rotor of a current rotating device during an operation process; after singular value decomposition is performed on said vibration signal, performing singular spectrum analysis, and extracting to obtain a plurality of principal component singular values to be diagnosed; and inputting said plurality of principal component singular values into an nSVDD model for rotor crack fault diagnosis, and obtaining a rotor crack fault diagnosis result of the current rotating device; the nSVDD model for rotor crack fault diagnosis is pre-trained and generated on the basis of a vibration signal sample set of a rotor of a rotating device during a historical operation process. According to the present invention, rotor crack fault diagnosis based on singular values and nSVDD may be achieved, improving the accuracy of rotor crack fault diagnosis.

The invention provides a seismic data reconstruction method based on a convolutional neural network, and the method comprises the steps: 1, randomly initializing a network parameter theta, and inputting noise Z; step 2, the reconstruction network f takes Z as an input and takes preliminary reconstruction data f theta (Z) with the same size as an output; 3, calculating local optimal parameters of the network; 4, reconstructing test data based on the trained network model; and step 5, evaluating the performance of the method in irregular and regular seismic data reconstruction. The seismic data reconstruction method based on the convolutional neural network is used for interpolation of irregular and rule missing channels in pre-stack and post-stack seismic data, and experimental results show that the method is superior to a processing type Cadzow method commonly used in traditional singular spectrum analysis and reconstruction of the data.

The invention belongs to the technical field of seismic data acquisition and processing, and particularly relates to a method and a device for acquiring a seismic diffraction body. The method comprises the following specific steps: preprocessing collected seismic data to obtain seismic shot gather data which can be directly used for migration; eliminating residual noise by using a multi-channel singular spectrum analysis technology to obtain optimized pre-stack gather data; a complete diffracted wave is obtained through forward and reverse transformation of a virtual seismic source and through offset-reflected wave removal-deoffset; and finally, accurately separating weak signals of the diffracted waves under the condition of intersection and tangency of seismic wave fields by researching characteristics that the polarity and the amplitude of the diffracted waves change along with an emergence angle and an azimuth angle, thereby realizing high-resolution diffraction body imaging of the fractured-vuggy reservoir, effectively improving the imaging precision of the karst fractured-vuggy reservoir, and achieving the purpose of fine detection of the fractured-vuggy reservoir. The method is of positive significance to clarify the seismic response of the fracture-vug type reservoir, implement fracture-vug distribution characteristics and guide effective identification of the karst type reservoir and optimization of a favorable target area.

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 provides an earth fluid effective angular momentum time sequence reconstruction method and device. The earth fluid effective angular momentum time sequence reconstruction method comprises the steps that earth fluid effective angular momentum is obtained, an original earth fluid effective angular momentum time sequence is obtained through linear interpolation, spectral analysis is conducted through fast Fourier transform, and an inherent periodic term is obtained; setting a lag window length based on an annual swing period and a Marcher swing period, performing singular value decomposition grouping and reconstruction, and obtaining a period time sequence, a trend time sequence and a residual time sequence; performing spectral analysis on the periodic time sequence, the trend time sequence and the residual time sequence obtained by decomposition through fast Fourier transform, judging whether the spectral characteristics are the same as those obtained by spectral analysis or not, and if not, continuing to decompose the periodic term; otherwise, reconstructing the earth fluid effective angular momentum time sequence. According to the method, a singular spectrum analysis scheme for setting a stable lag window is provided, signal integrity can be ensured, signal aliasing is avoided, and the extraction precision of quasi-periodic signals is improved; meanwhile, the data fusion consistency is ensured, and the earth fluid effective angular momentum reconstruction precision is improved.

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 monitoring and regulation system for a connector, and relates to the field of monitoring data processing, and the key points of the technical scheme are that a data acquisition module acquires comprehensive working data and environmental data of the connector; the data optimization module is used for preprocessing the comprehensive working data, and the preprocessing modes comprise data synchronous calibration, weighting processing on repeated current values or voltage values existing at a single time point, and interpolation method filling on data missing at the single time point; obtaining adjustment data; the feature analysis module is used for performing singular spectrum analysis SSA on the adjustment and calibration data to obtain feature data; and the prediction regulation and control module inputs the feature data into a pre-trained neural network model, a regulation and control decision of the connector is obtained through prediction, all the modules are connected in a wired and / or wireless mode, and intelligent monitoring and regulation and control of the connector are achieved.

The invention discloses a short-term wind power prediction method based on multi-scale dominant component analysis and mixed data driving, and the method comprises the steps: carrying out the decomposition of wind power data through VMD (variational mode decomposition), and decomposing a wind power sequence into n mode components; extracting a dominant component from each modal component based on singular spectrum analysis SSA and taking the dominant component as new modal data; meanwhile, superposing the residual part after extraction of each modal component with a variational modal decomposition (VMD) residual error to serve as an (n + 1) th modal; constructing a mixed data driving model based on TCN and SWLSTM, and respectively predicting multi-modal data obtained by variational mode decomposition (VMD) and singular spectrum analysis (SSA); and reconstructing prediction results of all modal data to obtain a final wind power prediction value. The invention provides a short-term wind power prediction method based on multi-scale dominant component analysis and mixed data driving, which can effectively improve the short-term wind power prediction precision and improve the prediction efficiency.

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 invention discloses a method for classifying various arrhythmia signals, belongs to the technical field of arrhythmia signal classification methods, and the accurate classification of arrhythmia has important significance for disease diagnosis and clinical treatment. However, the traditional detection method is often interfered by various factors such as signal non-stationarity, feature non-specificity, artifacts and noise caused by arrhythmia when processing various arrhythmia, and the classification precision and efficiency are difficult to meet the requirements. Therefore, the invention provides an improved power normalized cepstrum coefficient (SPNCC) method based on singular spectrum analysis (SSA), which is used for extracting key features from a photoplethysmogram (PPG) signal and reducing the influence of noise, and constructing a deep learning classification model in combination with a convolutional neural network and a bidirectional long short-term memory network (CNN-BiLSTM). Therefore, accurate identification of various arrhythmias can be realized.

The invention is applicable to the field of inverters, and discloses an inverter fault diagnosis method, device and equipment and a storage medium, and the method is used for carrying out online detection on real-time data of inverter equipment so as to timely find whether the inverter equipment has a fault or not. The method comprises the following steps: acquiring historical inverter data; using an improved singular spectrum analysis method to decompose and reconstruct the historical inverter data to obtain inverter data after noise reduction; the inverter data obtained after noise reduction is input into an Angle Transform initial model to be trained, and an inverter fault diagnosis model is obtained; and collecting real-time data of the inverter equipment, and inputting the collected real-time data into the inverter fault diagnosis model to obtain a diagnosis result.

The application discloses a distributed photovoltaic power station power ultra-short-term grid prediction method, and the technical scheme steps of the application comprise the following steps: firstly, based on an adaptive grid division method, a similar day screening method containing singular spectrum analysis is designed; a three-dimensional convolution network input feature set is constructed based on a Holt-Winters exponential smoothing method; finally, based on the grid division and similar day screening results, and according to the input feature set, a three-dimensional convolution neural network is constructed to establish a distributed photovoltaic power station cluster power ultra-short-term grid prediction model. The prediction method can fully consider the time and space characteristics of the distributed photovoltaic, and effectively improves the accuracy and robustness of the distributed photovoltaic power station cluster power prediction.

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.

Method and device for extracting noise-reduced signal information from a time series of sensor measurements during normal process operation and then actively checking whether present realizations of the process are departing from historical normal behavior. To extract signal information, the solution borrows ideas from singular spectrum analysis a non-parametric exploratory analysis tool for time series that is particularly suitable for separating the deterministic part of a dynamical system behavior from the chaotic part, purely from noisy time series of measurements.

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.