383 results about "Signal reconstruction" patented technology

The present invention relates to the technical field of cross-modal image signal reconstruction. Disclosed is a cross-modal joint source-channel coding and decoding method adaptable to changeable scenarios. The method comprises: first designing a Transformer encoder-based cross-modal channel coding and decoding optimization solution, so as to achieve the performance improvement and robustness of a channel encoder and a channel decoder; then designing a cross-modal source coding and decoding optimization solution for haptic-to-image generation based on a latent diffusion model, so that under an image signal loss scenario, haptic information is used to guide image generation; and finally, incorporating transfer learning technology, so as to reduce additional training costs caused by a system facing changeable cross-modal communication scenarios such as a changeable channel signal-to-noise ratio and different transmission tasks. Under cross-modal changeable communication scenarios, the joint source-channel coding and decoding method provided in the present invention can solve the problems of the inability of a receiving end to well complete image reconstruction, and additional model training costs caused by changeable channel environments and scenarios.

The invention provides a vibration signal space-time reconstruction method based on a multi-modal condition diffusion model, and relates to the technical field of vibration signal reconstruction, and the method comprises the steps: firstly collecting structure vibration response through multiple sensors, constructing a multi-dimensional vibration signal matrix, and automatically recognizing a space continuous missing region and a time random missing region; performing coarse reconstruction on the missing region by adopting self-adaptive multi-scale interpolation so as to recover the basic trend and frequency band characteristics of the signal; a pseudo-missing mask is further applied to complete data, a training sample is constructed through a self-supervision strategy, and the model is guided to learn spatio-temporal correlation features and missing modes; in a training stage, a diffusion model is used as a generation framework, Gaussian noise disturbance is applied to a missing region, four types of condition embedding of time, space, trend and frequency domain are introduced in a denoising inversion process, signal periodicity, multi-sensor space coupling, low-frequency change and a physical frequency spectrum structure are respectively described, and the noise is reduced; and high-fidelity signal reconstruction under multi-modal information joint constraint is realized.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention belongs to the technical field of fault positioning, and discloses a Beidou satellite time service-based power distribution network fault section positioning method, which comprises the following steps of: acquiring three-phase current, and performing phase synchronization alignment on the three-phase current by using a Beidou satellite to obtain a three-phase current synchronization data sequence; performing modulus transformation on the three-phase current synchronous data sequence to obtain a zero-mode current component and a line-mode current component; performing adaptive waveform decomposition on the zero mode current component and the line mode current component to obtain an intrinsic mode component, and performing signal reconstruction on a target component to obtain an enhanced fault traveling wave signal; performing waveform curvature sudden change detection on the enhanced fault traveling wave signal to obtain a wave head arrival time and a time difference observation sequence; performing geometric analysis on the topological structure based on the time difference observation sequence and the traveling wave propagation speed to obtain a suspected fault section; and comprehensively studying and judging the suspected fault section to obtain an actual fault section. The power distribution network fault positioning efficiency can be improved.

The invention discloses a state detection method, a related device, equipment, a medium and a program product. The state detection method comprises the following steps: decomposing a first electric signal of an electric drive system into a plurality of first modal components; selecting at least one noise mode component from the plurality of first mode components; performing filtering processing on the at least one noise mode component to obtain at least one second mode component; performing signal reconstruction by using the at least one second modal component and each unfiltered modal component in the plurality of first modal components to obtain a second electric signal; and determining a state detection result of the electric drive system at least by using the second electric signal. In this way, the accuracy of state detection of the electric drive system can be improved.

The invention relates to the technical field of signal reconstruction, in particular to a complex electromagnetic environment-oriented millimeter wave signal blind source separation and reconstruction system, which comprises a frequency spectrum trend division module, a path fading construction module, an initial cluster label generation module, a multi-solution path screening module and a fusion reconstruction execution module. According to the method, power spectral density sequence processing is carried out on millimeter wave frequency domain data, a multi-dimensional feature group is formed according to a path loss factor, an angle of arrival and the like, and an initial feature cluster is screened through an Euclidean distance, so that the accuracy of signal source classification is effectively improved; after the frequency domain response and the phase contour are continuously subjected to point comparison, path screening is completed by integrating a mean square error residual value, the path misjudgment probability is reduced, time domain resampling and phase frequency offset standardization are executed in fusion reconstruction, the consistency and fidelity of signal reconstruction are enhanced, and the reconstruction precision is improved. The whole process improves the separation accuracy and reconstruction precision of mixed signals in a complex electromagnetic environment.

The invention relates to the technical field of data fusion, in particular to a multi-source electrocardiosignal correction method and system based on adaptive fusion, and the method comprises the following steps: constructing a multi-channel input tensor, extracting local features through a weight calculation network, carrying out the adaptive weight fusion and dimension reduction of multiple paths of signals, and carrying out the correction of the multi-source electrocardiosignal. A nonlinear mapping relation is established through a deep reconstruction network, a standard waveform is reconstructed, and network parameters are optimized based on reconstruction error reverse iteration. According to the method, local neighborhood features of multichannel signals are extracted by constructing a weight calculation network, a dynamic channel weight sequence reflecting the real-time contribution degree of a signal source is constructed, the amplitude intensity is adaptively adjusted according to the signal quality, unstable channel noise interference is effectively inhibited, and high-quality signal components are enhanced; a deep reconstruction network is used for carrying out nonlinear feature transformation on a fusion sequence, accurate mapping from non-standard input to standard lead waveforms is established, and weight distribution and optimization of signal reconstruction parameters are achieved in combination with an error back propagation mechanism.

The invention discloses a hydropower station signal source side data anti-interference acquisition method and system. The method comprises the following steps: constructing a multi-source interference feature recognition network according to an environment state of a hydropower station, obtaining an interference source distribution feature spectrum, and obtaining an original signal through space-frequency domain joint filtering preprocessing based on the spectrum; performing recursion processing on the original signal by adopting an adaptive Kalman filtering algorithm to obtain a high-fidelity target signal; processing the high-fidelity target signal based on a multi-scale feature extraction and verification mechanism to obtain a credibility quantitative purification signal; and processing the credibility quantitative purification signal based on a compensation strategy to obtain a high-reliability output signal. According to the method, the influence of interference signals is gradually removed from interference source identification, filtering processing, signal reconstruction and quality evaluation to signal compensation, and finally, a result subjected to multiple anti-interference processing is obtained, so that various interferences in a complex environment of a hydropower station can be effectively resisted, and high-reliability data acquisition is realized.

The invention discloses a biomedical multi-mode signal anomaly detection and prediction method and system, and the method comprises the steps: modeling a biomedical multi-mode signal into a time-varying non-local dynamic system, capturing the long-time-history dependence characteristic of the signal through a memory mechanism of a Caputo fractional derivative, and introducing a time-varying input item to process interference; the method comprises the following steps of: carrying out high-precision numerical integration by adopting an Adams-Bashform-Module solver; optimizing model parameters in combination with a local domain normalization pre-training strategy and a fusion loss function; abnormal detection is realized by calculating comparison between signal reconstruction deviation and a self-adaptive threshold value; a future anomaly probability is generated based on the trajectory prediction. According to the method, the problems of insufficient non-local dependence capture, poor time-varying interference robustness, high false positive rate and the like in the prior art are effectively solved, the accuracy and real-time performance of abnormal detection and prediction of biomedical signals such as electroencephalogram and electrocardiogram are remarkably improved, and the method is suitable for wearable medical equipment and clinical monitoring systems.

A power transmission tower grounding resistance intelligent measurement method based on ground potential field distributed sensing comprises the steps that a ground potential sensing network is arranged around a power transmission tower grounding device, and a ground potential field distributed measurement system with an iron tower as the center is established; a broadband characteristic coding current signal is injected into the grounding system through a weak current injection module; acquiring potential response data of each node of the ground potential sensing network excited by the feature coding current signal by adopting a distributed synchronous acquisition technology, and reconstructing complete ground potential field spatial distribution from sparse node observation data based on a compressed sensing theory through a signal reconstruction algorithm; and inputting the reconstructed complete ground potential field spatial distribution map into a pre-trained map neural network model, and outputting an intelligent identification result and an anomaly diagnosis conclusion of the ground resistance. According to the invention, non-contact, distributed and high-precision grounding resistance measurement under the condition that a grounding lead does not need to be disconnected is realized, and the measurement precision and reliability under a complex working condition are remarkably improved.

The invention discloses a signal reconstruction and noise suppression method and system for a distributed Raman temperature measurement sensing system, and the method comprises the steps: obtaining original anti-Stokes light and Stokes light time domain signals of the distributed Raman temperature measurement sensing system, and constructing a space-time signal matrix; performing preprocessing and data enhancement on the space-time signal matrix; constructing a dual-path feature fusion network for Raman signal reconstruction and noise suppression, and initializing the network; defining a loss function fusing temperature physical constraints, and training the dual-path feature fusion network; and inputting an original Raman signal to be processed into the trained network, outputting the reconstructed anti-Stokes and Stokes signals with the high signal-to-noise ratio, and carrying out temperature demodulation according to the anti-Stokes and Stokes signals with the high signal-to-noise ratio. While the spatial resolution is maintained or improved, the noise of the Raman temperature measurement system is effectively suppressed, especially the real signal of a temperature abrupt change point can be recovered, the temperature demodulation precision, stability and reliability are improved, and reliable data are provided for infrastructure safety monitoring.

The invention discloses a substation double-pump rotation automatic drainage control method and system and a medium, and relates to the technical field of double-pump rotation automatic drainage control, and the method comprises the following steps: obtaining a liquid level signal and a double-pump start-stop action signal of a double-pump drainage system, and recognizing a disturbance area of the liquid level signal according to the double-pump start-stop action signal; performing signal reconstruction on the disturbance area to obtain a reconstructed liquid level sequence, and establishing a double-pump rotation evaluation model based on the reconstructed liquid level sequence; the next round of double-pump start-stop action time is evaluated based on the double-pump rotation evaluation model, and the optimal double-pump start-stop action state is obtained; coupling the optimal double-pump start-stop action state with a liquid level signal to obtain an equivalent liquid level value; the double-pump alternating control is executed through the equivalent liquid level value, the double-pump starting and stopping control signal is output, and the problem that when the pumps are started or stopped, the liquid level signal jumps disorderly due to instantaneous water flow disturbance, and consequently double-pump alternating misoperation is caused is solved.

The invention discloses an industrial signal enhancement method based on dynamic adaptive filtering and self-supervised learning, and the method comprises the steps: firstly, purifying an original signal through preprocessing steps including DC drift removal, abnormal point elimination and amplitude normalization, and extracting a time-frequency feature matrix of the signal through short-time Fourier transform; and secondly, dynamic adaptive filtering kernel parameters are generated in real time by adopting a lightweight 1D convolutional neural network, the preprocessed signals are filtered, and optimized signal output is generated. Then, a signal reconstruction network under a self-supervised learning mechanism is designed, the network takes filtered signals as input, statistical consistency between input signals and reconstructed signals is optimized through a composite loss function, and retention of useful signal components and noise suppression are achieved. Finally, the method is deployed in an industrial edge computing unit or an embedded industrial personal computer, signal enhancement and denoising operation can be executed in real time, and the efficiency and accuracy of an industrial automation system are improved.

The invention relates to the technical field of electrical variable measurement, in particular to a self-adaptive power quality monitoring method for a power cabinet, which comprises the following steps of: starting a low-frequency acquisition unit arranged at a monitoring node of the power cabinet, and acquiring a low-frequency coarse scanning voltage signal of the power cabinet in a current monitoring period at a preset low-frequency sampling rate; according to the method, compressed observation vectors are transmitted, sparse basis inversion calculation is executed by using an orthogonal matching pursuit algorithm, high-precision power quality time domain waveform data are reconstructed, rich high-frequency transient details are restored under the condition of low-data-volume transmission, distortion feature analysis is carried out based on reconstructed waveforms, disturbance types are identified, and a monitoring analysis report is generated and uploaded. The real-time dynamic adjustment of the sampling scale and the matrix structure along with the signal fluctuation state is realized, the optimal balance of the data compression ratio and the signal reconstruction quality is achieved, the contradiction between the storage and transmission bandwidth limitation of massive high-frequency data in a complex power grid environment is solved, and the sensitivity for transient abnormality is improved.

The invention discloses a power dispatching monitoring data anomaly detection method based on artificial intelligence, and relates to the field of data anomaly detection, and the method comprises the steps: firstly carrying out the multi-modal alignment and preprocessing of high-frequency time sequence monitoring data and sparse log text of power dispatching, and constructing the association mapping of heterogeneous data under a time window; a retrieval enhancement generation technology is utilized to deeply parse a log text to construct a global semantic context, and time series data is synchronously subjected to double-flow coding to extract inherent fluctuation features. Based on this, a cross-modal semantic gating feature modulation mechanism is established, and a dynamic reweighting sequential sequence is guided by using log semantics, so that when a semantic background of a specific scheduling operation is perceived, the attention on compliance data mutation is automatically reduced. And finally, performing signal reconstruction and adaptive anomaly judgment based on the feature sequence under semantic guidance. In this way, false alarms can be significantly reduced while the real fault sensing capability is guaranteed, and the intelligence and robustness of the power monitoring system are improved.

The invention belongs to the field of audio processing, and particularly discloses an audio depression detection method and system based on an improved convolutional neural network autoencoder, and the method comprises the steps: carrying out the potential feature extraction and compression of input original audio data, extracting the data into a low-dimensional and dense potential feature vector, and carrying out the recognition of the potential feature vector; and the information loss in the reconstruction process is reduced. According to the model part, a residual block structure is introduced into an encoder part, input low-layer information is reserved through jump connection, the learning ability of the network is enhanced, and the gradient disappearance problem is avoided. A transpose convolution operation is introduced into a decoder part, the spatial resolution is increased through up-sampling, and a high-quality signal reconstruction task is taken as a constraint, so that the extracted potential features are ensured to contain all key information for accurate classification. Finally, the potential features are directly used for depression classification, and experimental results show that the accuracy and robustness of depression detection can be remarkably improved, and the method has high practical application value.

The invention provides a high-precision mine water recharge dynamic monitoring system and method.The high-precision mine water recharge dynamic monitoring system comprises a distributed sound wave data acquisition subsystem, the distributed sound wave data acquisition subsystem is connected with one end of an armored optical cable, and the armored optical cable is fed into a recharge drill hole through an armored optical cable laying device; the recharge drill hole is further connected with a mine water recharge subsystem for mine water recharge. The sound wave signal processing and feature extraction subsystem comprises a wavelet multi-scale decomposition module, a target frequency band extraction module, a sub-band energy calculation module, a non-target frequency band suppression module and a signal reconstruction module; the intelligent injection channel identification and flow estimation subsystem comprises an injection channel identification module, a flow distribution calculation module and a dominant channel intelligent identification module. The system has the full-shaft and high-temporal-spatial-resolution dynamic monitoring capability, the injection profile dynamic analysis and dominant channel intelligent identification capability, the high reliability and long service life capability in an extreme environment, and the recharge process safety and optimization decision support capability.

The invention discloses a numerical control machine tool spindle bearing feature extraction method based on improved FMD, and belongs to the technical field of rotating machine fault diagnosis. The method aims at solving the problems that traditional feature mode decomposition is high in parameter dependency and fault feature extraction is difficult under the noise background. The core of the method is that firstly, noise is added into a collected bearing vibration signal, and an ETO-FMD model is input; secondly, using an exponential trigonometric function optimization algorithm to take weighted envelope spectrum kurtosis as a fitness function, performing adaptive global optimization on the mode number M of the FMD and the length L of a filter, and automatically obtaining an optimal parameter combination; and finally, calculating a weighted envelope spectrum kurtosis value of each IMF component after FMD decomposition, and screening out the most critical component to perform signal reconstruction so as to realize accurate extraction of fault features. According to the method, the limitation of manually setting parameters is overcome, the accuracy, the adaptability and the robustness of feature extraction are remarkably improved, and the method is suitable for diagnosis of various faults of the spindle bearing of the high-end numerical control machine tool.

The present invention relates to the technical field of haptic signal generation. Disclosed is a signal-to-noise ratio adaptive haptic signal reconstruction method with joint source-channel optimization, comprising: preprocessing an image corresponding to each object and a haptic vibration signal generated by sliding; constructing a cross-modal joint encoder for generating hierarchical fusion features, constructing a cross-modal joint decoder, and inputting preprocessed training data into the constructed cross-modal joint encoder and cross-modal joint decoder for training; and inputting in pairs haptic signals and image signals under test into the cross-modal joint encoder and the optimal cross-modal joint decoder to reconstruct a target haptic signal. The signal-to-noise ratio adaptive haptic signal reconstruction method with joint source-channel optimization provided by the present invention simplifies encoding training processes, better performs modal feature fusion, improves the robustness of models to channel variations, and allows the models to exhibit higher haptic signal reconstruction stability under a wider range of signal-to-noise ratio variations.

The invention discloses a virtual multichannel compressed sensing sampling method and system for partial discharge signals, and belongs to the field of power equipment state monitoring. The problems of high sampling cost, large data volume and low signal reconstruction precision in the existing partial discharge signal sampling method are solved, non-uniform sampling is carried out by adopting a single physical sampling channel, and enough information is obtained at a relatively low sampling rate for signal reconstruction; an observation sequence obtained by sampling is distributed to a plurality of virtual channels according to a time sequence, compressed sensing reconstruction is carried out by using a joint observation matrix matched with a random sampling rate and a virtual channel distribution mode, a complete partial discharge digital signal is recovered at one time, the hardware cost and the system power consumption are effectively reduced, and the system reliability is improved. Therefore, the precision and efficiency of signal reconstruction are improved, and an efficient and reliable solution is provided for state monitoring and fault diagnosis of power equipment.

The invention discloses a two-stage multi-mode bearing fault diagnosis method based on a pre-training large model, and belongs to the technical field of bearing fault diagnosis. The method aims at solving the problems that a traditional method is poor in generalization and poor in robustness under multiple working conditions and small sample conditions. The method comprises the following steps: firstly, constructing a learnable multi-modal Tokens which comprises a multi-scale patch Token, a feature Token and a fault Token, and realizing efficient extraction and fusion of multi-modal features; a time-frequency semantic fusion module is introduced, and comprehensive time-frequency features are output through adaptive frequency coding, time coding and multi-modal fusion; and inputting the multi-modal feature sequence into a pre-training BERT model, and adopting a two-stage training strategy, in the first stage, performing self-supervised pre-training by taking mask signal reconstruction as a target, and in the second stage, performing parameter fine tuning by taking fault classification as a target. According to the method, the diagnosis accuracy and the cross-working-condition generalization ability under the small sample condition can be remarkably improved.

The embodiment of the invention provides a voiceprint recognition method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring at least one reference voice sample corresponding to each channel type; respectively carrying out coding and decoding distortion simulation on each reference voice sample by adopting a frequency domain spectrum feature reconstruction mode and a noise mixing mode, and / or adopting an LPC domain parameter disturbance mode and a signal reconstruction mode; and training a voiceprint recognition model based on all the first distorted voice samples and / or all the second distorted voice samples obtained after coding and decoding distortion simulation so as to perform voiceprint recognition. According to the method, data enhancement can be realized by performing coding and decoding distortion simulation on the reference voice sample, so that the cross-channel voiceprint recognition performance is effectively improved, the problem of feature mismatch is solved, the stability of model noise resistance is kept, and a judgment threshold is avoided.

The invention discloses an rPPG physiological signal reconstruction recognition system based on generative adversarial optimization, and relates to the technical field of data processing. The system comprises a signal preprocessing module used for extracting an initial rPPG signal from an input video sequence; and the signal reconstruction module comprises a generator and is used for receiving the initial rPPG signal and outputting a reconstructed rPPG signal. According to the method, by introducing a multi-dimensional discriminator set and physiological prior loss collaborative optimization mechanism, the precision and robustness of rPPG signal reconstruction are remarkably improved. The system can restrain signal quality from multiple angles of time domain, frequency domain and time-frequency domain, and restrain irrational fluctuation in combination with physiological laws, thereby effectively overcoming motion artifacts and illumination interference. Meanwhile, the dynamic region-of-interest selection module adaptively focuses an optimal signal region through a learnable attention mechanism, the input quality is improved from the source, and finally high-reliability estimation of the physiological parameters such as the heart rate and the respiration rate in a complex scene is achieved.

The invention provides a bidirectional LSTM photovoltaic power generation prediction method based on wavelet decomposition and double attention, and belongs to the technical field of power generation prediction. Selecting an optimal wavelet basis function by using a particle swarm optimization algorithm to carry out adaptive noise complete set empirical mode decomposition on the power sequence to obtain a multi-layer intrinsic mode function component, and carrying out adaptive denoising and power signal reconstruction according to a multi-scale permutation entropy and a Bayesian risk minimization criterion in combination with meteorological conditions; key feature variables are extracted through a maximum information coefficient, a bidirectional long-short-term memory network prediction framework is established, a feature attention mechanism and a double-path time attention structure are introduced, and when power mutation or irradiance mutation is detected, a sparse attention weight rapid reconstruction mechanism is triggered to complete prediction. The technical problem that the prediction precision is reduced when the photovoltaic power generation power changes suddenly under the cloudy weather condition is solved.

The invention relates to the technical field of data processing, in particular to a nondestructive testing method for the prestress of an anchor cable. Obtaining the difference degree according to the difference characteristics of the time-frequency data of the sound wave signals in the adjacent preset time periods and the change characteristics of the sound wave signals; respectively segmenting the sound wave signal and the background signal according to the difference degree; eMD decomposition is carried out on the sound wave signal segment and the background signal segment, and the comprehensive interference degree is obtained according to the difference characteristics of the frequency spectrum data of the sound wave component and all the background components; adjusting the initial covariance matrix of the sound wave component according to the comprehensive interference degree, and denoising through a Kalman filtering algorithm to obtain a denoised component; and performing signal reconstruction according to the de-noising component and the comprehensive interference degree of the sound wave signal segment to obtain a de-noised wave signal segment. Corrected sound wave signals are obtained according to all de-noised wave signal segments; the anchor cable prestress is detected according to the corrected sound wave signal, and the accuracy and reliability of anchor cable detection are improved.

The invention provides an angle signal reconstruction and fault tolerance method and system based on multistage diagnosis. The system is used for an angle sensor outputting four paths of complementary signals, and comprises a signal acquisition module, a physical rationality diagnosis and replacement module, an algorithm rationality diagnosis and reconstruction module, an angle calculation module, a space-time reconstruction module and a self-excitation compensation module. Based on historical data, the rotating speed sensor and the inertial model, a current angle signal is generated through Kalman filtering or a prediction method and is fused with an existing signal to be output. The self-excitation compensation module injects a high-frequency micro-amplitude excitation signal into a corresponding sensor channel when a certain signal is continuously abnormal, collects an electrical response and calculates a compensation angle signal based on an equivalent electrical parameter. Through physical and algorithm double-layer diagnosis, signal reconstruction, space-time prediction and self-excitation compensation, fault tolerance and continuous output of angle signals under various abnormal working conditions are achieved, and the reliability and the anti-interference capacity of the system are remarkably improved.

The invention relates to the cross technical field of elevator fault diagnosis and signal processing, in particular to an elevator shaft breakage early fault detection signal denoising method. The method comprises the following steps: acquiring an original low and medium frequency discrete signal of elevator shaft body vibration, and filtering by adopting a four-order Butterworth band-pass filter to obtain a filtered signal; performing complementary set empirical mode decomposition on the filtering signal to obtain a plurality of IMF components and a residual component, and screening effective IMF components from all the IMF components; the center frequency of each effective IMF component is calculated, and Gaussian weight distribution is carried out on each effective IMF component based on the center frequency of each effective IMF component; performing signal reconstruction on all the effective IMF components and the residual components according to the weight of each effective IMF component to obtain a reconstructed signal; and smoothing residual noise in the reconstructed signal by adopting self-adaptive moving smoothing filtering to obtain a de-noised signal. According to the method, the 30-60Hz low and medium frequency characteristic signals generated in the elevator spindle microcrack propagation stage can be accurately extracted, so that fault judgment is facilitated.

The invention relates to the field of railway data analysis and processing, in particular to a railway irregularity data processing method based on cEEMDAN-Hilbert. The method comprises the following steps: acquiring and preprocessing track irregularity original signals to obtain a target track irregularity signal sequence; performing CEEMDAN (adaptive noise complete ensemble empirical mode decomposition) on the target track irregularity signal sequence, and performing iterative decomposition on the signal added with Gaussian white noise to obtain a plurality of IMF (intrinsic mode function) components and a residual component; performing Hilbert transformation on each IMF component, calculating an instantaneous frequency and an instantaneous amplitude, constructing an analysis signal in a complex form, and extracting time and frequency joint distribution information; according to the instantaneous frequency and the energy result, false wavelength components are removed, signal reconstruction is carried out, and reconstructed track irregularity signals are generated. According to the invention, the identification capability of local structure disturbance can be enhanced.

The invention relates to the technical field of artificial intelligence and intelligent medical treatment, and discloses a health care information service system based on deep learning and a knowledge graph, and the system uses implicit physiological semantic prototype nodes to establish a mapping access point of a continuous signal and a discrete graph, and generates a dynamic causal graph based on real-time physiological features. The causal deduction module predicts an intervention result on the dynamic map, and the signal reconstruction verification module performs reverse physical signal reconstruction on a map reasoning path and calculates an effectiveness score based on biophysical constraints. And the decision output module fuses the potential result predicted value and the validity score to generate an intervention suggestion. According to the method, deep fusion of time sequence data and symbol knowledge is realized, and physiological safety and interpretability of dynamic health maintenance decision are ensured through a bidirectional verification mechanism of a logic space and a physical signal space.

The invention relates to the technical field of engineering machinery control, and discloses a hydraulic control system under a variable load piling working condition, which comprises a multi-dimensional state acquisition module, a fluid parameter observation module, a signal reconstruction decoupling module, a virtual impedance mapping module and a fluid phase locking control module. The acquisition module acquires a basic physical state; the observation module solves the effective volume elastic modulus of the hydraulic oil based on a fluid continuity equation; the reconstruction module utilizes the modulus to correct signal lag, reconstructs external load force through an inverse dynamic model and decouples environmental acoustic impedance; the mapping module dynamically generates a target mechanical impedance parameter according to the environment impedance; and the control module calculates an ideal instruction based on the target parameter, corrects the gain by utilizing the fluid modulus and locks the rebound peak phase to adjust the action time sequence of the valve core. Control distortion caused by fluid parameter drift is solved, active flexible adaptation to variable load geology is achieved, energy hedging is avoided, and operation efficiency is improved.