1154 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 relates to the technical field of Beidou navigation, in particular to a big data-based Beidou navigation signal optimization and error correction system, which comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a multi-dimensional error analysis module, an error collaborative prediction module, a real-time correction parameter generation module and an optimization signal reconstruction module, the multi-source heterogeneous data acquisition module is used for acquiring multi-source heterogeneous data in real time; the data preprocessing module carries out space-time alignment; the multi-dimensional error analysis module constructs a dynamic multi-path feature library; the error collaborative prediction module generates a three-dimensional space error distribution matrix; the real-time correction parameter generation module generates a frequency domain-time domain joint correction parameter; the optimized signal reconstruction module outputs an optimized Beidou navigation signal; according to the invention, high-precision and high-stability Beidou navigation service is continuously provided, and a precise and efficient solution is provided for application of a Beidou system.

The invention relates to the technical field of anti-interference, in particular to a communication signal interference elimination method and system, and the method comprises the following steps: obtaining communication signal time domain data, calculating a signal energy change rate, a signal abrupt change rate and a phase drift rate, marking and distinguishing a signal region according to a calculation result, and obtaining a communication signal non-stationary interval identification result. According to the method, the time domain data of the communication signal is subjected to fine-grained calculation, and the signal energy change rate, the signal abrupt change rate and the phase drift rate are extracted, so that accurate signal region distinguishing is realized, the regression modeling parameter range is adjusted, the influence of instantaneous data of an abrupt change region is reduced, and the stability of signal reconstruction is improved; according to the method, signal-to-noise ratio calculation and interference power fluctuation amplitude evaluation are combined, channel state parameters are dynamically adjusted, interference intensity evaluation is more accurate, the filtering step length, the weight coefficient and the gain coefficient are optimized based on error information, signal processing adapts to different types of interference, and integrity and stability of communication signals are enhanced.

The invention discloses a switch loop data extraction method, device and equipment based on phase locking, and the method comprises the steps: carrying out the interval change analysis of operation data in a ring main unit, and recognizing a communication abnormal node through a phase locking technology; associating a mechanical vibration source based on the communication abnormal nodes, detecting a vibration conduction path, extracting current fluctuation characteristics, and determining position deviation; the operation current intensity is adjusted in combination with fluctuation characteristic parameters, and accurate off-position control is achieved through reverse braking force; constructing a dynamic threshold reference based on frequency domain analysis, and generating a complete operation record according to the dynamic threshold reference and the accurate parking data; performing typed grouping and transmission optimization on the data, and controlling data switching by adopting a gradually migrated beat sequence; and finally, through signal reconstruction and data fusion technologies, a switch loop data set with continuous time sequence and complete space is generated. The method can effectively cope with communication interference, mechanical vibration and other complex environmental factors, and improves the integrity and accuracy of data acquisition.

The invention provides a real-time dynamic harmonic detection method and system for a traction power supply system, and relates to the technical field of harmonic detection, and the detection method comprises the following steps: collecting a voltage signal of the traction power supply system in real time, and dynamically tracking fundamental frequency fluctuation through a phase-locked loop technology; dynamically adjusting the data window length of FFT analysis; dynamically adjusting a frequency spectrum calculation kernel of the FFT; carrying out linear compensation on the detected harmonic phase; taking the preliminarily detected harmonic parameter as an initial value, and iteratively updating the harmonic parameter until convergence by minimizing a signal reconstruction error; dynamically adjusting a sampling interval according to the fundamental frequency fluctuation, and carrying out anti-aliasing processing by adopting a filter with a variable cut-off frequency; and constructing a matrix model containing a fundamental frequency fluctuation parameter, and calculating a real harmonic parameter through a least square method. According to the method, the detection defect of the fixed window FFT during frequency fluctuation can be systematically solved, and high-reliability real-time data support is provided for harmonic resonance suppression of the electrified railway.

A distributed optical fiber sensing event classification and identification method based on mask auto-encoder self-supervised learning belongs to the technical field of distributed optical fiber sound and vibration sensing, on one hand, a pre-training network does not need label data, a downstream classification and identification network only needs a small amount of labeled data, and the demand of the labeled data is reduced; and on the other hand, in the pre-training process, the model extracts DAS signal features and compresses the calculation amount of the DAS signals, so that pixel-level reconstruction of DAS signal reconstruction is changed into feature reconstruction, a large amount of time is saved, and the accuracy is ensured.

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 signal reconstruction denoising method for circuit breaker mechanical state evaluation and a storage medium. In order to solve the problems of multi-source signal aliasing and strong noise interference of the double-break vacuum circuit breaker, a technical route combining mechanical transfer function modeling and a double-flow deep network is innovatively adopted. A complex vibration source is analyzed by constructing a signal coupling model, environmental noise is accurately filtered by using a multi-stage noise reduction strategy, and a multi-dimensional state vector is constructed by fusing time-frequency domain features, so that accurate evaluation of the health state of the equipment is realized. Experiments show that the method is remarkably superior to the prior art in signal denoising, feature extraction precision and diagnosis accuracy, and an efficient solution is provided for intelligent operation and maintenance of power equipment.

The invention relates to a wavenumber domain compressed sensing damage detection method suitable for a plate-shaped structure. The method comprises the following steps: S1, constructing a compressed sensing equation; s2, solving the compressed sensing equation, and reconstructing to obtain a wave number domain signal of a full wave field; and S3, selecting a threshold value for the reconstructed wavenumber domain data for filtering, returning to a spatial domain through inverse Fourier transform, and carrying out merging processing on results by adopting a root mean square method to obtain a damage morphology graph. According to the method, the sparse basis matrix meeting the compressed sensing requirement is constructed by optimizing the sparse representation of the signal, so that the precision of full-wave field signal reconstruction is remarkably improved; the number of sampling points is greatly reduced, the full-wave-field data acquisition efficiency is improved, and the potential of breaking through the limitation of the Nyquist sampling law is achieved; the position and the shape of the damage are detected, the detection effect is good, and the detection efficiency is high.

The invention discloses a hydraulic pump remote state monitoring method and system based on low-sampling-rate signal cloud intelligent reconstruction, and belongs to the field of remote state monitoring. According to the method, low-sampling-rate signals with small data volume are only uploaded to the cloud, so that the problems of high cost and delay when a traditional cloud platform uploads large-scale high-sampling-rate signals are effectively relieved, and the key bottleneck of timely remote state monitoring is overcome. After the data is uploaded, under the support of the strong parallel computing capability of the cloud platform, the low-sampling-rate signal is accurately and quickly reconstructed into the high-sampling-rate signal through the reconstruction model deployed at the cloud end. The high-frequency information in the original signal is recovered by the signal reconstructed by the model, so that more accurate state monitoring can be supported. In general, the proposed scheme achieves timely and accurate remote state monitoring of the hydraulic pump by reducing data transmission cost and time delay and ensuring the accuracy of cloud signal reconstruction.

The invention provides a rotating machine fault diagnosis method and a related system. The rotating machine fault diagnosis method comprises the following steps: S1, constructing a rotor vibration equation according to a vibration behavior of a rotor system under an operation condition; s2, acquiring real-time information of a to-be-diagnosed rotating machine, performing modal analysis based on a rotor vibration equation according to the real-time information of the to-be-diagnosed rotating machine to determine a sensitive frequency band and key features, and forming multi-dimensional feature data; s3, carrying out denoising processing on the multi-dimensional feature data and carrying out signal reconstruction to obtain a reconstructed signal; s4, inputting the reconstruction signal into a pre-trained rotating machine fault model to obtain a fault diagnosis result; and the rotating machine fault model is fused by adopting a neural network ordinary differential equation and a convolutional neural network-gating cycle unit. The method can accurately simulate and predict the continuous dynamic change of the system, improves the diagnosis precision, and enhances the real-time performance and robustness of the model.

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 discloses a nonlinear OFDM (Orthogonal Frequency Division Multiplexing) information transmission method and system based on frequency domain symbol block modulation, an information sending part adopts a frequency domain symbol block to bear bit information, the relative amplitude and phase state of two symbols in the frequency domain symbol block are used for loading the bit information, and the power constraint is met; performing OFDM modulation on the frequency domain symbol block to form an OFDM time domain signal, and performing time domain up-sampling, iterative clipping and filtering and digital pre-distortion to reduce the interference of an output signal of the nonlinear power amplifier on an adjacent channel; and the information receiving part is used for performing nonlinear signal reconstruction on the received signal with peak distortion by utilizing frequency domain symbol block constraint and signal distortion characteristics, performing OFDM demodulation, detection and frequency domain symbol block de-mapping on the reconstructed signal, and recovering and sending bit information. According to the method, the adjacent channel interference performance and the information transmission performance of the power amplifier under nonlinearity can be improved, the power rollback requirement of the power amplifier is reduced, and the energy efficiency of an OFDM system is improved.

The invention discloses a nuclear power plant multi-sensor failure detection and signal reconstruction method, and relates to the technical field of sensor measurement and signal reconstruction. After preprocessing the measurement data, constructing a training set; extracting a part of training set data, simulating a sensor fault by using the sensor fault mechanism model to obtain simulated sensor fault data, and constructing a test set; detecting the working state of the nuclear power plant sensor based on the training set and test set data, positioning the specific position of the fault sensor, and outputting the normal data of the sensor or the position data of the fault sensor; using a local fusion mode to reconstruct failure data of the fault sensor, and outputting reconstructed sensor data; the method can accurately judge the working state of the nuclear power plant sensor.

The invention discloses a bio-electricity signal acquisition electrode and an efficient electroencephalogram signal acquisition method, and relates to the technical field of signal processing. The method comprises the following steps: step 1, carrying out electrode-tissue interface dynamic impedance modeling based on ion diffusion and a capacitance effect; calculating density distribution of the conductive coupling agent based on the impedance function; 2, calculating a signal quality index; performing signal enhancement according to the signal quality index to obtain an enhanced signal; 3, performing feature extraction on the enhanced signal to obtain a feature signal; performing dynamic noise suppression according to the characteristic signal to obtain a noise suppression result; and 4, performing signal reconstruction according to a noise suppression result to obtain a reconstructed signal, and completing electroencephalogram signal acquisition. According to the electrode design, the contact quality is improved, the distribution of the conductive coupling agent is more uniform, and long-time stable monitoring is ensured. Meanwhile, the definition and fidelity of the signal are effectively improved through adaptive noise suppression and a signal reconstruction mechanism.

The invention discloses a broadband oscillation monitoring method and device, and the method comprises the steps: collecting an initial signal of a to-be-detected power system line in real time, and carrying out the preprocessing of the initial signal; performing frequency interval division on the preprocessed signal, and performing adaptive filtering; separating a plurality of key components from the filtered signal; performing parameter estimation on the plurality of key components, extracting target modal information, and performing signal reconstruction; performing multi-modal identification on the reconstructed signal, and extracting a plurality of modal components; and judging whether a broadband oscillation phenomenon exists or not by using the plurality of modal components. According to the method, accurate separation and parameter estimation of broadband oscillation signals are realized, and the identification precision of oscillation frequency, amplitude and phase is improved.

The invention provides a magnetotelluric data denoising method and device based on a depth fusion model, is applied to a magnetotelluric data denoising system, and relates to the technical field of magnetotelluric data processing. A deep fusion model with a'noise feature recognition-signal selective reconstruction 'framework is established, input signals are pre-classified through a front noise detection module, the problem of false attenuation of effective signals in traditional end-to-end denoising is effectively avoided, a denoising auto-encoder containing a residual structure is utilized, and the denoising accuracy is improved. The deep feature extraction capability of the residual structure and the signal reconstruction advantage of the denoising auto-encoder are combined, the fidelity and integrity of the reconstructed signal are remarkably improved, the denoising process does not depend on manual intervention, and the efficiency and accuracy of data processing are greatly improved.

The invention discloses a non-contact electrocardio detection method which comprises the following steps: transmitting a frequency modulation continuous wave signal to the heart part of a human body by using an MIMO millimeter wave radar so as to obtain an echo signal reflected by the human body and pre-process the echo signal to obtain a discrete three-dimensional radar signal; enhancing the discrete three-dimensional radar signal by using a 2D beam forming technology to obtain a 2D signal; extracting a target area signal from the 2D signal, and performing phase extraction on the extracted target area signal to obtain phase data; the method comprises the following steps: constructing a WaveGRU-Net network comprising an MODWT module, a CNN module and a Bi-GRU module; and training the WaveGRU-Net network by using the phase data so as to output a reconstructed electrocardiogram by using the trained WaveGRU-Net network, thereby realizing non-contact electrocardiogram detection. According to the method, accurate extraction of the electrocardiosignal is realized, and meanwhile, the signal reconstruction precision is improved.

The invention belongs to the technical field of integrated circuits, and particularly relates to a chip-level integrated circuit fault diagnosis method based on improved wavelet entropy, which comprises the following steps: S1, acquiring an original signal of a chip-level integrated circuit, and reconstructing the original signal by using a VMD signal reconstruction technology; s2, performing deep processing on the reconstructed signal by adopting an improved wavelet entropy method, and extracting key features of a fault signal; s3, monitoring the voltage change of each node through the key characteristics of the fault signal, and effectively positioning the fault position; and S4, setting a fault factor which can comprehensively consider the fault position, the attenuation degree and the fault length, comparing the fault factor with a set threshold value, and judging the severity of the fault, thereby realizing the online diagnosis of the fault of the chip-level integrated circuit. According to the invention, by introducing the adaptive wavelet basis selection and multi-scale wavelet entropy fusion technology, the accuracy and efficiency of diagnosis are significantly improved, the repeatability of fault signal feature extraction is reduced, and the real-time performance and stability of a diagnosis system are improved.

The invention discloses a big data-based machine tool fault diagnosis and analysis method and system. The method comprises the following steps of: acquiring signal data of each part of a machine tool by utilizing a sensor; carrying out de-noising processing on the acquired signal data; the de-noised signals are decomposed by using successive variational mode decomposition, screening is carried out based on a Pearson's correlation coefficient, and the unscreened signals are fused and then are subjected to signal reconstruction with the screened signals; the method comprises the following steps: constructing a fault diagnosis model based on an Adaboost-Reformer integrated algorithm, and optimizing hyper-parameters of the Reformer model by using an improved water circulation optimization algorithm; the reconstructed signals are divided into a training set and a test set, the training set is input into the optimized model for training, machine tool signals collected in real time are input into the trained model after being subjected to denoising and reconstruction processing, and corresponding fault categories are obtained. The method can effectively extract fault features from the multi-mode sensor signals and perform accurate diagnosis, has strong robustness and high efficiency, and is suitable for intelligent fault diagnosis of industrial equipment.

The invention discloses a multi-factor coupling dynamic error compensation method, system and device and a storage medium, and the method comprises the steps: obtaining an original signal sequence, and carrying out the time-frequency analysis of the original signal sequence, and obtaining a time-frequency matrix; performing feature extraction on the time-frequency matrix to obtain feature information; according to the feature information, calculating a Lagrange interpolation reference node of a corresponding time point, and carrying out signal reconstruction to obtain a synchronous sampling sequence; performing harmonic analysis on the synchronous sampling sequence to obtain harmonic parameters; the dynamic compensation amount is calculated in combination with the characteristic information and the harmonic parameters, the electric energy metering value is corrected, and the real-time performance and accuracy of electric energy measurement are effectively improved.

A system to configure a neurostimulation signal to a patient's body has a neurostimulator to create a base stimulation signal which has a series of first pulses. A computer processor within the neurostimulator deconstructs the first series of pulses into a first and second partial pulse series which have respective first and second partial electrical pulses where the first and second partial electrical pulses substantially summate to the base stimulation signal. A programmable signal generator provides a constant amplitude of combined stimulation pulses by applying the first and second partial pulses to a number of contacts of an electrode positionally located to stimulate a target tissue in the patient's body.

The invention provides an aero-engine blade damage detection method based on an improved ultrasonic sparse reconstruction algorithm, and relates to the technical field of aero-engine damage detection positioning, and the method comprises the steps: S1, setting a sensor array and a detection region of a to-be-detected blade, and obtaining an actual measurement damage reflection signal dictionary through a sensor; s2, wave number distribution of a detection area is determined according to a variable thickness structure Lamb wave propagation model, signal reconstruction is carried out, and a theoretical damage reflection signal dictionary is obtained; s3, an aero-engine blade damage detection model is constructed, an improved sparse reconstruction algorithm is adopted to solve and optimize, and damage judgment is carried out; and S4, according to the pixel value distribution condition of the aero-engine blade detection area, generating an aero-engine blade damage detection image, and positioning a damage position. According to the method, the reflected wave amplitude information can be obtained from the array sensor signals, and the reflected wave amplitude information is compared with the reconstructed theoretical reflected signals, so that the damage condition of the aero-engine blade is identified and positioned.

The invention relates to the technical field of rotary steerable drilling system dynamics analysis and control, in particular to a rotary steerable drilling system dynamics analysis system which comprises a data acquisition module used for acquiring ground engineering parameters and underground dynamic parameters in real time and intercepting drilling parameter adjusting instructions; the digital twin modeling module is used for outputting a complete state vector of a digital twin model; the noise prediction module is used for generating a control source noise signal; the signal reconstruction module is used for executing self-adaptive hedging processing on the original mixed signal so as to reconstruct a pure geological signal; the cooperative control module is used for predicting the vibration risk caused by the change of the front stratum and generating an optimal control instruction for actively avoiding the vibration risk; the occurrence probability of malignant vibration is remarkably reduced, the drilling efficiency is improved, and the service life of a drilling tool is prolonged.

The embodiment of the invention discloses a high-speed railway trackside space electromagnetic environment recording and simulating method and device. A high-speed railway system comprises a field acquisition test subsystem and a signal reconstruction test subsystem. Comprising the following steps: collecting field electromagnetic field intensity data externally generated by an electromagnetic interference source of the high-speed railway system through the field collecting and testing subsystem; obtaining related design parameters of the high-speed railway system and external area parameters of the simulation position; reconstructing the field electromagnetic field intensity data, the design parameters and the external area parameters through the signal reconstruction test subsystem to obtain a field electromagnetic interference field intensity signal; and inputting the field electromagnetic interference field intensity signal to the electromagnetic sensitive equipment to enable the electromagnetic sensitive equipment to perform operation simulation so as to obtain whether the electromagnetic sensitive equipment operates normally based on the field electromagnetic interference field intensity signal.

The invention discloses a radar signal noise reduction method for deep learning, and relates to the technical field of radar signal processing, and the method comprises the steps: converting a time domain radar signal into a two-dimensional time-frequency graph through short-time Fourier transform; frequency domain feature extraction is carried out on the two-dimensional time-frequency graph, and frequency domain feature representation is output through a DnCNN and a cascade CNN in sequence; performing time domain feature extraction on the time domain radar signal and outputting time domain feature representation; fusing the frequency domain features and the time domain features based on a multi-head attention mechanism to generate cross-domain joint features; and inputting the cross-domain joint feature into a residual shrinkage network for signal reconstruction, and outputting a denoised time domain signal. A time-frequency double-domain collaborative learning framework is constructed, and signal high-fidelity reconstruction in a complex noise environment is realized through deep fusion of time domain characteristics and a frequency domain structure distribution rule. According to the noise reduction method, an extremely low phase error and an extremely high feature retention rate can be kept in a strong noise environment.

The invention relates to the technical field of digital coding, in particular to a multi-bit control system based on digital coding, which comprises a network monitoring module, a coding adjustment module, a signal reconstruction module, a fault-tolerant control module and a signal processing module. According to the invention, by dynamically monitoring key network performance parameters such as bandwidth, delay and error rate, network state change is obtained in real time, and intelligent adjustment is carried out on the basis, so that the system can continuously optimize coding complexity and flexibly adjust signal compression ratio according to network conditions, and signals can be efficiently transmitted in a changeable network environment; when a signal is reconstructed, the system can adjust a decoding mode and correct errors based on real-time data feedback by comparing the difference between the reconstructed signal and a target signal, the decoding accuracy and the signal quality are remarkably improved, the power output of a control signal is optimized by means of execution signal data fed back in real time, and the signal stability is further enhanced.

The invention provides a cuff-free continuous blood pressure signal reconstruction method and system, and relates to the technical field of blood pressure monitoring, and the method comprises the steps: obtaining a PPG signal and an ECG signal which are synchronized; respectively extracting a PPG feature vector and an ECG feature vector from the PPG signal and the ECG signal by using the trained attention sequential network, carrying out multi-modal fusion on the PPG feature vector and the ECG feature vector, and reconstructing a cuff-free continuous blood pressure signal based on the fused feature vectors; according to feature vector extraction, an information screening machine and a time sequence learning machine are additionally arranged between an encoder and a decoder and are respectively used for learning a channel probability distribution vector and a time sequence weight vector, guiding the decoder to decode high-level semantic features and decoding the sequential relation between PPG and ECG; the attention sequential network is provided on the basis of multi-modal fusion, and continuous blood pressure signal reconstruction is achieved by analyzing photoelectric volume pulse wave signals and electrocardiosignals.

The invention discloses a blade strain field prediction method and system based on a blade tip timing band-pass reconstruction technology. The method comprises the following steps: firstly, constructing a three-dimensional finite element model of a rotor blade; secondly, establishing a generalized band-pass sampling model of a blade tip timing signal; determining a sensor arrangement scheme; constructing a frequency domain band-pass filtering model and solving a tip timing signal time domain interpolation reconstruction function; building a to-be-tested rotor test platform according to the sensor arrangement scheme; performing time sequence reconstruction of the blade tip timing signal, and calculating to obtain a blade vibration displacement reconstruction signal by using the measured blade tip timing signal and the interpolation reconstruction function; establishing a transient strain field reconstruction model based on a displacement-modal transfer ratio; and finally, blade transient strain field reconstruction and result visual output are carried out. According to the method, the number of the sensors and the sampling period can be controlled to be minimized while the blade tip timing signal reconstruction accuracy is ensured, so that the applicable range of the method is widened.

The invention discloses a deployment system for measuring multi-object vibration by using compressed sensing and a signal reconstruction method. The invention relates to an object vibration information acquisition and frequency period calculation system and method based on an RFID technology. The system aims at efficiently collecting vibration data of a plurality of objects through the RFID reader-writer and accurately processing and analyzing the vibration data. The system comprises an RFID vibration information acquisition module which is responsible for acquiring a unique identification EPC label, phase data and a timestamp of an object and storing the data in a CSV format; the data preprocessing module is used for cleaning collected data, deleting repeated timestamps and classifying the data; the measurement matrix generation module is used for constructing a signal measurement matrix so as to facilitate subsequent processing; the compressed sensing signal reconstruction module performs sparse representation and reconstruction on the vibration signal through a La sso regression algorithm, Fourier transform and other technologies, and recovers an original vibration signal of the object; and finally, the vibration period and frequency calculation module extracts the period characteristics of the signals through self-correlation analysis, and calculates the vibration frequency of the object. Through the system, the vibration states of multiple objects can be monitored and analyzed in real time, the system is widely applied to the fields of industrial monitoring, structural health monitoring and the like, and the precision and efficiency of object vibration detection are improved.